How to Convert Polars Series to Pandas Series in Python?

Polars and Pandas are both powerful Python libraries used for data analysis. Each has its own type of Series, which represents a one-dimensional array of data. When you convert a Polars Series to a Pandas Series, you're simply transforming the data from Polars' format to Pandas' format so that you can work with it using Pandas-specific tools and functions.

In this article, I’ll walk you through the syntax for converting a Polars Series to a Pandas Series, explain its parameters, and demonstrate how to use it to return a pandas.Series containing the same values as the original Polars Series.

Key Points –

• Use the to_pandas() method to convert a Polars Series to a Pandas Series.
• Handles various data types like integers, floats, strings, dates, and null values during conversion.
• Null values in Polars (represented as None) are converted to NaN or <NA> in Pandas, depending on the data type.
• For string data, the dtype in Pandas will be object unless an extension type is used.
• Datetime data is converted to datetime64[ns] in Pandas.
• By default, the conversion does not use PyArrow extension arrays, which means Pandas handles the data types like it normally does
• When set to True, it enables PyArrow extension arrays to improve performance and compatibility for certain column types.
• Nullable integer types are supported through PyArrow’s extension arrays, preserving None without converting to float.

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Syntax of polars.Series.to_pandas

Let’s know the syntax of the polars.Series.to_pandas

# Syntax of polars series to pandas series
Series.to_pandas(
    *, 
    use_pyarrow_extension_array: bool = False, 
    **kwargs: Any
) → pd.Series[Any]

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Parameters of the polars.Series.to_pandas

The following are the parameters of the polars.Series.to_pandas

• use_pyarrow_extension_array - If True, uses PyArrow extension arrays for better performance and memory handling (especially for strings, categoricals, and dates). Default is False.
• **kwargs - Any additional keyword arguments passed to the underlying pyarrow or pandas methods (if needed).

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Return Value

This function returns a pandas.Series with the same values as the original Polars Series.

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Usage of Convert Polars Series to Pandas Series

To convert a Polars Series to a Pandas Series in Python, we utilize the to_pandas() method provided by Polars. This method allows you to seamlessly convert data between Polars' and Pandas' data structures, enabling you to work with Pandas functions when necessary, even after performing operations using Polars.

To run some examples of how to convert a polars series to a pandas series, let’s create a Polars Series.

import polars as pl

# Create a Polars Series
polars_series = pl.Series("Fees", [22000,25000,30000,25000,28000])
print("Original Polars Series:\n", polars_series)

Yields below output.

To convert a Polars Series into a Pandas Series, simply use the to_pandas() method. This allows you to transform the Polars Series into a format that can be used with Pandas.

# Convert to Pandas Series
pandas_series = polars_series.to_pandas()
print("Converted Pandas Series:\n", pandas_series)

Here,

• The Polars Series is converted to a Pandas Series using the to_pandas() method.
• The result is a Pandas Series that can now be used with any Pandas operations.

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Conversion of Polars Series with Strings to Pandas Series

To convert a Polars Series containing strings into a Pandas Series in Python, you can easily use the to_pandas() method. This is especially useful when you need to apply Pandas functions to string-based data.

import polars as pl

# Create a Polars Series with strings
polars_series = pl.Series("Course", ["Python", "Data Science", "Hyperion", "Spark", "Pyspark"])

# Convert to Pandas Series
pandas_series = polars_series.to_pandas()
print("Converted Pandas Series:\n", pandas_series)

# Output:
# Converted Pandas Series:
# 0          Python
# 1    Data Science
# 2        Hyperion
# 3           Spark
# 4         Pyspark
# Name: Course, dtype: object

Here,

• pl.Series("Courses", […]) creates a Polars Series with string values.
• to_pandas() converts it into a Pandas Series.
• The string data type in Polars becomes object in Pandas (which is how Pandas represents strings internally).

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Conversion of Polars Series with Dates to Pandas Series

If you're working with date or datetime values in a Polars Series and need to move them into Pandas, you can convert them easily using to_pandas(). Polars and Pandas both support datetime types, so the conversion preserves the values and types well.

import polars as pl
from datetime import date

# Create a Polars Series with dates
polars_series = pl.Series("JoinDate", [date(2025, 1, 15), date(2025, 2, 20), date(2025, 3, 10)])

# Convert to Pandas Series
pandas_series = polars_series.to_pandas()
print("Converted Pandas Series:\n", pandas_series)

# Output:
# Converted Pandas Series:
# 0   2025-01-15
# 1   2025-02-20
# 2   2025-03-10
# Name: JoinDate, dtype: datetime64[ms]

Here,

• pl.Series() holds Python datetime.date objects.
• to_pandas() converts the Polars Series to a Pandas Series with datetime64[ns] dtype.
• Polars uses datetime[μs] to represent timestamps with microsecond precision.
• The name, values, and index are preserved.
• The to_pandas() method handles this conversion seamlessly.

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Conversion of Polars Series with Null Values to Pandas Series

You can easily convert a Polars Series with null values to a Pandas Series in Python. Handling null values (or None/NaN) is common in real-world data. Since both Polars and Pandas support null values in their Series, the conversion from Polars to Pandas is seamless.

import polars as pl

# Create a Polars Series with null values
polars_series = pl.Series("Age", [25, None, 30, None, 22])

# Convert to Pandas Series
pandas_series = polars_series.to_pandas()
print("Converted Pandas Series:\n", pandas_series)

# Output:
# Converted Pandas Series:
# 0    25.0
# 1     NaN
# 2    30.0
# 3     NaN
# 4    22.0
# Name: Age, dtype: float64

Here,

• The pl.Series() function is used to create a Series with some null (None) values.
• Polars uses None for missing values, which automatically gets converted to NaN in Pandas when converting using to_pandas().
• The resulting Pandas Series has NaN where the values are missing, and the dtype is float64 because Pandas uses float64 for columns with numeric and NaN values.

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Converting a Polars Series Using use_pyarrow_extension_array=True

When converting a Polars Series to a Pandas Series, you can pass the parameter use_pyarrow_extension_array=True to the to_pandas() method. This controls how the underlying data is represented in the resulting Pandas Series.

import polars as pl

# Polars Series with null values
polars_series = pl.Series("Age", [25, None, 30, None, 22])

# Convert using PyArrow extension array
pandas_series_arrow = polars_series.to_pandas(use_pyarrow_extension_array=True)
print("Converted Pandas Series:\n", pandas_series_arrow)
print("Dtype:", pandas_series_arrow.dtype)

# Output:
# Converted Pandas Series:
# 0      25
# 1    
# 2      30
# 3    
# 4      22
# Name: Age, dtype: int64[pyarrow]
# Dtype: int64[pyarrow]

Here,

• The null values (None) are represented as <NA> (Pandas' nullable integer type).
• The dtype becomes Int64 (not to be confused with NumPy's int64) this is a Pandas ExtensionDtype.
• This allows nulls in integer columns, which isn’t possible with regular int64 (as it uses NaN and casts to float).

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Conclusion

In summary, converting a Polars Series to a Pandas Series in Python is straightforward and efficient with the to_pandas() method. Whether you're dealing with numeric data, strings, dates, or null values, the process is smooth, allowing you to easily transition between Polars and Pandas and leverage Pandas' powerful functionality when needed.

Happy Learning!!

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Related Articles

• Polars Series max() Function
• Polars Series filter() Method with Examples

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References

• https://docs.pola.rs/api/python/stable/reference/series/api/polars.Series.to_torch.html