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

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

rotate()

pivot()

transpose()

reshape()

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

dropna()

removeNaN()

deleteMissing()

cleanData()

3. 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

4. Which Pandas function is used to calculate summary statistics of a DataFrame?

summary()

describe()

stats()

summarize()

5. 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

6. 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

7. 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

8. 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

9. 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')

10. 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

11. 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)

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

removeOutliers()

handleOutliers()

dropOutliers()

clipOutliers()

13. 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

14. 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)

15. 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()

16. 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

17. 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')

18. 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')

19. In Pandas, how can you select multiple columns from a DataFrame?

selectColumns()

columns()

select()

loc[]

20. How do you rename a column in a Pandas DataFrame?

renameColumn()

changeColumn()

rename()

modifyColumn()

21. 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

22. What does the Pandas function 'df.groupby()' allow you to do?

Sort the DataFrame by a specific column

Group data based on a column's unique values

Apply a function to each element of the DataFrame

Concatenate two DataFrames

23. In Pandas, how do you efficiently calculate a rolling window average for a specific column?

df['col'].rolling(window=3).average()

df['col'].rolling(window=3).mean()

averageRollingWindow(df['col'], window=3)

meanRollingWindow(df['col'], window=3)

24. What does the `mode()` function in Pandas calculate?

Mode of a DataFrame

Mode along the specified axis

Mode of each column

Mode of each row

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

percentageChange()

changePercentage()

pct_change()

calculateChange()

26. 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

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

convertTimeZone()

df.convert_timezone()

tz_convert()

timeZoneConversion(df)

28. 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

29. 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

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

DataFrame

Series

Array

List

31. 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

32. 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

33. 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

34. 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

35. 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))

36. How do you efficiently calculate the pairwise correlation matrix for selected columns in a Pandas DataFrame?

correlation_matrix(df[['col1', 'col2']])

pairwise_corr(df[['col1', 'col2']])

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

calculateCorrelationMatrix(df, columns=['col1', 'col2'])

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

elementwise()

applyElementwise()

mathOps()

add()

38. 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

39. How can you select a specific column 'column_name' from a Pandas DataFrame 'df'?

df.column_name

df['column_name']

df.select_column('column_name')

df.get_column('column_name')

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

fill()

replace()

fillna()

fix()

41. 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)

42. 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

43. How can you handle duplicate rows in a Pandas DataFrame?

dropDuplicates()

removeDuplicates()

drop()

excludeDuplicates()

44. 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

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

transformOutliers()

log1p()

outlierTransform()

powerTransform()

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

sort()

sortValues()

orderBy()

sortMultiple()

47. 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

48. How can you convert a Pandas DataFrame to a NumPy array?

toNumpyArray()

asArray()

toArray()

values()

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

sort_index()

multiSort()

sort_values(multiindex=True)

sortMultiIndex()

50. In Pandas, what is the purpose of the `nunique()` function?

Calculate the number of unique elements

Count occurrences of each unique element

Count unique values in each column

Find the nth unique element

Pandas MCQ Test 1