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[run] | ||
omit=pdcleaner/tests/* | ||
omit=tests/* |
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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 | ||
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- name: Build python package | ||
run: | | ||
python setup.py sdist bdist_wheel | ||
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.vscode/ | ||
.ipynb_checkpoints/ | ||
.idea/ | ||
.pytest_cache/ | ||
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#compiled sources | ||
*.py[ocd] | ||
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# Code of Conduct | ||
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## Our Pledge | ||
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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. | ||
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We pledge to act and interact in ways that contribute to an open, welcoming, | ||
diverse, inclusive, and healthy community. | ||
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## Our Standards | ||
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Examples of behavior that contributes to a positive environment for our | ||
community include: | ||
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* 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 | ||
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Examples of unacceptable behavior include: | ||
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* 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 | ||
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## Enforcement Responsibilities | ||
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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. | ||
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||
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. | ||
|
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## Scope | ||
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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. | ||
|
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## Enforcement | ||
|
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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. | ||
|
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All community leaders are obligated to respect the privacy and security of the | ||
reporter of any incident. | ||
|
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## Enforcement Guidelines | ||
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Community leaders will follow these Community Impact Guidelines in determining | ||
the consequences for any action they deem in violation of this Code of Conduct: | ||
|
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### 1. Correction | ||
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**Community Impact**: Use of inappropriate language or other behavior deemed | ||
unprofessional or unwelcome in the community. | ||
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**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. | ||
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### 2. Warning | ||
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**Community Impact**: A violation through a single incident or series of | ||
actions. | ||
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**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 | ||
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**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. | ||
|
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### 4. Permanent Ban | ||
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**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. | ||
|
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## Attribution | ||
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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]. | ||
|
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Community Impact Guidelines were inspired by | ||
[Mozilla's code of conduct enforcement ladder][Mozilla CoC]. | ||
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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]. | ||
|
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# Pandas-cleaner | ||
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<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"> | ||
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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...). | ||
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 | ||
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## Features | ||
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Pandas-cleaner offers functionnalities to automatically : | ||
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: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, | ||
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:arrow_right: **analyze** these errors, via reports and plots, to check the validity of the set and/or decide if any correction is needed, | ||
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:arrow_right: **clean** the datasets, either by dropping the lines with errors, emptying, correcting or replacing bad values, | ||
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:arrow_right: **reapply** the same rules to any other incoming fresh data. | ||
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## Usage | ||
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Import the package | ||
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```python | ||
import pandas as pd | ||
import pdcleaner | ||
``` | ||
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Create an example data series | ||
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```python | ||
series = pd.Series([1, 5, -6, 100, 10]) | ||
``` | ||
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Detect the errors in the series with a given method (such as `bounded`, `iqr`, `zscore` and many more depending the type of data...) | ||
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```python | ||
detector = series.cleaner.detect('bounded', lower=0, upper=10) | ||
``` | ||
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Inspect the result: | ||
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```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 | ||
============================================================================== | ||
``` | ||
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Check the potential errors that have been detected | ||
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```python | ||
detector.detected() | ||
``` | ||
``` | ||
2 -6 | ||
3 100 | ||
dtype: int64 | ||
``` | ||
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Clean the detected errors from the series using the chosen method among `drop`, `to_na`, `clip` | ||
, `replace`... | ||
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```python | ||
series.cleaner.clean("drop", detector, inplace=True) | ||
series | ||
``` | ||
``` | ||
0 1 | ||
1 5 | ||
4 10 | ||
dtype: int64 | ||
``` | ||
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## Contributing to pandas-cleaner | ||
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All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. | ||
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Issues and bugs can be reported at https://github.com/eurodecision/pandas-cleaner/issues |
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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"> | ||
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<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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