This repository contains a collection of Python data analysis practice notebooks focused on foundational data science skills. The notebooks use Python, pandas, NumPy, and exploratory data analysis techniques to clean, explore, summarize, and interpret different datasets.

This repository includes practice notebooks covering core Python data analysis concepts, including:

• pandas review
• NumPy review
• Exploratory data analysis
• Dataset exploration
• Data cleaning
• Summary statistics
• Data visualization
• Themed analysis exercises

This notebook focuses on using pandas for data analysis tasks such as loading datasets, inspecting data, selecting columns, filtering rows, creating summaries, and working with DataFrames.

Key concepts demonstrated:

• DataFrame operations
• Data filtering
• Column selection
• Summary statistics
• Data cleaning basics

This notebook reviews NumPy, a core Python library for numerical computing. It demonstrates array creation, indexing, slicing, mathematical operations, and working with structured numerical data.

Key concepts demonstrated:

• NumPy arrays
• Array indexing
• Mathematical operations
• Numerical computing
• Python data science foundations

This notebook explores a LEGO-themed dataset using Python data analysis methods. It demonstrates how to examine dataset structure, summarize variables, and identify patterns in the data.

Key concepts demonstrated:

• Exploratory data analysis
• Dataset inspection
• Grouping and aggregation
• Pattern recognition
• Data visualization

This notebook analyzes a Survivor-themed dataset. It applies data analysis techniques to explore trends, compare groups, and summarize information from the dataset.

Key concepts demonstrated:

• Categorical data analysis
• Group comparisons
• Summary tables
• Data exploration
• pandas workflows

This notebook explores a Halloween candy dataset using Python. It demonstrates how to analyze and compare candy-related variables through data exploration and summary statistics.

Key concepts demonstrated:

• Exploratory data analysis
• Data visualization
• Variable comparison
• Summary statistics
• Data interpretation

This notebook includes a picture phrase or visual-based Python exercise. It demonstrates basic Python logic, interpretation, and problem-solving in a notebook format.

Key concepts demonstrated:

• Python practice
• Notebook-based problem solving
• Logic development
• Interactive learning

Example files include:

• picture_phrase.ipynb
• pandas_review.ipynb
• lego.ipynb
• survivor.ipynb
• halloween_candy.ipynb
• numpy_review.ipynb

• Python
• Jupyter Notebook
• pandas
• NumPy
• Matplotlib
• Data analysis
• Exploratory data analysis

• Python programming
• Data cleaning
• Data exploration
• DataFrame manipulation
• Numerical computing
• Summary statistics
• Exploratory data analysis
• Dataset interpretation
• Reproducible notebook documentation

The purpose of this repository is to document foundational Python data analysis practice. These notebooks demonstrate the use of pandas, NumPy, and exploratory analysis methods to work with different datasets and strengthen core data science skills.

Gilbert Morgan

Data Science Graduate Student
Python | Data Analysis | pandas | NumPy | Exploratory Data Analysis