Fill Missing Values in DataFrame using Python

Solution

To solve this, we will follow the steps given below −

• Define a dataframe

• Apply df.interpolate funtion inside method =’linear’, limit_direction =’forward’ and fill NaN limit = 2

df.interpolate(method ='linear', limit_direction ='forward', limit = 2

Example

import pandas as pd
df = pd.DataFrame({"Id":[1, 2, 3, None, 5],
                     "Age":[12, 12, 14, 13, None],
                     "Mark":[80, 90, None, 95, 85],
                  })
print("Dataframe is:\n",df)
print("Interpolate missing values:")
print(df.interpolate(method ='linear', limit_direction ='forward', limit = 2))

Output

Dataframe is:
   Id     Age   Mark
0 1.0    12.0   80.0
1 2.0    12.0   90.0
2 3.0    14.0   NaN
3 NaN    13.0   95.0
4 5.0    NaN    85.0
Interpolate missing values:
   Id     Age    Mark
0 1.0    12.0    80.0
1 2.0    12.0    90.0
2 3.0    14.0    92.5
3 4.0    13.0    95.0
4 5.0    13.0    85.0