Pandas-Matplotlib-Analysis

Data Analysis and Visualization Project

Overview 📊

This project is a Python-based data analysis and visualization assignment that demonstrates fundamental skills in using the pandas and matplotlib libraries. The goal is to load, clean, analyze, and visualize a dataset to uncover insights and patterns.

The project fulfills the following objectives:

• Loading and exploring a dataset using pandas.
• Performing basic statistical analysis and data cleaning.
• Creating a variety of plots and charts with matplotlib to visualize key findings.

Dataset

This project uses a sample dataset that mimics the Iris dataset, a classic dataset for classification problems. The dataset contains information about various measurements of different iris flower species.

Files in this Repository

• data_analysis.py: The main Python script containing all the code for data loading, analysis, and visualization.
• README.md: This file, providing an overview and instructions for the project.

How to Run the Project 🚀

1. Clone the Repository:

   git clone [https://github.com/your-username/Data-Analysis-Project.git](https://github.com/your-username/Data-Analysis-Project.git)
   cd Data-Analysis-Project

2. Install Required Libraries: Make sure you have pandas and matplotlib installed. If not, you can install them using pip:

   pip install pandas matplotlib numpy

3. Execute the Script: Run the Python script from your terminal. This will perform the analysis and display the generated plots.

   python data_analysis.py

Analysis and Findings 📈

The script performs the following key analyses:

• Data Exploration: Checks for missing values and data types. Missing numerical values are filled with the mean of their respective columns.
• Descriptive Statistics: Computes the mean, median, standard deviation, and other statistics for numerical columns to summarize the data.
• Grouped Analysis: Calculates the average sepal_length and petal_length for each species, revealing significant differences between the groups.

Visualizations

The script generates four plots to visualize the data:

1. Scatter Plot: Shows the relationship between sepal_length and petal_length, which clearly separates the species.
2. Bar Chart: Compares the average petal_length across different species.
3. Histogram: Illustrates the distribution of sepal_width values.
4. Line Chart: Shows a trend of sepal_length over a dummy time-series, demonstrating how to plot data over time.

Feel free to explore the data_analysis.py file to see the code in detail. Contributions and feedback are welcome!