Pandas DataFrame Interpolate Method

Introduction

In this lab, we will explore the interpolate() method in the Pandas library for Python. The interpolate() method is used to fill missing or NaN (Not a Number) values in a DataFrame using various interpolation techniques. Interpolation is the process of estimating the missing values based on the existing data points.

VM Tips

After the VM startup is done, click the top left corner to switch to the Notebook tab to access Jupyter Notebook for practice.

Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.

If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.

Skills Graph

Import the necessary libraries

Let's begin by importing the pandas library and numpy library.

import pandas as pd
import numpy as np

Create a DataFrame with missing values

Next, let's create a DataFrame with some missing values.

df = pd.DataFrame([(0.0, np.nan, -1.0, 1.0), (np.nan, 2.0, np.nan, np.nan), (2.0, 3.0, np.nan, 9.0)], columns=list('abcd'))
print(df)

Output:

     a    b    c    d
0  0.0  NaN -1.0  1.0
1  NaN  2.0  NaN  NaN
2  2.0  3.0  NaN  9.0

Interpolate the missing values using the linear method

We can interpolate the missing values using the linear method. The linear method assumes that the values in the DataFrame are equally spaced.

interpolated_df = df.interpolate(method='linear')
print(interpolated_df)

Output:

     a    b    c    d
0  0.0  NaN -1.0  1.0
1  1.0  2.0 -1.0  5.0
2  2.0  3.0 -1.0  9.0

Interpolate the missing values using the polynomial method

We can also interpolate the missing values using the polynomial method. The polynomial method requires us to specify the order of the spline or polynomial.

interpolated_column = df['a'].interpolate(method='polynomial', order=1)
print(interpolated_column)

Output:

0    0.0
1    1.0
2    2.0
Name: a, dtype: float64

Interpolate the missing values using the pad method

Another method to interpolate the missing values is the pad method. The pad method fills the NaN values with the existing values in the DataFrame.

interpolated_df = df.interpolate(method='pad')
print(interpolated_df)

Output:

     a    b    c    d
0  0.0  NaN -1.0  1.0
1  0.0  2.0 -1.0  1.0
2  2.0  3.0 -1.0  9.0

Summary

In this lab, we learned how to use the interpolate() method in Pandas to fill missing or NaN values in a DataFrame. We explored different interpolation methods such as linear, polynomial, and pad. Interpolation is a useful technique for estimating missing values and making the data more complete for analysis.