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

The `groupby()` method is used to split the DataFrame into groups based on some criteria, in this case, the 'Region' column. Then, `['Sales']` selects the 'Sales' column from each group, and `.sum()` is an aggregation function that calculates the sum of the 'Sales' for each region.

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

The `fillna()` method is the standard way to replace missing values (NaN) in a Pandas Series or DataFrame. The first argument is the value to use for filling, and setting `inplace=True` modifies the DataFrame directly without needing to reassign it.

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

An 'inner' merge returns only the records that have matching keys in both DataFrames. This is equivalent to the intersection of the keys. An 'outer' merge would include all rows from both, a 'left' merge would include all rows from `df1` and matched rows from `df2`.

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

The `.loc[]` accessor is used for label-based indexing, which means you can select rows and columns using their index labels and column names. In contrast, `.iloc[]` is used for integer-position based indexing.

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

Pandas supports vectorized operations, which are highly efficient. Directly performing arithmetic operations on columns (which are Pandas Series) is the most direct and performant way to create a new column based on existing ones. Using `.apply()` or iterating with `iterrows()` is much less efficient for this type of calculation.

Data Science with Python Certification Practice Test

Data Science with Python Certification Practice Test

Data Science with Python Pandas DataFrame Operations Questions and Answers