dateprepkit 0.4

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:

# Import DataPrepKit
from DataPrepKit.DataPrepKit import DataPrepKit
# Initialize DataPrepKit with a DataFrame
prep_kit = DataPrepKit()

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
csv_data = prep_kit.read_data('student-dataset.csv', 'csv')

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

# Generate a data summary
summary = prep_kit.data_summary()
print("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
cleaned_data = prep_kit.handle_missing_values(strategy='remove')
print("\nData after handling missing values (removed):")
print(cleaned_data)

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

# Encode categorical data
encoded_df = prep_kit.encode_categorical_data(categorical_columns=['Gender', 'City'])
print("\nEncoded DataFrame:")
print(encoded_df)

Example

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

# Import DataPrepKit
from DataPrepKit.DataPrepKit import DataPrepKit
# Initialize DataPrepKit with a DataFrame
prep_kit = DataPrepKit()

# Read data from a CSV file
csv_data = prep_kit.read_data('student-dataset.csv', 'csv')

# Generate a data summary
summary = prep_kit.data_summary()
print("Data Summary:")
print(summary)

# Handle missing values by removing
cleaned_data = prep_kit.handle_missing_values(strategy='remove')
print("\nData after handling missing values (removed):")
print(cleaned_data)

# Encode categorical data
encoded_df = prep_kit.encode_categorical_data(categorical_columns=['Gender', 'City'])
print("\nEncoded DataFrame:")
print(encoded_df)