What is DataFrame.first_valid_index in pandas?

Overview

The first_valid_index method is used to return the index for the first non-NA or None value. The method returns None for the following conditions:

If all elements are non-NA or None.
If the DataFrame is empty.

Note: Click here to learn more about the pandas library.

Syntax

DataFrame.first_valid_index()

Parameter

The method has no parameters.

Example

import pandas as pd
import numpy as np
def index(dataframe):
    print("DataFrame is:")
    print(dataframe)
    print("Index of the first non-NA/null value is - ", dataframe.first_valid_index())
df = pd.DataFrame([[np.nan, np.nan, np.nan],[np.nan, 100,3],[1, 2, 3],[np.nan,-7,np.nan],[-9, -8, -7],[10, 8, 12]],columns=list('XYZ'))
df1 = pd.DataFrame([[np.nan, np.nan, np.nan],[np.nan, np.nan, np.nan],[np.nan, np.nan, np.nan],[np.nan,-3,np.nan],[10, 8, 12]],columns=list('XYZ'))
df2 = pd.DataFrame([[np.nan, np.nan, np.nan],[np.nan, np.nan, np.nan],[np.nan, np.nan, np.nan]],columns=list('XYZ'))
index(df)
index(df1)
index(df2)

Explanation

Lines 1-2: We import the pandas and numpy modules.
Lines 4–7: We define a function called index that accepts a DataFrame, prints it, and finds the first non-NA/null value index using the first_valid_index method.
Lines 10–12: We define three different DataFrames with different patterns.
Lines 14–16: We call the index method with different DataFrames.

Output

The output for df indicates that the 1st row has a non-NA/non-null value in its columns.
The output for df1 indicates that the 3rd row has a non-NA/non-null value in its columns.
The output for df2 indicates that none of the rows have a non-null/non-NA value.

License: Creative Commons-Attribution-ShareAlike 4.0 (CC-BY-SA 4.0)

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