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. 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')

3. 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')

4. 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')

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

transformOutliers()

log1p()

outlierTransform()

powerTransform()

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

correlation()

corr()

correlate()

calculateCorrelation()

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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')

13. 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)

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

toNumpyArray()

asArray()

toArray()

values()

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. 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()

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

merge()

concat()

combine()

join()

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. 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

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

renameColumn()

changeColumn()

rename()

modifyColumn()

21. 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

22. 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

23. 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)

24. 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)

25. 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)

26. 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

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

sort()

sortValues()

orderBy()

sortMultiple()

28. 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

29. 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

30. 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

Pandas MCQ Test 3