Pandas DataFrame Examples for AP Computer Science Principles

📚 Quick Study Guide

• 🐼 What is a Pandas DataFrame? A 2-dimensional labeled data structure with columns of potentially different types. Think of it like a spreadsheet or SQL table.
• 🔨 Creating a DataFrame: You can create DataFrames from lists, dictionaries, or even CSV files.
• 📝 Common Operations:
  • Selecting Columns: `df['column_name']`
  • Filtering Rows: `df[df['column_name'] > value]`
  • Adding New Columns: `df['new_column'] = df['existing_column'] * 2`
• ➕ Importing Pandas: Always start with `import pandas as pd`
• 💾 Reading CSV Files: Use `pd.read_csv('file.csv')` to load data.
• 📊 Basic Attributes: `.shape` (dimensions), `.head()` (first few rows), `.tail()` (last few rows), `.info()` (summary of the DataFrame).
• 🧮 Descriptive Statistics: `.describe()` gives count, mean, std, min, max, and quartiles.

Practice Quiz

1. What is the primary purpose of a Pandas DataFrame?
   1. A) To create graphical user interfaces.
   2. B) To efficiently store and manipulate tabular data.
   3. C) To perform complex mathematical computations.
   4. D) To manage operating system processes.
2. Which Pandas function is commonly used to read data from a CSV file into a DataFrame?
   1. A) `pd.read_table()`
   2. B) `pd.read_excel()`
   3. C) `pd.read_csv()`
   4. D) `pd.load_csv()`
3. How do you select a column named 'Age' from a DataFrame named 'df'?
   1. A) `df.Age()`
   2. B) `df['Age']`
   3. C) `df.get('Age')`
   4. D) `df.column('Age')`
4. Which attribute of a DataFrame returns the number of rows and columns?
   1. A) `.size`
   2. B) `.length`
   3. C) `.shape`
   4. D) `.dimension`
5. What does the `.head()` method of a DataFrame do?
   1. A) Returns the last 5 rows of the DataFrame.
   2. B) Returns the first 5 rows of the DataFrame.
   3. C) Returns a summary of the DataFrame.
   4. D) Returns the column headers of the DataFrame.
6. How would you filter a DataFrame 'df' to only include rows where the 'Score' column is greater than 80?
   1. A) `df[df.Score > 80]`
   2. B) `df.filter(Score > 80)`
   3. C) `df.where(Score > 80)`
   4. D) `df[Score > 80]`
7. What is the purpose of the `.describe()` method in Pandas DataFrames?
   1. A) To display the first few rows of the DataFrame.
   2. B) To provide descriptive statistics (e.g., mean, median, std) of the DataFrame.
   3. C) To rename columns in the DataFrame.
   4. D) To sort the DataFrame by a specific column.