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

1. 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

2. 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

3. 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

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

renameColumn()

changeColumn()

rename()

modifyColumn()

5. 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

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

transformOutliers()

log1p()

outlierTransform()

powerTransform()

7. 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

8. 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

9. 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)

10. 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

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

dtypes()

dataTypes()

columnTypes()

checkTypes()

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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)

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

df.remove_missing()

df.drop_na()

df.dropna()

df.exclude_missing()

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

removeOutliers()

handleOutliers()

dropOutliers()

clipOutliers()

20. 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

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

fill()

replace()

fillna()

fix()

22. 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

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

elementwise()

applyElementwise()

mathOps()

add()

24. 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')

25. 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

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

merge()

concat()

combine()

join()

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

percentageChange()

changePercentage()

pct_change()

calculateChange()

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

DataFrame

Series

Array

List

29. 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)

30. 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)

31. 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

32. 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')

33. What is the purpose of the `str.contains()` method in Pandas?

Check if a column contains a specific value

Concatenate strings in a column

Count occurrences of a substring

Check if a string column contains a substring

34. 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

35. 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')

36. 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()

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

convertTimeZone()

df.convert_timezone()

tz_convert()

timeZoneConversion(df)

38. 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')

39. 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

40. 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

41. 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')

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 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)

44. 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'])

45. 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

46. 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')

47. 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

48. 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))

49. 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

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

df.head()

df.preview()

df.show_first()

df.display_first()

Pandas MCQ Test 1