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I don't know what exponential weighted covariance is, but I've had pretty good luck converting time series-based analyses from pandas to polars (for patient presentations to my emergency department -- patients per hour, per day, per shift, etc.). Resample has a direct (and easier IMO) replacement in polars, and there is group_by_dynamic.

I've had trouble determining whether one timestamp falls between two others across tens of thousands of rows (with the polars team suggesting I use a massive cross product and filter -- which worked but excludes the memory requirement), whereas in pandas I was able to sort the timestamps and thereby only need to compare against the preceding / following few based on the index of the last match.

The other issue I've had with resampling is with polars automatically dropping time periods with zero events, giving me a null instead of zero for the count of events in certain time periods (which then gets dropped from aggregations). This has caught me a few times.

But other than that I've had good luck.

I'm curious how is polars group_by_dynamic easier than resample in pandas. In pandas if I want to resample to a monthly frequency anchored to the last business day of the month, I'd write:

> my_df.resample("BME").apply(...)

Done. I don't think it gets any easier than this. Every time I tried something similar with polars, I got bogged down in calendar treatment hell and large and obscure SQL like contraptions.

Edit: original tone was unintentionally combative - apologies.

Totally fair. And thank you for the rewording (sincerely). I haven't used polars for anything business or finance related, so this is likely one of many blind spots for me.

Reviewing my work, only needed an hourly aggregation, which was similarly easy in polars and pandas (I misspoke about being easier) -- what I found easier was grouping by time data that wasn't amenable to `resample`.

In polars I had no problems using a regular group_by with a pl.col.dt object, whereas in pandas I remember struggling to do so, even though it seemed straightforward.

Sorry, I wish I could remember more details; this was probably 5 years ago that I was writing the pandas code and just converted it to polars about a year ago, so it's possible that I just got better at python in the meantime (though I was writing much more python back then). And of course a rewrite is likely to feel easier the second time.

The other confounding issue is that the eager pandas code crashed with OOM regularly and took several minutes to run, whereas polars handles it very well (which I'm sure to some degree is it optimizing things that I could have done manually), but this made iterating on this codebase feel much less onerous.

> cross product and filter

`.join_where()`[1] was also added recently.

[1]: https://docs.pola.rs/api/python/stable/reference/dataframe/a...

Exactly the single reason why I use pandas when I need to use python. But coming from R, it still feels like “second best”.

Could you show how you write "calculate a monthly sum anchored on the last business day of the month" in pandas please?

Not OP.

But I'm guessing it's something like this:

import pandas as pd

def calculate_monthly_business_sum(df, date_column, value_column):

    """
    Calculate monthly sums anchored to the last business day of each month

    Parameters:
    df: DataFrame with dates and values
    date_column: name of date column
    value_column: name of value column to sum
    
    Returns:
    DataFrame with sums anchored to last business day
    """
    # Ensure date column is datetime
    df[date_column] = pd.to_datetime(df[date_column])
    
    # Group by end of business month and sum
    monthly_sum = df.groupby(pd.Grouper(
        key=date_column,
        freq='BME'  # Business Month End frequency
    ))[value_column].sum().reset_index()

    return monthly_sum

# Example usage:

df = pd.DataFrame({ 'date': ['2024-01-01', '2024-01-31', '2024-02-29'], 'amount': [100, 200, 300] })

result = calculate_monthly_business_sum(df, 'date', 'amount')

print(result)

Which you can run here => https://python-fiddle.com/examples/pandas?checkpoint=1732114...

It's actually much simpler than that. Assuming the index of the dataframe DF is composed of timestamps (which is normal for timeseries):

df.resample("BME").sum()

Done. One line of code and it is quite obvious what it is doing - with perhaps the small exception of BME, but if you want max readability you could do:

df.resample(pd.offsets.BusinessMonthEnd()).sum()

This is why people use pandas.

Answered the child comment but let me copy paste here too. It's literally one (short) line:

> df.resample("BME").sum()

Assuming `df` is a dataframe (ie table) indexed by a timestamp index, which is usual for timeseries analysis.

"BME" stands for BusinessMonthEnd, which you can type out if you want the code to be easier to read by someone not familiar with pandas.

This one liner example is one of the reason why some people use pandas and some people despise it.

It so easy for my analyst team because of daily uses but my developers probavly will never thought/know BME and decided to implement the code again.

A bit from memory as in transit, but something like df.groupby(df[date_col]+pd.offsets.MonthEnd(0))[agg_col].sum()

Is LazyFrame.group_by_dynamic not basically the same thing?