Answers for "nan pandas"

fill missing values in column pandas with mean

df.fillna(df.mean(), inplace=True)

represent NaN with pandas in python

import pandas as pd

if pd.isnull(float("Nan")):
  print("Null Value.")

pandas nan values in column

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

nan vs nat pandas

>>> import pandas as pd, datetime, numpy as np
>>> df = pd.DataFrame({'a': [datetime.datetime.now(), np.nan], 'b': [5, np.nan], 'c': [1, 2]})
>>> df
                           a    b  c
0 2019-02-17 18:06:15.231557  5.0  1
1                        NaT  NaN  2
>>> fill_dt = datetime.datetime.now()
>>> fill_value = 4
>>> dt_filled_df = df.select_dtypes('datetime').fillna(fill_dt)
>>> dt_filled_df
                           a
0 2019-02-17 18:06:15.231557
1 2019-02-17 18:06:36.040404
>>> value_filled_df = df.select_dtypes('int').fillna(fill_value)
>>> value_filled_df
   c
0  1
1  2
>>> dt_filled_df.columns = [col + '_notnull' for col in dt_filled_df]
>>> value_filled_df.columns = [col + '_notnull' for col in value_filled_df]
>>> df = df.join(value_filled_df)
>>> df = df.join(dt_filled_df)
>>> df
                           a    b  c  c_notnull                  a_notnull
0 2019-02-17 18:06:15.231557  5.0  1          1 2019-02-17 18:06:15.231557
1                        NaT  NaN  2          2 2019-02-17 18:06:36.040404

handling missing dvalues denoted by a '?' in pandas

# Making a list of missing value typesmissing_values = ["n/a", "na", "--"]df = pd.read_csv("property data.csv", na_values = missing_values)