CODEWITHSUNDEEP
pythonpandas
user1260503
user1260503

Reputation:

Do I want to join/merge, concat, or append these two Pandas DataFrames?

How would I paste these two tables together using join/merge/concat/append/add in such a way that the population age 0-14, and 15-64 columns are side-by-side?

I don't need the cartesian product of these two DataFrames.

I've tried:

population ages = t3.merge(t4, on='Country Name', how='inner')

t3

  Country Name  Year  Population Age 0 - 14
0        Aruba  1960              43.847771
1      Andorra  1960                    NaN
2  Afghanistan  1960              43.712284
3       Angola  1960              43.759289
4      Albania  1960              41.757282

t4 

  Country Name  Population Age 15 - 64
0        Aruba               53.667355
1      Andorra                     NaN
2  Afghanistan               53.834637
3       Angola               53.587101
4      Albania               52.941044

Ideally

  Country Name  Population Age 15 - 64  Population Ages 0 - 14 
0        Aruba               53.667355               43.847771
1      Andorra                     NaN                     NaN
2  Afghanistan               53.834637               43.712284
3       Angola               53.587101               43.759289
4      Albania               52.941044               41.757282

Test results:

population_ages = t3.merge(t4, on='Country Name', how='inner')

I received a DataFrame that's a cartesian product of t3, t4 with a shape of (734832, 4) instead of (13608, 4)

       Country Name  Year  Population Age 0 - 14  Population Age 15 - 64
0             Aruba  1960              43.847771               53.667355
1             Aruba  1960              43.847771               53.890141
2             Aruba  1960              43.847771               54.216911
3             Aruba  1960              43.847771               54.637810
4             Aruba  1960              43.847771               55.119324
5             Aruba  1960              43.847771               55.631104
6             Aruba  1960              43.847771               56.168560
7             Aruba  1960              43.847771               56.736549
8             Aruba  1960              43.847771               57.341782
9             Aruba  1960              43.847771               57.983109
10            Aruba  1960              43.847771               58.674343
11            Aruba  1960              43.847771               59.404758
12            Aruba  1960              43.847771               60.164749

Upvotes: 2

Views: 1523

Answers (1)

furas
furas

Reputation: 142641

How about 

t4['Population Age 0 - 14'] = t3['Population Age 0 - 14']

or

pd.concat( t4, t3['Population Age 0 - 14'], axis=1)

full working example:

import pandas as pd
from StringIO import StringIO

d1 = '''Country Name  Year  Population Age 0 - 14
        Aruba  1960              43.847771
      Andorra  1960                    NaN
  Afghanistan  1960              43.712284
       Angola  1960              43.759289
      Albania  1960              41.757282'''

d2 = '''Country Name  Population Age 15 - 64
        Aruba               53.667355
      Andorra                     NaN
  Afghanistan               53.834637
       Angola               53.587101
      Albania               52.941044'''

t3 = pd.DataFrame.from_csv( StringIO(d1), sep='\s{2,}', index_col=None )
print '\nt3:\n',t3

t4 = pd.DataFrame.from_csv( StringIO(d2), sep='\s{2,}', index_col=None )
print '\nt4:\n',t3

print '\n--- merge ---\n'

print pd.merge( t4, t3, on='Country Name')

print pd.merge( t4, t3[ ['Country Name', 'Population Age 0 - 14'] ], on='Country Name')

print '\n--- concat ---\n'

print pd.concat( (t4, t3['Population Age 0 - 14']), axis=1)

print '\n--- [xxx] = [xxx] ---\n'

t4['Population Age 0 - 14'] = t3['Population Age 0 - 14']

print t4

result:

  Country Name  Population Age 15 - 64  Population Age 0 - 14
0        Aruba               53.667355              43.847771
1      Andorra                     NaN                    NaN
2  Afghanistan               53.834637              43.712284
3       Angola               53.587101              43.759289
4      Albania               52.941044              41.757282

Upvotes: 1

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