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"""
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Loading and Exploring Data
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In real data science projects, you'll work with data stored in files.
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This example shows you how to load data from a CSV file and explore it.
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What you'll learn:
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- How to load data from a CSV file
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- How to view basic information about your dataset
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- How to display the first/last rows
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- How to get summary statistics
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Prerequisites: pandas library (install with: pip install pandas)
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"""
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# Import the pandas library - it's the most popular tool for working with data in Python
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# We give it the short name 'pd' so we can type less
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import pandas as pd
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print("=" * 70)
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print("Welcome to Data Loading and Exploration!")
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print("=" * 70)
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print()
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# Step 1: Load data from a CSV file
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# CSV stands for "Comma-Separated Values" - a common format for storing data
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# We'll use the birds dataset that comes with this repository
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print("📂 Loading data from birds.csv...")
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print()
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# Load the data into a DataFrame (think of it as a smart spreadsheet)
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# A DataFrame is pandas' main data structure - it organizes data in rows and columns
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data = pd.read_csv('../data/birds.csv')
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print("✅ Data loaded successfully!")
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print()
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# Step 2: Get basic information about the dataset
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print("-" * 70)
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print("BASIC DATASET INFORMATION")
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print("-" * 70)
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# How many rows and columns do we have?
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num_rows, num_columns = data.shape
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print(f"📊 Dataset size: {num_rows} rows × {num_columns} columns")
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print()
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# What are the column names?
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print("📋 Column names:")
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for i, column in enumerate(data.columns, 1):
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print(f" {i}. {column}")
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print()
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# Step 3: Look at the first few rows of data
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# This gives us a quick preview of what the data looks like
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print("-" * 70)
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print("FIRST 5 ROWS OF DATA (Preview)")
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print("-" * 70)
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print(data.head()) # head() shows the first 5 rows by default
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print()
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# Step 4: Look at the last few rows
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print("-" * 70)
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print("LAST 3 ROWS OF DATA")
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print("-" * 70)
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print(data.tail(3)) # tail(3) shows the last 3 rows
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print()
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# Step 5: Get information about data types
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print("-" * 70)
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print("DATA TYPES AND NON-NULL COUNTS")
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print("-" * 70)
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print(data.info()) # Shows column names, data types, and count of non-null values
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print()
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# Step 6: Get statistical summary
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print("-" * 70)
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print("STATISTICAL SUMMARY (for numerical columns)")
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print("-" * 70)
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# describe() gives us statistics like mean, std, min, max, etc.
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print(data.describe())
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print()
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# Step 7: Check for missing values
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print("-" * 70)
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print("MISSING VALUES CHECK")
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print("-" * 70)
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missing_values = data.isnull().sum()
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print("Number of missing values per column:")
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print(missing_values)
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print()
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if missing_values.sum() == 0:
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print("✅ Great! No missing values found.")
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else:
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print("⚠️ Some columns have missing values. You may need to handle them.")
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print()
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# Step 8: Get unique values in a column
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print("-" * 70)
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print("SAMPLE: UNIQUE VALUES")
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print("-" * 70)
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# Let's see what unique values exist in the first column
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first_column = data.columns[0]
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unique_count = data[first_column].nunique()
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print(f"The column '{first_column}' has {unique_count} unique value(s)")
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print()
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# Summary
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print("=" * 70)
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print("SUMMARY")
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print("=" * 70)
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print("You've learned how to:")
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print(" ✓ Load data from a CSV file using pandas")
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print(" ✓ Check the size and shape of your dataset")
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print(" ✓ View the first and last rows")
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print(" ✓ Understand data types")
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print(" ✓ Get statistical summaries")
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print(" ✓ Check for missing values")
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print()
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print("Next step: Try loading other CSV files from the data/ folder!")
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print("=" * 70)
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# Pro Tips:
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# - Always explore your data before analyzing it
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# - Check for missing values and understand why they might be missing
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# - Look at the data types to ensure they make sense
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# - Use head() and tail() to spot any obvious issues with your data
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