Polars Series null_count() – Usage & Examples

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  • Post last modified:July 4, 2025
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You are currently viewing Polars Series null_count() – Usage & Examples

In Polars, the null_count() method is used to count the number of null (missing) values in a Series. It helps identify how many entries are missing. In this article, I will explain the syntax of the Polars Series null_count() function, describe its parameters, and show how it returns an integer representing the number of null (missing) values in the Series.

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Key Points –

  • The null_count() method counts the number of null (missing) values in a Polars Series.
  • It returns a single integer indicating how many entries in the Series are null.
  • This method does not modify the original Series; it only computes and reports the count.
  • null_count() can be called on Series of any data type (numeric, string, boolean, etc.).
  • If the Series has no missing values, null_count() returns zero.
  • Can be combined with other methods like drop_nulls() or fill_null() for further handling of missing data.

Polars Series null_count() Introduction

Let’s know the syntax of the series null_count() function.


# Syntax of series null_count
Series.null_count() → int

Parameters of the Polars Series null_count()

No arguments needed.

Return Value

This function returns an integer representing how many null values are in the Series.

Usage of Polars Series null_count() Function

null_count() calculates how many null (missing) values are present in a Polars Series and returns that count as an integer.

Now, let’s create a Polars series using a list of values and use the null_count() function.


import polars as pl

# Create a Series with integer values and some nulls
ser = pl.Series("values", [5, None, 15, None, 25])
print("Original Series:\n", ser)

Yields below output.

polars series null count

Here’s a simple example demonstrating how to use null_count() with a Polars Series containing integers.


# Count the number of nulls
ser2 = ser.null_count()
print("Number of null values:", ser2)

Here,

  • The Series contains 5 elements.
  • Two of them are null, so null_count() returns 2.
polars series null count

Series with All Non-null Values

You can create a Polars Series with only non-null values and use the null_count() function to check how many null entries it contains.


import polars as pl

# Series with no nulls
ser = pl.Series("numbers", [10, 20, 30, 40, 50])

# Count nulls
ser2 = ser.null_count()
print("Number of null values:", ser2)

# Output:
# Number of null values: 0

Here,

  • All entries (10, 20, 30, 40, 50) are valid integers.
  • There are no None or NaN values.
  • null_count() returns 0 to confirm that no data is missing.

Series with All Null Values

Polars Series where all values are null and how null_count() behaves, here’s a demonstration of using null_count() on a Series consisting entirely of null values.


import polars as pl

# Series where every element is null
ser = pl.Series("empty_values", [None, None, None, None])

# Count the nulls
ser2 = ser.null_count()
print("Number of null values:", ser2)

# Output:
# Number of null values: 4

Here,

  • All 4 entries are None (Polars shows them as null).
  • null_count() correctly counts every element.
  • You get 4 because all rows are missing data.

Conclusion

In conclusion, the null_count() method in Polars is a straightforward way to identify the number of missing values in a Series. Whether your data contains no nulls or is entirely null, this function provides an accurate count to help you assess data completeness.

Happy Learning!!

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