CODEWITHSUNDEEP
pythonpandas
user1361529
user1361529

Reputation: 2697

Pandas dataframe: creating a new column that is a custom function using 2 other columns

Consider the following data set stored in a pandas DataFrame dfX:

A   B
1   2
4   6
7   9

I have a function that is:

def someThingSpecial(x,y)
  # z = do something special with x,y
  return z

I now want to create a new column in df that bears the computed z value

Looking at other SO examples, I've tried several variants including:

dfX['C'] = dfX.apply(lambda x: someThingSpecial(x=x['A'], y=x['B']), axis=1)

Which returns errors. What is the right way to do this?

Upvotes: 8

Views: 5056

Answers (2)

Trevor
Trevor

Reputation: 31

if your function only needs one column's value, then do this instead of coldspeed's answer:

dfX['A'].apply(your_func)

to store it:

dfX['C'] = dfX['A'].apply(your_func)

Upvotes: 3

cs95
cs95

Reputation: 402333

This seems to work for me on v0.21. Take a look - 

df

   A  B
0  1  2
1  4  6
2  7  9

def someThingSpecial(x,y):
     return x + y


df.apply(lambda x: someThingSpecial(x.A, x.B), 1)

0     3
1    10
2    16
dtype: int64

You might want to try upgrading your pandas version to the latest stable release (0.21 as of now).

Here's another option. You can vectorise your function.

v = np.vectorize(someThingSpecial)

v now accepts arrays, but operates on each pair of elements individually. Note that this just hides the loop, as apply does, but is much cleaner. Now, you can compute C as so - 

df['C'] = v(df.A, df.B)

Upvotes: 8

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