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March 12, 2018 20:39
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{ | |
"cells": [ | |
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"cell_type": "code", | |
"execution_count": 1, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"import numpy as np" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"import pandas as pd" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"from numpy.random import randn #randn return samples from the standard normal distribution" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Create a dataframe (2-Dimensional data size tabular data), giving 'data', 'index' and 'columns'" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"df = pd.DataFrame(randn(3,3), index = ['A','B','C'] , columns = 'X Y Z'.split())\n", | |
"#you can give the names of index, columns as a list or you can split a string to create the list" | |
] | |
}, | |
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" <thead>\n", | |
" <tr style=\"text-align: right;\">\n", | |
" <th></th>\n", | |
" <th>X</th>\n", | |
" <th>Y</th>\n", | |
" <th>Z</th>\n", | |
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" <td>-0.555143</td>\n", | |
" <td>0.694758</td>\n", | |
" <td>0.940296</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>B</th>\n", | |
" <td>0.122956</td>\n", | |
" <td>1.242026</td>\n", | |
" <td>0.220803</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>C</th>\n", | |
" <td>-0.207121</td>\n", | |
" <td>0.039789</td>\n", | |
" <td>-1.027087</td>\n", | |
" </tr>\n", | |
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"text/plain": [ | |
" X Y Z\n", | |
"A -0.555143 0.694758 0.940296\n", | |
"B 0.122956 1.242026 0.220803\n", | |
"C -0.207121 0.039789 -1.027087" | |
] | |
}, | |
"execution_count": 6, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"df" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Show first column" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"A -0.555143\n", | |
"B 0.122956\n", | |
"C -0.207121\n", | |
"Name: X, dtype: float64" | |
] | |
}, | |
"execution_count": 8, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"df['X'] #use []" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"The result is a 'Series'. So we can count each column of a 'Dataframe' as a 'Series'" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Show columns 'X' and 'Y'" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 9, | |
"metadata": {}, | |
"outputs": [ | |
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"text/plain": [ | |
" X Y\n", | |
"A -0.555143 0.694758\n", | |
"B 0.122956 1.242026\n", | |
"C -0.207121 0.039789" | |
] | |
}, | |
"execution_count": 9, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"df[['X', 'Y']] #notice the double [[]]" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Show the first two rows" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 10, | |
"metadata": {}, | |
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" <th>B</th>\n", | |
" <td>0.122956</td>\n", | |
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"text/plain": [ | |
" X Y Z\n", | |
"A -0.555143 0.694758 0.940296\n", | |
"B 0.122956 1.242026 0.220803" | |
] | |
}, | |
"execution_count": 10, | |
"metadata": {}, | |
"output_type": "execute_result" | |
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"source": [ | |
"df[:2]" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Show the last 2 rows of columns 'Y' and 'Z'" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 11, | |
"metadata": {}, | |
"outputs": [ | |
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" <td>1.242026</td>\n", | |
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" <th>C</th>\n", | |
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"text/plain": [ | |
" Y Z\n", | |
"B 1.242026 0.220803\n", | |
"C 0.039789 -1.027087" | |
] | |
}, | |
"execution_count": 11, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"df[-2:][['Y', 'Z']]" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Add a new column with the name 'SUM' at the end of the Dataframe containing the sum of X,Y,Z " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 12, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"df['SUM'] = df['X'] + df['Y'] + df['Z']" | |
] | |
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{ | |
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" <td>-0.555143</td>\n", | |
" <td>0.694758</td>\n", | |
" <td>0.940296</td>\n", | |
" <td>1.079911</td>\n", | |
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" <tr>\n", | |
" <th>B</th>\n", | |
" <td>0.122956</td>\n", | |
" <td>1.242026</td>\n", | |
" <td>0.220803</td>\n", | |
" <td>1.585785</td>\n", | |
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" <th>C</th>\n", | |
" <td>-0.207121</td>\n", | |
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"text/plain": [ | |
" X Y Z SUM\n", | |
"A -0.555143 0.694758 0.940296 1.079911\n", | |
"B 0.122956 1.242026 0.220803 1.585785\n", | |
"C -0.207121 0.039789 -1.027087 -1.194419" | |
] | |
}, | |
"execution_count": 13, | |
"metadata": {}, | |
"output_type": "execute_result" | |
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], | |
"source": [ | |
"df" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Drop column 'SUM' permanently " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 14, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"df.drop('SUM', axis=1 , inplace=True) \n", | |
"\n", | |
"#axis=1 because rows have axis=0 and columns have axis=1\n", | |
"#inplace=True for permanent drop. If False or don't include it the drop is not permanent" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 15, | |
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"text/plain": [ | |
" X Y Z\n", | |
"A -0.555143 0.694758 0.940296\n", | |
"B 0.122956 1.242026 0.220803\n", | |
"C -0.207121 0.039789 -1.027087" | |
] | |
}, | |
"execution_count": 15, | |
"metadata": {}, | |
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"source": [ | |
"df" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Locate using 'loc'. \n", | |
"Locate the four corners of your Dataframe (where rows 'A','C' meet columns 'X' and 'Z')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 16, | |
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"text/plain": [ | |
" X Z\n", | |
"A -0.555143 0.940296\n", | |
"C -0.207121 -1.027087" | |
] | |
}, | |
"execution_count": 16, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"df.loc[['A','C'] , ['X','Z']]" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Conditional Selection" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Condition on 'X' column, showing all the row: Show the rows where 'X' is negative" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 18, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
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" <td>-0.207121</td>\n", | |
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"text/plain": [ | |
" X Y Z\n", | |
"A -0.555143 0.694758 0.940296\n", | |
"C -0.207121 0.039789 -1.027087" | |
] | |
}, | |
"execution_count": 18, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
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"source": [ | |
"df[df['X']<0]" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Condition on 'X' column but data on 'Y' column" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 19, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"A 0.694758\n", | |
"C 0.039789\n", | |
"Name: Y, dtype: float64" | |
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"execution_count": 19, | |
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"df[df['X']<0]['Y']" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Dataframe is a collection of Series" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 22, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"pandas.core.frame.DataFrame" | |
] | |
}, | |
"execution_count": 22, | |
"metadata": {}, | |
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} | |
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"type(df)" | |
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{ | |
"cell_type": "code", | |
"execution_count": 21, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"pandas.core.series.Series" | |
] | |
}, | |
"execution_count": 21, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"type(df['X'])" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Load a CSV file from a URL or a local file" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Read a 'csv' file offered online by gov.uk which shows the volume of property sales in UK from 1995 to Oct 2017. The link is\n", | |
"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": "code", | |
"execution_count": 23, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"df_sales = 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": "code", | |
"execution_count": 24, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/html": [ | |
"<div>\n", | |
"<style>\n", | |
" .dataframe thead tr:only-child th {\n", | |
" text-align: right;\n", | |
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" }\n", | |
"\n", | |
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"</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", | |
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" <tbody>\n", | |
" <tr>\n", | |
" <th>0</th>\n", | |
" <td>1995-01-01</td>\n", | |
" <td>England</td>\n", | |
" <td>E92000001</td>\n", | |
" <td>47639</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1</th>\n", | |
" <td>1995-01-01</td>\n", | |
" <td>Wales</td>\n", | |
" <td>W92000004</td>\n", | |
" <td>1991</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>2</th>\n", | |
" <td>1995-01-01</td>\n", | |
" <td>Inner London</td>\n", | |
" <td>E13000001</td>\n", | |
" <td>2890</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>3</th>\n", | |
" <td>1995-01-01</td>\n", | |
" <td>Outer London</td>\n", | |
" <td>E13000002</td>\n", | |
" <td>4616</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>4</th>\n", | |
" <td>1995-01-01</td>\n", | |
" <td>London</td>\n", | |
" <td>E12000007</td>\n", | |
" <td>7506</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" Date Region_Name Area_Code Sales_Volume\n", | |
"0 1995-01-01 England E92000001 47639\n", | |
"1 1995-01-01 Wales W92000004 1991\n", | |
"2 1995-01-01 Inner London E13000001 2890\n", | |
"3 1995-01-01 Outer London E13000002 4616\n", | |
"4 1995-01-01 London E12000007 7506" | |
] | |
}, | |
"execution_count": 24, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"#show first rows of Dataframe\n", | |
"df_sales.head()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 25, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/html": [ | |
"<div>\n", | |
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" <th>Region_Name</th>\n", | |
" <th>Area_Code</th>\n", | |
" <th>Sales_Volume</th>\n", | |
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" <tr>\n", | |
" <th>115597</th>\n", | |
" <td>2017-10-01</td>\n", | |
" <td>Conwy</td>\n", | |
" <td>W06000003</td>\n", | |
" <td>188</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>115598</th>\n", | |
" <td>2017-10-01</td>\n", | |
" <td>Flintshire</td>\n", | |
" <td>W06000005</td>\n", | |
" <td>176</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>115599</th>\n", | |
" <td>2017-10-01</td>\n", | |
" <td>England and Wales</td>\n", | |
" <td>K04000001</td>\n", | |
" <td>67408</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>115600</th>\n", | |
" <td>2017-10-01</td>\n", | |
" <td>Great Britain</td>\n", | |
" <td>K03000001</td>\n", | |
" <td>76255</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>115601</th>\n", | |
" <td>2017-10-01</td>\n", | |
" <td>United Kingdom</td>\n", | |
" <td>K02000001</td>\n", | |
" <td>81756</td>\n", | |
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"</table>\n", | |
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], | |
"text/plain": [ | |
" Date Region_Name Area_Code Sales_Volume\n", | |
"115597 2017-10-01 Conwy W06000003 188\n", | |
"115598 2017-10-01 Flintshire W06000005 176\n", | |
"115599 2017-10-01 England and Wales K04000001 67408\n", | |
"115600 2017-10-01 Great Britain K03000001 76255\n", | |
"115601 2017-10-01 United Kingdom K02000001 81756" | |
] | |
}, | |
"execution_count": 25, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"#show last rows of Dataframe\n", | |
"df_sales.tail()" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Show sales for Reading" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 26, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/html": [ | |
"<div>\n", | |
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" .dataframe thead tr:only-child th {\n", | |
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" <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", | |
" <tr>\n", | |
" <th>2291</th>\n", | |
" <td>1995-06-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>209</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>2739</th>\n", | |
" <td>1995-07-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>241</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>3149</th>\n", | |
" <td>1995-08-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>244</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>3536</th>\n", | |
" <td>1995-09-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>223</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>3883</th>\n", | |
" <td>1995-10-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>182</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>4322</th>\n", | |
" <td>1995-11-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>206</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>4650</th>\n", | |
" <td>1995-12-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>196</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>5047</th>\n", | |
" <td>1996-01-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>151</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>5512</th>\n", | |
" <td>1996-02-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>164</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>5859</th>\n", | |
" <td>1996-03-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>244</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>6249</th>\n", | |
" <td>1996-04-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>203</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>6626</th>\n", | |
" <td>1996-05-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>251</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>7052</th>\n", | |
" <td>1996-06-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>281</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>7474</th>\n", | |
" <td>1996-07-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>290</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>7874</th>\n", | |
" <td>1996-08-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>345</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>8226</th>\n", | |
" <td>1996-09-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>311</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>8614</th>\n", | |
" <td>1996-10-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>286</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>9035</th>\n", | |
" <td>1996-11-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>280</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>9416</th>\n", | |
" <td>1996-12-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>303</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>9795</th>\n", | |
" <td>1997-01-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>210</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>10196</th>\n", | |
" <td>1997-02-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>248</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>10614</th>\n", | |
" <td>1997-03-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>279</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>10979</th>\n", | |
" <td>1997-04-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>269</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>11348</th>\n", | |
" <td>1997-05-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>290</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>11770</th>\n", | |
" <td>1997-06-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>324</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>...</th>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>102793</th>\n", | |
" <td>2015-05-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>305</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>103248</th>\n", | |
" <td>2015-06-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>254</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>103683</th>\n", | |
" <td>2015-07-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>285</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>104087</th>\n", | |
" <td>2015-08-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>288</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>104552</th>\n", | |
" <td>2015-09-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>259</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>104943</th>\n", | |
" <td>2015-10-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>281</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>105416</th>\n", | |
" <td>2015-11-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>243</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>105890</th>\n", | |
" <td>2015-12-01</td>\n", | |
" <td>Reading</td>\n", | |
" <td>E06000038</td>\n", | |
" <td>227</td>\n", | |
" </tr>\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", | |
"<p>274 rows × 4 columns</p>\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\n", | |
"2291 1995-06-01 Reading E06000038 209\n", | |
"2739 1995-07-01 Reading E06000038 241\n", | |
"3149 1995-08-01 Reading E06000038 244\n", | |
"3536 1995-09-01 Reading E06000038 223\n", | |
"3883 1995-10-01 Reading E06000038 182\n", | |
"4322 1995-11-01 Reading E06000038 206\n", | |
"4650 1995-12-01 Reading E06000038 196\n", | |
"5047 1996-01-01 Reading E06000038 151\n", | |
"5512 1996-02-01 Reading E06000038 164\n", | |
"5859 1996-03-01 Reading E06000038 244\n", | |
"6249 1996-04-01 Reading E06000038 203\n", | |
"6626 1996-05-01 Reading E06000038 251\n", | |
"7052 1996-06-01 Reading E06000038 281\n", | |
"7474 1996-07-01 Reading E06000038 290\n", | |
"7874 1996-08-01 Reading E06000038 345\n", | |
"8226 1996-09-01 Reading E06000038 311\n", | |
"8614 1996-10-01 Reading E06000038 286\n", | |
"9035 1996-11-01 Reading E06000038 280\n", | |
"9416 1996-12-01 Reading E06000038 303\n", | |
"9795 1997-01-01 Reading E06000038 210\n", | |
"10196 1997-02-01 Reading E06000038 248\n", | |
"10614 1997-03-01 Reading E06000038 279\n", | |
"10979 1997-04-01 Reading E06000038 269\n", | |
"11348 1997-05-01 Reading E06000038 290\n", | |
"11770 1997-06-01 Reading E06000038 324\n", | |
"... ... ... ... ...\n", | |
"102793 2015-05-01 Reading E06000038 305\n", | |
"103248 2015-06-01 Reading E06000038 254\n", | |
"103683 2015-07-01 Reading E06000038 285\n", | |
"104087 2015-08-01 Reading E06000038 288\n", | |
"104552 2015-09-01 Reading E06000038 259\n", | |
"104943 2015-10-01 Reading E06000038 281\n", | |
"105416 2015-11-01 Reading E06000038 243\n", | |
"105890 2015-12-01 Reading E06000038 227\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\n", | |
"\n", | |
"[274 rows x 4 columns]" | |
] | |
}, | |
"execution_count": 26, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"df_sales[df_sales['Region_Name']=='Reading']" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Find the max 'Sales_Volume' properties sold in Reading" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 30, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"463" | |
] | |
}, | |
"execution_count": 30, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"df_sales[df_sales['Region_Name']=='Reading']['Sales_Volume'].max()" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Find the index of the max 'Sales_Volume' sold in Reading" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 31, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"21616" | |
] | |
}, | |
"execution_count": 31, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"df_sales[df_sales['Region_Name']=='Reading']['Sales_Volume'].argmax()" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Locate a row using its index. Use 'iloc' method" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 36, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"Date 1999-07-01\n", | |
"Region_Name Reading\n", | |
"Area_Code E06000038\n", | |
"Sales_Volume 463\n", | |
"Name: 21616, dtype: object" | |
] | |
}, | |
"execution_count": 36, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"df_sales.iloc[21616]" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Find the Region where there was the minimum Sales_volume ever" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 44, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"'City of London'" | |
] | |
}, | |
"execution_count": 44, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"#Use loc and idxmin() methods\n", | |
"df_sales.loc[df_sales['Sales_Volume'].argmin()]['Region_Name']" | |
] | |
}, | |
{ | |
"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 | |
} |
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