Data Analysis and Data Visualisation with Python

This is one my data projects for HyperionDev Data Science Bootcamp (full portfolio available here).

data_viz_task.ipynb

Task description

The objective is to create graphs using a .csv dataset to explore and analyze the performance of various car models, aiming to answer specific questions based on the data:

• Which of the manufacturers has the car with the highest revs per mile?
• Is it generally more fuel efficient to drive in the city or on the highway?
• What happens when the wheelbase gets larger?
• Does a larger car mean more horsepower?

Benefits of Learning Python's libraries for Data Visualization

Learning and practicing data visualization libraries like Matplotlib and Seaborn is highly valuable because:

• They help communicate data insights clearly and intuitively through charts, graphs, and plots.
• These tools facilitate thorough exploration of data, revealing patterns, trends, and relationships.
• Visualizations make presentations more engaging and persuasive, improving understanding of data-driven insights.
• They aid decision-making by providing a clearer picture of data implications and trends.

Installation instructions

To utilize the Matplotlib and Seaborn libraries, you must install them. Additionally, installing pandas is essential for leveraging its dataframe structure.

Install pandas

Pandas can be installed from PyPI using the terminal:

pip install pandas

Install Matplotlib

Matplotlib can be installed from PyPI using the terminal:

python -m pip install -U matplotlib

Install Seaborn

Official releases of seaborn can be installed from PyPI using the terminal:

pip install seaborn

Usage instructions

In order to use the three libraries in your file, you must first import them at the beginning of the file:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns