Basic stats  2 - Set 1.ipynb

{
 "cells": [
  {
   "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 sn\n",
    "%matplotlib inline\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=[24.23,25.53,25.41,24.14,29.62,28.25,25.81,24.39,40.26,32.95,91.36,25.99,39.42,26.71,35.00]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>28.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>25.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>24.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>40.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>32.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>91.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>25.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>39.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>26.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>35.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0\n",
       "0   24.23\n",
       "1   25.53\n",
       "2   25.41\n",
       "3   24.14\n",
       "4   29.62\n",
       "5   28.25\n",
       "6   25.81\n",
       "7   24.39\n",
       "8   40.26\n",
       "9   32.95\n",
       "10  91.36\n",
       "11  25.99\n",
       "12  39.42\n",
       "13  26.71\n",
       "14  35.00"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x28c70a37748>,\n",
       "  <matplotlib.lines.Line2D at 0x28c70a37d08>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x28c70a37648>,\n",
       "  <matplotlib.lines.Line2D at 0x28c709f8408>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x28c70a37188>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x28c70a37688>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x28c70a103c8>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 12,
     "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": [
    "#for identifing outliers, here data has one outlier\n",
    "plt.boxplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([10.,  2.,  2.,  0.,  0.,  0.,  0.,  0.,  0.,  1.]),\n",
       " array([24.14 , 30.862, 37.584, 44.306, 51.028, 57.75 , 64.472, 71.194,\n",
       "        77.916, 84.638, 91.36 ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 16,
     "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": [
    "plt.hist(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>33.271333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.945401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>24.140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.470000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>26.710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>33.975000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>91.360000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0\n",
       "count  15.000000\n",
       "mean   33.271333\n",
       "std    16.945401\n",
       "min    24.140000\n",
       "25%    25.470000\n",
       "50%    26.710000\n",
       "75%    33.975000\n",
       "max    91.360000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean and standard deviation \n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    287.146612\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#variance\n",
    "df.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}