CODEWITHSUNDEEP
pythonpandas
ac24
ac24

Reputation: 5565

Selecting columns by column NAME dtype

import pandas as pd
import numpy as np
cols = ['string',pd.Timestamp('2017-10-13'), 'anotherstring', pd.Timestamp('2017-10-14')]
pd.DataFrame(np.random.rand(5,4), columns=cols)

How can I get back just the 2nd and 4th column (which have dtype 'date time.datetime')? The types of the column contents are exactly the same, so select_dtypes doesn't help.

Upvotes: 1

Views: 422

Answers (1)

jezrael
jezrael

Reputation: 862711

Use type with map:

df = df.loc[:, df.columns.map(type) == pd.Timestamp]
print (df)
   2017-10-13 00:00:00  2017-10-14 00:00:00
0             0.894932             0.502015
1             0.080334             0.155712
2             0.600152             0.206344
3             0.008913             0.919534
4             0.280229             0.951434

Details:

print (df.columns.map(type))
Index([                         <class 'str'>,
       <class 'pandas._libs.tslib.Timestamp'>,
                                <class 'str'>,
       <class 'pandas._libs.tslib.Timestamp'>]

print (df.columns.map(type) == pd.Timestamp)
[False  True False  True]

Alternative solution:

df1 = df.loc[:, [isinstance(i, pd.Timestamp) for i in df.columns]]
print (df1)
   2017-10-13 00:00:00  2017-10-14 00:00:00
0             0.818283             0.128299
1             0.570288             0.458400
2             0.857426             0.395963
3             0.595765             0.306861
4             0.196899             0.438231

Upvotes: 2

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