dataframe

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Description
Please provide a clear and concise description of the problem. This should contain all the steps nee

We can reduce friction by figuring out how to load data most efficiently to polars memory.

Problem statement

The algorithms cudf::reduce and cudf::segmented_reduce do not accept an initial value.

Similar algorithms like [std::reduce](https://e

I would like to convert a DataFrame to a JSON object the same way that Pandas does with to_dict().

toJSON() treats rows as elements in an array, and ignores the index labels. But to_dict() uses the index as keys.

Here is an example of what I have in mind:

function to_dict(df) {
    const rows = df.toJSON();
    const entries = df.index.map((e, i) => ({ [e]: rows[i] }));
  

For example, the data is (3.8,4.5,4.6,4.7,4.9)
while I'm using tech.tablesaw.aggregate.AggregateFunctions.percentile function, the 90th percentile is 4.9, however, if the percentile function supports linear interpolation, the 90th percentile should be 4.82, which is adopted by most other programming languages.

Which version are you running? The latest version is on Github. Pip is for major releases.

import pandas_ta as ta
print(ta.version)
0.3.64b0

Indicator addition to match another broker

Hi,
So kind of new around here, and I've been looking for indicator to match certain behavior of VWAP standard deviation bands like the bands that appears in Interactive brokers management

Is your feature request related to a problem? Please describe.
Implements classification_report for classification metrics.(https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html)

Is your feature request related to a problem or challenge? Please describe what you are trying to do.
PR apache/arrow-datafusion#2521 adds OFFSET to the logical plan. We should implement a physical plan for it.

Describe the solution you'd like
Implement OFFSET physical plan.

Describe alternatives you've considered
None

Additional context
None

Hi ,

I am using some basic functions from pyjanitor such as - clean_names() , collapse_levels() in one of my code which I want to productionise.
And there are limitations on the size of the production code base.
Currently ,if I just look at the requirements.txt for just "pyjanitor" , its huge .
I don't think I require all the dependencies in my code.
How can I remove the unnecessary ones ?

For pipeline stages provided by the pdpipe.basic_stages, supplying conditions to the prec and post keyword arguments may not return the correct error messages.

Example Code

import pandas as pd; import pdpipe as pdp;
df = pd.DataFrame([[1,4],[4,5],[1,11]], [1,2,3], ['a','b'])
pline = pdp.PdPipeline([
  pdp.FreqDrop(2, 'a', prec=pdp.cond.HasAllColumns(['x']))
])
pline.apply(

Is your feature request related to a problem? Please describe.
The friction to getting the examples up and running is installing the dependencies. A docker container with them already provided would reduce friction for people to get started with Hamilton.

Describe the solution you'd like

1. A docker container, that has different python virtual environments, that has the dependencies t