Handling Missing values of column in pandas python

Missing values of column in pandas python can be handled either by dropping the missing values or replacing the missing values. Which is listed below in detail.

• drop the rows that have missing values
• Replace missing value with zeros
• Replace missing value with Mean of the column
• Replace missing value with Median of the column

First let’s create a dataframe.

import pandas as pd
import numpy as np

#Create a DataFrame
df1 = {
    'Name':['George','Andrea','micheal','maggie','Ravi','Xien','Jalpa',np.nan],
    'State':['Arizona','Georgia','Newyork','Indiana','Florida','California',np.nan,np.nan],
    'Gender':["M","F","M","F","M","M",np.nan,np.nan],      
    'Score':[63,48,56,75,np.nan,77,np.nan,np.nan]
    
   }

df1 = pd.DataFrame(df1,columns=['Name','State','Gender','Score'])
print(df1)

So the resultant dataframe will be

Drop the rows that have missing values

Drop the rows even with single NaN or single missing values.

df1.dropna()

Outputs:

Replace missing value with zeros

Fill the missing values with zeros i.e. replace the missing values with zero

df1.fillna(0)

Outputs:

Replace missing value with Mean of the column:

Fill in the missing values with mean of the column. i.e.  replace missing value with mean of the column

df1["Score"].fillna(df1["Score"].mean(), inplace=True)

Output:

Replace missing value with Median of the column:

Fill in the missing values with median of the column. i.e.  replace missing value with median of the column

df1["Score"].fillna(df1["Score"].median(), inplace=True)

Output: