Python: Filter rows in data frame when multiple rows meet a unique condition

Question

I have a data set similar to:

    dt = {'A': [0,0,0,1], 
          'B': [0, 2,0,3],
          'C': [0,0,0,4],
          'D': [0,5,0,6]}
    dt = pd.DataFrame(dt)

I aim to filter all rows when columns ['A', 'B','C', 'D'] all are zero for that row. In real data set instead of 4 columns I have more than twenty columns . So the following solution is not feasible:

    dt = dt[(dt['A'] == 0) & (dt['B'] == 0) & (dt['C'] == 0) & (dt['D'] == 0)]

So I came up with the following solution:

    dt['new'] = np.nan
    lst = [0,1,2,3]

    for i in range(len(dt)):
        dt.iloc[i, 4] = all(dt.iloc[i, lst] == 0) 

And finally I can filter based on 'new' column.

I am looking for a more efficient solution, preferably something without a loop, any help would be appreciated.

Answer 1

You can try this using DataFrame.eq with DataFrame.all and boolean indexing

dt[dt.eq(0).all(1)]

   A  B  C  D
0  0  0  0  0
2  0  0  0  0

Another idea is to usenp.any or DataFrame.any as boolean mask

dt[~dt.any(1)] # @Sayandip Dutta's answer in the comments
dt[~np.any(dt, axis=1)]

Answer 2

Try this, DataFrame.sum(axis=1)

dt[dt.sum(axis=1).eq(0)]

---

   A  B  C  D
0  0  0  0  0
2  0  0  0  0