P08. Box Plot Practice.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Box Plot Practice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=[11, 12, 12, 13, 15, 15, 15, 16, 17, 20, 21, 21, 21, 22, 22, 22, 23, 24, 26, 27, 27, 27, 28, 29, 29,\n",
    "30, 31, 32, 34, 35, 37, 41, 41, 42, 45, 47, 50, 52, 53, 56, 60, 62]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "plt.boxplot(X);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LQ=np.percentile(X,25)\n",
    "LQ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median=np.median(X);median\n",
    "median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "UQ = np.percentile(X, 75)\n",
    "UQ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR=UQ-LQ\n",
    "IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "only integer scalar arrays can be converted to a scalar index",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-31561c52ae16>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mupper_whisker\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m<=\u001b[0m\u001b[0mUQ\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1.5\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mIQR\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m## it gives error, better to do it by calling dataframe\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: only integer scalar arrays can be converted to a scalar index"
     ]
    }
   ],
   "source": [
    "upper_whisker = X[X<=upper_quartile+1.5*IQR].max() ## it gives error, better to do it by calling dataframe\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box Plot - using dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.Series(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    11\n",
       "1    12\n",
       "2    12\n",
       "3    13\n",
       "4    15\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "plt.boxplot(df)\n",
    "ax.grid()\n",
    "ax.grid(which='major', linestyle='-', linewidth='0.5', color='red') # inserting Major grid lines\n",
    "ax.minorticks_on()\n",
    "ax.grid(which='minor', linestyle=':', linewidth='0.5', color='black') # inserting Minor grid lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.0"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LQ=np.percentile(df,25)\n",
    "LQ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.0"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Median=np.median(df)\n",
    "Median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40.0"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "UQ=np.percentile(df,75)\n",
    "UQ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.0"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR = UQ-LQ\n",
    "IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Upper_whisker=df[df<=UQ+1.5*IQR].max()\n",
    "Upper_whisker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lower_whisker=df[df>=LQ-1.5*IQR].min()\n",
    "Lower_whisker"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other simpler method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "B=plt.boxplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([21., 11.]), array([40., 62.])]"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[item.get_ydata() for item in B['whiskers']] ## [LQ,Lower whisker],[UQ,Upper whisker]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[21.0, 40.0]"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[item.get_ydata()[0] for item in B['whiskers']] ## [LQ,UQ] and [1] if whiskers only reqd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.0"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median = np.median(df)\n",
    "median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR=40-21\n",
    "IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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