Beginner Project • Basic Python

Python Data Explorer

Load, inspect, clean and summarize a small customer dataset before building your first Ai model.

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How to use this project

Prepare your first Python workspace

Project 02 introduces the shared Python and VS Code setup used by later coding projects. Complete this setup once; future projects will link back here instead of repeating it.

Part A

Install the required software

Download applications only from their official websites.

Required

1. Install Python

Download the current stable Python installer for your operating system and complete the installation.

Official Python download ↗python --versionOn some Windows systems, use py --version.

Required

2. Install VS Code

Download Visual Studio Code, run the installer and open the application after installation.

Official VS Code download ↗code --versionYou may also open VS Code from the Start menu.

Required

3. Add Python support

In VS Code, open Extensions, search for Python by Microsoft and select Install.

Microsoft Python extension ↗Python: Select InterpreterRun this command from the VS Code Command Palette.

Optional

4. Add Jupyter

Use Jupyter only if you prefer notebooks. The supplied Python script is enough to complete this project.

Official Jupyter installation ↗pip install jupyterlabSkip this installation when following the script route.

Part B

Create the project folders

Use File Explorer or Finder, then open the complete project folder in VS Code.

Create a folder named project-02-python-data-explorer.
Inside it, create two folders named data and outputs.
Open VS Code and select File → Open Folder.
Select project-02-python-data-explorer, then confirm that it appears in the Explorer panel.
To create a missing file manually, select the New File icon beside the folder name in the Explorer panel and type its exact name, including the extension.
Open the Command Palette with Ctrl + Shift + P, run Python: Select Interpreter and choose your installed Python version.

project-02-python-data-explorer/
├── data/
├── outputs/
├── data-explorer.py
├── README.md
└── requirements.txt

Part C

Download and place the starter files

Use the Starter Files section below, then move each download into the indicated location.

customer-data.csvMove to data/customer-data.csvdata-explorer.pyMove to the project’s main folderrequirements.txtMove to the project’s main folderREADME-template.pdfRead the PDF and use its sections to write your own README.mdProject worksheetOptional planning and findings record

Check file extensions

Confirm the files end with .py, .csv, .txt and .pdf. Windows may hide extensions; do not accidentally create names such as data-explorer.py.txt.

Part D

Install packages and confirm the setup

In VS Code, select Terminal → New Terminal. Confirm the terminal is inside your project folder, then run:

VS Code terminal

python -m pip install -r requirements.txt
python -c "import pandas, matplotlib; print('Setup complete')"
python data-explorer.py

Ready to begin when: the terminal prints Setup complete, the CSV loads, and the script displays the first data summaries without a file-not-found error.

What you will build

A complete first data exploration

Create a Python project that loads, inspects, cleans and summarizes customer data. You will understand what the dataset contains before using it for Ai.

Skills you will learn

Creating a Python projectLoading CSV dataUnderstanding rows and columnsChecking data typesFinding missing valuesRemoving duplicatesCalculating summariesFiltering and groupingCreating a chart

Requirements

LevelBeginner

CodingBasic Python

Time60–90 minutes

SoftwarePython, VS Code and Jupyter or .py

Librariespandas + matplotlib

Starter files

customer-data.csvSample dataset ↓data-explorer.pyComplete starter script ↓requirements.txtRequired packages ↓README-template.pdfProject documentation ↓project-02-worksheet.pdfFindings worksheet ↓

project-02-python-data-explorer/
├── data/customer-data.csv
├── outputs/
├── data-explorer.py
├── README.md
└── requirements.txt

Seven guided steps

Explore the customer dataset

Run each code block, confirm its output, then mark the step complete.

Step 01

Install the packages

Prepare pandas for data work and matplotlib for the chart.

VS Code terminal

pip install pandas matplotlib

requirements.txt

pandas
matplotlib

Step 1 completeBoth packages installed without an error.

Step 02

Load the dataset

Read the CSV file and handle a missing-file error clearly.

Python

import pandas as pd

file_path = "data/customer-data.csv"

try:
    df = pd.read_csv(file_path)
    print("Dataset loaded successfully.")
except FileNotFoundError:
    print(f"Dataset not found: {file_path}")

Expected result

Dataset loaded successfully.

Step 2 completeThe dataset loads without an error.

Step 03

Inspect the data

Understand its size, columns, values and data types.

Python

print(df.head())
print(df.shape)
print(df.columns)
df.info()

How many rows?How many columns?Which columns contain numbers?Which columns contain categories?What does each column represent?

Step 3 completeI inspected the rows, columns and data types.

Step 04

Check data quality

Find missing values and duplicates before changing the data.

Check quality

print("Missing values:")
print(df.isnull().sum())

print("\nDuplicate rows:")
print(df.duplicated().sum())

Clean the data

df = df.drop_duplicates()

df["Monthly Spending"] = df["Monthly Spending"].fillna(
    df["Monthly Spending"].median()
)

Missing values foundDuplicate rows found

Cleaning actions and reasons

Important

Replace each column name with the exact name used in your dataset. Check why a row is duplicated before removing it.

Step 4 completeI found and handled missing values and duplicates transparently.

Step 05

Summarize the dataset

Calculate useful numerical and category summaries.

Python

print(df.describe())

average_spending = df["Monthly Spending"].mean()
average_purchases = df["Number of Purchases"].mean()

print("Average monthly spending:", round(average_spending, 2))
print("Average number of purchases:", round(average_purchases, 2))
print(df["Membership Type"].value_counts())

Average monthly spendingAverage purchasesMembership counts

Step 5 completeI calculated summary statistics and category counts.

Step 06

Filter and group the data

Find high-spending customers and compare membership groups.

Python

high_spending_customers = df[df["Monthly Spending"] > 100]
print(high_spending_customers.head())

membership_summary = (
    df.groupby("Membership Type")["Monthly Spending"]
    .mean()
    .sort_values(ascending=False)
)

print(membership_summary)

What did the filter and group comparison reveal?

Step 6 completeI filtered customers and grouped spending by membership type.

Step 07

Create a simple chart

Visualise average spending across membership groups.

Python

import matplotlib.pyplot as plt

membership_summary.plot(kind="bar")

plt.title("Average Spending by Membership Type")
plt.xlabel("Membership Type")
plt.ylabel("Average Monthly Spending")
plt.tight_layout()
plt.show()

Expected result

A bar chart comparing average spending across membership groups.

What is the chart’s main message?

Step 7 completeI created and interpreted one chart.

Final output

Complete your project evidence

Cleaned datasetPython notebook or scriptData summaryOne chartThree findingsREADME file

Write three important findings

010203

Responsible data check

Personal data is not unnecessarily exposedCustomer IDs are used instead of full namesMissing values are handled transparentlyRows are not deleted without checking the reasonFindings are based on the data and not assumptions

Python Data Explorer

Prepare your first Python workspace

Install the required software

1. Install Python

2. Install VS Code

3. Add Python support

4. Add Jupyter

Create the project folders

Download and place the starter files

Install packages and confirm the setup

A complete first data exploration

Explore the customer dataset

Install the packages

Load the dataset

Inspect the data

Check data quality

Summarize the dataset

Filter and group the data

Create a simple chart

Complete your project evidence

Write three important findings

Responsible data check

Confirm your Data Explorer

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