{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Part III : Exploratory Data Analyses" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Jump to : \n", "* [Part 1](https://github.com/Niladri-B/Coursera_Captstone/blob/master/wk4/Capstone_part1.ipynb) *Extracting Street Addresses & Coordinates* \n", "* [Part 2](https://github.com/Niladri-B/Coursera_Captstone/blob/master/wk4/Capstone_part2-forUpload.ipynb), *Extracting Foursquare Data*\n", "* [Part 4](https://github.com/Niladri-B/Coursera_Captstone/blob/master/wk4/Capstone_part4.ipynb), *Clustering and Visualising*\n", "* [Part 5](https://github.com/Niladri-B/Coursera_Captstone/blob/master/wk4/Capstone_part5.ipynb), *Conclusion & Discussion*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1: Load up environment and data" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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StreetStreet LatitudeStreet LongitudeTrikkTrikk Distance01234567891011121314151617T-bane_1T-bane_2T-bane_3T-bane_4Train Station
0Gregers Grams vei59.93487610.647214NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1Åsbrekka59.97155910.664394NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2Lilleborggata59.93692910.770171Torshov (trikk)401.0Buss 55 BNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3Simensbrekka59.90077810.783550NaNNaNUnderveis med 34 bussenNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4Krossveien59.96255010.764100NaNNaN25-bussenNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
\n", "
" ], "text/plain": [ " Street Street Latitude Street Longitude Trikk \\\n", "0 Gregers Grams vei 59.934876 10.647214 NaN \n", "1 Åsbrekka 59.971559 10.664394 NaN \n", "2 Lilleborggata 59.936929 10.770171 Torshov (trikk) \n", "3 Simensbrekka 59.900778 10.783550 NaN \n", "4 Krossveien 59.962550 10.764100 NaN \n", "\n", " Trikk Distance 0 1 2 3 4 5 6 7 \\\n", "0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "2 401.0 Buss 55 B NaN NaN NaN NaN NaN NaN NaN \n", "3 NaN Underveis med 34 bussen NaN NaN NaN NaN NaN NaN NaN \n", "4 NaN 25-bussen NaN NaN NaN NaN NaN NaN NaN \n", "\n", " 8 9 10 11 12 13 14 15 16 17 T-bane_1 T-bane_2 \\\n", "0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "3 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "4 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "\n", " T-bane_3 T-bane_4 Train Station \n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "streetTrikkBussMetroTog = pd.read_csv('./streetData_TrikkBusMetroTog.csv')\n", "streetTrikkBussMetroTog.head()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2460, 28)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "streetTrikkBussMetroTog.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, in all we have information on **2460** streets in Oslo, Norway" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 2: Find total transport options" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index([, , , ,\n", " , , , ,\n", " , , , ,\n", " , , , ,\n", " , , , ,\n", " , , , ,\n", " , , , ],\n", " dtype='object')" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Check column type in the above dataframe\n", "streetTrikkBussMetroTog.columns.map(type)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[str,\n", " str,\n", " str,\n", " str,\n", " str,\n", " str,\n", " str,\n", " str,\n", " str,\n", " str,\n", " str,\n", " str,\n", " str,\n", " str,\n", " str,\n", " str,\n", " str,\n", " str,\n", " str,\n", " str,\n", " str,\n", " str,\n", " str,\n", " str]" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#To find total transport options/street, we sum the columns that are NOT null, followed by sorting in descending order to obtain streets with maximum number of transport options\n", "\n", "#First we must select the columns to sum on\n", "busCOLS = list(range(18))\n", "\n", "#Now we define a function that can automatically add text to numbers, useful for colns of T-bane\n", "def makeColnName (list_of_colnNum):\n", " \n", " metroCOLSlist = []\n", " for i in list_of_colnNum:\n", " #print('T-bane_'+str(i))\n", " metroCOLSlist.append('T-bane_'+str(i))#Choose whatever colnName you want to prepend to the numbers\n", " return(metroCOLSlist)\n", "\n", "#Makes columns of T-bane\n", "COLSlist = makeColnName(list(range(1,5)))\n", "\n", "#Join T-bane and bus lists together with additional list of Trikk+Tog\n", "COLSlist = ['Trikk','Train Station']+ COLSlist + busCOLS\n", "COLSlist#Total 24 transport colns, out of which max comes 22...wow...a street with 22 options..??\n", "\n", "#Check data type in the list\n", "#for i in COLSlist:\n", "# if (type(i)) == str:\n", "# print('YABBA',i)\n", "# else:\n", "# print('BOO',i)\n", "# \n", "# \n", "#Convert int types to str types\n", "def convertType (LIST):\n", " \n", " newLIST = list()\n", " for i in LIST:\n", " if type(i) == str:\n", " newLIST.append(i)\n", " else:\n", " #str(i)\n", " newLIST.append(str(i))\n", " \n", " return newLIST\n", "\n", "COLSlist_str = convertType(COLSlist)\n", "\n", "#Check correctly formatted\n", "[type(i) for i in COLSlist_str] " ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "#Sort in descending order of non-null values i.e. highest transport to lowest\n", "streetTrikkBussMetroTog[COLSlist_str].notnull().sum(axis = 'columns').sort_values(ascending = False) \n", "\n", "#Also store this in a new coln named totalTransport\n", "streetTrikkBussMetroTog['Total Transport']= streetTrikkBussMetroTog[COLSlist_str].notnull().sum(axis = 'columns').sort_values(ascending = False) #Sort in descending order of non-null values i.e. highest transport to lowest" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Int64Index([1993, 554, 1736, 1152, 2354, 79, 1654, 1555, 1439, 279,\n", " ...\n", " 1136, 1134, 1126, 1124, 1122, 1115, 1101, 1092, 1091, 0],\n", " dtype='int64', length=2460)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Obtain new index of the sorted order\n", "newIndex = streetTrikkBussMetroTog[COLSlist_str].notnull().sum(axis = 'columns').sort_values(ascending = False).index \n", "newIndex" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0, 1, 2, ..., 2457, 2458, 2459])" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#If you want to see all the values of the original dataframe (not the sorted)\n", "streetTrikkBussMetroTog.index.values" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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StreetStreet LatitudeStreet LongitudeTrikkTrikk Distance01234567891011121314151617T-bane_1T-bane_2T-bane_3T-bane_4Train StationTotal Transport
0Lilletorget59.91350010.755900Jernbanetorget (Trikk/B)356.0buss 500Buss 30Buss 252Buss 70Buss 34Buss 54Buss 143Buss 54Buss 490Eplatform 29 oslo bussterminalBuss 111Buss 31Buss 31 EBuss 542 Seiersten Ekspress150 Oslo Bussterminal - Gullhaug - Oslo Busst...plattform 20-Buss 501 enebakk-Lillestrøm550 Oslo Bussterminal - LillestrømPlattform 18 - Buss 411 Lillestrøm('Jernbanetorget (T)', 248)('Grønland (T)', 318)NaNNaN('Oslo Sentralstasjon', 332)22
1Europarådets plass59.91200010.749300Jernbanetorget (Trikk/B)48.0Buss 31 EBuss 34Buss 542 Seiersten EkspressBuss 54Buss 82EBuss 143Buss 542 Til DrøbakBuss 37Buss 70Buss 54Buss 30Buss 252buss 500Buss 83Buss 3181B-Bussen81A-bussenplatform 29 oslo bussterminal('Jernbanetorget (T)', 164)('Stortinget (T)', 420)NaNNaN('Oslo Sentralstasjon', 300)22
2Brugata59.91400510.755774Jernbanetorget (Trikk/B)375.0Buss 30buss 500Buss 252Buss 54Buss 70Buss 34Buss 111Buss 143Buss 490EBuss 54Buss 31Buss 31 Eplatform 29 oslo bussterminal150 Oslo Bussterminal - Gullhaug - Oslo Busst...plattform 20-Buss 501 enebakk-Lillestrøm550 Oslo Bussterminal - LillestrømPlattform 18 - Buss 411 LillestrømNaN('Jernbanetorget (T)', 277)('Grønland (T)', 345)NaNNaN('Oslo Sentralstasjon', 384)21
3Pløens gate59.91390010.749325Jernbanetorget (Trikk/B)187.0Buss 54Buss 31Buss 54Buss 30Buss 37Buss 34Buss 31 EBuss 70Buss 143Buss 252Buss 82EBuss 542 Til Drøbakbuss 500Buss 542 Seiersten Ekspress81A-bussen81B-Bussenplatform 29 oslo bussterminalNaN('Jernbanetorget (T)', 241)('Stortinget (T)', 428)NaNNaN('Oslo Sentralstasjon', 443)21
4Sonja Henies plass59.91270010.755500NaNNaNBuss 252platform 29 oslo bussterminalbuss 500Buss 70Buss 30Buss 143Buss 34Buss 490EBuss 54Buss 54Buss 31 EBuss 82EBuss 31Buss 542 Seiersten EkspressBuss 111150 Oslo Bussterminal - Gullhaug - Oslo Busst...550 Oslo Bussterminal - Lillestrømplattform 20-Buss 501 enebakk-Lillestrøm('Jernbanetorget (T)', 190)('Grønland (T)', 327)NaNNaN('Oslo Sentralstasjon', 240)21
\n", "
" ], "text/plain": [ " Street Street Latitude Street Longitude \\\n", "0 Lilletorget 59.913500 10.755900 \n", "1 Europarådets plass 59.912000 10.749300 \n", "2 Brugata 59.914005 10.755774 \n", "3 Pløens gate 59.913900 10.749325 \n", "4 Sonja Henies plass 59.912700 10.755500 \n", "\n", " Trikk Trikk Distance 0 \\\n", "0 Jernbanetorget (Trikk/B) 356.0 buss 500 \n", "1 Jernbanetorget (Trikk/B) 48.0 Buss 31 E \n", "2 Jernbanetorget (Trikk/B) 375.0 Buss 30 \n", "3 Jernbanetorget (Trikk/B) 187.0 Buss 54 \n", "4 NaN NaN Buss 252 \n", "\n", " 1 2 3 \\\n", "0 Buss 30 Buss 252 Buss 70 \n", "1 Buss 34 Buss 542 Seiersten Ekspress Buss 54 \n", "2 buss 500 Buss 252 Buss 54 \n", "3 Buss 31 Buss 54 Buss 30 \n", "4 platform 29 oslo bussterminal buss 500 Buss 70 \n", "\n", " 4 5 6 7 8 \\\n", "0 Buss 34 Buss 54 Buss 143 Buss 54 Buss 490E \n", "1 Buss 82E Buss 143 Buss 542 Til Drøbak Buss 37 Buss 70 \n", "2 Buss 70 Buss 34 Buss 111 Buss 143 Buss 490E \n", "3 Buss 37 Buss 34 Buss 31 E Buss 70 Buss 143 \n", "4 Buss 30 Buss 143 Buss 34 Buss 490E Buss 54 \n", "\n", " 9 10 11 \\\n", "0 platform 29 oslo bussterminal Buss 111 Buss 31 \n", "1 Buss 54 Buss 30 Buss 252 \n", "2 Buss 54 Buss 31 Buss 31 E \n", "3 Buss 252 Buss 82E Buss 542 Til Drøbak \n", "4 Buss 54 Buss 31 E Buss 82E \n", "\n", " 12 \\\n", "0 Buss 31 E \n", "1 buss 500 \n", "2 platform 29 oslo bussterminal \n", "3 buss 500 \n", "4 Buss 31 \n", "\n", " 13 \\\n", "0 Buss 542 Seiersten Ekspress \n", "1 Buss 83 \n", "2 150 Oslo Bussterminal - Gullhaug - Oslo Busst... \n", "3 Buss 542 Seiersten Ekspress \n", "4 Buss 542 Seiersten Ekspress \n", "\n", " 14 \\\n", "0 150 Oslo Bussterminal - Gullhaug - Oslo Busst... \n", "1 Buss 31 \n", "2 plattform 20-Buss 501 enebakk-Lillestrøm \n", "3 81A-bussen \n", "4 Buss 111 \n", "\n", " 15 \\\n", "0 plattform 20-Buss 501 enebakk-Lillestrøm \n", "1 81B-Bussen \n", "2 550 Oslo Bussterminal - Lillestrøm \n", "3 81B-Bussen \n", "4 150 Oslo Bussterminal - Gullhaug - Oslo Busst... \n", "\n", " 16 \\\n", "0 550 Oslo Bussterminal - Lillestrøm \n", "1 81A-bussen \n", "2 Plattform 18 - Buss 411 Lillestrøm \n", "3 platform 29 oslo bussterminal \n", "4 550 Oslo Bussterminal - Lillestrøm \n", "\n", " 17 T-bane_1 \\\n", "0 Plattform 18 - Buss 411 Lillestrøm ('Jernbanetorget (T)', 248) \n", "1 platform 29 oslo bussterminal ('Jernbanetorget (T)', 164) \n", "2 NaN ('Jernbanetorget (T)', 277) \n", "3 NaN ('Jernbanetorget (T)', 241) \n", "4 plattform 20-Buss 501 enebakk-Lillestrøm ('Jernbanetorget (T)', 190) \n", "\n", " T-bane_2 T-bane_3 T-bane_4 Train Station \\\n", "0 ('Grønland (T)', 318) NaN NaN ('Oslo Sentralstasjon', 332) \n", "1 ('Stortinget (T)', 420) NaN NaN ('Oslo Sentralstasjon', 300) \n", "2 ('Grønland (T)', 345) NaN NaN ('Oslo Sentralstasjon', 384) \n", "3 ('Stortinget (T)', 428) NaN NaN ('Oslo Sentralstasjon', 443) \n", "4 ('Grønland (T)', 327) NaN NaN ('Oslo Sentralstasjon', 240) \n", "\n", " Total Transport \n", "0 22 \n", "1 22 \n", "2 21 \n", "3 21 \n", "4 21 " ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Attempt to change old index to new custom ordered index\n", "orderedStreetTrikkBussMetroTog = streetTrikkBussMetroTog.iloc[newIndex,]\n", "\n", "#Right now, the index values will be like 554, 2354 etc, to get them in the order of 0,1,2...\n", "\n", "#Get range for the index values\n", "manualIndex2 = range(orderedStreetTrikkBussMetroTog.shape[0])\n", "\n", "#Assign this new index\n", "orderedStreetTrikkBussMetroTog.index = manualIndex2\n", "orderedStreetTrikkBussMetroTog.head()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/local/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " \n" ] }, { "data": { "text/html": [ "
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StreetStreet LatitudeStreet LongitudeTrikkTrikk Distance01234567891011121314151617T-bane_1T-bane_2T-bane_3T-bane_4Train StationTotal TransportTotal Bus
0Lilletorget59.91350010.755900Jernbanetorget (Trikk/B)356.0buss 500Buss 30Buss 252Buss 70Buss 34Buss 54Buss 143Buss 54Buss 490Eplatform 29 oslo bussterminalBuss 111Buss 31Buss 31 EBuss 542 Seiersten Ekspress150 Oslo Bussterminal - Gullhaug - Oslo Busst...plattform 20-Buss 501 enebakk-Lillestrøm550 Oslo Bussterminal - LillestrømPlattform 18 - Buss 411 Lillestrøm('Jernbanetorget (T)', 248)('Grønland (T)', 318)NaNNaN('Oslo Sentralstasjon', 332)2218
1Europarådets plass59.91200010.749300Jernbanetorget (Trikk/B)48.0Buss 31 EBuss 34Buss 542 Seiersten EkspressBuss 54Buss 82EBuss 143Buss 542 Til DrøbakBuss 37Buss 70Buss 54Buss 30Buss 252buss 500Buss 83Buss 3181B-Bussen81A-bussenplatform 29 oslo bussterminal('Jernbanetorget (T)', 164)('Stortinget (T)', 420)NaNNaN('Oslo Sentralstasjon', 300)2218
2Brugata59.91400510.755774Jernbanetorget (Trikk/B)375.0Buss 30buss 500Buss 252Buss 54Buss 70Buss 34Buss 111Buss 143Buss 490EBuss 54Buss 31Buss 31 Eplatform 29 oslo bussterminal150 Oslo Bussterminal - Gullhaug - Oslo Busst...plattform 20-Buss 501 enebakk-Lillestrøm550 Oslo Bussterminal - LillestrømPlattform 18 - Buss 411 LillestrømNaN('Jernbanetorget (T)', 277)('Grønland (T)', 345)NaNNaN('Oslo Sentralstasjon', 384)2117
3Pløens gate59.91390010.749325Jernbanetorget (Trikk/B)187.0Buss 54Buss 31Buss 54Buss 30Buss 37Buss 34Buss 31 EBuss 70Buss 143Buss 252Buss 82EBuss 542 Til Drøbakbuss 500Buss 542 Seiersten Ekspress81A-bussen81B-Bussenplatform 29 oslo bussterminalNaN('Jernbanetorget (T)', 241)('Stortinget (T)', 428)NaNNaN('Oslo Sentralstasjon', 443)2117
4Sonja Henies plass59.91270010.755500NaNNaNBuss 252platform 29 oslo bussterminalbuss 500Buss 70Buss 30Buss 143Buss 34Buss 490EBuss 54Buss 54Buss 31 EBuss 82EBuss 31Buss 542 Seiersten EkspressBuss 111150 Oslo Bussterminal - Gullhaug - Oslo Busst...550 Oslo Bussterminal - Lillestrømplattform 20-Buss 501 enebakk-Lillestrøm('Jernbanetorget (T)', 190)('Grønland (T)', 327)NaNNaN('Oslo Sentralstasjon', 240)2118
\n", "
" ], "text/plain": [ " Street Street Latitude Street Longitude \\\n", "0 Lilletorget 59.913500 10.755900 \n", "1 Europarådets plass 59.912000 10.749300 \n", "2 Brugata 59.914005 10.755774 \n", "3 Pløens gate 59.913900 10.749325 \n", "4 Sonja Henies plass 59.912700 10.755500 \n", "\n", " Trikk Trikk Distance 0 \\\n", "0 Jernbanetorget (Trikk/B) 356.0 buss 500 \n", "1 Jernbanetorget (Trikk/B) 48.0 Buss 31 E \n", "2 Jernbanetorget (Trikk/B) 375.0 Buss 30 \n", "3 Jernbanetorget (Trikk/B) 187.0 Buss 54 \n", "4 NaN NaN Buss 252 \n", "\n", " 1 2 3 \\\n", "0 Buss 30 Buss 252 Buss 70 \n", "1 Buss 34 Buss 542 Seiersten Ekspress Buss 54 \n", "2 buss 500 Buss 252 Buss 54 \n", "3 Buss 31 Buss 54 Buss 30 \n", "4 platform 29 oslo bussterminal buss 500 Buss 70 \n", "\n", " 4 5 6 7 8 \\\n", "0 Buss 34 Buss 54 Buss 143 Buss 54 Buss 490E \n", "1 Buss 82E Buss 143 Buss 542 Til Drøbak Buss 37 Buss 70 \n", "2 Buss 70 Buss 34 Buss 111 Buss 143 Buss 490E \n", "3 Buss 37 Buss 34 Buss 31 E Buss 70 Buss 143 \n", "4 Buss 30 Buss 143 Buss 34 Buss 490E Buss 54 \n", "\n", " 9 10 11 \\\n", "0 platform 29 oslo bussterminal Buss 111 Buss 31 \n", "1 Buss 54 Buss 30 Buss 252 \n", "2 Buss 54 Buss 31 Buss 31 E \n", "3 Buss 252 Buss 82E Buss 542 Til Drøbak \n", "4 Buss 54 Buss 31 E Buss 82E \n", "\n", " 12 \\\n", "0 Buss 31 E \n", "1 buss 500 \n", "2 platform 29 oslo bussterminal \n", "3 buss 500 \n", "4 Buss 31 \n", "\n", " 13 \\\n", "0 Buss 542 Seiersten Ekspress \n", "1 Buss 83 \n", "2 150 Oslo Bussterminal - Gullhaug - Oslo Busst... \n", "3 Buss 542 Seiersten Ekspress \n", "4 Buss 542 Seiersten Ekspress \n", "\n", " 14 \\\n", "0 150 Oslo Bussterminal - Gullhaug - Oslo Busst... \n", "1 Buss 31 \n", "2 plattform 20-Buss 501 enebakk-Lillestrøm \n", "3 81A-bussen \n", "4 Buss 111 \n", "\n", " 15 \\\n", "0 plattform 20-Buss 501 enebakk-Lillestrøm \n", "1 81B-Bussen \n", "2 550 Oslo Bussterminal - Lillestrøm \n", "3 81B-Bussen \n", "4 150 Oslo Bussterminal - Gullhaug - Oslo Busst... \n", "\n", " 16 \\\n", "0 550 Oslo Bussterminal - Lillestrøm \n", "1 81A-bussen \n", "2 Plattform 18 - Buss 411 Lillestrøm \n", "3 platform 29 oslo bussterminal \n", "4 550 Oslo Bussterminal - Lillestrøm \n", "\n", " 17 T-bane_1 \\\n", "0 Plattform 18 - Buss 411 Lillestrøm ('Jernbanetorget (T)', 248) \n", "1 platform 29 oslo bussterminal ('Jernbanetorget (T)', 164) \n", "2 NaN ('Jernbanetorget (T)', 277) \n", "3 NaN ('Jernbanetorget (T)', 241) \n", "4 plattform 20-Buss 501 enebakk-Lillestrøm ('Jernbanetorget (T)', 190) \n", "\n", " T-bane_2 T-bane_3 T-bane_4 Train Station \\\n", "0 ('Grønland (T)', 318) NaN NaN ('Oslo Sentralstasjon', 332) \n", "1 ('Stortinget (T)', 420) NaN NaN ('Oslo Sentralstasjon', 300) \n", "2 ('Grønland (T)', 345) NaN NaN ('Oslo Sentralstasjon', 384) \n", "3 ('Stortinget (T)', 428) NaN NaN ('Oslo Sentralstasjon', 443) \n", "4 ('Grønland (T)', 327) NaN NaN ('Oslo Sentralstasjon', 240) \n", "\n", " Total Transport Total Bus \n", "0 22 18 \n", "1 22 18 \n", "2 21 17 \n", "3 21 17 \n", "4 21 18 " ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Find total bus options, but first convert the busCOLS to str\n", "busCOLS_str = convertType(busCOLS)\n", "orderedStreetTrikkBussMetroTog[busCOLS_str].notnull().sum(axis = 'columns').sort_values(ascending = False) #Sort in descending order of non-null values i.e. highest transport to lowest\n", "\n", "#Add this info as a new column\n", "orderedStreetTrikkBussMetroTog['Total Bus'] = orderedStreetTrikkBussMetroTog[busCOLS_str].notnull().sum(axis = 'columns').sort_values(ascending = False)\n", "orderedStreetTrikkBussMetroTog.head()" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A total of 630 streets seem to have NO transport option within 400m\n" ] } ], "source": [ "#Find total streets with least amount of transport options\n", "print ('A total of ', orderedStreetTrikkBussMetroTog[orderedStreetTrikkBussMetroTog['Total Transport'] == 0].shape[0],'streets seem to have NO transport option within 400m')" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2460, 30)" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "orderedStreetTrikkBussMetroTog.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 6: Exploratory Analysis " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us explore some of the streets with the most, the least transport options. Let us also review the distribution of transport options across all streets." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.1 Top 10 streets with most transport options within 400m" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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StreetStreet LatitudeStreet LongitudeTrikkTrikk Distance01234567891011121314151617T-bane_1T-bane_2T-bane_3T-bane_4Train StationTotal TransportTotal Bus
0Lilletorget59.91350010.755900Jernbanetorget (Trikk/B)356.0buss 500Buss 30Buss 252Buss 70Buss 34Buss 54Buss 143Buss 54Buss 490Eplatform 29 oslo bussterminalBuss 111Buss 31Buss 31 EBuss 542 Seiersten Ekspress150 Oslo Bussterminal - Gullhaug - Oslo Busst...plattform 20-Buss 501 enebakk-Lillestrøm550 Oslo Bussterminal - LillestrømPlattform 18 - Buss 411 Lillestrøm('Jernbanetorget (T)', 248)('Grønland (T)', 318)NaNNaN('Oslo Sentralstasjon', 332)2218
1Europarådets plass59.91200010.749300Jernbanetorget (Trikk/B)48.0Buss 31 EBuss 34Buss 542 Seiersten EkspressBuss 54Buss 82EBuss 143Buss 542 Til DrøbakBuss 37Buss 70Buss 54Buss 30Buss 252buss 500Buss 83Buss 3181B-Bussen81A-bussenplatform 29 oslo bussterminal('Jernbanetorget (T)', 164)('Stortinget (T)', 420)NaNNaN('Oslo Sentralstasjon', 300)2218
2Brugata59.91400510.755774Jernbanetorget (Trikk/B)375.0Buss 30buss 500Buss 252Buss 54Buss 70Buss 34Buss 111Buss 143Buss 490EBuss 54Buss 31Buss 31 Eplatform 29 oslo bussterminal150 Oslo Bussterminal - Gullhaug - Oslo Busst...plattform 20-Buss 501 enebakk-Lillestrøm550 Oslo Bussterminal - LillestrømPlattform 18 - Buss 411 LillestrømNaN('Jernbanetorget (T)', 277)('Grønland (T)', 345)NaNNaN('Oslo Sentralstasjon', 384)2117
3Pløens gate59.91390010.749325Jernbanetorget (Trikk/B)187.0Buss 54Buss 31Buss 54Buss 30Buss 37Buss 34Buss 31 EBuss 70Buss 143Buss 252Buss 82EBuss 542 Til Drøbakbuss 500Buss 542 Seiersten Ekspress81A-bussen81B-Bussenplatform 29 oslo bussterminalNaN('Jernbanetorget (T)', 241)('Stortinget (T)', 428)NaNNaN('Oslo Sentralstasjon', 443)2117
4Sonja Henies plass59.91270010.755500NaNNaNBuss 252platform 29 oslo bussterminalbuss 500Buss 70Buss 30Buss 143Buss 34Buss 490EBuss 54Buss 54Buss 31 EBuss 82EBuss 31Buss 542 Seiersten EkspressBuss 111150 Oslo Bussterminal - Gullhaug - Oslo Busst...550 Oslo Bussterminal - Lillestrømplattform 20-Buss 501 enebakk-Lillestrøm('Jernbanetorget (T)', 190)('Grønland (T)', 327)NaNNaN('Oslo Sentralstasjon', 240)2118
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" ], "text/plain": [ " Street Street Latitude Street Longitude \\\n", "0 Lilletorget 59.913500 10.755900 \n", "1 Europarådets plass 59.912000 10.749300 \n", "2 Brugata 59.914005 10.755774 \n", "3 Pløens gate 59.913900 10.749325 \n", "4 Sonja Henies plass 59.912700 10.755500 \n", "\n", " Trikk Trikk Distance 0 \\\n", "0 Jernbanetorget (Trikk/B) 356.0 buss 500 \n", "1 Jernbanetorget (Trikk/B) 48.0 Buss 31 E \n", "2 Jernbanetorget (Trikk/B) 375.0 Buss 30 \n", "3 Jernbanetorget (Trikk/B) 187.0 Buss 54 \n", "4 NaN NaN Buss 252 \n", "\n", " 1 2 3 \\\n", "0 Buss 30 Buss 252 Buss 70 \n", "1 Buss 34 Buss 542 Seiersten Ekspress Buss 54 \n", "2 buss 500 Buss 252 Buss 54 \n", "3 Buss 31 Buss 54 Buss 30 \n", "4 platform 29 oslo bussterminal buss 500 Buss 70 \n", "\n", " 4 5 6 7 8 \\\n", "0 Buss 34 Buss 54 Buss 143 Buss 54 Buss 490E \n", "1 Buss 82E Buss 143 Buss 542 Til Drøbak Buss 37 Buss 70 \n", "2 Buss 70 Buss 34 Buss 111 Buss 143 Buss 490E \n", "3 Buss 37 Buss 34 Buss 31 E Buss 70 Buss 143 \n", "4 Buss 30 Buss 143 Buss 34 Buss 490E Buss 54 \n", "\n", " 9 10 11 \\\n", "0 platform 29 oslo bussterminal Buss 111 Buss 31 \n", "1 Buss 54 Buss 30 Buss 252 \n", "2 Buss 54 Buss 31 Buss 31 E \n", "3 Buss 252 Buss 82E Buss 542 Til Drøbak \n", "4 Buss 54 Buss 31 E Buss 82E \n", "\n", " 12 \\\n", "0 Buss 31 E \n", "1 buss 500 \n", "2 platform 29 oslo bussterminal \n", "3 buss 500 \n", "4 Buss 31 \n", "\n", " 13 \\\n", "0 Buss 542 Seiersten Ekspress \n", "1 Buss 83 \n", "2 150 Oslo Bussterminal - Gullhaug - Oslo Busst... \n", "3 Buss 542 Seiersten Ekspress \n", "4 Buss 542 Seiersten Ekspress \n", "\n", " 14 \\\n", "0 150 Oslo Bussterminal - Gullhaug - Oslo Busst... \n", "1 Buss 31 \n", "2 plattform 20-Buss 501 enebakk-Lillestrøm \n", "3 81A-bussen \n", "4 Buss 111 \n", "\n", " 15 \\\n", "0 plattform 20-Buss 501 enebakk-Lillestrøm \n", "1 81B-Bussen \n", "2 550 Oslo Bussterminal - Lillestrøm \n", "3 81B-Bussen \n", "4 150 Oslo Bussterminal - Gullhaug - Oslo Busst... \n", "\n", " 16 \\\n", "0 550 Oslo Bussterminal - Lillestrøm \n", "1 81A-bussen \n", "2 Plattform 18 - Buss 411 Lillestrøm \n", "3 platform 29 oslo bussterminal \n", "4 550 Oslo Bussterminal - Lillestrøm \n", "\n", " 17 T-bane_1 \\\n", "0 Plattform 18 - Buss 411 Lillestrøm ('Jernbanetorget (T)', 248) \n", "1 platform 29 oslo bussterminal ('Jernbanetorget (T)', 164) \n", "2 NaN ('Jernbanetorget (T)', 277) \n", "3 NaN ('Jernbanetorget (T)', 241) \n", "4 plattform 20-Buss 501 enebakk-Lillestrøm ('Jernbanetorget (T)', 190) \n", "\n", " T-bane_2 T-bane_3 T-bane_4 Train Station \\\n", "0 ('Grønland (T)', 318) NaN NaN ('Oslo Sentralstasjon', 332) \n", "1 ('Stortinget (T)', 420) NaN NaN ('Oslo Sentralstasjon', 300) \n", "2 ('Grønland (T)', 345) NaN NaN ('Oslo Sentralstasjon', 384) \n", "3 ('Stortinget (T)', 428) NaN NaN ('Oslo Sentralstasjon', 443) \n", "4 ('Grønland (T)', 327) NaN NaN ('Oslo Sentralstasjon', 240) \n", "\n", " Total Transport Total Bus \n", "0 22 18 \n", "1 22 18 \n", "2 21 17 \n", "3 21 17 \n", "4 21 18 " ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#sns.set(rc={'figure.figsize':(12,9)})\n", "top_streets = orderedStreetTrikkBussMetroTog.head(10)\n", "top_streets.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.2 10 streets with least transport options within 400m" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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StreetStreet LatitudeStreet LongitudeTrikkTrikk Distance01234567891011121314151617T-bane_1T-bane_2T-bane_3T-bane_4Train StationTotal TransportTotal Bus
2450Hellerudstubben59.91734510.848599NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN00
2451Gamle Herregårdsvei59.84415610.783600NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN00
2452Vassfaret59.95352110.681836NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN00
2453Vekterveien59.89066310.812804NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN00
2454Tryvannsveien59.98452910.669471NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN00
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" ], "text/plain": [ " Street Street Latitude Street Longitude Trikk \\\n", "2450 Hellerudstubben 59.917345 10.848599 NaN \n", "2451 Gamle Herregårdsvei 59.844156 10.783600 NaN \n", "2452 Vassfaret 59.953521 10.681836 NaN \n", "2453 Vekterveien 59.890663 10.812804 NaN \n", "2454 Tryvannsveien 59.984529 10.669471 NaN \n", "\n", " Trikk Distance 0 1 2 3 4 5 6 7 8 9 10 \\\n", "2450 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "2451 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "2452 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "2453 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "2454 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "\n", " 11 12 13 14 15 16 17 T-bane_1 T-bane_2 T-bane_3 T-bane_4 \\\n", "2450 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "2451 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "2452 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "2453 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "2454 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "\n", " Train Station Total Transport Total Bus \n", "2450 NaN 0 0 \n", "2451 NaN 0 0 \n", "2452 NaN 0 0 \n", "2453 NaN 0 0 \n", "2454 NaN 0 0 " ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bottom_streets = orderedStreetTrikkBussMetroTog.tail(10)\n", "bottom_streets.head()" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", " 17, 18, 19]), )" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Let us combine the top10 and bottom 10 dataframes into a single dataframe\n", "#We will use pd.concat that takes in a LIST of dataframes to concat\n", "df_list = [top_streets, bottom_streets]\n", "combinedTop10_bottom10streets = pd.concat(df_list)\n", "#combinedTop10_bottom10streets\n", "\n", "#Let us visualise this\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "sns.set(rc={'figure.figsize':(12,9)})\n", "\n", "sns.barplot(x = 'Street', y = 'Total Transport', data = combinedTop10_bottom10streets, palette = 'Blues_d')\n", "plt.xticks(rotation = 45)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.3 Distribution of transport options across all streets" ] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 129, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set_palette(\"Blues_r\")\n", "#sns.set(style=\"white\", palette=\"dark\", color_codes=True)\n", "sns.distplot(orderedStreetTrikkBussMetroTog['Total Transport'], kde = False)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(y = orderedStreetTrikkBussMetroTog['Total Transport'], width = 0.1)" ] }, { "cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 132, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set(style=\"whitegrid\", palette=\"muted\", color_codes=True)\n", "sns.violinplot(x=\"Total Transport\", data=orderedStreetTrikkBussMetroTog)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.4 Visualise relation between total bus options and total transport" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.jointplot(x = 'Total Transport', y = 'Total Bus', data = orderedStreetTrikkBussMetroTog, height= 9)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Make a density plot version of the above\n", "sns.jointplot(x = 'Total Transport', y = 'Total Bus', kind = 'kde', data = orderedStreetTrikkBussMetroTog, height = 9)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cmap = sns.cubehelix_palette(as_cmap=True, dark=0, light=1, reverse=True)\n", "sns.kdeplot(orderedStreetTrikkBussMetroTog['Total Transport'], orderedStreetTrikkBussMetroTog['Total Bus'], cmap=cmap, n_levels=60, shade=True);\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.4 Divide streets based on no. of transport options" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0. , 3.14285714, 6.28571429, 9.42857143, 12.57142857,\n", " 15.71428571, 18.85714286, 22. ])" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bins = np.linspace(min(orderedStreetTrikkBussMetroTog['Total Transport']), max(orderedStreetTrikkBussMetroTog['Total Transport']), 8)#Provide 1 more than the number of levels to create\n", "bins" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "group_names = ['0-3', '4-6', '7-9', '10-12', '13-15', '16-18', '19-22']" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/local/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " \"\"\"Entry point for launching an IPython kernel.\n" ] }, { "data": { "text/html": [ "
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Binned TransportTotal Transport
019-2222
119-2222
219-2221
319-2221
419-2221
519-2221
619-2221
719-2221
819-2220
919-2220
1019-2219
1116-1818
1216-1818
1316-1817
1416-1817
1516-1816
1613-1515
1713-1515
1813-1514
1913-1514
\n", "
" ], "text/plain": [ " Binned Transport Total Transport\n", "0 19-22 22\n", "1 19-22 22\n", "2 19-22 21\n", "3 19-22 21\n", "4 19-22 21\n", "5 19-22 21\n", "6 19-22 21\n", "7 19-22 21\n", "8 19-22 20\n", "9 19-22 20\n", "10 19-22 19\n", "11 16-18 18\n", "12 16-18 18\n", "13 16-18 17\n", "14 16-18 17\n", "15 16-18 16\n", "16 13-15 15\n", "17 13-15 15\n", "18 13-15 14\n", "19 13-15 14" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "orderedStreetTrikkBussMetroTog['Binned Transport'] = pd.cut(orderedStreetTrikkBussMetroTog['Total Transport'], bins, labels = group_names, include_lowest= True)\n", "orderedStreetTrikkBussMetroTog[['Binned Transport','Total Transport']].head(20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Let us find out how many streets belong to each category" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0-3 2012\n", "4-6 294\n", "7-9 95\n", "10-12 35\n", "19-22 11\n", "13-15 8\n", "16-18 5\n", "Name: Binned Transport, dtype: int64" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "orderedStreetTrikkBussMetroTog['Binned Transport'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Let us try to visualise this to get a better perspective..." ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Binned Transport
0-32012
4-6294
7-995
10-1235
19-2211
13-158
16-185
\n", "
" ], "text/plain": [ " Binned Transport\n", "0-3 2012\n", "4-6 294\n", "7-9 95\n", "10-12 35\n", "19-22 11\n", "13-15 8\n", "16-18 5" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_df = orderedStreetTrikkBussMetroTog['Binned Transport'].value_counts().to_frame()\n", "new_df" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Transport OptionsTotal Streets
00-32012
14-6294
27-995
310-1235
419-2211
513-158
616-185
\n", "
" ], "text/plain": [ " Transport Options Total Streets\n", "0 0-3 2012\n", "1 4-6 294\n", "2 7-9 95\n", "3 10-12 35\n", "4 19-22 11\n", "5 13-15 8\n", "6 16-18 5" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_df.reset_index(inplace = True)\n", "new_df.columns = ['Transport Options', 'Total Streets']\n", "new_df" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([0, 1, 2, 3, 4, 5, 6]), )" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.barplot(x = 'Transport Options', y = 'Total Streets', data = new_df, palette= 'Blues_d')\n", "plt.xticks(rotation = 45)" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [], "source": [ "#Save to drive\n", "orderedStreetTrikkBussMetroTog.to_csv(path_or_buf='./orderedStreetTrikkBusMetroTog.csv', index = False)" ] } ], "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": "3.5.7" } }, "nbformat": 4, "nbformat_minor": 2 }