20230102D.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x540 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3) = plt.subplots(3, sharex='col', sharey='col')\n",
    "fig.set_size_inches(10,7.5)\n",
    "\n",
    "ax1.bar(x=x_values1, height=y_values1, align='edge', width=(x_values1[1]-x_values1[0]), facecolor='#E5E7E9')\n",
    "ax2.bar(x=x_values2, height=y_values2, align='edge', width=(x_values2[1]-x_values2[0]), facecolor='#E5E7E9')\n",
    "ax3.bar(x=x_values3, height=y_values3, align='edge', width=(x_values3[1]-x_values3[0]), facecolor='#E5E7E9')\n",
    "\n",
    "ax1.title.set_text('Histogram plot for normally distributed data')\n",
    "ax2.title.set_text('Histogram plot for data with positive skew')\n",
    "ax3.title.set_text('Histogram plot for data with negative skew')\n",
    "\n",
    "max_y_data = plt.gca().get_ylim()[1]\n",
    "min_y = [max_y_data*0.35, max_y_data*0.35, max_y_data*0.35, 0]\n",
    "max_y = [max_y_data, max_y_data, max_y_data, max_y_data*0.65]\n",
    "colors =['#8E44AD', '#F39C12', '#2874A6', '#7F8C8D']\n",
    "\n",
    "ax1.vlines([data1_q1, data1_median, data1_q3, data1_mean], ymin=min_y, ymax=max_y, color=colors, alpha=0.75)\n",
    "ax2.vlines([data2_q1, data2_median, data2_q3, data2_mean], ymin=min_y, ymax=max_y, color=colors, alpha=0.75)\n",
    "ax3.vlines([data3_q1, data3_median, data3_q3, data3_mean], ymin=min_y, ymax=max_y, color=colors, alpha=0.75)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "newenv",
   "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": "3.10.5 | packaged by conda-forge | (main, Jun 14 2022, 06:57:50) [MSC v.1929 64 bit (AMD64)]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "65e74bcb0609f7f067e47051b00bfa25ce7004419ec87f30c427e7be065571b6"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}