MERGING DATAFRAMES WITH PANDAS. Merging DataFrames
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1 MERGING DATAFRAMES WITH PANDAS Merging DataFrames
2 Population DataFrame In [1]: import pandas as pd In [2]: population = pd.read_csv('pa_zipcode_population.csv') In [3]: print(population) Zipcode 2010 Census Population
3 Cities DataFrame In [4]: cities = pd.read_csv('pa_zipcode_city.csv') In [5]: print(cities) Zipcode City State MANHEIM PA PRESTON PARK PA BIGLERVILLE PA INDIANA PA CURWENSVILLE PA CROWN PA HARVEYS LAKE PA MINERAL SPRINGS PA CASSVILLE PA HANNASTOWN PA SALTSBURG PA TUNKHANNOCK PA PITTSBURGH PA LEMASTERS PA GREAT BEND PA
4 Merging In [6]: pd.merge(population, cities) Out[6]: Zipcode 2010 Census Population City State MINERAL SPRINGS PA SALTSBURG PA TUNKHANNOCK PA BIGLERVILLE PA HANNASTOWN PA
5 Medal DataFrames In [7]: bronze = pd.read_csv('bronze_sorted.csv') In [8]: gold = pd.read_csv('gold_sorted.csv') In [9]: print(bronze) NOC Country Total 0 USA United States URS Soviet Union GBR United Kingdom FRA France GER Germany In [10]: print(gold) NOC Country Total 0 USA United States URS Soviet Union GBR United Kingdom ITA Italy GER Germany 407.0
6 Merging all columns In [11]: pd.merge(bronze, gold) Out[11]: Empty DataFrame Columns: [NOC, Country, Total] Index: []
7 Merging on In [12]: pd.merge(bronze, gold, on='noc') Out[12]: NOC Country_x Total_x Country_y Total_y 0 USA United States United States URS Soviet Union Soviet Union GBR United Kingdom United Kingdom GER Germany Germany 407.0
8 Merging on multiple columns In [13]: pd.merge(bronze, gold, on=['noc', 'Country']) Out[13]: NOC Country Total_x Total_y 0 USA United States URS Soviet Union GBR United Kingdom GER Germany
9 Using suffixes In [14]: pd.merge(bronze, gold, on=['noc', 'Country'], suffixes=['_bronze', '_gold']) Out[14]: NOC Country Total_bronze Total_gold 0 USA United States URS Soviet Union GBR United Kingdom GER Germany
10 Counties DataFrame In [15]: counties = pd.read_csv('pa_counties.csv') In [16]: print(counties) CITY NAME COUNTY NAME 0 SALTSBURG INDIANA 1 MINERAL SPRINGS CLEARFIELD 2 BIGLERVILLE ADAMS 3 HANNASTOWN WESTMORELAND 4 TUNKHANNOCK WYOMING In [17]: print(cities.tail()) Zipcode City State SALTSBURG PA TUNKHANNOCK PA PITTSBURGH PA LEMASTERS PA GREAT BEND PA
11 Specifying columns to merge In [18]: pd.merge(counties, cities, left_on='city NAME', right_on='city') Out[18]: CITY NAME COUNTY NAME Zipcode City State 0 SALTSBURG INDIANA SALTSBURG PA 1 MINERAL SPRINGS CLEARFIELD MINERAL SPRINGS PA 2 BIGLERVILLE ADAMS BIGLERVILLE PA 3 HANNASTOWN WESTMORELAND HANNASTOWN PA 4 TUNKHANNOCK WYOMING TUNKHANNOCK PA
12 Switching left/right DataFrames In [19]: pd.merge(cities, counties, left_on='city', right_on='city NAME') Out[19]: Zipcode City State CITY NAME COUNTY NAME BIGLERVILLE PA BIGLERVILLE ADAMS MINERAL SPRINGS PA MINERAL SPRINGS CLEARFIELD HANNASTOWN PA HANNASTOWN WESTMORELAND SALTSBURG PA SALTSBURG INDIANA TUNKHANNOCK PA TUNKHANNOCK WYOMING
13 MERGING DATAFRAMES WITH PANDAS Let s practice!
14 MERGING DATAFRAMES WITH PANDAS Joining DataFrames
15 Medal DataFrames In [1]: import pandas as pd In [2]: bronze = pd.read_csv('bronze_sorted.csv') In [3]: gold = pd.read_csv('gold_sorted.csv') In [4]: print(bronze) NOC Country Total 0 USA United States URS Soviet Union GBR United Kingdom FRA France GER Germany In [5]: print(gold) NOC Country Total 0 USA United States URS Soviet Union GBR United Kingdom ITA Italy GER Germany 407.0
16 Merging with inner join In [6]: pd.merge(bronze, gold, on=['noc', 'Country'],...: suffixes=['_bronze', '_gold'], how='inner') Out[6]: NOC Country Total_bronze Total_gold 0 USA United States URS Soviet Union GBR United Kingdom GER Germany
17 Merging with left join Keeps all rows of the left DF in the merged DF For rows in the left DF with matches in the right DF: Non-joining columns of right DF are appended to left DF For rows in the left DF with no matches in the right DF: Non-joining columns are filled with nulls
18 Merging with left join In [7]: pd.merge(bronze, gold, on=['noc', 'Country'],...: suffixes=['_bronze', '_gold'], how='left') Out[7]: NOC Country Total_bronze Total_gold 0 USA United States URS Soviet Union GBR United Kingdom FRA France NaN 4 GER Germany
19 Merging with right join In [8]: pd.merge(bronze, gold, on=['noc', 'Country'],...: suffixes=['_bronze', '_gold'], how='right') Out[8]: NOC Country Total_bronze Total_gold 0 USA United States URS Soviet Union GBR United Kingdom GER Germany ITA Italy NaN 460.0
20 Merging with outer join In [9]: pd.merge(bronze, gold, on=['noc', 'Country'],...: suffixes=['_bronze', '_gold'], how='outer') Out[9]: NOC Country Total_bronze Total_gold 0 USA United States URS Soviet Union GBR United Kingdom FRA France NaN 4 GER Germany ITA Italy NaN 460.0
21 Population & unemployment data In [10]: population = pd.read_csv('population_00.csv', index_col=0) In [11]: unemployment = pd.read_csv('unemployment_00.csv', index_col=0) In [12]: print(population) 2010 Census Population Zip Code ZCTA In [13]: print(unemployment) unemployment participants Zip
22 Using.join(how= left ) In [16]: population.join(unemployment) Out[16]: 2010 Census Population unemployment participants Zip Code ZCTA NaN NaN NaN NaN NaN NaN
23 Using.join(how= right ) In [17]: population.join(unemployment, how= 'right') Out[17]: 2010 Census Population unemployment participants Zip NaN NaN NaN
24 Using.join(how= inner ) In [18]: population.join(unemployment, how='inner') Out[18]: 2010 Census Population unemployment participants
25 Using.join(how= outer ) In [19]: population.join(unemployment, how= 'outer') Out[19]: 2010 Census Population unemployment participants 1097 NaN NaN NaN NaN NaN NaN NaN NaN NaN
26 Which should you use? df1.append(df2): stacking vertically pd.concat([df1, df2]): stacking many horizontally or vertically simple inner/outer joins on Indexes df1.join(df2): inner/outer/left/right joins on Indexes pd.merge([df1, df2]): many joins on multiple columns
27 MERGING DATAFRAMES WITH PANDAS Let s practice!
28 MERGING DATAFRAMES WITH PANDAS Ordered merges
29 Software & hardware sales In [1]: import pandas as pd In [2]: software = pd.read_csv('feb-sales-software.csv', parse_dates=['date'])...:.sort_values('date') In [3]: hardware = pd.read_csv('feb-sales-hardware.csv', parse_dates=['date'])...:.sort_values('date')
30 Software & hardware sales In [4]: print(software) Date Company Product Units :33:01 Hooli Software :14:18 Initech Software :36:29 Streeplex Software :53:06 Acme Coporation Software :09:55 Mediacore Software :03:08 Initech Software :50:44 Hooli Software :09:19 Hooli Software :01:26 Mediacore Software 3 In [5]: print(hardware) Date Company Product Units :54:49 Mediacore Hardware :52:45 Acme Coporation Hardware :58:10 Acme Coporation Hardware :59:33 Mediacore Hardware :41:47 Hooli Hardware 3
31 Using merge() In [6]: pd.merge(hardware, software) Out[6]: Empty DataFrame Columns: [Date, Company, Product, Units] Index: []
32 Using merge(how= outer ) In [7]: pd.merge(hardware, software, how='outer') Out[7]: Date Company Product Units :54:49 Mediacore Hardware :52:45 Acme Coporation Hardware :58:10 Acme Coporation Hardware :59:33 Mediacore Hardware :41:47 Hooli Hardware :33:01 Hooli Software :14:18 Initech Software :36:29 Streeplex Software :53:06 Acme Coporation Software :09:55 Mediacore Software :03:08 Initech Software :50:44 Hooli Software :09:19 Hooli Software :01:26 Mediacore Software 3
33 Sorting merge(how= outer ) In [8]: pd.merge(hardware, software, how='outer').sorted_values('date') Out[8]: Date Company Product Units :54:49 Mediacore Hardware :52:45 Acme Coporation Hardware :58:10 Acme Coporation Hardware :59:33 Mediacore Hardware :41:47 Hooli Hardware :33:01 Hooli Software :14:18 Initech Software :36:29 Streeplex Software :53:06 Acme Coporation Software :09:55 Mediacore Software :03:08 Initech Software :50:44 Hooli Software :09:19 Hooli Software :01:26 Mediacore Software 3
34 Using merge_ordered() In [9]: pd.merge_ordered(hardware, software) Out[9]: Date Company Product Units :33:01 Hooli Software :54:49 Mediacore Hardware :14:18 Initech Software :36:29 Streeplex Software :52:45 Acme Coporation Hardware :53:06 Acme Coporation Software :58:10 Acme Coporation Hardware :09:55 Mediacore Software :03:08 Initech Software :50:44 Hooli Software :09:19 Hooli Software :59:33 Mediacore Hardware :01:26 Mediacore Software :41:47 Hooli Hardware 3.0
35 Using on & suffixes In [10]: pd.merge_ordered(hardware, software, on=['date', 'Company'],...: suffixes=['_hardware', '_software']).head() Out[10]: Date Company Product_hardware \ :33:01 Hooli NaN :54:49 Mediacore Hardware :14:18 Initech NaN :36:29 Streeplex NaN :52:45 Acme Coporation Hardware Units_hardware Product_software Units_software 0 NaN Software NaN NaN 2 NaN Software NaN Software NaN NaN
36 Stocks data In [11]: stocks = pd.read_csv('stocks-2013.csv') In [12]: print(stocks) Date AAPL IBM CSCO MSFT
37 GDP data In [13]: gdp = pd.read_csv('gdp-2013.csv') In [14]: print(gdp) Date GDP
38 Ordered merge In [15]: pd.merge_ordered(stocks, gdp, on='date') Out[15]: Date AAPL IBM CSCO MSFT GDP NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
39 Ordered merge with ffill In [16]: pd.merge_ordered(stocks, gdp, on='date', fill_method='ffill') Out[16]: Date AAPL IBM CSCO MSFT GDP NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
40 MERGING DATAFRAMES WITH PANDAS Let s practice!
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