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

1. 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

2. 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)

3. 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

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

renameColumn()

changeColumn()

rename()

modifyColumn()

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

dropna()

removeNaN()

deleteMissing()

cleanData()

6. 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

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

rotate()

pivot()

transpose()

reshape()

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

toNumpyArray()

asArray()

toArray()

values()

9. 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

10. 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))

11. 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'])

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

sort()

sortValues()

orderBy()

sortMultiple()

13. 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)

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

dtypes()

dataTypes()

columnTypes()

checkTypes()

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

elementwise()

applyElementwise()

mathOps()

add()

16. 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)

17. 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')

18. 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

19. 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

20. 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()

Pandas MCQ Test 4