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:

Describe the bug
Warnings raised by pandas when call groupby.agg:

/home/admin/kaisheng.hks/mars/mars/dataframe/groupby/aggregation.py:741: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will b

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

Is your feature request related to a problem? Please describe.
Would it be possible to get the support of the weekNumber function in Datetime

Describe the solution you'd like
For a given date, it should return the number of the week in the year, from 1 to 52/53 (or 0 to 51/52).

Thanks

Is your feature request related to a problem or challenge? Please describe what you are trying to do.
The doc site https://arrow.apache.org/datafusion/ is the public face of DataFusion. While it currently has a great foundation / layout, the content is a bit out of date (and could always use more love)

Making DataFusion more accessible to new users would be great way to get involved with t

It does not help users view the data when all that is printed on the screen is column names.

Here is pillar output where the number of columns goes into the thousands

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(