Pandas - conditionally select source column of data for a new column based on row value

Using DataFrame.where's other argument and pandas.concat:

>>> import pandas as pd
>>>
>>> foo = pd.DataFrame([
...     ['USA',1,2],
...     ['Canada',3,4],
...     ['Canada',5,6]
... ], columns=('Country', 'x', 'y'))
>>>
>>> z = foo['x'].where(foo['Country'] == 'USA', foo['y'])
>>> pd.concat([foo['Country'], z], axis=1)
  Country  x
0     USA  1
1  Canada  4
2  Canada  6

If you want z as column name, specify keys:

>>> pd.concat([foo['Country'], z], keys=['Country', 'z'], axis=1)
  Country  z
0     USA  1
1  Canada  4
2  Canada  6

This would work:

In [84]:

def func(x):
    if x['Country'] == 'USA':
        return x['x']
    if x['Country'] == 'Canada':
        return x['y']
    return NaN
foo['z'] = foo.apply(func(row), axis = 1)
foo
Out[84]:
  Country  x  y  z
0     USA  1  2  1
1  Canada  3  4  4
2  Canada  5  6  6

[3 rows x 4 columns]

You can use loc:

In [137]:

foo.loc[foo['Country']=='Canada','z'] = foo['y']
foo.loc[foo['Country']=='USA','z'] = foo['x']
foo
Out[137]:
  Country  x  y  z
0     USA  1  2  1
1  Canada  3  4  4
2  Canada  5  6  6

[3 rows x 4 columns]

EDIT

Although unwieldy using loc will scale better with larger dataframes as the apply here is called for every row whilst using boolean indexing will be vectorised.

Here is a generic solution to selecting arbitrary columns given a value in another column.

This has the additional benefit of separating the lookup logic in a simple dict structure which makes it easy to modify.

import pandas as pd
df = pd.DataFrame(
    [['UK', 'burgers', 4, 5, 6],
    ['USA', 4, 7, 9, 'make'],
    ['Canada', 6, 4, 6, 'you'],
    ['France', 3, 6, 'fat', 8]],
    columns = ('Country', 'a', 'b', 'c', 'd')
)

I extend to an operation where a conditional result is stored in an external lookup structure (dict)

lookup = {'Canada': 'd', 'France': 'c', 'UK': 'a', 'USA': 'd'}

Loop the pd.DataFrame for each column stored in the dict and use the values in the condition table to determine which column to select

for k,v in lookup.iteritems():
    filt = df['Country'] == k
    df.loc[filt, 'result'] = df.loc[filt, v] # modifies in place

To give the life lesson

In [69]: df
Out[69]:
  Country        a  b    c     d   result
0      UK  burgers  4    5     6  burgers
1     USA        4  7    9  make     make
2  Canada        6  4    6   you      you
3  France        3  6  fat     8      fat