Project_Plotting | Transport Data Analysis using Matplotlib and Seaborn

This project involves analysing a dataset related to transport (vehicles and fuel efficiency) using Python libraries such as Matplotlib and Seaborn. The analysis includes creating visualisations like histograms and heatmaps to understand the relationships between various features in the dataset.

Instructions

The most recent ASC23 considered four major systems-based tracks with a view to providing region-focused contributions to inform the global stocktake among them Cities, urban and rural settlements, infrastructure, and transport. In this assignment, you will be performing analysis more specifically on transport (vehicles and fuel efficiency).

• Import and use necessary libraries for data analysis and visualisation.
• Load and display the dataset to understand its structure.
• Create visualisations to analyse the data:
  • Histogram of a numerical feature.
  • Heatmap to visualise correlations between features.
• Interpret the visualisations to draw meaningful insights.

Files

• Plotting_Assignment.ipynb: The Notebook containing the code, plots, and interpretations.
• all graph plot files can be found in the plot_images directory.
• fuel_econ.csv: The dataset used for the analysis.

Libraries Used

The following Python libraries were used in this project:

• pandas: For data manipulation and analysis.
• numpy: For numerical operations.
• matplotlib: For creating static, animated, and interactive visualisations.
• seaborn: For making statistical graphics in Python.

How to Run the Notebook

1. Clone this repository to your local machine:

   git clone https://github.com/eadewusic/Project_Plotting.git

2. cd into the repo.

3. Install the required libraries if you haven't already:

   pip install pandas numpy matplotlib seaborn

4. Open the Jupyter Notebook:

   jupyter notebook Plotting_Assignment.ipynb

5. Run the cells in the notebook to generate the plots and view the interpretations.