dataframe

vaex.from_arrays(s=['a,b']).s.str.replace(r'(\w+)',r'--\g<1>==',regex=True)

when using capture group in str, it fails, while str_pandas.replace() is correct

Name: vaex
Version: 4.6.0
Summary: Out-of-Core DataFrames to visualize and explore big tabular datasets
Home-page:

What language are you using?

Python

What version of polars are you using?

0.13.1

What operating system are you using polars on?

CentOS Linux release 8.1.1911 (Core)

What language version are you using

python 3.7.9

Describe your bug.

When calling scan_csv on an empty file, a confusing message about buffers appears instead of simply saying th

Is your feature request related to a problem? Please describe.
While reviewing PR #9817 to introduce DataFrame.diff, I noticed that it is restricted to acting on numeric types.

A time-series diff is probably a very common user need, if provided a series of timestamps and seeking the durations between observations.

Pandas supports diffs on non-numeric types like timestamps:

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.

0.3.14b0

Do you have TA Lib also installed in your environment?

yes

Did you upgrade? Did the upgrade resolve the issue?

I try upgrade, not solve.

Describe the bug
The df.ta.to_utc is a property, but it is not idempotent. When you type df.ta. then press tab for auto

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] }));
  

Is your feature request related to a problem or challenge? Please describe what you are trying to do.

Follow up for apache/arrow-datafusion#1712 (comment).

The Dataframe trait was introduced in order to have two separate Dataframe implementation in Datafusion and Ballista. Since then, the design has changed and we are sharing the same dataframe imple

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(