Python Data Analytics Lesson 1

Data Analytics Lesson 1: Loading and Understanding Your Data

📘 Overview

Welcome to your first data analytics lesson! Data analytics is like being a detective - we use data to answer questions and discover patterns. Today, we’ll learn how to load data into Python and take our first look at what we’re working with.

Expected Duration: 30-45 minutes
This is an introductory lesson - no prior Python knowledge required!

Required packages:

  • pandas
  • numpy

📦 Package Installation

Before we start, we need to install some special Python tools (called libraries) that help us work with data:

# These are the tools we'll use for data analysis
import pandas as pd    # For working with data tables
import numpy as np     # For math operations
import os             # For working with files

# Don't worry if you see warnings - that's normal!
print("Libraries imported successfully!")

Explanation:

  • pandas is a powerful library for working with tabular data (like spreadsheets)
  • numpy provides mathematical operations and array handling
  • os helps us work with files and directories

📌 Loading Your First Dataset

Let’s start with a simple dataset about students and their test scores. In real life, data often comes in CSV files (Comma Separated Values).

# First, let's create some sample student data to practice with
student_data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eva', 'Frank', 'Grace', 'Henry'],
    'Age': [16, 17, 16, 18, 17, 16, 17, 18],
    'Grade': ['10th', '11th', '10th', '12th', '11th', '10th', '11th', '12th'],
    'Math_Score': [85, 92, 78, 95, 88, 76, 91, 87],
    'Science_Score': [89, 87, 82, 98, 85, 79, 93, 89],
    'English_Score': [92, 85, 88, 91, 94, 83, 89, 86]
}

# Convert this into a DataFrame (think of it as a digital spreadsheet)
df = pd.DataFrame(student_data)

# Let's see what our data looks like
print("Our Student Dataset:")
print(df)

What’s happening here?

  • We create a Python dictionary with student information
  • pd.DataFrame() converts our dictionary into a pandas DataFrame (like a spreadsheet)
  • The print() function displays our data

🔍 Understanding Your Data Structure

Now let’s explore what we have. Think of this like getting to know a new friend - we want to learn basic facts about our data:

# How big is our dataset? (rows and columns)
print("Dataset shape (rows, columns):", df.shape)

# What types of information do we have?
print("\nColumn names:")
print(df.columns.tolist())

# What kind of data is in each column?
print("\nData types:")
print(df.dtypes)

Understanding the output:

  • .shape tells us the dimensions: (rows, columns)
  • .columns lists all column names
  • .dtypes shows the data type of each column (numbers, text, etc.)

📊 Getting Basic Information About Your Data

Let’s learn some basic facts about our student data:

Statistical Summary

# Get a quick summary of numerical columns
print("Basic Statistics Summary:")
print(df.describe())

The .describe() method gives us:

  • count: Number of non-empty values
  • mean: Average value
  • std: Standard deviation (how spread out the values are)
  • min/max: Smallest and largest values
  • 25%, 50%, 75%: Quartile values

Counting Values

# Count how many students are in each grade
print("Students per grade:")
print(df['Grade'].value_counts())

Viewing Portions of Data

# Look at just the first few rows (useful for large datasets)
print("First 3 students:")
print(df.head(3))

# Look at the last few rows
print("\nLast 3 students:")
print(df.tail(3))

Why is this useful?

  • .head(n) shows the first n rows - great for previewing large datasets
  • .tail(n) shows the last n rows - useful for checking if data loaded completely

💡 Practice Exercise

Now it’s your turn! Try these exercises to practice what you’ve learned:

Exercise 1: Create Your Own Dataset

# Create your own small dataset about your friends or family
# Include at least 3 columns and 5 rows
# Replace this with your own data!

my_data = {
    'Name': ['Person1', 'Person2', 'Person3', 'Person4', 'Person5'],
    'Age': [20, 25, 30, 22, 28],
    'Hobby': ['Reading', 'Sports', 'Music', 'Art', 'Cooking']
}

my_df = pd.DataFrame(my_data)
print("My Dataset:")
print(my_df)

Exercise 2: Explore Your Dataset

# Use the methods we learned to understand your data

print("Shape of my dataset:", my_df.shape)
print("\nData types:")
print(my_df.dtypes)
print("\nFirst 2 rows:")
print(my_df.head(2))

✅ Summary

Congratulations! You’ve completed your first data analytics lesson. Here’s what you learned:

  1. How to import essential libraries (pandas, numpy)
  2. How to create a DataFrame from dictionary data
  3. How to explore data structure (shape, columns, data types)
  4. How to get basic statistics using .describe()
  5. How to view portions of your data using .head() and .tail()
  6. How to count values using .value_counts()

🚀 Next Steps

In Lesson 2, we’ll learn how to:

  • Clean messy data
  • Handle missing values
  • Filter and sort our data
  • Load data from real CSV files

Keep practicing with different datasets - the more you work with data, the more comfortable you’ll become!

📓 Interactive Notebook

Want to practice with the interactive Jupyter notebook version of this lesson?

Download the Jupyter Notebook

Tip: You can run this notebook in:

  • Google Colab (free, no installation needed)
  • Jupyter Lab on your computer
  • VS Code with Jupyter extension