2024/2401-canadian-nhl-players/canadian-nhl-players.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from dateutil.relativedelta import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data and process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "players = pd.read_csv(\"nhl_player_births.csv\")\n",
    "rosters = pd.read_csv(\"nhl_rosters.csv\")\n",
    "teams = pd.read_csv(\"nhl_teams.csv\")\n",
    "\n",
    "df = pd.merge(rosters, teams, left_on=\"team_code\", right_on=\"team_code\", how=\"left\")\n",
    "df = df.rename(columns={\"full_name\": \"team_name\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# season start year\n",
    "df[\"season_start\"] = pd.to_datetime([str(i)[:4] + \"-10-01\" for i in df[\"season\"]])\n",
    "\n",
    "# calculate age of player at season start\n",
    "df[\"birth_date\"] = pd.to_datetime(df[\"birth_date\"])\n",
    "df[\"age_season_start\"] = [relativedelta(i,j).years for i,j in zip(df[\"season_start\"], df[\"birth_date\"]) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualise\n",
    "\n",
    "- Median age over time: season average + per team\n",
    "- Broken axis [tutorial](https://matplotlib.org/stable/gallery/subplots_axes_and_figures/broken_axis.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>team_name</th>\n",
       "      <th>mean_age</th>\n",
       "      <th>season_label</th>\n",
       "      <th>x_pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19791980</td>\n",
       "      <td>Atlanta Flames</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1979/80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19791980</td>\n",
       "      <td>Boston Bruins</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1979/80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19791980</td>\n",
       "      <td>Buffalo Sabres</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1979/80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19791980</td>\n",
       "      <td>Chicago Blackhawks</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1979/80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19791980</td>\n",
       "      <td>Colorado Rockies</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1979/80</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1178</th>\n",
       "      <td>20232024</td>\n",
       "      <td>Toronto Maple Leafs</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2023/24</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1179</th>\n",
       "      <td>20232024</td>\n",
       "      <td>Vancouver Canucks</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2023/24</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1180</th>\n",
       "      <td>20232024</td>\n",
       "      <td>Vegas Golden Knights</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2023/24</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1181</th>\n",
       "      <td>20232024</td>\n",
       "      <td>Washington Capitals</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2023/24</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1182</th>\n",
       "      <td>20232024</td>\n",
       "      <td>Winnipeg Jets</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2023/24</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1183 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        season             team_name  mean_age season_label  x_pos\n",
       "0     19791980        Atlanta Flames      26.0      1979/80      0\n",
       "1     19791980         Boston Bruins      27.0      1979/80      0\n",
       "2     19791980        Buffalo Sabres      25.5      1979/80      0\n",
       "3     19791980    Chicago Blackhawks      24.0      1979/80      0\n",
       "4     19791980      Colorado Rockies      24.0      1979/80      0\n",
       "...        ...                   ...       ...          ...    ...\n",
       "1178  20232024   Toronto Maple Leafs      27.0      2023/24     43\n",
       "1179  20232024     Vancouver Canucks      27.0      2023/24     43\n",
       "1180  20232024  Vegas Golden Knights      26.0      2023/24     43\n",
       "1181  20232024   Washington Capitals      29.0      2023/24     43\n",
       "1182  20232024         Winnipeg Jets      28.0      2023/24     43\n",
       "\n",
       "[1183 rows x 5 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# avg age by team\n",
    "pivot = df.groupby([\"team_name\", \"season\"]).agg({\"age_season_start\":\"median\"})\n",
    "pivot = pivot.unstack().transpose().reset_index()\n",
    "pivot = pivot[pivot[\"season\"]>19790000]\n",
    "pivot = pivot.drop(columns=\"level_0\").set_index(\"season\")\n",
    "pivot = pivot.stack().to_frame().reset_index().rename(columns={0:\"mean_age\"})\n",
    "pivot[\"season_label\"] = [str(i)[:4] + \"/\"+ str(i)[6:] for i in pivot[\"season\"]]\n",
    "\n",
    "# x position for plot\n",
    "dict = {}\n",
    "for i, season in enumerate(pivot[\"season\"].unique()):\n",
    "    dict[season] = i\n",
    "pivot[\"x_pos\"] = pivot[\"season\"].map(dict)\n",
    "\n",
    "# get avg age per season\n",
    "total_avg = df.drop_duplicates(subset=[\"player_id\", \"season\"], keep=\"first\").reset_index(drop=True)\n",
    "total_avg = total_avg.groupby([\"season\"]).agg({\"age_season_start\": \"median\"}).reset_index()\n",
    "total_avg = total_avg[total_avg[\"season\"]>19790000].reset_index(drop=True)\n",
    "\n",
    "pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import font_manager\n",
    "#font_manager.findSystemFonts(fontpaths=None, fontext=\"ttf\")\n",
    "title_font = font_manager.FontProperties(fname='C:\\\\WINDOWS\\\\Fonts\\\\Inter-Black.ttf')\n",
    "subtitle_font = font_manager.FontProperties(fname='C:\\\\WINDOWS\\\\Fonts\\\\Inter-Regular.ttf')\n",
    "\n",
    "# set custom font\n",
    "plt.rcParams[\"font.family\"] = [\"Ubuntu\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = {\"dots\": \"#40B8E1\", \"bg\": \"#090C11\", \"line\": \"#FFF5EE\", \"text\":\"#FFF5EE\"}\n",
    "\n",
    "# layout -----------------------------------------------\n",
    "fig = plt.figure(figsize=(15,8))\n",
    "ax1 = plt.subplot2grid(shape=(12, 1), loc=(0, 0), rowspan=11)\n",
    "ax2 = plt.subplot2grid(shape=(14, 1), loc=(13, 0))\n",
    "fig.subplots_adjust(hspace=0.0)  # adjust space between axes\n",
    "fig.set_facecolor(cols[\"bg\"])\n",
    "\n",
    "# plot data -----------------------------------------------\n",
    "sns.stripplot(ax=ax1, data=pivot, x=\"season_label\", y=\"mean_age\", size=10, jitter=0,\n",
    "              edgecolor=\"black\",linewidth=0, color=cols[\"dots\"], dodge=True, \n",
    "              alpha=0.15, zorder=2)\n",
    "ax1.scatter( total_avg.index, total_avg[\"age_season_start\"], zorder=4, linewidths=1,\n",
    "                                    marker=\"o\", s=100, facecolor=\"None\", edgecolor=cols[\"bg\"])\n",
    "ax1.plot( total_avg.index, total_avg[\"age_season_start\"],zorder=1, color=cols[\"line\"],alpha=0.8, linewidth=2,\n",
    "                                    marker=\"o\", markersize=8, markerfacecolor=cols[\"bg\"], markeredgecolor=\"None\", markeredgewidth=1)\n",
    "\n",
    "# Styling ---------------------------------------------\n",
    "ax1.spines.bottom.set_visible(False)\n",
    "for ax in [ax1, ax2]:\n",
    "    ax.set_facecolor(cols[\"bg\"])\n",
    "    ax.spines['bottom'].set_color(cols[\"line\"]) \n",
    "    for pos in [\"top\", \"right\", \"left\"]:\n",
    "        ax.spines[pos].set_visible(False)\n",
    "\n",
    "# configure xaxis and labels\n",
    "ax1.axes.get_xaxis().set_label_text(\"\")\n",
    "ax1.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False, color=cols[\"text\"])\n",
    "ax2.set_xlim(ax1.get_xlim())\n",
    "custom_xlabels = [str(value) for value in pivot[\"season_label\"].unique()[::5]]\n",
    "ax2.set_xticks(np.arange(0, len(ax1.get_xticks()), 5), custom_xlabels, color=cols[\"text\"])\n",
    "plt.xticks(fontsize=13)\n",
    "\n",
    "# configure yaxis\n",
    "ax1.axes.get_yaxis().set_label_text(\"\")\n",
    "ax1.grid(visible=True, axis='y', zorder=1, color = \"#EDEDED\", alpha=0.15)\n",
    "ax1.tick_params(axis='y', length = 0, labelsize=13, labelcolor=cols[\"text\"])\n",
    "ax1.set_ylim(ymin=21.5, ymax=31.2)\n",
    "ax1.set_yticks(np.arange(22, 32, 1))\n",
    "ax2.set_ylim(ymin=0, ymax=1)\n",
    "ax2.set_yticks([0])\n",
    "plt.yticks(fontsize=13, color=cols[\"text\"])\n",
    "\n",
    "# draw lines to indicate broken axis\n",
    "d = .5  # proportion of vertical to horizontal extent of the slanted line\n",
    "kwargs = {\"marker\":[(-1, -d), (1, d)], \"markersize\":12,\n",
    "              \"linestyle\": \"None\",  \"mew\":1, \"clip_on\":False}\n",
    "ax1.plot([0, 0], [0, 0], transform=ax1.transAxes, color=cols[\"line\"], **kwargs)\n",
    "ax2.plot([0, 0], [1, 1], transform=ax2.transAxes, color=cols[\"line\"],**kwargs)\n",
    "\n",
    "# Custom legend -----------------------------------------------\n",
    "lgd = fig.add_axes([0.92, 0.68, 0.02, 0.2]) \n",
    "lgd.scatter(y=[1,2,2,2,2,3,3,3,3,3,3,3,3], x=[1,1,1,1,1,1,1,1,1,1,1,1,1], s=80, color=cols[\"dots\"], alpha=0.15, linewidths=0)\n",
    "lgd.plot([0,1,2],[-1.55,-1.55,-1.55],  markersize=8, marker=\"o\", color=cols[\"line\"], markerfacecolor=cols[\"bg\"], \n",
    "         markeredgecolor=cols[\"line\"], alpha=1, linewidth=1.5 ,clip_on=True)\n",
    "lgd.invert_yaxis()\n",
    "lgd.text(0.98,0.1,\"Number of teams\", fontsize=14, color=cols[\"text\"])\n",
    "lgd.text(1.05,1.05,\"1\", fontsize=14, va=\"center\", color=cols[\"text\"])\n",
    "lgd.text(1.05,2.05,\"4\", fontsize=14, va=\"center\", color=cols[\"text\"])\n",
    "lgd.text(1.05,3.05,\"8\", fontsize=14, va=\"center\", color=cols[\"text\"])\n",
    "lgd.text(1.05,-1.5,\"Season average\", fontsize=14, va=\"center\", color=cols[\"text\"])\n",
    "lgd.set_xlim(0.97, 1.03)\n",
    "lgd.axis(\"off\")\n",
    "\n",
    "# Title and footer -----------------------------------------------\n",
    "plt.figtext(0.12,1.05, \"Is the Canadian National Hockey League getting older?\", fontproperties = title_font, fontsize=32, color=cols[\"text\"], ha=\"left\")\n",
    "plt.figtext(0.12,0.97, \"Median age of NHL players at season start by team and season, 1979/80 to 2023/24\", fontproperties = subtitle_font, fontsize=16, color=cols[\"text\"], ha=\"left\")\n",
    "plt.figtext(0.12,-0.03, \"Data: TidyTuesday   |   Design: Lisa Hornung\", fontsize=12, color=cols[\"text\"], ha=\"left\", alpha=0.5)\n",
    "\n",
    "# save\n",
    "plt.savefig(\"nhl-avg-age-dotplot-v2.png\", bbox_inches='tight', dpi=120, pad_inches=1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = {\"dots\": \"#AFF3FE\", \"bg\": \"#090C11\", \"line\": \"white\", \"text\":\"white\"}\n",
    "\n",
    "# layout -----------------------------------------------\n",
    "fig = plt.figure(figsize=(15,8))\n",
    "ax1 = plt.subplot2grid(shape=(12, 1), loc=(0, 0), rowspan=11)\n",
    "ax2 = plt.subplot2grid(shape=(14, 1), loc=(13, 0))\n",
    "fig.subplots_adjust(hspace=0.0)  # adjust space between axes\n",
    "fig.set_facecolor(cols[\"bg\"])\n",
    "\n",
    "# plot data -----------------------------------------------\n",
    "sns.stripplot(ax=ax1, data=pivot, x=\"season_label\", y=\"mean_age\", size=10, jitter=0,\n",
    "              edgecolor=\"black\",linewidth=0, color=cols[\"dots\"], dodge=True, \n",
    "              alpha=0.15, zorder=2)\n",
    "ax1.scatter( total_avg.index, total_avg[\"age_season_start\"], zorder=4, linewidths=1,\n",
    "                                    marker=\"o\", s=100, facecolor=\"None\", edgecolor=\"black\")\n",
    "ax1.plot( total_avg.index, total_avg[\"age_season_start\"],zorder=1, color=cols[\"line\"],\n",
    "                                    marker=\"o\", markersize=8, markerfacecolor=cols[\"bg\"], markeredgecolor=\"None\", markeredgewidth=1)\n",
    "\n",
    "# Styling ---------------------------------------------\n",
    "# remove spines\n",
    "ax1.spines.bottom.set_visible(False)\n",
    "for pos in [\"top\", \"right\", \"left\"]:\n",
    "    ax1.spines[pos].set_visible(False)\n",
    "    ax2.spines[pos].set_visible(False)\n",
    "\n",
    "ax1.set_facecolor(cols[\"bg\"])\n",
    "ax2.set_facecolor(cols[\"bg\"])\n",
    "\n",
    "# configure xaxis and labels\n",
    "ax1.axes.get_xaxis().set_label_text(\"\")\n",
    "ax1.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False, color=cols[\"text\"])\n",
    "ax2.set_xlim(ax1.get_xlim())\n",
    "custom_xlabels = [str(value) for value in pivot[\"season_label\"].unique()[::5]]\n",
    "ax2.set_xticks(np.arange(0, len(ax1.get_xticks()), 5), custom_xlabels, color=cols[\"text\"])\n",
    "plt.xticks(fontsize=13)\n",
    "\n",
    "# configure yaxis\n",
    "ax1.axes.get_yaxis().set_label_text(\"\")\n",
    "ax1.grid(visible=True, axis='y', zorder=1, color = \"#EDEDED\", alpha=0.2)\n",
    "ax1.tick_params(axis='y', length = 0, labelsize=13, color=cols[\"text\"])\n",
    "ax1.set_ylim(ymin=21.5, ymax=31.2)\n",
    "ax1.set_yticks(np.arange(22, 32, 1))\n",
    "ax2.set_ylim(ymin=0, ymax=1)\n",
    "ax2.set_yticks([0])\n",
    "plt.yticks(fontsize=13, color=cols[\"text\"])\n",
    "\n",
    "# draw lines to indicate broken axis\n",
    "d = .5  # proportion of vertical to horizontal extent of the slanted line\n",
    "kwargs = {\"marker\":[(-1, -d), (1, d)], \"markersize\":12,\n",
    "              \"linestyle\": \"None\", \"color\":'k', \"mec\":'k', \"mew\":1, \"clip_on\":False}\n",
    "ax1.plot([0, 0], [0, 0], transform=ax1.transAxes, **kwargs)\n",
    "ax2.plot([0, 0], [1, 1], transform=ax2.transAxes, **kwargs)\n",
    "\n",
    "# Custom legend -----------------------------------------------\n",
    "lgd = fig.add_axes([0.92, 0.68, 0.02, 0.2]) \n",
    "lgd.scatter(y=[1,2,2,2,2,3,3,3,3,3,3,3,3], x=[1,1,1,1,1,1,1,1,1,1,1,1,1], s=80, color=cols[\"dots\"], alpha=0.15, linewidths=0)\n",
    "lgd.plot([0,1,2],[-1.55,-1.55,-1.55],  markersize=8, marker=\"o\", color=cols[\"line\"], markerfacecolor=cols[\"bg\"], markeredgecolor=cols[\"line\"], alpha=1, linewidth=1 ,clip_on=True)\n",
    "lgd.invert_yaxis()\n",
    "lgd.text(0.98,0.1,\"Number of teams\", fontsize=14)\n",
    "lgd.text(1.05,1.05,\"1\", fontsize=14, va=\"center\")\n",
    "lgd.text(1.05,2.05,\"4\", fontsize=14, va=\"center\")\n",
    "lgd.text(1.05,3.05,\"8\", fontsize=14, va=\"center\")\n",
    "lgd.text(1.05,-1.5,\"Season average\", fontsize=14, va=\"center\")\n",
    "lgd.set_xlim(0.97, 1.03)\n",
    "lgd.axis(\"off\")\n",
    "\n",
    "# Title and footer -----------------------------------------------\n",
    "plt.figtext(0.12,1.0, \"Are players in the Canadian National Hockey League getting older?\", fontproperties = title_font, fontsize=22, color=\"black\", ha=\"left\")\n",
    "plt.figtext(0.12,0.95, \"Median age of NHL players by team and season\", fontproperties = subtitle_font, fontsize=18, color=\"black\", ha=\"left\")\n",
    "plt.figtext(0.12,0, \"Data: TidyTuesday   |   Design: Lisa Hornung\", fontsize=11, color=\"black\", ha=\"left\", alpha=0.75)\n",
    "\n",
    "# save\n",
    "#plt.savefig(\"nhl-avg-age-dotplot.png\", bbox_inches='tight', dpi=120, pad_inches=1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# year filter\n",
    "year_filter = (df[\"season_start\"] > pd.to_datetime(\"1989-10-01\")) & ((df[\"season_start\"] < pd.to_datetime(\"2023-10-01\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# age range per player\n",
    "age_players = df.groupby([\"player_id\", \"first_name\", \"last_name\", \"birth_country\"]).agg({\"age_season_start\":[\"min\", \"max\", \"count\"], \"season\": \"min\"})\n",
    "age_players.columns = ['_'.join(col) for col in age_players.columns]\n",
    "age_players[\"age_range\"] = age_players[\"age_season_start_max\"] - age_players[\"age_season_start_min\"]\n",
    "\n",
    "\n",
    "age_players = age_players.sort_values(by='age_range', ascending=False)\n",
    "age_players = age_players.sort_values(by='age_season_start_max', ascending=False)\n",
    "age_players.head(20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# avg age by season\n",
    "pivot = df.groupby(\"season_start\")[\"age_season_start\"].max()\n",
    "pivot.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot.set_index(\"season\")[\"total\"].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Idea: Top 15 oldest players\n",
    "\n",
    "Stripes plot for top 15 players with \n",
    "\n",
    "- Guide on [how to recreate Warming Stripes in Matplotlib](https://matplotlib.org/matplotblog/posts/warming-stripes/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LISA~1.HOR\\AppData\\Local\\Temp/ipykernel_9236/2263873871.py:18: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  age_dummies = pd.get_dummies(df_filtered['age_season_start'].apply(pd.Series).stack()).sum(level=0)\n"
     ]
    },
    {
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8448930</th>\n",
       "      <td>Otto</td>\n",
       "      <td>Schmidt</td>\n",
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       "      <td>16</td>\n",
       "      <td>16</td>\n",
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       "      <td>1</td>\n",
       "      <td>[BOS]</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8472 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          first_name last_name birth_country birth_date  age_season_start_min  \\\n",
       "player_id                                                                       \n",
       "8448000       Gordie      Howe           CAN 1928-03-31                    18   \n",
       "8446053        Chris   Chelios           USA 1962-01-25                    21   \n",
       "8450104          Moe   Roberts           USA 1905-12-13                    19   \n",
       "8448208      Jaromir      Jagr           CZE 1972-02-15                    18   \n",
       "8449835       Johnny     Bower           CAN 1924-11-08                    28   \n",
       "...              ...       ...           ...        ...                   ...   \n",
       "8445299         Gord   Buttrey           CAN 1926-03-17                    17   \n",
       "8448928       Jackie   Schmidt           CAN 1924-11-11                    17   \n",
       "8448192         Eric  Prentice           CAN 1926-08-22                    17   \n",
       "8449387          Len   Wharton           CAN 1927-12-13                    16   \n",
       "8448930         Otto   Schmidt           CAN 1926-11-05                    16   \n",
       "\n",
       "           age_season_start_max  \\\n",
       "player_id                         \n",
       "8448000                      51   \n",
       "8446053                      47   \n",
       "8450104                      45   \n",
       "8448208                      45   \n",
       "8449835                      44   \n",
       "...                         ...   \n",
       "8445299                      17   \n",
       "8448928                      17   \n",
       "8448192                      17   \n",
       "8449387                      16   \n",
       "8448930                      16   \n",
       "\n",
       "                                            age_season_start  \\\n",
       "player_id                                                      \n",
       "8448000    [51, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 2...   \n",
       "8446053    [47, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 2...   \n",
       "8450104                                     [25, 27, 19, 45]   \n",
       "8448208    [41, 42, 40, 18, 19, 20, 21, 22, 23, 24, 25, 2...   \n",
       "8449835    [33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 4...   \n",
       "...                                                      ...   \n",
       "8445299                                                 [17]   \n",
       "8448928                                                 [17]   \n",
       "8448192                                                 [17]   \n",
       "8449387                                                 [16]   \n",
       "8448930                                                 [16]   \n",
       "\n",
       "           team_code_nunique                          team_code_unique  \\\n",
       "player_id                                                                \n",
       "8448000                    2                                [DET, HFD]   \n",
       "8446053                    4                      [ATL, CHI, DET, MTL]   \n",
       "8450104                    3                           [BOS, CHI, NYA]   \n",
       "8448208                    8  [BOS, CGY, FLA, NJD, NYR, PHI, PIT, WSH]   \n",
       "8449835                    2                                [NYR, TOR]   \n",
       "...                      ...                                       ...   \n",
       "8445299                    1                                     [CHI]   \n",
       "8448928                    1                                     [BOS]   \n",
       "8448192                    1                                     [TOR]   \n",
       "8449387                    1                                     [NYR]   \n",
       "8448930                    1                                     [BOS]   \n",
       "\n",
       "           season_min  ...  39  40  41  42  43  44  45  46  47  51  \n",
       "player_id              ...                                          \n",
       "8448000      19461947  ...   1   1   1   1   0   0   0   0   0   1  \n",
       "8446053      19831984  ...   1   1   1   0   1   1   1   1   1   0  \n",
       "8450104      19251926  ...   0   0   0   0   0   0   1   0   0   0  \n",
       "8448208      19901991  ...   1   1   1   1   1   1   1   0   0   0  \n",
       "8449835      19531954  ...   1   1   1   1   1   1   0   0   0   0  \n",
       "...               ...  ...  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  \n",
       "8445299      19431944  ...   0   0   0   0   0   0   0   0   0   0  \n",
       "8448928      19421943  ...   0   0   0   0   0   0   0   0   0   0  \n",
       "8448192      19431944  ...   0   0   0   0   0   0   0   0   0   0  \n",
       "8449387      19441945  ...   0   0   0   0   0   0   0   0   0   0  \n",
       "8448930      19431944  ...   0   0   0   0   0   0   0   0   0   0  \n",
       "\n",
       "[8472 rows x 45 columns]"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop duplicate entries per player per season (ie. if they switched team at some point)\n",
    "df_filtered = df.drop_duplicates(subset=[\"player_id\", \"season\"], keep=\"first\").reset_index(drop=True)\n",
    "\n",
    "df_filtered = df_filtered.groupby([\"player_id\", \"first_name\", \"last_name\", \"birth_country\", \"birth_date\"]).agg(\n",
    "    {\"age_season_start\": [\"min\", \"max\", lambda x: ','.join(map(str, x))],\n",
    "     \"team_code\": [\"nunique\", np.unique],\n",
    "     \"season\": [\"min\", \"max\",\"count\"],\n",
    "     })\n",
    "\n",
    "# merge columns and rename\n",
    "df_filtered.columns = ['_'.join(col) for col in df_filtered.columns]\n",
    "df_filtered = df_filtered.rename(columns=lambda x: x.replace('_<lambda_0>', ''))\n",
    "\n",
    "df_filtered[\"age_season_start\"] = df_filtered[\"age_season_start\"].str.split(\",\")\n",
    "#df_filtered[\"team_code\"] = df_filtered[\"team_code\"].str.split(\",\")\n",
    "\n",
    "df_filtered  = df_filtered.reset_index().set_index(\"player_id\")\n",
    "age_dummies = pd.get_dummies(df_filtered['age_season_start'].apply(pd.Series).stack()).sum(level=0)\n",
    "df_filtered = pd.concat([df_filtered, age_dummies], axis=1)\n",
    "\n",
    "df_filtered = df_filtered.sort_values(by=\"age_season_start_max\", ascending=False)\n",
    "df_filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_season_start</th>\n",
       "      <th>team_code</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>player_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8444850</th>\n",
       "      <td>[24]</td>\n",
       "      <td>[BOS]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8444851</th>\n",
       "      <td>[28]</td>\n",
       "      <td>[TSP]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8444852</th>\n",
       "      <td>[26, 28]</td>\n",
       "      <td>[DET, DET]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8444853</th>\n",
       "      <td>[24, 25, 25, 26]</td>\n",
       "      <td>[BOS, BOS, CHI, CHI]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8444854</th>\n",
       "      <td>[22]</td>\n",
       "      <td>[MTL]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8484166</th>\n",
       "      <td>[18]</td>\n",
       "      <td>[CBJ]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8484254</th>\n",
       "      <td>[23]</td>\n",
       "      <td>[VAN]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8484256</th>\n",
       "      <td>[23]</td>\n",
       "      <td>[NSH]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8484287</th>\n",
       "      <td>[21]</td>\n",
       "      <td>[VAN]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8484314</th>\n",
       "      <td>[26]</td>\n",
       "      <td>[OTT]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8472 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           age_season_start             team_code\n",
       "player_id                                        \n",
       "8444850                [24]                 [BOS]\n",
       "8444851                [28]                 [TSP]\n",
       "8444852            [26, 28]            [DET, DET]\n",
       "8444853    [24, 25, 25, 26]  [BOS, BOS, CHI, CHI]\n",
       "8444854                [22]                 [MTL]\n",
       "...                     ...                   ...\n",
       "8484166                [18]                 [CBJ]\n",
       "8484254                [23]                 [VAN]\n",
       "8484256                [23]                 [NSH]\n",
       "8484287                [21]                 [VAN]\n",
       "8484314                [26]                 [OTT]\n",
       "\n",
       "[8472 rows x 2 columns]"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sorted = df.sort_values(by=[\"player_id\", \"season\"])\n",
    "\n",
    "df_player = df.groupby(\"player_id\").agg({\"age_season_start\": lambda x: ','.join(map(str, x)),\n",
    "                            \"team_code\":  lambda x: ','.join(x)})\n",
    "\n",
    "df_player[\"team_code\"] = df_player[\"team_code\"].str.split(\",\")\n",
    "df_player[\"age_season_start\"] = df_player[\"age_season_start\"].str.split(\",\")\n",
    "df_player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_changed_all = []\n",
    "for _id in list(df_player.index):\n",
    "    age_changed = []\n",
    "    if len(df_player[\"team_code\"][_id])>1:\n",
    "        for i in range(1,len(df_player[\"team_code\"][_id])):\n",
    "            if df_player[\"team_code\"][_id][i] != df_player[\"team_code\"][_id][i-1]:\n",
    "                age_changed.append(df_player[\"age_season_start\"][_id][i])\n",
    "    age_changed_all.append(age_changed)\n",
    "    \n",
    "df_player[\"aged_changed\"] = age_changed_all\n",
    "df_player.to_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filtered = pd.merge(df_filtered, df_player, left_index=True, right_index=True, how=\"left\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "setting an array element with a sequence.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;31mTypeError\u001b[0m: float() argument must be a string or a number, not 'list'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\LISA~1.HOR\\AppData\\Local\\Temp/ipykernel_9236/2020375426.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mdata_for_plot\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_filtered\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf_filtered\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m14\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheatmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_for_plot\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcbar\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\Users\\lisa.hornung\\anaconda3\\envs\\py3_makeover\\lib\\site-packages\\seaborn\\_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     44\u001b[0m             )\n\u001b[0;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 46\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     47\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\lisa.hornung\\anaconda3\\envs\\py3_makeover\\lib\\site-packages\\seaborn\\matrix.py\u001b[0m in \u001b[0;36mheatmap\u001b[1;34m(data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, linewidths, linecolor, cbar, cbar_kws, cbar_ax, square, xticklabels, yticklabels, mask, ax, **kwargs)\u001b[0m\n\u001b[0;32m    538\u001b[0m     \"\"\"\n\u001b[0;32m    539\u001b[0m     \u001b[1;31m# Initialize the plotter object\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 540\u001b[1;33m     plotter = _HeatMapper(data, vmin, vmax, cmap, center, robust, annot, fmt,\n\u001b[0m\u001b[0;32m    541\u001b[0m                           \u001b[0mannot_kws\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcbar\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcbar_kws\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxticklabels\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    542\u001b[0m                           yticklabels, mask)\n",
      "\u001b[1;32mc:\\Users\\lisa.hornung\\anaconda3\\envs\\py3_makeover\\lib\\site-packages\\seaborn\\matrix.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, cbar, cbar_kws, xticklabels, yticklabels, mask)\u001b[0m\n\u001b[0;32m    157\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    158\u001b[0m         \u001b[1;31m# Determine good default values for the colormapping\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 159\u001b[1;33m         self._determine_cmap_params(plot_data, vmin, vmax,\n\u001b[0m\u001b[0;32m    160\u001b[0m                                     cmap, center, robust)\n\u001b[0;32m    161\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\lisa.hornung\\anaconda3\\envs\\py3_makeover\\lib\\site-packages\\seaborn\\matrix.py\u001b[0m in \u001b[0;36m_determine_cmap_params\u001b[1;34m(self, plot_data, vmin, vmax, cmap, center, robust)\u001b[0m\n\u001b[0;32m    191\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    192\u001b[0m         \u001b[1;31m# plot_data is a np.ma.array instance\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 193\u001b[1;33m         \u001b[0mcalc_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    194\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mvmin\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    195\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mrobust\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: setting an array element with a sequence."
     ]
    },
    {
     "data": {
      "image/png": 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l21OzTMfW0j7qh9NuoX2pqncnuSXJoe7+2Wmajd0tsn9rSW7fDK1zk1xZVSe7+1unZUJeyqJ/dz7b3b9K8ququifJpUnE1pm1yN5dl+TveuNFQMeq6idJLkny76dnRF6GPTXL9NOIPupnde26d1V1YZI7knzSv6bPOrvuX3df1N1v7+63J/mHJH8jtM4ai/zd+e0kH6yq/VX1miTvT/LYaZ6T37bI3j2ZjTuSqaq3ZuMDjp84rVOyV3tqltE7W+2jflbWgnv3uSRvTvLVzbsjJ9sHrJ4VFtw/zlKL7F93P1ZV30vyUJIXktzS3Tv+d3VOnwX/7H0hyW1V9XA2npa6sbufPWND8xtV9Y0klyc5t6qOJ/l8klclL69ZvIM8AMAg7yAPADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMCg/wWw2xl1y4WfzAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "\n",
    "data_for_plot = df_filtered[df_filtered.columns[14:].tolist()].replace(0, np.nan)\n",
    "sns.heatmap(data_for_plot[:15], vmin=0, vmax=1, ax=ax, cbar=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py3_makeover",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  },
  "orig_nbformat": 4
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 "nbformat": 4,
 "nbformat_minor": 2
}