Polars Datetime Operations

last modified March 1, 2025

Polars is a fast DataFrame library in Rust with Python bindings. It is designed for efficient data manipulation and analysis. This tutorial covers datetime operations in Polars, with practical examples.

Datetime operations are essential for time series analysis, data filtering, and feature engineering. Polars provides robust support for handling datetime data.

Creating a DataFrame with Datetime

This example shows how to create a Polars DataFrame with a datetime column.

create_datetime.py

import polars as pl from datetime import datetime

data = { “date”: [datetime(2023, 1, 1), datetime(2023, 1, 2), datetime(2023, 1, 3)], “value”: [10, 20, 30] }

df = pl.DataFrame(data) print(df)

The datetime module is used to create datetime objects. These are stored in a Polars DataFrame for further analysis.

Filtering by Date

This example demonstrates filtering rows based on a specific date.

filter_by_date.py

import polars as pl from datetime import datetime

data = { “date”: [datetime(2023, 1, 1), datetime(2023, 1, 2), datetime(2023, 1, 3)], “value”: [10, 20, 30] }

df = pl.DataFrame(data) filtered_df = df.filter(pl.col(“date”) == datetime(2023, 1, 2)) print(filtered_df)

The filter method is used to select rows where the date matches a specific value. This is useful for extracting specific time periods.

Extracting Date Components

This example shows how to extract year, month, and day from a datetime column.

extract_components.py

import polars as pl from datetime import datetime

data = { “date”: [datetime(2023, 1, 1), datetime(2023, 1, 2), datetime(2023, 1, 3)], “value”: [10, 20, 30] }

df = pl.DataFrame(data) df = df.with_columns([ pl.col(“date”).dt.year().alias(“year”), pl.col(“date”).dt.month().alias(“month”), pl.col(“date”).dt.day().alias(“day”) ]) print(df)

The dt.year, dt.month, and dt.day methods extract date components. These are useful for grouping or analysis.

Calculating Date Differences

This example demonstrates calculating the difference between dates.

date_difference.py

import polars as pl from datetime import datetime

data = { “start_date”: [datetime(2023, 1, 1), datetime(2023, 1, 2)], “end_date”: [datetime(2023, 1, 3), datetime(2023, 1, 5)] }

df = pl.DataFrame(data) df = df.with_columns([ (pl.col(“end_date”) - pl.col(“start_date”)).alias(“date_diff”) ]) print(df)

The difference between two datetime columns is calculated using subtraction. This is useful for measuring time intervals.

Adding Time Intervals

This example shows how to add a time interval to a datetime column.

add_time_interval.py

import polars as pl from datetime import datetime, timedelta

data = { “date”: [datetime(2023, 1, 1), datetime(2023, 1, 2)] }

df = pl.DataFrame(data) df = df.with_columns([ (pl.col(“date”) + timedelta(days=5)).alias(“new_date”) ]) print(df)

The timedelta object is used to add a 5-day interval to the datetime column. This is useful for forecasting or scheduling.

Grouping by Date

This example demonstrates grouping data by a date component.

group_by_date.py

import polars as pl from datetime import datetime

data = { “date”: [datetime(2023, 1, 1), datetime(2023, 1, 2), datetime(2023, 1, 1)], “value”: [10, 20, 30] }

df = pl.DataFrame(data) grouped_df = df.groupby(“date”).agg(pl.col(“value”).sum()) print(grouped_df)

The groupby method groups rows by the date column. Aggregations like sum can then be applied to each group.

Resampling Time Series Data

This example shows how to resample time series data to a different frequency.

resample_data.py

import polars as pl from datetime import datetime

data = { “date”: [datetime(2023, 1, 1), datetime(2023, 1, 2), datetime(2023, 1, 3)], “value”: [10, 20, 30] }

df = pl.DataFrame(data) resampled_df = df.set_sorted(“date”).groupby_dynamic(“date”, every=“1d”).agg(pl.col(“value”).sum()) print(resampled_df)

The groupby_dynamic method resamples data to a daily frequency. This is useful for time series analysis.

Handling Time Zones

This example demonstrates converting datetime columns to different time zones.

time_zone_conversion.py

import polars as pl from datetime import datetime import pytz

data = { “date”: [datetime(2023, 1, 1), datetime(2023, 1, 2)] }

df = pl.DataFrame(data) df = df.with_columns([ pl.col(“date”).dt.convert_time_zone(“UTC”).alias(“utc_date”), pl.col(“date”).dt.convert_time_zone(“America/New_York”).alias(“ny_date”) ]) print(df)

The dt.convert_time_zone method converts datetime columns to different time zones. This is useful for global data analysis.

Best Practices for Datetime Operations

• Use Consistent Formats: Ensure datetime data is in a consistent format.

• Handle Time Zones: Convert time zones when working with global data.

• Optimize Performance: Use Polars’ efficient datetime operations for large datasets.

• Validate Data: Check for missing or invalid datetime values.

Source

Polars Documentation

In this article, we have explored datetime operations in Polars.

Author

My name is Jan Bodnar, and I am a passionate programmer with extensive programming experience. I have been writing programming articles since 2007. To date, I have authored over 1,400 articles and 8 e-books. I possess more than ten years of experience in teaching programming.

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