intro_to_data_science_with_python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yu07mcYFqxad"
   },
   "source": [
    "# Intro to Data Science with Python\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kOx-Kuwmr3iJ"
   },
   "source": [
    "# Looking at Data with Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nNEkGK2pxQ1j"
   },
   "source": [
    "We're going to use a Dataset built in to sklearn.\n",
    "\n",
    "sklearn has 7 built in datasets to easiliy start playing with. check out the whole list [here](https://scikit-learn.org/stable/datasets/index.html)\n",
    "\n",
    "We're going to use the famous [iris datatset](https://en.wikipedia.org/wiki/Iris_flower_data_set). Which contains data about flowers. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wpeYWuNwsNFE"
   },
   "outputs": [],
   "source": [
    "# Import iris dataset\n",
    "from sklearn.datasets import load_iris\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "avs8IAauuvzV"
   },
   "outputs": [],
   "source": [
    "iris = load_iris()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9xmiKiKH4sq_"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])\n",
      "(150, 4)\n"
     ]
    }
   ],
   "source": [
    "# If we didn't know the key value pairs in the data we could find out like this:\n",
    "\n",
    "print(iris.keys())\n",
    "\n",
    "#150 rows (samples) 4 columns (features: Height & Width of Pedals & Sepals) \n",
    "print(iris.data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "f9SLw0Ze3gfF"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "byOioAXv3Z3G"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
       "       [4.4, 2.9, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5.4, 3.7, 1.5, 0.2],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [4.8, 3. , 1.4, 0.1],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [5.7, 4.4, 1.5, 0.4],\n",
       "       [5.4, 3.9, 1.3, 0.4],\n",
       "       [5.1, 3.5, 1.4, 0.3],\n",
       "       [5.7, 3.8, 1.7, 0.3],\n",
       "       [5.1, 3.8, 1.5, 0.3],\n",
       "       [5.4, 3.4, 1.7, 0.2],\n",
       "       [5.1, 3.7, 1.5, 0.4],\n",
       "       [4.6, 3.6, 1. , 0.2],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
       "       [5. , 3.4, 1.6, 0.4],\n",
       "       [5.2, 3.5, 1.5, 0.2],\n",
       "       [5.2, 3.4, 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.6, 0.2],\n",
       "       [4.8, 3.1, 1.6, 0.2],\n",
       "       [5.4, 3.4, 1.5, 0.4],\n",
       "       [5.2, 4.1, 1.5, 0.1],\n",
       "       [5.5, 4.2, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.2, 1.2, 0.2],\n",
       "       [5.5, 3.5, 1.3, 0.2],\n",
       "       [4.9, 3.6, 1.4, 0.1],\n",
       "       [4.4, 3. , 1.3, 0.2],\n",
       "       [5.1, 3.4, 1.5, 0.2],\n",
       "       [5. , 3.5, 1.3, 0.3],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
       "       [4.8, 3. , 1.4, 0.3],\n",
       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [4.6, 3.2, 1.4, 0.2],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [6.4, 3.2, 4.5, 1.5],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [6.5, 2.8, 4.6, 1.5],\n",
       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6.3, 3.3, 4.7, 1.6],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.1, 2.8, 4.7, 1.2],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [6. , 2.9, 4.5, 1.5],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [7.3, 2.9, 6.3, 1.8],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [7.7, 2.6, 6.9, 2.3],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [5.9, 3. , 5.1, 1.8]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "opkgYlYY4Ugv"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Types of iris: ['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "# print out the list of lables\n",
    "print(\"Types of iris: {}\".format(iris['target_names']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "agBoB50S3UmQ"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ML4IAK9j4guk"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature names: \n",
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
     ]
    }
   ],
   "source": [
    "# Print out a list of feature names\n",
    "print(\"Feature names: \\n{}\".format(iris['feature_names']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4CdNCSJO4mql"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample Data:\n",
      "[[5.1 3.5 1.4 0.2]\n",
      " [4.9 3.  1.4 0.2]\n",
      " [4.7 3.2 1.3 0.2]\n",
      " [4.6 3.1 1.5 0.2]\n",
      " [5.  3.6 1.4 0.2]]\n"
     ]
    }
   ],
   "source": [
    "# print out the first 5 sets of features from each flower\n",
    "print(\"Sample Data:\\n{}\".format(iris['data'][:5]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jOnOIgGt44zX"
   },
   "source": [
    "## Pandas Data Frames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6TO92A273deq"
   },
   "source": [
    "[Visit Pandas Docs here](https://pandas.pydata.org/)\n",
    "\n",
    "pandas is a software library written for the Python for data manipulation and analysis.[pa]\n",
    "\n",
    "A really neat feature of Pandas is creating DataFrames\n",
    "\n",
    "Data Frames are 2-dimensional labeled data structure with columns. Very similar looking to a spreadsheet.\n",
    "\n",
    "Lets use a dataframe to look more at our data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nbHETU935QEj"
   },
   "outputs": [],
   "source": [
    "#import Pandas\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MFWUSFEqwwgB"
   },
   "outputs": [],
   "source": [
    "# Make a Dataframe\n",
    "df = pd.DataFrame(iris.data, columns=iris.feature_names)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wZXeZaDFwpOG"
   },
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# returns first 5 rows(default)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "J33QsRjuvrx6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tc5D_8eh62rK"
   },
   "outputs": [],
   "source": [
    "# Change settings to display all of our rows. \n",
    "pd.options.display.max_rows = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yjb27cWb64o4"
   },
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ski-LTm_4zF8"
   },
   "source": [
    "## Make better data frame\n",
    "\n",
    "We're not going to cover much `numpy` in this workshop but I absolutley suggest you read more about it after! You can do that [here](http://www.numpy.org/)\n",
    "\n",
    "Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.\n",
    "\n",
    "You'll come accross it again as you keep learning about data science!\n",
    "\n",
    "We're using a function of it it here to essentially combine our iris `target` data onto our already exsiting iris data\n",
    "\n",
    "read more about it [here](https://docs.scipy.org/doc/numpy/reference/generated/numpy.c_.html)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "U40wdabY4FCk"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "betterdf = pd.DataFrame(data= np.c_[iris['data'], iris['target']],\n",
    "                     columns= iris['feature_names'] + ['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SDGVuWeq5EVp"
   },
   "outputs": [
    {
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      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target  \n",
       "0     0.0  \n",
       "1     0.0  \n",
       "2     0.0  \n",
       "3     0.0  \n",
       "4     0.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
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   "source": [
    "betterdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EspE5MtC4NKV"
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   "outputs": [
    {
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       "      <td>2.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>2.0</td>\n",
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       "    <tr>\n",
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       "      <td>6.7</td>\n",
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       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
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       "      <td>1.8</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>6.2</td>\n",
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       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
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       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
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       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
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       "      <td>2.2</td>\n",
       "      <td>2.0</td>\n",
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       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
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       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
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       "      <td>1.4</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
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       "      <td>2.4</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
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       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.0</td>\n",
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       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
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       "      <td>1.8</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                  5.1               3.5                1.4               0.2   \n",
       "1                  4.9               3.0                1.4               0.2   \n",
       "2                  4.7               3.2                1.3               0.2   \n",
       "3                  4.6               3.1                1.5               0.2   \n",
       "4                  5.0               3.6                1.4               0.2   \n",
       "5                  5.4               3.9                1.7               0.4   \n",
       "6                  4.6               3.4                1.4               0.3   \n",
       "7                  5.0               3.4                1.5               0.2   \n",
       "8                  4.4               2.9                1.4               0.2   \n",
       "9                  4.9               3.1                1.5               0.1   \n",
       "10                 5.4               3.7                1.5               0.2   \n",
       "11                 4.8               3.4                1.6               0.2   \n",
       "12                 4.8               3.0                1.4               0.1   \n",
       "13                 4.3               3.0                1.1               0.1   \n",
       "14                 5.8               4.0                1.2               0.2   \n",
       "15                 5.7               4.4                1.5               0.4   \n",
       "16                 5.4               3.9                1.3               0.4   \n",
       "17                 5.1               3.5                1.4               0.3   \n",
       "18                 5.7               3.8                1.7               0.3   \n",
       "19                 5.1               3.8                1.5               0.3   \n",
       "20                 5.4               3.4                1.7               0.2   \n",
       "21                 5.1               3.7                1.5               0.4   \n",
       "22                 4.6               3.6                1.0               0.2   \n",
       "23                 5.1               3.3                1.7               0.5   \n",
       "24                 4.8               3.4                1.9               0.2   \n",
       "25                 5.0               3.0                1.6               0.2   \n",
       "26                 5.0               3.4                1.6               0.4   \n",
       "27                 5.2               3.5                1.5               0.2   \n",
       "28                 5.2               3.4                1.4               0.2   \n",
       "29                 4.7               3.2                1.6               0.2   \n",
       "..                 ...               ...                ...               ...   \n",
       "120                6.9               3.2                5.7               2.3   \n",
       "121                5.6               2.8                4.9               2.0   \n",
       "122                7.7               2.8                6.7               2.0   \n",
       "123                6.3               2.7                4.9               1.8   \n",
       "124                6.7               3.3                5.7               2.1   \n",
       "125                7.2               3.2                6.0               1.8   \n",
       "126                6.2               2.8                4.8               1.8   \n",
       "127                6.1               3.0                4.9               1.8   \n",
       "128                6.4               2.8                5.6               2.1   \n",
       "129                7.2               3.0                5.8               1.6   \n",
       "130                7.4               2.8                6.1               1.9   \n",
       "131                7.9               3.8                6.4               2.0   \n",
       "132                6.4               2.8                5.6               2.2   \n",
       "133                6.3               2.8                5.1               1.5   \n",
       "134                6.1               2.6                5.6               1.4   \n",
       "135                7.7               3.0                6.1               2.3   \n",
       "136                6.3               3.4                5.6               2.4   \n",
       "137                6.4               3.1                5.5               1.8   \n",
       "138                6.0               3.0                4.8               1.8   \n",
       "139                6.9               3.1                5.4               2.1   \n",
       "140                6.7               3.1                5.6               2.4   \n",
       "141                6.9               3.1                5.1               2.3   \n",
       "142                5.8               2.7                5.1               1.9   \n",
       "143                6.8               3.2                5.9               2.3   \n",
       "144                6.7               3.3                5.7               2.5   \n",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "     target  \n",
       "0       0.0  \n",
       "1       0.0  \n",
       "2       0.0  \n",
       "3       0.0  \n",
       "4       0.0  \n",
       "5       0.0  \n",
       "6       0.0  \n",
       "7       0.0  \n",
       "8       0.0  \n",
       "9       0.0  \n",
       "10      0.0  \n",
       "11      0.0  \n",
       "12      0.0  \n",
       "13      0.0  \n",
       "14      0.0  \n",
       "15      0.0  \n",
       "16      0.0  \n",
       "17      0.0  \n",
       "18      0.0  \n",
       "19      0.0  \n",
       "20      0.0  \n",
       "21      0.0  \n",
       "22      0.0  \n",
       "23      0.0  \n",
       "24      0.0  \n",
       "25      0.0  \n",
       "26      0.0  \n",
       "27      0.0  \n",
       "28      0.0  \n",
       "29      0.0  \n",
       "..      ...  \n",
       "120     2.0  \n",
       "121     2.0  \n",
       "122     2.0  \n",
       "123     2.0  \n",
       "124     2.0  \n",
       "125     2.0  \n",
       "126     2.0  \n",
       "127     2.0  \n",
       "128     2.0  \n",
       "129     2.0  \n",
       "130     2.0  \n",
       "131     2.0  \n",
       "132     2.0  \n",
       "133     2.0  \n",
       "134     2.0  \n",
       "135     2.0  \n",
       "136     2.0  \n",
       "137     2.0  \n",
       "138     2.0  \n",
       "139     2.0  \n",
       "140     2.0  \n",
       "141     2.0  \n",
       "142     2.0  \n",
       "143     2.0  \n",
       "144     2.0  \n",
       "145     2.0  \n",
       "146     2.0  \n",
       "147     2.0  \n",
       "148     2.0  \n",
       "149     2.0  \n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print out longer list to see target value change\n",
    "betterdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(betterdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5nCX9axfyhxI"
   },
   "source": [
    "# Plotting with Pandas\n",
    "\n",
    "Read more about plotting and charts [here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html)\n",
    "\n",
    "Dataframes are great for initally looking at our data, but a chart is often much easier for us humans to see patterns!\n",
    "\n",
    "Lets make a quick chart for exploring our iris dataset!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7rKQTC2izqYh"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What are our feature (column) names again?\n",
    "\n",
    "iris.feature_names\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6-5vhQg_ylWO"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a20de5b00>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "betterdf.plot(kind='scatter', x = 'sepal length (cm)', y = 'petal width (cm)', c='target')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fKtya2nrSFzG"
   },
   "source": [
    "Lets add a more human freindly color!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RpU7LMPUQJhd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a20d377f0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FJL4Vw61Ywkw2nlPVPYPtGgARqcUagByPOe8micjOedxRVgoalSQi9ZhSuFNV/5rhlAWkBmhvjQWQOyVNPVYT56dYzPxBwJeDY6OxOPbrMIf0uZiTutz5FPBX4Eoso/greM/nznIcVjvpD9hK4CvkznGox0qV/BQr+3EQcHkBZcxAolZSHEOpPhtYUDrLfsBcVZ0HICJ3Y9/GZme9qosUTDGIiGDG5TdV9RcdnPYgcFlwk/sDK1V1UaFkcuKkN3B1B8fGkszirSROI1nozukahwZbZ+gDXFMAWTpBdP/BEBFpDL2erKqTOznbgSIyE/uS/D+qOovMZveCBeoUcsVwMPBp4HURmRHs+xb2lRJVvRGYioWwzMU8TGVehtFxnMqjU47lpao6IY/JXgG2UdU1InICFtM7nohm97gomGJQ1efJEU+mqkrn0zudsuY6zFTaI/j95G6QoRWLMnoe61twJck2ncuxsNT3gaOBz1O0sMiSQ4EpwMNYj4pvYX6kKqRIEUdhP6yqThWRG0RkCEU2u3vms1NEvoXlNSQ4Bfg7xfdBfIZkaeseWNjpdMx3sA8WdrsJeyDOAq4vsnylwveBn2N9IeqxUOTZWNXYKqNIikFEtgIWq6qKyH5YFMfHmMNuvIiMxf5BzyH+2i2bccXgFJFfZtj3DYqrGJZjyWnNweuN2Org+eBYE8ny1eswX8nPqb6PimIrukRvhWast8TfsAqqVUSMzmcRuQvrqDRERBYA3yOIvQ3M62cAl4pIC/bmnxNYVlpE5DLMe18L3BL4HgpCtf23O91KphIS6T0ECk0z7aO0JZAjkyyKmZ6q8aPSkvZaKf7fqxSIL3lNVSflOP4bMmdToqpTMb9swfEMHaeIZKr6WeyQz6GYuSgRF1+LxdYfhLXdrCfpU+iJhY13tVFPOSNY69Fw3kEd3V+5tZvwzGfHKRR/xvIBepBMfCtyTDoCPApMwhzPx2EJ+f0xx+pLmILYATOZ3Ftk+UqJm4FLgR2BT2Dmtih9ISqQKlMM1bg+drqNGkw5dDf9gD92cGx7yr9HcVw0YP6Vn3e3IN1LFRbRc8XgOI6TC1cMjtNNfHQPbHgaaraBra+AmoRtvxWLJJqPVQY4PHTRcqz09XqS/SESvIX56npjpqMBoWPXAU9hJqPrSH4UWoLxFgEHAoeErmnCTEvNWP5F1H7KjwKvYa09T6N78iLWA3cBy7BWo+HWJwuwch+CBcV4geNUqq9RjysGpzT4z1dhxA1Q0wpttfDR7TB8JkgN8EngWSy0tAHzTXwde1DvgYV4twJXYWagA4Pzj8ce9LXAD7FyzYOxxLV/BhM/gj0wF2I2g6OwnIZNmCP6OuCLwfE9sf4AbcB3gzn2ynFj/w9L6NuI+VZOAe6guMphPaZQ52FKrQ64HfP3vIVVVtgYyHQVdv9RlV6VUGUrhupSg05p0roBRv0fNDRDXZv93GIuLLoTe/g+hyVZtWC5Bd8GNgC/ApZiD75NwTmXBYN+KTh3U3B8CRYF+BFJpZBgMdYX4FGsIsFa7AG6DrgCUwQ/wlYnG4Ix1wTHstEUyJiQfS2WWPd6J96cOLgLUwrrsPtajzmVwbK+V2OKYQOWq/DdIstX4iR8DO58dpwi0rwyc3n91sWYozj9+0sN9jD7iGSiWoKPg5/L0vZvwhRAR1UEFpAamrlZCOyhuZj2cf25eoMsx1Y44dj/+gyyFZqPaf8+JSovLCG15E4bRW+CUw5U0EM/Cr5icLqfnsNg5TBoDX34RKH/RMzMEU6MqwW2werxn4L5DxL0IplFfRKpD/reWAz+7mT+PnQWVvcxPFcdVha6F3Bq2lyJHhTZGIuFwaY/VPbIcV3cHEnqPTcARwS/n0b7+/IKsqnE16inXIikGERkkIjsIiLbiogrEyd+Gp6Cj8dASy2s7g/LboMBu2PVhh/B4ucbMJv+E9jD9mTMxDMAe3ifQTK08nrsAdcLGAj8DCvkWwc8TVJp1AK/xxrUb4vVTRoRzLV/MDdYWZrvYCuYXli/4lyloOuDuXYNft8WeJLi1xraB7gNU6Y9MEVxV3Dsa8AlWGnrPlhfDa9r2Y4qUwxiZTgyHBAZgP2HTMI+JU1YKugwLAvoBlV9qkhybmbChAna2NiY+0THcaoeEZmeZxlsJmw3SBt/dlS0+c74S97zlQLZfAx/xmruHqqqK8IHRGQf4NMisq2qVkKnd8dxnA6orNVAFDpUDKp6TJZj07GYNicvFJiGOQf3oWpr3Vc6y5dB01IYNAi8Z3l54oqhPSKyOzAmfH4HPZydyLRhDs9Hsbe1Dauoe2B3CuXEzYxX4eGHobYWWlvhsMPg0MO6Wyqns7hiSEVEbsFCOWaRDNlQLFXS6TL3Y0phbWjfOVhvAKci2LDBlEJLi20Azz4Lu+wKgwd3r2xO53DF0I4DVHXngktSdcyjfW37gnXqc7qDNauhJi2Ir7YWVq5wxVBOaPWVxIhyt/8SEVcMsbMPqVldNcAu3SSLUxAGDKBdDkNbGwxxP0PZUWXhqlEUw22YcnhbRF4TkddF5LVCC1b5HIGVI6jHkopG4da5CqO+Ac6dBD16QH091NXBaadDv37dLZnTWapMMUQxJd0CfBor8JKpN6PTZb4LfBUrAjcCS7ZyKoptxsDXvw6r10DfPlCXqfaHU/LE9NAPfLYnAUtUddcMx8/DKi+CFeS6VFVnBsf+g9WCaQVaCpkvEUUxzFfVBwslgNMv2JyKpbYOBg7sbimcrhJvo55bsWqOUzo4/h7wCVVdLiLHA5OxFPwER6jq0riE6YgoiuEtEfkT8Hesmhjg4apOkVmzBpYuhREjoKEh//FaW2DVaujTJ57xyoKV2Op0a3x12hniMxOp6rMiMibL8RdDL1/C/lhFJ4pi6IUphGND+zxc1SkeDzxg+QAJTj0V9tiz6+Mt+ADuvNPyClThpE/CHsUubFdsvgf8BPvID8WaFI3tVonKiuhRSUNEJFyzZ7KqTu7irBeRLNYF9tz9h4go8Ps8xs1JTsWgqhcWanLHyck776QqBYD774fddoOaLnzrbWs1pbBhQ3LfQ3+H0aNgUKWGkD4O/C8WHr0J+ABr0vNKdwpVPnTOlLQ0Dtu/iByBKYZwC8GDVXWhiGwJPC4ib6nqs/nOlYmcalBEbhORgaHXgwIHiuMUnvfmZd7f1EUz65o1yWSzBLW1sGRJ18YrC14hNWemDctXdSJTxKikoNLETcApqppoMIKqLgx+LgH+hrXlKwhR1ke7h4voqepycvczdJx4GLZV5v1bdPHbfe8+7fe1tcHAYpfCLibbYuW2w4zsDkHKlOL1YxCR0ZiZ/tOqOie0v4+I9Ev8jpn238h7wg6IohhqRGTzp0ZEBuOd35xiscce5nAOc/DBXQ/7rKuD006zvIIePWycAw6AYcPyl7Vk+RT2HOmD9a7oD9zdrRKVHTEpBhG5C/gXsIOILBCRi0TkEhG5JDjlKmAL4AYRmRHyVwwDnheRmcDLwMOq+mj8N2pEecD/HHhRRP6MWdvOwjqrO05x+PzFMHs2LFkM48bD1nkGauy8C4wcCUuaLDt5y0qvalsD/AWr5LsMy7r37OvIKLGVxFDVSTmOfw74XIb98yhi678ozucpgdY6EsvvP11VZxdcMscJs/POtsXFgIG2VQ1CAU3SlU8FZTVHoUPFICJ9VXUNQKAI2imD8DmO4ziVSWWVu4hCthXDAyIyA2uCO11V1wKIyLZYoZ+zgD9gnd7aESH1+/Bg7PeCXX9V1VxNdJ24aWuDR6bCjBkgAgcdBJ843H7PxocL4L77YPVqKwp39tn5VQxdtQruvQcWLbKks9NOh7FBnP1H90Gfz0Pv1bBsa+gxFfrnKDjY0mxhrW+9ZX6Fo46CfYNvzKtmwsaTYPBCWNsf1t8Mw063Y+++C/f/DdatM9/G2WdD336W7/D8c/D88/ae7b47nHCiRTRl4+OP4Z677Wf//nDmWe19JumowpP/hH//217vtRccN7F9pVaneFSZYujwP01VjwL+CXwBmCUiq0TkY+AOYCvgAlXNqBQCbgUm5pj/OVXdM9hcKXQHzz4DM2daCGdzM7z4Irz6avZr1q2F22+HlSvtIdm0BG671XIEusodt8PChTbe6tVw159gxQpY/RYMPh/6rYTaNhj8AbQeAZqjbNcjj8Dbb1sS28aN8Pjj8M4c0BbQI2GLD228/itg4CRYO9c6rd1zt4W0trXBhx/CHXfaeK+/Bs89B5s22Xv12mvw1JPZZWhtsfelqcnGW7ECptwG69dnv65xmimF5mbbXn0VXng+8lvpFIAqK6KX9SuIqk5V1fNUdYyq9lfVLVT1IFX9oap+lOPaZzFPl1PKvPWWPXwSNDfD229lv2bRotTXqvawW7WqazJs2GDfqFWT+0QsQ3nlY6kfuFqFfstg3fzsY77zTmq+QnMzzHkH1r4LvVdBTWguFVjxBMz/IHWlpGoO7+bm9u9TSwvMmUNWVqwwpZTO4qwfncx/k7dy/E2cwpFIcHPFUDQOFJGZIvKIiHRoGxCRi0WkUUQam5qaiilf5dMnLa5fBPr2zX5Nr172DThMWxv07Nk1Geo7CD3t1Rtqh4Fo6n5R6LFFbhnD1NZaddOGIalKAex13VbtrwEz39TV2nuSbl7r3Tu7DD172YolTFtb5nnCZJqrT46/iVNAgkY9UbYKoTvv5BVgG1XdA/g/rNdlRlR1sqpOUNUJQ72Zerwce5wVkautNVt8z55w2CeyXzN8BIzf3h7oNTX28+BD7EHYFWpr4dhjU8cbuTVsOxa2OgOadoJNDdBSC5vqYf4lUJejIu0JJ9o4ifvq0wf22w8atoD5F9o4ifGW7AZbngzjxsHw4alyTJwIUgOHHmoP9Lo6G7Ohwez+2ejTx3IkwuPtuCNsmSNn4ogjghyLxFw94JhjOveeOvFSZSsGUdXcZ3V1cKsi+FAm53OGc/8DTMhVUnbChAna2NiY7RSns6xcYaaKmhqL8U9fRWRCFd56E5Yth+Fbwbbb5S/H/PdhwQJz0u68S9LZ2tYMH14Pre9Bj0Nh+DnRxlu6FOa+Yw/kXXZNXdEs+hNsfAFqx8HWl4MEcRhtrTBrlvk5Ro2CUaOT16xbC7Nm2zk77BA9W/rdubB4sTnnd9gxt2MfzM8xe7a9zzvtCP0HRJvLSUFEpudbu2jC6GHa+LWs6QfJ+b56fd7zlQKRMphFpBbLvNt8vqrmMPLmHHMrYLGqqojsh61ePs5xmVMIBgyE/Q/o3DUisFPMHV9Hb2NbOjX1MOp/Oj/ekCG2ZWL4ucC5Geaqhd12z3xN7z6w776dl2O7cbZ1hr59bYXjdD/x9mMoCiJysKq+kGtfR+RUDCJyOVazdzHJDm4KdPDp2XzdXcDhWBnaBcEY9QCqeiNwBnCpiLQA64FztJDLF8dxnK5SZooBM8/vHWFfRqKsGL4C7BCu8heFCKnfv8E6GTlxsXo1PPyQhUduNRxOPCFz0bhK4vX/hSG/hLpN8MExsOftXSvHnQ9tbRb2+8Ys6NnD/CWJlc/69fDww7Booa1eTjzJTGVg0UmPPGJmo3Hj4OhjzK+Qda5WePJJM/316g0TjzN/jFNAysd/ICIHAgcBQ0Xkv0OH+tOJ7kxRFMMHWOsnp5RpaYFbbraQ0bY2yzFoWgKXXFq5iVFv3gw7fBMagrDUAX+GGa2w9z3FleOfT8C0ackQ0zvugIs+B1sOTc1jWL4cbr4JLrvMFMYtt1heBNjfa80aOOPM7HNNfQRemxnM9THcdht84RLYIkeUlpMf5RNx1AD0xZ7t4QiNVZiVJhLZSmIktM084GkReZjU1p6/6Iy0ToH56CPL1k2Ekba2Whz9so8tM7kSaf1jUikANDTDmMeLL8erM9rnHcyeDfV7wLJlyb+JquU1LFwES5tS8zZaWuDNNy1xT7I8hDYrhYDWVss7OejgeO/JSVJGPgZVfQZ4RkRuVdX3RaRPompFZ8imBhNd6udjLaAaQvs8qLrUqKtNfdCAvc5VsqGcaWtIer0StHbD/damfYwSuQ8d/U3qaqE2w3cyEazYXba50u5PpLL/xqVC+YWrjhCR2cCbACKyh4jcEPXibCUxrlbVq4HZid9D+97MW2x6kWssAAAfdUlEQVQnXoYNsxj8hI26rg7GjK3sBjQDvw3NDfaNDiwnYcHFxZfj8COSSXoiluOwx57Qrz9st13yWF0dDB1qeSA77mjhswkzX3299ZnIFcp62GGpc/XoAbvuVpj7cgKK16gnRn4FHEcQ6amqM4HDol4cxcfwTeC+CPuc7kRq4NOftlpHixdbobYDDowWM1+ujD4K3v87rL4GatdD63mw13/nvi5u9tnHsqrfmAW9etoDPuFgPussq3u0YIH1fTjoYFMGPXvCJZdYUb5Vq2D8eNg9Qrn9Aw+ysd980/JNDjkkWt6Jkx+l9dCPhKp+IKmf/8jFzLL5GI4HTgBGisivQ4f6Ay2Zr3K6lbr63FnLlcY2x2LdybqZHXa0LZ2aWnuYZ6J3H8s87yy77GqbUxxibNRTRD4QkYMAFZEG4Mt0wtKTbcWwEJgOnBz8TLAauKILgjqO45Qn5bdiuAS4HmvuvQD4B/ClqBd3qBgCm9RMEblTVZs7Os+pYNra4IH74b33rIjbGWckwyKXL4Nbb7VIqIED4fOft5o+YCWqH37YIma23wHOzBGCmQ9Lm+DFf8GmjbDnXpYPkOCdOVZSvEcPM+EkZG9rg5f/DfPn275DD03Kvn6dlddescLKfOyzTzRz3FtvwhtvWL2oQw6ubN9O1RGf/yBCnxrBHugnAOuAz6rqK8GxC4DvBKf+QFVv62ieoLTQeV2VM5sp6XUCt55k+GCoatbMZ6cCuPkm65EAljz329/AV68w5+evQ9bFpUvhuuvg29+xMM2//jV5bPYsuGUV/NdF8cv38VL4wx+SuQBz5sApp5iZ5bXX4O8PJktvv/GG2fQHDbZGPInS1rW1dt3FF0NrG0yenMwFmTvXckGOPyG7HI2N8I/HbDwRmPUGXHqp1zeqJOJbMdyKJfZO6eD48cD4YNsf+B2wv4gMxqpHTMCey9NF5EFVXZ5pkDTzf4KVQKOqPpBLyGyGs5OATwKPBtt5wTaVDrq2ORVEW2tSKSRQNWfps8+0P7+lxVYWjz3W/tgHHxRGxmnTkkoB7MH89NP2+9NPp/Zj2LQJpk+3xLJZs5K5AIl8j/nzTRGEc0Gam+2hn6sB0TNPJ8dTtblmzozhBp2SIaaopAh9ak4BpqjxEjBQRIZjEUaPq+qyQBk8TvZGaD2BPYF3gm13YDBwkYj8Kpec2UxJ78Pmwkvh7JkrReQFwDuuVTLp/RYStLRAWwcfgOZN+XVx6ywtGWIgEv0PMsnR0mL7M5mGWlvb904Ae9C3afavUJl6U7QU8X1wCkvnEtyGiEi4/PNkVZ3cidlGYtUmEiwI9nW0vyPGAUeqaguAiPwO8zMcA7yeS4gorvY+InJI4kXg6fb4uEqnrh76Zeh5sO++Fo6ZjohVEN1///bHEqGbcbPHnqlNfurrzScAsPfe7Y/ttrtFAo0YkUwKE7Fjo0ZZ/4dw+ZC6Ohg3Pnf9oj0zyLFLzJVnnW6kU416liZ6xwRbZ5RCMFk7NMv+jhhJ6nO6DzBCVVsJVbDoiCh5DBcBt4hIwmC6AvivCNc55c4Xvwh//KP5EBoa4NRTLYkOYNIkuPde+5ZdXw+f+5w9bA851PwR06bZt+1Bg+BLkYMhOseoUXD22VZUrrkZ9trLcjcADj3MsotnzoD6Bjj6KBgZfME673x4ZKrlFgwaZIXtevSEHth9PDwVVq2EsWOjhZMefbQ5r2e9YY7uY47N3YzHKS+KF5W0ABgVer01FiG6AKtWHd7/dJZxfgbMEJGnMaVyGPAjEekDPJFLiMiNekSkf3B+txbU80Y9juNEJZZGPSNGauNFl0ab7wffzTlftgZmInIicBkWlbQ/8GtV3S9wPk8nWTb7FWAfVW3nrwgim7bG8s32wxTDy6q6MP3cjsgWlXS+qt6RVrp1c4SSF9FzHKcqiLGIXoQ+NVMxpTAXC1e9MDi2TESuBaYFQ12TSSkE56qI3K+q+wA5I5Aykc2UlLBP5Wiu61Q0H3xgETt9+8Kuu0Yr2LZxg4WHbtpkfoctt0wem/cuvPyy2e2PODL/ctHr19tcLc2w/fawRQcd2xwnH2JSDBH61CgdJKKp6i3ALRGneklE9lXVablPbU+2qKTfB7/+VFU3dGVwp8x55RV49BHzI9TWQuM0uPDC7I1wNqyHG39v/ZFbW+Gpp8wfMXZbmDHDEuYSzJpl/SKGddEev3Yt3Pg72LDB/BlPPQWf/oz5HhwnNkquQF4UjgC+ICLvA2sxc5JGzT+LEpX0hoi8ICI/EZETQk5op5JRNQdtc7OFXzY3w5Il8Pac7Nc1NsKa1anXPfywHZv6cPvzw4qis7z0L8s7aGkxJdTcbDI7TtxEj0oqFY4HtgOOxPLREnlpkch5J6o6DpiExb6ehJXJmNElUZ3yoa2tfVy/qplusrFuXfvrNgQLzkx5AuvWdV3GNWvb5xDkM57jZCLhYyijstuq+n6Qi7ae4A7IHt6aQk7FICJbAwcDhwJ7AbOAIvdOdIpOba3F+6e3Bd1mdPbrxo1Ljemvq0vWL8pkMtpxp67LuOMO7efafoeuj+c4HVFmikFEThaRd4D3gGeA/wCPRL0+ytpnPvBV4BFVPVBVT1TVH3dFWKfMmDTJ7PW1teZ8Puus3M7dbbeD4yZav4G6OnMIn3iiHfvshTB4cPLc8eNhYras/hzssCMceZTlDtTVwS67wLElUILbqTDKslHPtcABwBxVHQscBbwQ9eIoCW57AYcA54rIlVjdjWdU9eYuCOuUE3362sO8s+yzTzIDOUxDA1z+5fzlCnPAAbY5TiEprYd+FJpV9WMRqRGRGlV9SkR+GvXinIpBVWeKyLvAu5g56Xwsi84Vg+M4lU95NupZISJ9gWeBO0VkCRC5fUJOxRAUhOoBvAg8DxyWKLDndBNr1sCihdC7N4wYWdj2natXwaKPzJQ0YkTqsSVLYMVyGLqllZZwnEql/FYMM7EEuSuwqtgDgL5RL45iSjpeVZu6JpsTO/Pnw513mDJoazM7/RlnFkY5zJsHd99lDui2Nth5F+t3IAJPPwUvvGD+h9ZWOOmTsEeEnsWOU3aUnP8gCkeoahvQBtwGICKvRb04SriqK4VS4s/3WUbxxo0Wtz93Lrz9VmHmuu9emyMx1+xZ8N48aGoypdDSYsdaWuChv6f2RnCcSqJMnM8icmnQZG1HEXkttL0HRFYMUVYMTimxZk3q69ZWWL4i/nlaW5L5B2GWr7DVQ21taj8EEVi7BhoGt7/GccqdEnjoR+RPWFjqj4ErQ/tXd1RbKROuGMqNoUPtG3uiKm5NTbIUdpzU1lkv5xVpSmf4VtCnT/vEspoa6FegvguO053EWESv0ATVr1diScldJlt11dNzCPDXbMedAnH2OXDbbZbhq21w2GEwZkxh5jr3PJhym5mL2trgqKPM2Q3ma7j/flsp1NTCeefmbmjjOGWJlGNUUl5k+yRnq6uhgCuG7mDwYPjKV6wZTs+eltxVKIYOhSuusLl69bJmNAl22dWyjNeuhX59bYXhOJVKmawY4iJbddUuZDY5RaGmBgYUqZZhTS0MGJj5WH29mZscp9JxxdCeoKvQLkDPxD5VvSbHNbdgRfeWdNCpSIDrsaYU64DPquor0UWvEDZssK1//9S6RG1tsHgx1IjlCaTXLCoWba2wapWtGHr0zH1+gvXrYFMz9O8HEpJd1XIjauvMVxGVTRth7Tobz1cnTjEpIx9DXERJcLsR6I3V974JOAN4OcLYtwK/AaZ0cPx4YHyw7Q/8LvhZPTzzNDz3nD30e/WGz14AgwZbT4Prr09GBfXqBV/+MvTsVVz5mpbAlCkhH8PRcOCB2a9RtfLar75q/oeBA+GCz1qC3Ib1MOV2G1cVdtoJTjs9t9J79RXrw1wj5sc4//ykr8NxikGVKYYoX0MPUtXPAMtV9WrgQFKbVWdEVZ8FsoVHnQJMUeMlYKCIFCC8pkR5b57lAiT6CKxeBXcHRWun3J4aKrp+PdxxR/Fl/NOfLDy2uTlouvMkfPhh9mveeANmzrTzW1pg2TK4/2927OGHYcniZP+Et9+GaTkaTC1dClOnWvhsc7O9F3feaY53xykKZVlELy+iKIZEAf51IjICq7cxNoa5RwIfhF4vCPZVB4sWpfYnUIWlQS7hx0vbn780w75C0trSPlQV4KOPsl/34Yf2AE/Q1mb3mjgWvufmZvhgfvbxlixuv6LYtMnMSo5TLGJq1CMiE0XkbRGZGxQlTT/+SxGZEWxzRGRF6Fhr6NiDMd9hClGMtQ+JyEDgOuAVzOJ2UwxzZ1KvGRtJiMjFwMUAo0fn6AdQLgwaZEli4XyAfkF77b597Zt2mL6Ry5zEQ22dmbDSG/Pkqom0xWAz94ST3xIO6sGDTdkkcjDq6izyKRsDByXPTyACvYtsVnOql5h8DCJSC/wWOAb7IjxNRB5U1dmbp1K9InT+5Vh16wTrVXXPvAWJQJQVw89UdYWq/gXYBtgR+EEMcy8g1SS1NbAw04mqOllVJ6jqhKG5HiTlwo47Wp2j+noLOW1ogDPOsGOTJqV+S66pgXPPLb6MZ56ZlK++HnbdFcbmWCzuvQ+MHGn306OHKZdTT7NjJ33SHM6J+x06FA46KPt4I0bAvvuaEknIcfrp2ftOO07cxGNK2g+Yq6rzVHUTcDdmUu+IScBdMd1Bp4iyYvgXsDeAqm4ENorIK4l9efAgcJmI3I05nVeq6qI8xywfpMaK3y1aaMlqw0cko3SGDIWvf90cuAB77VV8xzPA2G3N6f1RUF11qwguoNpauOACWPChRRKNHJmUfeBAuOxy+HCBrUhGbR3tAX/MsVagb+UqGLYl9Pe2404x6ZT/YEhQkTrBZFWdHPyeyXyeMeBGRLbBTPZPhnb3DMZuAX6iqnk0TM9OtsznrbAb6SUie5E0/fTHopSyIiJ3AYdjb9QC4HtAPYCq3ghMxUJV52LhqtWXNyHScXRNz15wYI5v08Wgbz8Y169z10iNdX7LRI8e1uWts2w5zDbH6Q6iK4alqjqhg2ORzefAOcCfVTXcKH20qi4UkW2BJ0XkdVV9N6pgnSHbiuE44LOYiecXof2rgG/lGlhVs9bqUFUFvpRbRMdxnG4kvkY9kc3nmGJIeT6q6sLg5zwReRrzPxRXMajqbcBtIvKpwL/gOI5ThcQWijoNGC8iY4EPsYd/O+ehiOwADMLM+Il9g4B1qrpRRIYABwM/i0OoTERRgy+IyM0i8kgg4M4iclGhBHIcxyk5YnA+q2oLcBnwGPAmcK+qzhKRa0Tk5NCpk4C7A6tKgp2ARhGZCTyF+RhmUyCiOJ//GGzfDl7PAe7Bez47jlMtxJS8pqpTMf9qeN9Vaa+/n+G6F4HdYhEiAlFWDENU9V6sRVxC67Vmv8RxHKeCqLLM5ygrhrUisgWB91xEDsAaQTiO41Q+XkQvI/+N5RxsJyIvAEOxQnqO4zhVgDfqaYeqviIinwB2wOJw31bV5hyXOY7jVA6+YkhFRHoCXwQOwRZVz4nIjaqaoVO84zhOBeKKoR1TgNXA/wWvJwG3A2cWSijHcZySwX0MGdlBVfcIvX4qiKV1HMepAior4igKUTwqrwaRSACIyP7AC4UTyXEcp8SIqR9DuRBlxbA/8BkRSXRUGQ28KSKvYyWPdi+YdI7jON2Nm5IyMrHgUjiO45QyrhhSUdX3iyGI4zhOaVJ9PoYoKwbHcZzqxhWD4ziOk4IrBsdxHGcz8TXqKRtcMTiO42TFfQyO4zhOOq4YHMdxnBRcMTiO4zibqcIEt+ryqDiO43SFmDq4ichEEXlbROaKyJUZjn9WRJpEZEawfS507AIReSfYLoj5DlPwFYPjOE5W4mnUIyK1wG+BY4AFwDQReVBVZ6edeo+qXpZ27WDge8AEbA0zPbh2ed6CZcBXDI7jOLmIZ8WwHzBXVeep6ibgbuCUiBIcBzyuqssCZfA4BSxX5IqhVFkAXA/8Gviwm2VxnGom4WOIphiGiEhjaLs4NNJI4IPQ6wXBvnQ+JSKvicifRWRUJ6+NBTcllSJzgH2BjcHrq4BGYFy3SeQ41U105/NSVZ3QwbFMg2ja678Dd6nqRhG5BLgNODLitbHhK4ZS5JvAGkwxbMT6532rWyVynCom4moht/JYAIwKvd4aWBg+QVU/VtXEV8I/APtEvTZOXDGUIouBttDrtmCf4zjdQzyNeqYB40VkrIg0AOcAD4ZPEJHhoZcnA28Gvz8GHCsig0RkEHBssK8guCmpFDkdeBVYF7zuDXyq+8RxnKompjwGVW0RkcuwB3otcIuqzhKRa4BGVX0Q+LKInAy0AMuAzwbXLhORazHlAnCNqi7LW6gOcMVQinwV+Ai4AbMsfgm4vFslcpzqJqYEN1WdCkxN23dV6PdvYsbkTNfeAtwSiyA5cMVQitQAPws2x3G6GS+i5ziO46RTZYqhoM7nfNK/nSwsBh4G/k0BA9Ycx9lMTCUxyoWCrRjySf92svAClu9Yi7mnTgDuIXOUs+M4+VOFjXoKebf5pH87HXE2luOwElgLPAI80K0SOU6FE1seQ9lQSMWQT/p3CiJycSLFvKmpqRCylg8fpb1uBt7rDkEcp4pwxRAbUdO/x6jq7sATWPp3+4tUJ6vqBFWdMHTo0JjFLDN2IvWvVgvs3U2yOE614IohNvJJ/3Y64n7snewF1APfAT7RrRI5TmXTuSJ6FUEhw1U3p39j9UHPAc4NnyAiw1V1UfAynP7tdMR2wDxMxQ4C+navOI5TFVTQQz8KBVMM+aR/OzmoJXUt5jhOAYmnUU85UdAEt3zSv6sexRzNAgwj1WOzASvDPTrY8qUV8/D0AA6PYTwwNb8WCzeors+UU4lU2YrBP7KlyHos+2MsMAbLW9gQHPsnZj46FNgGOCrPud7FFMJE4Ijg95V5jKfAF4HhwA7ArrSPpHKccqIKfQyuGEqRq4AXSfZjeA74fnDsROwbfoIngV/lMdeEtPE2Bfu6yl3AlGCc9cA7QEHbljtOEXDF4HQ7L2AP1QTrg32Q7OoW5uk85sq0Ong/j/GmYSakBC3AK3mM5zjdjie4OaXAjlgoaoIGLH8BzPGczs55zNUzw75BeYy3PdY/IoFgJjHHKWfiadRTNlTOnVQS12FO5X7BNhr4SXDshrRzRwM/yGOu9HIaQn4rkM9hxVD6Av0xJZMxbdFxyoQq9DF42e1SZAvgDeBf2IP6AJLf7C8GDsYS3bbBMkPyUe/HYKajH2GrlKuBwXmMV485yP+N1XTaFxiYx3iOUwpU0EM/Cq4YSpWeWJRQJnYJtrgYDdwY43g1wIExjuc43UplrQai4KYkx3GcXMRkSorQo+a/RWR2UFj0nyKyTehYa6h3zYMx32EKrhjSeRjYEnP4HgosCfYr8F3Mdt4b68PcmmmANGaTdCaPA2ZGuKYN+FowTx/g68E+sLyDwZiJqQb4RoTxis2NmPmoJ/ApYF33iuM4eRODYgj1qDkeCxmZJCLpoSOvAhOCwqJ/JrXB73pV3TPYTo7v5trjiiHMm8BZQBNWzvolkh0kJgO/xEIx1wO3Aj/MMd56LJN4Dha2+S5wJLA6x3W/wB6u67GH6g3A9cGxCcDy4HfFHNX35BivmDyOKbWVWGjtVODSbpXIcfIj0agn/6iknD1qVPUpVU18lXoJK5lZdFwxhHku7XUL8HLw8wFS4/PXkbtBzhwsYzlcbLwFmJXjuvtJ/Za9LtgHsCLD+aWkGB4lVfYNWDMhxylbYstjiNqjJsFFpH56egZ9aV4SkVO7di/RcOdzmC1oryp7YbkDWwU/E+YjwUxO2RiMrTzCbArmycawQI6E+agmmD8xb3pXi2E5xismW2JlNcKJePlEOTlOKRDd+TxERBpDryer6uTg9yg9auxEkfMx+0C4qP5oVV0oItsCT4rI66r6blTBOoOvGMKcjNX26YP5GHphFkHBSlIMDPb1xHwNP88x3ijgkmC8+uDnZ4DxOa77CZa/0DPY+mPhpACXp53bC/hpjvGKySXACMw/0iP4+btulchx8if6imFpoqlYsE0OjZKzRw2AiBwNfBs4OdSvBlVdGPych2Ub7RX7fQb4iiFMPfAsZppZAhyCWQXBQjpnY+6gVuBUopW+/iVwLGY+2hGrdZSL8cFcf8WU0ulYUTowX8O+wH3YSuF/McVRKgzAHOz3YHkMx5HM2nacciSR4JY/UXrU7AX8HpioqktC+wcB61R1o4gMwbKZwo7pWHHFkE49cH4Hx7bEKod2luODrTOMAC7r4Nj5dCxjKdAPy4B2nEohBsUQsUfNdZg94j4RAZgfRCDtBPxeRNowS89PVHV23kJ1gCsGx3GcrMTXqCdCj5qjO7juRWC3WISIQHX6GGZg9n7B7OBRU0VOxN6xGqxMRYINwEGYX6IfcFPo2AuYH0Awf8GToWN3YGagBszNtCZ0bKvgGsFWDwmWYSsXwb5zhOskzQiua8DMXG+Hjv0rkHkHzHrZEuxvwxazPQM5vx66ZgXmc2nA/Ct/Iz8UKxG+M7A38FCe4zlOsaiyWkmimtEpXrJMmDBBGxsbc5/YEZuwh2D6bc8jexXQc7FeA2EOxXwSu2G1jcJMDY73yzDWEixn4hNp+8dh/Qu2AeanHRsbyNiPVAUCcC9W82gIqUl3DVg+wXuY4kmEkfbGTD3XY81U04vcXY31hBgBLEo79iqwZ4Z7isIvge+E5OiFBeOlvw+OExMiMl1V8+kwwoT6PbRxyGPR5vtoeN7zlQLVt2J4gMwBYr/Icd1fMuxL9EhIVwoAvyZ15RAmnLAWZm7wM10pgD3cob1SAPhxIF96JvYmbIXyN1LDR9dhzXTAnNjp3IitgtKVAliUVlf5Pak5DuuBP+YxnuMUiypbMVSfj6GjSp+9O9ifIFMfBAn9TFc2PbLM1Tc4Hhc9gjEzkQgbDedgQLLfQ6b7aqDj/4xM/Ruikn7PCfOa45Q0lfXQj0L1rRiOof3DqAarg5SNb2XYd3Hw87S0/YKVy/g0qQ13wB7ElwPX0v7dPy4kYzoTg5/bZjj2O6wmUXrY6lDgMCyCqR9JJdAbMxWBmXbS+SmmGPbPIPu3M5wflWsx8xHYe9QH+Goe4zlOsfBGPVVAE+ZU7QWMwUw3HX3jTvAd7IE5AHvIfp1k05y/AP+D+Qb2xMpo7II9SJswh28vYDvMPNMQ/D4Tc8KOxpTFo8F4/8CS7RLO51NJJsbPAY7GHu5bAs8Ec9ZhfRWOxhzPJwD/wf7CwzDH9BcxX8kdJENhvxHcx3aBnPcBZwfHXgQuDMbbF3NmJzKwu8LJWJHCz2BK9WUst8NxSpkqbNRTfc5nx3Gqhlicz3V7auOAJ3OfCMiyLSrC+Vx9PgbHcZxOUVmrgShUtinpAayM9hdIRvUUm01YnaPTsTDQDaFjM4DdsYopXyBZNK+rbMBqOp0ezLkpz/EcxzGqzJRUuSuGP2COzXWY+rsHeA2z5xcLBU4CnsdCMx8FnsD8Au9i/oWEJW9ysO+JLs7VivV6eBVTEI9iORaPkLmmo+M40amgh34UKnfFcDXJmPk2rJdCeiJXoXmHpFIg+PkqlvfwPdqHuP4zj7leC7bEimQ9phi6a6XkOJWCStVFJVXuiqEl7XUrqUlexaCZ9qq3BjPxxC1Ltrkcx8kPXzFUCBeRmrTWC/M3FJMdsBDWhuB1PRZiujtwRYbzt89jrj2wkhiJvIkGrIxGrt4PjuPkpsp8DJWrGK7FktJ2wYrHPYY9kItJHWbOOQ1TEqdguQENWK+Hu7G8iAas78OreczVAyuUd1Iw1+mYLyNTZrPjOJ2jyhRD5ZqSarAs3XwydeNgC0wBZOJskslkcTAMa+7jOE58xNeop2yoXMXgOI4TC5W1GohCQU1JIjJRRN4WkbkicmWG4z1E5J7g+L9FZEwh5XEcx+kSVRaVVLA7EZFarEjz8VhrlkkisnPaaRcBy1V1HFatv5Ta2juO4xhV5mMopIrbD5irqvNUdRNmaT8l7ZxTSGYX/Bk4SoJGp47jOCVBFRbRK6RiGAl8EHq9INiX8RxVbcH6jW2RPpCIXCwijSLS2NTUVCBxHcdxMhFRKURQDPmY10Xkm8H+t0XkuPRr46SQiiHTu5Se6xvlHFR1sqpOUNUJQ4cOjUU4x3GcyMSgGPIxrwfnnYMF4E8EbgjGKwiFVAwLsEr+CbYGFnZ0jojUYVH9ywook+M4TueJx/mcj3n9FOBuVd2oqu9hjYD3i+3+0ihkuOo0YLyIjAU+xLTduWnnPAhcgKVmnQE8qTkaREyfPn2piLxfAHnTGU3m7svFxuUoLRnA5Sg1GaBjObbJd+DpTH9MkCERT+8pIuGGMZNVdXLweybzenqfxBTzuogkzOsjgZfSrk03zcdGwRRDcFOXYTnHtcAtqjpLRK4BGlX1QeBm4HYRmYutFM6JMG5RbEki0lQKDTdcjtKSweUoPRkKLYeqTsx9ViTyMa9HMrvHRUET3FR1KjA1bd9Vod83AGcWUoY8WNHdAgS4HElKQQZwOcKUggxQOnJkozPm9QVp5vUo18ZG5WRkxM/K7hYgwOVIUgoygMsRphRkgNKRIxubzesi0oBZSB5MOydhXodU8/qDwDlB1FKiPObLhRLUS2J0zOTcpxQFlyNJKcgALkeYUpABSkeODsnHvB6cdy8wG2sq8CVVbS2UrJLD1+s4juNUGW5KchzHcVJwxeA4juOk4IrBcRzHScEVg+M4jpOCKwbHcRwnBVcMjuM4TgquGBzHcZwU/j8aSvVAC2NKWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add color map at the end\n",
    "betterdf.plot(kind='scatter', x = 'sepal length (cm)', y = 'petal width (cm)', c='target', cmap=('spring'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9Bz028E_X6rO"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets clean it up a bit more!\n",
    "# Limit the color to three. \n",
    "# Clean up formatting\n",
    "# Adding Semi colon at the end removes the <matplotlib.axes._subplots.AxesSubplot at 0x7fe3904d6748>\n",
    "\n",
    "# Reaching the limitations of whats built into dataframe plots\n",
    "# We can import matplot lib and start using more of custimization. \n",
    "\n",
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "betterdf.plot(kind='scatter', \n",
    "              x = 'sepal length (cm)', \n",
    "              y = 'petal width (cm)', \n",
    "              c='target',\n",
    "              cmap=plt.cm.get_cmap('spring',3));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mJvHLDMZUvW-"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "print(iris.target_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "70ItHN0sSuTX"
   },
   "source": [
    "Challenge! Do an exploratory analysis of one of the other built in Data Sets!\n",
    "\n",
    "Make a data frame and look at the data\n",
    "\n",
    "Make a chart, play around with different chart types to see what works best \n",
    "\n",
    "Just try things and see what happens! Don't be afraid of breaking anything!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Me0N03t_TASy"
   },
   "outputs": [],
   "source": [
    "# Get started \n",
    "\n",
    "# Import wine dataset\n",
    "from sklearn.datasets import load_wine\n",
    "\n",
    "wine = load_wine()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UWyzV6cVTA7q"
   },
   "source": [
    "### A Solution / Example | Try on your Own first!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YXG9a54NTFlr"
   },
   "outputs": [],
   "source": [
    "# Import wine dataset\n",
    "from sklearn.datasets import load_wine\n",
    "\n",
    "wine = load_wine()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "QV7ual1l-aWU",
    "outputId": "a6f5cd79-94e3-457e-affe-d3998eafa830"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])\n",
      "(178, 13)\n"
     ]
    }
   ],
   "source": [
    "# If we didn't know the key value pairs in the data we could find out like this:\n",
    "print(wine.keys())\n",
    "\n",
    "#150 rows (samples) 4 columns (features: Height & Width of Pedals & Sepals) \n",
    "print(wine.data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "A8E_EAGq-gaB",
    "outputId": "6be3f08f-af8c-4964-faab-4dbb11413b5a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['class_0' 'class_1' 'class_2']\n"
     ]
    }
   ],
   "source": [
    "print(wine.target_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IALzt3zz9-b0"
   },
   "outputs": [],
   "source": [
    "# Make a Dataframe\n",
    "winedf = pd.DataFrame(wine.data, columns=wine.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 5408
    },
    "colab_type": "code",
    "id": "bCaaChvu-Clw",
    "outputId": "413ef95e-426d-493c-b907-7a59abcf3af3"
   },
   "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>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.640000</td>\n",
       "      <td>1.040</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.380000</td>\n",
       "      <td>1.050</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.680000</td>\n",
       "      <td>1.030</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>0.860</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.320000</td>\n",
       "      <td>1.040</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.45</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112.0</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>1.050</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14.39</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.45</td>\n",
       "      <td>14.6</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.52</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.98</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>1.020</td>\n",
       "      <td>3.58</td>\n",
       "      <td>1290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14.06</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2.61</td>\n",
       "      <td>17.6</td>\n",
       "      <td>121.0</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.51</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.050000</td>\n",
       "      <td>1.060</td>\n",
       "      <td>3.58</td>\n",
       "      <td>1295.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14.83</td>\n",
       "      <td>1.64</td>\n",
       "      <td>2.17</td>\n",
       "      <td>14.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.98</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.98</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>1.080</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1045.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13.86</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.27</td>\n",
       "      <td>16.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.98</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.85</td>\n",
       "      <td>7.220000</td>\n",
       "      <td>1.010</td>\n",
       "      <td>3.55</td>\n",
       "      <td>1045.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14.10</td>\n",
       "      <td>2.16</td>\n",
       "      <td>2.30</td>\n",
       "      <td>18.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>3.32</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2.38</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>1.250</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1510.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14.12</td>\n",
       "      <td>1.48</td>\n",
       "      <td>2.32</td>\n",
       "      <td>16.8</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.43</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.57</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.170</td>\n",
       "      <td>2.82</td>\n",
       "      <td>1280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.75</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.41</td>\n",
       "      <td>16.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.81</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>1.150</td>\n",
       "      <td>2.90</td>\n",
       "      <td>1320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.75</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.39</td>\n",
       "      <td>11.4</td>\n",
       "      <td>91.0</td>\n",
       "      <td>3.10</td>\n",
       "      <td>3.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>1.250</td>\n",
       "      <td>2.73</td>\n",
       "      <td>1150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.38</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.38</td>\n",
       "      <td>12.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.64</td>\n",
       "      <td>0.29</td>\n",
       "      <td>2.96</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>1.200</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1547.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>13.63</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.70</td>\n",
       "      <td>17.2</td>\n",
       "      <td>112.0</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.91</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.46</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>1.280</td>\n",
       "      <td>2.88</td>\n",
       "      <td>1310.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>14.30</td>\n",
       "      <td>1.92</td>\n",
       "      <td>2.72</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.14</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.070</td>\n",
       "      <td>2.65</td>\n",
       "      <td>1280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>13.83</td>\n",
       "      <td>1.57</td>\n",
       "      <td>2.62</td>\n",
       "      <td>20.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>3.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.72</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>1.130</td>\n",
       "      <td>2.57</td>\n",
       "      <td>1130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>14.19</td>\n",
       "      <td>1.59</td>\n",
       "      <td>2.48</td>\n",
       "      <td>16.5</td>\n",
       "      <td>108.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.93</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.86</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>1.230</td>\n",
       "      <td>2.82</td>\n",
       "      <td>1680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>13.64</td>\n",
       "      <td>3.10</td>\n",
       "      <td>2.56</td>\n",
       "      <td>15.2</td>\n",
       "      <td>116.0</td>\n",
       "      <td>2.70</td>\n",
       "      <td>3.03</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.66</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>0.960</td>\n",
       "      <td>3.36</td>\n",
       "      <td>845.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>14.06</td>\n",
       "      <td>1.63</td>\n",
       "      <td>2.28</td>\n",
       "      <td>16.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.17</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.10</td>\n",
       "      <td>5.650000</td>\n",
       "      <td>1.090</td>\n",
       "      <td>3.71</td>\n",
       "      <td>780.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>12.93</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.65</td>\n",
       "      <td>18.6</td>\n",
       "      <td>102.0</td>\n",
       "      <td>2.41</td>\n",
       "      <td>2.41</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.98</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.030</td>\n",
       "      <td>3.52</td>\n",
       "      <td>770.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>13.71</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2.36</td>\n",
       "      <td>16.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.61</td>\n",
       "      <td>2.88</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.69</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>1.110</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1035.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>12.85</td>\n",
       "      <td>1.60</td>\n",
       "      <td>2.52</td>\n",
       "      <td>17.8</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2.48</td>\n",
       "      <td>2.37</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.46</td>\n",
       "      <td>3.930000</td>\n",
       "      <td>1.090</td>\n",
       "      <td>3.63</td>\n",
       "      <td>1015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>13.50</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.61</td>\n",
       "      <td>20.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.53</td>\n",
       "      <td>2.61</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1.66</td>\n",
       "      <td>3.520000</td>\n",
       "      <td>1.120</td>\n",
       "      <td>3.82</td>\n",
       "      <td>845.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>13.05</td>\n",
       "      <td>2.05</td>\n",
       "      <td>3.22</td>\n",
       "      <td>25.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>2.63</td>\n",
       "      <td>2.68</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.92</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>1.130</td>\n",
       "      <td>3.20</td>\n",
       "      <td>830.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>13.39</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.62</td>\n",
       "      <td>16.1</td>\n",
       "      <td>93.0</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.94</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.45</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>0.920</td>\n",
       "      <td>3.22</td>\n",
       "      <td>1195.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>13.30</td>\n",
       "      <td>1.72</td>\n",
       "      <td>2.14</td>\n",
       "      <td>17.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.35</td>\n",
       "      <td>3.950000</td>\n",
       "      <td>1.020</td>\n",
       "      <td>2.77</td>\n",
       "      <td>1285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>13.87</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.80</td>\n",
       "      <td>19.4</td>\n",
       "      <td>107.0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>2.97</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1.76</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.250</td>\n",
       "      <td>3.40</td>\n",
       "      <td>915.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>14.02</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2.21</td>\n",
       "      <td>16.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.33</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.98</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>1.040</td>\n",
       "      <td>3.59</td>\n",
       "      <td>1035.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>13.73</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.70</td>\n",
       "      <td>22.5</td>\n",
       "      <td>101.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.25</td>\n",
       "      <td>0.29</td>\n",
       "      <td>2.38</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>1.190</td>\n",
       "      <td>2.71</td>\n",
       "      <td>1285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>13.58</td>\n",
       "      <td>1.66</td>\n",
       "      <td>2.36</td>\n",
       "      <td>19.1</td>\n",
       "      <td>106.0</td>\n",
       "      <td>2.86</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.95</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>1.090</td>\n",
       "      <td>2.88</td>\n",
       "      <td>1515.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>13.68</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2.36</td>\n",
       "      <td>17.2</td>\n",
       "      <td>104.0</td>\n",
       "      <td>2.42</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.97</td>\n",
       "      <td>3.840000</td>\n",
       "      <td>1.230</td>\n",
       "      <td>2.87</td>\n",
       "      <td>990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>13.76</td>\n",
       "      <td>1.53</td>\n",
       "      <td>2.70</td>\n",
       "      <td>19.5</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>2.74</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.35</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>1.250</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>13.51</td>\n",
       "      <td>1.80</td>\n",
       "      <td>2.65</td>\n",
       "      <td>19.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>2.53</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.54</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>1.100</td>\n",
       "      <td>2.87</td>\n",
       "      <td>1095.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>13.48</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.41</td>\n",
       "      <td>20.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.70</td>\n",
       "      <td>2.98</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.86</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.040</td>\n",
       "      <td>3.47</td>\n",
       "      <td>920.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>13.28</td>\n",
       "      <td>1.64</td>\n",
       "      <td>2.84</td>\n",
       "      <td>15.5</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.68</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.36</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>1.090</td>\n",
       "      <td>2.78</td>\n",
       "      <td>880.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>13.05</td>\n",
       "      <td>1.65</td>\n",
       "      <td>2.55</td>\n",
       "      <td>18.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.43</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.44</td>\n",
       "      <td>4.250000</td>\n",
       "      <td>1.120</td>\n",
       "      <td>2.51</td>\n",
       "      <td>1105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>13.07</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.10</td>\n",
       "      <td>15.5</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.64</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1.37</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>1.180</td>\n",
       "      <td>2.69</td>\n",
       "      <td>1020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>14.22</td>\n",
       "      <td>3.99</td>\n",
       "      <td>2.51</td>\n",
       "      <td>13.2</td>\n",
       "      <td>128.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.04</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.08</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>0.890</td>\n",
       "      <td>3.53</td>\n",
       "      <td>760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>13.56</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.31</td>\n",
       "      <td>16.2</td>\n",
       "      <td>117.0</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0.34</td>\n",
       "      <td>2.34</td>\n",
       "      <td>6.130000</td>\n",
       "      <td>0.950</td>\n",
       "      <td>3.38</td>\n",
       "      <td>795.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>13.41</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.12</td>\n",
       "      <td>18.8</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.68</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.48</td>\n",
       "      <td>4.280000</td>\n",
       "      <td>0.910</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1035.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>13.88</td>\n",
       "      <td>1.89</td>\n",
       "      <td>2.59</td>\n",
       "      <td>15.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.56</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.70</td>\n",
       "      <td>5.430000</td>\n",
       "      <td>0.880</td>\n",
       "      <td>3.56</td>\n",
       "      <td>1095.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>13.24</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.29</td>\n",
       "      <td>17.5</td>\n",
       "      <td>103.0</td>\n",
       "      <td>2.64</td>\n",
       "      <td>2.63</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.66</td>\n",
       "      <td>4.360000</td>\n",
       "      <td>0.820</td>\n",
       "      <td>3.00</td>\n",
       "      <td>680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>13.05</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.10</td>\n",
       "      <td>17.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.03</td>\n",
       "      <td>5.040000</td>\n",
       "      <td>0.880</td>\n",
       "      <td>3.35</td>\n",
       "      <td>885.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>14.21</td>\n",
       "      <td>4.04</td>\n",
       "      <td>2.44</td>\n",
       "      <td>18.9</td>\n",
       "      <td>111.0</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.65</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.240000</td>\n",
       "      <td>0.870</td>\n",
       "      <td>3.33</td>\n",
       "      <td>1080.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>14.38</td>\n",
       "      <td>3.59</td>\n",
       "      <td>2.28</td>\n",
       "      <td>16.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.17</td>\n",
       "      <td>0.27</td>\n",
       "      <td>2.19</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>1.040</td>\n",
       "      <td>3.44</td>\n",
       "      <td>1065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>13.90</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2.12</td>\n",
       "      <td>16.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>3.10</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.21</td>\n",
       "      <td>2.14</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>0.910</td>\n",
       "      <td>3.33</td>\n",
       "      <td>985.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>14.10</td>\n",
       "      <td>2.02</td>\n",
       "      <td>2.40</td>\n",
       "      <td>18.8</td>\n",
       "      <td>103.0</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2.92</td>\n",
       "      <td>0.32</td>\n",
       "      <td>2.38</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.070</td>\n",
       "      <td>2.75</td>\n",
       "      <td>1060.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>13.94</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.27</td>\n",
       "      <td>17.4</td>\n",
       "      <td>108.0</td>\n",
       "      <td>2.88</td>\n",
       "      <td>3.54</td>\n",
       "      <td>0.32</td>\n",
       "      <td>2.08</td>\n",
       "      <td>8.900000</td>\n",
       "      <td>1.120</td>\n",
       "      <td>3.10</td>\n",
       "      <td>1260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>13.05</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.04</td>\n",
       "      <td>12.4</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2.72</td>\n",
       "      <td>3.27</td>\n",
       "      <td>0.17</td>\n",
       "      <td>2.91</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>1.120</td>\n",
       "      <td>2.91</td>\n",
       "      <td>1150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>13.83</td>\n",
       "      <td>1.65</td>\n",
       "      <td>2.60</td>\n",
       "      <td>17.2</td>\n",
       "      <td>94.0</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.99</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>1.240</td>\n",
       "      <td>3.37</td>\n",
       "      <td>1265.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>13.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2.42</td>\n",
       "      <td>14.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>3.88</td>\n",
       "      <td>3.74</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.87</td>\n",
       "      <td>7.050000</td>\n",
       "      <td>1.010</td>\n",
       "      <td>3.26</td>\n",
       "      <td>1190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>13.77</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.68</td>\n",
       "      <td>17.1</td>\n",
       "      <td>115.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.79</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.68</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>1.130</td>\n",
       "      <td>2.93</td>\n",
       "      <td>1375.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>13.74</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.25</td>\n",
       "      <td>16.4</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.62</td>\n",
       "      <td>5.850000</td>\n",
       "      <td>0.920</td>\n",
       "      <td>3.20</td>\n",
       "      <td>1060.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>13.56</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.46</td>\n",
       "      <td>20.5</td>\n",
       "      <td>116.0</td>\n",
       "      <td>2.96</td>\n",
       "      <td>2.78</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.45</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>0.980</td>\n",
       "      <td>3.03</td>\n",
       "      <td>1120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>14.22</td>\n",
       "      <td>1.70</td>\n",
       "      <td>2.30</td>\n",
       "      <td>16.3</td>\n",
       "      <td>118.0</td>\n",
       "      <td>3.20</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.26</td>\n",
       "      <td>2.03</td>\n",
       "      <td>6.380000</td>\n",
       "      <td>0.940</td>\n",
       "      <td>3.31</td>\n",
       "      <td>970.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>13.29</td>\n",
       "      <td>1.97</td>\n",
       "      <td>2.68</td>\n",
       "      <td>16.8</td>\n",
       "      <td>102.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.23</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.66</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.070</td>\n",
       "      <td>2.84</td>\n",
       "      <td>1270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>13.72</td>\n",
       "      <td>1.43</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.7</td>\n",
       "      <td>108.0</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.67</td>\n",
       "      <td>0.19</td>\n",
       "      <td>2.04</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>0.890</td>\n",
       "      <td>2.87</td>\n",
       "      <td>1285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>12.37</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.36</td>\n",
       "      <td>10.6</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1.98</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>1.050</td>\n",
       "      <td>1.82</td>\n",
       "      <td>520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>12.33</td>\n",
       "      <td>1.10</td>\n",
       "      <td>2.28</td>\n",
       "      <td>16.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.05</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.41</td>\n",
       "      <td>3.270000</td>\n",
       "      <td>1.250</td>\n",
       "      <td>1.67</td>\n",
       "      <td>680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>12.64</td>\n",
       "      <td>1.36</td>\n",
       "      <td>2.02</td>\n",
       "      <td>16.8</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.02</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.62</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>0.980</td>\n",
       "      <td>1.59</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>13.67</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.92</td>\n",
       "      <td>18.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.79</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.73</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>1.230</td>\n",
       "      <td>2.46</td>\n",
       "      <td>630.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>12.37</td>\n",
       "      <td>1.13</td>\n",
       "      <td>2.16</td>\n",
       "      <td>19.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>3.10</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.87</td>\n",
       "      <td>4.450000</td>\n",
       "      <td>1.220</td>\n",
       "      <td>2.87</td>\n",
       "      <td>420.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>12.17</td>\n",
       "      <td>1.45</td>\n",
       "      <td>2.53</td>\n",
       "      <td>19.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.03</td>\n",
       "      <td>2.950000</td>\n",
       "      <td>1.450</td>\n",
       "      <td>2.23</td>\n",
       "      <td>355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>12.37</td>\n",
       "      <td>1.21</td>\n",
       "      <td>2.56</td>\n",
       "      <td>18.1</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.42</td>\n",
       "      <td>2.65</td>\n",
       "      <td>0.37</td>\n",
       "      <td>2.08</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>1.190</td>\n",
       "      <td>2.30</td>\n",
       "      <td>678.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>13.11</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.70</td>\n",
       "      <td>15.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2.98</td>\n",
       "      <td>3.18</td>\n",
       "      <td>0.26</td>\n",
       "      <td>2.28</td>\n",
       "      <td>5.300000</td>\n",
       "      <td>1.120</td>\n",
       "      <td>3.18</td>\n",
       "      <td>502.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>12.37</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1.92</td>\n",
       "      <td>19.6</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2.11</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.04</td>\n",
       "      <td>4.680000</td>\n",
       "      <td>1.120</td>\n",
       "      <td>3.48</td>\n",
       "      <td>510.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>13.34</td>\n",
       "      <td>0.94</td>\n",
       "      <td>2.36</td>\n",
       "      <td>17.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2.53</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.42</td>\n",
       "      <td>3.170000</td>\n",
       "      <td>1.020</td>\n",
       "      <td>1.93</td>\n",
       "      <td>750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>12.21</td>\n",
       "      <td>1.19</td>\n",
       "      <td>1.75</td>\n",
       "      <td>16.8</td>\n",
       "      <td>151.0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.14</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.850000</td>\n",
       "      <td>1.280</td>\n",
       "      <td>3.07</td>\n",
       "      <td>718.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>12.29</td>\n",
       "      <td>1.61</td>\n",
       "      <td>2.21</td>\n",
       "      <td>20.4</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1.46</td>\n",
       "      <td>3.050000</td>\n",
       "      <td>0.906</td>\n",
       "      <td>1.82</td>\n",
       "      <td>870.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>13.86</td>\n",
       "      <td>1.51</td>\n",
       "      <td>2.67</td>\n",
       "      <td>25.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>2.86</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.87</td>\n",
       "      <td>3.380000</td>\n",
       "      <td>1.360</td>\n",
       "      <td>3.16</td>\n",
       "      <td>410.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>13.49</td>\n",
       "      <td>1.66</td>\n",
       "      <td>2.24</td>\n",
       "      <td>24.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>1.88</td>\n",
       "      <td>1.84</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.740000</td>\n",
       "      <td>0.980</td>\n",
       "      <td>2.78</td>\n",
       "      <td>472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>12.99</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.60</td>\n",
       "      <td>30.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2.89</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.96</td>\n",
       "      <td>3.350000</td>\n",
       "      <td>1.310</td>\n",
       "      <td>3.50</td>\n",
       "      <td>985.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>11.96</td>\n",
       "      <td>1.09</td>\n",
       "      <td>2.30</td>\n",
       "      <td>21.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>3.38</td>\n",
       "      <td>2.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>1.65</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>0.990</td>\n",
       "      <td>3.13</td>\n",
       "      <td>886.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>11.66</td>\n",
       "      <td>1.88</td>\n",
       "      <td>1.92</td>\n",
       "      <td>16.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.61</td>\n",
       "      <td>1.57</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.15</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>1.230</td>\n",
       "      <td>2.14</td>\n",
       "      <td>428.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>13.03</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.71</td>\n",
       "      <td>16.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.03</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.46</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>1.190</td>\n",
       "      <td>2.48</td>\n",
       "      <td>392.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>11.84</td>\n",
       "      <td>2.89</td>\n",
       "      <td>2.23</td>\n",
       "      <td>18.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.72</td>\n",
       "      <td>1.32</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.95</td>\n",
       "      <td>2.650000</td>\n",
       "      <td>0.960</td>\n",
       "      <td>2.52</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>12.33</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.95</td>\n",
       "      <td>14.8</td>\n",
       "      <td>136.0</td>\n",
       "      <td>1.90</td>\n",
       "      <td>1.85</td>\n",
       "      <td>0.35</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>1.060</td>\n",
       "      <td>2.31</td>\n",
       "      <td>750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>12.70</td>\n",
       "      <td>3.87</td>\n",
       "      <td>2.40</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.83</td>\n",
       "      <td>2.55</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.570000</td>\n",
       "      <td>1.190</td>\n",
       "      <td>3.13</td>\n",
       "      <td>463.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>12.00</td>\n",
       "      <td>0.92</td>\n",
       "      <td>2.00</td>\n",
       "      <td>19.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2.42</td>\n",
       "      <td>2.26</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.43</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1.380</td>\n",
       "      <td>3.12</td>\n",
       "      <td>278.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>12.72</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.20</td>\n",
       "      <td>18.8</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.53</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.77</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>1.160</td>\n",
       "      <td>3.14</td>\n",
       "      <td>714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>12.08</td>\n",
       "      <td>1.13</td>\n",
       "      <td>2.51</td>\n",
       "      <td>24.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.58</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.40</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>1.310</td>\n",
       "      <td>2.72</td>\n",
       "      <td>630.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>13.05</td>\n",
       "      <td>3.86</td>\n",
       "      <td>2.32</td>\n",
       "      <td>22.5</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.62</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>0.840</td>\n",
       "      <td>2.01</td>\n",
       "      <td>515.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>11.84</td>\n",
       "      <td>0.89</td>\n",
       "      <td>2.58</td>\n",
       "      <td>18.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.21</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2.35</td>\n",
       "      <td>3.050000</td>\n",
       "      <td>0.790</td>\n",
       "      <td>3.08</td>\n",
       "      <td>520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>12.67</td>\n",
       "      <td>0.98</td>\n",
       "      <td>2.24</td>\n",
       "      <td>18.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.94</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.46</td>\n",
       "      <td>2.620000</td>\n",
       "      <td>1.230</td>\n",
       "      <td>3.16</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>12.16</td>\n",
       "      <td>1.61</td>\n",
       "      <td>2.31</td>\n",
       "      <td>22.8</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.56</td>\n",
       "      <td>2.450000</td>\n",
       "      <td>1.330</td>\n",
       "      <td>2.26</td>\n",
       "      <td>495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>11.65</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.62</td>\n",
       "      <td>26.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1.92</td>\n",
       "      <td>1.61</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.34</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>1.360</td>\n",
       "      <td>3.21</td>\n",
       "      <td>562.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>11.64</td>\n",
       "      <td>2.06</td>\n",
       "      <td>2.46</td>\n",
       "      <td>21.6</td>\n",
       "      <td>84.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.75</td>\n",
       "      <td>680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>12.08</td>\n",
       "      <td>1.33</td>\n",
       "      <td>2.30</td>\n",
       "      <td>23.6</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1.740000</td>\n",
       "      <td>1.070</td>\n",
       "      <td>3.21</td>\n",
       "      <td>625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>12.08</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2.32</td>\n",
       "      <td>18.5</td>\n",
       "      <td>81.0</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.64</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>1.080</td>\n",
       "      <td>2.27</td>\n",
       "      <td>480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>12.00</td>\n",
       "      <td>1.51</td>\n",
       "      <td>2.42</td>\n",
       "      <td>22.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.63</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>1.050</td>\n",
       "      <td>2.65</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>12.69</td>\n",
       "      <td>1.53</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.7</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.62</td>\n",
       "      <td>3.050000</td>\n",
       "      <td>0.960</td>\n",
       "      <td>2.06</td>\n",
       "      <td>495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>12.29</td>\n",
       "      <td>2.83</td>\n",
       "      <td>2.22</td>\n",
       "      <td>18.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.99</td>\n",
       "      <td>2.150000</td>\n",
       "      <td>1.150</td>\n",
       "      <td>3.30</td>\n",
       "      <td>290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>11.62</td>\n",
       "      <td>1.99</td>\n",
       "      <td>2.28</td>\n",
       "      <td>18.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>3.02</td>\n",
       "      <td>2.26</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.35</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>1.160</td>\n",
       "      <td>2.96</td>\n",
       "      <td>345.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>12.47</td>\n",
       "      <td>1.52</td>\n",
       "      <td>2.20</td>\n",
       "      <td>19.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.27</td>\n",
       "      <td>0.32</td>\n",
       "      <td>3.28</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>1.160</td>\n",
       "      <td>2.63</td>\n",
       "      <td>937.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>11.81</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2.74</td>\n",
       "      <td>21.5</td>\n",
       "      <td>134.0</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.56</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.950</td>\n",
       "      <td>2.26</td>\n",
       "      <td>625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>12.29</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.98</td>\n",
       "      <td>16.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2.55</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.230</td>\n",
       "      <td>2.74</td>\n",
       "      <td>428.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>12.37</td>\n",
       "      <td>1.07</td>\n",
       "      <td>2.10</td>\n",
       "      <td>18.5</td>\n",
       "      <td>88.0</td>\n",
       "      <td>3.52</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.95</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.040</td>\n",
       "      <td>2.77</td>\n",
       "      <td>660.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>12.29</td>\n",
       "      <td>3.17</td>\n",
       "      <td>2.21</td>\n",
       "      <td>18.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.99</td>\n",
       "      <td>0.45</td>\n",
       "      <td>2.81</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>1.420</td>\n",
       "      <td>2.83</td>\n",
       "      <td>406.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>12.08</td>\n",
       "      <td>2.08</td>\n",
       "      <td>1.70</td>\n",
       "      <td>17.5</td>\n",
       "      <td>97.0</td>\n",
       "      <td>2.23</td>\n",
       "      <td>2.17</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.40</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>1.270</td>\n",
       "      <td>2.96</td>\n",
       "      <td>710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>12.60</td>\n",
       "      <td>1.34</td>\n",
       "      <td>1.90</td>\n",
       "      <td>18.5</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.450000</td>\n",
       "      <td>1.040</td>\n",
       "      <td>2.77</td>\n",
       "      <td>562.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>12.34</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.46</td>\n",
       "      <td>21.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.56</td>\n",
       "      <td>2.11</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.31</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>0.800</td>\n",
       "      <td>3.38</td>\n",
       "      <td>438.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>11.82</td>\n",
       "      <td>1.72</td>\n",
       "      <td>1.88</td>\n",
       "      <td>19.5</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.64</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1.42</td>\n",
       "      <td>2.060000</td>\n",
       "      <td>0.940</td>\n",
       "      <td>2.44</td>\n",
       "      <td>415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>12.51</td>\n",
       "      <td>1.73</td>\n",
       "      <td>1.98</td>\n",
       "      <td>20.5</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.48</td>\n",
       "      <td>2.940000</td>\n",
       "      <td>1.040</td>\n",
       "      <td>3.57</td>\n",
       "      <td>672.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>12.42</td>\n",
       "      <td>2.55</td>\n",
       "      <td>2.27</td>\n",
       "      <td>22.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1.84</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.42</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.860</td>\n",
       "      <td>3.30</td>\n",
       "      <td>315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>12.25</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.12</td>\n",
       "      <td>19.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>2.03</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1.63</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.17</td>\n",
       "      <td>510.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>12.72</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2.28</td>\n",
       "      <td>22.5</td>\n",
       "      <td>84.0</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1.76</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.63</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>0.880</td>\n",
       "      <td>2.42</td>\n",
       "      <td>488.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>12.22</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1.94</td>\n",
       "      <td>19.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.04</td>\n",
       "      <td>0.39</td>\n",
       "      <td>2.08</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.860</td>\n",
       "      <td>3.02</td>\n",
       "      <td>312.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>11.61</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.70</td>\n",
       "      <td>20.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>2.74</td>\n",
       "      <td>2.92</td>\n",
       "      <td>0.29</td>\n",
       "      <td>2.49</td>\n",
       "      <td>2.650000</td>\n",
       "      <td>0.960</td>\n",
       "      <td>3.26</td>\n",
       "      <td>680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>11.46</td>\n",
       "      <td>3.74</td>\n",
       "      <td>1.82</td>\n",
       "      <td>19.5</td>\n",
       "      <td>107.0</td>\n",
       "      <td>3.18</td>\n",
       "      <td>2.58</td>\n",
       "      <td>0.24</td>\n",
       "      <td>3.58</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>0.750</td>\n",
       "      <td>2.81</td>\n",
       "      <td>562.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>12.52</td>\n",
       "      <td>2.43</td>\n",
       "      <td>2.17</td>\n",
       "      <td>21.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2.55</td>\n",
       "      <td>2.27</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.22</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.900</td>\n",
       "      <td>2.78</td>\n",
       "      <td>325.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>11.76</td>\n",
       "      <td>2.68</td>\n",
       "      <td>2.92</td>\n",
       "      <td>20.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2.03</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>1.230</td>\n",
       "      <td>2.50</td>\n",
       "      <td>607.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>11.41</td>\n",
       "      <td>0.74</td>\n",
       "      <td>2.50</td>\n",
       "      <td>21.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2.48</td>\n",
       "      <td>2.01</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.44</td>\n",
       "      <td>3.080000</td>\n",
       "      <td>1.100</td>\n",
       "      <td>2.31</td>\n",
       "      <td>434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>12.08</td>\n",
       "      <td>1.39</td>\n",
       "      <td>2.50</td>\n",
       "      <td>22.5</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2.56</td>\n",
       "      <td>2.29</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>0.930</td>\n",
       "      <td>3.19</td>\n",
       "      <td>385.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>11.03</td>\n",
       "      <td>1.51</td>\n",
       "      <td>2.20</td>\n",
       "      <td>21.5</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2.46</td>\n",
       "      <td>2.17</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>1.710</td>\n",
       "      <td>2.87</td>\n",
       "      <td>407.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>11.82</td>\n",
       "      <td>1.47</td>\n",
       "      <td>1.99</td>\n",
       "      <td>20.8</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>0.950</td>\n",
       "      <td>3.33</td>\n",
       "      <td>495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>12.42</td>\n",
       "      <td>1.61</td>\n",
       "      <td>2.19</td>\n",
       "      <td>22.5</td>\n",
       "      <td>108.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.09</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.61</td>\n",
       "      <td>2.060000</td>\n",
       "      <td>1.060</td>\n",
       "      <td>2.96</td>\n",
       "      <td>345.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>12.77</td>\n",
       "      <td>3.43</td>\n",
       "      <td>1.98</td>\n",
       "      <td>16.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.63</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.83</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>0.700</td>\n",
       "      <td>2.12</td>\n",
       "      <td>372.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>12.00</td>\n",
       "      <td>3.43</td>\n",
       "      <td>2.00</td>\n",
       "      <td>19.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.64</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1.87</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>0.930</td>\n",
       "      <td>3.05</td>\n",
       "      <td>564.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>11.45</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.42</td>\n",
       "      <td>20.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.90</td>\n",
       "      <td>2.79</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.83</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>0.800</td>\n",
       "      <td>3.39</td>\n",
       "      <td>625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>11.56</td>\n",
       "      <td>2.05</td>\n",
       "      <td>3.23</td>\n",
       "      <td>28.5</td>\n",
       "      <td>119.0</td>\n",
       "      <td>3.18</td>\n",
       "      <td>5.08</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.87</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.930</td>\n",
       "      <td>3.69</td>\n",
       "      <td>465.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>12.42</td>\n",
       "      <td>4.43</td>\n",
       "      <td>2.73</td>\n",
       "      <td>26.5</td>\n",
       "      <td>102.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.13</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.080000</td>\n",
       "      <td>0.920</td>\n",
       "      <td>3.12</td>\n",
       "      <td>365.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>13.05</td>\n",
       "      <td>5.80</td>\n",
       "      <td>2.13</td>\n",
       "      <td>21.5</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2.62</td>\n",
       "      <td>2.65</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.730</td>\n",
       "      <td>3.10</td>\n",
       "      <td>380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>11.87</td>\n",
       "      <td>4.31</td>\n",
       "      <td>2.39</td>\n",
       "      <td>21.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2.86</td>\n",
       "      <td>3.03</td>\n",
       "      <td>0.21</td>\n",
       "      <td>2.91</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>0.750</td>\n",
       "      <td>3.64</td>\n",
       "      <td>380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>12.07</td>\n",
       "      <td>2.16</td>\n",
       "      <td>2.17</td>\n",
       "      <td>21.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.65</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.760000</td>\n",
       "      <td>0.860</td>\n",
       "      <td>3.28</td>\n",
       "      <td>378.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>12.43</td>\n",
       "      <td>1.53</td>\n",
       "      <td>2.29</td>\n",
       "      <td>21.5</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2.74</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.77</td>\n",
       "      <td>3.940000</td>\n",
       "      <td>0.690</td>\n",
       "      <td>2.84</td>\n",
       "      <td>352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>11.79</td>\n",
       "      <td>2.13</td>\n",
       "      <td>2.78</td>\n",
       "      <td>28.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2.13</td>\n",
       "      <td>2.24</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.76</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.970</td>\n",
       "      <td>2.44</td>\n",
       "      <td>466.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>12.37</td>\n",
       "      <td>1.63</td>\n",
       "      <td>2.30</td>\n",
       "      <td>24.5</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2.22</td>\n",
       "      <td>2.45</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>0.890</td>\n",
       "      <td>2.78</td>\n",
       "      <td>342.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>12.04</td>\n",
       "      <td>4.30</td>\n",
       "      <td>2.38</td>\n",
       "      <td>22.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.790</td>\n",
       "      <td>2.57</td>\n",
       "      <td>580.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>12.86</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.32</td>\n",
       "      <td>18.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.94</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>0.760</td>\n",
       "      <td>1.29</td>\n",
       "      <td>630.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>12.88</td>\n",
       "      <td>2.99</td>\n",
       "      <td>2.40</td>\n",
       "      <td>20.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.83</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>0.740</td>\n",
       "      <td>1.42</td>\n",
       "      <td>530.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>12.81</td>\n",
       "      <td>2.31</td>\n",
       "      <td>2.40</td>\n",
       "      <td>24.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.83</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>0.660</td>\n",
       "      <td>1.36</td>\n",
       "      <td>560.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>12.70</td>\n",
       "      <td>3.55</td>\n",
       "      <td>2.36</td>\n",
       "      <td>21.5</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.84</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.780</td>\n",
       "      <td>1.29</td>\n",
       "      <td>600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>12.51</td>\n",
       "      <td>1.24</td>\n",
       "      <td>2.25</td>\n",
       "      <td>17.5</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.450000</td>\n",
       "      <td>0.750</td>\n",
       "      <td>1.51</td>\n",
       "      <td>650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>12.60</td>\n",
       "      <td>2.46</td>\n",
       "      <td>2.20</td>\n",
       "      <td>18.5</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1.62</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.94</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.730</td>\n",
       "      <td>1.58</td>\n",
       "      <td>695.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>12.25</td>\n",
       "      <td>4.72</td>\n",
       "      <td>2.54</td>\n",
       "      <td>21.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.80</td>\n",
       "      <td>3.850000</td>\n",
       "      <td>0.750</td>\n",
       "      <td>1.27</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>12.53</td>\n",
       "      <td>5.51</td>\n",
       "      <td>2.64</td>\n",
       "      <td>25.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>1.79</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.10</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.820</td>\n",
       "      <td>1.69</td>\n",
       "      <td>515.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>13.49</td>\n",
       "      <td>3.59</td>\n",
       "      <td>2.19</td>\n",
       "      <td>19.5</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1.62</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.88</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>0.810</td>\n",
       "      <td>1.82</td>\n",
       "      <td>580.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>12.84</td>\n",
       "      <td>2.96</td>\n",
       "      <td>2.61</td>\n",
       "      <td>24.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.32</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.81</td>\n",
       "      <td>4.920000</td>\n",
       "      <td>0.890</td>\n",
       "      <td>2.15</td>\n",
       "      <td>590.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>12.93</td>\n",
       "      <td>2.81</td>\n",
       "      <td>2.70</td>\n",
       "      <td>21.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>1.54</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.75</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>0.770</td>\n",
       "      <td>2.31</td>\n",
       "      <td>600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>13.36</td>\n",
       "      <td>2.56</td>\n",
       "      <td>2.35</td>\n",
       "      <td>20.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.64</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.700</td>\n",
       "      <td>2.47</td>\n",
       "      <td>780.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>13.52</td>\n",
       "      <td>3.17</td>\n",
       "      <td>2.72</td>\n",
       "      <td>23.5</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.55</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>0.890</td>\n",
       "      <td>2.06</td>\n",
       "      <td>520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>13.62</td>\n",
       "      <td>4.95</td>\n",
       "      <td>2.35</td>\n",
       "      <td>20.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.02</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>0.910</td>\n",
       "      <td>2.05</td>\n",
       "      <td>550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>12.25</td>\n",
       "      <td>3.88</td>\n",
       "      <td>2.20</td>\n",
       "      <td>18.5</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.14</td>\n",
       "      <td>8.210000</td>\n",
       "      <td>0.650</td>\n",
       "      <td>2.00</td>\n",
       "      <td>855.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>13.16</td>\n",
       "      <td>3.57</td>\n",
       "      <td>2.15</td>\n",
       "      <td>21.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.30</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.600</td>\n",
       "      <td>1.68</td>\n",
       "      <td>830.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>13.88</td>\n",
       "      <td>5.04</td>\n",
       "      <td>2.23</td>\n",
       "      <td>20.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.68</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>0.580</td>\n",
       "      <td>1.33</td>\n",
       "      <td>415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>12.87</td>\n",
       "      <td>4.61</td>\n",
       "      <td>2.48</td>\n",
       "      <td>21.5</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.86</td>\n",
       "      <td>7.650000</td>\n",
       "      <td>0.540</td>\n",
       "      <td>1.86</td>\n",
       "      <td>625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>13.32</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.38</td>\n",
       "      <td>21.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1.93</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.25</td>\n",
       "      <td>8.420000</td>\n",
       "      <td>0.550</td>\n",
       "      <td>1.62</td>\n",
       "      <td>650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>13.08</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.36</td>\n",
       "      <td>21.5</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.14</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>0.570</td>\n",
       "      <td>1.33</td>\n",
       "      <td>550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>13.50</td>\n",
       "      <td>3.12</td>\n",
       "      <td>2.62</td>\n",
       "      <td>24.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.57</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.25</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>0.590</td>\n",
       "      <td>1.30</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>12.79</td>\n",
       "      <td>2.67</td>\n",
       "      <td>2.48</td>\n",
       "      <td>22.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.26</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>0.480</td>\n",
       "      <td>1.47</td>\n",
       "      <td>480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>13.11</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>116.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.56</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.610</td>\n",
       "      <td>1.33</td>\n",
       "      <td>425.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>13.23</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2.28</td>\n",
       "      <td>18.5</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.87</td>\n",
       "      <td>10.520000</td>\n",
       "      <td>0.560</td>\n",
       "      <td>1.51</td>\n",
       "      <td>675.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>12.58</td>\n",
       "      <td>1.29</td>\n",
       "      <td>2.10</td>\n",
       "      <td>20.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.40</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>0.580</td>\n",
       "      <td>1.55</td>\n",
       "      <td>640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>13.17</td>\n",
       "      <td>5.19</td>\n",
       "      <td>2.32</td>\n",
       "      <td>22.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>1.74</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.55</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.600</td>\n",
       "      <td>1.48</td>\n",
       "      <td>725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>13.84</td>\n",
       "      <td>4.12</td>\n",
       "      <td>2.38</td>\n",
       "      <td>19.5</td>\n",
       "      <td>89.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.56</td>\n",
       "      <td>9.010000</td>\n",
       "      <td>0.570</td>\n",
       "      <td>1.64</td>\n",
       "      <td>480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>12.45</td>\n",
       "      <td>3.03</td>\n",
       "      <td>2.64</td>\n",
       "      <td>27.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.90</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.14</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.670</td>\n",
       "      <td>1.73</td>\n",
       "      <td>880.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>14.34</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2.70</td>\n",
       "      <td>25.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2.70</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.570</td>\n",
       "      <td>1.96</td>\n",
       "      <td>660.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>13.48</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.64</td>\n",
       "      <td>22.5</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2.60</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2.29</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>0.570</td>\n",
       "      <td>1.78</td>\n",
       "      <td>620.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>12.36</td>\n",
       "      <td>3.83</td>\n",
       "      <td>2.38</td>\n",
       "      <td>21.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.04</td>\n",
       "      <td>7.650000</td>\n",
       "      <td>0.560</td>\n",
       "      <td>1.58</td>\n",
       "      <td>520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>13.69</td>\n",
       "      <td>3.26</td>\n",
       "      <td>2.54</td>\n",
       "      <td>20.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>1.83</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.80</td>\n",
       "      <td>5.880000</td>\n",
       "      <td>0.960</td>\n",
       "      <td>1.82</td>\n",
       "      <td>680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>12.85</td>\n",
       "      <td>3.27</td>\n",
       "      <td>2.58</td>\n",
       "      <td>22.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.96</td>\n",
       "      <td>5.580000</td>\n",
       "      <td>0.870</td>\n",
       "      <td>2.11</td>\n",
       "      <td>570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>12.96</td>\n",
       "      <td>3.45</td>\n",
       "      <td>2.35</td>\n",
       "      <td>18.5</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.94</td>\n",
       "      <td>5.280000</td>\n",
       "      <td>0.680</td>\n",
       "      <td>1.75</td>\n",
       "      <td>675.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>13.78</td>\n",
       "      <td>2.76</td>\n",
       "      <td>2.30</td>\n",
       "      <td>22.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.41</td>\n",
       "      <td>1.03</td>\n",
       "      <td>9.580000</td>\n",
       "      <td>0.700</td>\n",
       "      <td>1.68</td>\n",
       "      <td>615.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>13.73</td>\n",
       "      <td>4.36</td>\n",
       "      <td>2.26</td>\n",
       "      <td>22.5</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.15</td>\n",
       "      <td>6.620000</td>\n",
       "      <td>0.780</td>\n",
       "      <td>1.75</td>\n",
       "      <td>520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>13.45</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2.60</td>\n",
       "      <td>23.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.46</td>\n",
       "      <td>10.680000</td>\n",
       "      <td>0.850</td>\n",
       "      <td>1.56</td>\n",
       "      <td>695.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>12.82</td>\n",
       "      <td>3.37</td>\n",
       "      <td>2.30</td>\n",
       "      <td>19.5</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.97</td>\n",
       "      <td>10.260000</td>\n",
       "      <td>0.720</td>\n",
       "      <td>1.75</td>\n",
       "      <td>685.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>13.58</td>\n",
       "      <td>2.58</td>\n",
       "      <td>2.69</td>\n",
       "      <td>24.5</td>\n",
       "      <td>105.0</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.54</td>\n",
       "      <td>8.660000</td>\n",
       "      <td>0.740</td>\n",
       "      <td>1.80</td>\n",
       "      <td>750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>13.40</td>\n",
       "      <td>4.60</td>\n",
       "      <td>2.86</td>\n",
       "      <td>25.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.98</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.11</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>0.670</td>\n",
       "      <td>1.92</td>\n",
       "      <td>630.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>12.20</td>\n",
       "      <td>3.03</td>\n",
       "      <td>2.32</td>\n",
       "      <td>19.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.73</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>0.660</td>\n",
       "      <td>1.83</td>\n",
       "      <td>510.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>12.77</td>\n",
       "      <td>2.39</td>\n",
       "      <td>2.28</td>\n",
       "      <td>19.5</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.64</td>\n",
       "      <td>9.899999</td>\n",
       "      <td>0.570</td>\n",
       "      <td>1.63</td>\n",
       "      <td>470.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>14.16</td>\n",
       "      <td>2.51</td>\n",
       "      <td>2.48</td>\n",
       "      <td>20.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.24</td>\n",
       "      <td>9.700000</td>\n",
       "      <td>0.620</td>\n",
       "      <td>1.71</td>\n",
       "      <td>660.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>0.640</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>0.700</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>0.590</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.300000</td>\n",
       "      <td>0.600</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.610</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "5      14.20        1.76  2.45               15.2      112.0           3.27   \n",
       "6      14.39        1.87  2.45               14.6       96.0           2.50   \n",
       "7      14.06        2.15  2.61               17.6      121.0           2.60   \n",
       "8      14.83        1.64  2.17               14.0       97.0           2.80   \n",
       "9      13.86        1.35  2.27               16.0       98.0           2.98   \n",
       "10     14.10        2.16  2.30               18.0      105.0           2.95   \n",
       "11     14.12        1.48  2.32               16.8       95.0           2.20   \n",
       "12     13.75        1.73  2.41               16.0       89.0           2.60   \n",
       "13     14.75        1.73  2.39               11.4       91.0           3.10   \n",
       "14     14.38        1.87  2.38               12.0      102.0           3.30   \n",
       "15     13.63        1.81  2.70               17.2      112.0           2.85   \n",
       "16     14.30        1.92  2.72               20.0      120.0           2.80   \n",
       "17     13.83        1.57  2.62               20.0      115.0           2.95   \n",
       "18     14.19        1.59  2.48               16.5      108.0           3.30   \n",
       "19     13.64        3.10  2.56               15.2      116.0           2.70   \n",
       "20     14.06        1.63  2.28               16.0      126.0           3.00   \n",
       "21     12.93        3.80  2.65               18.6      102.0           2.41   \n",
       "22     13.71        1.86  2.36               16.6      101.0           2.61   \n",
       "23     12.85        1.60  2.52               17.8       95.0           2.48   \n",
       "24     13.50        1.81  2.61               20.0       96.0           2.53   \n",
       "25     13.05        2.05  3.22               25.0      124.0           2.63   \n",
       "26     13.39        1.77  2.62               16.1       93.0           2.85   \n",
       "27     13.30        1.72  2.14               17.0       94.0           2.40   \n",
       "28     13.87        1.90  2.80               19.4      107.0           2.95   \n",
       "29     14.02        1.68  2.21               16.0       96.0           2.65   \n",
       "30     13.73        1.50  2.70               22.5      101.0           3.00   \n",
       "31     13.58        1.66  2.36               19.1      106.0           2.86   \n",
       "32     13.68        1.83  2.36               17.2      104.0           2.42   \n",
       "33     13.76        1.53  2.70               19.5      132.0           2.95   \n",
       "34     13.51        1.80  2.65               19.0      110.0           2.35   \n",
       "35     13.48        1.81  2.41               20.5      100.0           2.70   \n",
       "36     13.28        1.64  2.84               15.5      110.0           2.60   \n",
       "37     13.05        1.65  2.55               18.0       98.0           2.45   \n",
       "38     13.07        1.50  2.10               15.5       98.0           2.40   \n",
       "39     14.22        3.99  2.51               13.2      128.0           3.00   \n",
       "40     13.56        1.71  2.31               16.2      117.0           3.15   \n",
       "41     13.41        3.84  2.12               18.8       90.0           2.45   \n",
       "42     13.88        1.89  2.59               15.0      101.0           3.25   \n",
       "43     13.24        3.98  2.29               17.5      103.0           2.64   \n",
       "44     13.05        1.77  2.10               17.0      107.0           3.00   \n",
       "45     14.21        4.04  2.44               18.9      111.0           2.85   \n",
       "46     14.38        3.59  2.28               16.0      102.0           3.25   \n",
       "47     13.90        1.68  2.12               16.0      101.0           3.10   \n",
       "48     14.10        2.02  2.40               18.8      103.0           2.75   \n",
       "49     13.94        1.73  2.27               17.4      108.0           2.88   \n",
       "50     13.05        1.73  2.04               12.4       92.0           2.72   \n",
       "51     13.83        1.65  2.60               17.2       94.0           2.45   \n",
       "52     13.82        1.75  2.42               14.0      111.0           3.88   \n",
       "53     13.77        1.90  2.68               17.1      115.0           3.00   \n",
       "54     13.74        1.67  2.25               16.4      118.0           2.60   \n",
       "55     13.56        1.73  2.46               20.5      116.0           2.96   \n",
       "56     14.22        1.70  2.30               16.3      118.0           3.20   \n",
       "57     13.29        1.97  2.68               16.8      102.0           3.00   \n",
       "58     13.72        1.43  2.50               16.7      108.0           3.40   \n",
       "59     12.37        0.94  1.36               10.6       88.0           1.98   \n",
       "60     12.33        1.10  2.28               16.0      101.0           2.05   \n",
       "61     12.64        1.36  2.02               16.8      100.0           2.02   \n",
       "62     13.67        1.25  1.92               18.0       94.0           2.10   \n",
       "63     12.37        1.13  2.16               19.0       87.0           3.50   \n",
       "64     12.17        1.45  2.53               19.0      104.0           1.89   \n",
       "65     12.37        1.21  2.56               18.1       98.0           2.42   \n",
       "66     13.11        1.01  1.70               15.0       78.0           2.98   \n",
       "67     12.37        1.17  1.92               19.6       78.0           2.11   \n",
       "68     13.34        0.94  2.36               17.0      110.0           2.53   \n",
       "69     12.21        1.19  1.75               16.8      151.0           1.85   \n",
       "70     12.29        1.61  2.21               20.4      103.0           1.10   \n",
       "71     13.86        1.51  2.67               25.0       86.0           2.95   \n",
       "72     13.49        1.66  2.24               24.0       87.0           1.88   \n",
       "73     12.99        1.67  2.60               30.0      139.0           3.30   \n",
       "74     11.96        1.09  2.30               21.0      101.0           3.38   \n",
       "75     11.66        1.88  1.92               16.0       97.0           1.61   \n",
       "76     13.03        0.90  1.71               16.0       86.0           1.95   \n",
       "77     11.84        2.89  2.23               18.0      112.0           1.72   \n",
       "78     12.33        0.99  1.95               14.8      136.0           1.90   \n",
       "79     12.70        3.87  2.40               23.0      101.0           2.83   \n",
       "80     12.00        0.92  2.00               19.0       86.0           2.42   \n",
       "81     12.72        1.81  2.20               18.8       86.0           2.20   \n",
       "82     12.08        1.13  2.51               24.0       78.0           2.00   \n",
       "83     13.05        3.86  2.32               22.5       85.0           1.65   \n",
       "84     11.84        0.89  2.58               18.0       94.0           2.20   \n",
       "85     12.67        0.98  2.24               18.0       99.0           2.20   \n",
       "86     12.16        1.61  2.31               22.8       90.0           1.78   \n",
       "87     11.65        1.67  2.62               26.0       88.0           1.92   \n",
       "88     11.64        2.06  2.46               21.6       84.0           1.95   \n",
       "89     12.08        1.33  2.30               23.6       70.0           2.20   \n",
       "90     12.08        1.83  2.32               18.5       81.0           1.60   \n",
       "91     12.00        1.51  2.42               22.0       86.0           1.45   \n",
       "92     12.69        1.53  2.26               20.7       80.0           1.38   \n",
       "93     12.29        2.83  2.22               18.0       88.0           2.45   \n",
       "94     11.62        1.99  2.28               18.0       98.0           3.02   \n",
       "95     12.47        1.52  2.20               19.0      162.0           2.50   \n",
       "96     11.81        2.12  2.74               21.5      134.0           1.60   \n",
       "97     12.29        1.41  1.98               16.0       85.0           2.55   \n",
       "98     12.37        1.07  2.10               18.5       88.0           3.52   \n",
       "99     12.29        3.17  2.21               18.0       88.0           2.85   \n",
       "100    12.08        2.08  1.70               17.5       97.0           2.23   \n",
       "101    12.60        1.34  1.90               18.5       88.0           1.45   \n",
       "102    12.34        2.45  2.46               21.0       98.0           2.56   \n",
       "103    11.82        1.72  1.88               19.5       86.0           2.50   \n",
       "104    12.51        1.73  1.98               20.5       85.0           2.20   \n",
       "105    12.42        2.55  2.27               22.0       90.0           1.68   \n",
       "106    12.25        1.73  2.12               19.0       80.0           1.65   \n",
       "107    12.72        1.75  2.28               22.5       84.0           1.38   \n",
       "108    12.22        1.29  1.94               19.0       92.0           2.36   \n",
       "109    11.61        1.35  2.70               20.0       94.0           2.74   \n",
       "110    11.46        3.74  1.82               19.5      107.0           3.18   \n",
       "111    12.52        2.43  2.17               21.0       88.0           2.55   \n",
       "112    11.76        2.68  2.92               20.0      103.0           1.75   \n",
       "113    11.41        0.74  2.50               21.0       88.0           2.48   \n",
       "114    12.08        1.39  2.50               22.5       84.0           2.56   \n",
       "115    11.03        1.51  2.20               21.5       85.0           2.46   \n",
       "116    11.82        1.47  1.99               20.8       86.0           1.98   \n",
       "117    12.42        1.61  2.19               22.5      108.0           2.00   \n",
       "118    12.77        3.43  1.98               16.0       80.0           1.63   \n",
       "119    12.00        3.43  2.00               19.0       87.0           2.00   \n",
       "120    11.45        2.40  2.42               20.0       96.0           2.90   \n",
       "121    11.56        2.05  3.23               28.5      119.0           3.18   \n",
       "122    12.42        4.43  2.73               26.5      102.0           2.20   \n",
       "123    13.05        5.80  2.13               21.5       86.0           2.62   \n",
       "124    11.87        4.31  2.39               21.0       82.0           2.86   \n",
       "125    12.07        2.16  2.17               21.0       85.0           2.60   \n",
       "126    12.43        1.53  2.29               21.5       86.0           2.74   \n",
       "127    11.79        2.13  2.78               28.5       92.0           2.13   \n",
       "128    12.37        1.63  2.30               24.5       88.0           2.22   \n",
       "129    12.04        4.30  2.38               22.0       80.0           2.10   \n",
       "130    12.86        1.35  2.32               18.0      122.0           1.51   \n",
       "131    12.88        2.99  2.40               20.0      104.0           1.30   \n",
       "132    12.81        2.31  2.40               24.0       98.0           1.15   \n",
       "133    12.70        3.55  2.36               21.5      106.0           1.70   \n",
       "134    12.51        1.24  2.25               17.5       85.0           2.00   \n",
       "135    12.60        2.46  2.20               18.5       94.0           1.62   \n",
       "136    12.25        4.72  2.54               21.0       89.0           1.38   \n",
       "137    12.53        5.51  2.64               25.0       96.0           1.79   \n",
       "138    13.49        3.59  2.19               19.5       88.0           1.62   \n",
       "139    12.84        2.96  2.61               24.0      101.0           2.32   \n",
       "140    12.93        2.81  2.70               21.0       96.0           1.54   \n",
       "141    13.36        2.56  2.35               20.0       89.0           1.40   \n",
       "142    13.52        3.17  2.72               23.5       97.0           1.55   \n",
       "143    13.62        4.95  2.35               20.0       92.0           2.00   \n",
       "144    12.25        3.88  2.20               18.5      112.0           1.38   \n",
       "145    13.16        3.57  2.15               21.0      102.0           1.50   \n",
       "146    13.88        5.04  2.23               20.0       80.0           0.98   \n",
       "147    12.87        4.61  2.48               21.5       86.0           1.70   \n",
       "148    13.32        3.24  2.38               21.5       92.0           1.93   \n",
       "149    13.08        3.90  2.36               21.5      113.0           1.41   \n",
       "150    13.50        3.12  2.62               24.0      123.0           1.40   \n",
       "151    12.79        2.67  2.48               22.0      112.0           1.48   \n",
       "152    13.11        1.90  2.75               25.5      116.0           2.20   \n",
       "153    13.23        3.30  2.28               18.5       98.0           1.80   \n",
       "154    12.58        1.29  2.10               20.0      103.0           1.48   \n",
       "155    13.17        5.19  2.32               22.0       93.0           1.74   \n",
       "156    13.84        4.12  2.38               19.5       89.0           1.80   \n",
       "157    12.45        3.03  2.64               27.0       97.0           1.90   \n",
       "158    14.34        1.68  2.70               25.0       98.0           2.80   \n",
       "159    13.48        1.67  2.64               22.5       89.0           2.60   \n",
       "160    12.36        3.83  2.38               21.0       88.0           2.30   \n",
       "161    13.69        3.26  2.54               20.0      107.0           1.83   \n",
       "162    12.85        3.27  2.58               22.0      106.0           1.65   \n",
       "163    12.96        3.45  2.35               18.5      106.0           1.39   \n",
       "164    13.78        2.76  2.30               22.0       90.0           1.35   \n",
       "165    13.73        4.36  2.26               22.5       88.0           1.28   \n",
       "166    13.45        3.70  2.60               23.0      111.0           1.70   \n",
       "167    12.82        3.37  2.30               19.5       88.0           1.48   \n",
       "168    13.58        2.58  2.69               24.5      105.0           1.55   \n",
       "169    13.40        4.60  2.86               25.0      112.0           1.98   \n",
       "170    12.20        3.03  2.32               19.0       96.0           1.25   \n",
       "171    12.77        2.39  2.28               19.5       86.0           1.39   \n",
       "172    14.16        2.51  2.48               20.0       91.0           1.68   \n",
       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
       "\n",
       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity  \\\n",
       "0          3.06                  0.28             2.29         5.640000   \n",
       "1          2.76                  0.26             1.28         4.380000   \n",
       "2          3.24                  0.30             2.81         5.680000   \n",
       "3          3.49                  0.24             2.18         7.800000   \n",
       "4          2.69                  0.39             1.82         4.320000   \n",
       "5          3.39                  0.34             1.97         6.750000   \n",
       "6          2.52                  0.30             1.98         5.250000   \n",
       "7          2.51                  0.31             1.25         5.050000   \n",
       "8          2.98                  0.29             1.98         5.200000   \n",
       "9          3.15                  0.22             1.85         7.220000   \n",
       "10         3.32                  0.22             2.38         5.750000   \n",
       "11         2.43                  0.26             1.57         5.000000   \n",
       "12         2.76                  0.29             1.81         5.600000   \n",
       "13         3.69                  0.43             2.81         5.400000   \n",
       "14         3.64                  0.29             2.96         7.500000   \n",
       "15         2.91                  0.30             1.46         7.300000   \n",
       "16         3.14                  0.33             1.97         6.200000   \n",
       "17         3.40                  0.40             1.72         6.600000   \n",
       "18         3.93                  0.32             1.86         8.700000   \n",
       "19         3.03                  0.17             1.66         5.100000   \n",
       "20         3.17                  0.24             2.10         5.650000   \n",
       "21         2.41                  0.25             1.98         4.500000   \n",
       "22         2.88                  0.27             1.69         3.800000   \n",
       "23         2.37                  0.26             1.46         3.930000   \n",
       "24         2.61                  0.28             1.66         3.520000   \n",
       "25         2.68                  0.47             1.92         3.580000   \n",
       "26         2.94                  0.34             1.45         4.800000   \n",
       "27         2.19                  0.27             1.35         3.950000   \n",
       "28         2.97                  0.37             1.76         4.500000   \n",
       "29         2.33                  0.26             1.98         4.700000   \n",
       "30         3.25                  0.29             2.38         5.700000   \n",
       "31         3.19                  0.22             1.95         6.900000   \n",
       "32         2.69                  0.42             1.97         3.840000   \n",
       "33         2.74                  0.50             1.35         5.400000   \n",
       "34         2.53                  0.29             1.54         4.200000   \n",
       "35         2.98                  0.26             1.86         5.100000   \n",
       "36         2.68                  0.34             1.36         4.600000   \n",
       "37         2.43                  0.29             1.44         4.250000   \n",
       "38         2.64                  0.28             1.37         3.700000   \n",
       "39         3.04                  0.20             2.08         5.100000   \n",
       "40         3.29                  0.34             2.34         6.130000   \n",
       "41         2.68                  0.27             1.48         4.280000   \n",
       "42         3.56                  0.17             1.70         5.430000   \n",
       "43         2.63                  0.32             1.66         4.360000   \n",
       "44         3.00                  0.28             2.03         5.040000   \n",
       "45         2.65                  0.30             1.25         5.240000   \n",
       "46         3.17                  0.27             2.19         4.900000   \n",
       "47         3.39                  0.21             2.14         6.100000   \n",
       "48         2.92                  0.32             2.38         6.200000   \n",
       "49         3.54                  0.32             2.08         8.900000   \n",
       "50         3.27                  0.17             2.91         7.200000   \n",
       "51         2.99                  0.22             2.29         5.600000   \n",
       "52         3.74                  0.32             1.87         7.050000   \n",
       "53         2.79                  0.39             1.68         6.300000   \n",
       "54         2.90                  0.21             1.62         5.850000   \n",
       "55         2.78                  0.20             2.45         6.250000   \n",
       "56         3.00                  0.26             2.03         6.380000   \n",
       "57         3.23                  0.31             1.66         6.000000   \n",
       "58         3.67                  0.19             2.04         6.800000   \n",
       "59         0.57                  0.28             0.42         1.950000   \n",
       "60         1.09                  0.63             0.41         3.270000   \n",
       "61         1.41                  0.53             0.62         5.750000   \n",
       "62         1.79                  0.32             0.73         3.800000   \n",
       "63         3.10                  0.19             1.87         4.450000   \n",
       "64         1.75                  0.45             1.03         2.950000   \n",
       "65         2.65                  0.37             2.08         4.600000   \n",
       "66         3.18                  0.26             2.28         5.300000   \n",
       "67         2.00                  0.27             1.04         4.680000   \n",
       "68         1.30                  0.55             0.42         3.170000   \n",
       "69         1.28                  0.14             2.50         2.850000   \n",
       "70         1.02                  0.37             1.46         3.050000   \n",
       "71         2.86                  0.21             1.87         3.380000   \n",
       "72         1.84                  0.27             1.03         3.740000   \n",
       "73         2.89                  0.21             1.96         3.350000   \n",
       "74         2.14                  0.13             1.65         3.210000   \n",
       "75         1.57                  0.34             1.15         3.800000   \n",
       "76         2.03                  0.24             1.46         4.600000   \n",
       "77         1.32                  0.43             0.95         2.650000   \n",
       "78         1.85                  0.35             2.76         3.400000   \n",
       "79         2.55                  0.43             1.95         2.570000   \n",
       "80         2.26                  0.30             1.43         2.500000   \n",
       "81         2.53                  0.26             1.77         3.900000   \n",
       "82         1.58                  0.40             1.40         2.200000   \n",
       "83         1.59                  0.61             1.62         4.800000   \n",
       "84         2.21                  0.22             2.35         3.050000   \n",
       "85         1.94                  0.30             1.46         2.620000   \n",
       "86         1.69                  0.43             1.56         2.450000   \n",
       "87         1.61                  0.40             1.34         2.600000   \n",
       "88         1.69                  0.48             1.35         2.800000   \n",
       "89         1.59                  0.42             1.38         1.740000   \n",
       "90         1.50                  0.52             1.64         2.400000   \n",
       "91         1.25                  0.50             1.63         3.600000   \n",
       "92         1.46                  0.58             1.62         3.050000   \n",
       "93         2.25                  0.25             1.99         2.150000   \n",
       "94         2.26                  0.17             1.35         3.250000   \n",
       "95         2.27                  0.32             3.28         2.600000   \n",
       "96         0.99                  0.14             1.56         2.500000   \n",
       "97         2.50                  0.29             1.77         2.900000   \n",
       "98         3.75                  0.24             1.95         4.500000   \n",
       "99         2.99                  0.45             2.81         2.300000   \n",
       "100        2.17                  0.26             1.40         3.300000   \n",
       "101        1.36                  0.29             1.35         2.450000   \n",
       "102        2.11                  0.34             1.31         2.800000   \n",
       "103        1.64                  0.37             1.42         2.060000   \n",
       "104        1.92                  0.32             1.48         2.940000   \n",
       "105        1.84                  0.66             1.42         2.700000   \n",
       "106        2.03                  0.37             1.63         3.400000   \n",
       "107        1.76                  0.48             1.63         3.300000   \n",
       "108        2.04                  0.39             2.08         2.700000   \n",
       "109        2.92                  0.29             2.49         2.650000   \n",
       "110        2.58                  0.24             3.58         2.900000   \n",
       "111        2.27                  0.26             1.22         2.000000   \n",
       "112        2.03                  0.60             1.05         3.800000   \n",
       "113        2.01                  0.42             1.44         3.080000   \n",
       "114        2.29                  0.43             1.04         2.900000   \n",
       "115        2.17                  0.52             2.01         1.900000   \n",
       "116        1.60                  0.30             1.53         1.950000   \n",
       "117        2.09                  0.34             1.61         2.060000   \n",
       "118        1.25                  0.43             0.83         3.400000   \n",
       "119        1.64                  0.37             1.87         1.280000   \n",
       "120        2.79                  0.32             1.83         3.250000   \n",
       "121        5.08                  0.47             1.87         6.000000   \n",
       "122        2.13                  0.43             1.71         2.080000   \n",
       "123        2.65                  0.30             2.01         2.600000   \n",
       "124        3.03                  0.21             2.91         2.800000   \n",
       "125        2.65                  0.37             1.35         2.760000   \n",
       "126        3.15                  0.39             1.77         3.940000   \n",
       "127        2.24                  0.58             1.76         3.000000   \n",
       "128        2.45                  0.40             1.90         2.120000   \n",
       "129        1.75                  0.42             1.35         2.600000   \n",
       "130        1.25                  0.21             0.94         4.100000   \n",
       "131        1.22                  0.24             0.83         5.400000   \n",
       "132        1.09                  0.27             0.83         5.700000   \n",
       "133        1.20                  0.17             0.84         5.000000   \n",
       "134        0.58                  0.60             1.25         5.450000   \n",
       "135        0.66                  0.63             0.94         7.100000   \n",
       "136        0.47                  0.53             0.80         3.850000   \n",
       "137        0.60                  0.63             1.10         5.000000   \n",
       "138        0.48                  0.58             0.88         5.700000   \n",
       "139        0.60                  0.53             0.81         4.920000   \n",
       "140        0.50                  0.53             0.75         4.600000   \n",
       "141        0.50                  0.37             0.64         5.600000   \n",
       "142        0.52                  0.50             0.55         4.350000   \n",
       "143        0.80                  0.47             1.02         4.400000   \n",
       "144        0.78                  0.29             1.14         8.210000   \n",
       "145        0.55                  0.43             1.30         4.000000   \n",
       "146        0.34                  0.40             0.68         4.900000   \n",
       "147        0.65                  0.47             0.86         7.650000   \n",
       "148        0.76                  0.45             1.25         8.420000   \n",
       "149        1.39                  0.34             1.14         9.400000   \n",
       "150        1.57                  0.22             1.25         8.600000   \n",
       "151        1.36                  0.24             1.26        10.800000   \n",
       "152        1.28                  0.26             1.56         7.100000   \n",
       "153        0.83                  0.61             1.87        10.520000   \n",
       "154        0.58                  0.53             1.40         7.600000   \n",
       "155        0.63                  0.61             1.55         7.900000   \n",
       "156        0.83                  0.48             1.56         9.010000   \n",
       "157        0.58                  0.63             1.14         7.500000   \n",
       "158        1.31                  0.53             2.70        13.000000   \n",
       "159        1.10                  0.52             2.29        11.750000   \n",
       "160        0.92                  0.50             1.04         7.650000   \n",
       "161        0.56                  0.50             0.80         5.880000   \n",
       "162        0.60                  0.60             0.96         5.580000   \n",
       "163        0.70                  0.40             0.94         5.280000   \n",
       "164        0.68                  0.41             1.03         9.580000   \n",
       "165        0.47                  0.52             1.15         6.620000   \n",
       "166        0.92                  0.43             1.46        10.680000   \n",
       "167        0.66                  0.40             0.97        10.260000   \n",
       "168        0.84                  0.39             1.54         8.660000   \n",
       "169        0.96                  0.27             1.11         8.500000   \n",
       "170        0.49                  0.40             0.73         5.500000   \n",
       "171        0.51                  0.48             0.64         9.899999   \n",
       "172        0.70                  0.44             1.24         9.700000   \n",
       "173        0.61                  0.52             1.06         7.700000   \n",
       "174        0.75                  0.43             1.41         7.300000   \n",
       "175        0.69                  0.43             1.35        10.200000   \n",
       "176        0.68                  0.53             1.46         9.300000   \n",
       "177        0.76                  0.56             1.35         9.200000   \n",
       "\n",
       "       hue  od280/od315_of_diluted_wines  proline  \n",
       "0    1.040                          3.92   1065.0  \n",
       "1    1.050                          3.40   1050.0  \n",
       "2    1.030                          3.17   1185.0  \n",
       "3    0.860                          3.45   1480.0  \n",
       "4    1.040                          2.93    735.0  \n",
       "5    1.050                          2.85   1450.0  \n",
       "6    1.020                          3.58   1290.0  \n",
       "7    1.060                          3.58   1295.0  \n",
       "8    1.080                          2.85   1045.0  \n",
       "9    1.010                          3.55   1045.0  \n",
       "10   1.250                          3.17   1510.0  \n",
       "11   1.170                          2.82   1280.0  \n",
       "12   1.150                          2.90   1320.0  \n",
       "13   1.250                          2.73   1150.0  \n",
       "14   1.200                          3.00   1547.0  \n",
       "15   1.280                          2.88   1310.0  \n",
       "16   1.070                          2.65   1280.0  \n",
       "17   1.130                          2.57   1130.0  \n",
       "18   1.230                          2.82   1680.0  \n",
       "19   0.960                          3.36    845.0  \n",
       "20   1.090                          3.71    780.0  \n",
       "21   1.030                          3.52    770.0  \n",
       "22   1.110                          4.00   1035.0  \n",
       "23   1.090                          3.63   1015.0  \n",
       "24   1.120                          3.82    845.0  \n",
       "25   1.130                          3.20    830.0  \n",
       "26   0.920                          3.22   1195.0  \n",
       "27   1.020                          2.77   1285.0  \n",
       "28   1.250                          3.40    915.0  \n",
       "29   1.040                          3.59   1035.0  \n",
       "30   1.190                          2.71   1285.0  \n",
       "31   1.090                          2.88   1515.0  \n",
       "32   1.230                          2.87    990.0  \n",
       "33   1.250                          3.00   1235.0  \n",
       "34   1.100                          2.87   1095.0  \n",
       "35   1.040                          3.47    920.0  \n",
       "36   1.090                          2.78    880.0  \n",
       "37   1.120                          2.51   1105.0  \n",
       "38   1.180                          2.69   1020.0  \n",
       "39   0.890                          3.53    760.0  \n",
       "40   0.950                          3.38    795.0  \n",
       "41   0.910                          3.00   1035.0  \n",
       "42   0.880                          3.56   1095.0  \n",
       "43   0.820                          3.00    680.0  \n",
       "44   0.880                          3.35    885.0  \n",
       "45   0.870                          3.33   1080.0  \n",
       "46   1.040                          3.44   1065.0  \n",
       "47   0.910                          3.33    985.0  \n",
       "48   1.070                          2.75   1060.0  \n",
       "49   1.120                          3.10   1260.0  \n",
       "50   1.120                          2.91   1150.0  \n",
       "51   1.240                          3.37   1265.0  \n",
       "52   1.010                          3.26   1190.0  \n",
       "53   1.130                          2.93   1375.0  \n",
       "54   0.920                          3.20   1060.0  \n",
       "55   0.980                          3.03   1120.0  \n",
       "56   0.940                          3.31    970.0  \n",
       "57   1.070                          2.84   1270.0  \n",
       "58   0.890                          2.87   1285.0  \n",
       "59   1.050                          1.82    520.0  \n",
       "60   1.250                          1.67    680.0  \n",
       "61   0.980                          1.59    450.0  \n",
       "62   1.230                          2.46    630.0  \n",
       "63   1.220                          2.87    420.0  \n",
       "64   1.450                          2.23    355.0  \n",
       "65   1.190                          2.30    678.0  \n",
       "66   1.120                          3.18    502.0  \n",
       "67   1.120                          3.48    510.0  \n",
       "68   1.020                          1.93    750.0  \n",
       "69   1.280                          3.07    718.0  \n",
       "70   0.906                          1.82    870.0  \n",
       "71   1.360                          3.16    410.0  \n",
       "72   0.980                          2.78    472.0  \n",
       "73   1.310                          3.50    985.0  \n",
       "74   0.990                          3.13    886.0  \n",
       "75   1.230                          2.14    428.0  \n",
       "76   1.190                          2.48    392.0  \n",
       "77   0.960                          2.52    500.0  \n",
       "78   1.060                          2.31    750.0  \n",
       "79   1.190                          3.13    463.0  \n",
       "80   1.380                          3.12    278.0  \n",
       "81   1.160                          3.14    714.0  \n",
       "82   1.310                          2.72    630.0  \n",
       "83   0.840                          2.01    515.0  \n",
       "84   0.790                          3.08    520.0  \n",
       "85   1.230                          3.16    450.0  \n",
       "86   1.330                          2.26    495.0  \n",
       "87   1.360                          3.21    562.0  \n",
       "88   1.000                          2.75    680.0  \n",
       "89   1.070                          3.21    625.0  \n",
       "90   1.080                          2.27    480.0  \n",
       "91   1.050                          2.65    450.0  \n",
       "92   0.960                          2.06    495.0  \n",
       "93   1.150                          3.30    290.0  \n",
       "94   1.160                          2.96    345.0  \n",
       "95   1.160                          2.63    937.0  \n",
       "96   0.950                          2.26    625.0  \n",
       "97   1.230                          2.74    428.0  \n",
       "98   1.040                          2.77    660.0  \n",
       "99   1.420                          2.83    406.0  \n",
       "100  1.270                          2.96    710.0  \n",
       "101  1.040                          2.77    562.0  \n",
       "102  0.800                          3.38    438.0  \n",
       "103  0.940                          2.44    415.0  \n",
       "104  1.040                          3.57    672.0  \n",
       "105  0.860                          3.30    315.0  \n",
       "106  1.000                          3.17    510.0  \n",
       "107  0.880                          2.42    488.0  \n",
       "108  0.860                          3.02    312.0  \n",
       "109  0.960                          3.26    680.0  \n",
       "110  0.750                          2.81    562.0  \n",
       "111  0.900                          2.78    325.0  \n",
       "112  1.230                          2.50    607.0  \n",
       "113  1.100                          2.31    434.0  \n",
       "114  0.930                          3.19    385.0  \n",
       "115  1.710                          2.87    407.0  \n",
       "116  0.950                          3.33    495.0  \n",
       "117  1.060                          2.96    345.0  \n",
       "118  0.700                          2.12    372.0  \n",
       "119  0.930                          3.05    564.0  \n",
       "120  0.800                          3.39    625.0  \n",
       "121  0.930                          3.69    465.0  \n",
       "122  0.920                          3.12    365.0  \n",
       "123  0.730                          3.10    380.0  \n",
       "124  0.750                          3.64    380.0  \n",
       "125  0.860                          3.28    378.0  \n",
       "126  0.690                          2.84    352.0  \n",
       "127  0.970                          2.44    466.0  \n",
       "128  0.890                          2.78    342.0  \n",
       "129  0.790                          2.57    580.0  \n",
       "130  0.760                          1.29    630.0  \n",
       "131  0.740                          1.42    530.0  \n",
       "132  0.660                          1.36    560.0  \n",
       "133  0.780                          1.29    600.0  \n",
       "134  0.750                          1.51    650.0  \n",
       "135  0.730                          1.58    695.0  \n",
       "136  0.750                          1.27    720.0  \n",
       "137  0.820                          1.69    515.0  \n",
       "138  0.810                          1.82    580.0  \n",
       "139  0.890                          2.15    590.0  \n",
       "140  0.770                          2.31    600.0  \n",
       "141  0.700                          2.47    780.0  \n",
       "142  0.890                          2.06    520.0  \n",
       "143  0.910                          2.05    550.0  \n",
       "144  0.650                          2.00    855.0  \n",
       "145  0.600                          1.68    830.0  \n",
       "146  0.580                          1.33    415.0  \n",
       "147  0.540                          1.86    625.0  \n",
       "148  0.550                          1.62    650.0  \n",
       "149  0.570                          1.33    550.0  \n",
       "150  0.590                          1.30    500.0  \n",
       "151  0.480                          1.47    480.0  \n",
       "152  0.610                          1.33    425.0  \n",
       "153  0.560                          1.51    675.0  \n",
       "154  0.580                          1.55    640.0  \n",
       "155  0.600                          1.48    725.0  \n",
       "156  0.570                          1.64    480.0  \n",
       "157  0.670                          1.73    880.0  \n",
       "158  0.570                          1.96    660.0  \n",
       "159  0.570                          1.78    620.0  \n",
       "160  0.560                          1.58    520.0  \n",
       "161  0.960                          1.82    680.0  \n",
       "162  0.870                          2.11    570.0  \n",
       "163  0.680                          1.75    675.0  \n",
       "164  0.700                          1.68    615.0  \n",
       "165  0.780                          1.75    520.0  \n",
       "166  0.850                          1.56    695.0  \n",
       "167  0.720                          1.75    685.0  \n",
       "168  0.740                          1.80    750.0  \n",
       "169  0.670                          1.92    630.0  \n",
       "170  0.660                          1.83    510.0  \n",
       "171  0.570                          1.63    470.0  \n",
       "172  0.620                          1.71    660.0  \n",
       "173  0.640                          1.74    740.0  \n",
       "174  0.700                          1.56    750.0  \n",
       "175  0.590                          1.56    835.0  \n",
       "176  0.600                          1.62    840.0  \n",
       "177  0.610                          1.60    560.0  "
      ]
     },
     "execution_count": 50,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winedf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ad6z8jQq-zYI"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "betterwinedf = pd.DataFrame(data= np.c_[wine['data'], wine['target']],\n",
    "                     columns= wine['feature_names'] + ['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LI_kibYx-6SG"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.640000</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.380000</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.680000</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.320000</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.45</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112.0</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>1.05</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1450.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14.39</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.45</td>\n",
       "      <td>14.6</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.52</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.98</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>1.02</td>\n",
       "      <td>3.58</td>\n",
       "      <td>1290.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14.06</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2.61</td>\n",
       "      <td>17.6</td>\n",
       "      <td>121.0</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.51</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.050000</td>\n",
       "      <td>1.06</td>\n",
       "      <td>3.58</td>\n",
       "      <td>1295.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14.83</td>\n",
       "      <td>1.64</td>\n",
       "      <td>2.17</td>\n",
       "      <td>14.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.98</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.98</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>1.08</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1045.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13.86</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.27</td>\n",
       "      <td>16.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.98</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.85</td>\n",
       "      <td>7.220000</td>\n",
       "      <td>1.01</td>\n",
       "      <td>3.55</td>\n",
       "      <td>1045.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14.10</td>\n",
       "      <td>2.16</td>\n",
       "      <td>2.30</td>\n",
       "      <td>18.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>3.32</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2.38</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1510.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14.12</td>\n",
       "      <td>1.48</td>\n",
       "      <td>2.32</td>\n",
       "      <td>16.8</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.43</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.57</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.17</td>\n",
       "      <td>2.82</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.75</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.41</td>\n",
       "      <td>16.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.81</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>1.15</td>\n",
       "      <td>2.90</td>\n",
       "      <td>1320.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.75</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.39</td>\n",
       "      <td>11.4</td>\n",
       "      <td>91.0</td>\n",
       "      <td>3.10</td>\n",
       "      <td>3.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.73</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.38</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.38</td>\n",
       "      <td>12.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.64</td>\n",
       "      <td>0.29</td>\n",
       "      <td>2.96</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>1.20</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1547.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>13.63</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.70</td>\n",
       "      <td>17.2</td>\n",
       "      <td>112.0</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.91</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.46</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>1.28</td>\n",
       "      <td>2.88</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>14.30</td>\n",
       "      <td>1.92</td>\n",
       "      <td>2.72</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.14</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.07</td>\n",
       "      <td>2.65</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>13.83</td>\n",
       "      <td>1.57</td>\n",
       "      <td>2.62</td>\n",
       "      <td>20.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>3.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.72</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>1.13</td>\n",
       "      <td>2.57</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>14.19</td>\n",
       "      <td>1.59</td>\n",
       "      <td>2.48</td>\n",
       "      <td>16.5</td>\n",
       "      <td>108.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.93</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.86</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>1.23</td>\n",
       "      <td>2.82</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>13.64</td>\n",
       "      <td>3.10</td>\n",
       "      <td>2.56</td>\n",
       "      <td>15.2</td>\n",
       "      <td>116.0</td>\n",
       "      <td>2.70</td>\n",
       "      <td>3.03</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.66</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>0.96</td>\n",
       "      <td>3.36</td>\n",
       "      <td>845.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>14.06</td>\n",
       "      <td>1.63</td>\n",
       "      <td>2.28</td>\n",
       "      <td>16.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.17</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.10</td>\n",
       "      <td>5.650000</td>\n",
       "      <td>1.09</td>\n",
       "      <td>3.71</td>\n",
       "      <td>780.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>12.93</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.65</td>\n",
       "      <td>18.6</td>\n",
       "      <td>102.0</td>\n",
       "      <td>2.41</td>\n",
       "      <td>2.41</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.98</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.52</td>\n",
       "      <td>770.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>13.71</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2.36</td>\n",
       "      <td>16.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.61</td>\n",
       "      <td>2.88</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.69</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>1.11</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>12.85</td>\n",
       "      <td>1.60</td>\n",
       "      <td>2.52</td>\n",
       "      <td>17.8</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2.48</td>\n",
       "      <td>2.37</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.46</td>\n",
       "      <td>3.930000</td>\n",
       "      <td>1.09</td>\n",
       "      <td>3.63</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>13.50</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.61</td>\n",
       "      <td>20.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.53</td>\n",
       "      <td>2.61</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1.66</td>\n",
       "      <td>3.520000</td>\n",
       "      <td>1.12</td>\n",
       "      <td>3.82</td>\n",
       "      <td>845.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>13.05</td>\n",
       "      <td>2.05</td>\n",
       "      <td>3.22</td>\n",
       "      <td>25.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>2.63</td>\n",
       "      <td>2.68</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.92</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>1.13</td>\n",
       "      <td>3.20</td>\n",
       "      <td>830.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>13.39</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.62</td>\n",
       "      <td>16.1</td>\n",
       "      <td>93.0</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.94</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.45</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>0.92</td>\n",
       "      <td>3.22</td>\n",
       "      <td>1195.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>13.30</td>\n",
       "      <td>1.72</td>\n",
       "      <td>2.14</td>\n",
       "      <td>17.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.35</td>\n",
       "      <td>3.950000</td>\n",
       "      <td>1.02</td>\n",
       "      <td>2.77</td>\n",
       "      <td>1285.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>13.87</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.80</td>\n",
       "      <td>19.4</td>\n",
       "      <td>107.0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>2.97</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1.76</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3.40</td>\n",
       "      <td>915.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>14.02</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2.21</td>\n",
       "      <td>16.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.33</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.98</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.59</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>13.32</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.38</td>\n",
       "      <td>21.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1.93</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.25</td>\n",
       "      <td>8.420000</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1.62</td>\n",
       "      <td>650.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>13.08</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.36</td>\n",
       "      <td>21.5</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.14</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.33</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>13.50</td>\n",
       "      <td>3.12</td>\n",
       "      <td>2.62</td>\n",
       "      <td>24.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.57</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.25</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.30</td>\n",
       "      <td>500.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>12.79</td>\n",
       "      <td>2.67</td>\n",
       "      <td>2.48</td>\n",
       "      <td>22.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.26</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.47</td>\n",
       "      <td>480.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>13.11</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>116.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.56</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.33</td>\n",
       "      <td>425.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>13.23</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2.28</td>\n",
       "      <td>18.5</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.87</td>\n",
       "      <td>10.520000</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.51</td>\n",
       "      <td>675.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>12.58</td>\n",
       "      <td>1.29</td>\n",
       "      <td>2.10</td>\n",
       "      <td>20.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.40</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.55</td>\n",
       "      <td>640.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>13.17</td>\n",
       "      <td>5.19</td>\n",
       "      <td>2.32</td>\n",
       "      <td>22.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>1.74</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.55</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.48</td>\n",
       "      <td>725.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>13.84</td>\n",
       "      <td>4.12</td>\n",
       "      <td>2.38</td>\n",
       "      <td>19.5</td>\n",
       "      <td>89.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.56</td>\n",
       "      <td>9.010000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.64</td>\n",
       "      <td>480.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>12.45</td>\n",
       "      <td>3.03</td>\n",
       "      <td>2.64</td>\n",
       "      <td>27.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.90</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.14</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.73</td>\n",
       "      <td>880.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>14.34</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2.70</td>\n",
       "      <td>25.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2.70</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.96</td>\n",
       "      <td>660.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>13.48</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.64</td>\n",
       "      <td>22.5</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2.60</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2.29</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.78</td>\n",
       "      <td>620.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>12.36</td>\n",
       "      <td>3.83</td>\n",
       "      <td>2.38</td>\n",
       "      <td>21.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.04</td>\n",
       "      <td>7.650000</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.58</td>\n",
       "      <td>520.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>13.69</td>\n",
       "      <td>3.26</td>\n",
       "      <td>2.54</td>\n",
       "      <td>20.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>1.83</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.80</td>\n",
       "      <td>5.880000</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.82</td>\n",
       "      <td>680.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>12.85</td>\n",
       "      <td>3.27</td>\n",
       "      <td>2.58</td>\n",
       "      <td>22.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.96</td>\n",
       "      <td>5.580000</td>\n",
       "      <td>0.87</td>\n",
       "      <td>2.11</td>\n",
       "      <td>570.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>12.96</td>\n",
       "      <td>3.45</td>\n",
       "      <td>2.35</td>\n",
       "      <td>18.5</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.94</td>\n",
       "      <td>5.280000</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.75</td>\n",
       "      <td>675.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>13.78</td>\n",
       "      <td>2.76</td>\n",
       "      <td>2.30</td>\n",
       "      <td>22.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.41</td>\n",
       "      <td>1.03</td>\n",
       "      <td>9.580000</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.68</td>\n",
       "      <td>615.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>13.73</td>\n",
       "      <td>4.36</td>\n",
       "      <td>2.26</td>\n",
       "      <td>22.5</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.15</td>\n",
       "      <td>6.620000</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1.75</td>\n",
       "      <td>520.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>13.45</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2.60</td>\n",
       "      <td>23.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.46</td>\n",
       "      <td>10.680000</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.56</td>\n",
       "      <td>695.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>12.82</td>\n",
       "      <td>3.37</td>\n",
       "      <td>2.30</td>\n",
       "      <td>19.5</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.97</td>\n",
       "      <td>10.260000</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.75</td>\n",
       "      <td>685.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>13.58</td>\n",
       "      <td>2.58</td>\n",
       "      <td>2.69</td>\n",
       "      <td>24.5</td>\n",
       "      <td>105.0</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.54</td>\n",
       "      <td>8.660000</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1.80</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>13.40</td>\n",
       "      <td>4.60</td>\n",
       "      <td>2.86</td>\n",
       "      <td>25.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.98</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.11</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.92</td>\n",
       "      <td>630.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>12.20</td>\n",
       "      <td>3.03</td>\n",
       "      <td>2.32</td>\n",
       "      <td>19.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.73</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.83</td>\n",
       "      <td>510.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>12.77</td>\n",
       "      <td>2.39</td>\n",
       "      <td>2.28</td>\n",
       "      <td>19.5</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.64</td>\n",
       "      <td>9.899999</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.63</td>\n",
       "      <td>470.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>14.16</td>\n",
       "      <td>2.51</td>\n",
       "      <td>2.48</td>\n",
       "      <td>20.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.24</td>\n",
       "      <td>9.700000</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.71</td>\n",
       "      <td>660.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.300000</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "5      14.20        1.76  2.45               15.2      112.0           3.27   \n",
       "6      14.39        1.87  2.45               14.6       96.0           2.50   \n",
       "7      14.06        2.15  2.61               17.6      121.0           2.60   \n",
       "8      14.83        1.64  2.17               14.0       97.0           2.80   \n",
       "9      13.86        1.35  2.27               16.0       98.0           2.98   \n",
       "10     14.10        2.16  2.30               18.0      105.0           2.95   \n",
       "11     14.12        1.48  2.32               16.8       95.0           2.20   \n",
       "12     13.75        1.73  2.41               16.0       89.0           2.60   \n",
       "13     14.75        1.73  2.39               11.4       91.0           3.10   \n",
       "14     14.38        1.87  2.38               12.0      102.0           3.30   \n",
       "15     13.63        1.81  2.70               17.2      112.0           2.85   \n",
       "16     14.30        1.92  2.72               20.0      120.0           2.80   \n",
       "17     13.83        1.57  2.62               20.0      115.0           2.95   \n",
       "18     14.19        1.59  2.48               16.5      108.0           3.30   \n",
       "19     13.64        3.10  2.56               15.2      116.0           2.70   \n",
       "20     14.06        1.63  2.28               16.0      126.0           3.00   \n",
       "21     12.93        3.80  2.65               18.6      102.0           2.41   \n",
       "22     13.71        1.86  2.36               16.6      101.0           2.61   \n",
       "23     12.85        1.60  2.52               17.8       95.0           2.48   \n",
       "24     13.50        1.81  2.61               20.0       96.0           2.53   \n",
       "25     13.05        2.05  3.22               25.0      124.0           2.63   \n",
       "26     13.39        1.77  2.62               16.1       93.0           2.85   \n",
       "27     13.30        1.72  2.14               17.0       94.0           2.40   \n",
       "28     13.87        1.90  2.80               19.4      107.0           2.95   \n",
       "29     14.02        1.68  2.21               16.0       96.0           2.65   \n",
       "..       ...         ...   ...                ...        ...            ...   \n",
       "148    13.32        3.24  2.38               21.5       92.0           1.93   \n",
       "149    13.08        3.90  2.36               21.5      113.0           1.41   \n",
       "150    13.50        3.12  2.62               24.0      123.0           1.40   \n",
       "151    12.79        2.67  2.48               22.0      112.0           1.48   \n",
       "152    13.11        1.90  2.75               25.5      116.0           2.20   \n",
       "153    13.23        3.30  2.28               18.5       98.0           1.80   \n",
       "154    12.58        1.29  2.10               20.0      103.0           1.48   \n",
       "155    13.17        5.19  2.32               22.0       93.0           1.74   \n",
       "156    13.84        4.12  2.38               19.5       89.0           1.80   \n",
       "157    12.45        3.03  2.64               27.0       97.0           1.90   \n",
       "158    14.34        1.68  2.70               25.0       98.0           2.80   \n",
       "159    13.48        1.67  2.64               22.5       89.0           2.60   \n",
       "160    12.36        3.83  2.38               21.0       88.0           2.30   \n",
       "161    13.69        3.26  2.54               20.0      107.0           1.83   \n",
       "162    12.85        3.27  2.58               22.0      106.0           1.65   \n",
       "163    12.96        3.45  2.35               18.5      106.0           1.39   \n",
       "164    13.78        2.76  2.30               22.0       90.0           1.35   \n",
       "165    13.73        4.36  2.26               22.5       88.0           1.28   \n",
       "166    13.45        3.70  2.60               23.0      111.0           1.70   \n",
       "167    12.82        3.37  2.30               19.5       88.0           1.48   \n",
       "168    13.58        2.58  2.69               24.5      105.0           1.55   \n",
       "169    13.40        4.60  2.86               25.0      112.0           1.98   \n",
       "170    12.20        3.03  2.32               19.0       96.0           1.25   \n",
       "171    12.77        2.39  2.28               19.5       86.0           1.39   \n",
       "172    14.16        2.51  2.48               20.0       91.0           1.68   \n",
       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
       "\n",
       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0          3.06                  0.28             2.29         5.640000  1.04   \n",
       "1          2.76                  0.26             1.28         4.380000  1.05   \n",
       "2          3.24                  0.30             2.81         5.680000  1.03   \n",
       "3          3.49                  0.24             2.18         7.800000  0.86   \n",
       "4          2.69                  0.39             1.82         4.320000  1.04   \n",
       "5          3.39                  0.34             1.97         6.750000  1.05   \n",
       "6          2.52                  0.30             1.98         5.250000  1.02   \n",
       "7          2.51                  0.31             1.25         5.050000  1.06   \n",
       "8          2.98                  0.29             1.98         5.200000  1.08   \n",
       "9          3.15                  0.22             1.85         7.220000  1.01   \n",
       "10         3.32                  0.22             2.38         5.750000  1.25   \n",
       "11         2.43                  0.26             1.57         5.000000  1.17   \n",
       "12         2.76                  0.29             1.81         5.600000  1.15   \n",
       "13         3.69                  0.43             2.81         5.400000  1.25   \n",
       "14         3.64                  0.29             2.96         7.500000  1.20   \n",
       "15         2.91                  0.30             1.46         7.300000  1.28   \n",
       "16         3.14                  0.33             1.97         6.200000  1.07   \n",
       "17         3.40                  0.40             1.72         6.600000  1.13   \n",
       "18         3.93                  0.32             1.86         8.700000  1.23   \n",
       "19         3.03                  0.17             1.66         5.100000  0.96   \n",
       "20         3.17                  0.24             2.10         5.650000  1.09   \n",
       "21         2.41                  0.25             1.98         4.500000  1.03   \n",
       "22         2.88                  0.27             1.69         3.800000  1.11   \n",
       "23         2.37                  0.26             1.46         3.930000  1.09   \n",
       "24         2.61                  0.28             1.66         3.520000  1.12   \n",
       "25         2.68                  0.47             1.92         3.580000  1.13   \n",
       "26         2.94                  0.34             1.45         4.800000  0.92   \n",
       "27         2.19                  0.27             1.35         3.950000  1.02   \n",
       "28         2.97                  0.37             1.76         4.500000  1.25   \n",
       "29         2.33                  0.26             1.98         4.700000  1.04   \n",
       "..          ...                   ...              ...              ...   ...   \n",
       "148        0.76                  0.45             1.25         8.420000  0.55   \n",
       "149        1.39                  0.34             1.14         9.400000  0.57   \n",
       "150        1.57                  0.22             1.25         8.600000  0.59   \n",
       "151        1.36                  0.24             1.26        10.800000  0.48   \n",
       "152        1.28                  0.26             1.56         7.100000  0.61   \n",
       "153        0.83                  0.61             1.87        10.520000  0.56   \n",
       "154        0.58                  0.53             1.40         7.600000  0.58   \n",
       "155        0.63                  0.61             1.55         7.900000  0.60   \n",
       "156        0.83                  0.48             1.56         9.010000  0.57   \n",
       "157        0.58                  0.63             1.14         7.500000  0.67   \n",
       "158        1.31                  0.53             2.70        13.000000  0.57   \n",
       "159        1.10                  0.52             2.29        11.750000  0.57   \n",
       "160        0.92                  0.50             1.04         7.650000  0.56   \n",
       "161        0.56                  0.50             0.80         5.880000  0.96   \n",
       "162        0.60                  0.60             0.96         5.580000  0.87   \n",
       "163        0.70                  0.40             0.94         5.280000  0.68   \n",
       "164        0.68                  0.41             1.03         9.580000  0.70   \n",
       "165        0.47                  0.52             1.15         6.620000  0.78   \n",
       "166        0.92                  0.43             1.46        10.680000  0.85   \n",
       "167        0.66                  0.40             0.97        10.260000  0.72   \n",
       "168        0.84                  0.39             1.54         8.660000  0.74   \n",
       "169        0.96                  0.27             1.11         8.500000  0.67   \n",
       "170        0.49                  0.40             0.73         5.500000  0.66   \n",
       "171        0.51                  0.48             0.64         9.899999  0.57   \n",
       "172        0.70                  0.44             1.24         9.700000  0.62   \n",
       "173        0.61                  0.52             1.06         7.700000  0.64   \n",
       "174        0.75                  0.43             1.41         7.300000  0.70   \n",
       "175        0.69                  0.43             1.35        10.200000  0.59   \n",
       "176        0.68                  0.53             1.46         9.300000  0.60   \n",
       "177        0.76                  0.56             1.35         9.200000  0.61   \n",
       "\n",
       "     od280/od315_of_diluted_wines  proline  target  \n",
       "0                            3.92   1065.0     0.0  \n",
       "1                            3.40   1050.0     0.0  \n",
       "2                            3.17   1185.0     0.0  \n",
       "3                            3.45   1480.0     0.0  \n",
       "4                            2.93    735.0     0.0  \n",
       "5                            2.85   1450.0     0.0  \n",
       "6                            3.58   1290.0     0.0  \n",
       "7                            3.58   1295.0     0.0  \n",
       "8                            2.85   1045.0     0.0  \n",
       "9                            3.55   1045.0     0.0  \n",
       "10                           3.17   1510.0     0.0  \n",
       "11                           2.82   1280.0     0.0  \n",
       "12                           2.90   1320.0     0.0  \n",
       "13                           2.73   1150.0     0.0  \n",
       "14                           3.00   1547.0     0.0  \n",
       "15                           2.88   1310.0     0.0  \n",
       "16                           2.65   1280.0     0.0  \n",
       "17                           2.57   1130.0     0.0  \n",
       "18                           2.82   1680.0     0.0  \n",
       "19                           3.36    845.0     0.0  \n",
       "20                           3.71    780.0     0.0  \n",
       "21                           3.52    770.0     0.0  \n",
       "22                           4.00   1035.0     0.0  \n",
       "23                           3.63   1015.0     0.0  \n",
       "24                           3.82    845.0     0.0  \n",
       "25                           3.20    830.0     0.0  \n",
       "26                           3.22   1195.0     0.0  \n",
       "27                           2.77   1285.0     0.0  \n",
       "28                           3.40    915.0     0.0  \n",
       "29                           3.59   1035.0     0.0  \n",
       "..                            ...      ...     ...  \n",
       "148                          1.62    650.0     2.0  \n",
       "149                          1.33    550.0     2.0  \n",
       "150                          1.30    500.0     2.0  \n",
       "151                          1.47    480.0     2.0  \n",
       "152                          1.33    425.0     2.0  \n",
       "153                          1.51    675.0     2.0  \n",
       "154                          1.55    640.0     2.0  \n",
       "155                          1.48    725.0     2.0  \n",
       "156                          1.64    480.0     2.0  \n",
       "157                          1.73    880.0     2.0  \n",
       "158                          1.96    660.0     2.0  \n",
       "159                          1.78    620.0     2.0  \n",
       "160                          1.58    520.0     2.0  \n",
       "161                          1.82    680.0     2.0  \n",
       "162                          2.11    570.0     2.0  \n",
       "163                          1.75    675.0     2.0  \n",
       "164                          1.68    615.0     2.0  \n",
       "165                          1.75    520.0     2.0  \n",
       "166                          1.56    695.0     2.0  \n",
       "167                          1.75    685.0     2.0  \n",
       "168                          1.80    750.0     2.0  \n",
       "169                          1.92    630.0     2.0  \n",
       "170                          1.83    510.0     2.0  \n",
       "171                          1.63    470.0     2.0  \n",
       "172                          1.71    660.0     2.0  \n",
       "173                          1.74    740.0     2.0  \n",
       "174                          1.56    750.0     2.0  \n",
       "175                          1.56    835.0     2.0  \n",
       "176                          1.62    840.0     2.0  \n",
       "177                          1.60    560.0     2.0  \n",
       "\n",
       "[178 rows x 14 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "betterwinedf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "colab_type": "code",
    "id": "DsFBrzqW_D_Q",
    "outputId": "ec3f7188-4280-42f3-8d15-53756a0c0d91"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "betterwinedf.plot(kind='scatter', \n",
    "              x = 'total_phenols', \n",
    "              y = 'color_intensity', \n",
    "              c='target',\n",
    "              cmap=plt.cm.get_cmap('spring',3));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "C9Jusitwr_Jm"
   },
   "source": [
    "# Visualize Data better with Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "k0XnVoDe-lkL"
   },
   "source": [
    "Using the `.plot` feature on a dataframe is a great quick way to look at your data. \n",
    "You probably noticed that it even used the `matplotlib` library. You can take even more control of your visualization using libraries like [Matplotlib](https://matplotlib.org/) or [Seaborn](https://seaborn.pydata.org/index.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "11d9LMXvRZQw"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x640 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# choose x & y index values. \n",
    "# Remember our list looks like this\n",
    "# ['sepal length (cm)',\n",
    "#  'sepal width (cm)',\n",
    "#  'petal length (cm)',\n",
    "#  'petal width (cm)']\n",
    "\n",
    "# 0 = sepal length (cm)\n",
    "# 3 = petal width (cm)\n",
    "x_index = 0\n",
    "y_index = 3\n",
    "\n",
    "# this formatter will label the colorbar with the correct target names\n",
    "# Look up lambda functions in python, they're neat!\n",
    "formatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)])\n",
    "\n",
    "\n",
    "# figsize: width, height Inches\n",
    "#dpi: Dots per inch\n",
    "plt.figure( figsize=(10, 8), dpi=80)\n",
    "\n",
    "plt.scatter(iris.data[:, x_index], iris.data[:, y_index],\n",
    "            c=iris.target, cmap=plt.cm.get_cmap('spring', 3))\n",
    "\n",
    "plt.colorbar(ticks=[0, 1, 2], format=formatter)\n",
    "#plt.clim(-0.5, 2.5)\n",
    "plt.xlabel(iris.feature_names[x_index])\n",
    "plt.ylabel(iris.feature_names[y_index]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pu2RQOvXAFxZ"
   },
   "source": [
    "Take a couple minutes and play around with numbers above to see how the chart changes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Br8uxIOosOY0"
   },
   "source": [
    "# Machine Learning with Scikit learn\n",
    "\n",
    "We don't have the time to dive super deep into machine learning, but this will give you a quick overview and some ideas of how to start using machine learning\n",
    "\n",
    "We're going to use a machine learning model called `k-nearest neighbors ` or k-NN for short."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "O7LfeBGhVkcP"
   },
   "source": [
    "# Why K-NN\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DyrCB9lZVBto"
   },
   "source": [
    "\n",
    "We want to predict the PROBABILITY of the flower type\n",
    "\n",
    "We're going to use k-nearest neighbors also referred to as knn\n",
    "\n",
    "knn takes the 'k' nearest categorized values to uncategorized data and assigns the majority value to the data its trying to categorize. You should be able to see why this model makes sense from our data analysis!\n",
    "\n",
    "The example below has the value 4 for 'k'. So we look at the 4 nearest neighboring data points. 3 of of them are labeled versicolor. So we would assign a high probability that our new data point should be labeled versicolor!\n",
    "\n",
    "\n",
    "![k-NN](https://github.com/sagecodes/intro-machine-learning/blob/master/img/knn4.png?raw=true)\n",
    "\n",
    "\n",
    "These lines are hand drawn and not 100% accurate but you could imagine the predictions falling into the categories like this\n",
    "\n",
    "![k-NN](https://github.com/sagecodes/intro-machine-learning/raw/master/img/predictsegments.png)\n",
    "\n",
    "\n",
    "3d exmaple here:\n",
    "\n",
    "https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-enLXercPjOP"
   },
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0uMPJlp2WPkS"
   },
   "source": [
    "- Build the Classifier:\n",
    "- Import the neighbors model\n",
    "- Data is typically denoted as X while labels are denoted with y\n",
    "- assign our classifier with 'k' value to a variable\n",
    "- train(fit) our model\n",
    "- output the prediction\n",
    "- output the probability behind the prediction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tDWDcIMNPm7d"
   },
   "outputs": [],
   "source": [
    "from sklearn import neighbors\n",
    "\n",
    "x = iris.data\n",
    "y = iris.target\n",
    "\n",
    "knn = neighbors.KNeighborsClassifier(n_neighbors=3)\n",
    "\n",
    "#pass features and labels into model\n",
    "knn.fit(x, y)\n",
    "\n",
    "# What kind of iris has 3cm x 5cm sepal and 4cm x 2cm petal?\n",
    "# 0 = setosa' 1 = 'versicolor' 2 = 'virginica']\n",
    "# Data: sepal length, sepal width, petal length, petal width\n",
    "\n",
    "# Comment / Uncoomment different flowers below to see the prediction. \n",
    "# test_flower = [3, 5, 4, 2]\n",
    "#test_flower = [5.1, 3.5, 1.4, .2]\n",
    "# test_flower = [5.1, 4.5, 2.8, 2]\n",
    "\n",
    "result = knn.predict([test_flower])\n",
    "print(result)\n",
    "\n",
    "\n",
    "knn.predict_proba([test_flower])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3niIo8BDBupk"
   },
   "outputs": [],
   "source": [
    "# Lets make it into a dataframe so we can read it better\n",
    "\n",
    "pred = knn.predict_proba([test_flower])\n",
    "\n",
    "preddf = pd.DataFrame(pred, columns=iris.target_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sVNSqor8B8I2"
   },
   "outputs": [],
   "source": [
    "# output DataFrame\n",
    "preddf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iXIVmjNwCNG7"
   },
   "outputs": [],
   "source": [
    "# Lets make it a chart so we can read even more easily!\n",
    "preddf.plot(kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qkCi0_vamgbx"
   },
   "source": [
    "Train test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "299uEoGNPr0k"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, \n",
    "                                                    test_size=0.3,\n",
    "                                                    random_state=21, \n",
    "                                                    stratify=y)\n",
    "\n",
    "\n",
    "knn = neighbors.KNeighborsClassifier(n_neighbors=3)\n",
    "\n",
    "knn.fit(x_train, y_train)\n",
    "\n",
    "y_pred = knn.predict(x_test)\n",
    "\n",
    "print(\"Test Predictions: \\n {}\".format(y_pred))\n",
    "\n",
    "\n",
    "\n",
    "# Print accuracy\n",
    "knn.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4CVvAvXungrK"
   },
   "source": [
    "\n",
    "## Back to github Repo:\n",
    "\n",
    "[https://github.com/sagecodes/intro-data-science](https://github.com/sagecodes/intro-data-science)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vKhX3f6btjnR"
   },
   "outputs": [],
   "source": [
    "import sklearn as skl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.20.1'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skl.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "version": "0.3.2"
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   "display_name": "Python 3",
   "language": "python",
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