Metadata-Version: 2.1
Name: prepdatakit
Version: 1.5.0
Summary: A Python toolkit for preprocessing datasets.
Home-page: UNKNOWN
Author: Abdulaziz Yabrak
Author-email: abdulaziz.mofid@gmail.com
License: UNKNOWN
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
Requires-Dist: scikit-learn (==1.2.1)
Requires-Dist: numpy (==1.23.5)
Requires-Dist: pandas (==1.4.2)

# 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
```


## 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)


```


