Updating multiple columns in pandas dataframe with function

Question

I've tried searching this on Stack Overflow but don't seem to be able to find the same scenario. I'm trying to update multiple columns in my pandas dataframe in one line of code with same function. I can update each column individually, but want the function to update multiple columns simultaneously.

The function is basic:

binary_values = lambda x: 0 if x == -99 else 1

I know I can assign scalar values to sections of the dataframe like this:

df.ix[:,22:26] = 1

I'm trying to do something like below:

df.ix[:,22:26] = df.ix[:,22:26].apply(binary_values)

...but it throws up ValueError: ('The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().', 'occurred at index..')

akuiper · Accepted Answer · 2017-07-24 00:36:49Z

1

Your lambda function is not vectorized, you need applymap to apply it to each element of your data frame:

df.iloc[:,22:26] = df.iloc[:,22:26].applymap(binary_values)

Example:

df = pd.DataFrame({
    "A":[1,2,3,-99,5,3],
    "B":[2,3,5,2,-99,2],
    "C":[-99,3,2,1,4,5]
})

binary_values = lambda x: 0 if x == -99 else 1
df.iloc[:,1:] = df.iloc[:,1:].applymap(binary_values)

df

#   A   B   C
#0  1   1   0
#1  2   1   1
#2  3   1   1
#3  -99 1   1
#4  5   0   1
#5  3   1   1

A simpler (vectorized) option is check the equality and then convert the data type to int:

df.iloc[:,1:] = df.iloc[:,1:].ne(-99).astype(int)

df
#   A   B   C
#0  1   1   0
#1  2   1   1
#2  3   1   1
#3  -99 1   1
#4  5   0   1
#5  3   1   1

edited Jul 24, 2017 at 0:36

answered Jul 24, 2017 at 0:31

akuiper

216k33 gold badges362 silver badges379 bronze badges

Sign up to request clarification or add additional context in comments.

Collectives™ on Stack Overflow

Updating multiple columns in pandas dataframe with function

1 Answer 1

Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

Comments

Your Answer

Sign up or log in

Post as a guest

Related