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.

Is your feature request related to a problem? Please describe.
Our Python docstrings have various style violations when compared against standards like pep257. Not only does this impact readability (which may be subjective), it also reduces the effectiveness of tools like Sphinx or numpydoc that rely on specific formatting in order to parse docstrings.

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.

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)

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

import pandas_ta as ta
print(ta.version)

Do you have TA Lib also installed in your environment?

$ pip list

Did you upgrade? Did the upgrade resolve the issue?

$ pip install -U git+https://github.com/twopirllc/pandas-ta

Describe the bug

Is your feature request related to a problem or challenge? Please describe what you are trying to do.
Currently, DataFusion creates column names by https://github.com/apache/arrow-datafusion/blob/7be0e268a69ffecbf06823c98ca572733dddb29e/datafusion/src/physical_plan/planner.rs#L91.
The approach has two existing problems:

1. result in potential bugs, such as https://github.com/apache/arrow-

Background

This thread is borne out of the discussion from #968 , in an effort to make documentation more beginner-friendly & more understandable.
One of the subtasks mentioned in that thread was to go through the function docstrings and include a minimal working example to each of the public functions in pyjanitor.

Criteria reiterated here for the benefit of discussion:

It sh

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(