Pandas Quiz: Test Your Knowledge

Pandas Quiz: Test Your Knowledge#

Part 1: Data Creation#

  1. What function would you use to create a one-dimensional labeled array in Pandas?

    • a) pd.DataFrame()

    • b) pd.Series()

    • c) pd.read_csv()

    • d) pd.to_excel()

  2. Which function creates a two-dimensional labeled table in Pandas?

    • a) pd.DataFrame()

    • b) pd.Series()

    • c) pd.pivot()

    • d) pd.melt()

Part 2: Data Input/Output (I/O)#

  1. Which function reads a CSV file into a DataFrame?

    • a) pd.read_csv()

    • b) pd.read_sql()

    • c) pd.read_excel()

    • d) pd.to_csv()

  2. If you need to export a DataFrame to an Excel file, which function should you use?

    • a) pd.read_excel()

    • b) pd.to_csv()

    • c) pd.to_excel()

    • d) pd.read_csv()

Part 3: Data Inspection#

  1. Which function provides a summary of a DataFrame, including data types and non-null counts?

    • a) df.head()

    • b) df.describe()

    • c) df.info()

    • d) df.shape

  2. How would you return the dimensions (number of rows and columns) of a DataFrame?

    • a) df.describe()

    • b) df.shape

    • c) df.dtypes

    • d) df.head()

Part 4: Data Selection#

  1. What function allows you to select rows or columns based on index positions?

    • a) df.loc[]

    • b) df.iloc[]

    • c) df['column_name']

    • d) df.groupby()

  2. How would you access multiple columns (e.g., ‘col1’ and ‘col2’) in a DataFrame?

    • a) df[['col1', 'col2']]

    • b) df['col1']

    • c) df.loc[]

    • d) df.iloc[]

Part 5: Data Cleaning#

  1. Which function detects missing values in a DataFrame?

    • a) df.dropna()

    • b) df.notnull()

    • c) df.isnull()

    • d) df.fillna()

  2. What function replaces missing values with a specified value?

    • a) df.dropna()

    • b) df.fillna(value)

    • c) df.rename()

    • d) df.replace()

Part 6: Data Transformation#

  1. To sort rows of a DataFrame based on a specific column, which function would you use?

    • a) df.groupby()

    • b) df.sort_index()

    • c) df.sort_values(by='column')

    • d) df.apply()

  2. Which function groups data for aggregation or transformation?

    • a) df.pivot_table()

    • b) df.groupby()

    • c) df.astype()

    • d) df.corr()

Part 7: Data Aggregation#

  1. Which function computes the mean of numeric columns in a DataFrame?

    • a) df.sum()

    • b) df.mean()

    • c) df.mode()

    • d) df.median()

  2. How would you calculate the cumulative sum of numeric values in a DataFrame?

    • a) df.cumsum()

    • b) df.sum()

    • c) df.mean()

    • d) df.count()

Part 8: Merging and Reshaping#

  1. If you need to combine two DataFrames based on a common column, which function should you use?

    • a) pd.concat()

    • b) pd.merge()

    • c) df.join()

    • d) df.melt()

  2. What function converts a DataFrame from wide format to long format?

    • a) pd.pivot()

    • b) df.melt()

    • c) df.pivot_table()

    • d) pd.concat()

Part 9: Statistical Operations#

  1. Which function calculates the correlation between numeric columns?

    • a) df.cov()

    • b) df.var()

    • c) df.corr()

    • d) df.std()

  2. How would you compute the variance of numeric columns?

    • a) df.corr()

    • b) df.var()

    • c) df.cumsum()

    • d) df.median()

Part 10: Time Series Functions#

  1. What function converts data into a datetime object in Pandas?

    • a) pd.to_datetime()

    • b) df.resample()

    • c) df.shift()

    • d) df.groupby()

  2. Which function aggregates time series data in Pandas?

    • a) df.shift()

    • b) pd.to_datetime()

    • c) df.resample()

    • d) df.groupby()

Answer Key (for self-assessment):

  1. b | 2. a | 3. a | 4. c | 5. c | 6. b | 7. b | 8. a | 9. c | 10. b | 11. c | 12. b | 13. b | 14. a | 15. b | 16. b | 17. c | 18. b | 19. a | 20. c

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