pandas interpolate

Pandas interpolate() is a method that is used to fill the missing values in a pandas DataFrame or Series object. It uses various interpolation techniques to estimate the missing values based on the existing data.

The interpolate() method takes various parameters, such as method, axis, limit, limit_direction, and inplace, which can be used to customize the interpolation process.

Some of the commonly used interpolation techniques include linear interpolation, polynomial interpolation, and spline interpolation. The method parameter can be used to specify the interpolation technique to be used.

For example, the following code snippet shows how to use the interpolate() method to fill the missing values using linear interpolation:

import pandas as pd

# create a sample dataframe with missing values
df = pd.DataFrame({'A': [1, 2, np.nan, 4, 5], 'B': [5, np.nan, 7, np.nan, 9]})

# fill the missing values using linear interpolation
df.interpolate(method='linear', inplace=True)

print(df)

Output:

     A    B
0  1.0  5.0
1  2.0  6.0
2  3.0  7.0
3  4.0  8.0
4  5.0  9.0

In the above example, the missing values in column A and B are filled using linear interpolation. The inplace parameter is set to True, which updates the original dataframe with the interpolated values.