pandas missing data handling code example

Example 1: how to fill missing values dataframe with mean

sub2['income'].fillna((sub2['income'].mean()), inplace=True)

Example 2: represent NaN with pandas in python

import pandas as pd

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

Example 3: filling the missing data in pandas

note:to fill a specific value

varable = 1
def fill_mod_acc(most_related_coloum_name,missing_data_coloum):
    if np.isnan(missing_data_coloum):
        return varable[most_related_coloum_name]
    else:
        return missing_data_coloum

df['missing_data_coloum'] = df.apply(lambda x:fill_mod_acc(x['most_related_coloum_name'],x['missing_data_coloum']),axis=1)


Note:to fill mean from existing closley related coloum

varable = df.groupby('most_related_coloum_name').mean()['missing_data_coloum']

def fill_mod_acc(most_related_coloum_name,missing_data_coloum):
    if np.isnan(missing_data_coloum):
        return varable[most_related_coloum_name]
    else:
        return missing_data_coloum

df['missing_data_coloum'] = df.apply(lambda x:fill_mod_acc(x['most_related_coloum_name'],x['missing_data_coloum']),axis=1)

Example 4: 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)