Metadata-Version: 2.1
Name: prepdatakit
Version: 1.5.5
Summary: A Python toolkit for preprocessing datasets.
Home-page: UNKNOWN
Author: Abdulaziz Yabrak
Author-email: abdulaziz.mofid@gmail.com
License: UNKNOWN
Description: # PrepDataKit
        
        PrepDataKit is a Python package that provides a toolkit for preprocessing datasets. It offers various functions to assist in reading data from different file formats, summarizing datasets, handling missing values, and encoding categorical data.
        
        ## Installation
        
        You can install PrepDataKit using pip:
        
        ```python 
        pip install prepdatakit
        ```
                            
        ## Sample Data
        | Category | Price | In Stock | Description |
        |---|---|---|---|
        | Fruit | 2.50 | True | Ripe and delicious |
        | Animal | None | False | Needs more data |
        | Color | 1.99 |  | Vivid and bright |
        | Tool | 9.99 | True | Heavy duty and reliable (Maybe) |
        
        
        [ Download CSV ](https://amzytest.great-site.net/zdownload.php?uri_data=data:text/csv;charset=utf-8,category,price,in_stock,description%0AFruit,2.50,True,Ripe%20and%20delicious%0AAnimal,None,False,Needs%20more%20data%0AColor,1.99,,Vivid%20and%20bright%0ATool,9.99,True,Heavy%20duty%20and%20reliable%20(Maybe)%0A)
        
        
        ## Usage
        
        Here's an example of how to use PrepDataKit:
        
        ```python
        from prepdatakit import prepdatakit
        import time
        
        # Read a CSV file
        data = prepdatakit.read_file('data.csv')
        print("Start after loading the file, summary")
        
        # Get summary statistics
        summary = prepdatakit.get_summary(data)
        print(summary)
        print("Finish summary")
        time.sleep(0.5)
        
        # Handle missing values
        print("Start clean_data")
        clean_data = prepdatakit.handle_missing_values(data, strategy='remove')
        print(clean_data)
        print("Finish clean_data")
        time.sleep(0.5)
        
        # Encode categorical data
        print("Start encoded_data")
        encoded_data = prepdatakit.one_hot_encode(clean_data, columns=['category'])
        print("End encoded_data")
        time.sleep(0.5)
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
