Data Processing with Pandas and NumPy

This repository contains code and resources for data processing using the popular Python libraries pandas and NumPy. It demonstrates a variety of data wrangling techniques such as cleaning, transformation, integration, and exploratory analysis, useful for preparing data for analysis and machine learning tasks.

Project Overview

This project provides examples and exercises for handling data, focusing on:

• Cleaning data by handling null values, duplicates, and inconsistent formats.
• Transforming data for analysis, including normalization, encoding, and feature engineering.
• Integrating data from multiple sources.
• Exploring data through summary statistics and visualization.

The code in this repository is designed for beginners and intermediate users looking to strengthen their data preprocessing skills in Python.

Requirements

• Python 3.x
• Libraries:
  • pandas
  • NumPy
  • Matplotlib (optional, for visualization examples)

Installation

To run the code in this repository, you need to have Python installed. Install the required libraries using:

pip install pandas numpy matplotlib
Usage
Each script in the repository focuses on a specific data preprocessing task, such as handling null values, merging data sets, and data exploration. To run a script, simply execute:


python script_name.py
Example
An example of data preprocessing in this repository includes:

Handling missing values: Fill null values with statistical measures or default values.
Data aggregation and grouping: Summarize data based on specific criteria.
Merging data sets: Combine data from multiple sources to enrich the data set.
Outlier detection: Identify and handle outliers using statistical methods.
Features
Data Cleaning: Handle missing values, duplicate data, and inconsistent formatting.
Data Transformation: Apply scaling, encoding, and feature engineering.
Data Integration: Merge and concatenate data sets to create a single unified view.
Exploratory Data Analysis (EDA): Summary statistics and basic data visualizations.
Examples
Here are some examples of tasks covered in the repository:

Filling missing values with mean values or default text.
Merging DataFrames to consolidate information from different sources.
Calculating statistics like mean, median, and standard deviation.
Visualizing data with histograms for an initial data overview.
Contributing
Contributions are welcome! If you would like to improve or expand this project, please open an issue or submit a pull request.