04_plot_combined_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 04: Load the combined dataframe, and do some plotting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import relevant packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import scipy.stats\n",
    "\n",
    "# change the font for the whole plot\n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "\n",
    "# Read in the data\n",
    "df = pd.read_csv(\"processed_data/sales_and_weather_2021-2022.csv\")\n",
    "df_cold = pd.read_csv(\"processed_data/sales_and_weather_2021-2022_ONLY_COLD.csv\")\n",
    "df_warm = pd.read_csv(\"processed_data/sales_and_weather_2021-2022_ONLY_WARM.csv\")\n",
    "\n",
    "# Drop the null values from the variables\n",
    "df = df.dropna(subset=[\"temperature_mean\", \"temperature_min\", \"temperature_max\", \"precipitation\"])\n",
    "df_cold = df_cold.dropna(subset=[\"temperature_mean\", \"temperature_min\", \"temperature_max\", \"precipitation\"])\n",
    "df_warm = df_warm.dropna(subset=[\"temperature_mean\", \"temperature_min\", \"temperature_max\", \"precipitation\"])\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set parameters for the plots\n",
    "\n",
    "general_color = \"cornflowerblue\"\n",
    "days = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\", \"Sunday\"]\n",
    "day_colors = [\"midnightblue\", \"mediumblue\", \"royalblue\", \"lightskyblue\", \"aquamarine\", \"palegreen\", \"mediumseagreen\"]\n",
    "\n",
    "axes_fontsize = 16\n",
    "title_fontsize = 24\n",
    "label_fontsize = 20\n",
    "legend_fontsize = 16\n",
    "\n",
    "page_width = 16 # inches\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Plot the entire sales and weather data on whole date range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.dates as mdates\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16, 3))\n",
    "\n",
    "# Plot the max temperature and the sales for each day of the week\n",
    "lns1 = ax.plot(df[\"date\"].astype(\"datetime64\"), df[\"Coffee_sales\"].astype(\"float\"), color=\"cornflowerblue\", label=\"Coffee Sales\")\n",
    "ax2 = ax.twinx()\n",
    "lns2 = ax2.plot(df[\"date\"].astype(\"datetime64\"), df[\"temperature_max\"].astype(\"float\"), color=\"darkorange\", label=\"Max. Temperature\")\n",
    "ax.set_ylim(0, 2200)\n",
    "ax2.set_ylim(-5, 38)\n",
    "ax.set_yticks(np.arange(0, 2500, 1000))\n",
    "ax2.set_yticks(np.arange(0, 40, 10))\n",
    "ax.set_xlim(pd.Timestamp('2021-01-01'), pd.Timestamp('2022-10-20'))\n",
    "ax.xaxis.set_major_locator(mdates.MonthLocator(interval=3, ))\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))\n",
    "ax.tick_params(axis=\"both\", which=\"major\", labelsize=axes_fontsize)\n",
    "ax2.tick_params(axis=\"both\", which=\"major\", labelsize=axes_fontsize)\n",
    "ax.set_xlabel(\"Date\", fontsize=label_fontsize)\n",
    "ax.set_ylabel(\"Coffee Sales (€)\", fontsize=label_fontsize)\n",
    "ax2.set_ylabel(\"Max. Temperature (°C)\", fontsize=label_fontsize)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax2.spines[\"top\"].set_visible(False)\n",
    "lns = lns1+lns2\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax.legend(lns, labs, loc=\"upper left\", fontsize=legend_fontsize)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"results/coffee_sales.pdf\", dpi=300, bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Plot general correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_variable = \"Coffee_sales\"\n",
    "\n",
    "# plot the correlation between sales and max temperature\n",
    "fig, axes = plt.subplots(1, 3, figsize=(page_width, 4), sharey=True)\n",
    "weather_variable_1 = \"temperature_max\"\n",
    "axes[0].scatter(df[weather_variable_1], df[sales_variable], s=20,  marker=\"o\", color=general_color)\n",
    "axes[0].set_xlabel(\"Maximum Temperature (°C)\", fontsize=label_fontsize)\n",
    "axes[0].set_ylabel(\"Coffee Sales (€)\",         fontsize=label_fontsize)\n",
    "axes[0].set_ylim(0, 1800)\n",
    "axes[0].set_yticks(np.arange(0, 2500, 1000))\n",
    "axes[0].set_xlim(-7, 37)\n",
    "axes[0].set_xticks(np.arange(0, 40, 10))\n",
    "X = df[weather_variable_1].values.reshape(-1, 1)\n",
    "y = df[sales_variable].values.reshape(-1, 1)\n",
    "reg = LinearRegression().fit(X, y)\n",
    "axes[0].plot(X, reg.predict(X), color=\"black\")\n",
    "corr = scipy.stats.pearsonr(df[sales_variable], df[weather_variable_1])[0]\n",
    "pval = scipy.stats.pearsonr(df[sales_variable], df[weather_variable_1])[1]\n",
    "\n",
    "# plot the correlation between sales and sunshine duration\n",
    "weather_variable_2 = \"sunshine_duration\"\n",
    "axes[1].scatter(df[weather_variable_2], df[sales_variable], s=20,  marker=\"o\", color=general_color)\n",
    "axes[1].set_xlabel(\"Sunshine Duration (h)\", fontsize=label_fontsize)\n",
    "axes[1].set_xlim(-1, 17)\n",
    "axes[1].set_xticks(np.arange(0, 20, 5))\n",
    "X = df[weather_variable_2].values.reshape(-1, 1)\n",
    "reg = LinearRegression().fit(X, y)\n",
    "axes[1].plot(X, reg.predict(X), color=\"black\")\n",
    "corr = scipy.stats.pearsonr(df[sales_variable], df[weather_variable_2])[0]\n",
    "pval = scipy.stats.pearsonr(df[sales_variable], df[weather_variable_2])[1]\n",
    "\n",
    "# plot the correlation between sales and precipitation\n",
    "weather_variable_3 = \"precipitation\"\n",
    "axes[2].scatter(df[weather_variable_3], df[sales_variable], s=20,  marker=\"o\", color=general_color)\n",
    "axes[2].set_xlabel(\"Precipitation (mm/m²/day)\", fontsize=label_fontsize)\n",
    "axes[2].set_xlim(-2, 35)\n",
    "axes[2].set_xticks(np.arange(0, 40, 10))\n",
    "X = df[weather_variable_3].values.reshape(-1, 1)\n",
    "reg = LinearRegression().fit(X, y)\n",
    "axes[2].plot(X, reg.predict(X), color=\"black\")\n",
    "corr = scipy.stats.pearsonr(df[sales_variable], df[weather_variable_3])[0]\n",
    "pval = scipy.stats.pearsonr(df[sales_variable], df[weather_variable_3])[1]\n",
    "\n",
    "for ax in axes:\n",
    "    ax.tick_params(axis=\"both\", which=\"major\", labelsize=axes_fontsize)\n",
    "    ax.spines[\"right\"].set_visible(False)\n",
    "    ax.spines[\"top\"].set_visible(False)\n",
    "\n",
    "fig.suptitle(\"Correlation between Coffee Sales and Weather Variables\", fontsize=title_fontsize)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"results/temp_sun_precip.pdf\", bbox_inches=\"tight\", dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the correlation between sales and mean temperature\n",
    "\n",
    "#corr = df[\"Getränke_sales\"].corr(df[\"temperature_max\"])\n",
    "corr = scipy.stats.pearsonr(df[\"Coffee_sales\"], df[\"temperature_max\"])[0]\n",
    "pval = scipy.stats.pearsonr(df[\"Coffee_sales\"], df[\"temperature_max\"])[1]\n",
    "print(\"Correlation between overall sales and max temperature is about: {:.2f}\".format(corr) + f\". The correlation has a p-value of: {pval}\")\n",
    "\n",
    "#corr = df[\"Getränke_sales\"].corr(df[\"temperature_max\"])\n",
    "corr = scipy.stats.pearsonr(df[\"Coffee_sales\"], df[\"sunshine_duration\"])[0]\n",
    "pval = scipy.stats.pearsonr(df[\"Coffee_sales\"], df[\"sunshine_duration\"])[1]\n",
    "print(\"Correlation between overall sales and sunshine duration is about: {:.2f}\".format(corr) + f\". The correlation has a p-value of: {pval}\")\n",
    "\n",
    "#corr = df[\"Getränke_sales\"].corr(df[\"temperature_max\"])\n",
    "corr = scipy.stats.pearsonr(df[\"Coffee_sales\"], df[\"precipitation\"])[0]\n",
    "pval = scipy.stats.pearsonr(df[\"Coffee_sales\"], df[\"precipitation\"])[1]\n",
    "print(\"Correlation between overall sales and precipitation is about: {:.2f}\".format(corr) + f\". The correlation has a p-value of: {pval}\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Plot average coffee sales per weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_variable = \"temperature_max\"\n",
    "sales_variable = \"Coffee_sales\"\n",
    "\n",
    "# Plot the average coffee sales per weekday in a bar plot with error bars.\n",
    "fig, ax = plt.subplots(1, 1, figsize=(page_width*0.3, 4), sharey=True, sharex=True)\n",
    "fig.supylabel(\"average Coffee Sales (€)\", fontsize=label_fontsize, x=0.050, y=0.58)\n",
    "width = 0.4\n",
    "for i, day in enumerate(days):\n",
    "    ax.bar(i+1-0.5*width, df[(df[\"day_of_week\"] == day) & (df[\"year\"] == 2021)][sales_variable].astype(\"float\").mean(), label=day, color=day_colors[i], width=width, alpha=0.4, )\n",
    "    ax.bar(i+1+0.5*width, df[(df[\"day_of_week\"] == day) & (df[\"year\"] == 2022)][sales_variable].astype(\"float\").mean(), label=day, color=day_colors[i], width=width)\n",
    "    ax.errorbar(i+1-0.5*width, df[(df[\"day_of_week\"] == day) & (df[\"year\"] == 2021)][sales_variable].astype(\"float\").mean(), yerr=df[(df[\"day_of_week\"] == day) & (df[\"year\"] == 2021)][sales_variable].astype(\"float\").std(), fmt=\"none\", color=day_colors[i], capsize=3)\n",
    "    ax.errorbar(i+1+0.5*width, df[(df[\"day_of_week\"] == day) & (df[\"year\"] == 2022)][sales_variable].astype(\"float\").mean(), yerr=df[(df[\"day_of_week\"] == day) & (df[\"year\"] == 2022)][sales_variable].astype(\"float\").std(), fmt=\"none\", color=day_colors[i], capsize=3)\n",
    "    ax.errorbar(i+1-0.5*width, df[(df[\"day_of_week\"] == day) & (df[\"year\"] == 2021)][sales_variable].astype(\"float\").mean(), yerr=df[(df[\"day_of_week\"] == day) & (df[\"year\"] == 2021)][sales_variable].astype(\"float\").std(), fmt=\"none\", color=\"black\", capsize=3, alpha=0.6, label = \"2021\")\n",
    "    ax.errorbar(i+1+0.5*width, df[(df[\"day_of_week\"] == day) & (df[\"year\"] == 2022)][sales_variable].astype(\"float\").mean(), yerr=df[(df[\"day_of_week\"] == day) & (df[\"year\"] == 2022)][sales_variable].astype(\"float\").std(), fmt=\"none\", color=\"black\", capsize=3, alpha=0.6)\n",
    "    for tick in ax.get_xticklabels():\n",
    "        tick.set_rotation(30)\n",
    "        tick.set_horizontalalignment(\"right\")\n",
    "    ax.set_xticks(np.arange(1, 8))\n",
    "    ax.set_xticklabels(days)\n",
    "    ax.set_ylim(0, 1800)\n",
    "    ax.set_yticks(np.arange(0, 1600, 500))\n",
    "    ax.tick_params(axis=\"both\", which=\"major\", labelsize=axes_fontsize)\n",
    "    \n",
    "t = [\"2021\", \"2022\", \"± 1 std. dev.\"]\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "leg = ax.legend(handles[:3], t, fontsize=legend_fontsize)\n",
    "leg.legendHandles[0].set_color('grey')\n",
    "leg.legendHandles[1].set_color('dimgrey')\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"results/mean_sales_by_day_of_week.pdf\", dpi=300, format=\"pdf\")\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Plot the per day correlation between sales and maximum temperature / sunshine hours / precipitation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_correlation(df, weather_variable, sales_variable, condition, condition_value, ax, s, marker, color=None, show_condition=False):\n",
    "    df= df[df[condition] == condition_value]\n",
    "    ax.scatter(df[weather_variable], df[sales_variable], color=color, s=s, marker=marker)\n",
    "    X = df[weather_variable].values.reshape(-1, 1)\n",
    "    y = df[sales_variable].values.reshape(-1, 1)\n",
    "    reg = LinearRegression().fit(X, y)\n",
    "    ax.plot(X, reg.predict(X), color=\"black\")\n",
    "    corr = scipy.stats.pearsonr(df[sales_variable], df[weather_variable])[0]\n",
    "    pval = scipy.stats.pearsonr(df[sales_variable], df[weather_variable])[1]\n",
    "    print(\"Correlation between sales and \" + weather_variable + \" on \" + condition_value + \" is about: {:.2f}\".format(corr) + f\". The correlation has a p-value of: {pval}\")\n",
    "\n",
    "    ax.set_title(f\"{condition_value}\", fontsize=18, alpha=1.0 if show_condition else 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_variable = \"temperature_max\"\n",
    "sales_variable = \"Coffee_sales\"\n",
    "\n",
    "# Plot the correlation between the max temperature and the sales for each day of the week.\n",
    "fig, axes = plt.subplots(1, 7, figsize=(page_width, 3), sharey=True, sharex=True, frameon=True)\n",
    "fig.supxlabel(\"Maximum Temperature (°C)\", fontsize=label_fontsize, x=0.5, y=0.08) \n",
    "fig.supylabel(\"Coffee Sales (€)\",         fontsize=label_fontsize, x=0.015, y=0.57)\n",
    "for i, day in enumerate(days):\n",
    "    plot_correlation(df, weather_variable, sales_variable, \"day_of_week\", day, axes[i], 20, \"o\", day_colors[i], True)\n",
    "    axes[i].set_ylim(0, 2200)\n",
    "    axes[i].set_yticks(np.arange(0, 2500, 1000))\n",
    "    axes[i].set_xlim(-7, 37)\n",
    "    axes[i].set_xticks(np.arange(0, 40, 10))\n",
    "    axes[i].tick_params(axis=\"both\", which=\"major\", labelsize=axes_fontsize)\n",
    "    axes[i].spines[\"right\"].set_visible(False)\n",
    "    axes[i].spines[\"top\"].set_visible(False)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"results/correlation_by_day_of_week.pdf\", dpi=300, bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_variable = \"sunshine_duration\"\n",
    "sales_variable = \"Coffee_sales\"\n",
    "\n",
    "# Plot the correlation between sunshine hours and the sales for each day of the week.\n",
    "fig, axes = plt.subplots(1, 7, figsize=(page_width, 3), sharey=True, sharex=True, frameon=True)\n",
    "fig.supxlabel(\"Sunshine Duration (h)\", fontsize=label_fontsize, x=0.5, y=0.08) \n",
    "fig.supylabel(\"Coffee Sales (€)\",      fontsize=label_fontsize, x=0.015, y=0.57)\n",
    "for i, day in enumerate(days):\n",
    "    plot_correlation(df, weather_variable, sales_variable, \"day_of_week\", day, axes[i], 20, \"o\", day_colors[i])\n",
    "    axes[i].set_ylim(0, 2200)\n",
    "    axes[i].set_yticks(np.arange(0, 2500, 1000))\n",
    "    axes[i].set_xlim(-2, 17)\n",
    "    axes[i].set_xticks(np.arange(0, 20, 5))\n",
    "    axes[i].tick_params(axis=\"both\", which=\"major\", labelsize=axes_fontsize)\n",
    "    axes[i].spines[\"right\"].set_visible(False)\n",
    "    axes[i].spines[\"top\"].set_visible(False)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"results/correlation_sunshine_by_day_of_week.pdf\", dpi=300, bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_variable = \"precipitation\"\n",
    "sales_variable = \"Coffee_sales\"\n",
    "\n",
    "# Plot the correlation between the precipitation and the sales for each day of the week.\n",
    "fig, axes = plt.subplots(1, 7, figsize=(page_width, 3), sharey=True, sharex=True, frameon=True)\n",
    "fig.supxlabel(\"Precipitation (mm/m²/day)\", fontsize=label_fontsize, x=0.5, y=0.08)\n",
    "fig.supylabel(\"Coffee Sales (€)\",          fontsize=label_fontsize, x=0.015, y=0.57)\n",
    "for i, day in enumerate(days):\n",
    "    plot_correlation(df, weather_variable, sales_variable, \"day_of_week\", day, axes[i], 20, \"o\", day_colors[i])\n",
    "    axes[i].set_ylim(0, 2200)\n",
    "    axes[i].set_yticks(np.arange(0, 2500, 1000))\n",
    "    axes[i].set_xlim(-2, 35)\n",
    "    axes[i].set_xticks(np.arange(0, 40, 10))\n",
    "    axes[i].tick_params(axis=\"both\", which=\"major\", labelsize=axes_fontsize)\n",
    "    axes[i].spines[\"right\"].set_visible(False)\n",
    "    axes[i].spines[\"top\"].set_visible(False)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"results/correlation_precipitation_by_day_of_week.pdf\", dpi=300, bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Plot the correlation between sales data for cold / warm drinks and the maximum temperature\n",
    "\n",
    "We found that cold drinks had a higher correlation with the temperature than warm ones.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "day_colors_warm = [\"#C11718\", \"#C9311E\", \"#D04526\", \"#D95B2F\", \"#E2703B\", \"#EB8548\", \"#F49A56\"]\n",
    "day_colors_cold = [\"#1C3E8F\", \"#27509D\", \"#3163AB\", \"#3E77BB\", \"#4E8DCA\", \"#60A2D8\", \"#74B9E7\"]\n",
    "\n",
    "sales_variable = \"Coffee_sales\"\n",
    "weather_variable = \"temperature_max\"\n",
    "\n",
    "# Plot the correlation between the max temperature and sales data for warm drinks for each day of the week\n",
    "fig, axes = plt.subplots(1, 7, figsize=(page_width, 3), sharey=True, sharex=True, frameon=True)\n",
    "fig.supxlabel(\"Maximum Temperature (°C)\", fontsize=label_fontsize, x=0.5, y=0.08) \n",
    "fig.supylabel(\"Coffee Sales (€)\", fontsize=label_fontsize, x=0.02, y=0.6)\n",
    "for i, day in enumerate(days):\n",
    "    plot_correlation(df_warm, weather_variable, sales_variable, \"day_of_week\", day, axes[i], 20, \"o\", day_colors_warm[i])\n",
    "    axes[i].set_ylim(0, 2200)\n",
    "    axes[i].set_yticks(np.arange(0, 2500, 1000))\n",
    "    axes[i].set_xlim(-7, 37)\n",
    "    axes[i].set_xticks(np.arange(0, 40, 10))\n",
    "    axes[i].tick_params(axis=\"both\", which=\"major\", labelsize=axes_fontsize)\n",
    "    axes[i].spines[\"right\"].set_visible(False)\n",
    "    axes[i].spines[\"top\"].set_visible(False)\n",
    "# tight layout to avoid overlapping of the x-axis labels\n",
    "# set the title of the whole figure\n",
    "fig.suptitle(\"Correlation between the maximum temperature and the sales of warm drinks\", fontsize=title_fontsize, y=0.87)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Plot the correlation between the max temperature and sales data of cold drinks for each day of the week\n",
    "fig, axes = plt.subplots(1, 7, figsize=(16, 3), sharey=True, sharex=True, frameon=True)\n",
    "fig.supxlabel(\"Maximum Temperature (°C)\", fontsize=label_fontsize, x=0.5, y=0.08) \n",
    "fig.supylabel(\"Coffee Sales (€)\", fontsize=label_fontsize, x=0.02, y=0.6)\n",
    "for i, day in enumerate(days):\n",
    "    plot_correlation(df_cold, weather_variable, sales_variable, \"day_of_week\", day, axes[i], 20, \"o\", day_colors_cold[i])\n",
    "    axes[i].set_ylim(0, 2200)\n",
    "    axes[i].set_yticks(np.arange(0, 2500, 1000))\n",
    "    axes[i].set_xlim(-7, 37)\n",
    "    axes[i].set_xticks(np.arange(0, 40, 10))\n",
    "    axes[i].tick_params(axis=\"both\", which=\"major\", labelsize=axes_fontsize)\n",
    "    axes[i].spines[\"right\"].set_visible(False)\n",
    "    axes[i].spines[\"top\"].set_visible(False)\n",
    "# tight layout to avoid overlapping of the x-axis labels\n",
    "# set the title of the whole figure\n",
    "fig.suptitle(\"Correlation between the maximum temperature and the sales of cold drinks\", fontsize=title_fontsize, y=0.87)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.10 ('PytorchGeometric')",
   "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": "3.7.10"
  },
  "vscode": {
   "interpreter": {
    "hash": "b264b2ac6437c9bf059af297d6e83aaee783728e002e0b2ed5d328e2da20d4ac"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}