pandas apply function on column only if condition on another column is met-CodePudding

I have a dataframe:

df =  A. Cond Val
      1. True 0.8
      5. False 0.8
      2. False 0.6
      4. False 0.5

I want to update the value of the columns 'Val' by truncate it in 0.1, only when Cond is False and val is higher than 0.55. So new df will be:

df =  A. Cond Val
      1. True 0.8
      5. False 0.7
      2. False 0.5
      2. False 0.5

What is the best way to do it?

CodePudding user response：

Use boolean indexing with DataFrame.loc, for test False values invert maks by ~ and chain another mask by Series.gt:

df.loc[df['Val'].gt(0.55) & ~df['Cond'], 'Val'] -= 0.1
print (df)
    A.   Cond  Val
0  1.0   True  0.8
1  5.0  False  0.7
2  2.0  False  0.5
3  4.0  False  0.5

CodePudding user response：

Use boolean indexing with two conditions and AND (&):

df.loc[df['Val'].gt(0.55) & ~df['Cond'], 'Val'] -= 0.1

Output:

    A.   Cond  Val
0  1.0   True  0.8
1  5.0  False  0.7
2  2.0  False  0.5
3  4.0  False  0.5

Conditions:

    A.   Cond  Val  df['Val'].gt(0.55)  ~df['Cond']    AND
0  1.0   True  0.8                True        False  False
1  5.0  False  0.8                True         True   True
2  2.0  False  0.6                True         True   True
3  4.0  False  0.5               False         True  False

CodePudding user response：

Using the int values of booleans, this would work too:

df['val'] -= 0.1*(~df['cond'])*(df['val'] > 0.55)