Data Analytics Lesson 1: Loading and Understanding Your Data
Introduction to Data Analytics
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.
What You’ll Need
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!
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)
Output:
Our Student Dataset:
Name Age Grade Math_Score Science_Score English_Score
0 Alice 16 10th 85 89 92
1 Bob 17 11th 92 87 85
2 Charlie 16 10th 78 82 88
3 Diana 18 12th 95 98 91
4 Eva 17 11th 88 85 94
5 Frank 16 10th 76 79 83
6 Grace 17 11th 91 93 89
7 Henry 18 12th 87 89 86
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)
Expected Output:
Dataset shape (rows, columns): (8, 6)
Column names:
['Name', 'Age', 'Grade', 'Math_Score', 'Science_Score', 'English_Score']
Data types:
Name object
Age int64
Grade object
Math_Score int64
Science_Score int64
English_Score int64
dtype: object
Getting Basic Information About Your Data
Let’s learn some basic facts about our student data:
# Get a quick summary of numerical columns
print("Basic Statistics Summary:")
print(df.describe())
# Count how many students are in each grade
print("\nStudents per grade:")
print(df['Grade'].value_counts())
# Look at just the first few rows (useful for large datasets)
print("\nFirst 3 students:")
print(df.head(3))
# Look at the last few rows
print("\nLast 3 students:")
print(df.tail(3))
Expected Output:
Basic Statistics Summary:
Age Math_Score Science_Score English_Score
count 8.000000 8.000000 8.000000 8.000000
mean 16.750000 86.500000 87.750000 88.500000
std 0.886405 6.345177 6.135528 3.778153
min 16.000000 76.000000 79.000000 83.000000
25% 16.250000 82.750000 84.000000 86.000000
50% 17.000000 87.500000 88.000000 88.500000
75% 17.750000 91.250000 92.250000 91.250000
max 18.000000 95.000000 98.000000 94.000000
Students per grade:
10th 3
11th 3
12th 2
Name: Grade, dtype: int64
First 3 students:
Name Age Grade Math_Score Science_Score English_Score
0 Alice 16 10th 85 89 92
1 Bob 17 11th 92 87 85
2 Charlie 16 10th 78 82 88
Last 3 students:
Name Age Grade Math_Score Science_Score English_Score
5 Frank 16 10th 76 79 83
6 Grace 17 11th 91 93 89
7 Henry 18 12th 87 89 86
Loading Data from a Real File
In real projects, you’ll often load data from files. Here’s how to do it:
# If you had a CSV file, you would load it like this:
# df = pd.read_csv('student_data.csv')
# Let's save our data to a file first, then load it back
df.to_csv('student_scores.csv', index=False) # Save without row numbers
# Now load it back (this is what you'd do with real data)
loaded_df = pd.read_csv('student_scores.csv')
print("Data loaded from file:")
print(loaded_df.head())
# Check if it's the same as our original data
print(f"\nAre they identical? {df.equals(loaded_df)}")
Handling Missing Data
Real data is often messy and has missing values. Let’s see how to deal with this:
# Let's create some data with missing values to practice
messy_data = student_data.copy()
messy_data['Math_Score'][2] = None # Charlie's math score is missing
messy_data['Age'][5] = None # Frank's age is missing
df_messy = pd.DataFrame(messy_data)
print("Data with missing values:")
print(df_messy)
# Check for missing data
print("\nMissing values per column:")
print(df_messy.isnull().sum())
# Fill in missing values (we'll learn better methods later)
df_clean = df_messy.fillna(df_messy.mean(numeric_only=True))
print("\nAfter filling missing values with averages:")
print(df_clean)
Key Learning Points
- DataFrames are like digital spreadsheets - they organize data in rows and columns
- Always explore your data first - use
.shape,.head(),.describe()to understand what you have - Real data is often messy - missing values and errors are common
- CSV files are the most common way to store and share data
- pandas is your best friend for working with data in Python
Practice Exercises
- Create Your Own Dataset: Make a DataFrame with information about your favorite movies (title, year, rating, genre)
- Explore the Data: Use
.describe(),.head(), and.info()on your movie dataset - Save and Load: Save your dataset to a CSV file and load it back
- Handle Missing Data: Add some missing values to your dataset and practice filling them in
What’s Next?
Now that you can load and examine data, in the next lesson we’ll learn how to ask good questions about our data and start exploring patterns. We’ll discover what kinds of questions our student dataset can answer!