How to Convert String to Date or Datetime in Polars

In Polars, you can convert a string column to a Date or Datetime using the str.to_date() and str.to_datetime() methods. To transform a Polars string column into a datetime type (datetime[μs]), use str.to_datetime(). Likewise, to convert a string column into a date type (date), use str.to_date() method. In this article, I will explain converting strings to date or DateTime in Polars using simple examples.

Key Points –

• str.to_date() converts a string to a date type (YYYY-MM-DD).
• str.to_datetime() converts a string to a datetime type (YYYY-MM-DD HH:MM:SS).
• You can specify custom formats when converting string dates/datetimes using format specifiers (e.g., %d/%m/%Y).
• Once converted, you can extract date components like year, month, and day using Polars’ built-in functions.
• Datetime conversion in Polars supports time zone-aware datetimes, allowing for localized time handling.
• str.to_date() ignores time information when converting to a date.
• str.to_datetime() preserves both date and time information, allowing precise temporal data handling.
• Converted date and datetime types allow advanced time-series analysis and operations.

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Quick Examples of Convert String to Date or Datetime

If you are in a hurry, below are some quick examples of how to convert string to date or datetime in polars.

# Quick examples of converting string to date or datetime

# Convert 'date_str' to Date type
df2 = df.with_columns(
    pl.col("date_str").str.to_date().alias("date"))

# Convert 'date_str' to Date with specified format
df = df.with_columns(
    pl.col("date_str").str.strptime(pl.Date, "%Y-%m-%d").alias("date"))

# Convert 'datetime_str' to Datetime type
df2 = df.with_columns(
    pl.col("datetime_str").str.to_datetime().alias("datetime"))

# Convert 'datetime_str' to Datetime type 
# Using a custom format
df2 = df.with_columns(
    pl.col("datetime_str").str.to_datetime(format="%Y-%m-%d %H:%M:%S").alias("datetime"))

# Convert strings to date and datetime
df2 = df.with_columns([
    pl.col("date_str").str.strptime(pl.Date, "%Y-%m-%d").alias("date"),
    pl.col("datetime_str").str.strptime(pl.Datetime, "%Y-%m-%d %H:%M:%S").alias("datetime")])

# Convert string to date 
# Using the custom format
df2 = df.with_columns(
    pl.col("date_str").str.to_date("%d/%m/%Y").alias("custom_date"))

# Convert string to datetime 
# Using the custom format
df = df.with_columns(
    pl.col("datetime_str").str.to_datetime("%d/%m/%Y %H:%M:%S").alias("custom_datetime"))

To demonstrate how to convert a string to date or datetime in Polars, let's first create a Polars DataFrame.

import polars as pl

df = pl.DataFrame({
    'Courses':["Spark","PySpark", "Pandas"],
    'Fee' :[22000,25000,23000],
    "date_str": ["2024-01-20", "2023-12-15", "2025-06-10"],
    "datetime_str": ["2024-01-20 14:30:00", "2023-12-15 08:15:45", "2025-06-10 23:59:59"]})
print("Original DataFrame:\n", df)

Yields below output.

<a id="h-converting-string-to-a-date-object"></a>

Converting String to a Date Object

To convert the date_str column from a string to a Date object, you can use the str.strptime() method with the appropriate date format in Polars.

<a id="h-convert-string-to-date"></a>

Convert String to Date

To convert a string to a Date object in Polars, you can use the .str.to_date() method. This method automatically parses the string as a date, assuming the string is in a recognized date format (like "YYYY-MM-DD").

# Convert 'date_str' to Date type
df2 = df.with_columns(
    pl.col("date_str").str.to_date().alias("date"))
print("Updated DataFrame with Date type:\n", df2)

Here,

• pl.col("date_str") - This selects the date_str column from the DataFrame df.
• .str.to_date() - This function converts the string values in the date_str column to Date objects. It automatically recognizes the format "YYYY-MM-DD", so it doesn't require a custom format string.
• .alias("date") - This renames the resulting column to "date".

<a id="h-specifying-the-format"></a>

Specifying the Format

When converting a string to a Date object in Polars, you can define the date string's format to align with the format of your data, ensuring accurate parsing.

To convert string columns into date or datetime formats, with the pl.col().str.strptime() function facilitating the conversion. You can define the date format, such as %Y-%m-%d, which represents the year, month, and day format.

# Convert 'date_str' to Date with specified format
df = df.with_columns(
    pl.col("date_str").str.strptime(pl.Date, "%Y-%m-%d").alias("date"))
print("Updated DataFrame with Date type:\n", df)

# Output:
# Updated DataFrame with Date type:
# shape: (3, 5)
┌─────────┬───────┬────────────┬─────────────────────┬────────────┐
│ Courses ┆ Fee   ┆ date_str   ┆ datetime_str        ┆ date       │
│ ---     ┆ ---   ┆ ---        ┆ ---                 ┆ ---        │
│ str     ┆ i64   ┆ str        ┆ str                 ┆ date       │
╞═════════╪═══════╪════════════╪═════════════════════╪════════════╡
│ Spark   ┆ 22000 ┆ 2024-01-20 ┆ 2024-01-20 14:30:00 ┆ 2024-01-20 │
│ PySpark ┆ 25000 ┆ 2023-12-15 ┆ 2023-12-15 08:15:45 ┆ 2023-12-15 │
│ Pandas  ┆ 23000 ┆ 2025-06-10 ┆ 2025-06-10 23:59:59 ┆ 2025-06-10 │
└─────────┴───────┴────────────┴─────────────────────┴────────────┘

Here,

• "%Y-%m-%d" - The format "%Y-%m-%d" specifies that the string is in the "year-month-day" format (e.g., "2024-01-20").
  • %Y - 4-digit year (e.g., 2024)
  • %m - 2-digit month (e.g., 01)
  • %d - 2-digit day (e.g., 20)
• You use .str.strptime(pl.Date, "%Y-%m-%d") to specify the format during conversion.

<a id="h-converting-string-to-datetime"></a>

Converting String to DateTime

To convert a string to a DateTime object in Polars, you can use the str.to_datetime() method. This method automatically recognizes the standard datetime formats (such as "YYYY-MM-DD HH:MM:SS") and converts the string to a DateTime object.

# Convert 'datetime_str' to Datetime type
df2 = df.with_columns(
    pl.col("datetime_str").str.to_datetime().alias("datetime"))
print("Updated DataFrame with Datetime type:\n", df2)

# Output:
# Updated DataFrame with Datetime type:
# shape: (3, 5)
┌─────────┬───────┬────────────┬─────────────────────┬─────────────────────┐
│ Courses ┆ Fee   ┆ date_str   ┆ datetime_str        ┆ datetime            │
│ ---     ┆ ---   ┆ ---        ┆ ---                 ┆ ---                 │
│ str     ┆ i64   ┆ str        ┆ str                 ┆ datetime[μs]        │
╞═════════╪═══════╪════════════╪═════════════════════╪═════════════════════╡
│ Spark   ┆ 22000 ┆ 2024-01-20 ┆ 2024-01-20 14:30:00 ┆ 2024-01-20 14:30:00 │
│ PySpark ┆ 25000 ┆ 2023-12-15 ┆ 2023-12-15 08:15:45 ┆ 2023-12-15 08:15:45 │
│ Pandas  ┆ 23000 ┆ 2025-06-10 ┆ 2025-06-10 23:59:59 ┆ 2025-06-10 23:59:59 │
└─────────┴───────┴────────────┴─────────────────────┴─────────────────────┘

Here,

• pl.col("datetime_str") - This selects the datetime_str column from the DataFrame df2.
• .str.to_datetime() - This function converts the string values in the datetime_str column to Datetime objects. It recognizes the format "YYYY-MM-DD HH:MM:SS" by default and parses the strings accordingly.
• .alias("datetime") - This renames the resulting column to "datetime".

<a id="h-specifying-the-datetime-format"></a>

Specifying the Datetime Format

To specify a custom datetime format in Polars, you can use the .str.to_datetime() method along with a format string. The format string should follow the strftime conventions, which allow you to specify the exact structure of the datetime string you're working with.

# Convert 'datetime_str' to Datetime type 
# Using a custom format
df2 = df.with_columns(
    pl.col("datetime_str").str.to_datetime(format="%Y-%m-%d %H:%M:%S").alias("datetime")
)
print("Updated DataFrame with Datetime type:\n", df2)

# Output:
# Updated DataFrame with Datetime type:
# shape: (3, 5)
┌─────────┬───────┬────────────┬─────────────────────┬─────────────────────┐
│ Courses ┆ Fee   ┆ date_str   ┆ datetime_str        ┆ datetime            │
│ ---     ┆ ---   ┆ ---        ┆ ---                 ┆ ---                 │
│ str     ┆ i64   ┆ str        ┆ str                 ┆ datetime[μs]        │
╞═════════╪═══════╪════════════╪═════════════════════╪═════════════════════╡
│ Spark   ┆ 22000 ┆ 2024-01-20 ┆ 2024-01-20 14:30:00 ┆ 2024-01-20 14:30:00 │
│ PySpark ┆ 25000 ┆ 2023-12-15 ┆ 2023-12-15 08:15:45 ┆ 2023-12-15 08:15:45 │
│ Pandas  ┆ 23000 ┆ 2025-06-10 ┆ 2025-06-10 23:59:59 ┆ 2025-06-10 23:59:59 │
└─────────┴───────┴────────────┴─────────────────────┴─────────────────────┘

Here,

• pl.col("datetime_str") - Selects the datetime_str column.
• .str.to_datetime("%d/%m/%Y %H:%M:%S") - Specifies the custom format "%d/%m/%Y %H:%M:%S", which matches the structure of the datetime strings (DD/MM/YYYY HH:MM:SS).
• .alias("datetime") - Renames the resulting column to "datetime".

<a id="h-convert-strings-to-date-and-datetime"></a>

Convert Strings to Date and DateTime

You can use .str.strptime() to convert string columns into proper Date and Datetime types. Alternatively, the .str.to_date() and .str.to_datetime() methods provide a simple way to perform these conversions.

# Convert strings to date and datetime
df2 = df.with_columns([
    pl.col("date_str").str.strptime(pl.Date, "%Y-%m-%d").alias("date"),
    pl.col("datetime_str").str.strptime(pl.Datetime, "%Y-%m-%d %H:%M:%S").alias("datetime")
])
print("DataFrame after conversion:\n", df2)

# Output:
# DataFrame after conversion:
# shape: (3, 6)
┌─────────┬───────┬────────────┬─────────────────────┬────────────┬─────────────────────┐
│ Courses ┆ Fee   ┆ date_str   ┆ datetime_str        ┆ date       ┆ datetime            │
│ ---     ┆ ---   ┆ ---        ┆ ---                 ┆ ---        ┆ ---                 │
│ str     ┆ i64   ┆ str        ┆ str                 ┆ date       ┆ datetime[μs]        │
╞═════════╪═══════╪════════════╪═════════════════════╪════════════╪═════════════════════╡
│ Spark   ┆ 22000 ┆ 2024-01-20 ┆ 2024-01-20 14:30:00 ┆ 2024-01-20 ┆ 2024-01-20 14:30:00 │
│ PySpark ┆ 25000 ┆ 2023-12-15 ┆ 2023-12-15 08:15:45 ┆ 2023-12-15 ┆ 2023-12-15 08:15:45 │
│ Pandas  ┆ 23000 ┆ 2025-06-10 ┆ 2025-06-10 23:59:59 ┆ 2025-06-10 ┆ 2025-06-10 23:59:59 │
└─────────┴───────┴────────────┴─────────────────────┴────────────┴─────────────────────┘

<a id="h-converting-with-custom-formats"></a>

Converting with Custom Formats

In some cases, date and datetime strings may not follow the standard "YYYY-MM-DD" or "YYYY-MM-DD HH:MM:SS" formats. Polars allows you to specify a custom format using str.to_date(format) and str.to_datetime(format).

<a id="h-convert-string-to-date-with-a-custom-format"></a>

Convert String to Date with a Custom Format

If your date strings are in a non-standard format (e.g., "DD/MM/YYYY" or "MM-DD-YYYY"), you can use str.to_date(format).

import polars as pl

df = pl.DataFrame({
    'Courses':["Spark","PySpark", "Pandas"],
    'Fee' :[22000,25000,23000],
    "date_str": ["20/01/2024", "15/12/2023", "10/06/2025"]})

# Convert string to date using the custom format
df2 = df.with_columns(
    pl.col("date_str").str.to_date("%d/%m/%Y").alias("custom_date")
)
print("Updated DataFrame with Custom Date Format:\n", df2)

# Output:
# Updated DataFrame with Custom Date Format:
# shape: (3, 4)
┌─────────┬───────┬────────────┬─────────────┐
│ Courses ┆ Fee   ┆ date_str   ┆ custom_date │
│ ---     ┆ ---   ┆ ---        ┆ ---         │
│ str     ┆ i64   ┆ str        ┆ date        │
╞═════════╪═══════╪════════════╪═════════════╡
│ Spark   ┆ 22000 ┆ 20/01/2024 ┆ 2024-01-20  │
│ PySpark ┆ 25000 ┆ 15/12/2023 ┆ 2023-12-15  │
│ Pandas  ┆ 23000 ┆ 10/06/2025 ┆ 2025-06-10  │
└─────────┴───────┴────────────┴─────────────┘

Here,

• %d/%m/%Y - Specifies the format as day/month/year.
• .str.to_date("%d/%m/%Y") - Converts the string to a Date column.

<a id="h-convert-string-to-datetime-with-a-custom-format"></a>

Convert String to Datetime with a Custom Format

If your datetime strings are in non-standard formats (e.g., "DD/MM/YYYY HH:MM:SS" or "MM-DD-YYYY HH:MM"), use str.to_datetime(format).

import polars as pl

df = pl.DataFrame({
    "Courses": ["Spark", "PySpark", "Pandas"],
    "Fee": [22000, 25000, 23000],
    "datetime_str": ["20/01/2024 14:30:00", "15/12/2023 08:15:45", "10/06/2025 23:59:59"]
})

# Convert string to datetime using the custom format
df = df.with_columns(
    pl.col("datetime_str").str.to_datetime("%d/%m/%Y %H:%M:%S").alias("custom_datetime")
)
print("Updated DataFrame with Custom Datetime Format:\n", df)

# Output:
# Updated DataFrame with Custom Datetime Format:
# shape: (3, 4)
┌─────────┬───────┬─────────────────────┬─────────────────────┐
│ Courses ┆ Fee   ┆ datetime_str        ┆ custom_datetime     │
│ ---     ┆ ---   ┆ ---                 ┆ ---                 │
│ str     ┆ i64   ┆ str                 ┆ datetime[μs]        │
╞═════════╪═══════╪═════════════════════╪═════════════════════╡
│ Spark   ┆ 22000 ┆ 20/01/2024 14:30:00 ┆ 2024-01-20 14:30:00 │
│ PySpark ┆ 25000 ┆ 15/12/2023 08:15:45 ┆ 2023-12-15 08:15:45 │
│ Pandas  ┆ 23000 ┆ 10/06/2025 23:59:59 ┆ 2025-06-10 23:59:59 │
└─────────┴───────┴─────────────────────┴─────────────────────┘

Here,

• %d/%m/%Y %H:%M:%S - Specifies the format as day/month/year hours:minutes:seconds.
• .str.to_datetime("%d/%m/%Y %H:%M:%S") - Converts the string to a Datetime colum.

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Conclusion

In summary, converting string data to date and datetime types in Polars is both simple and efficient, significantly improving the ability to work with temporal data. The str.to_date() and str.to_datetime() methods facilitate the easy transformation of string-based date and time values, unlocking more advanced capabilities for time-series analysis and temporal data operations.

Happy Learning!!

<a id="h-related-articles"></a>

Related Articles

• Polars Cast String to Integer
• Convert Polars Cast Int to String
• Convert Polars Cast String to Float
• Convert Polars Cast Float to String
• Polars DataFrame drop() Method
• Polars DataFrame select() Method
• Polars Cast Multiple Columns
• How to Transpose DataFrame in Polars

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References

• https://docs.pola.rs/api/python/dev/reference/expressions/api/polars.Expr.str.to_datetime.html