I have the following dataframe:
import pandas as pd
import numpy as np
df = pd.DataFrame(dict(A = np.arange(3),
B = np.random.randn(3),
C = ['foo','bar','bah'],
D = pd.Timestamp('20130101')))
print(df)
A B C D
0 0 -1.087180 foo 2013-01-01
1 1 -1.343424 bar 2013-01-01
2 2 -0.193371 bah 2013-01-01
dtypes
for columns:
print(df.dtypes)
A int32
B float64
C object
D datetime64[ns]
dtype: object
But after using apply
they all changes to object:
print(df.apply(lambda x: x.dtype))
A object
B object
C object
D object
dtype: object
Why are dtypes
coerced to object? I thought that in apply
only columns should be taken in account.
pandas 0.17.1
python 3.4.3
You can use parameter reduce=False
and more info here:
print (df.apply(lambda x: x.dtype, reduce=False))
A int32
B float64
C object
D datetime64[ns]
dtype: object
In newer versions of pandas is possible use:
print (df.apply(lambda x: x.dtype, result_type='expand'))
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