codeflix.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2da6d0a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "labels = ['Dec-2016', 'Jan-2017','Feb-2017','March-2017']\n",
    "usergain = np.array([570,507,460,463])\n",
    "userloss = np.array([0,92,186,342])\n",
    "\n",
    "width = 2\n",
    "x = np.arange(len(labels))\n",
    "x1=np.arange(0,6*width,6*width/4)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "currentbottom=0\n",
    "lossbottom=0\n",
    "\n",
    "ax.bar(x1[0]+width/2, usergain[0], width/2,bottom=0, color=(0.76,0.76,0.74,1),\n",
    "       edgecolor='gray',align='center',label='Subscribers gained')\n",
    "ax.bar(x1[0]+width/2, userloss[0], width/2,bottom=0, color=(0.95,0.57,0.52,1),\n",
    "       edgecolor='gray',align='center',label='Subscribers lost')\n",
    "\n",
    "for i in range(0, len(labels)-1):\n",
    "    usercount=usergain[i]\n",
    "    currentbottom+=(usergain[i]-userloss[i])\n",
    "    lossbottom=(usergain[i+1]+currentbottom-userloss[i+1])\n",
    "    p1=ax.bar(x1[i+1]-width/2, np.zeros_like(usergain[i+1]), width/2, bottom=currentbottom, fill=False,edgecolor='gray')\n",
    "    p2=ax.bar(x1[i+1], usergain[i+1], width/2,bottom=currentbottom, color=(0.76,0.76,0.74,1),edgecolor='gray')\n",
    "    p3=ax.bar(x1[i+1]+width/2, userloss[i+1], width/2,\n",
    "                    bottom=lossbottom, color=(0.95,0.57,0.52,1),edgecolor='gray')\n",
    "    ax.bar_label(p1,label_type='edge')\n",
    "    \n",
    "last_value=str(int(currentbottom+usergain[len(labels)-1]-userloss[len(labels)-1]))\n",
    "ax.bar_label(p3,fmt=last_value,label_type='edge', padding=-userloss[-1]/3-25)\n",
    "\n",
    "ax.set_xticks(x1+width/2)\n",
    "ax.set_xticklabels(labels,fontsize=12, color='dimgray' )\n",
    "ax.set_xlim(0,12)\n",
    "ax.set_yticks([0,400,800,1200,1600,2000])\n",
    "ax.grid(axis='y')\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "#Adding the legend\n",
    "handles, labels_legend = plt.gca().get_legend_handles_labels()\n",
    "order = [0,1]\n",
    "plt.legend([handles[idx] for idx in order],[labels_legend[idx] for idx in order], fontsize=10,\n",
    "           frameon = False,bbox_to_anchor=(0.95,0.1)) \n",
    "\n",
    "ax.set_ylim(bottom=0)\n",
    "ax.set_ylabel('Subscribers',fontsize=14, color='dimgray' )\n",
    "fig.set_size_inches(18, 12)\n",
    "fig.set(facecolor=(0.996,0.992,0.968,1))\n",
    "ax.set_facecolor(color=(0.996,0.992,0.968,1))\n",
    "#plt.gca().set_title('Subscriber activity', fontsize=25, fontweight ='bold', loc='left', color='dimgray')\n",
    "plt.savefig('codiflix_1.png', dpi=600)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}