This project demonstrates a complete data analysis workflow using Python. The steps include setting up the environment, preparing the data, exploring and cleaning the data, analyzing the data, building models (if applicable), and evaluating the results. Make sure to have python and pip installed before attemption the installation below.
- Python 3.12.3
- pip
- Clone the repository:
git clone https://github.com/jtwiley1996/Python-Project.git cd Python-Project - Create and activate a virtual environment:
python3 -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate`
- Install the required packages
pip install pandas matplotlib scikit-learn
- Prepare Data
Place your CSV file in the project directory. Ensure it is named data.csv or update the script accordingly.
- Place your CSV file
Make sure the data.csv file is located in the same directory as data_analysis.py.
-
Run the script
python3 data_analysis.py
-
Output
The script will display the contents of the CSV file, a statistical summary of the data, and data types.
data_analysis.py: Reads data.csv, prints the data, shows statistical summaries, and displays data types.
```bash
import pandas as pd
# Load data
data = pd.read_csv('data.csv')
# Display data
print("Data:")
print(data)
# Display statistical summary
print("\nStatistical Summary:")
print(data.describe())
# Display data types
print("\nData Types:")
print(data.dtypes)
```
##Acknowledgements
The pandas library for data manipulation and analysis.
Feel free to modify any section to better fit your project's specifics or personal preferences!