Polars Series count() Function with Examples

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  • Post last modified:June 26, 2025
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In Polars, the series.count() function is used to count the number of non-null (non-missing) values present in that Series. It excludes null (missing) values and counts only the valid data points.

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In this article, I will explain the syntax of the Polars Series count() function, its parameters, and how it returns an integer (int) representing the number of non-null values in the Series.

Key Points –

  • The count() function returns the number of non-null (non–missing) values in a Series.
  • The result is an integer (int) representing the total valid entries.
  • count() works with all data types, including strings, integers, floats, booleans, etc.
  • It is useful for data validation, quality checks, and handling missing data.
  • The function is applied directly to a Series object in Polars.
  • It ignores nulls, making it useful for measuring data completeness.

Polars Series count() Introduction

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


# Syntax of count()
Series.count() → int

Parameters of the Polars Series count()

No parameters. This is a method called directly on a Series object.

Return Value

This method returns an int representing the number of non-null (valid) elements in the Series.

Usage of Polars Series count() Function

The count() function in a Polars Series returns the number of non-null (non-missing) values present in the Series.

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


import polars as pl

ser = pl.Series("numbers", [2, 4, 6, 8])
print("Original Series:\n", ser)

Yields below output.

polars Series count

To perform a simple count of non-null values in a Polars Series, use the count() method.


# Count non-null values
ser2 = ser.count()
print("Non-null value count:", ser2)

Here,

  • Since there are no nulls, count() returns 4, the total number of elements.
polars Series count

Series with Null Values

You can create a Polars Series containing null values and use the count() function to determine the number of non-null entries.


import polars as pl

# Series with null (None) values
ser = pl.Series("values", [5, None, 15, None, 25])

# Count non-null values
ser2 = ser.count()
print("Non-null value count:", ser2)

# Output:
# Non-null value count: 3

Here,

  • None is automatically treated as null in Polars.
  • count() ignores nulls and only returns the count of actual (non-null) values.

Series of Strings with Nulls

You can create a Polars Series of strings that contains null values and use the count() function to see how it handles non-null entries.


import polars as pl

# Create a Series of strings with some null values
ser = pl.Series("Tech", ["Spark", None, "Pandas", "Hadoop", None])

# Count non-null values
ser2 = ser.count()
print("Non-null value count:", ser2)

# Output:
# Non-null value count: 3

Here,

  • The Series contains 5 elements, 2 of which are null.
  • count() ignores null and returns the count of valid (non-null) strings, 3.

Series with Booleans and Null

You can create a Polars Series containing Boolean values along with nulls and use the count() function to observe how it handles non-null Boolean entries.


import polars as pl

# Boolean Series with nulls
ser = pl.Series("flags", [True, None, False, True, None])
print("Non-null count:", ser.count())

# Output:
# Non-null count: 3

Here,

  • The Series contains 5 boolean entries, including 2 nulls.
  • count() skips the nulls and returns 3, the number of non-null boolean values (True, False, True).

Conclusion

In conclusion, the count() function in Polars Series is a simple yet powerful tool to determine the number of non-null (valid) values in your data. Whether you’re working with strings, booleans, or any other data type, count() helps you quickly assess the presence of meaningful data by ignoring nulls.

Happy Learning!!

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