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# How does the pandas series.first_valid_index() method work?

The pandas series.first_valid_index() method is used to get the index of the first valid data. This means the first_valid_index() method returns the index of the first non_null element of the series.

It will return a single scalar based on the type of series index, and it will return None if the given series has all null/NA values or if it is empty. The first_valid_index() method doesn’t take any parameter.

## Example 1

Let’s take a series object and try to retrieve the first valid index.

# importing packages import pandas as pd import numpy as np # create a series s = pd.Series([None, np.nan, 27, 61,np.nan, 34, 52, np.nan, 17], index=list('abcdefghi')) print(s) # apply first_valid_index() method result = s.first_valid_index() print("Result:") print(result)

## Explanation

Initially, we have created a pandas series object by using the pandas.Series constructor with a list of Nan’s and integer values and the index of the series is specified by an index parameter with a list of strings.

## Output

The output is as follows −

a NaN b NaN c 27.0 d 61.0 e NaN f 34.0 g 52.0 h NaN i 17.0 dtype: float64 Result: c

For the above example, the first valid index is “c”, because the elements at index positions a, b are Null/NA values.

## Example 2

Here, let’s take an empty series object and see how the first_valid_index() method works for an empty series.

# importing packages import pandas as pd # create a series sr = pd.Series([]) print(sr) # apply first_valid_index() method result = sr.first_valid_index() print("Result:") print(result)

## Output

The output is given below −

Series([], dtype: float64) Result: None

The first_valid_index() method returned None for the empty series object.

## Example 3

In this following example, we have created a pandas series object with all Null/Nan values and applied the first_valid_index() method to get the 1st valid index.

# importing packages import pandas as pd import numpy as np # create a series sr = pd.Series([None, np.nan]) print(sr) # apply first_valid_index() method result = sr.first_valid_index() print("Result:") print(result)

## Output

The output is given below −

0 NaN 1 NaN dtype: float64 Result: None

For this example, also the first_valid_index() method returned None because there is no valid element available in the given series object.

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