dataframe

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

We now have native ODBC support upstream. This has to be exposed in polars similarly to existing IO readers and writers.

Describe the bug
series.unique() returns a cuDF.Series while it returns a numpy.ndarray for pandas.

Steps/Code to reproduce bug

In [1]: import cudf
In [2]: import pandas as pd

In [3]: type(pd.Series([1,1]).unique())
Out[3]: numpy.ndarray

In [4]: type(cudf.Series([1,1]).unique())
Out[4]: cudf.core.series.Series

Expected behavior
I would exp

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 lastest version is on Github. Pip is for major releases.
lattest

Is your feature request related to a problem? Please describe.
No

Describe the solution you'd like
I want to use SSL channel Indicator (Semaphore Signal Level channel)

Describe alternatives you've considered
I dont have Any Idea, are there alternatives of SSL channel ind

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.

ScalarValue::List and ScalarValue::Struct contain a Box<Vec<_>> this adds a redundant level of boxing. Clippy in fact complains about this, but this has been suppressed with #[allow(clippy::box_collection)]

Describe the solution you'd like

Remove the additional Box

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(