Select dataframe columns based on column values in Pandas

Question

My dataframe looks like:

A  B  C  D  ....  Y   Z
0  5  12 14       4   2
3  6  15 10       1   30
2  10 20 12       5   15

I want to create another dataframe that only contains the columns with an average value greater than 10:

C  D  .... Z
12 14      2
15 10      30
20 12      15

jezrael · Accepted Answer · 2017-12-04 06:34:12Z

1

Use:

df = df.loc[:, df.mean() > 10]
print (df)
    C   D   Z
0  12  14   2
1  15  10  30
2  20  12  15

Detail:

print (df.mean())
A     1.666667
B     7.000000
C    15.666667
D    12.000000
Y     3.333333
Z    15.666667
dtype: float64

print (df.mean() > 10)
A    False
B    False
C     True
D     True
Y    False
Z     True
dtype: bool

Alternative:

print (df[df.columns[df.mean() > 10]])
    C   D   Z
0  12  14   2
1  15  10  30
2  20  12  15

Detail:

print (df.columns[df.mean() > 10])
Index(['C', 'D', 'Z'], dtype='object')

edited Dec 4, 2017 at 6:34

answered Dec 4, 2017 at 6:27

jezrael

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2 Comments

fume_hood_geologist Over a year ago

thanks, i tried it on my dataframe and got: IndexingError: Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match for some reason. I suppose there must be an issue with the dataframe itself I didnt notice

jezrael Over a year ago

It seems you forget for loc. I add another solution, please chek it.

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