Polars syntax for Pandas complex queries

I am trying to benchmark Polars but I am stuck on how to replicate the following Pandas expression in Polars.

df['ll_lat'] = (df['lat'] // 0.1 * 0.1).round(1)
df['ll_lon'] = (df['lon'] // 0.1 * 0.1).round(1)
df['temporalBasket'] = df['eventtime'].astype(str).str[:13]
df = df.groupby(['ll_lat', 'll_lon', 'temporalBasket']).agg(strikes=('lat', 'count'))
df

Can someone help me translate and explain how I should be thinking about Polars column creation etc. please?

Here is a df.head() output to make things a little clearer.

You can do something similar in Polars to what you are doing in Pandas. However, you can use truncate the extract the day + hour instead of slicing the string. This should be faster, and also easier to read.

For rounding down to the nearest decimal, I did not find a Polars method for it. So I kept your logic.

# Sample data
data = {
    'lat': [45.123, 45.155, 45.171, 45.191, 45.123],
    'lon': [12.321, 12.322, 12.345, 12.366, 12.321],
    'eventtime': [
        datetime(2023, 4, 1, 10, 20),
        datetime(2023, 4, 1, 12, 30),
        datetime(2023, 4, 1, 10, 45),
        datetime(2023, 4, 2, 9, 15),
        datetime(2023, 4, 2, 11, 50),
    ],
}

df_pl = pl.DataFrame(data)

df_pl.groupby(
    (pl.col('lat') // 0.1 * 0.1).alias('ll_lat'),
    (pl.col('lon') // 0.1 * 0.1).alias('ll_lon'),
    pl.col('eventtime').dt.truncate('1h').alias('temporalBasket')
).agg(
    strikes=pl.col('lat').count()
)

# Output
┌────────┬────────┬─────────────────────┬─────────┐
│ ll_lat ┆ ll_lon ┆ temporalBasket      ┆ strikes │
│ ---    ┆ ---    ┆ ---                 ┆ ---     │
│ f64    ┆ f64    ┆ datetime[μs]        ┆ u32     │
╞════════╪════════╪═════════════════════╪═════════╡
│ 45.1   ┆ 12.3   ┆ 2023-04-01 12:00:00 ┆ 1       │
│ 45.1   ┆ 12.3   ┆ 2023-04-02 09:00:00 ┆ 1       │
│ 45.1   ┆ 12.3   ┆ 2023-04-01 10:00:00 ┆ 2       │
│ 45.1   ┆ 12.3   ┆ 2023-04-02 11:00:00 ┆ 1       │
└────────┴────────┴─────────────────────┴─────────┘