pandas where
# replaces values with other where condition is False
DataFrame.where(cond, other=nan, inplace=False, axis=None, level=None, errors='raise', try_cast=False)
import pandas as pd
df = pd.DataFrame({'values':[1,2,3,4]})
df.where(df['values'] % 2 == 0, -1) # output : [-1, 2, -3, 4]
4
1
In [20]: df
Out[20]:
A B C
0 1 2 1
1 2 3 0
2 3 4 0
3 4 5 1
# Note that df.C is a mask
# Note that this is np.where, not df.where. That is different.
In [21]: df['D'] = np.where(df.C, df.A, df.B)
In [22]: df
Out[22]:
A B C D
0 1 2 1 1
1 2 3 0 3
2 3 4 0 4
3 4 5 1 4
Thank you!
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