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

1. 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

2. 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

3. 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)

4. 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

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 `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

7. 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()

8. How do you check the data types of columns in a Pandas DataFrame?

dtypes()

dataTypes()

columnTypes()

checkTypes()

9. 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

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

transformOutliers()

log1p()

outlierTransform()

powerTransform()

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

rotate()

pivot()

transpose()

reshape()

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

toNumpyArray()

asArray()

toArray()

values()

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

dropna()

removeNaN()

deleteMissing()

cleanData()

14. 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

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

removeOutliers()

handleOutliers()

dropOutliers()

clipOutliers()

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

sort_index()

multiSort()

sort_values(multiindex=True)

sortMultiIndex()

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

correlation()

corr()

correlate()

calculateCorrelation()

18. 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

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

selectColumns()

columns()

select()

loc[]

20. 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

21. 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'])

22. 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

23. 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'])

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

sort()

sortValues()

orderBy()

sortMultiple()

25. 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

26. 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)

27. 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')

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

fill()

replace()

fillna()

fix()

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 do you rename a column in a Pandas DataFrame?

renameColumn()

changeColumn()

rename()

modifyColumn()

31. 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

32. 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

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

dropDuplicates()

removeDuplicates()

drop()

excludeDuplicates()

34. 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

35. 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')

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

summary()

describe()

stats()

summarize()

37. 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

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

merge()

concat()

combine()

join()

39. 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))

40. 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')

41. How can you sort a Pandas DataFrame 'df' by the values in the column 'column_name' in ascending order?

df.sort('column_name', ascending=True)

df.order_by('column_name', ascending=True)

df.sort_values('column_name', ascending=True)

df.arrange('column_name', ascending=True)

42. 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

43. 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

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

df.head()

df.preview()

df.show_first()

df.display_first()

45. 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

46. 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')

47. 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

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

elementwise()

applyElementwise()

mathOps()

add()

49. 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)

50. 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)

Pandas MCQ Test 1