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v0.0.2
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ed-rhilbert authored Oct 17, 2022
2 parents c94ef2d + 2d9eff9 commit 6acea99
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2 changes: 1 addition & 1 deletion .coveragerc
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[run]
omit=pdcleaner/tests/*
omit=tests/*
20 changes: 20 additions & 0 deletions .github/workflows/main.yml
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# exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
- name: Install python package
run: |
python setup.py develop
- name: Test with pytest
run: |
pytest
- name: Get Coverage for badge
run: |
SUMMARY="$(pytest --cov --cov-report term-missing | tail -3 | head -1)"
TOKENS=($SUMMARY)
echo "COVERAGE=$(echo ${TOKENS[-1]})" >> $GITHUB_ENV
- name: Create the Badge
uses: schneegans/[email protected]
with:
auth: ${{ secrets.GIST_TOKEN }}
gistID: 74e11571ef9b1a732e44fe9fbbdc7f0b
filename: pdcleaner_coverage.json
label: Test Coverage
message: ${{ env.COVERAGE }}
color: green

- name: Build python package
run: |
python setup.py sdist bdist_wheel
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1 change: 1 addition & 0 deletions .gitignore
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.vscode/
.ipynb_checkpoints/
.idea/
.pytest_cache/

#compiled sources
*.py[ocd]
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128 changes: 128 additions & 0 deletions CODE_OF_CONDUCT.md
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# Code of Conduct

## Our Pledge

We as members, contributors, and leaders pledge to make participation in our
community a harassment-free experience for everyone, regardless of age, body
size, visible or invisible disability, ethnicity, sex characteristics, gender
identity and expression, level of experience, education, socio-economic status,
nationality, personal appearance, race, caste, color, religion, or sexual
identity and orientation.

We pledge to act and interact in ways that contribute to an open, welcoming,
diverse, inclusive, and healthy community.

## Our Standards

Examples of behavior that contributes to a positive environment for our
community include:

* Demonstrating empathy and kindness toward other people
* Being respectful of differing opinions, viewpoints, and experiences
* Giving and gracefully accepting constructive feedback
* Accepting responsibility and apologizing to those affected by our mistakes,
and learning from the experience
* Focusing on what is best not just for us as individuals, but for the overall
community

Examples of unacceptable behavior include:

* The use of sexualized language or imagery, and sexual attention or advances of
any kind
* Trolling, insulting or derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or email address,
without their explicit permission
* Other conduct which could reasonably be considered inappropriate in a
professional setting

## Enforcement Responsibilities

Community leaders are responsible for clarifying and enforcing our standards of
acceptable behavior and will take appropriate and fair corrective action in
response to any behavior that they deem inappropriate, threatening, offensive,
or harmful.

Community leaders have the right and responsibility to remove, edit, or reject
comments, commits, code, wiki edits, issues, and other contributions that are
not aligned to this Code of Conduct, and will communicate reasons for moderation
decisions when appropriate.

## Scope

This Code of Conduct applies within all community spaces, and also applies when
an individual is officially representing the community in public spaces.
Examples of representing our community include using an official e-mail address,
posting via an official social media account, or acting as an appointed
representative at an online or offline event.

## Enforcement

Instances of abusive, harassing, or otherwise unacceptable behavior may be
reported to the community leaders responsible for enforcement at
[email protected].
All complaints will be reviewed and investigated promptly and fairly.

All community leaders are obligated to respect the privacy and security of the
reporter of any incident.

## Enforcement Guidelines

Community leaders will follow these Community Impact Guidelines in determining
the consequences for any action they deem in violation of this Code of Conduct:

### 1. Correction

**Community Impact**: Use of inappropriate language or other behavior deemed
unprofessional or unwelcome in the community.

**Consequence**: A private, written warning from community leaders, providing
clarity around the nature of the violation and an explanation of why the
behavior was inappropriate. A public apology may be requested.

### 2. Warning

**Community Impact**: A violation through a single incident or series of
actions.

**Consequence**: A warning with consequences for continued behavior. No
interaction with the people involved, including unsolicited interaction with
those enforcing the Code of Conduct, for a specified period of time. This
includes avoiding interactions in community spaces as well as external channels
like social media. Violating these terms may lead to a temporary or permanent
ban.

### 3. Temporary Ban

**Community Impact**: A serious violation of community standards, including
sustained inappropriate behavior.

**Consequence**: A temporary ban from any sort of interaction or public
communication with the community for a specified period of time. No public or
private interaction with the people involved, including unsolicited interaction
with those enforcing the Code of Conduct, is allowed during this period.
Violating these terms may lead to a permanent ban.

### 4. Permanent Ban

**Community Impact**: Demonstrating a pattern of violation of community
standards, including sustained inappropriate behavior, harassment of an
individual, or aggression toward or disparagement of classes of individuals.

**Consequence**: A permanent ban from any sort of public interaction within the
community.

## Attribution

This Code of Conduct is adapted from the [Contributor Covenant][homepage],
version 2.1, available at
[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html].

Community Impact Guidelines were inspired by
[Mozilla's code of conduct enforcement ladder][Mozilla CoC].

For answers to common questions about this code of conduct, see the FAQ at
[https://www.contributor-covenant.org/faq]. Translations are available at
[https://www.contributor-covenant.org/translations].

88 changes: 88 additions & 0 deletions README.md
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# Pandas-cleaner

<img src=https://img.shields.io/pypi/v/pandas-cleaner.svg target=https://pypi.python.org/pypi/pandas-cleaner> <img src=https://img.shields.io/pypi/l/pandas-cleaner.svg target=https://pypi.python.org/pypi/pandas-cleaner> <img src="https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/ed-ialifinaritra/74e11571ef9b1a732e44fe9fbbdc7f0b/raw/pdcleaner_coverage.json">

Pandas-cleaner is a Python package, built on top of pandas, that provides methods detect, analyze and clean errors in datasets with different types of data (numerical, categorical, text, datetimes...).

![](https://github.com/eurodecision/pandas-cleaner/blob/master/docs/source/pandas-cleaner.png)

## Features

Pandas-cleaner offers functionnalities to automatically :

:arrow_right: **detect** different kind of potential errors in datasets such as outliers, inconsistencies, typos, wrong-typed ..., given predefined rules or statistiscal estimations, via an easy-to-use API extending pandas,

:arrow_right: **analyze** these errors, via reports and plots, to check the validity of the set and/or decide if any correction is needed,

:arrow_right: **clean** the datasets, either by dropping the lines with errors, emptying, correcting or replacing bad values,

:arrow_right: **reapply** the same rules to any other incoming fresh data.

## Usage

Import the package

```python
import pandas as pd
import pdcleaner
```

Create an example data series

```python
series = pd.Series([1, 5, -6, 100, 10])
```

Detect the errors in the series with a given method (such as `bounded`, `iqr`, `zscore` and many more depending the type of data...)

```python
detector = series.cleaner.detect('bounded', lower=0, upper=10)
```

Inspect the result:

```python
detector.report()
```
```none
Detection report
==============================================================================
Method: bounded Nb samples: 5
Date: January 24,2022 Nb errors: 2
Time: 16:06:08 Nb rows with NaN: 0
------------------------------------------------------------------------------
lower 0 upper 10
inclusive both sided both
==============================================================================
```

Check the potential errors that have been detected

```python
detector.detected()
```
```
2 -6
3 100
dtype: int64
```

Clean the detected errors from the series using the chosen method among `drop`, `to_na`, `clip`
, `replace`...

```python
series.cleaner.clean("drop", detector, inplace=True)
series
```
```
0 1
1 5
4 10
dtype: int64
```

## Contributing to pandas-cleaner

All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.

Issues and bugs can be reported at https://github.com/eurodecision/pandas-cleaner/issues
32 changes: 31 additions & 1 deletion RELEASE.md
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## Version 0.0.1
### What's new in 0.0.2 (October 17, 2022)

+ **New detection methods**
+ `'value'` to detect cells different from a given value
+ ``'custom'`` to apply an user-defined python callable to detect the errors
+ ``'spaces'`` to detect extra spaces before or after strings
+ ``'duplicated'`` to detect duplicated records (whole row or subset of columns)
+ ``'date_range'`` to detect dates outside a given time range
+ **New cleaning method**
+ ``'cast'`` associated to the detector ``castable`` to cast strings into floats, integers, dates or NaNs
+ **Enhancements**
+ The detectors have two new useful methods `.detected()` and `.valid()` that return valid/unvalid rows
+ Gaussian detectors (``iqr``, ``zscore``, ``modzscore``) now have a systematic normality test and an option to transform the distribution
(Box-Cox, Yeo-Johnson) before applying the detector
+ Reports are more detailed when using a multivariate qualitative/ quantitative detector
+ Detector ``castable`` now applies to booleans
+ Detector ``values`` is now called ``enum``
+ Detector ``keycollision``
+ is now called ``alternatives``
+ is much faster
+ now offers the choice to keep either most frequent representation or the first encountered
+ Plot method for ``freq`` and ``cat`` detectors now have options ``nfirst`` and ``nlast``
+ API for detector ``length`` has been simplified
+ **Bug fixes**
+ Detector ``pattern`` now works with compiled regex
+ Cleaning methods applied to DataFrames have been fixed
+ Booleans are better displayed in reports
+ Fixed bug with ``association`` detector plot method
+ **Code**
+ Detector classes now have much simplier names
+ ``tests`` folder has been reorganized to mimic ``src`` structure

### What's new in 0.0.1 (February 25, 2022)

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pandas-cleaner
==============

.. image:: https://img.shields.io/pypi/v/pandas-cleaner.svg
:target: https://pypi.python.org/pypi/pandas-cleaner

.. image:: https://img.shields.io/pypi/l/pandas-cleaner.svg
:target: https://pypi.python.org/pypi/pandas-cleaner

.. image:: https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/ed-ialifinaritra/74e11571ef9b1a732e44fe9fbbdc7f0b/raw/pdcleaner_coverage.json


What is it ?
------------
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.. code-block:: python
detector.detected
detector.detected()
.. parsed-literal::
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4 10
dtype: int64
Documentation
-------------

The documentation is still a **work in progress**.

* Clone the project

* Build the documentation using :

.. code-block:: bash
cd docs
make html
* Open `docs/build/html/index.html` in your browser
Contributing to pandas-cleaner
------------------------------
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4 changes: 2 additions & 2 deletions docs/source/_templates/logo.html
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<div class="sidebar-end-items">
<a href="https://www.eurodecision.eu/" target="_blank">
<a href="https://www.eurodecision.com/" target="_blank">

<img src="https://www.eurodecision.com/wp-content/themes/eurodecision/img/logo-eurod.jpg" width=250>
<img src="https://www.eurodecision.com/wp-content/themes/eurodecision/img/logo-eurod.jpg" width=125>
</a>
</div>

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