Change cell value according to values within another column [pandas]
Question:
I have a dataframe such as
Names Value COLA COLB COLC
A 100 0 4 1
B NaN 0 2 1
C 20 3 0 0
D 1 0 1 0
E 300 3 0 0
And I would like to change all the COLA,B and C values (except the 0) :
- to 1 if the
Value col > 30
- to 2 if the
Value col <=30 or NaN.
I should then get
Names Value COLA COLB COLC
A 100 0 1 1
B NaN 0 2 2
C 20 2 0 0
D 1 0 2 0
E 300 1 0 0
Does someone have a sugestion ?
Answers:
Use numpy.where with chain condition used for broadcasting – assign mask from Series to multiple columns, for set 0 multiple ouput to boolean mask for set 0:
cols = ['COLA','COLB','COLC']
df[cols] = np.where(df['Value'].gt(30).to_numpy()[:, None], 1, 2) * df[cols].ne(0)
print (df)
Names Value COLA COLB COLC
0 A 100.0 0 1 1
1 B NaN 0 2 2
2 C 20.0 2 0 0
3 D 1.0 0 2 0
4 E 300.0 1 0 0
I have a dataframe such as
Names Value COLA COLB COLC
A 100 0 4 1
B NaN 0 2 1
C 20 3 0 0
D 1 0 1 0
E 300 3 0 0
And I would like to change all the COLA,B and C values (except the 0) :
- to 1 if the
Value col > 30 - to 2 if the
Value col <=30orNaN.
I should then get
Names Value COLA COLB COLC
A 100 0 1 1
B NaN 0 2 2
C 20 2 0 0
D 1 0 2 0
E 300 1 0 0
Does someone have a sugestion ?
Use numpy.where with chain condition used for broadcasting – assign mask from Series to multiple columns, for set 0 multiple ouput to boolean mask for set 0:
cols = ['COLA','COLB','COLC']
df[cols] = np.where(df['Value'].gt(30).to_numpy()[:, None], 1, 2) * df[cols].ne(0)
print (df)
Names Value COLA COLB COLC
0 A 100.0 0 1 1
1 B NaN 0 2 2
2 C 20.0 2 0 0
3 D 1.0 0 2 0
4 E 300.0 1 0 0