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Online Test
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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