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