Dropping rows from a PANDAS dataframe where some of the columns have value 0

Question

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

Answer 1

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

Answer 2

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()