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

1. 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

2. 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')

3. 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)

4. 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

5. 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')

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

merge()

concat()

combine()

join()

7. 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)

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

toNumpyArray()

asArray()

toArray()

values()

9. 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

10. 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)

11. 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

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

dropna()

removeNaN()

deleteMissing()

cleanData()

13. 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))

14. 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

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

rotate()

pivot()

transpose()

reshape()

16. 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

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

dropDuplicates()

removeDuplicates()

drop()

excludeDuplicates()

18. 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

19. 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

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

renameColumn()

changeColumn()

rename()

modifyColumn()

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

dtypes()

dataTypes()

columnTypes()

checkTypes()

22. 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

23. 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)

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

removeOutliers()

handleOutliers()

dropOutliers()

clipOutliers()

25. 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')

26. 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

27. What is the purpose of the Pandas function 'df.mean()'?

Calculating the median of the DataFrame

Computing the mean of each column

Finding the mode of the DataFrame

Summing up all values in the DataFrame

28. 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)

29. 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

30. 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

31. 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

32. 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

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

summary()

describe()

stats()

summarize()

34. 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')

35. 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

36. 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()

37. 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

38. 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

39. 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

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

elementwise()

applyElementwise()

mathOps()

add()

41. 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

42. 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')

43. 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'])

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. 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'])

46. 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

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. 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')

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

sort_index()

multiSort()

sort_values(multiindex=True)

sortMultiIndex()

50. 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')

Pandas MCQ Test 1