EDA percentage and values.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "df = pd.read_csv(r\"C:\\Users\\Gouse MG\\Desktop\\Machine Learning\\Medical care banglore\\newweek.csv\")\n",
    "df.head(10)\n",
    "df.drop(['Total_Patients'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>OPI_BIN</th>\n",
       "      <th>Nos_PT_Called</th>\n",
       "      <th>Nos_PT_Texted</th>\n",
       "      <th>Nos_PT_Emailed</th>\n",
       "      <th>Nos_PT_Anymedia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>High_OPI</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Medium_OPI</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Low_OPI</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      OPI_BIN  Nos_PT_Called  Nos_PT_Texted  Nos_PT_Emailed  Nos_PT_Anymedia\n",
       "0    High_OPI              8             11               0              108\n",
       "1  Medium_OPI              2              5               1               89\n",
       "2     Low_OPI              4              7               0              259"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Stacked Bar Graph by dataframe'}, xlabel='OPI_BIN'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x='OPI_BIN', kind='bar', stacked=True,\n",
    "        title='Stacked Bar Graph by dataframe')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "df = df.set_index('OPI_BIN')\n",
    "result_pct = df.div(df.sum(axis=1), axis=0)\n",
    "\n",
    "print(result_pct.reset_index())"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# ax = result_pct.plot(kind='bar',stacked=True,figsize=(20,5),width = 0.2,edgecolor=None)\n",
    "plt.legend(labels=df.columns,fontsize= 14)\n",
    "plt.title(\"Patient Distribution accross OPI Level for TEST POOL(GLOBAL)\",fontsize= 16,loc='center',pad=35)\n",
    "plt.xlabel(\"OPI_BIN\",fontsize=14,loc='center')\n",
    "plt.ylabel(\"No.of Patients\",fontsize=14,loc='center')\n",
    "plt.locator_params(axis=\"y\", nbins=10)\n",
    "\n",
    "plt.xticks(fontsize=14)\n",
    "\n",
    "\n",
    "for spine in plt.gca().spines.values():\n",
    "    spine.set_visible(False)\n",
    "plt.yticks()\n",
    "\n",
    "# Add this loop to add the annotations\n",
    "for p in ax.patches:\n",
    "    width = p.get_width()\n",
    "    height = p.get_height()\n",
    "    x, y = p.get_xy() \n",
    "    ax.annotate(f'{height:5.0%}', (x + width/2, y + height*0.02), ha='center')\n",
    "    \n",
    "    \n",
    "# rename xtick labels; remove time\n",
    "ticks, labels = plt.xticks(rotation=90)\n",
    "labels = [label.get_text()[:10] for label in labels]\n",
    "plt.xticks(ticks=ticks, labels=labels)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df['OPI_BIN']\n",
    "y1 = np.array(df['Nos_PT_Called'])\n",
    "y2 = np.array(df['Nos_PT_Texted'])\n",
    "y3 = df['Nos_PT_Emailed']\n",
    "y4 = np.array(df['Nos_PT_Anymedia'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# memo of sample number\n",
    "snum = y1+y2+y3+y4\n",
    "\n",
    "# normalization\n",
    "called_per = y1/snum*100\n",
    "texted_per = y2/snum*100\n",
    "email_per  = y3/snum*100\n",
    "media_per  = y4/snum*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    0\n",
       "Name: Nos_PT_Emailed, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Nos_PT_Emailed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.patches import Rectangle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "\n",
    "plt.bar(x, y1, label='Nos_PT_Called',width=0.9,color='#FFD700',align = \"center\",edgecolor='black')\n",
    "plt.bar(x, y2,bottom=y1,label='Nos_PT_Texted',width=0.9,color='#EEE8AA',align = \"center\",edgecolor='black')\n",
    "plt.bar(x, y3,bottom=y1+y2,label='Nos_PT_Emailed',width=0.9,color='#8B4513',align = \"center\")\n",
    "plt.bar(x, y4,bottom=y1+y2+y3,label='Nos_PT_Anymedia',width=0.9,color='#87CEEB',align = \"center\",edgecolor='black')\n",
    "\n",
    "#plt.gcf().subplots_adjust(bottom=1.35, top=2.35)\n",
    "\n",
    "plt.xticks(fontsize=14)\n",
    "plt.title(\"Patient Distribution accross OPI Level for TEST POOL(GLOBAL)\",fontsize= 16,loc='center',pad=35)\n",
    "plt.xlabel(\"OPI_BIN\",fontsize=14,loc='center')\n",
    "plt.ylabel(\"No.of Patients\",fontsize=14,loc='center')\n",
    "plt.locator_params(axis=\"y\", nbins=10)\n",
    "\n",
    "\n",
    "\n",
    "# add text annotation corresponding to the values/percentage of each bar.\n",
    "\n",
    "\n",
    "for xpos, ypos, yval in zip(x, y1/8, y1):\n",
    "    plt.text(xpos, ypos, yval, ha=\"right\", va=\"center\",fontsize=14,color = 'black')\n",
    "for xpos, ypos, yval in zip(x, called_per/6, called_per):\n",
    "    plt.text(xpos, ypos, \"(%%=%.1f)  \"%yval, ha=\"right\", va=\"center\",color='black',fontsize=14)\n",
    "    \n",
    "    \n",
    "for xpos, ypos, yval in zip(x, y1+y2/18, y2):\n",
    "    plt.text(xpos, ypos, yval, ha=\"left\", va=\"center\",fontsize=14,rotation_mode='anchor')\n",
    "for xpos, ypos, yval in zip(x, texted_per/2, texted_per):\n",
    "    plt.text(xpos, ypos, \"   (%%=%.1f)\"%yval, ha=\"left\", va=\"baseline\",fontsize=14,rotation=5)\n",
    "\n",
    "\n",
    "for xpos, ypos, yval in zip(x, y1+y2+y3/3, y3):\n",
    "    plt.text(xpos, ypos, yval, ha=\"left\", va=\"bottom\",fontsize=14)\n",
    "     \n",
    "    \n",
    "    \n",
    "for xpos,ypos, yval in zip(x, y1+y2+y3+y4/3.5, y4):\n",
    "    plt.text(xpos, ypos, yval, ha=\"center\", va=\"center\",fontsize=18)\n",
    "for xpos, ypos, yval in zip(x, media_per/2, media_per):\n",
    "    plt.text(xpos, ypos, \"      (%%=%.1f)\"%yval, ha=\"center\", va=\"center\",fontsize=14)\n",
    "\n",
    "    \n",
    "    \n",
    "# add text annotation corresponding to the \"total\" value of each bar\n",
    "for xpos, ypos, yval in zip(x, y1+y2+y3+y4, y1+y2+y3+y4):\n",
    "    plt.text(xpos, ypos, \"%d\"%yval, ha=\"left\", va=\"baseline\",fontsize=18)\n",
    "\n",
    "    \n",
    "\n",
    "    \n",
    "plt.ylim(0,350)\n",
    "\n",
    "plt.legend(title='Categories',title_fontsize=20,fontsize=12)\n",
    "plt.tight_layout( pad=5.08, h_pad=2.5, w_pad=None, rect=(1,1,1,2))\n",
    "plt.savefig('stacked_barplot_with_number.png', bbox_inches='tight', pad_inches=0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>OPI_BIN</th>\n",
       "      <th>Nos_PT_Called</th>\n",
       "      <th>Nos_PT_Texted</th>\n",
       "      <th>Nos_PT_Emailed</th>\n",
       "      <th>Nos_PT_Anymedia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>High_OPI</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Medium_OPI</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Low_OPI</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      OPI_BIN  Nos_PT_Called  Nos_PT_Texted  Nos_PT_Emailed  Nos_PT_Anymedia\n",
       "0    High_OPI              8             11               0              108\n",
       "1  Medium_OPI              2              5               1               89\n",
       "2     Low_OPI              4              7               0              259"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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