Pandas DataFrame Last_valid_index Method

Introduction

The last_valid_index() method in pandas is used to get the index of the last non-null/NaN value in a DataFrame. It returns a scalar value that represents the index. If all the elements are non-null/NaN, it returns None. If the DataFrame is empty, it also returns None. This method is helpful for finding the position of the last non-null/NaN value in a DataFrame.

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.

Create a DataFrame with null values

First, let's create a DataFrame with null values using the np.nan value and the pandas library. This DataFrame will have some non-null/NaN values as well.

## Importing pandas as pd
import pandas as pd
## Importing numpy as np
import numpy as np

df = pd.DataFrame([[np.nan, np.nan, np.nan],
                   [np.nan, 2, 5],
                   [1, 3, 4],
                   [np.nan, 3, np.nan],
                   [2, 8, 0],
                   [7, 5, 4]],
                  columns=list('ABC'))
print("-----The DataFrame is-----")
print(df)

Get the index of the last non-null/NaN value

Next, we will use the last_valid_index() method on the DataFrame to get the index of the last non-null/NaN value.

print("Index for last non-null/NaN value is:", df.last_valid_index())

Create a DataFrame with only null values

Now, let's create another DataFrame with only null values using the np.nan value and the pandas library.

## Importing pandas as pd
import pandas as pd
## Importing numpy as np
import numpy as np

df = pd.DataFrame([[np.nan, np.nan, np.nan],
                   [np.nan, np.nan, np.nan],
                   [np.nan, np.nan, np.nan],
                   [2, 8, 0],
                   [7, 5, 4],
                   [np.nan, np.nan, np.nan]],
                  columns=list('ABC'))
print("-----The DataFrame is-----")
print(df)

Get the index of the last non-null/NaN value in the second DataFrame

Similarly, we will use the last_valid_index() method on this DataFrame to get the index of the last non-null/NaN value.

print("Index for last non-null/NaN value is:", df.last_valid_index())

Create a DataFrame with only null values

Lastly, let's create a DataFrame with only null values again using the np.nan value and the pandas library.

## Importing pandas as pd
import pandas as pd
## Importing numpy as np
import numpy as np

df = pd.DataFrame([[np.nan, np.nan, np.nan],
                   [np.nan, np.nan, np.nan],
                   [np.nan, np.nan, np.nan],
                   [np.nan, np.nan, np.nan]],
                  columns=list('ABC'))
print("-----The DataFrame is-----")
print(df)

Get the index of the last non-null/NaN value in the third DataFrame

Finally, we will use the last_valid_index() method on this DataFrame to get the index of the last non-null/NaN value.

print("Index for last non-null/NaN value is:", df.last_valid_index())

Summary

In this lab, we learned about the last_valid_index() method in pandas. We saw how to use this method to get the index of the last non-null/NaN value in a DataFrame. We also observed that this method returns None if all the elements are non-null/NaN, or if the DataFrame is empty. This method is useful for finding the position of the last non-null/NaN value in a DataFrame.