Question: I have this code that generates a toy DataFrame (production df is much complex): import polars as pl import numpy as np import pandas as pd def create_timeseries_df(num_rows): date_rng = pd.date_range(start='1/1/2020', end='1/01/2021', freq='T') data = { 'date': np.random.choice(date_rng, num_rows), 'category': np.random.choice(['A', 'B', 'C', 'D'], num_rows), 'subcategory': np.random.choice(['X', 'Y', 'Z'], num_rows), 'value': np.random.rand(num_rows) * 100 } df = pd.DataFrame(data) df = df.sort_values('date') df.set_index('date', inplace=True, drop=False) df.index = pd.to_datetime(df.index) return df num_rows = 1000000 # for example df = create_timeseries_df(num_rows)  Then perform this transformations with Pandas. df_pd = df.copy() df_pd = df_pd.groupby(['category', 'subcategory']) df_pd = df_pd.resample('W-MON') df_pd.agg({ 'value': ['sum', 'mean', 'max', 'min'] }).reset_index()  But, obviously it is quite slow with Pandas (at least in production). Thus, I'd like to use Polars to speed up time. This is what I have so far: #Convert to Polars DataFrame df_pl = pl.from_pandas(df) #Groupby, resample and aggregate df_pl = df_pl.group_by('category', 'subcategory') df_pl = df_pl.group_by_dynamic('date', every='1w', closed='right') df_pl.agg( pl.col('value').sum().alias('value_sum'), pl.col('value').mean().alias('value_mean'), pl.col('value').max().alias('value_max'), pl.col('value').min().alias('value_min') )  But I get AttributeError: 'GroupBy' object has no attribute 'group_by_dynamic'. Any ideas on how to use groupby followed by resample in Polars?

Answer: You can pass additional columns to group by in a call to group_by_dynamic by passing a list with the named argument group_by=: df_pl = df_pl.group_by_dynamic( "date", every="1w", closed="right", group_by=["category", "subcategory"] )  With this, I get a dataframe that looks similar to the one your pandas code produces: shape: (636, 7) ┌──────────┬─────────────┬─────────────────────┬──────────────┬───────────┬───────────┬──────────┐ │ category ┆ subcategory ┆ date ┆ sum ┆ mean ┆ max ┆ min │ │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ str ┆ datetime[ns] ┆ f64 ┆ f64 ┆ f64 ┆ f64 │ ╞══════════╪═════════════╪═════════════════════╪══════════════╪═══════════╪═══════════╪══════════╡ │ D ┆ Z ┆ 2019-12-30 00:00:00 ┆ 55741.652346 ┆ 50.399324 ┆ 99.946595 ┆ 0.008139 │ │ D ┆ Z ┆ 2020-01-06 00:00:00 ┆ 76161.42206 ┆ 50.139185 ┆ 99.96917 ┆ 0.138366 │ │ D ┆ Z ┆ 2020-01-13 00:00:00 ┆ 80222.894298 ┆ 49.581517 ┆ 99.937069 ┆ 0.117216 │ │ D ┆ Z ┆ 2020-01-20 00:00:00 ┆ 82042.968995 ┆ 50.456931 ┆ 99.981101 ┆ 0.009077 │ │ D ┆ Z ┆ 2020-01-27 00:00:00 ┆ 82408.144078 ┆ 49.494381 ┆ 99.954734 ┆ 0.023769 │ │ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │ │ B ┆ Z ┆ 2020-11-30 00:00:00 ┆ 79530.963748 ┆ 49.737939 ┆ 99.973554 ┆ 0.007446 │ │ B ┆ Z ┆ 2020-12-07 00:00:00 ┆ 80050.524653 ┆ 49.566888 ┆ 99.975546 ┆ 0.003066 │ │ B ┆ Z ┆ 2020-12-14 00:00:00 ┆ 77896.578291 ┆ 50.029915 ┆ 99.969098 ┆ 0.033222 │ │ B ┆ Z ┆ 2020-12-21 00:00:00 ┆ 76490.507942 ┆ 49.636929 ┆ 99.953563 ┆ 0.021683 │ │ B ┆ Z ┆ 2020-12-28 00:00:00 ┆ 46964.533378 ┆ 50.553857 ┆ 99.653981 ┆ 0.042546 │ └──────────┴─────────────┴─────────────────────┴──────────────┴───────────┴───────────┴──────────┘