Learn Dates & Times | Strings, Dates, Missing Data

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When working with real-world datasets, you will often encounter date and time information stored as plain text. To analyze or manipulate these dates, you need to convert them into a datetime format that Polars can understand. In this chapter, you will learn how to parse release_date strings to datetime and extract the release year using the .dt namespace.

Suppose you have a DataFrame with a column called release_date, where each value is a string like "2015-07-14". To work with these as dates, you must first convert the column to a datetime type. Then, you can extract useful information, such as the year, using Polars' powerful .dt accessor.


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import polars as pl

# Sample DataFrame with string dates
df = pl.DataFrame({
    "title": ["Movie A", "Movie B", "Movie C"],
    "release_date": ["2015-07-14", "2018-03-22", "2020-11-05"]
})

# Convert 'release_date' to datetime
df = df.with_columns(
    pl.col("release_date").str.strptime(pl.Date, "%Y-%m-%d").alias("release_date_dt")
)

# Extract the release year as a new column
df = df.with_columns(
    pl.col("release_date_dt").dt.year().alias("release_year")
)

print(df)

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Section 3. Chapter 3