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 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

3. 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')

4. How do you efficiently apply a function to elements in a specific column of a Pandas DataFrame?

applyFunction(df, 'col', func)

df['col'].apply(func)

transformColumn(df, 'col', func)

func.apply(df['col'])

5. 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

6. 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

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

df.head()

df.preview()

df.show_first()

df.display_first()

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

toNumpyArray()

asArray()

toArray()

values()

9. 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

10. 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'])

11. 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')

12. 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')

13. In Pandas, how do you create a new column based on the maximum value from multiple columns?

df['new_column'] = df.max(axis=1)

df['new_column'] = max(df.columns)

df['new_column'] = df.max()

df['new_column'] = max(df)

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

rotate()

pivot()

transpose()

reshape()

15. 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

16. 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

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

renameColumn()

changeColumn()

rename()

modifyColumn()

18. 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)

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

sort()

sortValues()

orderBy()

sortMultiple()

20. 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))

21. 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

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 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

24. 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

25. 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

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

selectColumns()

columns()

select()

loc[]

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

summary()

describe()

stats()

summarize()

28. 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

29. What does the Pandas function 'df.describe()' provide?

Summary statistics of numerical columns

Information about non-null values

A concise summary of the DataFrame

Details about missing values

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

dropDuplicates()

removeDuplicates()

drop()

excludeDuplicates()

31. 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)

32. 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

33. 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

34. 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

35. 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)

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

convertTimeZone()

df.convert_timezone()

tz_convert()

timeZoneConversion(df)

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

transformOutliers()

log1p()

outlierTransform()

powerTransform()

38. 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

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

dropna()

removeNaN()

deleteMissing()

cleanData()

40. 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

Pandas MCQ Test 2