I have a dataframe
df=
A B C
1 2 55
0 44 0
0 0 0
and I want to change values to 1 if the value is >0.
Is this the right approach: df.loc[df>0,]=1
to give:
A B C
1 1 1
0 1 0
0 0 0
Asked
Active
Viewed 1.0k times
2
fred.schwartz
- 2,023
- 4
- 26
- 53
-
1Are you after `df.loc[df.gt(0).all(1), :] = 1` ? – Jon Clements Nov 24 '18 at 14:17
-
Just tried it and that didn't work but thanks for trying. Any other ideas? – fred.schwartz Nov 24 '18 at 14:18
-
2well, that's if all values are >0 for row, if you just want to max each value then `df.clip_upper(1)`? Your question title and body don't seem to be quite in tandem with what you might be asking. – Jon Clements Nov 24 '18 at 14:19
2 Answers
6
Use clip_upper:
df = df.clip_upper(1)
print (df)
A B C
0 1 1 1
1 0 1 0
2 0 0 0
Numpy alternative:
df = pd.DataFrame(np.clip(df.values, a_min=0, a_max=1),
index=df.index,
columns=df.columns)
print (df)
A B C
0 1 1 1
1 0 1 0
2 0 0 0
And solution if no negative integer values - compare by ge (>=) and cast mask to integers:
print (df.ge(1))
A B C
0 True True True
1 False True False
2 False False False
df = df.ge(1).astype(int)
print (df)
A B C
0 1 1 1
1 0 1 0
2 0 0 0
jezrael
- 822,522
- 95
- 1,334
- 1,252
1
You can do this:
df.clip_upper(1)
or this:
df.where(df < 1, other=1)
Piotr
- 2,029
- 1
- 13
- 8
-
-
This is great but there is a added complication that the value in question will not always be 0. It might be 20. – fred.schwartz Nov 24 '18 at 14:31
-
-
-
-