Filtering date columns

Filtering date columns works in the same way as with other types of columns using the .filter method.

Polars uses Python's native datetime, date and timedelta for equality comparisons between the datatypes pl.Datetime, pl.Date and pl.Duration.

In the following example we use a time series of Apple stock prices.

import polars as pl
from datetime import datetime

df = pl.read_csv("data/appleStock.csv", try_parse_dates=True)
print(df)

shape: (100, 2)
┌────────────┬────────┐
│ Date       ┆ Close  │
│ ---        ┆ ---    │
│ date       ┆ f64    │
╞════════════╪════════╡
│ 1981-02-23 ┆ 24.62  │
│ 1981-05-06 ┆ 27.38  │
│ 1981-05-18 ┆ 28.0   │
│ 1981-09-25 ┆ 14.25  │
│ …          ┆ …      │
│ 2012-12-04 ┆ 575.85 │
│ 2013-07-05 ┆ 417.42 │
│ 2013-11-07 ┆ 512.49 │
│ 2014-02-25 ┆ 522.06 │
└────────────┴────────┘

Filtering by single dates

We can filter by a single date by casting the desired date string to a Date object in a filter expression:

filtered_df = df.filter(
    pl.col("Date") == datetime(1995, 10, 16),
)

shape: (1, 2)
┌────────────┬───────┐
│ Date       ┆ Close │
│ ---        ┆ ---   │
│ date       ┆ f64   │
╞════════════╪═══════╡
│ 1995-10-16 ┆ 36.13 │
└────────────┴───────┘

Note we are using the lowercase datetime method rather than the uppercase Datetime data type.

Filtering by a date range

We can filter by a range of dates using the is_between method in a filter expression with the start and end dates:

filtered_range_df = df.filter(
    pl.col("Date").is_between(datetime(1995, 7, 1), datetime(1995, 11, 1)),
)

shape: (2, 2)
┌────────────┬───────┐
│ Date       ┆ Close │
│ ---        ┆ ---   │
│ date       ┆ f64   │
╞════════════╪═══════╡
│ 1995-07-06 ┆ 47.0  │
│ 1995-10-16 ┆ 36.13 │
└────────────┴───────┘

Filtering with negative dates

Say you are working with an archeologist and are dealing in negative dates. Polars can parse and store them just fine, but the Python datetime library does not. So for filtering, you should use attributes in the .dt namespace:

ts = pl.Series(["-1300-05-23", "-1400-03-02"]).str.strptime(pl.Date)

negative_dates_df = pl.DataFrame({"ts": ts, "values": [3, 4]})

negative_dates_filtered_df = negative_dates_df.filter(pl.col("ts").dt.year() < -1300)

shape: (1, 2)
┌─────────────┬────────┐
│ ts          ┆ values │
│ ---         ┆ ---    │
│ date        ┆ i64    │
╞═════════════╪════════╡
│ -1400-03-02 ┆ 4      │
└─────────────┴────────┘