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Filtering

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.

read_csv 

import polars as pl
from datetime import datetime

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

CsvReader · Available on feature csv 

let df = CsvReader::from_path("docs/data/apple_stock.csv")
    .unwrap()
    .with_try_parse_dates(true)
    .finish()
    .unwrap();
println!("{}", &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:

filter 

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

filter 

let filtered_df = df
    .clone()
    .lazy()
    .filter(col("Date").eq(lit(NaiveDate::from_ymd_opt(1995, 10, 16).unwrap())))
    .collect()?;
println!("{}", &filtered_df);


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:

filter · is_between 

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

filter 

let filtered_range_df = df
    .clone()
    .lazy()
    .filter(
        col("Date")
            .gt(lit(NaiveDate::from_ymd_opt(1995, 7, 1).unwrap()))
            .and(col("Date").lt(lit(NaiveDate::from_ymd_opt(1995, 11, 1).unwrap()))),
    )
    .collect()?;
println!("{}", &filtered_range_df);


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:

strptime 

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)
print(negative_dates_filtered_df)


    let negative_dates_df = df!(
    "ts"=> &["-1300-05-23", "-1400-03-02"],
    "values"=> &[3, 4])?
    .lazy()
    .with_column(
        col("ts")
            .str()
            .strptime(DataType::Date, StrptimeOptions::default()),
    )
    .collect()?;

    let negative_dates_filtered_df = negative_dates_df
        .clone()
        .lazy()
        .filter(col("ts").dt().year().lt(-1300))
        .collect()?;
    println!("{}", &negative_dates_filtered_df);

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