Data Science with Python Certification Data Analysis with Python 5

Question 1

Which pandas method efficiently applies a function along an axis of a DataFrame without using a Python loop?

Accepted Answer

df.apply(func, axis=1)

Answer

df.apply() applies a function along rows (axis=1) or columns (axis=0), leveraging pandas internals for efficiency.

Question 2

What is the effect of setting inplace=True in df.drop(columns=['col'], inplace=True)?

Accepted Answer

Modifies the original DataFrame and returns None

Answer

inplace=True mutates the DataFrame directly and returns None instead of a new object.

Question 3

Which NumPy function computes the cumulative sum of array elements along a given axis?

Accepted Answer

np.cumsum(arr)

Answer

np.cumsum() returns an array of the same shape where each element is the sum of all preceding elements along the axis.

Question 4

In pandas, what does the .resample('M').sum() operation require and what does it produce?

Accepted Answer

A DatetimeIndex; monthly totals

Answer

resample() requires a DatetimeIndex and resamples data to a new time frequency; 'M' groups by calendar month, summing each group.

Question 5

What is the primary difference between pd.merge() and pd.concat()?

Accepted Answer

merge() joins on key columns (SQL-style); concat() stacks DataFrames along an axis

Answer

pd.merge() aligns rows by common key columns, while pd.concat() simply stacks DataFrames without key-based alignment.

Data Science with Python Certification Practice Test