count nan pandas
#Python, pandas
#Count missing values for each column of the dataframe df
df.isnull().sum()count nan pandas
#Python, pandas
#Count missing values for each column of the dataframe df
df.isnull().sum()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.")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 18:06:36.040404handling 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)Copyright © 2021 Codeinu
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