Pandas: How can I use the apply () function for a single column?

Question

I have a pandas data frame with two columns. I need to change the values ​​of the first column without affecting the second, and return the entire data frame with only the values ​​of the first column changed. How can I do this using pandas?

Answer 1

For sample df data like:

 a,b 1,2 2,3 3,4 4,5 

What would you like:

 df['a'] = df['a'].apply(lambda x: x + 1) 

which returns:

  ab 0 2 2 1 3 3 2 4 4 3 5 5 

Answer 2

You don't need a function at all. You can work with the whole column directly.

Sample data:

 >>> df = pd.DataFrame({'a': [100, 1000], 'b': [200, 2000], 'c': [300, 3000]}) >>> df abc 0 100 200 300 1 1000 2000 3000 

Half of all values ​​in column a :

 >>> df.a = df.a / 2 >>> df abc 0 50 200 300 1 500 2000 3000 

Answer 3

For a single column, it is better to use map() , for example:

 df = pd.DataFrame([{'a': 15, 'b': 15, 'c': 5}, {'a': 20, 'b': 10, 'c': 7}, {'a': 25, 'b': 30, 'c': 9}]) abc 0 15 15 5 1 20 10 7 2 25 30 9 df['a'] = df['a'].map(lambda a: a / 2.) abc 0 7.5 15 5 1 10.0 10 7 2 12.5 30 9 

Answer 4

Although the answers given are correct, they change the initial data frame, which is not always desirable (and given that the OP asked for examples “using apply ”, they probably need a version that returns a new data frame, as apply does).

This is possible using the assign command: this is valid for assign existing columns, as the documentation reads (highlighted mine):

Assign new columns to the DataFrame.

Returns a new object with all source columns in addition to new ones. Existing columns to be reassigned will be overwritten .

In short:

 In [1]: import pandas as pd In [2]: df = pd.DataFrame([{'a': 15, 'b': 15, 'c': 5}, {'a': 20, 'b': 10, 'c': 7}, {'a': 25, 'b': 30, 'c': 9}]) In [3]: df.assign(a=lambda df: df.a / 2) Out[3]: abc 0 7.5 15 5 1 10.0 10 7 2 12.5 30 9 In [4]: df Out[4]: abc 0 15 15 5 1 20 10 7 2 25 30 9