semvec/pypi-packages huggingface dataset:
CloudsOfArx 0.4.0
Web Scraper package for generating wordclouds from research paper abstracts.
License: MIT
Requires Python: >=3.6, <4
Keywords: ArXiv,ADS,wordcloud

# CloudsOfArx 
[![codecov](https://codecov.io/gh/James11222/CloudsOfArx/branch/main/graph/badge.svg?token=LBJRT9BTNA)](https://codecov.io/gh/James11222/CloudsOfArx)
![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/James11222/CloudsOfArx/run_tests.yml)
![PyPI](https://img.shields.io/pypi/v/CloudsOfArx)

An automated webscraper package to make wordcloud images out of the abstracts of your first-author papers. 

## Installation

To use this package just install via pip

`pip install CloudsOfArx`

## Usage

This package is simple and straightforward. To use it simply run the following lines in your python environment of choice after installation via pip.

``` python
import CloudsOfArx

CloudsOfArx.create_wordcloud(ADS_TOKEN, author, image_file, orcid=None, save_name=None)
```

The `ADS_TOKEN` is required to use the NASA ADS API. Make an account on NASA ADS to acquire an API token key, then copy and paste the key as a string for that argument. The `author` argument is the name of the first-author in a "LastName, FirstName" formatted string. `image_file` is a string pointing to the desired image for masking the wordcloud into. I also include the optional `orcid` parameter for authors who wish to use their ORCID to ensure the papers used are their own work. The `save_name` argument is an optional argument for naming the saved wordcloud file.

An example of this is shown below
![This is an image](examples/example.png)
a-pandas-ex-apply-ignore-exceptions 0.10
Use DataFrame.apply / Series.apply with a default value for Exceptions
License: MIT
Keywords: DataFrame,Series,apply,Exception


# Use DataFrame.apply / Series.apply with a default value for Exceptions 

```python
pip install a-pandas-ex-apply-ignore-exceptions
```

```python

from a_pandas_ex_apply_ignore_exceptions import pd_add_apply_ignore_exceptions
import pandas as pd
pd_add_apply_ignore_exceptions()
df = pd.read_csv(
    "https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv"
)

# the Exception value is the first arg, the rest is just like apply
df1 = df.PassengerId.ds_apply_ignore(pd.NA, lambda x: x / 3)
df2 = df.ds_apply_ignore(pd.NA, lambda x: x["PassengerId"] / 3, axis=1)
df3 = df.PassengerId.ds_apply_ignore(pd.NA, lambda x: x / 0)
df4 = df.ds_apply_ignore(pd.NA, lambda x: x["PassengerId"] / 0, axis=1)
print(df1)
print(df2)
print(df3)
print(df4)


r"""
0        0.333333
1        0.666667
2        1.000000
3        1.333333
4        1.666667
          ...    
886    295.666667
887    296.000000
888    296.333333
889    296.666667
890    297.000000
Name: PassengerId, Length: 891, dtype: float64
0        0.333333
1        0.666667
2        1.000000
3        1.333333
4        1.666667
          ...    
886    295.666667
887    296.000000
888    296.333333
889    296.666667
890    297.000000
Length: 891, dtype: float64
0      <NA>
1      <NA>
2      <NA>
3      <NA>
4      <NA>
       ... 
886    <NA>
887    <NA>
888    <NA>
889    <NA>
890    <NA>
Name: PassengerId, Length: 891, dtype: object
0      <NA>
1      <NA>
2      <NA>
3      <NA>
4      <NA>
       ... 
886    <NA>
887    <NA>
888    <NA>
889    <NA>
890    <NA>
Length: 891, dtype: object

"""

		
```