DATA Analysis with Python

This project provides a collection of Python scripts and notebooks for data analysis tasks. It demonstrates how to load, clean, visualize, and analyze datasets using popular libraries such as pandas, NumPy, and matplotlib. The examples are suitable for beginners and intermediate users interested in learning practical data analysis techniques.

Features

• Data loading and preprocessing
• Exploratory data analysis (EDA)
• Data visualization
• Statistical analysis

Requirements

• Python 3.x
• pandas
• numpy
• matplotlib

Getting Started

If on Mac, create and activate a virtual environment using the following command:

python3 -m venv venv
source venv/bin/activate

Check if the virtual environment is activated by running:

ls -la

If you see a venv directory, the virtual environment is active. Install the required packages using pip:

pip install pandas numpy matplotlib seaborn scipy scikit-learn plotly

(This command will install the following essential libraries for data science and machine learning in Python: Pandas: A powerful library for data manipulation and analysis, providing data structures like DataFrames. NumPy: The fundamental library for numerical computing in Python, offering support for arrays and mathematical functions. Matplotlib: A comprehensive library for creating static, animated, and interactive visualizations in Python. Seaborn: A statistical data visualization library based on Matplotlib, providing a high-level interface for drawing attractive and informative statistical graphics. SciPy: A library of scientific and technical computing routines, building on NumPy. Scikit-learn: A machine learning library that provides tools for data mining and data analysis. Plotly: A library for creating interactive, publication-quality graphs online.)

Usage

You can run the scripts or Jupyter notebooks in this repository to perform various data analysis tasks. Each notebook contains detailed explanations and code examples. To run a Jupyter notebook, first ensure you have Jupyter installed:

pip install jupyter

Then, start Jupyter Notebook:

jupyter notebook

This will open a web interface where you can navigate to the notebooks in this repository.

Clone the repository and follow the instructions in each notebook or script to explore different data analysis workflows.