maheshagodekere/sample.ipynb

## sample.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy import stats\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>rank</th>\n",
       "      <th>name</th>\n",
       "      <th>country</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Mauro Vinicius DA SILVA</td>\n",
       "      <td>BRA</td>\n",
       "      <td>8.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Marquise GOODWIN</td>\n",
       "      <td>USA</td>\n",
       "      <td>8.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Aleksandr MENKOV</td>\n",
       "      <td>RUS</td>\n",
       "      <td>8.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Greg RUTHERFORD</td>\n",
       "      <td>GBR</td>\n",
       "      <td>8.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Christopher TOMLINSON</td>\n",
       "      <td>GBR</td>\n",
       "      <td>8.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rank                     name country  distance\n",
       "0     1  Mauro Vinicius DA SILVA     BRA      8.11\n",
       "1     2         Marquise GOODWIN     USA      8.11\n",
       "2     3         Aleksandr MENKOV     RUS      8.09\n",
       "3     4          Greg RUTHERFORD     GBR      8.08\n",
       "4     5    Christopher TOMLINSON     GBR      8.06"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income = pd.read_csv('../data/us.income.csv')\n",
    "income.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    40.000000\n",
       "mean      7.674500\n",
       "std       0.370003\n",
       "min       6.550000\n",
       "25%       7.500000\n",
       "50%       7.765000\n",
       "75%       7.955000\n",
       "max       8.110000\n",
       "Name: distance, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Descriptive statistics summary\n",
    "income['distance'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11722ef98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(income['distance']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 10,
	"metadata": {},
	"outputs": [],
	"source": [
	"import pandas as pd\n",
	"import matplotlib.pyplot as plt\n",
	"import seaborn as sns\n",
	"import numpy as np\n",
	"from scipy.stats import norm\n",
	"from sklearn.preprocessing import StandardScaler\n",
	"from scipy import stats\n",
	"import warnings\n",
	"warnings.filterwarnings('ignore')\n",
	"%matplotlib inline"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 13,
	"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>rank</th>\n",
	" <th>name</th>\n",
	" <th>country</th>\n",
	" <th>distance</th>\n",
	" </tr>\n",
	" </thead>\n",
	" <tbody>\n",
	" <tr>\n",
	" <th>0</th>\n",
	" <td>1</td>\n",
	" <td>Mauro Vinicius DA SILVA</td>\n",
	" <td>BRA</td>\n",
	" <td>8.11</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>1</th>\n",
	" <td>2</td>\n",
	" <td>Marquise GOODWIN</td>\n",
	" <td>USA</td>\n",
	" <td>8.11</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>2</th>\n",
	" <td>3</td>\n",
	" <td>Aleksandr MENKOV</td>\n",
	" <td>RUS</td>\n",
	" <td>8.09</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>3</th>\n",
	" <td>4</td>\n",
	" <td>Greg RUTHERFORD</td>\n",
	" <td>GBR</td>\n",
	" <td>8.08</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>4</th>\n",
	" <td>5</td>\n",
	" <td>Christopher TOMLINSON</td>\n",
	" <td>GBR</td>\n",
	" <td>8.06</td>\n",
	" </tr>\n",
	" </tbody>\n",
	"</table>\n",
	"</div>"
	],
	"text/plain": [
	" rank name country distance\n",
	"0 1 Mauro Vinicius DA SILVA BRA 8.11\n",
	"1 2 Marquise GOODWIN USA 8.11\n",
	"2 3 Aleksandr MENKOV RUS 8.09\n",
	"3 4 Greg RUTHERFORD GBR 8.08\n",
	"4 5 Christopher TOMLINSON GBR 8.06"
	]
	},
	"execution_count": 13,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"income = pd.read_csv('../data/us.income.csv')\n",
	"income.head()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 15,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"count 40.000000\n",
	"mean 7.674500\n",
	"std 0.370003\n",
	"min 6.550000\n",
	"25% 7.500000\n",
	"50% 7.765000\n",
	"75% 7.955000\n",
	"max 8.110000\n",
	"Name: distance, dtype: float64"
	]
	},
	"execution_count": 15,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"# Descriptive statistics summary\n",
	"income['distance'].describe()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 16,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"image/png": 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\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x11722ef98>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"sns.distplot(income['distance']);"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {},
	"outputs": [],
	"source": []
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {},
	"outputs": [],
	"source": []
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {},
	"outputs": [],
	"source": []
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "Python [conda root]",
	"language": "python",
	"name": "conda-root-py"
	},
	"language_info": {
	"codemirror_mode": {
	"name": "ipython",
	"version": 3
	},
	"file_extension": ".py",
	"mimetype": "text/x-python",
	"name": "python",
	"nbconvert_exporter": "python",
	"pygments_lexer": "ipython3",
	"version": "3.6.0"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}