Quizalize logo
placeholder image to represent content

Python

Quiz by Support - BusinessPromoted .com

Feel free to use or edit a copy

includes Teacher and Student dashboards

Measure skills
from any curriculum

Tag the questions with any skills you have. Your dashboard will track each student's mastery of each skill.

With a free account, teachers can
  • edit the questions
  • save a copy for later
  • start a class game
  • automatically assign follow-up activities based on students’ scores
  • assign as homework
  • share a link with colleagues
  • print as a bubble sheet

Our brand new solo games combine with your quiz, on the same screen

Correct quiz answers unlock more play!

New Quizalize solo game modes
20 questions
Show answers
  • Q1
    1. How can you embed a Python script in Power BI to perform data transformation?
    Use SQL Script in Power Query Editor
    Use Python Script in Power Query Editor
    Use DAX Script in Power Query Editor
    Use R Script in Power Query Editor
    120s
  • Q2
    2. Which Python library is most commonly used for data manipulation and analysis in Power BI?
    numpy
    pandas
    seaborn
    matplotlib
    120s
  • Q3
    3. How can you handle missing values in a dataset using Python in Power BI?
    df.fillna()
    df.interpolate()
    df.replace()
    df.dropna()
    120s
  • Q4
    4. What is the purpose of using 'df.groupby()' in a Python script within Power BI?
    To group data based on one or more columns
    To sort data
    To filter data
    To merge dataframes
    120s
  • Q5
    5. How can you visualize data using Python in Power BI?
    Use matplotlib or seaborn within a Python visual
    Use numpy
    Use pandas
    Use SQL
    120s
  • Q6
    6. How do you ensure your Python environment is set up correctly for Power BI integration?
    Install Java and required libraries
    Install R and required libraries
    Install SQL Server and required libraries
    Install Python and required libraries, and set the correct Python home directory in Power BI
    120s
  • Q7
    7. How do you read a CSV file into a pandas DataFrame in Power BI?
    pd.get_csv('file_path')
    pd.open_csv('file_path')
    pd.read_csv('file_path')
    pd.load_csv('file_path')
    120s
  • Q8
    8. How can you merge two DataFrames in Python for use in Power BI?
    pd.concat(df1, df2)
    pd.append(df1, df2)
    pd.merge(df1, df2, on='key')
    pd.join(df1, df2, on='key')
    120s
  • Q9
    9. What method would you use to remove duplicates from a DataFrame in Python within Power BI?
    df.remove_duplicates()
    df.clear_duplicates()
    df.delete_duplicates()
    df.drop_duplicates()
    120s
  • Q10
    10. How can you create a new column in a DataFrame in Python to calculate the total sales from quantity and price columns?
    df['Total Sales'] = df['Quantity'] * df['Price']
    df['Total Sales'] = df['Quantity'] - df['Price']
    df['Total Sales'] = df['Quantity'] / df['Price']
    df['Total Sales'] = df['Quantity'] + df['Price']
    120s
  • Q11
    11. How do you handle large datasets in Python within Power BI to ensure performance?
    Use chunking with pd.read_csv()
    Use SQL queries
    Use Excel for pre-processing
    Load the entire dataset at once
    120s
  • Q12
    12. How can you apply a custom function to each element of a column in a DataFrame in Power BI using Python?
    df['column'].map(custom_function)
    df['column'].sort(custom_function)
    df['column'].filter(custom_function)
    df['column'].apply(custom_function)
    120s
  • Q13
    13. How can you perform a left join between two DataFrames in Python within Power BI?
    pd.concat(df1, df2, how='left')
    pd.merge(df1, df2, how='left')
    pd.join(df1, df2, how='left')
    pd.append(df1, df2, how='left')
    120s
  • Q14
    14. Which method would you use to pivot a DataFrame in Python within Power BI?
    df.stack()
    df.unstack()
    df.pivot()
    df.melt()
    120s
  • Q15
    15. How do you filter rows in a DataFrame based on a condition in Python for Power BI?
    df[df['column'] > value]
    df.where('column' > value)
    df.select('column' > value)
    df.filter('column' > value)
    120s

Teachers give this quiz to your class