trjhtr

I am dropping rows from a PANDAS dataframe when some of its columns have 0 value. I got the output by using the below code, but I hope we can do the same with less code — perhaps in a single line.

df:

 A B C
 0 1 2 5
 1 4 4 0
 2 6 8 4
 3 0 4 2

My code:

 drop_A=df.index[df["A"] == 0].tolist()
 drop_B=df.index[df["C"] == 0].tolist()
 c=drop_A+drop_B
 df=df.drop(df.index[c])

[out]

 A B C
 0 1 2 5
 2 6 8 4

I think you need create boolean DataFrame by compare all filtered columns values by scalar for not equality and then check all Trues per rows by all:

df = df[(df[['A','C']] != 0).all(axis=1)]
print (df)
 A B C
0 1 2 5
2 6 8 4

Details:

print (df[['A','C']] != 0)
 A C
0 True True
1 True False
2 True True
3 False True

print ((df[['A','C']] != 0).all(axis=1))

0 True
1 False
2 True
3 False
dtype: bool

I think you need create boolean DataFrame by compare all values by scalar and then check any Trues per rows by any and last invert mask by ~:

df = df[~(df[['A','C']] == 0).any(axis=1)]

Details:

print (df[['A','C']])
 A C
0 1 5
1 4 0
2 6 4
3 0 2

print (df[['A','C']] == 0)
 A C
0 False False
1 False True
2 False False
3 True False

print ((df[['A','C']] == 0).any(axis=1))
0 False
1 True
2 False
3 True
dtype: bool

print (~(df[['A','C']] == 0).any(axis=1))
0 True
1 False
2 True
3 False
dtype: bool

One line hack using .dropna()

import pandas as pd

df = pd.DataFrame('A':[1,4,6,0],'B':[2,4,8,4],'C':[5,0,4,2])
print df
 A B C
0 1 2 5
1 4 4 0
2 6 8 4
3 0 4 2

columns = ['A', 'C']
df = df.replace(0, pd.np.nan).dropna(axis=0, how='any', subset=columns).fillna(0).astype(int)

print df
 A B C
0 1 2 5
2 6 8 4

So, what's happening is:

1. Replace 0 by NaN with .replace()
   


2. Use .dropna() to drop NaN considering only columns A and C
   


3. Replace NaN back to 0 with .fillna() (not needed if you use all columns instead of only a subset)


4. Correct the data type from float to int with .astype()