Co-authored-by: leestott <2511341+leestott@users.noreply.github.com>copilot/fix-68b23bbf-3e40-4d4b-a759-7d2a26a17f58
copilot-swe-agent[bot]
2 months ago
parent
1ecb020675
commit
2298145600
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"""
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Hello World - Data Science Style!
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This is your very first data science program. It introduces you to the basic
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concepts of working with data in Python.
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What you'll learn:
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- How to create a simple dataset
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- How to display data
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- How to work with Python lists and dictionaries
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- Basic data manipulation
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Prerequisites: Just Python installed on your computer!
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"""
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# Let's start with the classic "Hello, World!" but with a data science twist
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print("=" * 50)
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print("Hello, World of Data Science!")
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print("=" * 50)
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print()
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# In data science, we work with data. Let's create our first simple dataset.
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# We'll use a list to store information about students and their test scores.
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# A list is a collection of items in Python, written with square brackets []
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students = ["Alice", "Bob", "Charlie", "Diana", "Eve"]
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scores = [85, 92, 78, 95, 88]
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print("Our Dataset:")
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print("-" * 50)
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print("Students:", students)
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print("Scores:", scores)
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print()
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# Now let's do something useful with this data!
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# We can find basic statistics about the scores
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# Find the highest score
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highest_score = max(scores)
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print(f"📊 Highest score: {highest_score}")
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# Find the lowest score
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lowest_score = min(scores)
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print(f"📊 Lowest score: {lowest_score}")
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# Calculate the average score
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# sum() adds all numbers together, len() tells us how many items we have
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average_score = sum(scores) / len(scores)
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print(f"📊 Average score: {average_score:.2f}") # .2f means show 2 decimal places
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print()
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# Let's find who got the highest score
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# We use index() to find where the highest_score is in our list
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top_student_index = scores.index(highest_score)
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top_student = students[top_student_index]
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print(f"🏆 Top student: {top_student} with a score of {highest_score}")
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print()
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# Now let's organize this data in a more structured way
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# We'll use a dictionary - it pairs keys (student names) with values (scores)
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print("Student Scores (organized as key-value pairs):")
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print("-" * 50)
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# Create a dictionary by pairing students with their scores
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student_scores = {}
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for i in range(len(students)):
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student_scores[students[i]] = scores[i]
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# Display each student and their score
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for student, score in student_scores.items():
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# Add a special marker for the top student
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marker = "⭐" if student == top_student else " "
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print(f"{marker} {student}: {score} points")
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print()
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print("=" * 50)
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print("Congratulations! You've completed your first data science program!")
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print("=" * 50)
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# What did we just do?
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# 1. Created a simple dataset (student names and scores)
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# 2. Performed basic analysis (max, min, average)
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# 3. Found insights (who is the top student)
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# 4. Organized the data in a useful structure (dictionary)
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#
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# These are the fundamental building blocks of data science!
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# Next, you'll learn to work with real datasets using powerful libraries.
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"""
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Simple Data Analysis
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Learn how to analyze data and answer questions about it.
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This example demonstrates common data analysis operations.
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What you'll learn:
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- How to calculate statistics on your data
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- How to filter data based on conditions
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- How to group and aggregate data
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- How to sort data
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Prerequisites: pandas library (install with: pip install pandas)
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"""
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import pandas as pd
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print("=" * 70)
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print("Simple Data Analysis Tutorial")
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print("=" * 70)
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print()
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# Load a dataset - we'll use the honey production data
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print("📂 Loading honey production data...")
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data = pd.read_csv('../data/honey.csv')
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print("✅ Data loaded!\n")
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# Quick look at the data
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print("-" * 70)
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print("FIRST FEW ROWS")
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print("-" * 70)
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print(data.head(3))
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print()
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# SECTION 1: Basic Statistics
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print("=" * 70)
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print("SECTION 1: CALCULATING STATISTICS")
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print("=" * 70)
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print()
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# Let's look at the 'totalprod' column (total production)
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if 'totalprod' in data.columns:
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total_production = data['totalprod']
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print("Total Honey Production Statistics:")
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print("-" * 70)
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print(f" Mean (Average): {total_production.mean():,.2f}")
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print(f" Median (Middle): {total_production.median():,.2f}")
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print(f" Mode (Most common): {total_production.mode().values[0]:,.2f}")
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print(f" Std Dev: {total_production.std():,.2f}")
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print(f" Minimum: {total_production.min():,.2f}")
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print(f" Maximum: {total_production.max():,.2f}")
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print()
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# SECTION 2: Filtering Data
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print("=" * 70)
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print("SECTION 2: FILTERING DATA")
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print("=" * 70)
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print()
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# Let's filter the data to show only records from a specific year
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if 'year' in data.columns:
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year_to_filter = 2000
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filtered_data = data[data['year'] == year_to_filter]
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print(f"Showing data for year {year_to_filter}:")
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print("-" * 70)
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print(f"Found {len(filtered_data)} records")
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print()
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print(filtered_data.head())
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print()
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# Filter based on multiple conditions
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if 'totalprod' in data.columns and 'year' in data.columns:
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# Find records where production was above 10 million pounds after 2010
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high_production = data[(data['totalprod'] > 10000000) & (data['year'] > 2010)]
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print("High production years (>10M pounds after 2010):")
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print("-" * 70)
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print(f"Found {len(high_production)} records")
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print()
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# SECTION 3: Grouping and Aggregating
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print("=" * 70)
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print("SECTION 3: GROUPING AND AGGREGATING DATA")
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print("=" * 70)
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print()
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# Group by state and calculate average production
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if 'state' in data.columns and 'totalprod' in data.columns:
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# Group the data by state and calculate mean production
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state_averages = data.groupby('state')['totalprod'].mean()
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# Sort to see which states have highest average production
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state_averages_sorted = state_averages.sort_values(ascending=False)
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print("Top 10 States by Average Honey Production:")
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print("-" * 70)
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for i, (state, avg_prod) in enumerate(state_averages_sorted.head(10).items(), 1):
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print(f"{i:2d}. {state:20s} {avg_prod:,.0f} pounds")
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print()
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# SECTION 4: Sorting Data
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print("=" * 70)
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print("SECTION 4: SORTING DATA")
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print("=" * 70)
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print()
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if 'totalprod' in data.columns:
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# Sort by total production in descending order
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sorted_data = data.sort_values('totalprod', ascending=False)
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print("Records with Highest Production:")
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print("-" * 70)
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# Show the top 5 records
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columns_to_show = ['state', 'year', 'totalprod'] if all(col in data.columns for col in ['state', 'year', 'totalprod']) else data.columns[:3]
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print(sorted_data[columns_to_show].head())
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print()
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# SECTION 5: Counting Values
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print("=" * 70)
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print("SECTION 5: COUNTING VALUES")
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print("=" * 70)
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print()
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if 'state' in data.columns:
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# Count how many records we have for each state
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state_counts = data['state'].value_counts()
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print("Number of records per state (top 10):")
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print("-" * 70)
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for state, count in state_counts.head(10).items():
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print(f"{state:20s} {count:3d} records")
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print()
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# SECTION 6: Answering a Question
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print("=" * 70)
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print("SECTION 6: ANSWERING A REAL QUESTION")
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print("=" * 70)
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print()
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# Question: Which state had the highest honey production in 2012?
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if all(col in data.columns for col in ['state', 'year', 'totalprod']):
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year_2012 = data[data['year'] == 2012]
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if len(year_2012) > 0:
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# Find the row with maximum production in 2012
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max_prod_idx = year_2012['totalprod'].idxmax()
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max_prod_state = year_2012.loc[max_prod_idx, 'state']
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max_prod_amount = year_2012.loc[max_prod_idx, 'totalprod']
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print("Question: Which state had the highest honey production in 2012?")
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print("-" * 70)
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print(f"Answer: {max_prod_state}")
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print(f"Production: {max_prod_amount:,.0f} pounds")
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print()
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# Summary
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print("=" * 70)
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print("CONGRATULATIONS!")
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print("=" * 70)
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print("You've learned how to:")
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print(" ✓ Calculate basic statistics (mean, median, mode, etc.)")
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print(" ✓ Filter data based on conditions")
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print(" ✓ Group data and calculate aggregates")
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print(" ✓ Sort data to find top/bottom values")
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print(" ✓ Count occurrences of values")
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print(" ✓ Answer real questions using data")
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print()
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print("Try this yourself:")
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print(" • Find the state with the lowest average production")
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print(" • Calculate total production by year")
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print(" • Find trends over time")
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print("=" * 70)
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"""
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Basic Data Visualization
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Learn how to create simple, effective visualizations to communicate your findings.
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Visualizations help you and others understand data at a glance.
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What you'll learn:
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- How to create bar charts
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- How to create line plots
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- How to create pie charts
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- How to customize and save your visualizations
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Prerequisites:
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- pandas library (install with: pip install pandas)
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- matplotlib library (install with: pip install matplotlib)
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"""
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import pandas as pd
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import matplotlib.pyplot as plt
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print("=" * 70)
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print("Basic Data Visualization Tutorial")
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print("=" * 70)
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print()
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# Load data
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print("📂 Loading honey production data...")
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data = pd.read_csv('../data/honey.csv')
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print("✅ Data loaded!\n")
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# For better display, we'll use a subset of the data
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# Let's focus on a few states in recent years
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if 'state' in data.columns and 'year' in data.columns:
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# Get data for a few states in recent years
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states_to_show = ['CA', 'FL', 'ND', 'SD', 'MT']
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recent_data = data[(data['year'] >= 2010) & (data['state'].isin(states_to_show))]
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# VISUALIZATION 1: Bar Chart
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print("=" * 70)
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print("VISUALIZATION 1: BAR CHART")
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print("=" * 70)
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print()
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if 'state' in data.columns and 'totalprod' in data.columns:
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# Calculate average production by state (for top 10 states)
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state_avg = data.groupby('state')['totalprod'].mean().sort_values(ascending=False).head(10)
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print("Creating a bar chart of average honey production by state...")
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print()
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# Create the figure and axis
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plt.figure(figsize=(12, 6)) # Width: 12 inches, Height: 6 inches
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# Create the bar chart
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plt.bar(state_avg.index, state_avg.values, color='gold', edgecolor='orange')
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# Add labels and title
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plt.xlabel('State', fontsize=12)
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plt.ylabel('Average Production (pounds)', fontsize=12)
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plt.title('Top 10 States by Average Honey Production', fontsize=14, fontweight='bold')
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# Rotate x-axis labels for better readability
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plt.xticks(rotation=45)
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# Add a grid for easier reading (behind the bars)
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plt.grid(axis='y', alpha=0.3, linestyle='--')
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# Adjust layout to prevent label cutoff
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plt.tight_layout()
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# Save the figure
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plt.savefig('bar_chart_example.png', dpi=300, bbox_inches='tight')
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print("✅ Bar chart saved as 'bar_chart_example.png'")
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plt.close() # Close to free memory
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print()
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# VISUALIZATION 2: Line Plot
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print("=" * 70)
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print("VISUALIZATION 2: LINE PLOT")
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print("=" * 70)
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print()
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if 'year' in data.columns and 'totalprod' in data.columns:
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# Calculate total production by year
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yearly_production = data.groupby('year')['totalprod'].sum()
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print("Creating a line plot of honey production over time...")
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print()
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plt.figure(figsize=(12, 6))
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# Create the line plot
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plt.plot(yearly_production.index, yearly_production.values,
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marker='o', # Add circular markers at each data point
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linewidth=2, # Line thickness
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color='darkorange', # Line color
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markersize=6, # Size of markers
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markerfacecolor='gold') # Fill color of markers
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# Add labels and title
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plt.xlabel('Year', fontsize=12)
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plt.ylabel('Total Production (pounds)', fontsize=12)
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plt.title('Honey Production Over Time', fontsize=14, fontweight='bold')
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# Add a grid
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plt.grid(True, alpha=0.3, linestyle='--')
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plt.tight_layout()
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plt.savefig('line_plot_example.png', dpi=300, bbox_inches='tight')
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print("✅ Line plot saved as 'line_plot_example.png'")
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plt.close()
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print()
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# VISUALIZATION 3: Pie Chart
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print("=" * 70)
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print("VISUALIZATION 3: PIE CHART")
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print("=" * 70)
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print()
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if 'state' in data.columns and 'totalprod' in data.columns:
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# Get total production for top 5 states
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top5_states = data.groupby('state')['totalprod'].sum().sort_values(ascending=False).head(5)
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print("Creating a pie chart of production share (top 5 states)...")
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print()
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plt.figure(figsize=(10, 8))
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# Create the pie chart
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colors = ['gold', 'orange', 'lightsalmon', 'lightcoral', 'peachpuff']
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plt.pie(top5_states.values,
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labels=top5_states.index, # State names
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autopct='%1.1f%%', # Show percentages
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startangle=90, # Start from top
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colors=colors,
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explode=(0.1, 0, 0, 0, 0)) # Slightly separate the first slice
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plt.title('Top 5 States Share of Total Honey Production',
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fontsize=14, fontweight='bold', pad=20)
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plt.savefig('pie_chart_example.png', dpi=300, bbox_inches='tight')
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print("✅ Pie chart saved as 'pie_chart_example.png'")
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plt.close()
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print()
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# VISUALIZATION 4: Multiple Lines on One Plot
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print("=" * 70)
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print("VISUALIZATION 4: COMPARING MULTIPLE SERIES")
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print("=" * 70)
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print()
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if 'year' in data.columns and 'totalprod' in data.columns and 'state' in data.columns:
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# Compare production trends for a few states
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states_to_compare = ['CA', 'ND', 'SD']
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print(f"Creating a comparison plot for states: {', '.join(states_to_compare)}...")
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print()
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plt.figure(figsize=(12, 6))
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# Plot a line for each state
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colors_map = {'CA': 'blue', 'ND': 'green', 'SD': 'red'}
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for state in states_to_compare:
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if state in data['state'].values:
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state_data = data[data['state'] == state].groupby('year')['totalprod'].sum()
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plt.plot(state_data.index, state_data.values,
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marker='o',
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label=state, # This will appear in the legend
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linewidth=2,
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color=colors_map.get(state, 'gray'))
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plt.xlabel('Year', fontsize=12)
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plt.ylabel('Total Production (pounds)', fontsize=12)
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plt.title('Honey Production Comparison by State', fontsize=14, fontweight='bold')
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plt.legend(title='State') # Add a legend
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plt.grid(True, alpha=0.3, linestyle='--')
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plt.tight_layout()
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plt.savefig('comparison_plot_example.png', dpi=300, bbox_inches='tight')
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print("✅ Comparison plot saved as 'comparison_plot_example.png'")
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plt.close()
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print()
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# Summary
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print("=" * 70)
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print("CONGRATULATIONS!")
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print("=" * 70)
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print("You've learned how to:")
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print(" ✓ Create bar charts to compare categories")
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print(" ✓ Create line plots to show trends over time")
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print(" ✓ Create pie charts to show proportions")
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print(" ✓ Plot multiple data series on one chart")
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print(" ✓ Customize colors, labels, and titles")
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print(" ✓ Save your visualizations as image files")
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print()
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print("Your visualizations have been saved in the examples/ folder!")
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print()
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print("Try this yourself:")
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print(" • Change the colors of your charts")
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print(" • Add more states to the comparison plot")
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print(" • Create a horizontal bar chart")
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print(" • Experiment with different chart styles")
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print()
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print("Pro tip: Always choose the right chart type for your data:")
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print(" • Bar charts: Compare categories")
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print(" • Line plots: Show trends over time")
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print(" • Pie charts: Show parts of a whole")
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print(" • Scatter plots: Show relationships between variables")
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print("=" * 70)
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@ -0,0 +1,135 @@
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# Beginner-Friendly Data Science Examples
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|
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Welcome to the examples directory! This collection of simple, well-commented examples is designed to help you get started with data science, even if you're a complete beginner.
|
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|
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## 📚 What You'll Find Here
|
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|
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Each example is self-contained and includes:
|
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- **Clear comments** explaining every step
|
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- **Simple, readable code** that demonstrates one concept at a time
|
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- **Real-world context** to help you understand when and why to use these techniques
|
||||
- **Expected output** so you know what to look for
|
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|
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## 🚀 Getting Started
|
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|
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### Prerequisites
|
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Before running these examples, make sure you have:
|
||||
- Python 3.7 or higher installed
|
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- Basic understanding of how to run Python scripts
|
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|
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### Installing Required Libraries
|
||||
```bash
|
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pip install pandas numpy matplotlib
|
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```
|
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|
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## 📖 Examples Overview
|
||||
|
||||
### 1. Hello World - Data Science Style
|
||||
**File:** `01_hello_world_data_science.py`
|
||||
|
||||
Your first data science program! Learn how to:
|
||||
- Load a simple dataset
|
||||
- Display basic information about your data
|
||||
- Print your first data science output
|
||||
|
||||
Perfect for absolute beginners who want to see their first data science program in action.
|
||||
|
||||
---
|
||||
|
||||
### 2. Loading and Exploring Data
|
||||
**File:** `02_loading_data.py`
|
||||
|
||||
Learn the fundamentals of working with data:
|
||||
- Read data from CSV files
|
||||
- View the first few rows of your dataset
|
||||
- Get basic statistics about your data
|
||||
- Understand data types
|
||||
|
||||
This is often the first step in any data science project!
|
||||
|
||||
---
|
||||
|
||||
### 3. Simple Data Analysis
|
||||
**File:** `03_simple_analysis.py`
|
||||
|
||||
Perform your first data analysis:
|
||||
- Calculate basic statistics (mean, median, mode)
|
||||
- Find maximum and minimum values
|
||||
- Count occurrences of values
|
||||
- Filter data based on conditions
|
||||
|
||||
See how to answer simple questions about your data.
|
||||
|
||||
---
|
||||
|
||||
### 4. Data Visualization Basics
|
||||
**File:** `04_basic_visualization.py`
|
||||
|
||||
Create your first visualizations:
|
||||
- Make a simple bar chart
|
||||
- Create a line plot
|
||||
- Generate a pie chart
|
||||
- Save your visualizations as images
|
||||
|
||||
Learn to communicate your findings visually!
|
||||
|
||||
---
|
||||
|
||||
### 5. Working with Real Data
|
||||
**File:** `05_real_world_example.py`
|
||||
|
||||
Put it all together with a complete example:
|
||||
- Load real data from the repository
|
||||
- Clean and prepare the data
|
||||
- Perform analysis
|
||||
- Create meaningful visualizations
|
||||
- Draw conclusions
|
||||
|
||||
This example shows you a complete workflow from start to finish.
|
||||
|
||||
---
|
||||
|
||||
## 🎯 How to Use These Examples
|
||||
|
||||
1. **Start from the beginning**: The examples are numbered in order of difficulty. Begin with `01_hello_world_data_science.py` and work your way through.
|
||||
|
||||
2. **Read the comments**: Each file has detailed comments explaining what the code does and why. Read them carefully!
|
||||
|
||||
3. **Experiment**: Try modifying the code. What happens if you change a value? Break things and fix them - that's how you learn!
|
||||
|
||||
4. **Run the code**: Execute each example and observe the output. Compare it with what you expected.
|
||||
|
||||
5. **Build on it**: Once you understand an example, try extending it with your own ideas.
|
||||
|
||||
## 💡 Tips for Beginners
|
||||
|
||||
- **Don't rush**: Take time to understand each example before moving to the next one
|
||||
- **Type the code yourself**: Don't just copy-paste. Typing helps you learn and remember
|
||||
- **Look up unfamiliar concepts**: If you see something you don't understand, search for it online or in the main lessons
|
||||
- **Ask questions**: Join the [discussion forum](https://github.com/microsoft/Data-Science-For-Beginners/discussions) if you need help
|
||||
- **Practice regularly**: Try to code a little bit every day rather than long sessions once a week
|
||||
|
||||
## 🔗 Next Steps
|
||||
|
||||
After completing these examples, you're ready to:
|
||||
- Work through the main curriculum lessons
|
||||
- Try the assignments in each lesson folder
|
||||
- Explore the Jupyter notebooks for more in-depth learning
|
||||
- Create your own data science projects
|
||||
|
||||
## 📚 Additional Resources
|
||||
|
||||
- [Main Curriculum](../README.md) - The complete 20-lesson course
|
||||
- [For Teachers](../for-teachers.md) - Using this curriculum in your classroom
|
||||
- [Microsoft Learn](https://docs.microsoft.com/learn/) - Free online learning resources
|
||||
- [Python Documentation](https://docs.python.org/3/) - Official Python reference
|
||||
|
||||
## 🤝 Contributing
|
||||
|
||||
Found a bug or have an idea for a new example? We welcome contributions! Please see our [Contributing Guide](../CONTRIBUTING.md).
|
||||
|
||||
---
|
||||
|
||||
**Happy Learning! 🎉**
|
||||
|
||||
Remember: Every expert was once a beginner. Take it one step at a time, and don't be afraid to make mistakes - they're part of the learning process!
|
||||
Loading…
Reference in new issue