Innovative Pandas Test - 73+ MCQs: Pandas test: Evaluate Your Data Manipulation Skills with Pandas Questions and Answers

1. How can you filter rows in a Pandas DataFrame 'df' where the column 'column_name' is equal to 10?

df.filter(column_name=10)

df.where(column_name==10)

df[df['column_name'] == 10]

df.select_rows(column_name=10)

2. What does the `groupby()` function in Pandas allow you to do?

Sort DataFrame by a column

Group data based on a column

Calculate cumulative sum

Filter rows by a condition

3. How can you check the first few rows of a Pandas DataFrame 'df'?

df.head()

df.preview()

df.show_first()

df.display_first()

4. What is the purpose of the `ffill()` and `bfill()` functions in Pandas?

Calculate forward and backward fill for numerical columns

Fill missing values forward and backward in a column

Perform fast fill operations on a DataFrame

Filter rows based on forward and backward conditions

5. What is the purpose of the `cummin()` function in Pandas?

Calculate cumulative minimum along the specified axis

Count occurrences of a value

Convert values to cumulative sums

Count cumulative occurrences of each value

6. In Pandas, what does the `str.replace()` method do?

Remove special characters

Replace values in a string column

Replace NaN values

Check if a string contains a substring

7. How can you efficiently aggregate and count the occurrence of unique combinations in two columns of a Pandas DataFrame?

df.groupby(['col1', 'col2']).count()

count_unique_combinations(df, 'col1', 'col2')

aggregateUniqueCombinations(df, 'col1', 'col2')

df.groupby(['col1', 'col2']).agg('count')

8. Which Pandas function is used to merge two DataFrames based on a common column?

merge()

concat()

combine()

join()

9. How can you find the number of unique values in a column 'column_name' in a Pandas DataFrame 'df'?

df.unique_count('column_name')

df.count_unique('column_name')

df['column_name'].nunique()

df.calculate_unique('column_name')

10. How can you efficiently handle missing values in a Pandas DataFrame considering both forward and backward filling?

fillna(method='ffill', limit=1)

forwardBackwardFill()

fillna(method='bfill', limit=1)

handleMissingValues('fb')

11. What does the `pd.cut()` function in Pandas allow you to do?

Create a new DataFrame

Cut a DataFrame into specified bins

Calculate cumulative sum

Count unique values in a column

12. What is the purpose of the `between_time()` method in Pandas?

Filter rows between specified times

Calculate time duration

Convert time to numeric values

Count occurrences between times

13. What is the primary data structure in Pandas for handling one-dimensional labeled data?

DataFrame

Series

Array

List

14. What is the purpose of the `pivot_table()` function in Pandas?

Create a pivot chart

Create a pivot table

Transpose a DataFrame

Sort values in a DataFrame

15. What does the Pandas function 'df['column_name'].unique()' return?

Unique values in the specified column

Count of unique values in the specified column

A boolean indicating if the column has only unique values

List of all column names in the DataFrame

16. How can you efficiently calculate the percentage change in a Pandas DataFrame for multiple columns?

pct_change(columns=['col1', 'col2'])

percentageChange(['col1', 'col2'])

df.pct_change().select(['col1', 'col2'])

df[['col1', 'col2']].pct_change()

17. In Pandas, how can you efficiently handle outliers by transforming them based on a power transformation?

transformOutliers()

log1p()

outlierTransform()

powerTransform()

18. Which Pandas method is used to pivot a DataFrame based on column values?

rotate()

pivot()

transpose()

reshape()

19. What is the purpose of the `apply()` function in Pandas?

Apply a function along the axis

Append data to a DataFrame

Adjust column width

Aggregate data

20. In Pandas, what does the `isin()` method do?

Checks if a value is NaN

Filters rows based on a condition

Identifies duplicate values

Checks if a value is in a list

21. How can you efficiently reshape a Pandas DataFrame by converting column values into separate columns with binary indicators?

pivot_table()

unstack()

get_dummies()

reshape_binary()

22. In Pandas, how can you efficiently encode categorical variables using one-hot encoding with a specified prefix for column names?

df.onehot_encode(prefix='prefix')

encode_categorical(df, prefix='prefix')

pd.onehot(df, prefix='prefix')

pd.get_dummies(df, prefix='prefix')

oneHotEncode(df, prefix='prefix')

23. What does the `filter()` function in Pandas allow you to do?

Filter rows based on a condition

Filter columns based on a condition

Filter rows and columns simultaneously

Filter DataFrame by excluding specific values

24. Which Pandas function is used to perform element-wise mathematical operations on two DataFrames?

elementwise()

applyElementwise()

mathOps()

add()

25. How can you select multiple columns 'col1' and 'col2' from a Pandas DataFrame 'df'?

df[['col1', 'col2']]

df.select('col1', 'col2')

df.get_columns(['col1', 'col2'])

df.columns(['col1', 'col2'])

26. In Pandas, what method is used to drop missing values from a DataFrame?

dropna()

removeNaN()

deleteMissing()

cleanData()

27. How can you calculate the median of a specific column 'column_name' in a Pandas DataFrame 'df'?

df.median(column_name)

df.calculate_median('column_name')

df['column_name'].median()

df.find_median(column_name)

28. What is the purpose of the Pandas function 'df.drop_duplicates()'?

Removes rows with duplicate values

Deletes columns with duplicate names

Drops the last occurrence of duplicate rows

Drops all occurrences of duplicate rows

29. How do you efficiently handle time zone conversion in a Pandas DataFrame?

convertTimeZone()

df.convert_timezone()

tz_convert()

timeZoneConversion(df)

30. Which Pandas method is used to calculate the correlation between columns in a DataFrame?

correlation()

corr()

correlate()

calculateCorrelation()

Pandas MCQ Test 3