ikoustou/seaborn1.ipynb

## seaborn1.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the same method we used on \"Pandas Tutorial\" and load the sales volume of properties of UK."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('http://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Sales-2017-12.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=sales&utm_term=9.30_13_02_18')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Query the DataFrame and find the sales volume in Reading, England. ('Region_Name'=='Reading')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_R = df[df['Region_Name']=='Reading']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Region_Name</th>\n",
       "      <th>Area_Code</th>\n",
       "      <th>Sales_Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>1995-02-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1169</th>\n",
       "      <td>1995-03-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1538</th>\n",
       "      <td>1995-04-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>1995-05-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date Region_Name  Area_Code  Sales_Volume\n",
       "380   1995-01-01     Reading  E06000038           137\n",
       "719   1995-02-01     Reading  E06000038           161\n",
       "1169  1995-03-01     Reading  E06000038           217\n",
       "1538  1995-04-01     Reading  E06000038           153\n",
       "1967  1995-05-01     Reading  E06000038           232"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_R.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the results as barplot using seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#the following command is used only for Jupyter Notebooks (as we described on previous tutorial)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._axes.Axes at 0x13171a58>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wJEmSpPbcdXqtqeFeR49sU8MHvnTvQDliRBetCaidGi7v05LGkdMvnAHA0W+5puJIJEka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\nJElSSUy4JEmSJKkk/x9vrNfQ4UPyuQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13171828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,4))\n",
    "axes = fig.add_axes([.1, .1, .9, .9])\n",
    "\n",
    "sns.barplot(x='Date' , y='Sales_Volume' , data = df_R, ax = axes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the distribution of the data using seaborn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x142e8208>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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liWa2ysw2mlmZmf14sBok/nPO8fOlOxg9IpEvnl/kdzhRpzQ3hWkFGfzmzV10ampsGYCT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\nCKCEICIiHiUEEREBlBBERMTz/wGghQzD69tWJgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c5e0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_R['Sales_Volume'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdf69898>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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13iF1uo+fNYJrpwzjp4s28re39UeQnDyxJISlQKGZjTSzVCIniee36DMfuDlYngkscncP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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14f29518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_R['Sales_Volume'], bins=50) #using more histogram bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot data as bars for years 2016 and 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Region_Name</th>\n",
       "      <th>Area_Code</th>\n",
       "      <th>Sales_Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>106278</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106744</th>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107168</th>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107591</th>\n",
       "      <td>2016-04-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108077</th>\n",
       "      <td>2016-05-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108485</th>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108949</th>\n",
       "      <td>2016-07-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109365</th>\n",
       "      <td>2016-08-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109832</th>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110245</th>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110678</th>\n",
       "      <td>2016-11-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111127</th>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111585</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112015</th>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112490</th>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112891</th>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113374</th>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113786</th>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114237</th>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114675</th>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115136</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115556</th>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>Reading</td>\n",
       "      <td>E06000038</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date Region_Name  Area_Code  Sales_Volume\n",
       "106278  2016-01-01     Reading  E06000038           188\n",
       "106744  2016-02-01     Reading  E06000038           225\n",
       "107168  2016-03-01     Reading  E06000038           461\n",
       "107591  2016-04-01     Reading  E06000038           194\n",
       "108077  2016-05-01     Reading  E06000038           155\n",
       "108485  2016-06-01     Reading  E06000038           261\n",
       "108949  2016-07-01     Reading  E06000038           291\n",
       "109365  2016-08-01     Reading  E06000038           255\n",
       "109832  2016-09-01     Reading  E06000038           213\n",
       "110245  2016-10-01     Reading  E06000038           201\n",
       "110678  2016-11-01     Reading  E06000038           184\n",
       "111127  2016-12-01     Reading  E06000038           214\n",
       "111585  2017-01-01     Reading  E06000038           194\n",
       "112015  2017-02-01     Reading  E06000038           188\n",
       "112490  2017-03-01     Reading  E06000038           204\n",
       "112891  2017-04-01     Reading  E06000038           154\n",
       "113374  2017-05-01     Reading  E06000038           161\n",
       "113786  2017-06-01     Reading  E06000038           231\n",
       "114237  2017-07-01     Reading  E06000038           204\n",
       "114675  2017-08-01     Reading  E06000038           215\n",
       "115136  2017-09-01     Reading  E06000038           173\n",
       "115556  2017-10-01     Reading  E06000038           173"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_R[df_R['Date']>'2016']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fWZ0kaaH88ulvHHV7//zE3xx1e5KkHc9KBqKdq+rq4faTgROq6nTg9GVXjpMkSZKkhbOS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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1725f390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = plt.figure(figsize=(12,4))\n",
    "axes2 = fig2.add_axes([.1, .1, .9, .9])\n",
    "\n",
    "\n",
    "sns.barplot(x='Date', y='Sales_Volume', data=df_R[df_R['Date']>'2016'], ax=axes2)\n",
    "\n",
    "#rotate x labels\n",
    "for tick in axes2.get_xticklabels():\n",
    "    tick.set_rotation(90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}