Pandas DataFrame, How do I remove all columns and rows that sum to 0

Question

I have a dataFrame with rows and columns that sum to 0.

    A   B   C    D
0   1   1   0    1
1   0   0   0    0 
2   1   0   0    1
3   0   1   0    0  
4           


        

Answer 1

df.loc[row_indexer, column_indexer] allows you to select rows and columns using boolean masks:

In [88]: df.loc[(df.sum(axis=1) != 0), (df.sum(axis=0) != 0)]
Out[88]: 
   A  B  D
0  1  1  1
2  1  0  1
3  0  1  0
4  1  1  1

[4 rows x 3 columns]

df.sum(axis=1) != 0 is True if and only if the row does not sum to 0.

df.sum(axis=0) != 0 is True if and only if the column does not sum to 0.

Answer 2

This is my way to do it:

import pandas as pd 
hl = []
df =  pd.read_csv("my.csv")
l = list(df.columns.values)
for l in l:
    if sum(df[l]) != 0:
        hl.append(l)
df2 = df[hl]

to write reduced_Data:

df2.to_csv("my_reduced_data.csv")

It will only check columns but ignore Rows

Answer 3

building on Drop rows with all zeros in pandas data frame to avoid using the sum()

df = pd.DataFrame({'A': [1,0,1,0,1],
                   'B': [1,0,0,1,1],
                   'C': [0,0,0,0,0],
                   'D': [1,0,1,0,1]})

df.loc[(df!=0).any(1), (df!=0).any(0)]

   A  B  D
0  1  1  1
2  1  0  1
3  0  1  0
4  1  1  1