What about the polars API doesn't work well for your use case?
You can do the same with Polars, but you have to start messing about with datetimes and convert the simple problem "I want to calculate a monthly sum anchored on the last business day of the month" to SQL-like operations.
Pandas grew a large and obtuse API because it provides specialized functions for 99% of the tasks one needs to do on timeseries. If I want to calculate an exponential weighted covariance between two time series, I can trivially do this with pandas: series1.ewm(...).cov(series2). I welcome people to try and do this with Polars. It'll be a horrible and barely readable contraption.
YC is mostly populated by technologists, and technologists are often completely ignorant about what makes pandas useful and popular. It was built by quants/scientists, for doing (interactive) research. In this respect it is similar to R, which is not a language well liked by technologists, but it is (surprise) deeply loved by many scientists.
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.
> 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.
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.
`.join_where()`[1] was also added recently.
[1]: https://docs.pola.rs/api/python/stable/reference/dataframe/a...
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
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...
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.
> 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.
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.