dataframe

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

I really like the printSchema() function in PySpark as it massively simplifies working with (large) tables:

Having this function in polars as well would aid its usage comfort :)

Now we are going to have set operations (rapidsai/cudf#11043). To be consistent with other libraries/framework (like Presto: https://prestodb.io/docs/current/functions/array.html), we should rename lists::drop_list_duplicates into lists::distinct. The implementation should be moved into set_operations.hpp|cu to be easily located and for consistency, as mentioned above

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.

Ulcer Index

in development version: I am ignoring everget

highest_close = close.rolling(length).max()
downside = scalar * (close - highest_close)
downside /= highest_close
d2 = downside * downside
_ui = d2.rolling(length).sum()
ui = np.sqrt(_ui / length)

In development version, sometime I am getting RuntimeWarning: invalid value encountered in sqrt after searching abo

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.
Someone is asking about this feature on StackOverflow: https://stackoverflow.com/questions/72876397/impute-and-add-new-calculated-column-with-rust-datafusion

Describe the solution you'd like
Add with_column(&self, name: &str, expr: Expr). This should add the new column to the data f

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