KrishnaVyshak / DataMunge

DataMunge is a python module that helps clean and organize data for analysis. It includes functions for handling missing values, removing outliers, encoding categorical variables, normalizing data, reducing dimensionality and removing duplicate rows. It can be used in data analysis projects to prepare data for further analysis.

DataMunge

DataMunge is a python module that helps clean and organize data for analysis. It includes functions for handling missing values, removing outliers, encoding categorical variables, normalizing data, reducing dimensionality and removing duplicate rows. It can be used in data analysis projects to prepare data for further analysis.

Installation

pip install DataMunge

Functions

remove_outliers(data, column, threshold)

Remove outliers from a given column of a dataframe

Params:

• data: dataframe
• column: string, column name
• threshold: int, threshold value

Returns:

• dataframe

handle_missing_values(data, strategy="mean")

Handle missing values in a dataframe

Params:

• data: dataframe
• strategy: string, strategy for handling missing values (mean, median or mode)

Returns:

• dataframe

encode_categorical_variables(data, columns)

Encode categorical variables in a dataframe

Params:

• data: dataframe
• columns: list of strings, column names

Returns:

• dataframe

normalize_data(data, columns)

Normalize data in a dataframe

Params:

• data: dataframe
• columns: list of strings, column names

Returns:

• dataframe

reduce_dimensionality(data, n_components)

Reduce dimensionality of a dataframe using PCA

Params:

• data: dataframe
• n_components: int, number of components

Returns:

• dataframe

remove_duplicates(data)

Remove duplicate rows from a dataframe

Params:

• data: dataframe

Returns:

• dataframe

File Structure

|-- DataMunge/
	 |-- __init__.py
	 |-- functions.py
|-- tests/
	 |-- test_functions.py 
|-- setup.py 
|-- README.md 
|-- LICENSE

Contribution: Contributions are always welcomed. If you have any ideas for new features or improvements, feel free to open an issue or submit a pull request.

Note:

This module is designed to be flexible and adaptable to different types of data and use cases.
It is important to understand the underlying assumptions and limitations of each function and how they apply to your specific data before using them. 
It is also recommended to test the functions on a small subset of your data before applying them to the entire dataset.