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data-cleaning-1.0.1
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مشاهده بیشترتوضیحات
An utility to clean the data and return you the cleaned data
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | data-cleaning-1.0.1 |
| نام | data-cleaning |
| نسخه کتابخانه | 1.0.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Murali krishna mopi devi, Induraj P.Ramamurthy |
| ایمیل نویسنده | mopidevi@gmail.com, induraj.gandhian@yahoo.com |
| آدرس صفحه اصلی | https://github.com/DataPreprocessing/DataCleaning |
| آدرس اینترنتی | https://pypi.org/project/data-cleaning/ |
| مجوز | - |
<img src="https://github.com/DataPreprocessing/DataCleaning/blob/mopidevimu/img/datacleaning.png" width="100%"/>
<h1 align="center">DATA CLEANING</h1>
## Description
<p>In any Machine Learning process, Data Preprocessing is the primary step wherein the raw/unclean data are transformed
into cleaned data, So that in the later stage, machine learning algorithms can be applied. This python paackage make the
data preprocessing very easy in just 2 lines of code. All you have to do is just input a raw data(CSV file), this library
will clean your data and return you the cleaned dataframe on which further you can apply feature engineering,
feature selection and modeling.
- What this does?
* Cleans special character
* Removes duplicates
* Fixes abnormality in column names
* Imputes the data (categorical & numerical)
</p>
## Data Cleaning
<p>Data-cleaning is a python package for data preprocessing. This cleans the CSV file and returns the <b>cleaned data frame</b>.
It does the work of imputation, removing duplicates, replacing special characters, and many more.</p>
## How to use:
Step 1:
Install the libaray
````python
pip install data-cleaning
````
Step 2:
Import the library, and specify the path of the csv file.
````python
from datacleaning import DataCleaning
dp = DataCleaning(file_uploaded='filename.csv')
cleaned_df = dp.start_cleaning()
````
There are some optional parameters that you can specify as listed below,
## Usage:
````python
from datacleaning import DataCleaning
DataCleaning(file_uploaded='filename.csv', separator=",", row_threshold=None, col_threshold=None,
special_character=None, action=None, ignore_columns=None, imputation_type="RDF")
````
## Parameters
------
| Parameter | Default Value | Limit | Example |
| ------ | ------ | ------ | ------ |
| file_uploaded | ***none*** | Provide a CSV file. | filename.csv |
| separator | ***,*** | Separator used in csv file | ****;****
| row_threshold | ***none*** | 0 to 100 | 80 |
| col_threshold | ***none*** | 0 to 100 | 80 |
| special_character | Check the list below |Sspecify the character <br> that is not listed in default_list (see below) | [ '$' , '?' ] |
| action | ***none*** | ***add*** or ***remove*** | add |
| ignore_columns | ***none*** | Provide list of column names <br> to ignoring the special characters operation. | [ 'column1', 'column2' ] |
| imputation_type | ***RDF*** | Select your preferred imputation <br> ***RDF***, ***KNN***, ***mean***, ***median***, ***most_frequent***, ***constant*** . | KNN |
## Examples of using parameters
### - Appending extra special characters to the existing default_list
The DEFAULT SPECIAL CHARACTERS included in the package are shown below,
````python
default_list = ["!", '"', "#", "%", "&", "'", "(", ")",
"*", "+", ",", "-", ".", "/", ":", ";", "<",
"=", ">", "?", "@", "[", "\\", "]", "^", "_",
"`", "{", "|", "}", "~", "–", "//", "%*", ":/", ".;", "Ø", "§",'$',"£"]
````
How to remove a special character, say for example if you want to remove "?" and "%".
<i>Note:- Do not forget to give <b> action = 'remove' </b></i>
````python
from datacleaning import DataCleaning
dp = DataCleaning(file_uploaded='filename.csv', special_character =['?', '%'], action='remove')
cleaned_df = dp.start_cleaning()
````
How to add a special character, say for example if you want to add "é" that is not in the default_list given above.
<i>Note:- Do not forget to give <b> action = 'add' </b></i>
````python
from datacleaning import DataCleaning
dp = DataCleaning(file_uploaded='filename.csv', special_character =['é'], action='add')
cleaned_df = dp.start_cleaning()
````
### - Ignoring a particular columns and adding a special character
Say for example, column named "timestamp" and "date" needs to be removed and a special character needs to be added 'é'
````python
from datacleaning import DataCleaning
dp = DataCleaning(file_uploaded='filename.csv', special_character =['é'],
action='add', ignore_columns=['timestamp', 'date'])
cleaned_df = dp.start_cleaning()
````
### - Changing threshold to remove null rows/columns above this given threshold value
````python
from datacleaning import DataCleaning
dp = DataCleaning(file_uploaded='filename.csv', row_threshold=50, col_threshold=90)
cleaned_df = dp.start_cleaning()
````
### - Imputation methods available
- RDF (RandomForest) -> (DEFAULT)
- KNN (k-nearest neighbors)
- mean
- median
- most_frequent
- constant
````python
# Example for KNN imputation.
from datacleaning import DataCleaning
dp = DataCleaning(file_uploaded='filename.csv', imputation_type='KNN')
cleaned_df = dp.start_cleaning()
````
<h2 align="center"> >> THANK YOU << </h2>
نیازمندی
| مقدار | نام |
|---|---|
| - | missingpy |
| - | joblib |
| - | numpy |
| - | pandas |
| - | python-dateutil |
| - | pytz |
| - | scikit-learn |
| - | scipy |
| - | six |
| - | threadpoolctl |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl data-cleaning-1.0.1:
pip install data-cleaning-1.0.1.whl
نصب پکیج tar.gz data-cleaning-1.0.1:
pip install data-cleaning-1.0.1.tar.gz