This project was completed as part of a Data Technician Bootcamp and focuses on learning the fundamentals of Python programming and introductory data analysis using the Pandas library.
The project demonstrates how Python can be used to build logical programs, perform calculations, interact with users, and analyse structured datasets. These foundational programming and data analysis skills are widely used in data analytics and automation.
This project consists of several Python exercises completed in Google Colab notebooks. The exercises focus on building programming logic using variables, loops, and conditional statements while also introducing data analysis using Pandas.
The notebooks demonstrate how Python can be used to process user input, perform calculations, and analyse datasets in a structured and reproducible way.
This project demonstrates several core Python programming concepts including:
- Using variables
- Displaying output with
print() - Collecting user input with
input() - Performing type casting using
int() - Writing if statements for logical decision making
- Using for loops and while loops
- Performing arithmetic calculations
- Building simple interactive programs
The project also introduces data analysis using the Pandas library, which is widely used for working with structured data.
Pandas was used to:
- Create and manipulate DataFrames
- Load and explore datasets
- Analyse data using built-in functions
- Identify patterns and key metrics within datasets
This program asks the user for their name and age and prints a personalised greeting message.
This program asks the user to enter a four-digit number and performs calculations to rearrange the digits.
This program uses nested for loops to generate a number pattern.
This program calculates the sum of numbers from 1 to a user-defined value using a loop.
This example demonstrates creating a Pandas DataFrame from a list of dictionaries, showing how structured data can be organised for analysis.
This example analyses a dataset to identify the top five vehicles with the highest average mileage.
The .info() method provides an overview of dataset structure including column names, data types, and missing values.
- Python
- Pandas
- Google Colab
- Jupyter Notebooks
My-Python-Projects
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├── Data_Technician_Workbook_Week_6_2026.docx
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├── Python_Day_1_Exercises_Grant_Riches.ipynb
├── Python_Day_2_Exercises_Grant_Riches.ipynb
│
├── PandasDataFrames_01_Grant_Riches.ipynb
├── PandasDataFrames_02_Grant_Riches.ipynb
├── Pandas_DataFrames_03_Grant_Riches.ipynb
│
├── user-input-greeting.png
├── digit-reversal.png
├── nested-loops-pattern.png
├── sum-loop.png
│
├── pandas-dataframe.png
├── pandas-analysis.png
├── pandas-info.png
│
└── README.md
Through this project I developed skills in:
- Writing Python programs using loops and conditional logic
- Building interactive programs using user input
- Performing calculations and logical operations
- Working with datasets using Pandas
- Exploring and analysing structured data
- Using Google Colab notebooks for Python development