Code Sample, a copy-pastable example if possible

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [7, 5, np.nan, 3, 2]})
df.plot(x='A', y='B')
df = df.astype('Int64')
df.plot(x='A', y='B')

Problem description

The first plotting command works, the second throws the error message

TypeError: float() argument must be a string o

I was having a very hard time figuring out

fill = A.stack().mean()
A.add(B, fill_value=fill)

fill = 4.5. However I computed a value of 3.2 because I was taking the mean from the column of A not the DataFrame A.
This coming after the Indexing chapter where "explicit is better than implicit." I was thinking that this should be a little more explicit.

"Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of novel graphics in the style of D3.js, but also deliver this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easi

I think we should keep dependencies in requirements.txt, and we should read dependencies from requirements.txt instead of keep another copy in setup.py.

Hi, I'm trying to visualise the graph involved when xarray writes to a netcdf file, but no visualisation is appearing.

import dask
import xarray

ds = xarray.Dataset({'a': 0})
task = ds.to_netcdf('dat.nc',compute=False)
task.compute()
print(task)
task.visualise(filename='netcdf.svg')  # This file is never produced


def inc(x):
    return x + 1

z = dask.delayed(inc)(1)

Missing functionality

On the back of the issue raised - pandas-profiling/pandas-profiling#315 - I would like to request for improved documentation about removed features and alternative ways to overcome them when using the new version of pandas-profiling, starting v2.4.0.

Proposed feature

• docs on why style={'full_width': True}, minify_html=True ca

System information

• OS Platform and Distribution (e.g., Linux Ubuntu 16.04):

Ubuntu 19.10

• Modin version (modin.__version__):

0.7.1+2.g4f36f23

• Python version:

Python 3.7.5

• Code we can use to reproduce:

#import pandas as pd
import ray
ray.init(huge_pages=False, plasma_directory="/localdisk/gashiman/plasma", memory=1024*1024*1024*200, ob

The iloc in cudf documentation for 0.12 and 0.13 shows:

>>> df = DataFrame([('a', list(range(20))),
...                 ('b', list(range(20))),
...                 ('c', list(range(20)))])

https://rapidsai.github.io/projects/cudf/en/0.12.0/api.html#cudf.core.dataframe.DataFrame.iloc
https://rapidsai.github.io/projects/cudf/en/0.13.0/api.html#cudf.core.dataframe.DataFrame.iloc
which

Most of the people who start out new don't find a latest feed of community hyped resources on ML and DL topics. It would be pretty good if we add a Reddit section.
If you're fine with this suggestion I'll put up a PR with the update 😄

Please add support for richt text formatting as in write_rich_string for cell comments (write_comment). This is not covered in the current implementation, where you can set some basic formatting properties (like font size and background color) for the entir

I suggest keeping the README short and to the point: badges showing status etc. of the package, what is the purpose of the package, how to install, 1-2 basic code examples, and contributing information.

More examples, function API docs, and detailed descriptions can be moved to the docs site.

I also suggest adding a gallery to the docs, [similar to Seaborn](https://seaborn.pydata.org/example

Implement Series.reindex.

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.reindex.html

It's a fairly common request to get the size of data for each symbol in VersionStore and currently I just use a Mongo js script to get it, but it would be nice to have a util in VersionStore or otherwise to do the same.

As listed in https://docs.scipy.org/doc/scipy/reference/special.html#bessel-functions, we can implement the universal functions in the list first.

MCVE Code Sample

# Your code here
import numpy as np
import xarray as xr

data = np.zeros((10, 4))
example_xr = xr.DataArray(data, coords=[range(10), ["

The procedure for downloading the "en_vectors_web_lg" in spacy. by downloading and unzipping the file, and shifting it to the appropriate directory, as illustrated here is l

It would be nice to have some general developer documentation for potential contributors to help in cases such as #510, etc.

What are the best steps to take towards accomplishing this? Maybe something similar (albeit not all details needed) to the Pandas developer docs?

I've begun an implementation of this on my fork, basicall

They say
from ibis.pandas import udf
but I think it should be
from ibis.pandas.udf import udf

Similarly from ibis.bigquery import udf should be from ibis.bigquery.udf import udf

https://docs.ibis-project.org/udf.html

Change the default value for quantiles in this function to quantiles=None. When using bins instead of quantiles with the utils.get_clean_factor_and_forward_returns() function, there is an error in the utils.quantize_factor() inferring that no bin size or quantile sizes were passed but it is actually because utils.quantize_factor() passes quantiles = 5 by default, regardless of if bins in not None.