nan vs nat pandas

1#Python, pandas
2#Count missing values for each column of the dataframe df
3
4df.isnull().sum()
5

1df['your column name'].isnull()

1>>> import pandas as pd, datetime, numpy as np
2>>> df = pd.DataFrame({'a': [datetime.datetime.now(), np.nan], 'b': [5, np.nan], 'c': [1, 2]})
3>>> df
4                           a    b  c
50 2019-02-17 18:06:15.231557  5.0  1
61                        NaT  NaN  2
7>>> fill_dt = datetime.datetime.now()
8>>> fill_value = 4
9>>> dt_filled_df = df.select_dtypes('datetime').fillna(fill_dt)
10>>> dt_filled_df
11                           a
120 2019-02-17 18:06:15.231557
131 2019-02-17 18:06:36.040404
14>>> value_filled_df = df.select_dtypes('int').fillna(fill_value)
15>>> value_filled_df
16   c
170  1
181  2
19>>> dt_filled_df.columns = [col + '_notnull' for col in dt_filled_df]
20>>> value_filled_df.columns = [col + '_notnull' for col in value_filled_df]
21>>> df = df.join(value_filled_df)
22>>> df = df.join(dt_filled_df)
23>>> df
24                           a    b  c  c_notnull                  a_notnull
250 2019-02-17 18:06:15.231557  5.0  1          1 2019-02-17 18:06:15.231557
261                        NaT  NaN  2          2 2019-02-17 18:06:36.040404
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