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1). How can you perform a left join between two DataFrames 'df1' and 'df2' on a column 'key'??

A) df1.merge(df2, on='key', how='left')

B) df1.join(df2, on='key', how='left')

C) df1.concat(df2, on='key', how='left')

D) df1.append(df2, on='key', how='left')

2). Which method is used to change the data type of a DataFrame column??

A) astype()

B) convert()

C) change_type()

D) cast()

3). How can you calculate the rolling mean of a column 'sales' with a window size of 3??

A) df['sales'].rolling(window=3).mean()

B) df['sales'].rolling(window=3).average()

C) df['sales'].mean(window=3)

D) df['sales'].average(window=3)

4). How do you drop rows with missing values in a DataFrame??

A) dropna()

B) remove_na()

C) drop_missing()

D) discard_na()

5). Which method is used to get the unique values in a DataFrame column??

A) unique()

B) distinct()

C) get_unique()

D) unique_values()

6). How can you group a DataFrame by a column 'category' and calculate the mean of 'value' for each group??

A) df.groupby('category')['value'].mean()

B) df.group_by('category').mean('value')

C) df.groupby('category').mean('value')

D) df.aggregate('category', 'mean', 'value')

7). What method would you use to pivot a DataFrame with columns 'index', 'columns', and 'values'??

A) pivot()

B) pivot_table()

C) reshape()

D) transform()

8). How can you concatenate two DataFrames 'df1' and 'df2' along columns??

A) pd.concat([df1, df2], axis=1)

B) pd.merge([df1, df2], axis=1)

C) pd.append([df1, df2], axis=1)

D) pd.join([df1, df2], axis=1)

9). How can you handle missing values by filling them with a specified value in a DataFrame??

A) fillna(value)

B) replace_na(value)

C) impute(value)

D) setna(value)

10). Which method would you use to get a summary of the DataFrame's columns and their data types??

A) info()

B) describe()

C) summary()

D) columns()

11). How can you apply a function to each row of a DataFrame??

A) apply(func, axis=1)

B) map(func, axis=1)

C) apply_func(func, axis=1)

D) transform(func, axis=1)

12). What method is used to remove a column from a DataFrame??

A) drop()

B) remove()

C) discard()

D) delete()

13). How can you set a column 'date' as the index of a DataFrame??

A) df.set_index('date')

B) df.change_index('date')

C) df.index('date')

D) df.index_col('date')

14). How do you calculate the cumulative sum of a DataFrame column 'amount'??

A) cumsum()

B) cum_sum()

C) total_sum()

D) accumulate()

15). How can you check for duplicates in a DataFrame??

A) duplicated()

B) check_duplicates()

C) find_duplicates()

D) drop_duplicates()

16). What function is used to merge DataFrames based on a common index??

A) join()

B) merge()

C) combine()

D) concatenate()

17). How do you save a DataFrame to a CSV file??

A) to_csv()

B) save_csv()

C) export_csv()

D) write_csv()

18). How can you group data by multiple columns and calculate the sum of another column??

A) df.groupby(['col1', 'col2'])['value'].sum()

B) df.group_by(['col1', 'col2']).sum('value')

C) df.groupby(['col1', 'col2']).aggregate('value', 'sum')

D) df.aggregate(['col1', 'col2'], 'sum', 'value')

19). How can you pivot a DataFrame with 'index' and 'values' but no 'columns'??

A) pivot(index='index', values='values')

B) pivot_table(index='index', values='values')

C) reshape(index='index', values='values')

D) transform(index='index', values='values')

20). What method is used to sort a DataFrame by multiple columns??

A) sort_values()

B) order_by()

C) sort()

D) arrange()

21). How do you extract a substring from a DataFrame column 'text'??

A) str.slice()

B) str.extract()

C) str.substring()

D) str.sub()

22). How can you compute the correlation matrix of a DataFrame??

A) corr()

B) correlation()

C) compute_corr()

D) correlation_matrix()

23). What method is used to reset the index of a DataFrame and move the current index to a column??

A) reset_index()

B) set_index()

C) reindex()

D) index_reset()

24). How can you filter rows where the value of 'score' is between 50 and 100??

A) df[(df['score'] >= 50) & (df['score'] <= 100)]

B) df[df['score'].between(50, 100)]

C) df[df['score'] > 50 & df['score'] < 100]

D) df.query('50 <= score <= 100')

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