Select all row where all columns are equal to value

Question

I have a pandas dataframe and I need to select the rows where all the columns are equal to 0.00. How can I do that?

Does this answer your question? [How to select rows from a DataFrame based on column values](https://stackoverflow.com/questions/17071871/how-to-select-rows-from-a-dataframe-based-on-column-values) — bluevulture, Apr 21 '21 at 11:28
More or less, I need that all columns have this value, not only a specific one — Denise Lavezzari, Apr 21 '21 at 11:34

Thomas Kimber · Accepted Answer · 2021-04-21T11:41:51.110

First, we'll setup some example data to work on - here there are 3 columns, but it could be more, or fewer in your example.

import pandas as pd

data = [
    {"A": 1, "B": 1, "C" : 1},
    {"A": 0, "B": 1, "C" : 1},
    {"A": 1, "B": 0, "C" : 1},
    {"A": 0, "B": 0, "C" : 1},
    {"A": 1, "B": 1, "C" : 0},
    {"A": 0, "B": 1, "C" : 0},
    {"A": 1, "B": 0, "C" : 0},
    {"A": 0, "B": 0, "C" : 0}
    
]

df = pd.DataFrame(data)

And define a function that accepts a row from a dataframe, and performs some test on it - in this case, check that all values in the row are equal to zero.

def all_zero(row):
    if all([v==0 for v in row.values]):
        return True
    else:
        return False

Once the test function is defined apply it to the dataframe along axis 1 (row by row):

df.apply(all_zero, axis=1)

Which returns a boolean series, showing for each row, whether the test passed or failed.

0    False
1    False
2    False
3    False
4    False
5    False
6    False
7     True
dtype: bool

And you can use this boolean series as an index-level selector (or mask, as some people call it), to retrieve only those rows that return True.

df[df.apply(all_zero, axis=1)]

index	A	B	C
7	0	0	0

Select all row where all columns are equal to value

1 Answers1