Pandas Advanced Operations

df.iloc['column_name']

df['column_name']

df.select('column_name')

df.column_name

Value at index 2 in the 'Age' column

Value at row 2 and column 'Age'

Value at index 2 in the DataFrame

Value at row 2 and column index 2

df[df['Age'] > 30]

df.select_rows('Age' > 30)

df.query('Age > 30')

Both a and c

df.group()

df.aggregate()

df.groupby()

df.group_by()

Groups data by 'Category' and finds the mean of 'Price' for each category

Groups data by 'Price' and calculates the mean for 'Category'

Filters data based on the 'Price' column

Creates a new column 'mean_price' in the DataFrame

df.groupby('Region')['Sales'].sum()

df['Sales'].groupby('Region').sum()

df.groupby('Region').sum()['Sales']

Both a and c

Concatenates two DataFrames

Merges two DataFrames based on the 'ID' column

Joins two DataFrames with an outer join

Joins two DataFrames with a left join

Inner join

Left join

Right join

All of the above

pd.merge(df1, df2, on='ID')

pd.merge(df1, df2, left_on='ID', right_on='UserID')

df1.merge(df2, left='ID', right='UserID')

Both b and c

Sorts the DataFrame by 'Age' in ascending order

Sorts the DataFrame by 'Age' in descending order

Sorts the DataFrame by 'Age' in alphabetical order

None of the above