I need to set the value of one column based on the value of another in a Pandas dataframe. This is the logic:
if df['c1'] == 'Value':
df['c2'] = 10
else:
df['c2'] = df['c3']
I am unable to get this to do what I want, which is to simply create a column with new values (or change the value of an existing column: either one works for me).
If I try to run the code above or if I write it as a function and use the apply method, I get the following:
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
one way to do this would be to use indexing with .loc
.
Example
In the absence of an example dataframe, I'll make one up here:
import numpy as np
import pandas as pd
df = pd.DataFrame({'c1': list('abcdefg')})
df.loc[5, 'c1'] = 'Value'
>>> df
c1
0 a
1 b
2 c
3 d
4 e
5 Value
6 g
Assuming you wanted to create a new column c2
, equivalent to c1
except where c1
is Value
, in which case, you would like to assign it to 10:
First, you could create a new column c2
, and set it to equivalent as c1
, using one of the following two lines (they essentially do the same thing):
df = df.assign(c2 = df['c1'])
# OR:
df['c2'] = df['c1']
Then, find all the indices where c1
is equal to 'Value'
using .loc
, and assign your desired value in c2
at those indices:
df.loc[df['c1'] == 'Value', 'c2'] = 10
And you end up with this:
>>> df
c1 c2
0 a a
1 b b
2 c c
3 d d
4 e e
5 Value 10
6 g g
If, as you suggested in your question, you would perhaps sometimes just want to replace the values in the column you already have, rather than create a new column, then just skip the column creation, and do the following:
df['c1'].loc[df['c1'] == 'Value'] = 10
# or:
df.loc[df['c1'] == 'Value', 'c1'] = 10
Giving you:
>>> df
c1
0 a
1 b
2 c
3 d
4 e
5 10
6 g