dateprepkit 0.3

DataPrepKit is a Python class for data preparation and analysis. It provides functionalities for reading various data formats, summarizing statistics, handling missing values, and encoding categorical data.

DataPrepKit

DataPrepKit is a Python utility class for simplifying common data preparation tasks such as reading data from different file formats, generating summary statistics, handling missing values, and encoding categorical data.

Installation

You can install DataPrepKit using pip:

pip install dateprepkit

Usage

To use DataPrepKit in your Python project, follow the steps below:

from data_prep_kit import DataPrepKit
import pandas as pd

# Sample data
data = pd.read_csv('your_data.csv')

# Initialize DataPrepKit object
data_prep = DataPrepKit(data)

1. Reading Data Use the read_data method to load data from various file formats such as CSV, Excel, or JSON:

# Read data from a CSV file
data = data_prep.read_data('your_data.csv', format='csv')

2. Generating Data Summary Generate summary statistics for the loaded data using the data_summary method:

# Generate summary statistics
summary = data_prep.data_summary()
print(summary)

3. Handling Missing Values Handle missing values in the DataFrame by either removing or imputing them using the handle_missing_values method:

# Handle missing values by removing rows with missing values
cleaned_data = data_prep.handle_missing_values(strategy='remove')

4. Encoding Categorical Data Encode categorical columns in the DataFrame using one-hot encoding with the encode_categorical_data method:

# Encode categorical columns
encoded_data = data_prep.encode_categorical_data(categorical_columns=['category'])

Example

Here's a complete example of how to use DataPrepKit:

from data_prep_kit import DataPrepKit
import pandas as pd

# Sample data
data = pd.read_csv('your_data.csv')

# Initialize DataPrepKit object
data_prep = DataPrepKit(data)

# Read data from a CSV file
data = data_prep.read_data('your_data.csv', format='csv')

# Generate summary statistics
summary = data_prep.data_summary()
print(summary)

# Handle missing values by removing rows with missing values
cleaned_data = data_prep.handle_missing_values(strategy='remove')

# Encode categorical columns
encoded_data = data_prep.encode_categorical_data(categorical_columns=['category'])