MiniBatch_Consumer_KMeans_KMeansPCA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cruzc\\Anaconda3\\envs\\fnb\\lib\\site-packages\\sklearn\\utils\\deprecation.py:143: FutureWarning: The sklearn.datasets.samples_generator module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.datasets. Anything that cannot be imported from sklearn.datasets is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import pyodbc\n",
    "import time\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import math as math\n",
    "%matplotlib inline\n",
    "import sklearn_extensions as ske\n",
    "from sklearn_extensions.fuzzy_kmeans import KMedians, FuzzyKMeans, KMeans\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.cluster import KMeans, MiniBatchKMeans, MeanShift, AgglomerativeClustering\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pd.options.mode.chained_assignment = None # gets rid of chaining warning\n",
    "\n",
    "pd.set_option('max_rows', 150)\n",
    "pd.set_option('max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Variables removed for privacy reasons\n",
    "\n",
    "colors = ['red','orange','yellow','green','blue','purple','black','grey','brown','olive','aqua','coral','azure','indigo','gold']\n",
    "\n",
    "def demo_graphs(data_hhold, data_freq, n_clusters):\n",
    "    fig, ax = plt.subplots(2,3,figsize = (15,10))\n",
    "    \n",
    "    pd.crosstab(data_hhold['Segment'],[data_hhold[''],data_hhold['']]).plot(kind = 'bar', ax = ax[0][0])\n",
    "    ax[0][0].legend([\"NA\", \"\",''])\n",
    "    pd.crosstab(data_hhold['Segment'],[data_hhold[''],data_hhold['']]).plot(kind = 'bar',ax = ax[0][1])\n",
    "    ax[0][1].legend([\"NA\", \"\",''])\n",
    "    pd.crosstab(data_hhold['Segment'],[data_hhold[''],data_hhold[''],data_hhold['']]).plot(kind = 'bar', ax = ax[0][2])\n",
    "    ax[0][2].legend([\"NA\",'','', \"\"]);\n",
    "    pd.crosstab(data_hhold['Segment'],[data_hhold[''],data_hhold[''],data_hhold['']]).plot(kind = 'bar', ax = ax[1][0])\n",
    "    ax[1][0].legend([\"NA\",'','', \"\"]);\n",
    "    pd.crosstab(data_hhold['Segment'],[data_hhold[''],data_hhold['']]).plot(kind = 'bar', ax = ax[1][1])\n",
    "    ax[1][1].legend([\"NA\",'', \"\"]);\n",
    "\n",
    "    x = np.arange(n_clusters)\n",
    "    ax[1][2].bar(x,data_freq['avg_age'],0.2,color = 'navy', label = 'Avg Age')\n",
    "    ax[1][2].legend(loc = 'lower right');\n",
    "    \n",
    "def bar_chart_ob_fees(data,y_list):\n",
    "    list_num = 0\n",
    "    fig, ax = plt.subplots(2,math.ceil((len(y_list)/2)),figsize = (15,10))\n",
    "\n",
    "    for i in range(2):\n",
    "        for j in range(math.ceil((len(y_list)/2))):\n",
    "            if list_num < len(y_list):\n",
    "                ax[i][j].bar(data.index,data[y_list[list_num]])\n",
    "                ax[i][j].set_title(y_list[list_num])\n",
    "                list_num += 1\n",
    "                \n",
    "def bar_chart_trans(data,y_list):\n",
    "    list_num = 0\n",
    "    fig, ax = plt.subplots(math.ceil((len(y_list)/2)),2,figsize = (15,45))\n",
    "\n",
    "    for i in range(math.ceil((len(y_list)/2))):\n",
    "        for j in range(2):\n",
    "            if list_num < math.ceil((len(y_list)/2)):\n",
    "                if j == 0:\n",
    "                    ax[i][j].bar(data.index,data[y_list[list_num]])\n",
    "                    ax[i][j].set_title(y_list[list_num])              \n",
    "                elif j == 1:\n",
    "                    ax[i][j].bar(data.index,data[y_list[list_num + math.ceil((len(y_list)/2))]])\n",
    "                    ax[i][j].set_title(y_list[list_num + math.ceil((len(y_list)/2))])\n",
    "        list_num += 1\n",
    "        \n",
    "def zip_graphs(data, n_clust):\n",
    "    fig, ax = plt.subplots(math.ceil((n_clust/2)),2,figsize = (15,25))\n",
    "    col_count = 0\n",
    "    \n",
    "    for i in range(math.ceil((n_clust/2))):\n",
    "        for j in range(2):\n",
    "            new_data = data[data[\"Segment\"] == i]\n",
    "            ax[i][j].bar(new_data.loc[:, \"\":\"\"].columns, new_data.loc[:, \"\":\"\"].sum().values, \n",
    "                         tick_label = [str(x)[4:] for x in new_data.loc[:, \"\":\"\"].columns], color = colors[col_count])\n",
    "            ax[i][j].set_title(f'Segment {col_count}')\n",
    "            col_count += 1\n",
    "        \n",
    "    plt.show();\n",
    "    \n",
    "def networth_graphs(data, n_clust):\n",
    "    fig, ax = plt.subplots(math.ceil((n_clust/2)),2,figsize = (15,25))\n",
    "    col_count = 0\n",
    "   \n",
    "    for i in range(math.ceil((n_clust/2))):\n",
    "        for j in range(2):\n",
    "            new_data = data[data[\"Segment\"] == i]\n",
    "            X = [str(x)[9:] for x in new_data.loc[:, '':''].columns]\n",
    "            X = list(map(float, X))\n",
    "            X = list(map(int, X))\n",
    "            ascending = pd.DataFrame(data = X, columns = [\"networth\"])\n",
    "            ascending[\"counts\"] = new_data.loc[:, '':''].sum().values\n",
    "            ascending = ascending.sort_values(\"networth\")\n",
    "            ax[i][j].bar(ascending[\"networth\"], ascending[\"counts\"], tick_label = ascending[\"networth\"], color = colors[col_count])\n",
    "            ax[i][j].set_title(f'Segment {col_count}')\n",
    "            col_count += 1\n",
    "       \n",
    "    plt.show();\n",
    "    \n",
    "def bit_graphs(data_freq, n_clust):\n",
    "    fig, ax = plt.subplots(2,1,figsize = (15,20)) \n",
    "    \n",
    "    x = np.arange(n_clust)\n",
    "    ax[0].bar(x-0.2,data_freq[''],0.2,color = 'navy', label = 'Brkge')\n",
    "    ax[0].bar(x,data_freq[''],0.2,color = 'orange', label = 'Insur')\n",
    "    ax[0].bar(x+0.2,data_freq[''],0.2,color = 'grey',label = 'Trust')\n",
    "    ax[0].set_xlabel(\"Segment\")\n",
    "    ax[0].set_ylabel(\"Avg Num of Accounts\")\n",
    "    ax[0].legend(loc = 'lower right');\n",
    "    \n",
    "    x1 = np.arange(n_clust)\n",
    "    ax[1].bar(x1-0.2,data_freq[''],0.2,color = 'green', label = 'Full')\n",
    "    ax[1].bar(x1,data_freq[''],0.2,color = 'red', label = 'Partial')\n",
    "    ax[1].set_xlabel(\"Segment\")\n",
    "    ax[1].legend(loc = 'lower right');\n",
    "    \n",
    "def sbloan_graphs(data, n_clusters):\n",
    "    x = np.arange(n_clusters)\n",
    "    plt.bar(x,data['']);\n",
    "    \n",
    "def feature_table(model_list, pca_truth_list, model_names, threshold,n_clust):\n",
    "    \n",
    "    top_features = np.full((n_clust, 2), str)\n",
    "   \n",
    "    for i in range(len(model_list)):\n",
    "       \n",
    "        if not pca_truth_list[i]:\n",
    "            cluster_centers = pd.DataFrame(data = model_list[i].cluster_centers_, columns = active_accts_dummies.columns)\n",
    "        else:\n",
    "            cluster_centers = pd.DataFrame(data = model_list[i].cluster_centers_, columns = scores_pca.columns)\n",
    "   \n",
    "        for j in range(cluster_centers.shape[0]):\n",
    "            curr_row = cluster_centers.iloc[j].copy()\n",
    "            indexes = (np.where(curr_row.abs() > threshold))\n",
    "            top_features[j][i] = abs(curr_row.iloc[indexes]).nlargest(3).index.values\n",
    "        \n",
    "    return pd.DataFrame(data = top_features, columns = model_names, index = np.arange(n_clust))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CSV Data Retrieval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data removed for privacy reasons\n",
    "active_accts = pd.read_csv(, index_col = \"hkey\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing the numeric categorical variables to a string\n",
    "active_accts[\"ZIP\"] = active_accts[\"ZIP\"].astype(str)\n",
    "active_accts['ZIP'] = [str(x)[0:2] for x in active_accts['ZIP']]   \n",
    "active_accts[\"NETWORTH\"] = active_accts[\"NETWORTH\"].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identified outliers\n",
    "active_accts = active_accts.drop(labels = [227071, 387064,501403], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter to the consumer only group\n",
    "active_accts = active_accts[(active_accts[\"sum_smallbiz_flag\"] == 0) & (active_accts[\"sum_consumer_flag\"] > 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter out people older than 110\n",
    "active_accts = active_accts[active_accts['avg_age'] < 110]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "active_accts = active_accts[active_accts.avg_age > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making the hkey the index\n",
    "hkey_list = active_accts.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(494253, 91)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_accts.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummy Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Variables removed for privacy reasons\n",
    "dummies = pd.get_dummies(active_accts[[]])\n",
    "active_accts_dummies = pd.concat([active_accts, dummies], axis = 1)\n",
    "active_accts_dummies = active_accts_dummies.drop(columns = [])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "# Shuffles the data\n",
    "active_accts_dummies = active_accts_dummies.sample(frac = 1, random_state = 42)\n",
    "\n",
    "active_accts_std = scaler.fit_transform(active_accts_dummies)\n",
    "\n",
    "active_accts_std = pd.DataFrame(active_accts_std, columns = active_accts_dummies.columns)\n",
    "\n",
    "active_accts_std = active_accts_std.set_index(hkey_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster Sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "inertia = []\n",
    "for i in range(2,21):\n",
    "    model = MiniBatchKMeans(n_clusters=i, random_state=0, init = 'k-means++').fit(active_accts_std)\n",
    "    inertia.append(model.inertia_)\n",
    "    \n",
    "plt.figure(figsize = (10,8))\n",
    "plt.xticks(range(0,19), range(2,21))\n",
    "plt.suptitle('Small Business Mini Batch',fontsize=20)\n",
    "plt.plot(inertia, marker = 'o');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "inertia =[]\n",
    "sil =[]\n",
    "    \n",
    "for k in range(2,14):\n",
    "    print(k)\n",
    "    kmeans_rand = MiniBatchKMeans(n_clusters=k, init='k-means++', random_state=0)\n",
    "    kmeans_rand.fit(active_accts_std)\n",
    "    y_pred = kmeans_rand.predict(active_accts_std)\n",
    "        \n",
    "    inertia.append(kmeans_rand.inertia_)\n",
    "    sil.append((k, silhouette_score(active_accts_std, y_pred, sample_size = 10000, random_state = 14)))\n",
    "        \n",
    "fig, ax = plt.subplots(1, 2, figsize=(12,4))\n",
    "ax[0].plot(range(2,14), inertia, marker = 'o')\n",
    "ax[0].set_title('Elbow Method')\n",
    "ax[0].set_xlabel('Number of clusters')\n",
    "ax[0].set_ylabel('Inertia')\n",
    "    \n",
    "x_sil = [x[0] for x in sil]\n",
    "y_sil = [x[1] for x in sil]\n",
    "ax[1].plot(x_sil, y_sil, marker = 'o')\n",
    "ax[1].set_xlabel('Number of Clusters')\n",
    "ax[1].set_ylabel('Silhouetter Score')\n",
    "ax[1].set_title('Silhouetter Score Curve')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KMiniBatch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "mini_clusters = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MiniBatchKMeans(n_clusters=5, random_state=14)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minikmeans = MiniBatchKMeans(n_clusters = mini_clusters, init = 'k-means++', random_state = 14)\n",
    "minikmeans.fit(active_accts_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MiniKMeans: Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "active_accts_minikmeans = active_accts_dummies.copy()\n",
    "active_accts_minikmeans[\"Segment\"] = minikmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_inv_bal</th>\n",
       "      <th>avg_ckcom_bal</th>\n",
       "      <th>avg_ckcon_bal</th>\n",
       "      <th>avg_indln_bal</th>\n",
       "      <th>avg_mtg_bal</th>\n",
       "      <th>avg_comln_bal</th>\n",
       "      <th>avg_conln_bal</th>\n",
       "      <th>avg_inv_num</th>\n",
       "      <th>avg_ckcom_num</th>\n",
       "      <th>avg_ckcon_num</th>\n",
       "      <th>avg_indln_num</th>\n",
       "      <th>avg_mtg_num</th>\n",
       "      <th>avg_comln_num</th>\n",
       "      <th>avg_conln_num</th>\n",
       "      <th>avg_bill_pay</th>\n",
       "      <th>avg_direct_deposit</th>\n",
       "      <th>avg_estatement</th>\n",
       "      <th>avg_mobile_app</th>\n",
       "      <th>avg_online_banking</th>\n",
       "      <th>sum_YTDINTPAID</th>\n",
       "      <th>sum_YTDITEMNSF</th>\n",
       "      <th>sum_YTDNSFFEE</th>\n",
       "      <th>sum_YTDNSFRTFA</th>\n",
       "      <th>sum_YTDODFEEAT</th>\n",
       "      <th>sum_YTDPDFEES</th>\n",
       "      <th>sum_YTDOTRCHG</th>\n",
       "      <th>sum_YTDPDINT</th>\n",
       "      <th>sum_YTDRTNNSF</th>\n",
       "      <th>sum_YTDSVC</th>\n",
       "      <th>sum_YTDSVCWVD</th>\n",
       "      <th>sum_YTDTIMEOD</th>\n",
       "      <th>sum_brokerage</th>\n",
       "      <th>sum_insurance</th>\n",
       "      <th>sum_trust</th>\n",
       "      <th>sum_full_charge_off</th>\n",
       "      <th>sum_partial_charge_off</th>\n",
       "      <th>sum_sbloan_flag</th>\n",
       "      <th>sum_smallbiz_flag</th>\n",
       "      <th>sum_consumer_flag</th>\n",
       "      <th>avg_age</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>BIZRETL</th>\n",
       "      <th>ANNSPEND</th>\n",
       "      <th>educate</th>\n",
       "      <th>income</th>\n",
       "      <th>DWELTYPE</th>\n",
       "      <th>MORESPON</th>\n",
       "      <th>RISKIND</th>\n",
       "      <th>PROPTYPE</th>\n",
       "      <th>homemktvalue</th>\n",
       "      <th>LOR</th>\n",
       "      <th>NETWORTH</th>\n",
       "      <th>ownrent</th>\n",
       "      <th>total_ACH_DEPOSIT_trans</th>\n",
       "      <th>total_ATM_DEBIT_trans</th>\n",
       "      <th>total_AUTO_CR_TRANSFER_trans</th>\n",
       "      <th>total_AUTO_TRANSFER_DR_trans</th>\n",
       "      <th>total_Auto_Loan_Payment_trans</th>\n",
       "      <th>total_CHECK_trans</th>\n",
       "      <th>total_DEBIT_CARD_PURCHASE_trans</th>\n",
       "      <th>total_DEPOSIT_trans</th>\n",
       "      <th>total_FIRST_DESKTOP_DEPOSIT_trans</th>\n",
       "      <th>total_LOCKBOX_trans</th>\n",
       "      <th>total_MISC_CREDIT_trans</th>\n",
       "      <th>total_MISC_DEBIT_trans</th>\n",
       "      <th>total_MOBILE_DEPOSIT_trans</th>\n",
       "      <th>total_ONLINE_DEBIT_trans</th>\n",
       "      <th>total_POS_RETURN_trans</th>\n",
       "      <th>total_TRANSFER_CREDIT_trans</th>\n",
       "      <th>WIRE_trans</th>\n",
       "      <th>total_WITHDRAWAL_trans</th>\n",
       "      <th>total_ZELLE_trans</th>\n",
       "      <th>total_ACH_DEPOSIT_num</th>\n",
       "      <th>total_ATM_DEBIT_num</th>\n",
       "      <th>total_AUTO_CR_TRANSFER_num</th>\n",
       "      <th>total_AUTO_TRANSFER_DR_num</th>\n",
       "      <th>total_Auto_Loan_Payment_num</th>\n",
       "      <th>total_CHECK_num</th>\n",
       "      <th>total_DEBIT_CARD_PURCHASE_num</th>\n",
       "      <th>total_DEPOSIT_num</th>\n",
       "      <th>total_FIRST_DESKTOP_DEPOSIT_num</th>\n",
       "      <th>total_LOCKBOX_num</th>\n",
       "      <th>total_MISC_CREDIT_num</th>\n",
       "      <th>total_MISC_DEBIT_num</th>\n",
       "      <th>total_MOBILE_DEPOSIT_num</th>\n",
       "      <th>total_ONLINE_DEBIT_num</th>\n",
       "      <th>total_POS_RETURN_num</th>\n",
       "      <th>total_TRANSFER_CREDIT_num</th>\n",
       "      <th>WIRE_num</th>\n",
       "      <th>total_WITHDRAWAL_num</th>\n",
       "      <th>total_ZELLE_num</th>\n",
       "      <th>Segment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hkey</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>268972</td>\n",
       "      <td>0</td>\n",
       "      <td>3101</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>618.24</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>618.24</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>60.666666</td>\n",
       "      <td>44</td>\n",
       "      <td>R</td>\n",
       "      <td>888.0</td>\n",
       "      <td>C</td>\n",
       "      <td>40to60</td>\n",
       "      <td>S</td>\n",
       "      <td>R</td>\n",
       "      <td>794.0</td>\n",
       "      <td>C</td>\n",
       "      <td>LTE300k</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>O</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8749.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5377.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>1131.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42782</td>\n",
       "      <td>0</td>\n",
       "      <td>6437</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.416667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.85</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.85</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>16</td>\n",
       "      <td>R</td>\n",
       "      <td>11126.0</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>R</td>\n",
       "      <td>815.0</td>\n",
       "      <td>C</td>\n",
       "      <td>LTE300k</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>O</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32365.0</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7462.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2107.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27509</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>21</td>\n",
       "      <td>R</td>\n",
       "      <td>888.0</td>\n",
       "      <td>C</td>\n",
       "      <td>40to60</td>\n",
       "      <td>S</td>\n",
       "      <td>R</td>\n",
       "      <td>817.0</td>\n",
       "      <td>C</td>\n",
       "      <td>GT300k</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>O</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28168.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7546.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>119110</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>764.97</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>764.97</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>15</td>\n",
       "      <td>R</td>\n",
       "      <td>888.0</td>\n",
       "      <td>C</td>\n",
       "      <td>40to60</td>\n",
       "      <td>S</td>\n",
       "      <td>R</td>\n",
       "      <td>766.0</td>\n",
       "      <td>C</td>\n",
       "      <td>LTE300k</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>O</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>130002</td>\n",
       "      <td>0</td>\n",
       "      <td>99482</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113.91</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113.91</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>15</td>\n",
       "      <td>R</td>\n",
       "      <td>888.0</td>\n",
       "      <td>C</td>\n",
       "      <td>20to40</td>\n",
       "      <td>S</td>\n",
       "      <td>R</td>\n",
       "      <td>711.0</td>\n",
       "      <td>C</td>\n",
       "      <td>LTE100k</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>O</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17776.0</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17607.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      avg_inv_bal  avg_ckcom_bal  avg_ckcon_bal  avg_indln_bal  avg_mtg_bal  \\\n",
       "hkey                                                                          \n",
       "1          268972              0           3101              0            0   \n",
       "2           42782              0           6437              0            0   \n",
       "4               0              0          27509              0            0   \n",
       "7          119110              0              0              0            0   \n",
       "12         130002              0          99482              0            0   \n",
       "\n",
       "      avg_comln_bal  avg_conln_bal  avg_inv_num  avg_ckcom_num  avg_ckcon_num  \\\n",
       "hkey                                                                            \n",
       "1                 0              0     8.000000            0.0            1.0   \n",
       "2                 0              0     1.416667            0.0            1.0   \n",
       "4                 0              0     0.000000            0.0            1.0   \n",
       "7                 0              0     2.000000            0.0            0.0   \n",
       "12                0              0     2.000000            0.0            1.0   \n",
       "\n",
       "      avg_indln_num  avg_mtg_num  avg_comln_num  avg_conln_num  avg_bill_pay  \\\n",
       "hkey                                                                           \n",
       "1               0.0          0.0            0.0            0.0             0   \n",
       "2               0.0          0.0            0.0            0.0             0   \n",
       "4               0.0          0.0            0.0            0.0             1   \n",
       "7               0.0          0.0            0.0            0.0             0   \n",
       "12              0.0          0.0            0.0            0.0             0   \n",
       "\n",
       "      avg_direct_deposit  avg_estatement  avg_mobile_app  avg_online_banking  \\\n",
       "hkey                                                                           \n",
       "1                      1               0               0                   1   \n",
       "2                      1               1               0                   0   \n",
       "4                      1               1               0                   1   \n",
       "7                      0               0               0                   0   \n",
       "12                     1               0               0                   0   \n",
       "\n",
       "      sum_YTDINTPAID  sum_YTDITEMNSF  sum_YTDNSFFEE  sum_YTDNSFRTFA  \\\n",
       "hkey                                                                  \n",
       "1             618.24               0            0.0             0.0   \n",
       "2               2.85               0            0.0             0.0   \n",
       "4               0.00               0            0.0             0.0   \n",
       "7             764.97               0            0.0             0.0   \n",
       "12            113.91               0            0.0             0.0   \n",
       "\n",
       "      sum_YTDODFEEAT  sum_YTDPDFEES  sum_YTDOTRCHG  sum_YTDPDINT  \\\n",
       "hkey                                                               \n",
       "1                0.0            0.0            0.0        618.24   \n",
       "2                0.0            0.0            0.0          2.85   \n",
       "4                0.0            0.0            0.0          0.00   \n",
       "7                0.0            0.0            0.0        764.97   \n",
       "12               0.0            0.0            0.0        113.91   \n",
       "\n",
       "      sum_YTDRTNNSF  sum_YTDSVC  sum_YTDSVCWVD  sum_YTDTIMEOD  sum_brokerage  \\\n",
       "hkey                                                                           \n",
       "1                 0         0.0            0.0              0            4.0   \n",
       "2                 0         0.0            0.0              0            7.0   \n",
       "4                 0        35.0            0.0              0            4.0   \n",
       "7                 0         0.0            0.0              0            1.0   \n",
       "12                0         0.0            0.0              0            2.0   \n",
       "\n",
       "      sum_insurance  sum_trust  sum_full_charge_off  sum_partial_charge_off  \\\n",
       "hkey                                                                          \n",
       "1               0.0        0.0                  0.0                     0.0   \n",
       "2               0.0        0.0                  0.0                     0.0   \n",
       "4               0.0        0.0                  0.0                     0.0   \n",
       "7               0.0        0.0                  0.0                     0.0   \n",
       "12              0.0        0.0                  0.0                     0.0   \n",
       "\n",
       "      sum_sbloan_flag  sum_smallbiz_flag  sum_consumer_flag    avg_age ZIP  \\\n",
       "hkey                                                                         \n",
       "1                 0.0                0.0                9.0  60.666666  44   \n",
       "2                 0.0                0.0                2.0  64.000000  16   \n",
       "4                 0.0                0.0                1.0  70.000000  21   \n",
       "7                 0.0                0.0                2.0  69.000000  15   \n",
       "12                0.0                0.0                3.0  73.000000  15   \n",
       "\n",
       "     BIZRETL  ANNSPEND educate  income DWELTYPE MORESPON  RISKIND PROPTYPE  \\\n",
       "hkey                                                                         \n",
       "1          R     888.0       C  40to60        S        R    794.0        C   \n",
       "2          R   11126.0       C     NaN        S        R    815.0        C   \n",
       "4          R     888.0       C  40to60        S        R    817.0        C   \n",
       "7          R     888.0       C  40to60        S        R    766.0        C   \n",
       "12         R     888.0       C  20to40        S        R    711.0        C   \n",
       "\n",
       "     homemktvalue   LOR NETWORTH ownrent  total_ACH_DEPOSIT_trans  \\\n",
       "hkey                                                                \n",
       "1         LTE300k   6.0      8.0       O                     10.0   \n",
       "2         LTE300k  15.0      9.0       O                      6.0   \n",
       "4          GT300k   2.0     10.0       O                     10.0   \n",
       "7         LTE300k   3.0      7.0       O                      0.0   \n",
       "12        LTE100k  15.0      7.0       O                     11.0   \n",
       "\n",
       "      total_ATM_DEBIT_trans  total_AUTO_CR_TRANSFER_trans  \\\n",
       "hkey                                                        \n",
       "1                       3.0                           0.0   \n",
       "2                      20.0                           0.0   \n",
       "4                       1.0                           0.0   \n",
       "7                       0.0                           0.0   \n",
       "12                     13.0                           0.0   \n",
       "\n",
       "      total_AUTO_TRANSFER_DR_trans  total_Auto_Loan_Payment_trans  \\\n",
       "hkey                                                                \n",
       "1                              0.0                            0.0   \n",
       "2                              0.0                            0.0   \n",
       "4                              0.0                            0.0   \n",
       "7                              0.0                            0.0   \n",
       "12                             0.0                            0.0   \n",
       "\n",
       "      total_CHECK_trans  total_DEBIT_CARD_PURCHASE_trans  total_DEPOSIT_trans  \\\n",
       "hkey                                                                            \n",
       "1                  58.0                             40.0                  1.0   \n",
       "2                  14.0                              4.0                  1.0   \n",
       "4                  18.0                              1.0                  0.0   \n",
       "7                   0.0                              0.0                  0.0   \n",
       "12                 27.0                              0.0                  1.0   \n",
       "\n",
       "      total_FIRST_DESKTOP_DEPOSIT_trans  total_LOCKBOX_trans  \\\n",
       "hkey                                                           \n",
       "1                                   0.0                  0.0   \n",
       "2                                   0.0                  0.0   \n",
       "4                                   0.0                  0.0   \n",
       "7                                   0.0                  0.0   \n",
       "12                                  0.0                  0.0   \n",
       "\n",
       "      total_MISC_CREDIT_trans  total_MISC_DEBIT_trans  \\\n",
       "hkey                                                    \n",
       "1                         0.0                     0.0   \n",
       "2                         0.0                     0.0   \n",
       "4                         0.0                     0.0   \n",
       "7                         0.0                     0.0   \n",
       "12                        0.0                     0.0   \n",
       "\n",
       "      total_MOBILE_DEPOSIT_trans  total_ONLINE_DEBIT_trans  \\\n",
       "hkey                                                         \n",
       "1                            0.0                       0.0   \n",
       "2                            0.0                       2.0   \n",
       "4                            0.0                       0.0   \n",
       "7                            0.0                       0.0   \n",
       "12                           0.0                       0.0   \n",
       "\n",
       "      total_POS_RETURN_trans  total_TRANSFER_CREDIT_trans  WIRE_trans  \\\n",
       "hkey                                                                    \n",
       "1                        0.0                          0.0         0.0   \n",
       "2                        0.0                          0.0         0.0   \n",
       "4                        0.0                          0.0         0.0   \n",
       "7                        0.0                          0.0         0.0   \n",
       "12                       0.0                          0.0         0.0   \n",
       "\n",
       "      total_WITHDRAWAL_trans  total_ZELLE_trans  total_ACH_DEPOSIT_num  \\\n",
       "hkey                                                                     \n",
       "1                        0.0                0.0                 8749.0   \n",
       "2                        0.0                0.0                32365.0   \n",
       "4                        0.0                0.0                28168.0   \n",
       "7                        0.0                0.0                    0.0   \n",
       "12                       0.0                0.0                17776.0   \n",
       "\n",
       "      total_ATM_DEBIT_num  total_AUTO_CR_TRANSFER_num  \\\n",
       "hkey                                                    \n",
       "1                   550.0                         0.0   \n",
       "2                  1940.0                         0.0   \n",
       "4                    60.0                         0.0   \n",
       "7                     0.0                         0.0   \n",
       "12                 6500.0                         0.0   \n",
       "\n",
       "      total_AUTO_TRANSFER_DR_num  total_Auto_Loan_Payment_num  \\\n",
       "hkey                                                            \n",
       "1                            0.0                          0.0   \n",
       "2                            0.0                          0.0   \n",
       "4                            0.0                          0.0   \n",
       "7                            0.0                          0.0   \n",
       "12                           0.0                          0.0   \n",
       "\n",
       "      total_CHECK_num  total_DEBIT_CARD_PURCHASE_num  total_DEPOSIT_num  \\\n",
       "hkey                                                                      \n",
       "1              5377.0                         1999.0             1131.0   \n",
       "2              7462.0                          248.0             2800.0   \n",
       "4              7546.0                           46.0                0.0   \n",
       "7                 0.0                            0.0                0.0   \n",
       "12            17607.0                            0.0               23.0   \n",
       "\n",
       "      total_FIRST_DESKTOP_DEPOSIT_num  total_LOCKBOX_num  \\\n",
       "hkey                                                       \n",
       "1                                 0.0                0.0   \n",
       "2                                 0.0                0.0   \n",
       "4                                 0.0                0.0   \n",
       "7                                 0.0                0.0   \n",
       "12                                0.0                0.0   \n",
       "\n",
       "      total_MISC_CREDIT_num  total_MISC_DEBIT_num  total_MOBILE_DEPOSIT_num  \\\n",
       "hkey                                                                          \n",
       "1                       0.0                   0.0                       0.0   \n",
       "2                       0.0                   0.0                       0.0   \n",
       "4                       0.0                   0.0                       0.0   \n",
       "7                       0.0                   0.0                       0.0   \n",
       "12                      0.0                   0.0                       0.0   \n",
       "\n",
       "      total_ONLINE_DEBIT_num  total_POS_RETURN_num  total_TRANSFER_CREDIT_num  \\\n",
       "hkey                                                                            \n",
       "1                        0.0                   0.0                        0.0   \n",
       "2                     2107.0                   0.0                        0.0   \n",
       "4                        0.0                   0.0                        0.0   \n",
       "7                        0.0                   0.0                        0.0   \n",
       "12                       0.0                   0.0                        0.0   \n",
       "\n",
       "      WIRE_num  total_WITHDRAWAL_num  total_ZELLE_num  Segment  \n",
       "hkey                                                            \n",
       "1          0.0                   0.0              0.0        3  \n",
       "2          0.0                   0.0              0.0        3  \n",
       "4          0.0                   0.0              0.0        3  \n",
       "7          0.0                   0.0              0.0        3  \n",
       "12         0.0                   0.0              0.0        3  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add segments back to data without dummies for scatterplot\n",
    "sorted_segments = active_accts_minikmeans.sort_index().loc[:,'Segment']\n",
    "active_accts['Segment'] = sorted_segments\n",
    "active_accts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Segment</th>\n",
       "      <th>avg_inv_bal</th>\n",
       "      <th>avg_ckcom_bal</th>\n",
       "      <th>avg_ckcon_bal</th>\n",
       "      <th>avg_indln_bal</th>\n",
       "      <th>avg_mtg_bal</th>\n",
       "      <th>avg_comln_bal</th>\n",
       "      <th>avg_conln_bal</th>\n",
       "      <th>avg_inv_num</th>\n",
       "      <th>avg_ckcom_num</th>\n",
       "      <th>avg_ckcon_num</th>\n",
       "      <th>avg_indln_num</th>\n",
       "      <th>avg_mtg_num</th>\n",
       "      <th>avg_comln_num</th>\n",
       "      <th>avg_conln_num</th>\n",
       "      <th>avg_bill_pay</th>\n",
       "      <th>avg_direct_deposit</th>\n",
       "      <th>avg_estatement</th>\n",
       "      <th>avg_mobile_app</th>\n",
       "      <th>avg_online_banking</th>\n",
       "      <th>sum_YTDINTPAID</th>\n",
       "      <th>sum_YTDITEMNSF</th>\n",
       "      <th>sum_YTDNSFFEE</th>\n",
       "      <th>sum_YTDNSFRTFA</th>\n",
       "      <th>sum_YTDODFEEAT</th>\n",
       "      <th>sum_YTDPDFEES</th>\n",
       "      <th>sum_YTDOTRCHG</th>\n",
       "      <th>sum_YTDPDINT</th>\n",
       "      <th>sum_YTDRTNNSF</th>\n",
       "      <th>sum_YTDSVC</th>\n",
       "      <th>sum_YTDSVCWVD</th>\n",
       "      <th>sum_YTDTIMEOD</th>\n",
       "      <th>sum_brokerage</th>\n",
       "      <th>sum_insurance</th>\n",
       "      <th>sum_trust</th>\n",
       "      <th>sum_full_charge_off</th>\n",
       "      <th>sum_partial_charge_off</th>\n",
       "      <th>sum_sbloan_flag</th>\n",
       "      <th>sum_consumer_flag</th>\n",
       "      <th>avg_age</th>\n",
       "      <th>ANNSPEND</th>\n",
       "      <th>RISKIND</th>\n",
       "      <th>LOR</th>\n",
       "      <th>total_ACH_DEPOSIT_trans</th>\n",
       "      <th>total_ATM_DEBIT_trans</th>\n",
       "      <th>total_AUTO_CR_TRANSFER_trans</th>\n",
       "      <th>total_AUTO_TRANSFER_DR_trans</th>\n",
       "      <th>total_Auto_Loan_Payment_trans</th>\n",
       "      <th>total_CHECK_trans</th>\n",
       "      <th>total_DEBIT_CARD_PURCHASE_trans</th>\n",
       "      <th>total_DEPOSIT_trans</th>\n",
       "      <th>total_FIRST_DESKTOP_DEPOSIT_trans</th>\n",
       "      <th>total_LOCKBOX_trans</th>\n",
       "      <th>total_MISC_CREDIT_trans</th>\n",
       "      <th>total_MISC_DEBIT_trans</th>\n",
       "      <th>total_MOBILE_DEPOSIT_trans</th>\n",
       "      <th>total_ONLINE_DEBIT_trans</th>\n",
       "      <th>total_POS_RETURN_trans</th>\n",
       "      <th>total_TRANSFER_CREDIT_trans</th>\n",
       "      <th>WIRE_trans</th>\n",
       "      <th>total_WITHDRAWAL_trans</th>\n",
       "      <th>total_ZELLE_trans</th>\n",
       "      <th>total_ACH_DEPOSIT_num</th>\n",
       "      <th>total_ATM_DEBIT_num</th>\n",
       "      <th>total_AUTO_CR_TRANSFER_num</th>\n",
       "      <th>total_AUTO_TRANSFER_DR_num</th>\n",
       "      <th>total_Auto_Loan_Payment_num</th>\n",
       "      <th>total_CHECK_num</th>\n",
       "      <th>total_DEBIT_CARD_PURCHASE_num</th>\n",
       "      <th>total_DEPOSIT_num</th>\n",
       "      <th>total_FIRST_DESKTOP_DEPOSIT_num</th>\n",
       "      <th>total_LOCKBOX_num</th>\n",
       "      <th>total_MISC_CREDIT_num</th>\n",
       "      <th>total_MISC_DEBIT_num</th>\n",
       "      <th>total_MOBILE_DEPOSIT_num</th>\n",
       "      <th>total_ONLINE_DEBIT_num</th>\n",
       "      <th>total_POS_RETURN_num</th>\n",
       "      <th>total_TRANSFER_CREDIT_num</th>\n",
       "      <th>WIRE_num</th>\n",
       "      <th>total_WITHDRAWAL_num</th>\n",
       "      <th>total_ZELLE_num</th>\n",
       "      <th>ZIP_14</th>\n",
       "      <th>ZIP_15</th>\n",
       "      <th>ZIP_16</th>\n",
       "      <th>ZIP_17</th>\n",
       "      <th>ZIP_18</th>\n",
       "      <th>ZIP_19</th>\n",
       "      <th>ZIP_20</th>\n",
       "      <th>ZIP_21</th>\n",
       "      <th>ZIP_22</th>\n",
       "      <th>ZIP_23</th>\n",
       "      <th>ZIP_26</th>\n",
       "      <th>ZIP_27</th>\n",
       "      <th>ZIP_28</th>\n",
       "      <th>ZIP_29</th>\n",
       "      <th>ZIP_32</th>\n",
       "      <th>ZIP_33</th>\n",
       "      <th>ZIP_34</th>\n",
       "      <th>ZIP_37</th>\n",
       "      <th>ZIP_43</th>\n",
       "      <th>ZIP_44</th>\n",
       "      <th>educate_C</th>\n",
       "      <th>educate_NC</th>\n",
       "      <th>income_0to20</th>\n",
       "      <th>income_100to150</th>\n",
       "      <th>income_150plus</th>\n",
       "      <th>income_20to40</th>\n",
       "      <th>income_40to60</th>\n",
       "      <th>income_60to75</th>\n",
       "      <th>income_75to100</th>\n",
       "      <th>homemktvalue_GT300k</th>\n",
       "      <th>homemktvalue_LTE100k</th>\n",
       "      <th>homemktvalue_LTE300k</th>\n",
       "      <th>DWELTYPE_M</th>\n",
       "      <th>DWELTYPE_S</th>\n",
       "      <th>MORESPON_R</th>\n",
       "      <th>PROPTYPE_A</th>\n",
       "      <th>PROPTYPE_B</th>\n",
       "      <th>PROPTYPE_C</th>\n",
       "      <th>NETWORTH_1.0</th>\n",
       "      <th>NETWORTH_10.0</th>\n",
       "      <th>NETWORTH_11.0</th>\n",
       "      <th>NETWORTH_2.0</th>\n",
       "      <th>NETWORTH_3.0</th>\n",
       "      <th>NETWORTH_4.0</th>\n",
       "      <th>NETWORTH_5.0</th>\n",
       "      <th>NETWORTH_6.0</th>\n",
       "      <th>NETWORTH_7.0</th>\n",
       "      <th>NETWORTH_8.0</th>\n",
       "      <th>NETWORTH_9.0</th>\n",
       "      <th>ownrent_O</th>\n",
       "      <th>ownrent_R</th>\n",
       "      <th>N Obs</th>\n",
       "      <th>Prop Obs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sorted Segments</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>18665.957458</td>\n",
       "      <td>1631.207648</td>\n",
       "      <td>13661.768549</td>\n",
       "      <td>32.749304</td>\n",
       "      <td>10765.637883</td>\n",
       "      <td>2723.953406</td>\n",
       "      <td>107.085591</td>\n",
       "      <td>0.434927</td>\n",
       "      <td>0.010849</td>\n",
       "      <td>0.946643</td>\n",
       "      <td>0.003587</td>\n",
       "      <td>0.140657</td>\n",
       "      <td>0.014145</td>\n",
       "      <td>0.110176</td>\n",
       "      <td>0.078501</td>\n",
       "      <td>0.537098</td>\n",
       "      <td>0.496075</td>\n",
       "      <td>0.288934</td>\n",
       "      <td>0.356293</td>\n",
       "      <td>868.122644</td>\n",
       "      <td>0.222841</td>\n",
       "      <td>6.849076</td>\n",
       "      <td>0.936946</td>\n",
       "      <td>1.169185</td>\n",
       "      <td>23.112801</td>\n",
       "      <td>7.552965</td>\n",
       "      <td>868.122644</td>\n",
       "      <td>0.026589</td>\n",
       "      <td>3.936619</td>\n",
       "      <td>0.046075</td>\n",
       "      <td>0.104837</td>\n",
       "      <td>0.062547</td>\n",
       "      <td>0.005065</td>\n",
       "      <td>0.004811</td>\n",
       "      <td>0.013168</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.770575</td>\n",
       "      <td>60.846002</td>\n",
       "      <td>10926.049633</td>\n",
       "      <td>762.579894</td>\n",
       "      <td>7.926564</td>\n",
       "      <td>6.941251</td>\n",
       "      <td>1.083565</td>\n",
       "      <td>0.045834</td>\n",
       "      <td>0.072677</td>\n",
       "      <td>0.211193</td>\n",
       "      <td>12.507217</td>\n",
       "      <td>29.327425</td>\n",
       "      <td>1.620663</td>\n",
       "      <td>0.020258</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.043809</td>\n",
       "      <td>0.044315</td>\n",
       "      <td>0.293239</td>\n",
       "      <td>0.557356</td>\n",
       "      <td>0.413016</td>\n",
       "      <td>0.013421</td>\n",
       "      <td>0.021524</td>\n",
       "      <td>0.005318</td>\n",
       "      <td>0.223348</td>\n",
       "      <td>11134.122309</td>\n",
       "      <td>201.793365</td>\n",
       "      <td>45.367435</td>\n",
       "      <td>37.377817</td>\n",
       "      <td>162.156242</td>\n",
       "      <td>11510.798683</td>\n",
       "      <td>1504.513041</td>\n",
       "      <td>7642.898202</td>\n",
       "      <td>928.603697</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>388.818182</td>\n",
       "      <td>226.444670</td>\n",
       "      <td>214.298557</td>\n",
       "      <td>1356.519878</td>\n",
       "      <td>35.589516</td>\n",
       "      <td>633.968853</td>\n",
       "      <td>1750.934920</td>\n",
       "      <td>33.554318</td>\n",
       "      <td>61.659661</td>\n",
       "      <td>0.001013</td>\n",
       "      <td>0.050393</td>\n",
       "      <td>0.066852</td>\n",
       "      <td>0.057736</td>\n",
       "      <td>0.012155</td>\n",
       "      <td>0.014940</td>\n",
       "      <td>0.025070</td>\n",
       "      <td>0.067612</td>\n",
       "      <td>0.008103</td>\n",
       "      <td>0.005571</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>0.581666</td>\n",
       "      <td>0.046341</td>\n",
       "      <td>0.010129</td>\n",
       "      <td>0.003545</td>\n",
       "      <td>0.004558</td>\n",
       "      <td>0.003292</td>\n",
       "      <td>0.001519</td>\n",
       "      <td>0.001519</td>\n",
       "      <td>0.015700</td>\n",
       "      <td>0.321854</td>\n",
       "      <td>0.494302</td>\n",
       "      <td>0.020512</td>\n",
       "      <td>0.036718</td>\n",
       "      <td>0.013421</td>\n",
       "      <td>0.070651</td>\n",
       "      <td>0.100532</td>\n",
       "      <td>0.614839</td>\n",
       "      <td>0.069131</td>\n",
       "      <td>0.846543</td>\n",
       "      <td>0.004052</td>\n",
       "      <td>0.149405</td>\n",
       "      <td>0.008610</td>\n",
       "      <td>0.987592</td>\n",
       "      <td>0.856926</td>\n",
       "      <td>0.003798</td>\n",
       "      <td>0.022284</td>\n",
       "      <td>0.963788</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.043809</td>\n",
       "      <td>0.017473</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>0.000506</td>\n",
       "      <td>0.000760</td>\n",
       "      <td>0.000506</td>\n",
       "      <td>0.004052</td>\n",
       "      <td>0.003292</td>\n",
       "      <td>0.009116</td>\n",
       "      <td>0.919473</td>\n",
       "      <td>0.937199</td>\n",
       "      <td>0.004305</td>\n",
       "      <td>3949</td>\n",
       "      <td>0.007990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>16869.894187</td>\n",
       "      <td>0.419723</td>\n",
       "      <td>8146.859622</td>\n",
       "      <td>187.060384</td>\n",
       "      <td>411.854543</td>\n",
       "      <td>6.965152</td>\n",
       "      <td>112.965435</td>\n",
       "      <td>0.638209</td>\n",
       "      <td>0.000647</td>\n",
       "      <td>0.825380</td>\n",
       "      <td>0.020496</td>\n",
       "      <td>0.043195</td>\n",
       "      <td>0.000941</td>\n",
       "      <td>0.109669</td>\n",
       "      <td>0.021586</td>\n",
       "      <td>0.552624</td>\n",
       "      <td>0.234058</td>\n",
       "      <td>0.109904</td>\n",
       "      <td>0.162528</td>\n",
       "      <td>133.127740</td>\n",
       "      <td>0.233070</td>\n",
       "      <td>7.031462</td>\n",
       "      <td>1.106659</td>\n",
       "      <td>1.169300</td>\n",
       "      <td>1.754506</td>\n",
       "      <td>7.578379</td>\n",
       "      <td>133.127740</td>\n",
       "      <td>0.030756</td>\n",
       "      <td>1.384831</td>\n",
       "      <td>0.009876</td>\n",
       "      <td>0.105672</td>\n",
       "      <td>0.094244</td>\n",
       "      <td>0.004374</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>0.060525</td>\n",
       "      <td>0.000988</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.677765</td>\n",
       "      <td>69.784075</td>\n",
       "      <td>8987.312077</td>\n",
       "      <td>693.307985</td>\n",
       "      <td>12.162669</td>\n",
       "      <td>4.674379</td>\n",
       "      <td>1.999012</td>\n",
       "      <td>0.060243</td>\n",
       "      <td>0.106236</td>\n",
       "      <td>0.166337</td>\n",
       "      <td>9.821670</td>\n",
       "      <td>22.806857</td>\n",
       "      <td>1.132760</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.032026</td>\n",
       "      <td>0.039362</td>\n",
       "      <td>0.060807</td>\n",
       "      <td>0.153499</td>\n",
       "      <td>0.295147</td>\n",
       "      <td>0.000988</td>\n",
       "      <td>0.002540</td>\n",
       "      <td>0.004374</td>\n",
       "      <td>0.066733</td>\n",
       "      <td>4715.923674</td>\n",
       "      <td>307.887133</td>\n",
       "      <td>15.729966</td>\n",
       "      <td>16.804881</td>\n",
       "      <td>53.853837</td>\n",
       "      <td>3518.068567</td>\n",
       "      <td>982.381772</td>\n",
       "      <td>2400.535694</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>73.601439</td>\n",
       "      <td>77.963742</td>\n",
       "      <td>31.897291</td>\n",
       "      <td>138.836766</td>\n",
       "      <td>23.010158</td>\n",
       "      <td>1437.217833</td>\n",
       "      <td>194.790209</td>\n",
       "      <td>22.630502</td>\n",
       "      <td>12.226157</td>\n",
       "      <td>0.000423</td>\n",
       "      <td>0.224323</td>\n",
       "      <td>0.469526</td>\n",
       "      <td>0.084650</td>\n",
       "      <td>0.031603</td>\n",
       "      <td>0.011005</td>\n",
       "      <td>0.000423</td>\n",
       "      <td>0.006490</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.004656</td>\n",
       "      <td>0.076608</td>\n",
       "      <td>0.046699</td>\n",
       "      <td>0.006067</td>\n",
       "      <td>0.000564</td>\n",
       "      <td>0.000988</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>0.001834</td>\n",
       "      <td>0.005784</td>\n",
       "      <td>0.019328</td>\n",
       "      <td>0.289644</td>\n",
       "      <td>0.434537</td>\n",
       "      <td>0.790632</td>\n",
       "      <td>0.002257</td>\n",
       "      <td>0.002540</td>\n",
       "      <td>0.100451</td>\n",
       "      <td>0.065886</td>\n",
       "      <td>0.015519</td>\n",
       "      <td>0.007477</td>\n",
       "      <td>0.011428</td>\n",
       "      <td>0.418172</td>\n",
       "      <td>0.569131</td>\n",
       "      <td>0.019611</td>\n",
       "      <td>0.965435</td>\n",
       "      <td>0.913657</td>\n",
       "      <td>0.013967</td>\n",
       "      <td>0.014108</td>\n",
       "      <td>0.910130</td>\n",
       "      <td>0.005079</td>\n",
       "      <td>0.000564</td>\n",
       "      <td>0.000988</td>\n",
       "      <td>0.014814</td>\n",
       "      <td>0.010299</td>\n",
       "      <td>0.022291</td>\n",
       "      <td>0.897009</td>\n",
       "      <td>0.021868</td>\n",
       "      <td>0.019611</td>\n",
       "      <td>0.005643</td>\n",
       "      <td>0.001834</td>\n",
       "      <td>0.960920</td>\n",
       "      <td>0.009594</td>\n",
       "      <td>7088</td>\n",
       "      <td>0.014341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>28112.435255</td>\n",
       "      <td>7788.419552</td>\n",
       "      <td>15346.574429</td>\n",
       "      <td>1180.774657</td>\n",
       "      <td>6233.710779</td>\n",
       "      <td>26854.437980</td>\n",
       "      <td>491.996532</td>\n",
       "      <td>1.545479</td>\n",
       "      <td>0.059016</td>\n",
       "      <td>2.745513</td>\n",
       "      <td>0.098708</td>\n",
       "      <td>0.110222</td>\n",
       "      <td>0.064399</td>\n",
       "      <td>0.214898</td>\n",
       "      <td>0.295289</td>\n",
       "      <td>1.770025</td>\n",
       "      <td>2.800391</td>\n",
       "      <td>2.759870</td>\n",
       "      <td>1.003096</td>\n",
       "      <td>1055.461930</td>\n",
       "      <td>4.212389</td>\n",
       "      <td>111.631421</td>\n",
       "      <td>12.618343</td>\n",
       "      <td>8.887421</td>\n",
       "      <td>73.653069</td>\n",
       "      <td>62.879064</td>\n",
       "      <td>1055.461930</td>\n",
       "      <td>0.386115</td>\n",
       "      <td>20.959614</td>\n",
       "      <td>0.270544</td>\n",
       "      <td>1.368356</td>\n",
       "      <td>0.134938</td>\n",
       "      <td>0.016099</td>\n",
       "      <td>0.007604</td>\n",
       "      <td>0.015282</td>\n",
       "      <td>0.003344</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>4.891366</td>\n",
       "      <td>44.349888</td>\n",
       "      <td>10903.222098</td>\n",
       "      <td>732.323525</td>\n",
       "      <td>9.215956</td>\n",
       "      <td>41.046639</td>\n",
       "      <td>13.445807</td>\n",
       "      <td>0.606925</td>\n",
       "      <td>1.170357</td>\n",
       "      <td>1.054342</td>\n",
       "      <td>20.534502</td>\n",
       "      <td>287.624461</td>\n",
       "      <td>4.654406</td>\n",
       "      <td>0.291450</td>\n",
       "      <td>0.011765</td>\n",
       "      <td>0.161193</td>\n",
       "      <td>0.126988</td>\n",
       "      <td>2.743449</td>\n",
       "      <td>13.318472</td>\n",
       "      <td>8.180909</td>\n",
       "      <td>0.190519</td>\n",
       "      <td>0.201813</td>\n",
       "      <td>0.019542</td>\n",
       "      <td>2.228885</td>\n",
       "      <td>53471.912741</td>\n",
       "      <td>1874.895131</td>\n",
       "      <td>7496.096374</td>\n",
       "      <td>7466.241765</td>\n",
       "      <td>1905.471739</td>\n",
       "      <td>24817.925447</td>\n",
       "      <td>12344.547209</td>\n",
       "      <td>13801.873632</td>\n",
       "      <td>8837.202135</td>\n",
       "      <td>279.427131</td>\n",
       "      <td>2321.377273</td>\n",
       "      <td>971.223411</td>\n",
       "      <td>1441.773270</td>\n",
       "      <td>17385.123421</td>\n",
       "      <td>978.912914</td>\n",
       "      <td>7911.129588</td>\n",
       "      <td>26916.398350</td>\n",
       "      <td>26.207658</td>\n",
       "      <td>446.187819</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.309184</td>\n",
       "      <td>0.226532</td>\n",
       "      <td>0.192401</td>\n",
       "      <td>0.024050</td>\n",
       "      <td>0.043023</td>\n",
       "      <td>0.004062</td>\n",
       "      <td>0.030267</td>\n",
       "      <td>0.000372</td>\n",
       "      <td>0.000223</td>\n",
       "      <td>0.006365</td>\n",
       "      <td>0.065190</td>\n",
       "      <td>0.042651</td>\n",
       "      <td>0.004954</td>\n",
       "      <td>0.000545</td>\n",
       "      <td>0.000669</td>\n",
       "      <td>0.000619</td>\n",
       "      <td>0.000421</td>\n",
       "      <td>0.005895</td>\n",
       "      <td>0.038342</td>\n",
       "      <td>0.335860</td>\n",
       "      <td>0.472012</td>\n",
       "      <td>0.060435</td>\n",
       "      <td>0.080894</td>\n",
       "      <td>0.054837</td>\n",
       "      <td>0.158840</td>\n",
       "      <td>0.240254</td>\n",
       "      <td>0.160004</td>\n",
       "      <td>0.128647</td>\n",
       "      <td>0.147124</td>\n",
       "      <td>0.247313</td>\n",
       "      <td>0.601897</td>\n",
       "      <td>0.024050</td>\n",
       "      <td>0.962922</td>\n",
       "      <td>0.812429</td>\n",
       "      <td>0.018601</td>\n",
       "      <td>0.042948</td>\n",
       "      <td>0.913533</td>\n",
       "      <td>0.044757</td>\n",
       "      <td>0.065834</td>\n",
       "      <td>0.052014</td>\n",
       "      <td>0.040620</td>\n",
       "      <td>0.037153</td>\n",
       "      <td>0.063977</td>\n",
       "      <td>0.081216</td>\n",
       "      <td>0.126393</td>\n",
       "      <td>0.207411</td>\n",
       "      <td>0.155818</td>\n",
       "      <td>0.096869</td>\n",
       "      <td>0.839674</td>\n",
       "      <td>0.021945</td>\n",
       "      <td>40374</td>\n",
       "      <td>0.081687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>3808.826041</td>\n",
       "      <td>61.225876</td>\n",
       "      <td>3636.344711</td>\n",
       "      <td>182.387172</td>\n",
       "      <td>2842.288607</td>\n",
       "      <td>221.406830</td>\n",
       "      <td>51.993476</td>\n",
       "      <td>0.233403</td>\n",
       "      <td>0.002555</td>\n",
       "      <td>0.996128</td>\n",
       "      <td>0.017177</td>\n",
       "      <td>0.037065</td>\n",
       "      <td>0.001457</td>\n",
       "      <td>0.047746</td>\n",
       "      <td>0.025116</td>\n",
       "      <td>0.468243</td>\n",
       "      <td>0.561625</td>\n",
       "      <td>0.369889</td>\n",
       "      <td>0.264879</td>\n",
       "      <td>158.316391</td>\n",
       "      <td>1.116451</td>\n",
       "      <td>29.517894</td>\n",
       "      <td>3.989257</td>\n",
       "      <td>6.636254</td>\n",
       "      <td>7.704915</td>\n",
       "      <td>21.621848</td>\n",
       "      <td>158.316391</td>\n",
       "      <td>0.117243</td>\n",
       "      <td>2.743107</td>\n",
       "      <td>0.010859</td>\n",
       "      <td>0.488034</td>\n",
       "      <td>0.018654</td>\n",
       "      <td>0.001137</td>\n",
       "      <td>0.000416</td>\n",
       "      <td>0.028566</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>1.336791</td>\n",
       "      <td>46.786039</td>\n",
       "      <td>10364.582436</td>\n",
       "      <td>643.912389</td>\n",
       "      <td>3.942304</td>\n",
       "      <td>6.295821</td>\n",
       "      <td>4.014820</td>\n",
       "      <td>0.032714</td>\n",
       "      <td>0.078358</td>\n",
       "      <td>0.067294</td>\n",
       "      <td>3.395538</td>\n",
       "      <td>62.429444</td>\n",
       "      <td>1.163758</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.054128</td>\n",
       "      <td>0.045903</td>\n",
       "      <td>0.320246</td>\n",
       "      <td>0.604328</td>\n",
       "      <td>1.352466</td>\n",
       "      <td>0.016529</td>\n",
       "      <td>0.011025</td>\n",
       "      <td>0.003372</td>\n",
       "      <td>0.486874</td>\n",
       "      <td>5314.687713</td>\n",
       "      <td>590.363640</td>\n",
       "      <td>7.865365</td>\n",
       "      <td>11.716451</td>\n",
       "      <td>28.679864</td>\n",
       "      <td>1714.025108</td>\n",
       "      <td>2546.348067</td>\n",
       "      <td>1744.767545</td>\n",
       "      <td>4.992684</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47.272900</td>\n",
       "      <td>53.717572</td>\n",
       "      <td>169.583455</td>\n",
       "      <td>367.338195</td>\n",
       "      <td>130.508978</td>\n",
       "      <td>10.172069</td>\n",
       "      <td>285.896730</td>\n",
       "      <td>5.347550</td>\n",
       "      <td>95.357571</td>\n",
       "      <td>0.001106</td>\n",
       "      <td>0.244201</td>\n",
       "      <td>0.167263</td>\n",
       "      <td>0.227476</td>\n",
       "      <td>0.030440</td>\n",
       "      <td>0.053846</td>\n",
       "      <td>0.010711</td>\n",
       "      <td>0.031938</td>\n",
       "      <td>0.003709</td>\n",
       "      <td>0.002149</td>\n",
       "      <td>0.005544</td>\n",
       "      <td>0.091335</td>\n",
       "      <td>0.052842</td>\n",
       "      <td>0.008422</td>\n",
       "      <td>0.001404</td>\n",
       "      <td>0.002713</td>\n",
       "      <td>0.001490</td>\n",
       "      <td>0.000894</td>\n",
       "      <td>0.007371</td>\n",
       "      <td>0.036972</td>\n",
       "      <td>0.158770</td>\n",
       "      <td>0.354199</td>\n",
       "      <td>0.117902</td>\n",
       "      <td>0.017439</td>\n",
       "      <td>0.009535</td>\n",
       "      <td>0.182977</td>\n",
       "      <td>0.146287</td>\n",
       "      <td>0.062770</td>\n",
       "      <td>0.037168</td>\n",
       "      <td>0.069748</td>\n",
       "      <td>0.317039</td>\n",
       "      <td>0.579683</td>\n",
       "      <td>0.325853</td>\n",
       "      <td>0.592802</td>\n",
       "      <td>0.316231</td>\n",
       "      <td>0.279958</td>\n",
       "      <td>0.052984</td>\n",
       "      <td>0.558175</td>\n",
       "      <td>0.313910</td>\n",
       "      <td>0.003756</td>\n",
       "      <td>0.002070</td>\n",
       "      <td>0.184012</td>\n",
       "      <td>0.077072</td>\n",
       "      <td>0.074077</td>\n",
       "      <td>0.045676</td>\n",
       "      <td>0.042657</td>\n",
       "      <td>0.034745</td>\n",
       "      <td>0.018011</td>\n",
       "      <td>0.008751</td>\n",
       "      <td>0.161844</td>\n",
       "      <td>0.118756</td>\n",
       "      <td>127530</td>\n",
       "      <td>0.258026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>16260.758176</td>\n",
       "      <td>90.801359</td>\n",
       "      <td>10333.611008</td>\n",
       "      <td>454.945917</td>\n",
       "      <td>7706.542520</td>\n",
       "      <td>642.493124</td>\n",
       "      <td>177.399902</td>\n",
       "      <td>0.567588</td>\n",
       "      <td>0.003038</td>\n",
       "      <td>0.985849</td>\n",
       "      <td>0.039359</td>\n",
       "      <td>0.115326</td>\n",
       "      <td>0.003663</td>\n",
       "      <td>0.104536</td>\n",
       "      <td>0.073426</td>\n",
       "      <td>0.607402</td>\n",
       "      <td>0.595068</td>\n",
       "      <td>0.336841</td>\n",
       "      <td>0.295216</td>\n",
       "      <td>563.330452</td>\n",
       "      <td>0.304543</td>\n",
       "      <td>8.884708</td>\n",
       "      <td>1.143401</td>\n",
       "      <td>1.525275</td>\n",
       "      <td>9.586035</td>\n",
       "      <td>10.933917</td>\n",
       "      <td>563.330452</td>\n",
       "      <td>0.033082</td>\n",
       "      <td>2.330471</td>\n",
       "      <td>0.025423</td>\n",
       "      <td>0.152830</td>\n",
       "      <td>0.096562</td>\n",
       "      <td>0.005293</td>\n",
       "      <td>0.002268</td>\n",
       "      <td>0.025533</td>\n",
       "      <td>0.000666</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>1.900977</td>\n",
       "      <td>58.832279</td>\n",
       "      <td>9337.514012</td>\n",
       "      <td>737.350275</td>\n",
       "      <td>10.692733</td>\n",
       "      <td>7.144666</td>\n",
       "      <td>2.592274</td>\n",
       "      <td>0.050299</td>\n",
       "      <td>0.139722</td>\n",
       "      <td>0.355911</td>\n",
       "      <td>9.078237</td>\n",
       "      <td>41.539301</td>\n",
       "      <td>1.440101</td>\n",
       "      <td>0.000609</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.046494</td>\n",
       "      <td>0.040246</td>\n",
       "      <td>0.344833</td>\n",
       "      <td>0.616259</td>\n",
       "      <td>0.533678</td>\n",
       "      <td>0.001383</td>\n",
       "      <td>0.012848</td>\n",
       "      <td>0.004415</td>\n",
       "      <td>0.191991</td>\n",
       "      <td>8681.652189</td>\n",
       "      <td>403.817692</td>\n",
       "      <td>22.630214</td>\n",
       "      <td>29.260184</td>\n",
       "      <td>187.132684</td>\n",
       "      <td>4653.043271</td>\n",
       "      <td>1858.511982</td>\n",
       "      <td>4034.837513</td>\n",
       "      <td>9.078763</td>\n",
       "      <td>0.003263</td>\n",
       "      <td>276.443678</td>\n",
       "      <td>184.814533</td>\n",
       "      <td>195.133084</td>\n",
       "      <td>848.941119</td>\n",
       "      <td>43.646014</td>\n",
       "      <td>27.832902</td>\n",
       "      <td>876.883880</td>\n",
       "      <td>19.438861</td>\n",
       "      <td>45.545412</td>\n",
       "      <td>0.000932</td>\n",
       "      <td>0.266729</td>\n",
       "      <td>0.186193</td>\n",
       "      <td>0.156166</td>\n",
       "      <td>0.035045</td>\n",
       "      <td>0.025026</td>\n",
       "      <td>0.012508</td>\n",
       "      <td>0.042824</td>\n",
       "      <td>0.004396</td>\n",
       "      <td>0.002109</td>\n",
       "      <td>0.006936</td>\n",
       "      <td>0.095518</td>\n",
       "      <td>0.063074</td>\n",
       "      <td>0.007713</td>\n",
       "      <td>0.001589</td>\n",
       "      <td>0.001811</td>\n",
       "      <td>0.001503</td>\n",
       "      <td>0.001437</td>\n",
       "      <td>0.008214</td>\n",
       "      <td>0.067447</td>\n",
       "      <td>0.394241</td>\n",
       "      <td>0.435432</td>\n",
       "      <td>0.077609</td>\n",
       "      <td>0.093660</td>\n",
       "      <td>0.062662</td>\n",
       "      <td>0.187900</td>\n",
       "      <td>0.242978</td>\n",
       "      <td>0.156480</td>\n",
       "      <td>0.132881</td>\n",
       "      <td>0.186095</td>\n",
       "      <td>0.231406</td>\n",
       "      <td>0.581729</td>\n",
       "      <td>0.002096</td>\n",
       "      <td>0.994301</td>\n",
       "      <td>0.899538</td>\n",
       "      <td>0.000615</td>\n",
       "      <td>0.032501</td>\n",
       "      <td>0.950310</td>\n",
       "      <td>0.003967</td>\n",
       "      <td>0.081164</td>\n",
       "      <td>0.077752</td>\n",
       "      <td>0.013330</td>\n",
       "      <td>0.033088</td>\n",
       "      <td>0.068659</td>\n",
       "      <td>0.071491</td>\n",
       "      <td>0.142602</td>\n",
       "      <td>0.239531</td>\n",
       "      <td>0.170019</td>\n",
       "      <td>0.096685</td>\n",
       "      <td>0.962789</td>\n",
       "      <td>0.004196</td>\n",
       "      <td>315312</td>\n",
       "      <td>0.637957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Segment   avg_inv_bal  avg_ckcom_bal  avg_ckcon_bal  \\\n",
       "Sorted Segments                                                        \n",
       "0                      0  18665.957458    1631.207648   13661.768549   \n",
       "1                      4  16869.894187       0.419723    8146.859622   \n",
       "2                      1  28112.435255    7788.419552   15346.574429   \n",
       "3                      2   3808.826041      61.225876    3636.344711   \n",
       "4                      3  16260.758176      90.801359   10333.611008   \n",
       "\n",
       "                 avg_indln_bal   avg_mtg_bal  avg_comln_bal  avg_conln_bal  \\\n",
       "Sorted Segments                                                              \n",
       "0                    32.749304  10765.637883    2723.953406     107.085591   \n",
       "1                   187.060384    411.854543       6.965152     112.965435   \n",
       "2                  1180.774657   6233.710779   26854.437980     491.996532   \n",
       "3                   182.387172   2842.288607     221.406830      51.993476   \n",
       "4                   454.945917   7706.542520     642.493124     177.399902   \n",
       "\n",
       "                 avg_inv_num  avg_ckcom_num  avg_ckcon_num  avg_indln_num  \\\n",
       "Sorted Segments                                                             \n",
       "0                   0.434927       0.010849       0.946643       0.003587   \n",
       "1                   0.638209       0.000647       0.825380       0.020496   \n",
       "2                   1.545479       0.059016       2.745513       0.098708   \n",
       "3                   0.233403       0.002555       0.996128       0.017177   \n",
       "4                   0.567588       0.003038       0.985849       0.039359   \n",
       "\n",
       "                 avg_mtg_num  avg_comln_num  avg_conln_num  avg_bill_pay  \\\n",
       "Sorted Segments                                                            \n",
       "0                   0.140657       0.014145       0.110176      0.078501   \n",
       "1                   0.043195       0.000941       0.109669      0.021586   \n",
       "2                   0.110222       0.064399       0.214898      0.295289   \n",
       "3                   0.037065       0.001457       0.047746      0.025116   \n",
       "4                   0.115326       0.003663       0.104536      0.073426   \n",
       "\n",
       "                 avg_direct_deposit  avg_estatement  avg_mobile_app  \\\n",
       "Sorted Segments                                                       \n",
       "0                          0.537098        0.496075        0.288934   \n",
       "1                          0.552624        0.234058        0.109904   \n",
       "2                          1.770025        2.800391        2.759870   \n",
       "3                          0.468243        0.561625        0.369889   \n",
       "4                          0.607402        0.595068        0.336841   \n",
       "\n",
       "                 avg_online_banking  sum_YTDINTPAID  sum_YTDITEMNSF  \\\n",
       "Sorted Segments                                                       \n",
       "0                          0.356293      868.122644        0.222841   \n",
       "1                          0.162528      133.127740        0.233070   \n",
       "2                          1.003096     1055.461930        4.212389   \n",
       "3                          0.264879      158.316391        1.116451   \n",
       "4                          0.295216      563.330452        0.304543   \n",
       "\n",
       "                 sum_YTDNSFFEE  sum_YTDNSFRTFA  sum_YTDODFEEAT  sum_YTDPDFEES  \\\n",
       "Sorted Segments                                                                 \n",
       "0                     6.849076        0.936946        1.169185      23.112801   \n",
       "1                     7.031462        1.106659        1.169300       1.754506   \n",
       "2                   111.631421       12.618343        8.887421      73.653069   \n",
       "3                    29.517894        3.989257        6.636254       7.704915   \n",
       "4                     8.884708        1.143401        1.525275       9.586035   \n",
       "\n",
       "                 sum_YTDOTRCHG  sum_YTDPDINT  sum_YTDRTNNSF  sum_YTDSVC  \\\n",
       "Sorted Segments                                                           \n",
       "0                     7.552965    868.122644       0.026589    3.936619   \n",
       "1                     7.578379    133.127740       0.030756    1.384831   \n",
       "2                    62.879064   1055.461930       0.386115   20.959614   \n",
       "3                    21.621848    158.316391       0.117243    2.743107   \n",
       "4                    10.933917    563.330452       0.033082    2.330471   \n",
       "\n",
       "                 sum_YTDSVCWVD  sum_YTDTIMEOD  sum_brokerage  sum_insurance  \\\n",
       "Sorted Segments                                                               \n",
       "0                     0.046075       0.104837       0.062547       0.005065   \n",
       "1                     0.009876       0.105672       0.094244       0.004374   \n",
       "2                     0.270544       1.368356       0.134938       0.016099   \n",
       "3                     0.010859       0.488034       0.018654       0.001137   \n",
       "4                     0.025423       0.152830       0.096562       0.005293   \n",
       "\n",
       "                 sum_trust  sum_full_charge_off  sum_partial_charge_off  \\\n",
       "Sorted Segments                                                           \n",
       "0                 0.004811             0.013168                0.000000   \n",
       "1                 0.000141             0.060525                0.000988   \n",
       "2                 0.007604             0.015282                0.003344   \n",
       "3                 0.000416             0.028566                0.000133   \n",
       "4                 0.002268             0.025533                0.000666   \n",
       "\n",
       "                 sum_sbloan_flag  sum_consumer_flag    avg_age      ANNSPEND  \\\n",
       "Sorted Segments                                                                \n",
       "0                       0.000000           1.770575  60.846002  10926.049633   \n",
       "1                       0.000000           1.677765  69.784075   8987.312077   \n",
       "2                       0.000050           4.891366  44.349888  10903.222098   \n",
       "3                       0.000047           1.336791  46.786039  10364.582436   \n",
       "4                       0.000029           1.900977  58.832279   9337.514012   \n",
       "\n",
       "                    RISKIND        LOR  total_ACH_DEPOSIT_trans  \\\n",
       "Sorted Segments                                                   \n",
       "0                762.579894   7.926564                 6.941251   \n",
       "1                693.307985  12.162669                 4.674379   \n",
       "2                732.323525   9.215956                41.046639   \n",
       "3                643.912389   3.942304                 6.295821   \n",
       "4                737.350275  10.692733                 7.144666   \n",
       "\n",
       "                 total_ATM_DEBIT_trans  total_AUTO_CR_TRANSFER_trans  \\\n",
       "Sorted Segments                                                        \n",
       "0                             1.083565                      0.045834   \n",
       "1                             1.999012                      0.060243   \n",
       "2                            13.445807                      0.606925   \n",
       "3                             4.014820                      0.032714   \n",
       "4                             2.592274                      0.050299   \n",
       "\n",
       "                 total_AUTO_TRANSFER_DR_trans  total_Auto_Loan_Payment_trans  \\\n",
       "Sorted Segments                                                                \n",
       "0                                    0.072677                       0.211193   \n",
       "1                                    0.106236                       0.166337   \n",
       "2                                    1.170357                       1.054342   \n",
       "3                                    0.078358                       0.067294   \n",
       "4                                    0.139722                       0.355911   \n",
       "\n",
       "                 total_CHECK_trans  total_DEBIT_CARD_PURCHASE_trans  \\\n",
       "Sorted Segments                                                       \n",
       "0                        12.507217                        29.327425   \n",
       "1                         9.821670                        22.806857   \n",
       "2                        20.534502                       287.624461   \n",
       "3                         3.395538                        62.429444   \n",
       "4                         9.078237                        41.539301   \n",
       "\n",
       "                 total_DEPOSIT_trans  total_FIRST_DESKTOP_DEPOSIT_trans  \\\n",
       "Sorted Segments                                                           \n",
       "0                           1.620663                           0.020258   \n",
       "1                           1.132760                           0.000000   \n",
       "2                           4.654406                           0.291450   \n",
       "3                           1.163758                           0.000321   \n",
       "4                           1.440101                           0.000609   \n",
       "\n",
       "                 total_LOCKBOX_trans  total_MISC_CREDIT_trans  \\\n",
       "Sorted Segments                                                 \n",
       "0                           0.000000                 0.043809   \n",
       "1                           0.000000                 0.032026   \n",
       "2                           0.011765                 0.161193   \n",
       "3                           0.000000                 0.054128   \n",
       "4                           0.000006                 0.046494   \n",
       "\n",
       "                 total_MISC_DEBIT_trans  total_MOBILE_DEPOSIT_trans  \\\n",
       "Sorted Segments                                                       \n",
       "0                              0.044315                    0.293239   \n",
       "1                              0.039362                    0.060807   \n",
       "2                              0.126988                    2.743449   \n",
       "3                              0.045903                    0.320246   \n",
       "4                              0.040246                    0.344833   \n",
       "\n",
       "                 total_ONLINE_DEBIT_trans  total_POS_RETURN_trans  \\\n",
       "Sorted Segments                                                     \n",
       "0                                0.557356                0.413016   \n",
       "1                                0.153499                0.295147   \n",
       "2                               13.318472                8.180909   \n",
       "3                                0.604328                1.352466   \n",
       "4                                0.616259                0.533678   \n",
       "\n",
       "                 total_TRANSFER_CREDIT_trans  WIRE_trans  \\\n",
       "Sorted Segments                                            \n",
       "0                                   0.013421    0.021524   \n",
       "1                                   0.000988    0.002540   \n",
       "2                                   0.190519    0.201813   \n",
       "3                                   0.016529    0.011025   \n",
       "4                                   0.001383    0.012848   \n",
       "\n",
       "                 total_WITHDRAWAL_trans  total_ZELLE_trans  \\\n",
       "Sorted Segments                                              \n",
       "0                              0.005318           0.223348   \n",
       "1                              0.004374           0.066733   \n",
       "2                              0.019542           2.228885   \n",
       "3                              0.003372           0.486874   \n",
       "4                              0.004415           0.191991   \n",
       "\n",
       "                 total_ACH_DEPOSIT_num  total_ATM_DEBIT_num  \\\n",
       "Sorted Segments                                               \n",
       "0                         11134.122309           201.793365   \n",
       "1                          4715.923674           307.887133   \n",
       "2                         53471.912741          1874.895131   \n",
       "3                          5314.687713           590.363640   \n",
       "4                          8681.652189           403.817692   \n",
       "\n",
       "                 total_AUTO_CR_TRANSFER_num  total_AUTO_TRANSFER_DR_num  \\\n",
       "Sorted Segments                                                           \n",
       "0                                 45.367435                   37.377817   \n",
       "1                                 15.729966                   16.804881   \n",
       "2                               7496.096374                 7466.241765   \n",
       "3                                  7.865365                   11.716451   \n",
       "4                                 22.630214                   29.260184   \n",
       "\n",
       "                 total_Auto_Loan_Payment_num  total_CHECK_num  \\\n",
       "Sorted Segments                                                 \n",
       "0                                 162.156242     11510.798683   \n",
       "1                                  53.853837      3518.068567   \n",
       "2                                1905.471739     24817.925447   \n",
       "3                                  28.679864      1714.025108   \n",
       "4                                 187.132684      4653.043271   \n",
       "\n",
       "                 total_DEBIT_CARD_PURCHASE_num  total_DEPOSIT_num  \\\n",
       "Sorted Segments                                                     \n",
       "0                                  1504.513041        7642.898202   \n",
       "1                                   982.381772        2400.535694   \n",
       "2                                 12344.547209       13801.873632   \n",
       "3                                  2546.348067        1744.767545   \n",
       "4                                  1858.511982        4034.837513   \n",
       "\n",
       "                 total_FIRST_DESKTOP_DEPOSIT_num  total_LOCKBOX_num  \\\n",
       "Sorted Segments                                                       \n",
       "0                                     928.603697           0.000000   \n",
       "1                                       0.000000           0.000000   \n",
       "2                                    8837.202135         279.427131   \n",
       "3                                       4.992684           0.000000   \n",
       "4                                       9.078763           0.003263   \n",
       "\n",
       "                 total_MISC_CREDIT_num  total_MISC_DEBIT_num  \\\n",
       "Sorted Segments                                                \n",
       "0                           388.818182            226.444670   \n",
       "1                            73.601439             77.963742   \n",
       "2                          2321.377273            971.223411   \n",
       "3                            47.272900             53.717572   \n",
       "4                           276.443678            184.814533   \n",
       "\n",
       "                 total_MOBILE_DEPOSIT_num  total_ONLINE_DEBIT_num  \\\n",
       "Sorted Segments                                                     \n",
       "0                              214.298557             1356.519878   \n",
       "1                               31.897291              138.836766   \n",
       "2                             1441.773270            17385.123421   \n",
       "3                              169.583455              367.338195   \n",
       "4                              195.133084              848.941119   \n",
       "\n",
       "                 total_POS_RETURN_num  total_TRANSFER_CREDIT_num  \\\n",
       "Sorted Segments                                                    \n",
       "0                           35.589516                 633.968853   \n",
       "1                           23.010158                1437.217833   \n",
       "2                          978.912914                7911.129588   \n",
       "3                          130.508978                  10.172069   \n",
       "4                           43.646014                  27.832902   \n",
       "\n",
       "                     WIRE_num  total_WITHDRAWAL_num  total_ZELLE_num  \\\n",
       "Sorted Segments                                                        \n",
       "0                 1750.934920             33.554318        61.659661   \n",
       "1                  194.790209             22.630502        12.226157   \n",
       "2                26916.398350             26.207658       446.187819   \n",
       "3                  285.896730              5.347550        95.357571   \n",
       "4                  876.883880             19.438861        45.545412   \n",
       "\n",
       "                   ZIP_14    ZIP_15    ZIP_16    ZIP_17    ZIP_18    ZIP_19  \\\n",
       "Sorted Segments                                                               \n",
       "0                0.001013  0.050393  0.066852  0.057736  0.012155  0.014940   \n",
       "1                0.000423  0.224323  0.469526  0.084650  0.031603  0.011005   \n",
       "2                0.000471  0.309184  0.226532  0.192401  0.024050  0.043023   \n",
       "3                0.001106  0.244201  0.167263  0.227476  0.030440  0.053846   \n",
       "4                0.000932  0.266729  0.186193  0.156166  0.035045  0.025026   \n",
       "\n",
       "                   ZIP_20    ZIP_21    ZIP_22    ZIP_23    ZIP_26    ZIP_27  \\\n",
       "Sorted Segments                                                               \n",
       "0                0.025070  0.067612  0.008103  0.005571  0.000253  0.581666   \n",
       "1                0.000423  0.006490  0.000282  0.000282  0.004656  0.076608   \n",
       "2                0.004062  0.030267  0.000372  0.000223  0.006365  0.065190   \n",
       "3                0.010711  0.031938  0.003709  0.002149  0.005544  0.091335   \n",
       "4                0.012508  0.042824  0.004396  0.002109  0.006936  0.095518   \n",
       "\n",
       "                   ZIP_28    ZIP_29    ZIP_32    ZIP_33    ZIP_34    ZIP_37  \\\n",
       "Sorted Segments                                                               \n",
       "0                0.046341  0.010129  0.003545  0.004558  0.003292  0.001519   \n",
       "1                0.046699  0.006067  0.000564  0.000988  0.000847  0.001834   \n",
       "2                0.042651  0.004954  0.000545  0.000669  0.000619  0.000421   \n",
       "3                0.052842  0.008422  0.001404  0.002713  0.001490  0.000894   \n",
       "4                0.063074  0.007713  0.001589  0.001811  0.001503  0.001437   \n",
       "\n",
       "                   ZIP_43    ZIP_44  educate_C  educate_NC  income_0to20  \\\n",
       "Sorted Segments                                                            \n",
       "0                0.001519  0.015700   0.321854    0.494302      0.020512   \n",
       "1                0.005784  0.019328   0.289644    0.434537      0.790632   \n",
       "2                0.005895  0.038342   0.335860    0.472012      0.060435   \n",
       "3                0.007371  0.036972   0.158770    0.354199      0.117902   \n",
       "4                0.008214  0.067447   0.394241    0.435432      0.077609   \n",
       "\n",
       "                 income_100to150  income_150plus  income_20to40  \\\n",
       "Sorted Segments                                                   \n",
       "0                       0.036718        0.013421       0.070651   \n",
       "1                       0.002257        0.002540       0.100451   \n",
       "2                       0.080894        0.054837       0.158840   \n",
       "3                       0.017439        0.009535       0.182977   \n",
       "4                       0.093660        0.062662       0.187900   \n",
       "\n",
       "                 income_40to60  income_60to75  income_75to100  \\\n",
       "Sorted Segments                                                 \n",
       "0                     0.100532       0.614839        0.069131   \n",
       "1                     0.065886       0.015519        0.007477   \n",
       "2                     0.240254       0.160004        0.128647   \n",
       "3                     0.146287       0.062770        0.037168   \n",
       "4                     0.242978       0.156480        0.132881   \n",
       "\n",
       "                 homemktvalue_GT300k  homemktvalue_LTE100k  \\\n",
       "Sorted Segments                                              \n",
       "0                           0.846543              0.004052   \n",
       "1                           0.011428              0.418172   \n",
       "2                           0.147124              0.247313   \n",
       "3                           0.069748              0.317039   \n",
       "4                           0.186095              0.231406   \n",
       "\n",
       "                 homemktvalue_LTE300k  DWELTYPE_M  DWELTYPE_S  MORESPON_R  \\\n",
       "Sorted Segments                                                             \n",
       "0                            0.149405    0.008610    0.987592    0.856926   \n",
       "1                            0.569131    0.019611    0.965435    0.913657   \n",
       "2                            0.601897    0.024050    0.962922    0.812429   \n",
       "3                            0.579683    0.325853    0.592802    0.316231   \n",
       "4                            0.581729    0.002096    0.994301    0.899538   \n",
       "\n",
       "                 PROPTYPE_A  PROPTYPE_B  PROPTYPE_C  NETWORTH_1.0  \\\n",
       "Sorted Segments                                                     \n",
       "0                  0.003798    0.022284    0.963788      0.000000   \n",
       "1                  0.013967    0.014108    0.910130      0.005079   \n",
       "2                  0.018601    0.042948    0.913533      0.044757   \n",
       "3                  0.279958    0.052984    0.558175      0.313910   \n",
       "4                  0.000615    0.032501    0.950310      0.003967   \n",
       "\n",
       "                 NETWORTH_10.0  NETWORTH_11.0  NETWORTH_2.0  NETWORTH_3.0  \\\n",
       "Sorted Segments                                                             \n",
       "0                     0.043809       0.017473      0.000253      0.000506   \n",
       "1                     0.000564       0.000988      0.014814      0.010299   \n",
       "2                     0.065834       0.052014      0.040620      0.037153   \n",
       "3                     0.003756       0.002070      0.184012      0.077072   \n",
       "4                     0.081164       0.077752      0.013330      0.033088   \n",
       "\n",
       "                 NETWORTH_4.0  NETWORTH_5.0  NETWORTH_6.0  NETWORTH_7.0  \\\n",
       "Sorted Segments                                                           \n",
       "0                    0.000760      0.000506      0.004052      0.003292   \n",
       "1                    0.022291      0.897009      0.021868      0.019611   \n",
       "2                    0.063977      0.081216      0.126393      0.207411   \n",
       "3                    0.074077      0.045676      0.042657      0.034745   \n",
       "4                    0.068659      0.071491      0.142602      0.239531   \n",
       "\n",
       "                 NETWORTH_8.0  NETWORTH_9.0  ownrent_O  ownrent_R   N Obs  \\\n",
       "Sorted Segments                                                             \n",
       "0                    0.009116      0.919473   0.937199   0.004305    3949   \n",
       "1                    0.005643      0.001834   0.960920   0.009594    7088   \n",
       "2                    0.155818      0.096869   0.839674   0.021945   40374   \n",
       "3                    0.018011      0.008751   0.161844   0.118756  127530   \n",
       "4                    0.170019      0.096685   0.962789   0.004196  315312   \n",
       "\n",
       "                 Prop Obs  \n",
       "Sorted Segments            \n",
       "0                0.007990  \n",
       "1                0.014341  \n",
       "2                0.081687  \n",
       "3                0.258026  \n",
       "4                0.637957  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_accts_mini_freq = active_accts_minikmeans.groupby([\"Segment\"]).mean()\n",
    "active_accts_mini_freq[\"N Obs\"] = active_accts_minikmeans[[\"Segment\", \"avg_inv_bal\"]].groupby([\"Segment\"]).count()\n",
    "active_accts_mini_freq[\"Prop Obs\"] = active_accts_mini_freq[\"N Obs\"] / active_accts_mini_freq[\"N Obs\"].sum()\n",
    "active_accts_mini_freq = active_accts_mini_freq.sort_values('N Obs')\n",
    "active_accts_mini_freq = active_accts_mini_freq.reset_index()\n",
    "active_accts_mini_freq.index.name = 'Sorted Segments'\n",
    "active_accts_mini_freq\n",
    "\n",
    "active_accts_minikmeans['Segment'] = active_accts_minikmeans['Segment'].map(dict(zip(list(active_accts_mini_freq[\"Segment\"]),list(active_accts_mini_freq.index))))\n",
    "active_accts_mini_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#bar_chart_trans(active_accts_mini_freq,active_accts_mini_freq.loc[:,'avg_inv_bal':'avg_conln_num'].columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#demo_graphs(active_accts_minikmeans, active_accts_mini_freq, mini_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#bar_chart_ob_fees(active_accts_mini_freq, active_accts_mini_freq.loc[:,'avg_bill_pay':'avg_online_banking'].columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#bar_chart_ob_fees(active_accts_mini_freq,active_accts_mini_freq.loc[:,'sum_YTDINTPAID':'sum_YTDTIMEOD'].columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#bar_chart_trans(active_accts_mini_freq,active_accts_mini_freq.loc[:,'total_ACH_DEPOSIT_trans':'total_ZELLE_num'].columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#zip_graphs(active_accts_minikmeans, mini_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#networth_graphs(active_accts_minikmeans, mini_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#bit_graphs(active_accts_mini_freq, mini_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sbloan_graphs(active_accts_mini_freq, mini_clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MiniBatch with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA()"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA()\n",
    "pca.fit(active_accts_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cumulative Explained Variance')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 9))\n",
    "plt.plot(range(1, len(pca.explained_variance_ratio_)+1), pca.explained_variance_ratio_.cumsum(), marker = 'o', linestyle = '--')\n",
    "plt.title(\"Explained Variance by Components\")\n",
    "plt.xlabel(\"Number of Components\")\n",
    "plt.ylabel(\"Cumulative Explained Variance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=80)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_comps = 80\n",
    "pca = PCA(n_components = n_comps)\n",
    "pca.fit(active_accts_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_inv_bal</th>\n",
       "      <th>avg_ckcom_bal</th>\n",
       "      <th>avg_ckcon_bal</th>\n",
       "      <th>avg_indln_bal</th>\n",
       "      <th>avg_mtg_bal</th>\n",
       "      <th>avg_comln_bal</th>\n",
       "      <th>avg_conln_bal</th>\n",
       "      <th>avg_inv_num</th>\n",
       "      <th>avg_ckcom_num</th>\n",
       "      <th>avg_ckcon_num</th>\n",
       "      <th>avg_indln_num</th>\n",
       "      <th>avg_mtg_num</th>\n",
       "      <th>avg_comln_num</th>\n",
       "      <th>avg_conln_num</th>\n",
       "      <th>avg_bill_pay</th>\n",
       "      <th>avg_direct_deposit</th>\n",
       "      <th>avg_estatement</th>\n",
       "      <th>avg_mobile_app</th>\n",
       "      <th>avg_online_banking</th>\n",
       "      <th>sum_YTDINTPAID</th>\n",
       "      <th>sum_YTDITEMNSF</th>\n",
       "      <th>sum_YTDNSFFEE</th>\n",
       "      <th>sum_YTDNSFRTFA</th>\n",
       "      <th>sum_YTDODFEEAT</th>\n",
       "      <th>sum_YTDPDFEES</th>\n",
       "      <th>sum_YTDOTRCHG</th>\n",
       "      <th>sum_YTDPDINT</th>\n",
       "      <th>sum_YTDRTNNSF</th>\n",
       "      <th>sum_YTDSVC</th>\n",
       "      <th>sum_YTDSVCWVD</th>\n",
       "      <th>sum_YTDTIMEOD</th>\n",
       "      <th>sum_brokerage</th>\n",
       "      <th>sum_insurance</th>\n",
       "      <th>sum_trust</th>\n",
       "      <th>sum_full_charge_off</th>\n",
       "      <th>sum_partial_charge_off</th>\n",
       "      <th>sum_sbloan_flag</th>\n",
       "      <th>sum_consumer_flag</th>\n",
       "      <th>avg_age</th>\n",
       "      <th>ANNSPEND</th>\n",
       "      <th>RISKIND</th>\n",
       "      <th>LOR</th>\n",
       "      <th>total_ACH_DEPOSIT_trans</th>\n",
       "      <th>total_ATM_DEBIT_trans</th>\n",
       "      <th>total_AUTO_CR_TRANSFER_trans</th>\n",
       "      <th>total_AUTO_TRANSFER_DR_trans</th>\n",
       "      <th>total_Auto_Loan_Payment_trans</th>\n",
       "      <th>total_CHECK_trans</th>\n",
       "      <th>total_DEBIT_CARD_PURCHASE_trans</th>\n",
       "      <th>total_DEPOSIT_trans</th>\n",
       "      <th>total_FIRST_DESKTOP_DEPOSIT_trans</th>\n",
       "      <th>total_LOCKBOX_trans</th>\n",
       "      <th>total_MISC_CREDIT_trans</th>\n",
       "      <th>total_MISC_DEBIT_trans</th>\n",
       "      <th>total_MOBILE_DEPOSIT_trans</th>\n",
       "      <th>total_ONLINE_DEBIT_trans</th>\n",
       "      <th>total_POS_RETURN_trans</th>\n",
       "      <th>total_TRANSFER_CREDIT_trans</th>\n",
       "      <th>WIRE_trans</th>\n",
       "      <th>total_WITHDRAWAL_trans</th>\n",
       "      <th>total_ZELLE_trans</th>\n",
       "      <th>total_ACH_DEPOSIT_num</th>\n",
       "      <th>total_ATM_DEBIT_num</th>\n",
       "      <th>total_AUTO_CR_TRANSFER_num</th>\n",
       "      <th>total_AUTO_TRANSFER_DR_num</th>\n",
       "      <th>total_Auto_Loan_Payment_num</th>\n",
       "      <th>total_CHECK_num</th>\n",
       "      <th>total_DEBIT_CARD_PURCHASE_num</th>\n",
       "      <th>total_DEPOSIT_num</th>\n",
       "      <th>total_FIRST_DESKTOP_DEPOSIT_num</th>\n",
       "      <th>total_LOCKBOX_num</th>\n",
       "      <th>total_MISC_CREDIT_num</th>\n",
       "      <th>total_MISC_DEBIT_num</th>\n",
       "      <th>total_MOBILE_DEPOSIT_num</th>\n",
       "      <th>total_ONLINE_DEBIT_num</th>\n",
       "      <th>total_POS_RETURN_num</th>\n",
       "      <th>total_TRANSFER_CREDIT_num</th>\n",
       "      <th>WIRE_num</th>\n",
       "      <th>total_WITHDRAWAL_num</th>\n",
       "      <th>total_ZELLE_num</th>\n",
       "      <th>ZIP_14</th>\n",
       "      <th>ZIP_15</th>\n",
       "      <th>ZIP_16</th>\n",
       "      <th>ZIP_17</th>\n",
       "      <th>ZIP_18</th>\n",
       "      <th>ZIP_19</th>\n",
       "      <th>ZIP_20</th>\n",
       "      <th>ZIP_21</th>\n",
       "      <th>ZIP_22</th>\n",
       "      <th>ZIP_23</th>\n",
       "      <th>ZIP_26</th>\n",
       "      <th>ZIP_27</th>\n",
       "      <th>ZIP_28</th>\n",
       "      <th>ZIP_29</th>\n",
       "      <th>ZIP_32</th>\n",
       "      <th>ZIP_33</th>\n",
       "      <th>ZIP_34</th>\n",
       "      <th>ZIP_37</th>\n",
       "      <th>ZIP_43</th>\n",
       "      <th>ZIP_44</th>\n",
       "      <th>educate_C</th>\n",
       "      <th>educate_NC</th>\n",
       "      <th>income_0to20</th>\n",
       "      <th>income_100to150</th>\n",
       "      <th>income_150plus</th>\n",
       "      <th>income_20to40</th>\n",
       "      <th>income_40to60</th>\n",
       "      <th>income_60to75</th>\n",
       "      <th>income_75to100</th>\n",
       "      <th>homemktvalue_GT300k</th>\n",
       "      <th>homemktvalue_LTE100k</th>\n",
       "      <th>homemktvalue_LTE300k</th>\n",
       "      <th>DWELTYPE_M</th>\n",
       "      <th>DWELTYPE_S</th>\n",
       "      <th>MORESPON_R</th>\n",
       "      <th>PROPTYPE_A</th>\n",
       "      <th>PROPTYPE_B</th>\n",
       "      <th>PROPTYPE_C</th>\n",
       "      <th>NETWORTH_1.0</th>\n",
       "      <th>NETWORTH_10.0</th>\n",
       "      <th>NETWORTH_11.0</th>\n",
       "      <th>NETWORTH_2.0</th>\n",
       "      <th>NETWORTH_3.0</th>\n",
       "      <th>NETWORTH_4.0</th>\n",
       "      <th>NETWORTH_5.0</th>\n",
       "      <th>NETWORTH_6.0</th>\n",
       "      <th>NETWORTH_7.0</th>\n",
       "      <th>NETWORTH_8.0</th>\n",
       "      <th>NETWORTH_9.0</th>\n",
       "      <th>ownrent_O</th>\n",
       "      <th>ownrent_R</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Component 1</th>\n",
       "      <td>0.081132</td>\n",
       "      <td>0.243362</td>\n",
       "      <td>0.048842</td>\n",
       "      <td>0.017434</td>\n",
       "      <td>0.037801</td>\n",
       "      <td>0.192703</td>\n",
       "      <td>0.015686</td>\n",
       "      <td>0.060969</td>\n",
       "      <td>0.211254</td>\n",
       "      <td>0.105350</td>\n",
       "      <td>0.019349</td>\n",
       "      <td>0.019866</td>\n",
       "      <td>0.183878</td>\n",
       "      <td>0.023885</td>\n",
       "      <td>0.054233</td>\n",
       "      <td>0.105745</td>\n",
       "      <td>0.097615</td>\n",
       "      <td>0.102203</td>\n",
       "      <td>0.077463</td>\n",
       "      <td>0.152307</td>\n",
       "      <td>0.031034</td>\n",
       "      <td>0.027777</td>\n",
       "      <td>0.015431</td>\n",
       "      <td>0.010652</td>\n",
       "      <td>0.131449</td>\n",
       "      <td>0.006639</td>\n",
       "      <td>0.152307</td>\n",
       "      <td>0.013847</td>\n",
       "      <td>0.219453</td>\n",
       "      <td>0.064122</td>\n",
       "      <td>0.033929</td>\n",
       "      <td>0.013808</td>\n",
       "      <td>0.036338</td>\n",
       "      <td>0.028326</td>\n",
       "      <td>-0.013157</td>\n",
       "      <td>0.000862</td>\n",
       "      <td>0.003779</td>\n",
       "      <td>0.108278</td>\n",
       "      <td>-0.009875</td>\n",
       "      <td>0.017097</td>\n",
       "      <td>0.025834</td>\n",
       "      <td>0.014611</td>\n",
       "      <td>0.174235</td>\n",
       "      <td>0.043417</td>\n",
       "      <td>0.150027</td>\n",
       "      <td>0.137379</td>\n",
       "      <td>0.086951</td>\n",
       "      <td>0.232182</td>\n",
       "      <td>0.076388</td>\n",
       "      <td>0.147835</td>\n",
       "      <td>0.212687</td>\n",
       "      <td>0.052077</td>\n",
       "      <td>0.077120</td>\n",
       "      <td>0.053132</td>\n",
       "      <td>0.045721</td>\n",
       "      <td>0.123070</td>\n",
       "      <td>0.037223</td>\n",
       "      <td>0.180196</td>\n",
       "      <td>0.147703</td>\n",
       "      <td>0.016645</td>\n",
       "      <td>0.013069</td>\n",
       "      <td>0.186542</td>\n",
       "      <td>0.037807</td>\n",
       "      <td>0.140989</td>\n",
       "      <td>0.139943</td>\n",
       "      <td>0.099082</td>\n",
       "      <td>0.244838</td>\n",
       "      <td>0.080515</td>\n",
       "      <td>0.077017</td>\n",
       "      <td>0.189393</td>\n",
       "      <td>0.046137</td>\n",
       "      <td>0.154631</td>\n",
       "      <td>0.069693</td>\n",
       "      <td>0.043441</td>\n",
       "      <td>0.204779</td>\n",
       "      <td>0.025473</td>\n",
       "      <td>0.187296</td>\n",
       "      <td>0.134856</td>\n",
       "      <td>0.001990</td>\n",
       "      <td>0.018069</td>\n",
       "      <td>-0.001406</td>\n",
       "      <td>0.004026</td>\n",
       "      <td>0.008199</td>\n",
       "      <td>-0.002847</td>\n",
       "      <td>-0.001237</td>\n",
       "      <td>-0.001349</td>\n",
       "      <td>-0.000516</td>\n",
       "      <td>0.001909</td>\n",
       "      <td>-0.001010</td>\n",
       "      <td>-0.002297</td>\n",
       "      <td>-0.000716</td>\n",
       "      <td>-0.005002</td>\n",
       "      <td>-0.000994</td>\n",
       "      <td>-0.002107</td>\n",
       "      <td>-0.001443</td>\n",
       "      <td>-0.002428</td>\n",
       "      <td>-0.001286</td>\n",
       "      <td>-0.001379</td>\n",
       "      <td>-0.002768</td>\n",
       "      <td>-0.001492</td>\n",
       "      <td>0.015637</td>\n",
       "      <td>-0.000572</td>\n",
       "      <td>-0.012025</td>\n",
       "      <td>0.011312</td>\n",
       "      <td>0.014464</td>\n",
       "      <td>-0.008986</td>\n",
       "      <td>0.001310</td>\n",
       "      <td>0.007183</td>\n",
       "      <td>0.011038</td>\n",
       "      <td>0.020422</td>\n",
       "      <td>-0.010559</td>\n",
       "      <td>-0.004456</td>\n",
       "      <td>-0.025136</td>\n",
       "      <td>0.025910</td>\n",
       "      <td>0.025109</td>\n",
       "      <td>-0.023984</td>\n",
       "      <td>-0.002797</td>\n",
       "      <td>0.022294</td>\n",
       "      <td>-0.024325</td>\n",
       "      <td>0.011932</td>\n",
       "      <td>0.021919</td>\n",
       "      <td>-0.012644</td>\n",
       "      <td>-0.007936</td>\n",
       "      <td>-0.005985</td>\n",
       "      <td>-0.002257</td>\n",
       "      <td>0.000263</td>\n",
       "      <td>0.007220</td>\n",
       "      <td>0.010162</td>\n",
       "      <td>0.009068</td>\n",
       "      <td>0.030702</td>\n",
       "      <td>-0.012883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Component 2</th>\n",
       "      <td>0.021366</td>\n",
       "      <td>0.096343</td>\n",
       "      <td>-0.013627</td>\n",
       "      <td>-0.045008</td>\n",
       "      <td>0.056094</td>\n",
       "      <td>0.092591</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.089403</td>\n",
       "      <td>0.073930</td>\n",
       "      <td>-0.279813</td>\n",
       "      <td>-0.053577</td>\n",
       "      <td>0.062971</td>\n",
       "      <td>0.069407</td>\n",
       "      <td>-0.045045</td>\n",
       "      <td>-0.112582</td>\n",
       "      <td>-0.246651</td>\n",
       "      <td>-0.256347</td>\n",
       "      <td>-0.273396</td>\n",
       "      <td>-0.200596</td>\n",
       "      <td>0.099987</td>\n",
       "      <td>-0.143859</td>\n",
       "      <td>-0.132508</td>\n",
       "      <td>-0.084267</td>\n",
       "      <td>-0.086938</td>\n",
       "      <td>0.064731</td>\n",
       "      <td>-0.016358</td>\n",
       "      <td>0.099987</td>\n",
       "      <td>-0.078358</td>\n",
       "      <td>0.071675</td>\n",
       "      <td>0.029379</td>\n",
       "      <td>-0.152230</td>\n",
       "      <td>-0.018966</td>\n",
       "      <td>0.004295</td>\n",
       "      <td>0.003112</td>\n",
       "      <td>0.031692</td>\n",
       "      <td>-0.000361</td>\n",
       "      <td>0.003052</td>\n",
       "      <td>-0.211833</td>\n",
       "      <td>0.085960</td>\n",
       "      <td>0.016264</td>\n",
       "      <td>-0.009548</td>\n",
       "      <td>-0.005411</td>\n",
       "      <td>-0.169472</td>\n",
       "      <td>-0.178145</td>\n",
       "      <td>0.015375</td>\n",
       "      <td>-0.009864</td>\n",
       "      <td>-0.054009</td>\n",
       "      <td>0.018238</td>\n",
       "      <td>-0.286428</td>\n",
       "      <td>-0.036949</td>\n",
       "      <td>0.101510</td>\n",
       "      <td>0.026300</td>\n",
       "      <td>0.003124</td>\n",
       "      <td>-0.003733</td>\n",
       "      <td>-0.122654</td>\n",
       "      <td>-0.160147</td>\n",
       "      <td>-0.164773</td>\n",
       "      <td>0.083383</td>\n",
       "      <td>0.079366</td>\n",
       "      <td>-0.013813</td>\n",
       "      <td>-0.049791</td>\n",
       "      <td>0.032308</td>\n",
       "      <td>-0.151020</td>\n",
       "      <td>0.060937</td>\n",
       "      <td>0.060936</td>\n",
       "      <td>0.045612</td>\n",
       "      <td>0.103395</td>\n",
       "      <td>-0.279164</td>\n",
       "      <td>0.008366</td>\n",
       "      <td>0.095155</td>\n",
       "      <td>0.024579</td>\n",
       "      <td>0.081518</td>\n",
       "      <td>0.032721</td>\n",
       "      <td>-0.059734</td>\n",
       "      <td>0.086844</td>\n",
       "      <td>-0.120090</td>\n",
       "      <td>0.092698</td>\n",
       "      <td>0.075236</td>\n",
       "      <td>-0.000585</td>\n",
       "      <td>-0.056299</td>\n",
       "      <td>0.002918</td>\n",
       "      <td>-0.021428</td>\n",
       "      <td>-0.030975</td>\n",
       "      <td>-0.024223</td>\n",
       "      <td>0.007363</td>\n",
       "      <td>-0.012437</td>\n",
       "      <td>0.033200</td>\n",
       "      <td>0.026321</td>\n",
       "      <td>0.027910</td>\n",
       "      <td>0.015628</td>\n",
       "      <td>-0.002624</td>\n",
       "      <td>0.014873</td>\n",
       "      <td>0.020390</td>\n",
       "      <td>0.009264</td>\n",
       "      <td>0.007167</td>\n",
       "      <td>0.008129</td>\n",
       "      <td>0.006140</td>\n",
       "      <td>0.009020</td>\n",
       "      <td>0.004385</td>\n",
       "      <td>0.030449</td>\n",
       "      <td>0.016815</td>\n",
       "      <td>-0.031914</td>\n",
       "      <td>0.014765</td>\n",
       "      <td>0.010311</td>\n",
       "      <td>0.021406</td>\n",
       "      <td>-0.003168</td>\n",
       "      <td>-0.026112</td>\n",
       "      <td>-0.011644</td>\n",
       "      <td>0.001343</td>\n",
       "      <td>0.060043</td>\n",
       "      <td>-0.017146</td>\n",
       "      <td>-0.031584</td>\n",
       "      <td>0.037427</td>\n",
       "      <td>-0.041368</td>\n",
       "      <td>-0.020628</td>\n",
       "      <td>0.032946</td>\n",
       "      <td>0.012210</td>\n",
       "      <td>-0.039355</td>\n",
       "      <td>0.010337</td>\n",
       "      <td>0.020768</td>\n",
       "      <td>0.046546</td>\n",
       "      <td>0.000888</td>\n",
       "      <td>-0.002016</td>\n",
       "      <td>-0.005259</td>\n",
       "      <td>-0.009176</td>\n",
       "      <td>-0.017050</td>\n",
       "      <td>-0.025184</td>\n",
       "      <td>-0.013221</td>\n",
       "      <td>0.005060</td>\n",
       "      <td>-0.016612</td>\n",
       "      <td>0.002937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Component 3</th>\n",
       "      <td>-0.069161</td>\n",
       "      <td>0.047395</td>\n",
       "      <td>-0.064657</td>\n",
       "      <td>-0.024712</td>\n",
       "      <td>-0.036662</td>\n",
       "      <td>0.004910</td>\n",
       "      <td>-0.014991</td>\n",
       "      <td>-0.128672</td>\n",
       "      <td>0.017773</td>\n",
       "      <td>-0.030381</td>\n",
       "      <td>-0.028011</td>\n",
       "      <td>-0.074827</td>\n",
       "      <td>0.003312</td>\n",
       "      <td>-0.058334</td>\n",
       "      <td>-0.073168</td>\n",
       "      <td>-0.058104</td>\n",
       "      <td>-0.054064</td>\n",
       "      <td>-0.031997</td>\n",
       "      <td>-0.034389</td>\n",
       "      <td>-0.051146</td>\n",
       "      <td>0.156161</td>\n",
       "      <td>0.146193</td>\n",
       "      <td>0.110462</td>\n",
       "      <td>0.142167</td>\n",
       "      <td>0.005456</td>\n",
       "      <td>0.014529</td>\n",
       "      <td>-0.051146</td>\n",
       "      <td>0.103545</td>\n",
       "      <td>0.040070</td>\n",
       "      <td>0.021646</td>\n",
       "      <td>0.161249</td>\n",
       "      <td>-0.050844</td>\n",
       "      <td>-0.009350</td>\n",
       "      <td>-0.011902</td>\n",
       "      <td>0.005788</td>\n",
       "      <td>-0.002503</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.148580</td>\n",
       "      <td>-0.139039</td>\n",
       "      <td>0.010252</td>\n",
       "      <td>-0.180623</td>\n",
       "      <td>-0.207591</td>\n",
       "      <td>-0.001403</td>\n",
       "      <td>0.072431</td>\n",
       "      <td>0.012908</td>\n",
       "      <td>0.004428</td>\n",
       "      <td>-0.074134</td>\n",
       "      <td>-0.026051</td>\n",
       "      <td>0.060824</td>\n",
       "      <td>0.000775</td>\n",
       "      <td>0.051781</td>\n",
       "      <td>0.012819</td>\n",
       "      <td>0.021782</td>\n",
       "      <td>0.016006</td>\n",
       "      <td>-0.016389</td>\n",
       "      <td>0.013798</td>\n",
       "      <td>0.082796</td>\n",
       "      <td>0.059953</td>\n",
       "      <td>0.057408</td>\n",
       "      <td>-0.004079</td>\n",
       "      <td>0.021611</td>\n",
       "      <td>0.014166</td>\n",
       "      <td>0.060061</td>\n",
       "      <td>0.018461</td>\n",
       "      <td>0.018288</td>\n",
       "      <td>0.007340</td>\n",
       "      <td>0.051612</td>\n",
       "      <td>0.054137</td>\n",
       "      <td>-0.011147</td>\n",
       "      <td>0.044514</td>\n",
       "      <td>0.012639</td>\n",
       "      <td>0.037317</td>\n",
       "      <td>0.021217</td>\n",
       "      <td>-0.009678</td>\n",
       "      <td>0.043863</td>\n",
       "      <td>0.069663</td>\n",
       "      <td>0.046852</td>\n",
       "      <td>0.055572</td>\n",
       "      <td>-0.005035</td>\n",
       "      <td>0.021952</td>\n",
       "      <td>0.000939</td>\n",
       "      <td>-0.009512</td>\n",
       "      <td>-0.010828</td>\n",
       "      <td>0.058818</td>\n",
       "      <td>-0.001238</td>\n",
       "      <td>0.037351</td>\n",
       "      <td>-0.016429</td>\n",
       "      <td>-0.026215</td>\n",
       "      <td>-0.011462</td>\n",
       "      <td>-0.004703</td>\n",
       "      <td>0.001403</td>\n",
       "      <td>-0.010483</td>\n",
       "      <td>-0.013509</td>\n",
       "      <td>0.003191</td>\n",
       "      <td>-0.001648</td>\n",
       "      <td>0.002160</td>\n",
       "      <td>-0.001086</td>\n",
       "      <td>-0.002026</td>\n",
       "      <td>0.001530</td>\n",
       "      <td>-0.033715</td>\n",
       "      <td>-0.128584</td>\n",
       "      <td>0.008486</td>\n",
       "      <td>0.045388</td>\n",
       "      <td>-0.083038</td>\n",
       "      <td>-0.082435</td>\n",
       "      <td>0.029695</td>\n",
       "      <td>-0.015950</td>\n",
       "      <td>-0.053340</td>\n",
       "      <td>-0.077995</td>\n",
       "      <td>-0.121113</td>\n",
       "      <td>0.075114</td>\n",
       "      <td>0.009818</td>\n",
       "      <td>0.276492</td>\n",
       "      <td>-0.292695</td>\n",
       "      <td>-0.236952</td>\n",
       "      <td>0.270454</td>\n",
       "      <td>0.022135</td>\n",
       "      <td>-0.254926</td>\n",
       "      <td>0.213563</td>\n",
       "      <td>-0.093734</td>\n",
       "      <td>-0.112280</td>\n",
       "      <td>0.112453</td>\n",
       "      <td>0.059992</td>\n",
       "      <td>0.038509</td>\n",
       "      <td>0.008946</td>\n",
       "      <td>-0.022276</td>\n",
       "      <td>-0.079054</td>\n",
       "      <td>-0.091518</td>\n",
       "      <td>-0.087453</td>\n",
       "      <td>-0.306357</td>\n",
       "      <td>0.115557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Component 4</th>\n",
       "      <td>-0.030493</td>\n",
       "      <td>0.078991</td>\n",
       "      <td>0.008673</td>\n",
       "      <td>0.004739</td>\n",
       "      <td>-0.157695</td>\n",
       "      <td>-0.217206</td>\n",
       "      <td>-0.020431</td>\n",
       "      <td>0.021963</td>\n",
       "      <td>-0.097418</td>\n",
       "      <td>0.027102</td>\n",
       "      <td>0.010454</td>\n",
       "      <td>-0.135851</td>\n",
       "      <td>-0.143685</td>\n",
       "      <td>0.028810</td>\n",
       "      <td>-0.004192</td>\n",
       "      <td>0.041010</td>\n",
       "      <td>-0.011162</td>\n",
       "      <td>-0.015488</td>\n",
       "      <td>0.003357</td>\n",
       "      <td>-0.282151</td>\n",
       "      <td>-0.069293</td>\n",
       "      <td>-0.066330</td>\n",
       "      <td>-0.057178</td>\n",
       "      <td>-0.054472</td>\n",
       "      <td>-0.081980</td>\n",
       "      <td>-0.004700</td>\n",
       "      <td>-0.282151</td>\n",
       "      <td>-0.054959</td>\n",
       "      <td>-0.032074</td>\n",
       "      <td>0.022520</td>\n",
       "      <td>-0.059950</td>\n",
       "      <td>0.020949</td>\n",
       "      <td>-0.011879</td>\n",
       "      <td>-0.017844</td>\n",
       "      <td>0.017728</td>\n",
       "      <td>-0.003428</td>\n",
       "      <td>-0.004822</td>\n",
       "      <td>0.008783</td>\n",
       "      <td>0.074222</td>\n",
       "      <td>-0.134905</td>\n",
       "      <td>-0.029453</td>\n",
       "      <td>0.082915</td>\n",
       "      <td>-0.062684</td>\n",
       "      <td>0.016795</td>\n",
       "      <td>-0.122945</td>\n",
       "      <td>-0.099847</td>\n",
       "      <td>-0.048448</td>\n",
       "      <td>0.114452</td>\n",
       "      <td>0.001703</td>\n",
       "      <td>-0.089766</td>\n",
       "      <td>0.124571</td>\n",
       "      <td>-0.021789</td>\n",
       "      <td>0.009431</td>\n",
       "      <td>-0.019569</td>\n",
       "      <td>-0.017848</td>\n",
       "      <td>0.072267</td>\n",
       "      <td>-0.017380</td>\n",
       "      <td>0.282720</td>\n",
       "      <td>0.329540</td>\n",
       "      <td>-0.001144</td>\n",
       "      <td>-0.016355</td>\n",
       "      <td>-0.108269</td>\n",
       "      <td>0.014940</td>\n",
       "      <td>-0.163042</td>\n",
       "      <td>-0.162035</td>\n",
       "      <td>-0.042424</td>\n",
       "      <td>0.219191</td>\n",
       "      <td>-0.004129</td>\n",
       "      <td>-0.039832</td>\n",
       "      <td>0.077900</td>\n",
       "      <td>-0.014953</td>\n",
       "      <td>0.161845</td>\n",
       "      <td>0.038459</td>\n",
       "      <td>-0.031903</td>\n",
       "      <td>0.047663</td>\n",
       "      <td>-0.016557</td>\n",
       "      <td>0.068039</td>\n",
       "      <td>0.336556</td>\n",
       "      <td>-0.000903</td>\n",
       "      <td>-0.023181</td>\n",
       "      <td>0.002358</td>\n",
       "      <td>0.037404</td>\n",
       "      <td>0.038381</td>\n",
       "      <td>0.003655</td>\n",
       "      <td>0.013789</td>\n",
       "      <td>-0.008284</td>\n",
       "      <td>-0.066802</td>\n",
       "      <td>-0.047916</td>\n",
       "      <td>-0.050701</td>\n",
       "      <td>-0.020591</td>\n",
       "      <td>0.007446</td>\n",
       "      <td>-0.001124</td>\n",
       "      <td>-0.017287</td>\n",
       "      <td>-0.010833</td>\n",
       "      <td>-0.003391</td>\n",
       "      <td>-0.005627</td>\n",
       "      <td>-0.002745</td>\n",
       "      <td>-0.004739</td>\n",
       "      <td>0.003369</td>\n",
       "      <td>-0.029274</td>\n",
       "      <td>-0.026882</td>\n",
       "      <td>0.044632</td>\n",
       "      <td>0.038961</td>\n",
       "      <td>-0.040080</td>\n",
       "      <td>-0.064199</td>\n",
       "      <td>0.052661</td>\n",
       "      <td>0.046824</td>\n",
       "      <td>0.001760</td>\n",
       "      <td>-0.021925</td>\n",
       "      <td>-0.155236</td>\n",
       "      <td>0.058418</td>\n",
       "      <td>0.065829</td>\n",
       "      <td>-0.060424</td>\n",
       "      <td>0.065581</td>\n",
       "      <td>0.062732</td>\n",
       "      <td>-0.054202</td>\n",
       "      <td>-0.021572</td>\n",
       "      <td>0.059805</td>\n",
       "      <td>-0.017984</td>\n",
       "      <td>-0.068115</td>\n",
       "      <td>-0.113096</td>\n",
       "      <td>-0.000270</td>\n",
       "      <td>0.009760</td>\n",
       "      <td>0.022415</td>\n",
       "      <td>0.032032</td>\n",
       "      <td>0.045894</td>\n",
       "      <td>0.057851</td>\n",
       "      <td>0.023425</td>\n",
       "      <td>-0.022347</td>\n",
       "      <td>0.051925</td>\n",
       "      <td>-0.001916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Component 5</th>\n",
       "      <td>-0.028136</td>\n",
       "      <td>0.002130</td>\n",
       "      <td>-0.031195</td>\n",
       "      <td>-0.015471</td>\n",
       "      <td>0.028583</td>\n",
       "      <td>-0.008665</td>\n",
       "      <td>-0.002819</td>\n",
       "      <td>-0.084502</td>\n",
       "      <td>-0.013519</td>\n",
       "      <td>-0.111353</td>\n",
       "      <td>-0.019606</td>\n",
       "      <td>0.039703</td>\n",
       "      <td>-0.014156</td>\n",
       "      <td>-0.027988</td>\n",
       "      <td>-0.063647</td>\n",
       "      <td>-0.097397</td>\n",
       "      <td>-0.120508</td>\n",
       "      <td>-0.109387</td>\n",
       "      <td>-0.079577</td>\n",
       "      <td>0.017412</td>\n",
       "      <td>0.380274</td>\n",
       "      <td>0.375491</td>\n",
       "      <td>0.315468</td>\n",
       "      <td>0.303138</td>\n",
       "      <td>0.006036</td>\n",
       "      <td>0.023587</td>\n",
       "      <td>0.017412</td>\n",
       "      <td>0.304478</td>\n",
       "      <td>0.003918</td>\n",
       "      <td>0.014167</td>\n",
       "      <td>0.309239</td>\n",
       "      <td>-0.020915</td>\n",
       "      <td>-0.007152</td>\n",
       "      <td>-0.007677</td>\n",
       "      <td>0.004475</td>\n",
       "      <td>0.005411</td>\n",
       "      <td>-0.000220</td>\n",
       "      <td>-0.115890</td>\n",
       "      <td>0.011638</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.054660</td>\n",
       "      <td>0.055630</td>\n",
       "      <td>-0.084918</td>\n",
       "      <td>0.014466</td>\n",
       "      <td>-0.051588</td>\n",
       "      <td>-0.055902</td>\n",
       "      <td>-0.021239</td>\n",
       "      <td>-0.018276</td>\n",
       "      <td>-0.043590</td>\n",
       "      <td>-0.046358</td>\n",
       "      <td>0.034529</td>\n",
       "      <td>-0.003095</td>\n",
       "      <td>0.037787</td>\n",
       "      <td>0.012744</td>\n",
       "      <td>-0.042494</td>\n",
       "      <td>-0.041318</td>\n",
       "      <td>0.009488</td>\n",
       "      <td>0.050544</td>\n",
       "      <td>0.057568</td>\n",
       "      <td>-0.005588</td>\n",
       "      <td>-0.008785</td>\n",
       "      <td>-0.035766</td>\n",
       "      <td>0.016495</td>\n",
       "      <td>-0.026901</td>\n",
       "      <td>-0.026259</td>\n",
       "      <td>0.012151</td>\n",
       "      <td>0.032512</td>\n",
       "      <td>-0.033420</td>\n",
       "      <td>-0.017958</td>\n",
       "      <td>0.027499</td>\n",
       "      <td>-0.002436</td>\n",
       "      <td>0.046366</td>\n",
       "      <td>0.019696</td>\n",
       "      <td>-0.020243</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>0.019726</td>\n",
       "      <td>0.023061</td>\n",
       "      <td>0.058373</td>\n",
       "      <td>-0.001496</td>\n",
       "      <td>-0.009967</td>\n",
       "      <td>0.000391</td>\n",
       "      <td>-0.020526</td>\n",
       "      <td>-0.016520</td>\n",
       "      <td>-0.011442</td>\n",
       "      <td>0.004572</td>\n",
       "      <td>-0.005192</td>\n",
       "      <td>0.015125</td>\n",
       "      <td>0.018220</td>\n",
       "      <td>0.013019</td>\n",
       "      <td>0.008142</td>\n",
       "      <td>0.003030</td>\n",
       "      <td>0.018196</td>\n",
       "      <td>0.015196</td>\n",
       "      <td>0.005671</td>\n",
       "      <td>-0.000463</td>\n",
       "      <td>-0.003798</td>\n",
       "      <td>-0.002055</td>\n",
       "      <td>0.003558</td>\n",
       "      <td>0.004418</td>\n",
       "      <td>0.017067</td>\n",
       "      <td>0.029432</td>\n",
       "      <td>0.004362</td>\n",
       "      <td>-0.017806</td>\n",
       "      <td>0.026340</td>\n",
       "      <td>0.030693</td>\n",
       "      <td>-0.007046</td>\n",
       "      <td>0.004767</td>\n",
       "      <td>0.011768</td>\n",
       "      <td>0.019970</td>\n",
       "      <td>0.059410</td>\n",
       "      <td>-0.010708</td>\n",
       "      <td>-0.027611</td>\n",
       "      <td>-0.199977</td>\n",
       "      <td>0.204585</td>\n",
       "      <td>0.068219</td>\n",
       "      <td>-0.193880</td>\n",
       "      <td>-0.040348</td>\n",
       "      <td>0.189685</td>\n",
       "      <td>-0.073152</td>\n",
       "      <td>0.036590</td>\n",
       "      <td>0.054132</td>\n",
       "      <td>-0.034767</td>\n",
       "      <td>-0.013778</td>\n",
       "      <td>-0.003345</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.010263</td>\n",
       "      <td>0.014462</td>\n",
       "      <td>0.016683</td>\n",
       "      <td>0.022811</td>\n",
       "      <td>0.101848</td>\n",
       "      <td>-0.044195</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             avg_inv_bal  avg_ckcom_bal  avg_ckcon_bal  avg_indln_bal  \\\n",
       "Component 1     0.081132       0.243362       0.048842       0.017434   \n",
       "Component 2     0.021366       0.096343      -0.013627      -0.045008   \n",
       "Component 3    -0.069161       0.047395      -0.064657      -0.024712   \n",
       "Component 4    -0.030493       0.078991       0.008673       0.004739   \n",
       "Component 5    -0.028136       0.002130      -0.031195      -0.015471   \n",
       "\n",
       "             avg_mtg_bal  avg_comln_bal  avg_conln_bal  avg_inv_num  \\\n",
       "Component 1     0.037801       0.192703       0.015686     0.060969   \n",
       "Component 2     0.056094       0.092591      -0.005625    -0.089403   \n",
       "Component 3    -0.036662       0.004910      -0.014991    -0.128672   \n",
       "Component 4    -0.157695      -0.217206      -0.020431     0.021963   \n",
       "Component 5     0.028583      -0.008665      -0.002819    -0.084502   \n",
       "\n",
       "             avg_ckcom_num  avg_ckcon_num  avg_indln_num  avg_mtg_num  \\\n",
       "Component 1       0.211254       0.105350       0.019349     0.019866   \n",
       "Component 2       0.073930      -0.279813      -0.053577     0.062971   \n",
       "Component 3       0.017773      -0.030381      -0.028011    -0.074827   \n",
       "Component 4      -0.097418       0.027102       0.010454    -0.135851   \n",
       "Component 5      -0.013519      -0.111353      -0.019606     0.039703   \n",
       "\n",
       "             avg_comln_num  avg_conln_num  avg_bill_pay  avg_direct_deposit  \\\n",
       "Component 1       0.183878       0.023885      0.054233            0.105745   \n",
       "Component 2       0.069407      -0.045045     -0.112582           -0.246651   \n",
       "Component 3       0.003312      -0.058334     -0.073168           -0.058104   \n",
       "Component 4      -0.143685       0.028810     -0.004192            0.041010   \n",
       "Component 5      -0.014156      -0.027988     -0.063647           -0.097397   \n",
       "\n",
       "             avg_estatement  avg_mobile_app  avg_online_banking  \\\n",
       "Component 1        0.097615        0.102203            0.077463   \n",
       "Component 2       -0.256347       -0.273396           -0.200596   \n",
       "Component 3       -0.054064       -0.031997           -0.034389   \n",
       "Component 4       -0.011162       -0.015488            0.003357   \n",
       "Component 5       -0.120508       -0.109387           -0.079577   \n",
       "\n",
       "             sum_YTDINTPAID  sum_YTDITEMNSF  sum_YTDNSFFEE  sum_YTDNSFRTFA  \\\n",
       "Component 1        0.152307        0.031034       0.027777        0.015431   \n",
       "Component 2        0.099987       -0.143859      -0.132508       -0.084267   \n",
       "Component 3       -0.051146        0.156161       0.146193        0.110462   \n",
       "Component 4       -0.282151       -0.069293      -0.066330       -0.057178   \n",
       "Component 5        0.017412        0.380274       0.375491        0.315468   \n",
       "\n",
       "             sum_YTDODFEEAT  sum_YTDPDFEES  sum_YTDOTRCHG  sum_YTDPDINT  \\\n",
       "Component 1        0.010652       0.131449       0.006639      0.152307   \n",
       "Component 2       -0.086938       0.064731      -0.016358      0.099987   \n",
       "Component 3        0.142167       0.005456       0.014529     -0.051146   \n",
       "Component 4       -0.054472      -0.081980      -0.004700     -0.282151   \n",
       "Component 5        0.303138       0.006036       0.023587      0.017412   \n",
       "\n",
       "             sum_YTDRTNNSF  sum_YTDSVC  sum_YTDSVCWVD  sum_YTDTIMEOD  \\\n",
       "Component 1       0.013847    0.219453       0.064122       0.033929   \n",
       "Component 2      -0.078358    0.071675       0.029379      -0.152230   \n",
       "Component 3       0.103545    0.040070       0.021646       0.161249   \n",
       "Component 4      -0.054959   -0.032074       0.022520      -0.059950   \n",
       "Component 5       0.304478    0.003918       0.014167       0.309239   \n",
       "\n",
       "             sum_brokerage  sum_insurance  sum_trust  sum_full_charge_off  \\\n",
       "Component 1       0.013808       0.036338   0.028326            -0.013157   \n",
       "Component 2      -0.018966       0.004295   0.003112             0.031692   \n",
       "Component 3      -0.050844      -0.009350  -0.011902             0.005788   \n",
       "Component 4       0.020949      -0.011879  -0.017844             0.017728   \n",
       "Component 5      -0.020915      -0.007152  -0.007677             0.004475   \n",
       "\n",
       "             sum_partial_charge_off  sum_sbloan_flag  sum_consumer_flag  \\\n",
       "Component 1                0.000862         0.003779           0.108278   \n",
       "Component 2               -0.000361         0.003052          -0.211833   \n",
       "Component 3               -0.002503        -0.000208          -0.148580   \n",
       "Component 4               -0.003428        -0.004822           0.008783   \n",
       "Component 5                0.005411        -0.000220          -0.115890   \n",
       "\n",
       "              avg_age  ANNSPEND   RISKIND       LOR  total_ACH_DEPOSIT_trans  \\\n",
       "Component 1 -0.009875  0.017097  0.025834  0.014611                 0.174235   \n",
       "Component 2  0.085960  0.016264 -0.009548 -0.005411                -0.169472   \n",
       "Component 3 -0.139039  0.010252 -0.180623 -0.207591                -0.001403   \n",
       "Component 4  0.074222 -0.134905 -0.029453  0.082915                -0.062684   \n",
       "Component 5  0.011638  0.000010  0.054660  0.055630                -0.084918   \n",
       "\n",
       "             total_ATM_DEBIT_trans  total_AUTO_CR_TRANSFER_trans  \\\n",
       "Component 1               0.043417                      0.150027   \n",
       "Component 2              -0.178145                      0.015375   \n",
       "Component 3               0.072431                      0.012908   \n",
       "Component 4               0.016795                     -0.122945   \n",
       "Component 5               0.014466                     -0.051588   \n",
       "\n",
       "             total_AUTO_TRANSFER_DR_trans  total_Auto_Loan_Payment_trans  \\\n",
       "Component 1                      0.137379                       0.086951   \n",
       "Component 2                     -0.009864                      -0.054009   \n",
       "Component 3                      0.004428                      -0.074134   \n",
       "Component 4                     -0.099847                      -0.048448   \n",
       "Component 5                     -0.055902                      -0.021239   \n",
       "\n",
       "             total_CHECK_trans  total_DEBIT_CARD_PURCHASE_trans  \\\n",
       "Component 1           0.232182                         0.076388   \n",
       "Component 2           0.018238                        -0.286428   \n",
       "Component 3          -0.026051                         0.060824   \n",
       "Component 4           0.114452                         0.001703   \n",
       "Component 5          -0.018276                        -0.043590   \n",
       "\n",
       "             total_DEPOSIT_trans  total_FIRST_DESKTOP_DEPOSIT_trans  \\\n",
       "Component 1             0.147835                           0.212687   \n",
       "Component 2            -0.036949                           0.101510   \n",
       "Component 3             0.000775                           0.051781   \n",
       "Component 4            -0.089766                           0.124571   \n",
       "Component 5            -0.046358                           0.034529   \n",
       "\n",
       "             total_LOCKBOX_trans  total_MISC_CREDIT_trans  \\\n",
       "Component 1             0.052077                 0.077120   \n",
       "Component 2             0.026300                 0.003124   \n",
       "Component 3             0.012819                 0.021782   \n",
       "Component 4            -0.021789                 0.009431   \n",
       "Component 5            -0.003095                 0.037787   \n",
       "\n",
       "             total_MISC_DEBIT_trans  total_MOBILE_DEPOSIT_trans  \\\n",
       "Component 1                0.053132                    0.045721   \n",
       "Component 2               -0.003733                   -0.122654   \n",
       "Component 3                0.016006                   -0.016389   \n",
       "Component 4               -0.019569                   -0.017848   \n",
       "Component 5                0.012744                   -0.042494   \n",
       "\n",
       "             total_ONLINE_DEBIT_trans  total_POS_RETURN_trans  \\\n",
       "Component 1                  0.123070                0.037223   \n",
       "Component 2                 -0.160147               -0.164773   \n",
       "Component 3                  0.013798                0.082796   \n",
       "Component 4                  0.072267               -0.017380   \n",
       "Component 5                 -0.041318                0.009488   \n",
       "\n",
       "             total_TRANSFER_CREDIT_trans  WIRE_trans  total_WITHDRAWAL_trans  \\\n",
       "Component 1                     0.180196    0.147703                0.016645   \n",
       "Component 2                     0.083383    0.079366               -0.013813   \n",
       "Component 3                     0.059953    0.057408               -0.004079   \n",
       "Component 4                     0.282720    0.329540               -0.001144   \n",
       "Component 5                     0.050544    0.057568               -0.005588   \n",
       "\n",
       "             total_ZELLE_trans  total_ACH_DEPOSIT_num  total_ATM_DEBIT_num  \\\n",
       "Component 1           0.013069               0.186542             0.037807   \n",
       "Component 2          -0.049791               0.032308            -0.151020   \n",
       "Component 3           0.021611               0.014166             0.060061   \n",
       "Component 4          -0.016355              -0.108269             0.014940   \n",
       "Component 5          -0.008785              -0.035766             0.016495   \n",
       "\n",
       "             total_AUTO_CR_TRANSFER_num  total_AUTO_TRANSFER_DR_num  \\\n",
       "Component 1                    0.140989                    0.139943   \n",
       "Component 2                    0.060937                    0.060936   \n",
       "Component 3                    0.018461                    0.018288   \n",
       "Component 4                   -0.163042                   -0.162035   \n",
       "Component 5                   -0.026901                   -0.026259   \n",
       "\n",
       "             total_Auto_Loan_Payment_num  total_CHECK_num  \\\n",
       "Component 1                     0.099082         0.244838   \n",
       "Component 2                     0.045612         0.103395   \n",
       "Component 3                     0.007340         0.051612   \n",
       "Component 4                    -0.042424         0.219191   \n",
       "Component 5                     0.012151         0.032512   \n",
       "\n",
       "             total_DEBIT_CARD_PURCHASE_num  total_DEPOSIT_num  \\\n",
       "Component 1                       0.080515           0.077017   \n",
       "Component 2                      -0.279164           0.008366   \n",
       "Component 3                       0.054137          -0.011147   \n",
       "Component 4                      -0.004129          -0.039832   \n",
       "Component 5                      -0.033420          -0.017958   \n",
       "\n",
       "             total_FIRST_DESKTOP_DEPOSIT_num  total_LOCKBOX_num  \\\n",
       "Component 1                         0.189393           0.046137   \n",
       "Component 2                         0.095155           0.024579   \n",
       "Component 3                         0.044514           0.012639   \n",
       "Component 4                         0.077900          -0.014953   \n",
       "Component 5                         0.027499          -0.002436   \n",
       "\n",
       "             total_MISC_CREDIT_num  total_MISC_DEBIT_num  \\\n",
       "Component 1               0.154631              0.069693   \n",
       "Component 2               0.081518              0.032721   \n",
       "Component 3               0.037317              0.021217   \n",
       "Component 4               0.161845              0.038459   \n",
       "Component 5               0.046366              0.019696   \n",
       "\n",
       "             total_MOBILE_DEPOSIT_num  total_ONLINE_DEBIT_num  \\\n",
       "Component 1                  0.043441                0.204779   \n",
       "Component 2                 -0.059734                0.086844   \n",
       "Component 3                 -0.009678                0.043863   \n",
       "Component 4                 -0.031903                0.047663   \n",
       "Component 5                 -0.020243                0.016047   \n",
       "\n",
       "             total_POS_RETURN_num  total_TRANSFER_CREDIT_num  WIRE_num  \\\n",
       "Component 1              0.025473                   0.187296  0.134856   \n",
       "Component 2             -0.120090                   0.092698  0.075236   \n",
       "Component 3              0.069663                   0.046852  0.055572   \n",
       "Component 4             -0.016557                   0.068039  0.336556   \n",
       "Component 5              0.019726                   0.023061  0.058373   \n",
       "\n",
       "             total_WITHDRAWAL_num  total_ZELLE_num    ZIP_14    ZIP_15  \\\n",
       "Component 1              0.001990         0.018069 -0.001406  0.004026   \n",
       "Component 2             -0.000585        -0.056299  0.002918 -0.021428   \n",
       "Component 3             -0.005035         0.021952  0.000939 -0.009512   \n",
       "Component 4             -0.000903        -0.023181  0.002358  0.037404   \n",
       "Component 5             -0.001496        -0.009967  0.000391 -0.020526   \n",
       "\n",
       "               ZIP_16    ZIP_17    ZIP_18    ZIP_19    ZIP_20    ZIP_21  \\\n",
       "Component 1  0.008199 -0.002847 -0.001237 -0.001349 -0.000516  0.001909   \n",
       "Component 2 -0.030975 -0.024223  0.007363 -0.012437  0.033200  0.026321   \n",
       "Component 3 -0.010828  0.058818 -0.001238  0.037351 -0.016429 -0.026215   \n",
       "Component 4  0.038381  0.003655  0.013789 -0.008284 -0.066802 -0.047916   \n",
       "Component 5 -0.016520 -0.011442  0.004572 -0.005192  0.015125  0.018220   \n",
       "\n",
       "               ZIP_22    ZIP_23    ZIP_26    ZIP_27    ZIP_28    ZIP_29  \\\n",
       "Component 1 -0.001010 -0.002297 -0.000716 -0.005002 -0.000994 -0.002107   \n",
       "Component 2  0.027910  0.015628 -0.002624  0.014873  0.020390  0.009264   \n",
       "Component 3 -0.011462 -0.004703  0.001403 -0.010483 -0.013509  0.003191   \n",
       "Component 4 -0.050701 -0.020591  0.007446 -0.001124 -0.017287 -0.010833   \n",
       "Component 5  0.013019  0.008142  0.003030  0.018196  0.015196  0.005671   \n",
       "\n",
       "               ZIP_32    ZIP_33    ZIP_34    ZIP_37    ZIP_43    ZIP_44  \\\n",
       "Component 1 -0.001443 -0.002428 -0.001286 -0.001379 -0.002768 -0.001492   \n",
       "Component 2  0.007167  0.008129  0.006140  0.009020  0.004385  0.030449   \n",
       "Component 3 -0.001648  0.002160 -0.001086 -0.002026  0.001530 -0.033715   \n",
       "Component 4 -0.003391 -0.005627 -0.002745 -0.004739  0.003369 -0.029274   \n",
       "Component 5 -0.000463 -0.003798 -0.002055  0.003558  0.004418  0.017067   \n",
       "\n",
       "             educate_C  educate_NC  income_0to20  income_100to150  \\\n",
       "Component 1   0.015637   -0.000572     -0.012025         0.011312   \n",
       "Component 2   0.016815   -0.031914      0.014765         0.010311   \n",
       "Component 3  -0.128584    0.008486      0.045388        -0.083038   \n",
       "Component 4  -0.026882    0.044632      0.038961        -0.040080   \n",
       "Component 5   0.029432    0.004362     -0.017806         0.026340   \n",
       "\n",
       "             income_150plus  income_20to40  income_40to60  income_60to75  \\\n",
       "Component 1        0.014464      -0.008986       0.001310       0.007183   \n",
       "Component 2        0.021406      -0.003168      -0.026112      -0.011644   \n",
       "Component 3       -0.082435       0.029695      -0.015950      -0.053340   \n",
       "Component 4       -0.064199       0.052661       0.046824       0.001760   \n",
       "Component 5        0.030693      -0.007046       0.004767       0.011768   \n",
       "\n",
       "             income_75to100  homemktvalue_GT300k  homemktvalue_LTE100k  \\\n",
       "Component 1        0.011038             0.020422             -0.010559   \n",
       "Component 2        0.001343             0.060043             -0.017146   \n",
       "Component 3       -0.077995            -0.121113              0.075114   \n",
       "Component 4       -0.021925            -0.155236              0.058418   \n",
       "Component 5        0.019970             0.059410             -0.010708   \n",
       "\n",
       "             homemktvalue_LTE300k  DWELTYPE_M  DWELTYPE_S  MORESPON_R  \\\n",
       "Component 1             -0.004456   -0.025136    0.025910    0.025109   \n",
       "Component 2             -0.031584    0.037427   -0.041368   -0.020628   \n",
       "Component 3              0.009818    0.276492   -0.292695   -0.236952   \n",
       "Component 4              0.065829   -0.060424    0.065581    0.062732   \n",
       "Component 5             -0.027611   -0.199977    0.204585    0.068219   \n",
       "\n",
       "             PROPTYPE_A  PROPTYPE_B  PROPTYPE_C  NETWORTH_1.0  NETWORTH_10.0  \\\n",
       "Component 1   -0.023984   -0.002797    0.022294     -0.024325       0.011932   \n",
       "Component 2    0.032946    0.012210   -0.039355      0.010337       0.020768   \n",
       "Component 3    0.270454    0.022135   -0.254926      0.213563      -0.093734   \n",
       "Component 4   -0.054202   -0.021572    0.059805     -0.017984      -0.068115   \n",
       "Component 5   -0.193880   -0.040348    0.189685     -0.073152       0.036590   \n",
       "\n",
       "             NETWORTH_11.0  NETWORTH_2.0  NETWORTH_3.0  NETWORTH_4.0  \\\n",
       "Component 1       0.021919     -0.012644     -0.007936     -0.005985   \n",
       "Component 2       0.046546      0.000888     -0.002016     -0.005259   \n",
       "Component 3      -0.112280      0.112453      0.059992      0.038509   \n",
       "Component 4      -0.113096     -0.000270      0.009760      0.022415   \n",
       "Component 5       0.054132     -0.034767     -0.013778     -0.003345   \n",
       "\n",
       "             NETWORTH_5.0  NETWORTH_6.0  NETWORTH_7.0  NETWORTH_8.0  \\\n",
       "Component 1     -0.002257      0.000263      0.007220      0.010162   \n",
       "Component 2     -0.009176     -0.017050     -0.025184     -0.013221   \n",
       "Component 3      0.008946     -0.022276     -0.079054     -0.091518   \n",
       "Component 4      0.032032      0.045894      0.057851      0.023425   \n",
       "Component 5      0.000402      0.010263      0.014462      0.016683   \n",
       "\n",
       "             NETWORTH_9.0  ownrent_O  ownrent_R  \n",
       "Component 1      0.009068   0.030702  -0.012883  \n",
       "Component 2      0.005060  -0.016612   0.002937  \n",
       "Component 3     -0.087453  -0.306357   0.115557  \n",
       "Component 4     -0.022347   0.051925  -0.001916  \n",
       "Component 5      0.022811   0.101848  -0.044195  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp_list = []\n",
    "for i in range(1, n_comps+1):\n",
    "    comp_list.append(f'Component {i}')\n",
    "\n",
    "active_accts_pca_comp = pd.DataFrame(data = pca.components_,\n",
    "                        columns = active_accts_dummies.columns.values,\n",
    "                        index = comp_list)\n",
    "active_accts_pca_comp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores_pca = pca.transform(active_accts_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores_pca = pd.DataFrame(scores_pca, columns = comp_list)\n",
    "\n",
    "scores_pca = scores_pca.set_index(hkey_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "inertia = []\n",
    "for i in range(2,21):\n",
    "    minikmeans_pca = MiniBatchKMeans(n_clusters = i, init = 'k-means++', random_state = 14).fit(scores_pca)\n",
    "    inertia.append(minikmeans_pca.inertia_)\n",
    "    #print(i)\n",
    "    \n",
    "plt.figure(figsize = (10,8))\n",
    "plt.xticks(range(0,19), range(2,21))\n",
    "plt.suptitle('Small Business Mini Batch + PCA',fontsize=20)\n",
    "plt.plot(inertia, marker = 'o');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cluster Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "inertia =[]\n",
    "sil =[]\n",
    "    \n",
    "for k in range(2,14):\n",
    "    #print(k)\n",
    "    kmeans_rand = MiniBatchKMeans(n_clusters=k, init='k-means++', random_state=0)\n",
    "    kmeans_rand.fit(scores_pca)\n",
    "    y_pred = kmeans_rand.predict(scores_pca)\n",
    "        \n",
    "    inertia.append(kmeans_rand.inertia_)\n",
    "    sil.append((k, silhouette_score(scores_pca, y_pred, sample_size = 10000, random_state = 14)))\n",
    "        \n",
    "fig, ax = plt.subplots(1, 2, figsize=(12,4))\n",
    "ax[0].plot(range(2,14), inertia, marker = 'o')\n",
    "ax[0].set_title('Elbow Method')\n",
    "ax[0].set_xlabel('Number of clusters')\n",
    "ax[0].set_ylabel('Inertia')\n",
    "    \n",
    "x_sil = [x[0] for x in sil]\n",
    "y_sil = [x[1] for x in sil]\n",
    "ax[1].plot(x_sil, y_sil, marker = 'o')\n",
    "ax[1].set_xlabel('Number of Clusters')\n",
    "ax[1].set_ylabel('Silhouetter Score')\n",
    "ax[1].set_title('Silhouetter Score Curve')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "minipca_clusters = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MiniBatchKMeans(n_clusters=4, random_state=14)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minikmeans_pca = MiniBatchKMeans(n_clusters = minipca_clusters, init = 'k-means++', random_state = 14)\n",
    "minikmeans_pca.fit(scores_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "active_accts_mini_pca = pd.concat([active_accts_dummies, scores_pca], axis = 1)\n",
    "active_accts_mini_pca.columns.values[0-n_comps:] = comp_list\n",
    "active_accts_mini_pca[\"Segment\"] = minikmeans_pca.labels_\n",
    "active_accts_mini_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Segment</th>\n",
       "      <th>avg_inv_bal</th>\n",
       "      <th>avg_ckcom_bal</th>\n",
       "      <th>avg_ckcon_bal</th>\n",
       "      <th>avg_indln_bal</th>\n",
       "      <th>avg_mtg_bal</th>\n",
       "      <th>avg_comln_bal</th>\n",
       "      <th>avg_conln_bal</th>\n",
       "      <th>avg_inv_num</th>\n",
       "      <th>avg_ckcom_num</th>\n",
       "      <th>avg_ckcon_num</th>\n",
       "      <th>avg_indln_num</th>\n",
       "      <th>avg_mtg_num</th>\n",
       "      <th>avg_comln_num</th>\n",
       "      <th>avg_conln_num</th>\n",
       "      <th>avg_bill_pay</th>\n",
       "      <th>avg_direct_deposit</th>\n",
       "      <th>avg_estatement</th>\n",
       "      <th>avg_mobile_app</th>\n",
       "      <th>avg_online_banking</th>\n",
       "      <th>sum_YTDINTPAID</th>\n",
       "      <th>sum_YTDITEMNSF</th>\n",
       "      <th>sum_YTDNSFFEE</th>\n",
       "      <th>sum_YTDNSFRTFA</th>\n",
       "      <th>sum_YTDODFEEAT</th>\n",
       "      <th>sum_YTDPDFEES</th>\n",
       "      <th>sum_YTDOTRCHG</th>\n",
       "      <th>sum_YTDPDINT</th>\n",
       "      <th>sum_YTDRTNNSF</th>\n",
       "      <th>sum_YTDSVC</th>\n",
       "      <th>sum_YTDSVCWVD</th>\n",
       "      <th>sum_YTDTIMEOD</th>\n",
       "      <th>sum_brokerage</th>\n",
       "      <th>sum_insurance</th>\n",
       "      <th>sum_trust</th>\n",
       "      <th>sum_full_charge_off</th>\n",
       "      <th>sum_partial_charge_off</th>\n",
       "      <th>sum_sbloan_flag</th>\n",
       "      <th>sum_consumer_flag</th>\n",
       "      <th>avg_age</th>\n",
       "      <th>ANNSPEND</th>\n",
       "      <th>RISKIND</th>\n",
       "      <th>LOR</th>\n",
       "      <th>total_ACH_DEPOSIT_trans</th>\n",
       "      <th>total_ATM_DEBIT_trans</th>\n",
       "      <th>total_AUTO_CR_TRANSFER_trans</th>\n",
       "      <th>total_AUTO_TRANSFER_DR_trans</th>\n",
       "      <th>total_Auto_Loan_Payment_trans</th>\n",
       "      <th>total_CHECK_trans</th>\n",
       "      <th>total_DEBIT_CARD_PURCHASE_trans</th>\n",
       "      <th>total_DEPOSIT_trans</th>\n",
       "      <th>total_FIRST_DESKTOP_DEPOSIT_trans</th>\n",
       "      <th>total_LOCKBOX_trans</th>\n",
       "      <th>total_MISC_CREDIT_trans</th>\n",
       "      <th>total_MISC_DEBIT_trans</th>\n",
       "      <th>total_MOBILE_DEPOSIT_trans</th>\n",
       "      <th>total_ONLINE_DEBIT_trans</th>\n",
       "      <th>total_POS_RETURN_trans</th>\n",
       "      <th>total_TRANSFER_CREDIT_trans</th>\n",
       "      <th>WIRE_trans</th>\n",
       "      <th>total_WITHDRAWAL_trans</th>\n",
       "      <th>total_ZELLE_trans</th>\n",
       "      <th>total_ACH_DEPOSIT_num</th>\n",
       "      <th>total_ATM_DEBIT_num</th>\n",
       "      <th>total_AUTO_CR_TRANSFER_num</th>\n",
       "      <th>total_AUTO_TRANSFER_DR_num</th>\n",
       "      <th>total_Auto_Loan_Payment_num</th>\n",
       "      <th>total_CHECK_num</th>\n",
       "      <th>total_DEBIT_CARD_PURCHASE_num</th>\n",
       "      <th>total_DEPOSIT_num</th>\n",
       "      <th>total_FIRST_DESKTOP_DEPOSIT_num</th>\n",
       "      <th>total_LOCKBOX_num</th>\n",
       "      <th>total_MISC_CREDIT_num</th>\n",
       "      <th>total_MISC_DEBIT_num</th>\n",
       "      <th>total_MOBILE_DEPOSIT_num</th>\n",
       "      <th>total_ONLINE_DEBIT_num</th>\n",
       "      <th>total_POS_RETURN_num</th>\n",
       "      <th>total_TRANSFER_CREDIT_num</th>\n",
       "      <th>WIRE_num</th>\n",
       "      <th>total_WITHDRAWAL_num</th>\n",
       "      <th>total_ZELLE_num</th>\n",
       "      <th>ZIP_14</th>\n",
       "      <th>ZIP_15</th>\n",
       "      <th>ZIP_16</th>\n",
       "      <th>ZIP_17</th>\n",
       "      <th>ZIP_18</th>\n",
       "      <th>ZIP_19</th>\n",
       "      <th>ZIP_20</th>\n",
       "      <th>ZIP_21</th>\n",
       "      <th>ZIP_22</th>\n",
       "      <th>ZIP_23</th>\n",
       "      <th>ZIP_26</th>\n",
       "      <th>ZIP_27</th>\n",
       "      <th>ZIP_28</th>\n",
       "      <th>ZIP_29</th>\n",
       "      <th>ZIP_32</th>\n",
       "      <th>ZIP_33</th>\n",
       "      <th>ZIP_34</th>\n",
       "      <th>ZIP_37</th>\n",
       "      <th>ZIP_43</th>\n",
       "      <th>ZIP_44</th>\n",
       "      <th>educate_C</th>\n",
       "      <th>educate_NC</th>\n",
       "      <th>income_0to20</th>\n",
       "      <th>income_100to150</th>\n",
       "      <th>income_150plus</th>\n",
       "      <th>income_20to40</th>\n",
       "      <th>income_40to60</th>\n",
       "      <th>income_60to75</th>\n",
       "      <th>income_75to100</th>\n",
       "      <th>homemktvalue_GT300k</th>\n",
       "      <th>homemktvalue_LTE100k</th>\n",
       "      <th>homemktvalue_LTE300k</th>\n",
       "      <th>DWELTYPE_M</th>\n",
       "      <th>DWELTYPE_S</th>\n",
       "      <th>MORESPON_R</th>\n",
       "      <th>PROPTYPE_A</th>\n",
       "      <th>PROPTYPE_B</th>\n",
       "      <th>PROPTYPE_C</th>\n",
       "      <th>NETWORTH_1.0</th>\n",
       "      <th>NETWORTH_10.0</th>\n",
       "      <th>NETWORTH_11.0</th>\n",
       "      <th>NETWORTH_2.0</th>\n",
       "      <th>NETWORTH_3.0</th>\n",
       "      <th>NETWORTH_4.0</th>\n",
       "      <th>NETWORTH_5.0</th>\n",
       "      <th>NETWORTH_6.0</th>\n",
       "      <th>NETWORTH_7.0</th>\n",
       "      <th>NETWORTH_8.0</th>\n",
       "      <th>NETWORTH_9.0</th>\n",
       "      <th>ownrent_O</th>\n",
       "      <th>ownrent_R</th>\n",
       "      <th>Component 1</th>\n",
       "      <th>Component 2</th>\n",
       "      <th>Component 3</th>\n",
       "      <th>Component 4</th>\n",
       "      <th>Component 5</th>\n",
       "      <th>Component 6</th>\n",
       "      <th>Component 7</th>\n",
       "      <th>Component 8</th>\n",
       "      <th>Component 9</th>\n",
       "      <th>Component 10</th>\n",
       "      <th>Component 11</th>\n",
       "      <th>Component 12</th>\n",
       "      <th>Component 13</th>\n",
       "      <th>Component 14</th>\n",
       "      <th>Component 15</th>\n",
       "      <th>Component 16</th>\n",
       "      <th>Component 17</th>\n",
       "      <th>Component 18</th>\n",
       "      <th>Component 19</th>\n",
       "      <th>Component 20</th>\n",
       "      <th>Component 21</th>\n",
       "      <th>Component 22</th>\n",
       "      <th>Component 23</th>\n",
       "      <th>Component 24</th>\n",
       "      <th>Component 25</th>\n",
       "      <th>Component 26</th>\n",
       "      <th>Component 27</th>\n",
       "      <th>Component 28</th>\n",
       "      <th>Component 29</th>\n",
       "      <th>Component 30</th>\n",
       "      <th>Component 31</th>\n",
       "      <th>Component 32</th>\n",
       "      <th>Component 33</th>\n",
       "      <th>Component 34</th>\n",
       "      <th>Component 35</th>\n",
       "      <th>Component 36</th>\n",
       "      <th>Component 37</th>\n",
       "      <th>Component 38</th>\n",
       "      <th>Component 39</th>\n",
       "      <th>Component 40</th>\n",
       "      <th>Component 41</th>\n",
       "      <th>Component 42</th>\n",
       "      <th>Component 43</th>\n",
       "      <th>Component 44</th>\n",
       "      <th>Component 45</th>\n",
       "      <th>Component 46</th>\n",
       "      <th>Component 47</th>\n",
       "      <th>Component 48</th>\n",
       "      <th>Component 49</th>\n",
       "      <th>Component 50</th>\n",
       "      <th>Component 51</th>\n",
       "      <th>Component 52</th>\n",
       "      <th>Component 53</th>\n",
       "      <th>Component 54</th>\n",
       "      <th>Component 55</th>\n",
       "      <th>Component 56</th>\n",
       "      <th>Component 57</th>\n",
       "      <th>Component 58</th>\n",
       "      <th>Component 59</th>\n",
       "      <th>Component 60</th>\n",
       "      <th>Component 61</th>\n",
       "      <th>Component 62</th>\n",
       "      <th>Component 63</th>\n",
       "      <th>Component 64</th>\n",
       "      <th>Component 65</th>\n",
       "      <th>Component 66</th>\n",
       "      <th>Component 67</th>\n",
       "      <th>Component 68</th>\n",
       "      <th>Component 69</th>\n",
       "      <th>Component 70</th>\n",
       "      <th>Component 71</th>\n",
       "      <th>Component 72</th>\n",
       "      <th>Component 73</th>\n",
       "      <th>Component 74</th>\n",
       "      <th>Component 75</th>\n",
       "      <th>Component 76</th>\n",
       "      <th>Component 77</th>\n",
       "      <th>Component 78</th>\n",
       "      <th>Component 79</th>\n",
       "      <th>Component 80</th>\n",
       "      <th>N Obs</th>\n",
       "      <th>Prop Obs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sorted Segments</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>14420.150798</td>\n",
       "      <td>774.413798</td>\n",
       "      <td>8926.983931</td>\n",
       "      <td>446.854578</td>\n",
       "      <td>6278.420819</td>\n",
       "      <td>2750.435604</td>\n",
       "      <td>164.177299</td>\n",
       "      <td>0.567049</td>\n",
       "      <td>0.009038</td>\n",
       "      <td>1.139184</td>\n",
       "      <td>0.039027</td>\n",
       "      <td>0.094325</td>\n",
       "      <td>0.008816</td>\n",
       "      <td>0.101980</td>\n",
       "      <td>0.080404</td>\n",
       "      <td>0.669896</td>\n",
       "      <td>0.765808</td>\n",
       "      <td>0.554185</td>\n",
       "      <td>0.347396</td>\n",
       "      <td>506.846605</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>22.524515</td>\n",
       "      <td>2.704783</td>\n",
       "      <td>3.393351</td>\n",
       "      <td>16.826405</td>\n",
       "      <td>17.939122</td>\n",
       "      <td>506.846605</td>\n",
       "      <td>0.079482</td>\n",
       "      <td>4.470343</td>\n",
       "      <td>0.169565</td>\n",
       "      <td>0.342200</td>\n",
       "      <td>0.078820</td>\n",
       "      <td>0.004995</td>\n",
       "      <td>0.002086</td>\n",
       "      <td>0.025839</td>\n",
       "      <td>0.000802</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.014203</td>\n",
       "      <td>54.676431</td>\n",
       "      <td>9749.196437</td>\n",
       "      <td>712.290784</td>\n",
       "      <td>8.778025</td>\n",
       "      <td>9.729999</td>\n",
       "      <td>3.826252</td>\n",
       "      <td>0.106343</td>\n",
       "      <td>0.214191</td>\n",
       "      <td>0.337847</td>\n",
       "      <td>8.619744</td>\n",
       "      <td>68.201212</td>\n",
       "      <td>1.640507</td>\n",
       "      <td>0.046582</td>\n",
       "      <td>0.000823</td>\n",
       "      <td>0.057996</td>\n",
       "      <td>0.048828</td>\n",
       "      <td>0.543332</td>\n",
       "      <td>1.682435</td>\n",
       "      <td>1.376605</td>\n",
       "      <td>0.028747</td>\n",
       "      <td>0.021505</td>\n",
       "      <td>0.005758</td>\n",
       "      <td>0.433598</td>\n",
       "      <td>11156.353595</td>\n",
       "      <td>576.806131</td>\n",
       "      <td>737.147167</td>\n",
       "      <td>718.407178</td>\n",
       "      <td>238.457591</td>\n",
       "      <td>5911.484393</td>\n",
       "      <td>2938.178162</td>\n",
       "      <td>4003.273552</td>\n",
       "      <td>1441.425132</td>\n",
       "      <td>30.517935</td>\n",
       "      <td>254.030212</td>\n",
       "      <td>360.034946</td>\n",
       "      <td>285.393095</td>\n",
       "      <td>2793.637358</td>\n",
       "      <td>144.350746</td>\n",
       "      <td>1271.112743</td>\n",
       "      <td>1135.330204</td>\n",
       "      <td>17.488565</td>\n",
       "      <td>93.817465</td>\n",
       "      <td>0.000802</td>\n",
       "      <td>0.260873</td>\n",
       "      <td>0.188794</td>\n",
       "      <td>0.176236</td>\n",
       "      <td>0.032118</td>\n",
       "      <td>0.034224</td>\n",
       "      <td>0.011334</td>\n",
       "      <td>0.039079</td>\n",
       "      <td>0.003711</td>\n",
       "      <td>0.002126</td>\n",
       "      <td>0.006319</td>\n",
       "      <td>0.095510</td>\n",
       "      <td>0.059421</td>\n",
       "      <td>0.006961</td>\n",
       "      <td>0.001485</td>\n",
       "      <td>0.001866</td>\n",
       "      <td>0.001364</td>\n",
       "      <td>0.001244</td>\n",
       "      <td>0.007142</td>\n",
       "      <td>0.055770</td>\n",
       "      <td>0.324948</td>\n",
       "      <td>0.417208</td>\n",
       "      <td>0.094808</td>\n",
       "      <td>0.069872</td>\n",
       "      <td>0.047464</td>\n",
       "      <td>0.181732</td>\n",
       "      <td>0.217481</td>\n",
       "      <td>0.133065</td>\n",
       "      <td>0.104879</td>\n",
       "      <td>0.154730</td>\n",
       "      <td>0.258044</td>\n",
       "      <td>0.577937</td>\n",
       "      <td>0.086503</td>\n",
       "      <td>0.888722</td>\n",
       "      <td>0.738385</td>\n",
       "      <td>0.073303</td>\n",
       "      <td>0.038256</td>\n",
       "      <td>0.846854</td>\n",
       "      <td>0.089271</td>\n",
       "      <td>0.058698</td>\n",
       "      <td>0.053322</td>\n",
       "      <td>0.059762</td>\n",
       "      <td>0.045498</td>\n",
       "      <td>0.066281</td>\n",
       "      <td>0.076472</td>\n",
       "      <td>0.113926</td>\n",
       "      <td>0.178202</td>\n",
       "      <td>0.126324</td>\n",
       "      <td>0.077496</td>\n",
       "      <td>0.742939</td>\n",
       "      <td>0.035026</td>\n",
       "      <td>-0.778017</td>\n",
       "      <td>0.822290</td>\n",
       "      <td>4.210034</td>\n",
       "      <td>-0.645714</td>\n",
       "      <td>-2.174290</td>\n",
       "      <td>0.856326</td>\n",
       "      <td>2.209995</td>\n",
       "      <td>0.155269</td>\n",
       "      <td>0.068528</td>\n",
       "      <td>0.026740</td>\n",
       "      <td>-0.029541</td>\n",
       "      <td>0.316047</td>\n",
       "      <td>0.440845</td>\n",
       "      <td>0.016222</td>\n",
       "      <td>-0.115427</td>\n",
       "      <td>0.227296</td>\n",
       "      <td>-0.136139</td>\n",
       "      <td>-0.225230</td>\n",
       "      <td>0.104767</td>\n",
       "      <td>0.154402</td>\n",
       "      <td>0.066590</td>\n",
       "      <td>0.082838</td>\n",
       "      <td>-0.240280</td>\n",
       "      <td>0.005133</td>\n",
       "      <td>-0.079737</td>\n",
       "      <td>0.012033</td>\n",
       "      <td>-0.056234</td>\n",
       "      <td>0.006352</td>\n",
       "      <td>-0.032776</td>\n",
       "      <td>-0.089311</td>\n",
       "      <td>0.027352</td>\n",
       "      <td>0.112623</td>\n",
       "      <td>0.098707</td>\n",
       "      <td>0.032339</td>\n",
       "      <td>0.073307</td>\n",
       "      <td>-0.090479</td>\n",
       "      <td>-0.076835</td>\n",
       "      <td>0.044041</td>\n",
       "      <td>0.066618</td>\n",
       "      <td>0.093698</td>\n",
       "      <td>-0.038691</td>\n",
       "      <td>0.065482</td>\n",
       "      <td>-0.074969</td>\n",
       "      <td>-0.066319</td>\n",
       "      <td>0.131131</td>\n",
       "      <td>0.032858</td>\n",
       "      <td>-0.064498</td>\n",
       "      <td>0.042274</td>\n",
       "      <td>0.103905</td>\n",
       "      <td>-0.004000</td>\n",
       "      <td>0.045355</td>\n",
       "      <td>-0.001683</td>\n",
       "      <td>0.025938</td>\n",
       "      <td>0.010761</td>\n",
       "      <td>-0.025104</td>\n",
       "      <td>0.035764</td>\n",
       "      <td>0.104936</td>\n",
       "      <td>-0.005059</td>\n",
       "      <td>-0.024084</td>\n",
       "      <td>-0.025818</td>\n",
       "      <td>-0.065512</td>\n",
       "      <td>-0.010529</td>\n",
       "      <td>0.025556</td>\n",
       "      <td>0.020836</td>\n",
       "      <td>-0.020358</td>\n",
       "      <td>-0.009702</td>\n",
       "      <td>-0.061509</td>\n",
       "      <td>0.006736</td>\n",
       "      <td>0.051462</td>\n",
       "      <td>-0.015433</td>\n",
       "      <td>-0.012172</td>\n",
       "      <td>0.080094</td>\n",
       "      <td>-0.007695</td>\n",
       "      <td>0.039451</td>\n",
       "      <td>-0.089667</td>\n",
       "      <td>0.051923</td>\n",
       "      <td>-0.039311</td>\n",
       "      <td>0.008221</td>\n",
       "      <td>0.071013</td>\n",
       "      <td>-0.031076</td>\n",
       "      <td>49848</td>\n",
       "      <td>0.100855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>13638.559321</td>\n",
       "      <td>617.532395</td>\n",
       "      <td>9132.629031</td>\n",
       "      <td>428.353271</td>\n",
       "      <td>6096.591552</td>\n",
       "      <td>3315.976069</td>\n",
       "      <td>148.458963</td>\n",
       "      <td>0.556732</td>\n",
       "      <td>0.007737</td>\n",
       "      <td>1.132523</td>\n",
       "      <td>0.038757</td>\n",
       "      <td>0.093770</td>\n",
       "      <td>0.008321</td>\n",
       "      <td>0.098549</td>\n",
       "      <td>0.078823</td>\n",
       "      <td>0.661233</td>\n",
       "      <td>0.764657</td>\n",
       "      <td>0.541579</td>\n",
       "      <td>0.342917</td>\n",
       "      <td>498.612801</td>\n",
       "      <td>0.829690</td>\n",
       "      <td>22.457294</td>\n",
       "      <td>2.701693</td>\n",
       "      <td>3.486130</td>\n",
       "      <td>13.902795</td>\n",
       "      <td>17.275451</td>\n",
       "      <td>498.612801</td>\n",
       "      <td>0.081447</td>\n",
       "      <td>3.878895</td>\n",
       "      <td>0.028593</td>\n",
       "      <td>0.336157</td>\n",
       "      <td>0.078752</td>\n",
       "      <td>0.004678</td>\n",
       "      <td>0.002382</td>\n",
       "      <td>0.026583</td>\n",
       "      <td>0.000685</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>1.991999</td>\n",
       "      <td>54.597783</td>\n",
       "      <td>9746.295954</td>\n",
       "      <td>712.923744</td>\n",
       "      <td>8.826837</td>\n",
       "      <td>9.612972</td>\n",
       "      <td>3.828705</td>\n",
       "      <td>0.084628</td>\n",
       "      <td>0.196423</td>\n",
       "      <td>0.332207</td>\n",
       "      <td>8.512614</td>\n",
       "      <td>67.240277</td>\n",
       "      <td>1.599666</td>\n",
       "      <td>0.024216</td>\n",
       "      <td>0.001155</td>\n",
       "      <td>0.056818</td>\n",
       "      <td>0.051555</td>\n",
       "      <td>0.529642</td>\n",
       "      <td>1.650252</td>\n",
       "      <td>1.361329</td>\n",
       "      <td>0.026697</td>\n",
       "      <td>0.023375</td>\n",
       "      <td>0.005448</td>\n",
       "      <td>0.425562</td>\n",
       "      <td>11683.742695</td>\n",
       "      <td>571.050186</td>\n",
       "      <td>1441.100444</td>\n",
       "      <td>1447.734637</td>\n",
       "      <td>425.281693</td>\n",
       "      <td>5307.401560</td>\n",
       "      <td>2895.427331</td>\n",
       "      <td>4257.021521</td>\n",
       "      <td>863.992641</td>\n",
       "      <td>15.630043</td>\n",
       "      <td>434.273649</td>\n",
       "      <td>190.531482</td>\n",
       "      <td>295.515937</td>\n",
       "      <td>2092.946962</td>\n",
       "      <td>136.834168</td>\n",
       "      <td>689.731014</td>\n",
       "      <td>1192.439824</td>\n",
       "      <td>17.017984</td>\n",
       "      <td>92.516622</td>\n",
       "      <td>0.000984</td>\n",
       "      <td>0.265064</td>\n",
       "      <td>0.186426</td>\n",
       "      <td>0.174917</td>\n",
       "      <td>0.032060</td>\n",
       "      <td>0.035782</td>\n",
       "      <td>0.011595</td>\n",
       "      <td>0.038877</td>\n",
       "      <td>0.003708</td>\n",
       "      <td>0.002225</td>\n",
       "      <td>0.006803</td>\n",
       "      <td>0.094739</td>\n",
       "      <td>0.057659</td>\n",
       "      <td>0.007773</td>\n",
       "      <td>0.001255</td>\n",
       "      <td>0.001811</td>\n",
       "      <td>0.001212</td>\n",
       "      <td>0.001269</td>\n",
       "      <td>0.008129</td>\n",
       "      <td>0.055106</td>\n",
       "      <td>0.324962</td>\n",
       "      <td>0.421298</td>\n",
       "      <td>0.095409</td>\n",
       "      <td>0.072192</td>\n",
       "      <td>0.046478</td>\n",
       "      <td>0.182832</td>\n",
       "      <td>0.215077</td>\n",
       "      <td>0.134115</td>\n",
       "      <td>0.104508</td>\n",
       "      <td>0.154038</td>\n",
       "      <td>0.256920</td>\n",
       "      <td>0.579429</td>\n",
       "      <td>0.086724</td>\n",
       "      <td>0.889174</td>\n",
       "      <td>0.741981</td>\n",
       "      <td>0.073076</td>\n",
       "      <td>0.038463</td>\n",
       "      <td>0.847802</td>\n",
       "      <td>0.086196</td>\n",
       "      <td>0.056960</td>\n",
       "      <td>0.054137</td>\n",
       "      <td>0.060112</td>\n",
       "      <td>0.044025</td>\n",
       "      <td>0.069767</td>\n",
       "      <td>0.076256</td>\n",
       "      <td>0.113165</td>\n",
       "      <td>0.178996</td>\n",
       "      <td>0.126143</td>\n",
       "      <td>0.080207</td>\n",
       "      <td>0.747429</td>\n",
       "      <td>0.034655</td>\n",
       "      <td>2.231001</td>\n",
       "      <td>-4.103691</td>\n",
       "      <td>-0.186906</td>\n",
       "      <td>-0.148788</td>\n",
       "      <td>-0.065446</td>\n",
       "      <td>0.401002</td>\n",
       "      <td>-0.307042</td>\n",
       "      <td>0.149636</td>\n",
       "      <td>0.099839</td>\n",
       "      <td>0.083774</td>\n",
       "      <td>-0.051369</td>\n",
       "      <td>0.086069</td>\n",
       "      <td>0.103618</td>\n",
       "      <td>-0.039734</td>\n",
       "      <td>-0.138710</td>\n",
       "      <td>0.090265</td>\n",
       "      <td>-0.008262</td>\n",
       "      <td>0.050160</td>\n",
       "      <td>-0.082863</td>\n",
       "      <td>-0.017197</td>\n",
       "      <td>-0.011502</td>\n",
       "      <td>0.003520</td>\n",
       "      <td>0.021725</td>\n",
       "      <td>0.008879</td>\n",
       "      <td>-0.024449</td>\n",
       "      <td>0.031021</td>\n",
       "      <td>-0.080072</td>\n",
       "      <td>0.004457</td>\n",
       "      <td>-0.016063</td>\n",
       "      <td>0.024096</td>\n",
       "      <td>0.031209</td>\n",
       "      <td>0.013305</td>\n",
       "      <td>0.018750</td>\n",
       "      <td>-0.007190</td>\n",
       "      <td>-0.009776</td>\n",
       "      <td>-0.036647</td>\n",
       "      <td>0.009231</td>\n",
       "      <td>-0.039175</td>\n",
       "      <td>-0.022307</td>\n",
       "      <td>-0.007301</td>\n",
       "      <td>0.014302</td>\n",
       "      <td>-0.021455</td>\n",
       "      <td>0.001327</td>\n",
       "      <td>0.007855</td>\n",
       "      <td>-0.043694</td>\n",
       "      <td>-0.027643</td>\n",
       "      <td>0.003241</td>\n",
       "      <td>0.013216</td>\n",
       "      <td>-0.005891</td>\n",
       "      <td>0.000869</td>\n",
       "      <td>0.004855</td>\n",
       "      <td>-0.014765</td>\n",
       "      <td>0.003961</td>\n",
       "      <td>0.002560</td>\n",
       "      <td>-0.005411</td>\n",
       "      <td>-0.006734</td>\n",
       "      <td>-0.002236</td>\n",
       "      <td>0.001831</td>\n",
       "      <td>0.014732</td>\n",
       "      <td>-0.007163</td>\n",
       "      <td>0.015372</td>\n",
       "      <td>-0.015118</td>\n",
       "      <td>-0.031441</td>\n",
       "      <td>0.016111</td>\n",
       "      <td>0.086437</td>\n",
       "      <td>0.016267</td>\n",
       "      <td>0.037588</td>\n",
       "      <td>-0.016730</td>\n",
       "      <td>0.008628</td>\n",
       "      <td>-0.029144</td>\n",
       "      <td>-0.021047</td>\n",
       "      <td>0.094216</td>\n",
       "      <td>-0.055135</td>\n",
       "      <td>-0.016080</td>\n",
       "      <td>-0.013498</td>\n",
       "      <td>-0.012283</td>\n",
       "      <td>-0.091481</td>\n",
       "      <td>-0.034125</td>\n",
       "      <td>0.006889</td>\n",
       "      <td>0.011852</td>\n",
       "      <td>70119</td>\n",
       "      <td>0.141869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>13969.393905</td>\n",
       "      <td>667.794902</td>\n",
       "      <td>8929.645728</td>\n",
       "      <td>429.226203</td>\n",
       "      <td>6301.474360</td>\n",
       "      <td>2170.170349</td>\n",
       "      <td>179.962247</td>\n",
       "      <td>0.556145</td>\n",
       "      <td>0.006524</td>\n",
       "      <td>1.127942</td>\n",
       "      <td>0.037139</td>\n",
       "      <td>0.094532</td>\n",
       "      <td>0.007105</td>\n",
       "      <td>0.098440</td>\n",
       "      <td>0.077808</td>\n",
       "      <td>0.665386</td>\n",
       "      <td>0.759280</td>\n",
       "      <td>0.536257</td>\n",
       "      <td>0.343234</td>\n",
       "      <td>489.290400</td>\n",
       "      <td>0.820732</td>\n",
       "      <td>22.446527</td>\n",
       "      <td>2.851804</td>\n",
       "      <td>3.418971</td>\n",
       "      <td>13.476422</td>\n",
       "      <td>18.616274</td>\n",
       "      <td>489.290400</td>\n",
       "      <td>0.084097</td>\n",
       "      <td>3.875361</td>\n",
       "      <td>0.025422</td>\n",
       "      <td>0.333554</td>\n",
       "      <td>0.077937</td>\n",
       "      <td>0.004901</td>\n",
       "      <td>0.002079</td>\n",
       "      <td>0.025450</td>\n",
       "      <td>0.000849</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>1.989395</td>\n",
       "      <td>54.743360</td>\n",
       "      <td>9736.984113</td>\n",
       "      <td>712.563188</td>\n",
       "      <td>8.848325</td>\n",
       "      <td>9.622603</td>\n",
       "      <td>3.808611</td>\n",
       "      <td>0.084085</td>\n",
       "      <td>0.202328</td>\n",
       "      <td>0.329683</td>\n",
       "      <td>8.567498</td>\n",
       "      <td>66.512400</td>\n",
       "      <td>1.611705</td>\n",
       "      <td>0.019764</td>\n",
       "      <td>0.001739</td>\n",
       "      <td>0.057447</td>\n",
       "      <td>0.047105</td>\n",
       "      <td>0.529997</td>\n",
       "      <td>1.647420</td>\n",
       "      <td>1.362131</td>\n",
       "      <td>0.018557</td>\n",
       "      <td>0.040195</td>\n",
       "      <td>0.005013</td>\n",
       "      <td>0.423132</td>\n",
       "      <td>11410.952942</td>\n",
       "      <td>566.288416</td>\n",
       "      <td>351.783389</td>\n",
       "      <td>352.573981</td>\n",
       "      <td>260.938068</td>\n",
       "      <td>5524.298928</td>\n",
       "      <td>2873.109764</td>\n",
       "      <td>4058.358752</td>\n",
       "      <td>606.554317</td>\n",
       "      <td>40.097513</td>\n",
       "      <td>395.127888</td>\n",
       "      <td>135.477639</td>\n",
       "      <td>288.385888</td>\n",
       "      <td>1818.098602</td>\n",
       "      <td>143.295525</td>\n",
       "      <td>471.049676</td>\n",
       "      <td>5709.374001</td>\n",
       "      <td>14.819169</td>\n",
       "      <td>88.395334</td>\n",
       "      <td>0.000990</td>\n",
       "      <td>0.260823</td>\n",
       "      <td>0.186716</td>\n",
       "      <td>0.177359</td>\n",
       "      <td>0.032243</td>\n",
       "      <td>0.033554</td>\n",
       "      <td>0.011337</td>\n",
       "      <td>0.038187</td>\n",
       "      <td>0.003923</td>\n",
       "      <td>0.001792</td>\n",
       "      <td>0.006283</td>\n",
       "      <td>0.096395</td>\n",
       "      <td>0.058507</td>\n",
       "      <td>0.007736</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.002009</td>\n",
       "      <td>0.001511</td>\n",
       "      <td>0.001212</td>\n",
       "      <td>0.007841</td>\n",
       "      <td>0.056574</td>\n",
       "      <td>0.327352</td>\n",
       "      <td>0.417510</td>\n",
       "      <td>0.096447</td>\n",
       "      <td>0.071179</td>\n",
       "      <td>0.046935</td>\n",
       "      <td>0.181926</td>\n",
       "      <td>0.213970</td>\n",
       "      <td>0.134025</td>\n",
       "      <td>0.105957</td>\n",
       "      <td>0.156025</td>\n",
       "      <td>0.253878</td>\n",
       "      <td>0.580897</td>\n",
       "      <td>0.087950</td>\n",
       "      <td>0.887519</td>\n",
       "      <td>0.742450</td>\n",
       "      <td>0.075020</td>\n",
       "      <td>0.037876</td>\n",
       "      <td>0.845011</td>\n",
       "      <td>0.087435</td>\n",
       "      <td>0.058717</td>\n",
       "      <td>0.055251</td>\n",
       "      <td>0.059561</td>\n",
       "      <td>0.043761</td>\n",
       "      <td>0.068503</td>\n",
       "      <td>0.076883</td>\n",
       "      <td>0.113008</td>\n",
       "      <td>0.179180</td>\n",
       "      <td>0.125967</td>\n",
       "      <td>0.078446</td>\n",
       "      <td>0.745935</td>\n",
       "      <td>0.035985</td>\n",
       "      <td>-0.490960</td>\n",
       "      <td>0.873970</td>\n",
       "      <td>0.309860</td>\n",
       "      <td>-0.394697</td>\n",
       "      <td>0.352749</td>\n",
       "      <td>0.477537</td>\n",
       "      <td>-1.101535</td>\n",
       "      <td>-0.261401</td>\n",
       "      <td>-0.155099</td>\n",
       "      <td>0.271944</td>\n",
       "      <td>0.070422</td>\n",
       "      <td>-0.118990</td>\n",
       "      <td>-0.428510</td>\n",
       "      <td>-0.099081</td>\n",
       "      <td>0.133685</td>\n",
       "      <td>-0.078130</td>\n",
       "      <td>0.058509</td>\n",
       "      <td>0.080452</td>\n",
       "      <td>0.040744</td>\n",
       "      <td>-0.051674</td>\n",
       "      <td>-0.136481</td>\n",
       "      <td>0.005992</td>\n",
       "      <td>0.100040</td>\n",
       "      <td>-0.021485</td>\n",
       "      <td>0.062626</td>\n",
       "      <td>-0.014412</td>\n",
       "      <td>0.026332</td>\n",
       "      <td>0.001298</td>\n",
       "      <td>-0.050159</td>\n",
       "      <td>-0.012361</td>\n",
       "      <td>0.066725</td>\n",
       "      <td>-0.031044</td>\n",
       "      <td>-0.025429</td>\n",
       "      <td>-0.045883</td>\n",
       "      <td>-0.005954</td>\n",
       "      <td>-0.011357</td>\n",
       "      <td>0.034097</td>\n",
       "      <td>-0.048274</td>\n",
       "      <td>-0.012938</td>\n",
       "      <td>-0.034439</td>\n",
       "      <td>-0.025619</td>\n",
       "      <td>-0.047732</td>\n",
       "      <td>0.007530</td>\n",
       "      <td>-0.033671</td>\n",
       "      <td>-0.101499</td>\n",
       "      <td>-0.041316</td>\n",
       "      <td>-0.010894</td>\n",
       "      <td>-0.007450</td>\n",
       "      <td>-0.037018</td>\n",
       "      <td>-0.029963</td>\n",
       "      <td>-0.034152</td>\n",
       "      <td>0.003309</td>\n",
       "      <td>-0.018160</td>\n",
       "      <td>-0.028036</td>\n",
       "      <td>0.037110</td>\n",
       "      <td>-0.009250</td>\n",
       "      <td>-0.040204</td>\n",
       "      <td>-0.001175</td>\n",
       "      <td>-0.010531</td>\n",
       "      <td>0.012049</td>\n",
       "      <td>-0.002296</td>\n",
       "      <td>0.045732</td>\n",
       "      <td>-0.029677</td>\n",
       "      <td>0.017258</td>\n",
       "      <td>-0.006736</td>\n",
       "      <td>0.004063</td>\n",
       "      <td>0.078905</td>\n",
       "      <td>-0.029016</td>\n",
       "      <td>-0.051093</td>\n",
       "      <td>-0.013620</td>\n",
       "      <td>0.037202</td>\n",
       "      <td>-0.050727</td>\n",
       "      <td>0.017961</td>\n",
       "      <td>-0.009797</td>\n",
       "      <td>0.039885</td>\n",
       "      <td>-0.017742</td>\n",
       "      <td>0.017002</td>\n",
       "      <td>0.006567</td>\n",
       "      <td>0.010643</td>\n",
       "      <td>-0.003027</td>\n",
       "      <td>170767</td>\n",
       "      <td>0.345505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14153.959075</td>\n",
       "      <td>793.007503</td>\n",
       "      <td>9056.159543</td>\n",
       "      <td>443.346169</td>\n",
       "      <td>6255.030110</td>\n",
       "      <td>2877.564380</td>\n",
       "      <td>168.677996</td>\n",
       "      <td>0.565532</td>\n",
       "      <td>0.007895</td>\n",
       "      <td>1.127705</td>\n",
       "      <td>0.038032</td>\n",
       "      <td>0.093272</td>\n",
       "      <td>0.008683</td>\n",
       "      <td>0.098935</td>\n",
       "      <td>0.078214</td>\n",
       "      <td>0.665063</td>\n",
       "      <td>0.759074</td>\n",
       "      <td>0.538299</td>\n",
       "      <td>0.343693</td>\n",
       "      <td>496.355682</td>\n",
       "      <td>0.840899</td>\n",
       "      <td>22.696426</td>\n",
       "      <td>2.845007</td>\n",
       "      <td>3.447058</td>\n",
       "      <td>14.581618</td>\n",
       "      <td>17.406921</td>\n",
       "      <td>496.355682</td>\n",
       "      <td>0.084813</td>\n",
       "      <td>3.929034</td>\n",
       "      <td>0.028386</td>\n",
       "      <td>0.340253</td>\n",
       "      <td>0.080725</td>\n",
       "      <td>0.005410</td>\n",
       "      <td>0.002304</td>\n",
       "      <td>0.026012</td>\n",
       "      <td>0.000668</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>1.997091</td>\n",
       "      <td>54.739006</td>\n",
       "      <td>9733.475042</td>\n",
       "      <td>712.109990</td>\n",
       "      <td>8.826729</td>\n",
       "      <td>9.685430</td>\n",
       "      <td>3.838035</td>\n",
       "      <td>0.096060</td>\n",
       "      <td>0.212953</td>\n",
       "      <td>0.338799</td>\n",
       "      <td>8.618154</td>\n",
       "      <td>66.219115</td>\n",
       "      <td>1.649340</td>\n",
       "      <td>0.023020</td>\n",
       "      <td>0.000285</td>\n",
       "      <td>0.057911</td>\n",
       "      <td>0.049293</td>\n",
       "      <td>0.526712</td>\n",
       "      <td>1.628782</td>\n",
       "      <td>1.366413</td>\n",
       "      <td>0.018780</td>\n",
       "      <td>0.020308</td>\n",
       "      <td>0.005592</td>\n",
       "      <td>0.443502</td>\n",
       "      <td>11436.243746</td>\n",
       "      <td>568.970794</td>\n",
       "      <td>556.261897</td>\n",
       "      <td>564.547693</td>\n",
       "      <td>267.049951</td>\n",
       "      <td>5640.494593</td>\n",
       "      <td>2859.344744</td>\n",
       "      <td>4461.939760</td>\n",
       "      <td>628.667073</td>\n",
       "      <td>8.933156</td>\n",
       "      <td>385.156187</td>\n",
       "      <td>252.270520</td>\n",
       "      <td>286.179364</td>\n",
       "      <td>2094.605811</td>\n",
       "      <td>142.354144</td>\n",
       "      <td>737.043539</td>\n",
       "      <td>1438.641621</td>\n",
       "      <td>17.524747</td>\n",
       "      <td>91.429862</td>\n",
       "      <td>0.000899</td>\n",
       "      <td>0.262324</td>\n",
       "      <td>0.188729</td>\n",
       "      <td>0.174480</td>\n",
       "      <td>0.033510</td>\n",
       "      <td>0.032857</td>\n",
       "      <td>0.011114</td>\n",
       "      <td>0.038896</td>\n",
       "      <td>0.003896</td>\n",
       "      <td>0.001985</td>\n",
       "      <td>0.006486</td>\n",
       "      <td>0.095190</td>\n",
       "      <td>0.058309</td>\n",
       "      <td>0.007744</td>\n",
       "      <td>0.001523</td>\n",
       "      <td>0.001995</td>\n",
       "      <td>0.001459</td>\n",
       "      <td>0.001204</td>\n",
       "      <td>0.007616</td>\n",
       "      <td>0.056132</td>\n",
       "      <td>0.327026</td>\n",
       "      <td>0.417268</td>\n",
       "      <td>0.097023</td>\n",
       "      <td>0.071163</td>\n",
       "      <td>0.047263</td>\n",
       "      <td>0.181998</td>\n",
       "      <td>0.213115</td>\n",
       "      <td>0.134725</td>\n",
       "      <td>0.105685</td>\n",
       "      <td>0.156148</td>\n",
       "      <td>0.256143</td>\n",
       "      <td>0.578039</td>\n",
       "      <td>0.088193</td>\n",
       "      <td>0.887028</td>\n",
       "      <td>0.741970</td>\n",
       "      <td>0.074553</td>\n",
       "      <td>0.038596</td>\n",
       "      <td>0.845164</td>\n",
       "      <td>0.086980</td>\n",
       "      <td>0.058761</td>\n",
       "      <td>0.054378</td>\n",
       "      <td>0.059213</td>\n",
       "      <td>0.044266</td>\n",
       "      <td>0.068524</td>\n",
       "      <td>0.077231</td>\n",
       "      <td>0.111862</td>\n",
       "      <td>0.179109</td>\n",
       "      <td>0.125885</td>\n",
       "      <td>0.079963</td>\n",
       "      <td>0.745916</td>\n",
       "      <td>0.034975</td>\n",
       "      <td>-0.166143</td>\n",
       "      <td>0.479130</td>\n",
       "      <td>-1.226765</td>\n",
       "      <td>0.540596</td>\n",
       "      <td>0.259117</td>\n",
       "      <td>-0.748586</td>\n",
       "      <td>0.488758</td>\n",
       "      <td>0.129750</td>\n",
       "      <td>0.078957</td>\n",
       "      <td>-0.263593</td>\n",
       "      <td>-0.034155</td>\n",
       "      <td>-0.007222</td>\n",
       "      <td>0.215874</td>\n",
       "      <td>0.092852</td>\n",
       "      <td>-0.036110</td>\n",
       "      <td>-0.021215</td>\n",
       "      <td>-0.012903</td>\n",
       "      <td>-0.029621</td>\n",
       "      <td>-0.031298</td>\n",
       "      <td>0.011465</td>\n",
       "      <td>0.102170</td>\n",
       "      <td>-0.026530</td>\n",
       "      <td>-0.032574</td>\n",
       "      <td>0.013711</td>\n",
       "      <td>-0.024594</td>\n",
       "      <td>-0.001542</td>\n",
       "      <td>0.019266</td>\n",
       "      <td>-0.004180</td>\n",
       "      <td>0.055649</td>\n",
       "      <td>0.023945</td>\n",
       "      <td>-0.073439</td>\n",
       "      <td>-0.006120</td>\n",
       "      <td>-0.009299</td>\n",
       "      <td>0.033055</td>\n",
       "      <td>-0.009592</td>\n",
       "      <td>0.044316</td>\n",
       "      <td>-0.012970</td>\n",
       "      <td>0.043216</td>\n",
       "      <td>0.002224</td>\n",
       "      <td>0.008463</td>\n",
       "      <td>0.026045</td>\n",
       "      <td>0.031404</td>\n",
       "      <td>0.011586</td>\n",
       "      <td>0.041790</td>\n",
       "      <td>0.068101</td>\n",
       "      <td>0.036143</td>\n",
       "      <td>0.023822</td>\n",
       "      <td>-0.008657</td>\n",
       "      <td>0.007641</td>\n",
       "      <td>0.025821</td>\n",
       "      <td>0.015875</td>\n",
       "      <td>0.002723</td>\n",
       "      <td>0.007520</td>\n",
       "      <td>0.020007</td>\n",
       "      <td>-0.023125</td>\n",
       "      <td>0.001322</td>\n",
       "      <td>0.008803</td>\n",
       "      <td>0.001594</td>\n",
       "      <td>0.009660</td>\n",
       "      <td>-0.001319</td>\n",
       "      <td>0.012676</td>\n",
       "      <td>-0.030585</td>\n",
       "      <td>0.029474</td>\n",
       "      <td>-0.025135</td>\n",
       "      <td>-0.019142</td>\n",
       "      <td>-0.006637</td>\n",
       "      <td>-0.064092</td>\n",
       "      <td>0.028461</td>\n",
       "      <td>0.027294</td>\n",
       "      <td>0.025249</td>\n",
       "      <td>-0.020982</td>\n",
       "      <td>-0.009515</td>\n",
       "      <td>0.005810</td>\n",
       "      <td>0.004097</td>\n",
       "      <td>-0.006853</td>\n",
       "      <td>0.006401</td>\n",
       "      <td>0.026880</td>\n",
       "      <td>0.004234</td>\n",
       "      <td>-0.028697</td>\n",
       "      <td>0.006068</td>\n",
       "      <td>203519</td>\n",
       "      <td>0.411771</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Segment   avg_inv_bal  avg_ckcom_bal  avg_ckcon_bal  \\\n",
       "Sorted Segments                                                        \n",
       "0                      2  14420.150798     774.413798    8926.983931   \n",
       "1                      0  13638.559321     617.532395    9132.629031   \n",
       "2                      3  13969.393905     667.794902    8929.645728   \n",
       "3                      1  14153.959075     793.007503    9056.159543   \n",
       "\n",
       "                 avg_indln_bal  avg_mtg_bal  avg_comln_bal  avg_conln_bal  \\\n",
       "Sorted Segments                                                             \n",
       "0                   446.854578  6278.420819    2750.435604     164.177299   \n",
       "1                   428.353271  6096.591552    3315.976069     148.458963   \n",
       "2                   429.226203  6301.474360    2170.170349     179.962247   \n",
       "3                   443.346169  6255.030110    2877.564380     168.677996   \n",
       "\n",
       "                 avg_inv_num  avg_ckcom_num  avg_ckcon_num  avg_indln_num  \\\n",
       "Sorted Segments                                                             \n",
       "0                   0.567049       0.009038       1.139184       0.039027   \n",
       "1                   0.556732       0.007737       1.132523       0.038757   \n",
       "2                   0.556145       0.006524       1.127942       0.037139   \n",
       "3                   0.565532       0.007895       1.127705       0.038032   \n",
       "\n",
       "                 avg_mtg_num  avg_comln_num  avg_conln_num  avg_bill_pay  \\\n",
       "Sorted Segments                                                            \n",
       "0                   0.094325       0.008816       0.101980      0.080404   \n",
       "1                   0.093770       0.008321       0.098549      0.078823   \n",
       "2                   0.094532       0.007105       0.098440      0.077808   \n",
       "3                   0.093272       0.008683       0.098935      0.078214   \n",
       "\n",
       "                 avg_direct_deposit  avg_estatement  avg_mobile_app  \\\n",
       "Sorted Segments                                                       \n",
       "0                          0.669896        0.765808        0.554185   \n",
       "1                          0.661233        0.764657        0.541579   \n",
       "2                          0.665386        0.759280        0.536257   \n",
       "3                          0.665063        0.759074        0.538299   \n",
       "\n",
       "                 avg_online_banking  sum_YTDINTPAID  sum_YTDITEMNSF  \\\n",
       "Sorted Segments                                                       \n",
       "0                          0.347396      506.846605        0.833333   \n",
       "1                          0.342917      498.612801        0.829690   \n",
       "2                          0.343234      489.290400        0.820732   \n",
       "3                          0.343693      496.355682        0.840899   \n",
       "\n",
       "                 sum_YTDNSFFEE  sum_YTDNSFRTFA  sum_YTDODFEEAT  sum_YTDPDFEES  \\\n",
       "Sorted Segments                                                                 \n",
       "0                    22.524515        2.704783        3.393351      16.826405   \n",
       "1                    22.457294        2.701693        3.486130      13.902795   \n",
       "2                    22.446527        2.851804        3.418971      13.476422   \n",
       "3                    22.696426        2.845007        3.447058      14.581618   \n",
       "\n",
       "                 sum_YTDOTRCHG  sum_YTDPDINT  sum_YTDRTNNSF  sum_YTDSVC  \\\n",
       "Sorted Segments                                                           \n",
       "0                    17.939122    506.846605       0.079482    4.470343   \n",
       "1                    17.275451    498.612801       0.081447    3.878895   \n",
       "2                    18.616274    489.290400       0.084097    3.875361   \n",
       "3                    17.406921    496.355682       0.084813    3.929034   \n",
       "\n",
       "                 sum_YTDSVCWVD  sum_YTDTIMEOD  sum_brokerage  sum_insurance  \\\n",
       "Sorted Segments                                                               \n",
       "0                     0.169565       0.342200       0.078820       0.004995   \n",
       "1                     0.028593       0.336157       0.078752       0.004678   \n",
       "2                     0.025422       0.333554       0.077937       0.004901   \n",
       "3                     0.028386       0.340253       0.080725       0.005410   \n",
       "\n",
       "                 sum_trust  sum_full_charge_off  sum_partial_charge_off  \\\n",
       "Sorted Segments                                                           \n",
       "0                 0.002086             0.025839                0.000802   \n",
       "1                 0.002382             0.026583                0.000685   \n",
       "2                 0.002079             0.025450                0.000849   \n",
       "3                 0.002304             0.026012                0.000668   \n",
       "\n",
       "                 sum_sbloan_flag  sum_consumer_flag    avg_age     ANNSPEND  \\\n",
       "Sorted Segments                                                               \n",
       "0                       0.000000           2.014203  54.676431  9749.196437   \n",
       "1                       0.000071           1.991999  54.597783  9746.295954   \n",
       "2                       0.000035           1.989395  54.743360  9736.984113   \n",
       "3                       0.000029           1.997091  54.739006  9733.475042   \n",
       "\n",
       "                    RISKIND       LOR  total_ACH_DEPOSIT_trans  \\\n",
       "Sorted Segments                                                  \n",
       "0                712.290784  8.778025                 9.729999   \n",
       "1                712.923744  8.826837                 9.612972   \n",
       "2                712.563188  8.848325                 9.622603   \n",
       "3                712.109990  8.826729                 9.685430   \n",
       "\n",
       "                 total_ATM_DEBIT_trans  total_AUTO_CR_TRANSFER_trans  \\\n",
       "Sorted Segments                                                        \n",
       "0                             3.826252                      0.106343   \n",
       "1                             3.828705                      0.084628   \n",
       "2                             3.808611                      0.084085   \n",
       "3                             3.838035                      0.096060   \n",
       "\n",
       "                 total_AUTO_TRANSFER_DR_trans  total_Auto_Loan_Payment_trans  \\\n",
       "Sorted Segments                                                                \n",
       "0                                    0.214191                       0.337847   \n",
       "1                                    0.196423                       0.332207   \n",
       "2                                    0.202328                       0.329683   \n",
       "3                                    0.212953                       0.338799   \n",
       "\n",
       "                 total_CHECK_trans  total_DEBIT_CARD_PURCHASE_trans  \\\n",
       "Sorted Segments                                                       \n",
       "0                         8.619744                        68.201212   \n",
       "1                         8.512614                        67.240277   \n",
       "2                         8.567498                        66.512400   \n",
       "3                         8.618154                        66.219115   \n",
       "\n",
       "                 total_DEPOSIT_trans  total_FIRST_DESKTOP_DEPOSIT_trans  \\\n",
       "Sorted Segments                                                           \n",
       "0                           1.640507                           0.046582   \n",
       "1                           1.599666                           0.024216   \n",
       "2                           1.611705                           0.019764   \n",
       "3                           1.649340                           0.023020   \n",
       "\n",
       "                 total_LOCKBOX_trans  total_MISC_CREDIT_trans  \\\n",
       "Sorted Segments                                                 \n",
       "0                           0.000823                 0.057996   \n",
       "1                           0.001155                 0.056818   \n",
       "2                           0.001739                 0.057447   \n",
       "3                           0.000285                 0.057911   \n",
       "\n",
       "                 total_MISC_DEBIT_trans  total_MOBILE_DEPOSIT_trans  \\\n",
       "Sorted Segments                                                       \n",
       "0                              0.048828                    0.543332   \n",
       "1                              0.051555                    0.529642   \n",
       "2                              0.047105                    0.529997   \n",
       "3                              0.049293                    0.526712   \n",
       "\n",
       "                 total_ONLINE_DEBIT_trans  total_POS_RETURN_trans  \\\n",
       "Sorted Segments                                                     \n",
       "0                                1.682435                1.376605   \n",
       "1                                1.650252                1.361329   \n",
       "2                                1.647420                1.362131   \n",
       "3                                1.628782                1.366413   \n",
       "\n",
       "                 total_TRANSFER_CREDIT_trans  WIRE_trans  \\\n",
       "Sorted Segments                                            \n",
       "0                                   0.028747    0.021505   \n",
       "1                                   0.026697    0.023375   \n",
       "2                                   0.018557    0.040195   \n",
       "3                                   0.018780    0.020308   \n",
       "\n",
       "                 total_WITHDRAWAL_trans  total_ZELLE_trans  \\\n",
       "Sorted Segments                                              \n",
       "0                              0.005758           0.433598   \n",
       "1                              0.005448           0.425562   \n",
       "2                              0.005013           0.423132   \n",
       "3                              0.005592           0.443502   \n",
       "\n",
       "                 total_ACH_DEPOSIT_num  total_ATM_DEBIT_num  \\\n",
       "Sorted Segments                                               \n",
       "0                         11156.353595           576.806131   \n",
       "1                         11683.742695           571.050186   \n",
       "2                         11410.952942           566.288416   \n",
       "3                         11436.243746           568.970794   \n",
       "\n",
       "                 total_AUTO_CR_TRANSFER_num  total_AUTO_TRANSFER_DR_num  \\\n",
       "Sorted Segments                                                           \n",
       "0                                737.147167                  718.407178   \n",
       "1                               1441.100444                 1447.734637   \n",
       "2                                351.783389                  352.573981   \n",
       "3                                556.261897                  564.547693   \n",
       "\n",
       "                 total_Auto_Loan_Payment_num  total_CHECK_num  \\\n",
       "Sorted Segments                                                 \n",
       "0                                 238.457591      5911.484393   \n",
       "1                                 425.281693      5307.401560   \n",
       "2                                 260.938068      5524.298928   \n",
       "3                                 267.049951      5640.494593   \n",
       "\n",
       "                 total_DEBIT_CARD_PURCHASE_num  total_DEPOSIT_num  \\\n",
       "Sorted Segments                                                     \n",
       "0                                  2938.178162        4003.273552   \n",
       "1                                  2895.427331        4257.021521   \n",
       "2                                  2873.109764        4058.358752   \n",
       "3                                  2859.344744        4461.939760   \n",
       "\n",
       "                 total_FIRST_DESKTOP_DEPOSIT_num  total_LOCKBOX_num  \\\n",
       "Sorted Segments                                                       \n",
       "0                                    1441.425132          30.517935   \n",
       "1                                     863.992641          15.630043   \n",
       "2                                     606.554317          40.097513   \n",
       "3                                     628.667073           8.933156   \n",
       "\n",
       "                 total_MISC_CREDIT_num  total_MISC_DEBIT_num  \\\n",
       "Sorted Segments                                                \n",
       "0                           254.030212            360.034946   \n",
       "1                           434.273649            190.531482   \n",
       "2                           395.127888            135.477639   \n",
       "3                           385.156187            252.270520   \n",
       "\n",
       "                 total_MOBILE_DEPOSIT_num  total_ONLINE_DEBIT_num  \\\n",
       "Sorted Segments                                                     \n",
       "0                              285.393095             2793.637358   \n",
       "1                              295.515937             2092.946962   \n",
       "2                              288.385888             1818.098602   \n",
       "3                              286.179364             2094.605811   \n",
       "\n",
       "                 total_POS_RETURN_num  total_TRANSFER_CREDIT_num     WIRE_num  \\\n",
       "Sorted Segments                                                                 \n",
       "0                          144.350746                1271.112743  1135.330204   \n",
       "1                          136.834168                 689.731014  1192.439824   \n",
       "2                          143.295525                 471.049676  5709.374001   \n",
       "3                          142.354144                 737.043539  1438.641621   \n",
       "\n",
       "                 total_WITHDRAWAL_num  total_ZELLE_num    ZIP_14    ZIP_15  \\\n",
       "Sorted Segments                                                              \n",
       "0                           17.488565        93.817465  0.000802  0.260873   \n",
       "1                           17.017984        92.516622  0.000984  0.265064   \n",
       "2                           14.819169        88.395334  0.000990  0.260823   \n",
       "3                           17.524747        91.429862  0.000899  0.262324   \n",
       "\n",
       "                   ZIP_16    ZIP_17    ZIP_18    ZIP_19    ZIP_20    ZIP_21  \\\n",
       "Sorted Segments                                                               \n",
       "0                0.188794  0.176236  0.032118  0.034224  0.011334  0.039079   \n",
       "1                0.186426  0.174917  0.032060  0.035782  0.011595  0.038877   \n",
       "2                0.186716  0.177359  0.032243  0.033554  0.011337  0.038187   \n",
       "3                0.188729  0.174480  0.033510  0.032857  0.011114  0.038896   \n",
       "\n",
       "                   ZIP_22    ZIP_23    ZIP_26    ZIP_27    ZIP_28    ZIP_29  \\\n",
       "Sorted Segments                                                               \n",
       "0                0.003711  0.002126  0.006319  0.095510  0.059421  0.006961   \n",
       "1                0.003708  0.002225  0.006803  0.094739  0.057659  0.007773   \n",
       "2                0.003923  0.001792  0.006283  0.096395  0.058507  0.007736   \n",
       "3                0.003896  0.001985  0.006486  0.095190  0.058309  0.007744   \n",
       "\n",
       "                   ZIP_32    ZIP_33    ZIP_34    ZIP_37    ZIP_43    ZIP_44  \\\n",
       "Sorted Segments                                                               \n",
       "0                0.001485  0.001866  0.001364  0.001244  0.007142  0.055770   \n",
       "1                0.001255  0.001811  0.001212  0.001269  0.008129  0.055106   \n",
       "2                0.001452  0.002009  0.001511  0.001212  0.007841  0.056574   \n",
       "3                0.001523  0.001995  0.001459  0.001204  0.007616  0.056132   \n",
       "\n",
       "                 educate_C  educate_NC  income_0to20  income_100to150  \\\n",
       "Sorted Segments                                                         \n",
       "0                 0.324948    0.417208      0.094808         0.069872   \n",
       "1                 0.324962    0.421298      0.095409         0.072192   \n",
       "2                 0.327352    0.417510      0.096447         0.071179   \n",
       "3                 0.327026    0.417268      0.097023         0.071163   \n",
       "\n",
       "                 income_150plus  income_20to40  income_40to60  income_60to75  \\\n",
       "Sorted Segments                                                                \n",
       "0                      0.047464       0.181732       0.217481       0.133065   \n",
       "1                      0.046478       0.182832       0.215077       0.134115   \n",
       "2                      0.046935       0.181926       0.213970       0.134025   \n",
       "3                      0.047263       0.181998       0.213115       0.134725   \n",
       "\n",
       "                 income_75to100  homemktvalue_GT300k  homemktvalue_LTE100k  \\\n",
       "Sorted Segments                                                              \n",
       "0                      0.104879             0.154730              0.258044   \n",
       "1                      0.104508             0.154038              0.256920   \n",
       "2                      0.105957             0.156025              0.253878   \n",
       "3                      0.105685             0.156148              0.256143   \n",
       "\n",
       "                 homemktvalue_LTE300k  DWELTYPE_M  DWELTYPE_S  MORESPON_R  \\\n",
       "Sorted Segments                                                             \n",
       "0                            0.577937    0.086503    0.888722    0.738385   \n",
       "1                            0.579429    0.086724    0.889174    0.741981   \n",
       "2                            0.580897    0.087950    0.887519    0.742450   \n",
       "3                            0.578039    0.088193    0.887028    0.741970   \n",
       "\n",
       "                 PROPTYPE_A  PROPTYPE_B  PROPTYPE_C  NETWORTH_1.0  \\\n",
       "Sorted Segments                                                     \n",
       "0                  0.073303    0.038256    0.846854      0.089271   \n",
       "1                  0.073076    0.038463    0.847802      0.086196   \n",
       "2                  0.075020    0.037876    0.845011      0.087435   \n",
       "3                  0.074553    0.038596    0.845164      0.086980   \n",
       "\n",
       "                 NETWORTH_10.0  NETWORTH_11.0  NETWORTH_2.0  NETWORTH_3.0  \\\n",
       "Sorted Segments                                                             \n",
       "0                     0.058698       0.053322      0.059762      0.045498   \n",
       "1                     0.056960       0.054137      0.060112      0.044025   \n",
       "2                     0.058717       0.055251      0.059561      0.043761   \n",
       "3                     0.058761       0.054378      0.059213      0.044266   \n",
       "\n",
       "                 NETWORTH_4.0  NETWORTH_5.0  NETWORTH_6.0  NETWORTH_7.0  \\\n",
       "Sorted Segments                                                           \n",
       "0                    0.066281      0.076472      0.113926      0.178202   \n",
       "1                    0.069767      0.076256      0.113165      0.178996   \n",
       "2                    0.068503      0.076883      0.113008      0.179180   \n",
       "3                    0.068524      0.077231      0.111862      0.179109   \n",
       "\n",
       "                 NETWORTH_8.0  NETWORTH_9.0  ownrent_O  ownrent_R  \\\n",
       "Sorted Segments                                                     \n",
       "0                    0.126324      0.077496   0.742939   0.035026   \n",
       "1                    0.126143      0.080207   0.747429   0.034655   \n",
       "2                    0.125967      0.078446   0.745935   0.035985   \n",
       "3                    0.125885      0.079963   0.745916   0.034975   \n",
       "\n",
       "                 Component 1  Component 2  Component 3  Component 4  \\\n",
       "Sorted Segments                                                       \n",
       "0                  -0.778017     0.822290     4.210034    -0.645714   \n",
       "1                   2.231001    -4.103691    -0.186906    -0.148788   \n",
       "2                  -0.490960     0.873970     0.309860    -0.394697   \n",
       "3                  -0.166143     0.479130    -1.226765     0.540596   \n",
       "\n",
       "                 Component 5  Component 6  Component 7  Component 8  \\\n",
       "Sorted Segments                                                       \n",
       "0                  -2.174290     0.856326     2.209995     0.155269   \n",
       "1                  -0.065446     0.401002    -0.307042     0.149636   \n",
       "2                   0.352749     0.477537    -1.101535    -0.261401   \n",
       "3                   0.259117    -0.748586     0.488758     0.129750   \n",
       "\n",
       "                 Component 9  Component 10  Component 11  Component 12  \\\n",
       "Sorted Segments                                                          \n",
       "0                   0.068528      0.026740     -0.029541      0.316047   \n",
       "1                   0.099839      0.083774     -0.051369      0.086069   \n",
       "2                  -0.155099      0.271944      0.070422     -0.118990   \n",
       "3                   0.078957     -0.263593     -0.034155     -0.007222   \n",
       "\n",
       "                 Component 13  Component 14  Component 15  Component 16  \\\n",
       "Sorted Segments                                                           \n",
       "0                    0.440845      0.016222     -0.115427      0.227296   \n",
       "1                    0.103618     -0.039734     -0.138710      0.090265   \n",
       "2                   -0.428510     -0.099081      0.133685     -0.078130   \n",
       "3                    0.215874      0.092852     -0.036110     -0.021215   \n",
       "\n",
       "                 Component 17  Component 18  Component 19  Component 20  \\\n",
       "Sorted Segments                                                           \n",
       "0                   -0.136139     -0.225230      0.104767      0.154402   \n",
       "1                   -0.008262      0.050160     -0.082863     -0.017197   \n",
       "2                    0.058509      0.080452      0.040744     -0.051674   \n",
       "3                   -0.012903     -0.029621     -0.031298      0.011465   \n",
       "\n",
       "                 Component 21  Component 22  Component 23  Component 24  \\\n",
       "Sorted Segments                                                           \n",
       "0                    0.066590      0.082838     -0.240280      0.005133   \n",
       "1                   -0.011502      0.003520      0.021725      0.008879   \n",
       "2                   -0.136481      0.005992      0.100040     -0.021485   \n",
       "3                    0.102170     -0.026530     -0.032574      0.013711   \n",
       "\n",
       "                 Component 25  Component 26  Component 27  Component 28  \\\n",
       "Sorted Segments                                                           \n",
       "0                   -0.079737      0.012033     -0.056234      0.006352   \n",
       "1                   -0.024449      0.031021     -0.080072      0.004457   \n",
       "2                    0.062626     -0.014412      0.026332      0.001298   \n",
       "3                   -0.024594     -0.001542      0.019266     -0.004180   \n",
       "\n",
       "                 Component 29  Component 30  Component 31  Component 32  \\\n",
       "Sorted Segments                                                           \n",
       "0                   -0.032776     -0.089311      0.027352      0.112623   \n",
       "1                   -0.016063      0.024096      0.031209      0.013305   \n",
       "2                   -0.050159     -0.012361      0.066725     -0.031044   \n",
       "3                    0.055649      0.023945     -0.073439     -0.006120   \n",
       "\n",
       "                 Component 33  Component 34  Component 35  Component 36  \\\n",
       "Sorted Segments                                                           \n",
       "0                    0.098707      0.032339      0.073307     -0.090479   \n",
       "1                    0.018750     -0.007190     -0.009776     -0.036647   \n",
       "2                   -0.025429     -0.045883     -0.005954     -0.011357   \n",
       "3                   -0.009299      0.033055     -0.009592      0.044316   \n",
       "\n",
       "                 Component 37  Component 38  Component 39  Component 40  \\\n",
       "Sorted Segments                                                           \n",
       "0                   -0.076835      0.044041      0.066618      0.093698   \n",
       "1                    0.009231     -0.039175     -0.022307     -0.007301   \n",
       "2                    0.034097     -0.048274     -0.012938     -0.034439   \n",
       "3                   -0.012970      0.043216      0.002224      0.008463   \n",
       "\n",
       "                 Component 41  Component 42  Component 43  Component 44  \\\n",
       "Sorted Segments                                                           \n",
       "0                   -0.038691      0.065482     -0.074969     -0.066319   \n",
       "1                    0.014302     -0.021455      0.001327      0.007855   \n",
       "2                   -0.025619     -0.047732      0.007530     -0.033671   \n",
       "3                    0.026045      0.031404      0.011586      0.041790   \n",
       "\n",
       "                 Component 45  Component 46  Component 47  Component 48  \\\n",
       "Sorted Segments                                                           \n",
       "0                    0.131131      0.032858     -0.064498      0.042274   \n",
       "1                   -0.043694     -0.027643      0.003241      0.013216   \n",
       "2                   -0.101499     -0.041316     -0.010894     -0.007450   \n",
       "3                    0.068101      0.036143      0.023822     -0.008657   \n",
       "\n",
       "                 Component 49  Component 50  Component 51  Component 52  \\\n",
       "Sorted Segments                                                           \n",
       "0                    0.103905     -0.004000      0.045355     -0.001683   \n",
       "1                   -0.005891      0.000869      0.004855     -0.014765   \n",
       "2                   -0.037018     -0.029963     -0.034152      0.003309   \n",
       "3                    0.007641      0.025821      0.015875      0.002723   \n",
       "\n",
       "                 Component 53  Component 54  Component 55  Component 56  \\\n",
       "Sorted Segments                                                           \n",
       "0                    0.025938      0.010761     -0.025104      0.035764   \n",
       "1                    0.003961      0.002560     -0.005411     -0.006734   \n",
       "2                   -0.018160     -0.028036      0.037110     -0.009250   \n",
       "3                    0.007520      0.020007     -0.023125      0.001322   \n",
       "\n",
       "                 Component 57  Component 58  Component 59  Component 60  \\\n",
       "Sorted Segments                                                           \n",
       "0                    0.104936     -0.005059     -0.024084     -0.025818   \n",
       "1                   -0.002236      0.001831      0.014732     -0.007163   \n",
       "2                   -0.040204     -0.001175     -0.010531      0.012049   \n",
       "3                    0.008803      0.001594      0.009660     -0.001319   \n",
       "\n",
       "                 Component 61  Component 62  Component 63  Component 64  \\\n",
       "Sorted Segments                                                           \n",
       "0                   -0.065512     -0.010529      0.025556      0.020836   \n",
       "1                    0.015372     -0.015118     -0.031441      0.016111   \n",
       "2                   -0.002296      0.045732     -0.029677      0.017258   \n",
       "3                    0.012676     -0.030585      0.029474     -0.025135   \n",
       "\n",
       "                 Component 65  Component 66  Component 67  Component 68  \\\n",
       "Sorted Segments                                                           \n",
       "0                   -0.020358     -0.009702     -0.061509      0.006736   \n",
       "1                    0.086437      0.016267      0.037588     -0.016730   \n",
       "2                   -0.006736      0.004063      0.078905     -0.029016   \n",
       "3                   -0.019142     -0.006637     -0.064092      0.028461   \n",
       "\n",
       "                 Component 69  Component 70  Component 71  Component 72  \\\n",
       "Sorted Segments                                                           \n",
       "0                    0.051462     -0.015433     -0.012172      0.080094   \n",
       "1                    0.008628     -0.029144     -0.021047      0.094216   \n",
       "2                   -0.051093     -0.013620      0.037202     -0.050727   \n",
       "3                    0.027294      0.025249     -0.020982     -0.009515   \n",
       "\n",
       "                 Component 73  Component 74  Component 75  Component 76  \\\n",
       "Sorted Segments                                                           \n",
       "0                   -0.007695      0.039451     -0.089667      0.051923   \n",
       "1                   -0.055135     -0.016080     -0.013498     -0.012283   \n",
       "2                    0.017961     -0.009797      0.039885     -0.017742   \n",
       "3                    0.005810      0.004097     -0.006853      0.006401   \n",
       "\n",
       "                 Component 77  Component 78  Component 79  Component 80  \\\n",
       "Sorted Segments                                                           \n",
       "0                   -0.039311      0.008221      0.071013     -0.031076   \n",
       "1                   -0.091481     -0.034125      0.006889      0.011852   \n",
       "2                    0.017002      0.006567      0.010643     -0.003027   \n",
       "3                    0.026880      0.004234     -0.028697      0.006068   \n",
       "\n",
       "                  N Obs  Prop Obs  \n",
       "Sorted Segments                    \n",
       "0                 49848  0.100855  \n",
       "1                 70119  0.141869  \n",
       "2                170767  0.345505  \n",
       "3                203519  0.411771  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_accts_mini_pca_freq = active_accts_mini_pca.groupby([\"Segment\"]).mean()\n",
    "active_accts_mini_pca_freq[\"N Obs\"] = active_accts_mini_pca[[\"Segment\", \"avg_inv_bal\"]].groupby([\"Segment\"]).count()\n",
    "active_accts_mini_pca_freq[\"Prop Obs\"] = active_accts_mini_pca_freq[\"N Obs\"] / active_accts_mini_pca_freq[\"N Obs\"].sum()\n",
    "active_accts_mini_pca_freq = active_accts_mini_pca_freq.sort_values('N Obs')\n",
    "active_accts_mini_pca_freq = active_accts_mini_pca_freq.reset_index()\n",
    "active_accts_mini_pca_freq.index.name = 'Sorted Segments'\n",
    "\n",
    "active_accts_mini_pca['Segment'] = active_accts_mini_pca['Segment'].map(dict(zip(list(active_accts_mini_pca_freq[\"Segment\"]),list(active_accts_mini_pca_freq.index))))\n",
    "active_accts_mini_pca_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MiniBatch + PCA Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x3240 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#bar_chart_trans(active_accts_mini_pca_freq,active_accts_mini_pca_freq.loc[:,'avg_inv_bal':'avg_conln_num'].columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#demo_graphs(active_accts_mini_pca, active_accts_mini_pca_freq, minipca_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#bar_chart_ob_fees(active_accts_mini_pca_freq, active_accts_mini_pca_freq.loc[:,'avg_bill_pay':'avg_online_banking'].columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#bar_chart_ob_fees(active_accts_mini_pca_freq,active_accts_mini_pca_freq.loc[:,'sum_YTDINTPAID':'sum_YTDTIMEOD'].columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x3240 with 38 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#bar_chart_trans(active_accts_mini_pca_freq,active_accts_mini_pca_freq.loc[:,'total_ACH_DEPOSIT_trans':'total_ZELLE_num'].columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#zip_graphs(active_accts_mini_pca, minipca_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#networth_graphs(active_accts_mini_pca, minipca_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#bit_graphs(active_accts_mini_pca_freq, minipca_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sbloan_graphs(active_accts_mini_pca_freq, minipca_clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>KMiniBatch Prop</th>\n",
       "      <th>KMiniBatch N</th>\n",
       "      <th>KMini + PCA Prop</th>\n",
       "      <th>KMini + PCA N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.007990</td>\n",
       "      <td>3949</td>\n",
       "      <td>0.100855</td>\n",
       "      <td>49848.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.014341</td>\n",
       "      <td>7088</td>\n",
       "      <td>0.141869</td>\n",
       "      <td>70119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.081687</td>\n",
       "      <td>40374</td>\n",
       "      <td>0.345505</td>\n",
       "      <td>170767.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.258026</td>\n",
       "      <td>127530</td>\n",
       "      <td>0.411771</td>\n",
       "      <td>203519.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.637957</td>\n",
       "      <td>315312</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   KMiniBatch Prop  KMiniBatch N  KMini + PCA Prop  KMini + PCA N\n",
       "0         0.007990          3949          0.100855        49848.0\n",
       "1         0.014341          7088          0.141869        70119.0\n",
       "2         0.081687         40374          0.345505       170767.0\n",
       "3         0.258026        127530          0.411771       203519.0\n",
       "4         0.637957        315312               NaN            NaN"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_table_prop = pd.DataFrame({'KMiniBatch Prop': active_accts_mini_freq.loc[:,'Prop Obs'],\n",
    "                                'KMiniBatch N': active_accts_mini_freq.loc[:,'N Obs'],\n",
    "                               'KMini + PCA Prop': active_accts_mini_pca_freq.loc[:,'Prop Obs'],\n",
    "                               'KMini + PCA N': active_accts_mini_pca_freq.loc[:,'N Obs']},\n",
    "                          index = np.arange(max(minipca_clusters,mini_clusters)))\n",
    "dist_table_prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mini</th>\n",
       "      <th>Mini PCA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[NETWORTH_9.0, ZIP_27, income_60to75]</td>\n",
       "      <td>[Component 2, Component 1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[total_POS_RETURN_trans, avg_mobile_app, total_POS_RETURN_num]</td>\n",
       "      <td>[Component 3, Component 6, Component 7]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[ownrent_O, MORESPON_R, DWELTYPE_S]</td>\n",
       "      <td>[Component 3, Component 7, Component 5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[]</td>\n",
       "      <td>[Component 7, Component 2, Component 1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[NETWORTH_5.0, income_0to20, ZIP_16]</td>\n",
       "      <td>&lt;class 'str'&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             Mini  \\\n",
       "0                           [NETWORTH_9.0, ZIP_27, income_60to75]   \n",
       "1  [total_POS_RETURN_trans, avg_mobile_app, total_POS_RETURN_num]   \n",
       "2                             [ownrent_O, MORESPON_R, DWELTYPE_S]   \n",
       "3                                                              []   \n",
       "4                            [NETWORTH_5.0, income_0to20, ZIP_16]   \n",
       "\n",
       "                                  Mini PCA  \n",
       "0               [Component 2, Component 1]  \n",
       "1  [Component 3, Component 6, Component 7]  \n",
       "2  [Component 3, Component 7, Component 5]  \n",
       "3  [Component 7, Component 2, Component 1]  \n",
       "4                            <class 'str'>  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_features_all = feature_table([minikmeans, minikmeans_pca], [False, True], [\"Mini\", \"Mini PCA\"], 0.5,max(minipca_clusters,mini_clusters))\n",
    "pd.options.display.max_colwidth = 500\n",
    "top_features_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mini</th>\n",
       "      <th>Mini PCA</th>\n",
       "      <th>Mini PCA Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[NETWORTH_9.0, ZIP_27, income_60to75]</td>\n",
       "      <td>[Component 2, Component 1]</td>\n",
       "      <td>[([total_DEBIT_CARD_PURCHASE_trans, avg_ckcon_num, total_DEBIT_CARD_PURCHASE_num], [-0.286, -0.28, -0.279]), ([total_CHECK_num, avg_ckcom_bal, total_CHECK_trans], [0.245, 0.243, 0.232])]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[total_POS_RETURN_trans, avg_mobile_app, total_POS_RETURN_num]</td>\n",
       "      <td>[Component 3, Component 6, Component 7]</td>\n",
       "      <td>[([ownrent_O, DWELTYPE_S, DWELTYPE_M], [-0.306, -0.293, 0.276]), ([homemktvalue_GT300k, ANNSPEND, NETWORTH_11.0], [0.319, 0.238, 0.232]), ([DWELTYPE_M, PROPTYPE_C, PROPTYPE_A], [0.304, -0.295, 0.285])]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[ownrent_O, MORESPON_R, DWELTYPE_S]</td>\n",
       "      <td>[Component 3, Component 7, Component 5]</td>\n",
       "      <td>[([ownrent_O, DWELTYPE_S, DWELTYPE_M], [-0.306, -0.293, 0.276]), ([DWELTYPE_M, PROPTYPE_C, PROPTYPE_A], [0.304, -0.295, 0.285]), ([sum_YTDITEMNSF, sum_YTDNSFFEE, sum_YTDNSFRTFA], [0.38, 0.375, 0.315])]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[]</td>\n",
       "      <td>[Component 7, Component 2, Component 1]</td>\n",
       "      <td>[([DWELTYPE_M, PROPTYPE_C, PROPTYPE_A], [0.304, -0.295, 0.285]), ([total_DEBIT_CARD_PURCHASE_trans, avg_ckcon_num, total_DEBIT_CARD_PURCHASE_num], [-0.286, -0.28, -0.279]), ([total_CHECK_num, avg_ckcom_bal, total_CHECK_trans], [0.245, 0.243, 0.232])]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[NETWORTH_5.0, income_0to20, ZIP_16]</td>\n",
       "      <td>&lt;class 'str'&gt;</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             Mini  \\\n",
       "0                           [NETWORTH_9.0, ZIP_27, income_60to75]   \n",
       "1  [total_POS_RETURN_trans, avg_mobile_app, total_POS_RETURN_num]   \n",
       "2                             [ownrent_O, MORESPON_R, DWELTYPE_S]   \n",
       "3                                                              []   \n",
       "4                            [NETWORTH_5.0, income_0to20, ZIP_16]   \n",
       "\n",
       "                                  Mini PCA  \\\n",
       "0               [Component 2, Component 1]   \n",
       "1  [Component 3, Component 6, Component 7]   \n",
       "2  [Component 3, Component 7, Component 5]   \n",
       "3  [Component 7, Component 2, Component 1]   \n",
       "4                            <class 'str'>   \n",
       "\n",
       "                                                                                                                                                                                                                                            Mini PCA Features  \n",
       "0                                                                  [([total_DEBIT_CARD_PURCHASE_trans, avg_ckcon_num, total_DEBIT_CARD_PURCHASE_num], [-0.286, -0.28, -0.279]), ([total_CHECK_num, avg_ckcom_bal, total_CHECK_trans], [0.245, 0.243, 0.232])]  \n",
       "1                                                   [([ownrent_O, DWELTYPE_S, DWELTYPE_M], [-0.306, -0.293, 0.276]), ([homemktvalue_GT300k, ANNSPEND, NETWORTH_11.0], [0.319, 0.238, 0.232]), ([DWELTYPE_M, PROPTYPE_C, PROPTYPE_A], [0.304, -0.295, 0.285])]  \n",
       "2                                                   [([ownrent_O, DWELTYPE_S, DWELTYPE_M], [-0.306, -0.293, 0.276]), ([DWELTYPE_M, PROPTYPE_C, PROPTYPE_A], [0.304, -0.295, 0.285]), ([sum_YTDITEMNSF, sum_YTDNSFFEE, sum_YTDNSFRTFA], [0.38, 0.375, 0.315])]  \n",
       "3  [([DWELTYPE_M, PROPTYPE_C, PROPTYPE_A], [0.304, -0.295, 0.285]), ([total_DEBIT_CARD_PURCHASE_trans, avg_ckcon_num, total_DEBIT_CARD_PURCHASE_num], [-0.286, -0.28, -0.279]), ([total_CHECK_num, avg_ckcom_bal, total_CHECK_trans], [0.245, 0.243, 0.232])]  \n",
       "4                                                                                                                                                                                                                                                              "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_features_all[\"Mini PCA Features\"] = ''\n",
    "\n",
    "for i in range(minipca_clusters):\n",
    "    # filters PCA eigenvectors based on important components to each cluster\n",
    "    comp_ind = list(top_features_all[\"Mini PCA\"][i])\n",
    "    top_pca_feats = []\n",
    "    for j in range(len(comp_ind)):\n",
    "        # Selects 3 most important features of each component\n",
    "        top_ind = abs(active_accts_pca_comp.loc[comp_ind[j]]).nlargest(3).index\n",
    "        # Appends the names of the top 3 features per correlated component\n",
    "        top_pca_feats.append((top_ind.values, active_accts_pca_comp.loc[comp_ind[j]][top_ind].round(3)))\n",
    "    top_features_all[\"Mini PCA Features\"][i] = top_pca_feats\n",
    "    \n",
    "pd.options.display.max_colwidth = 1000\n",
    "top_features_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# active_accts['NETWORTH'] = pd.to_numeric(active_accts[\"NETWORTH\"])\n",
    "# plt.figure(figsize=(6, 6))\n",
    "# scatter = plt.scatter(active_accts['avg_ckcom_bal'],active_accts['sum_YTDINTPAID'],c = active_accts['Segment'], s = 3)\n",
    "# plt.xlabel(\"Avg Ckncom Bal Rq1\")\n",
    "# plt.ylabel(\"Fees\")\n",
    "# plt.legend(handles=scatter.legend_elements()[0], labels = [0,1,2,3,4,5,6,7,8,9,10]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.spatial import ConvexHull\n",
    "from matplotlib.patches import Polygon\n",
    " \n",
    "def hull_graphs(col1, col2):\n",
    "    colors = ['C0','C1','C2','C3']\n",
    "    centroids = minikmeans_pca.cluster_centers_\n",
    "    cen_x = [i[0] for i in centroids] \n",
    "    cen_y = [i[1] for i in centroids]\n",
    "\n",
    "\n",
    "    fig, ax=plt.subplots(1,1,figsize=(14,7))\n",
    "    ## plot centers\n",
    "    ax.scatter(cen_x, cen_y, marker='o', c=colors, s=60)\n",
    "    for i in active_accts_mini_pca[\"Segment\"].unique():\n",
    "        points = active_accts_mini_pca[active_accts_mini_pca[\"Segment\"] == i][[col1, col2]].values\n",
    "        # get convex hull\n",
    "        hull = ConvexHull(points)\n",
    "        # get x and y coordinates\n",
    "        # repeat last point to close the polygon\n",
    "        x_hull = np.append(points[hull.vertices,0],\n",
    "                           points[hull.vertices,0][0])\n",
    "        y_hull = np.append(points[hull.vertices,1],\n",
    "                           points[hull.vertices,1][0])\n",
    "        # plot shape\n",
    "        ax.fill(x_hull, y_hull, alpha=0.2, c=colors[i])\n",
    "\n",
    "    ax.legend(active_accts_mini_pca[\"Segment\"].unique())\n",
    "    ax.set_title(\"HULL PLOT EXAMPLE\")\n",
    "    ax.set_xlabel(col1)\n",
    "    ax.set_ylabel(col2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hull_graphs('Component 35', 'Component 46')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:fnb] *",
   "language": "python",
   "name": "conda-env-fnb-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}