Multiple Choice Questions on Data Manipulation with Pandas

📚 Quick Study Guide

• 🐍 Pandas DataFrames: Two-dimensional, size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns).
• 📊 Series: One-dimensional labeled array capable of holding any data type.
• 📍 Indexing: Selecting data based on labels (.loc[]) or integer positions (.iloc[]).
• ✂️ Slicing: Selecting a contiguous subset of rows and columns.
• 🧮 Filtering: Selecting rows based on conditions (Boolean indexing).
• ➕ Adding Columns: Creating new columns or modifying existing ones.
• 🗑️ Dropping Columns: Removing columns from a DataFrame.
• 🤝 Concatenation: Combining DataFrames vertically or horizontally.
• ⚙️ Merging: Joining DataFrames based on common columns.
• 🔍 GroupBy: Split-apply-combine strategy for data aggregation.
• 📈 Pivot Tables: Reshaping data for multi-dimensional analysis.
• 🧹 Handling Missing Data: Using .fillna(), .dropna(), and .interpolate().

🤔 Practice Quiz

1. Which Pandas function is used to read a CSV file into a DataFrame?
   1. A. pd.read_table()
   2. B. pd.read_csv()
   3. C. pd.load_csv()
   4. D. pd.open_csv()
2. How do you select a column named 'Age' from a DataFrame called 'df'?
   1. A. df.loc['Age']
   2. B. df['Age']
   3. C. df.iloc['Age']
   4. D. df.get_column('Age')
3. Which method is used to filter rows where the 'Salary' column is greater than 50000?
   1. A. df[df.Salary > 50000]
   2. B. df.filter(df.Salary > 50000)
   3. C. df.where(df.Salary > 50000)
   4. D. df.select(df.Salary > 50000)
4. How do you add a new column named 'Bonus' to a DataFrame 'df', calculated as 10% of the 'Salary' column?
   1. A. df.add_column('Bonus', df.Salary * 0.1)
   2. B. df['Bonus'] = df.Salary * 0.1
   3. C. df.insert_column('Bonus', df.Salary * 0.1)
   4. D. df.new_column('Bonus', df.Salary * 0.1)
5. Which function is used to group data by a column named 'Category' and calculate the mean of each group?
   1. A. df.groupby('Category').average()
   2. B. df.group('Category').mean()
   3. C. df.pivot_table('Category', aggfunc='mean')
   4. D. df.groupby('Category').mean()
6. How do you fill missing values in a DataFrame 'df' with the mean of each column?
   1. A. df.fillna(df.mean())
   2. B. df.replace_na(df.mean())
   3. C. df.fillna(df.average())
   4. D. df.impute(df.mean())
7. Which Pandas function is used to merge two DataFrames, 'df1' and 'df2', based on a common column 'ID'?
   1. A. pd.concat(df1, df2, on='ID')
   2. B. df1.join(df2, on='ID')
   3. C. pd.merge(df1, df2, on='ID')
   4. D. df1.merge(df2, on='ID')