Welcome to the Exploratory Data Analysis (EDA) using NumPy repository! This project is dedicated to demonstrating the use of NumPy, a fundamental package for scientific computing in Python, for conducting effective EDA within Jupyter Notebooks.
Exploratory Data Analysis is a critical step in understanding and preparing data for further analysis or machine learning modeling. Using NumPy in Jupyter Notebook, this repository guides you through various EDA techniques to gain insights from your data efficiently.
- Introduction to NumPy: Basics of NumPy and why it's essential for data analysis.
- Data Exploration Techniques: Using NumPy for data exploration including statistical analysis, data manipulation, and cleaning.
- Comprehensive EDA Examples: Step-by-step Jupyter Notebooks demonstrating EDA on various datasets.
- Visualization with NumPy: Basic visualization techniques to support EDA.
- Basic knowledge of Python.
- Jupyter Notebook installed.
- NumPy library installed.
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Clone the Repository
git clone https://github.com/uannabi/NumPy.git -
Navigate to the Repository
cd NumPy -
Open Jupyter Notebooks
Launch Jupyter Notebook in your environment and open the provided notebooks to start exploring.
Each notebook in this repository is self-contained and includes both explanations and code. Run each cell in the notebook to see how NumPy can be used for different EDA tasks.
Contributions to enhance the examples, add new datasets, or improve documentation are highly welcomed. Feel free to fork this repository and submit your pull requests.