Data Science with Python Pandas DataFrame Operations Questions and Answers

Question 1

Given a Pandas DataFrame named `sales`, which of the following code snippets correctly calculates the total sales for each 'Region' by summing the 'Sales' column?

Accepted Answer

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

Answer

sales.sum('Sales').by('Region')

Answer

sales.aggregate('Sales').on('Region')

Answer

sales.pivot(index='Region', values='Sales', aggfunc='sum')

Question 2

A data scientist has a DataFrame `df` with missing values in the 'Age' column. They want to fill these missing values with the mean age of all non-missing entries in that column. Which of the following is the correct way to achieve this in place?

Accepted Answer

df['Age'].fillna(df['Age'].mean(), inplace=True)

Answer

df['Age'].fill(df['Age'].mean())

Answer

df['Age'].replace(np.nan, df['Age'].mean())

Answer

df.loc[df['Age'].isnull(), 'Age'] = df['Age'].mean(skipna=True)

Question 3

You have two DataFrames, `df1` and `df2`, with a common column 'employee_id'. You want to create a new DataFrame that contains only the rows where 'employee_id' exists in *both* `df1` and `df2`. Which merge operation should you use?

Accepted Answer

pd.merge(df1, df2, on='employee_id', how='inner')

Answer

pd.merge(df1, df2, on='employee_id', how='outer')

Answer

pd.merge(df1, df2, on='employee_id', how='left')

Answer

pd.concat([df1, df2], on='employee_id')

Question 4

What is the primary purpose of the `df.loc[]` accessor in a Pandas DataFrame?

Accepted Answer

To select data by label-based indexing.

Answer

To select data by integer-based position.

Answer

To perform complex mathematical computations.

Answer

To select a single value using its row and column number.

Question 5

A data analyst is working with a DataFrame `market_data` and needs to create a new column called 'ProfitMargin'. This column should be calculated as `(market_data['Revenue'] - market_data['Cost']) / market_data['Revenue']`. Which of the following is the most idiomatic Pandas way to add this new column?

Accepted Answer

market_data['ProfitMargin'] = (market_data['Revenue'] - market_data['Cost']) / market_data['Revenue']

Answer

market_data.apply(lambda row: (row['Revenue'] - row['Cost']) / row['Revenue'], axis=1)

Answer

for index, row in market_data.iterrows(): market_data.loc[index, 'ProfitMargin'] = (row['Revenue'] - row['Cost']) / row['Revenue']

Answer

market_data.insert(column='ProfitMargin', value=(market_data.Revenue - market_data.Cost) / market_data.Revenue)

Question 6

Which of the following methods would you use to remove duplicate rows from a DataFrame `df` based on the values in the 'email' column, keeping the first occurrence?

Accepted Answer

df.drop_duplicates(subset=['email'], keep='first')

Answer

df.unique(subset=['email'])

Answer

df.delete_duplicates(on='email')

Answer

df.remove(duplicates=True, column='email')