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

1. What does the `melt()` function in Pandas allow you to do?

Transform a DataFrame into molten lava

Unpivot a DataFrame

Melt columns into a single column

Convert categorical data to numerical

2. What does the `loc` function in Pandas allow you to do?

Filter rows by index

Select columns by name

Both A and B

None of the above

3. How can you handle outliers in a Pandas DataFrame?

removeOutliers()

handleOutliers()

dropOutliers()

clipOutliers()

4. 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

5. What does the `pd.to_datetime()` function in Pandas do?

Convert a DataFrame to datetime format

Convert a column to datetime format

Extract time information from a column

Check if a date is in the correct format

6. Which method is used to fill missing values in a Pandas DataFrame?

fill()

replace()

fillna()

fix()

7. 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')

8. What does the Pandas function 'df.isnull()' return?

Count of null values in each column

A boolean DataFrame indicating null values

The sum of null values in the DataFrame

A list of columns with null values

9. How can you efficiently handle imbalanced data in a classification problem using Pandas?

df.balance_class(target='class_column')

oversample(df, target='class_column')

undersample(df, target='class_column')

resampleClass(df, target='class_column')

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

df.head()

df.preview()

df.show_first()

df.display_first()

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

DataFrame

Series

Array

List

12. What does the Pandas function 'df.drop()' do?

Deletes the specified column

Drops rows with null values

Removes the last row in the DataFrame

Drops specified labels from rows or columns

13. In Pandas, what is the purpose of the `nsmallest()` function?

Find the smallest values in a column

Extract the first n smallest values in a column

Select the smallest elements along a specified axis

Retrieve the indices of the n smallest values

14. How can you perform element-wise addition of two Pandas Series 's1' and 's2'?

s1 + s2

add(s1, s2)

s1.add(s2)

sum(s1, s2)

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

transformOutliers()

log1p()

outlierTransform()

powerTransform()

16. 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

17. What is the purpose of the `resample()` method in Pandas?

Reshape a DataFrame

Convert time series to desired frequency

Aggregate data based on a time interval

Calculate time series entropy

18. What does the `diff()` function in Pandas allow you to calculate?

Difference between consecutive elements in a column

Differentiate a DataFrame

Count the number of differences in each column

Calculate differentiated values based on a specified interval

19. In Pandas, how can you calculate the percentage change in a DataFrame?

percentageChange()

changePercentage()

pct_change()

calculateChange()

20. 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')

21. In Pandas, how can you efficiently handle heavy-tailed distributions by applying a power transformation?

heavyTailTransform(df, power=0.5)

df.apply_power_transform(power=0.5)

powerTransform(df, power=0.5)

boxcox(df, lmbda=0.5)

22. What does the Pandas function 'df.info()' provide?

Summary statistics of the DataFrame

Information about null values and data types

A concise summary of the DataFrame

Details about the unique values in each column

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

rotate()

pivot()

transpose()

reshape()

24. What is the purpose of the `at_time()` method in Pandas?

Select data at a specific time

Calculate absolute time difference

Filter rows based on time

Convert time to string format

25. In Pandas, what does the `explode()` function do?

Remove duplicate values

Expand lists into separate rows

Explode the entire DataFrame

Filter rows based on a condition

26. 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

27. 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()

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

correlation()

corr()

correlate()

calculateCorrelation()

29. How do you perform multi-index sorting in a Pandas DataFrame?

sort_index()

multiSort()

sort_values(multiindex=True)

sortMultiIndex()

30. How can you drop rows with missing values in a Pandas DataFrame 'df'?

df.remove_missing()

df.drop_na()

df.dropna()

df.exclude_missing()

31. In Pandas, how can you sort a DataFrame based on multiple columns?

sort()

sortValues()

orderBy()

sortMultiple()

32. How can you add a new column 'new_column' with values 1, 2, 3, ... N to a Pandas DataFrame 'df'?

df.add_column('new_column', range(1, N+1))

df.new_column = range(1, N+1)

df['new_column'] = range(1, N+1)

df.append_column('new_column', range(1, N+1))

33. 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'])

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

elementwise()

applyElementwise()

mathOps()

add()

35. What does the Pandas function 'df.fillna()' do?

Fills null values with the mean of the column

Replaces all values with NaN

Fills null values with a specified value

Deletes rows with null values

36. What is the purpose of the Pandas function 'df.iloc[]'?

Selecting rows and columns by labels

Accessing a group of rows and columns by integer positions

Filtering rows based on a condition

Sorting the DataFrame by specified columns

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

merge()

concat()

combine()

join()

38. How can you rename a column 'old_name' to 'new_name' in a Pandas DataFrame 'df'?

df.rename_column('old_name', 'new_name')

df.rename(columns={'old_name': 'new_name'})

df.column('old_name').rename('new_name')

df.change_column_name('old_name', 'new_name')

39. In Pandas, what does the `interpolate()` function do?

Insert NaN values

Fill missing values based on linear interpolation

Interpolate categorical data

Calculate interpolation of columns

40. 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

Pandas MCQ Test 2