DA0-002 Data Acquisition and Preparation Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  import csv; df = csv.read('sales.csv')

  Why it's wrong here

  The csv module has no read function returning a DataFrame; it exposes reader and DictReader yielding rows, so this line raises an AttributeError. It is tempting because the csv module genuinely parses CSV files, and would be correct for lightweight row-by-row processing without pandas.

• ✗

  import pandas as pd; df = pd.read('sales.csv')

  Why it's wrong here

  pandas exposes read_csv, not read; pd.read does not exist, so this raises an AttributeError rather than loading the file. It is tempting because the import and DataFrame assignment are otherwise correct, and read_csv is the genuine pandas function for this task.

• ✗

  import numpy as np; df = np.read_csv('sales.csv')

  Why it's wrong here

  NumPy provides no read_csv function; genfromtxt and loadtxt handle delimited text but return arrays, not DataFrames. It is tempting because NumPy underpins pandas and is genuinely used for numeric array loading, which would suit pure numerical computation without labelled columns.

• ✓

  import pandas as pd; df = pd.read_csv('sales.csv')

  Why this is correct

  `pd.read_csv('sales.csv')` is pandas' dedicated CSV parser, returning a DataFrame directly from the file path. The import aliases pandas as `pd`, satisfying the stem's requirement to read `sales.csv` into a DataFrame in one line, with no extra arguments needed for a standard comma-delimited file.