Python Pandas Quiz: Test Your Data Analysis Skills for AP Computer Science

📚 Quick Study Guide

• 🐼 What is Pandas? Pandas is a powerful Python library used for data manipulation and analysis. It provides data structures like DataFrames and Series that make it easy to work with structured data.
• 📊 Series: A one-dimensional labeled array capable of holding any data type. Think of it as a single column in a spreadsheet.
• 🧮 DataFrame: A two-dimensional labeled data structure with columns of potentially different types. It's like a spreadsheet or SQL table.
• 🔨 Key Operations:
  • ✏️ Creating DataFrames: From lists, dictionaries, and NumPy arrays.
  • 🔍 Selecting Data: Using labels (`.loc`) and integer positions (`.iloc`).
  • 🧩 Filtering Data: Using boolean indexing.
  • 🗑️ Handling Missing Data: Using `dropna()` and `fillna()`.
  • ➕ Adding Columns: Assigning new columns to a DataFrame.
  • ➗ Applying Functions: Using `apply()` to perform operations on rows or columns.
  • 📈 Grouping and Aggregating Data: Using `groupby()` to perform calculations on subsets of data.
• 💡 Boolean Indexing: Selecting rows based on a condition. For example, `df[df['column_name'] > 5]` selects rows where the value in 'column_name' is greater than 5.

🧪 Practice Quiz

1. What is the primary data structure in Pandas for working with tabular data?
   1. A) Series
   2. B) DataFrame
   3. C) Array
   4. D) List
2. How do you select data using labels in a Pandas DataFrame?
   1. A) `.iloc`
   2. B) `.loc`
   3. C) `.at`
   4. D) `.iat`
3. Which method is used to fill missing values in a Pandas DataFrame?
   1. A) `dropna()`
   2. B) `fillna()`
   3. C) `replace()`
   4. D) `interpolate()`
4. How do you filter rows in a DataFrame where the 'age' column is greater than 25?
   1. A) `df[df.age > 25]`
   2. B) `df.filter(age > 25)`
   3. C) `df.where(age > 25)`
   4. D) `df.select(age > 25)`
5. What does the `groupby()` method in Pandas allow you to do?
   1. A) Sort the DataFrame
   2. B) Filter the DataFrame
   3. C) Group rows based on a column and perform aggregate functions
   4. D) Merge two DataFrames
6. Which of the following is NOT a valid way to create a Pandas DataFrame?
   1. A) From a list of lists
   2. B) From a dictionary
   3. C) From a NumPy array
   4. D) From a text file (directly without using read_csv)
7. How do you apply a function to each element in a Pandas Series?
   1. A) `map()`
   2. B) `apply()`
   3. C) `transform()`
   4. D) `iterate()`