Pandas Quiz: Test Your Data Analysis Skills (AP CSP)

📚 Quick Study Guide: Pandas Essentials

• 💡 What is Pandas? It's a powerful Python library built on NumPy, designed for data manipulation and analysis. Think of it as Excel on steroids for programmatic use!
• 📊 Key Data Structures:
  • 🔢 Series: A one-dimensional labeled array capable of holding any data type (integers, strings, floats, Python objects, etc.). It's like a single column in a spreadsheet.
  • 📈 DataFrame: A two-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table. It's the most commonly used Pandas object.
• 🛠️ Creating Data Structures:
  • 📝 Series: `pd.Series([10, 20, 30], index=['a', 'b', 'c'])`
  • 🏗️ DataFrame: `pd.DataFrame({'Col1': [1, 2], 'Col2': [3, 4]})`
• 🔍 Common Operations:
  • 📂 Reading Data: Easily load data from various sources, e.g., `pd.read_csv('filename.csv')`, `pd.read_excel('filename.xlsx')`.
  • 🎯 Selection: Access columns using `df['ColumnName']` or `df.ColumnName`. For row/column label-based selection, use `df.loc[row_label, col_label]`. For integer-location based selection, use `df.iloc[row_index, col_index]`.
  • ✅ Filtering Data: Select rows based on conditions, e.g., `df[df['Score'] > 80]`.
  • 🚫 Handling Missing Data: Identify missing values with `df.isnull().sum()`. Drop rows/columns with `df.dropna()`. Fill missing values with `df.fillna(value)`.
  • ➕ Aggregation: Group data and apply functions, e.g., `df.groupby('Category')['Value'].mean()`.
  • ↕️ Sorting: Arrange data by values in columns, e.g., `df.sort_values(by='ColumnName', ascending=True)`.
  • 📐 Dimensions: `df.shape` returns a tuple (rows, columns).

🧠 Practice Quiz: Pandas Data Analysis

Test your knowledge with these multiple-choice questions! Choose the best answer for each.

1. What is the primary data structure in Pandas for a 1-dimensional labeled array?
   A) DataFrame
   B) Series
   C) Array
   D) List
2. How would you read a CSV file named 'data.csv' into a Pandas DataFrame?
   A) `pandas.open_csv('data.csv')`
   B) `pd.load_csv('data.csv')`
   C) `pd.read_csv('data.csv')`
   D) `pd.import_csv('data.csv')`
3. To select a single column named 'Age' from a DataFrame `df`, which of the following is the most common method?
   A) `df.Age`
   B) `df['Age']`
   C) Both A and B
   D) `df(Age)`
4. Which method is used to check for missing values in a Pandas DataFrame?
   A) `df.has_null()`
   B) `df.is_na()`
   C) `df.isnull()`
   D) `df.missing()`
5. If you want to filter a DataFrame `df` to only include rows where the 'Score' column is greater than 80, which code snippet would you use?
   A) `df[df['Score'] > 80]`
   B) `df.filter(Score > 80)`
   C) `df.loc[Score > 80]`
   D) `df.query('Score' > 80)`
6. What is the correct way to sort a DataFrame `df` by the 'Name' column in ascending order?
   A) `df.order_by('Name')`
   B) `df.sort_index(by='Name')`
   C) `df.sort_values(by='Name', ascending=True)`
   D) `df.arrange(by='Name')`
7. In a Pandas DataFrame, what does `df.shape` return?
   A) The data type of each column.
   B) A tuple representing the dimensions (rows, columns).
   C) The memory usage of the DataFrame.
   D) The number of unique values in each column.