DA0-002 Data Acquisition and Preparation Practice Question
An analyst wants to use Python (pandas) to compute the average sales amount per region from a DataFrame 'df' with columns 'region' and 'sales'. Which TWO pandas operations are needed? (Select TWO).
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
df.pivot_table(index='region', values='sales', aggfunc='mean')
To compute average per group, you can use groupby() followed by mean(), or pivot_table() with aggfunc='mean'. merge() combines DataFrames, apply() can be used but is less direct, and fillna() handles missing values.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
df.fillna(0)
Why it's wrong here
Fillna handles missing values, not aggregation.
- ✓
df.pivot_table(index='region', values='sales', aggfunc='mean')
Why this is correct
Pivot table with mean aggregation.
- ✗
df['sales'].apply(np.sqrt)
Why it's wrong here
Apply with sqrt is not for aggregation.
- ✗
df.merge(df2, on='region')
Why it's wrong here
Merge is for joining DataFrames, not aggregation.
- ✓
df.groupby('region')['sales'].mean()
Why this is correct
Groups by region and computes mean sales.
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