AnalystReps

Python practice: Datetime Intelligence with .dt Accessor

Chapter 24 · 8 questions · included in full access

  1. Question 1 · easy

    Parse dates with pd.to_datetime

    Load "sales.csv". Convert Date column using sales["Date"] = pd.to_datetime(sales["Date"]). Print str(sales["Date"].dtype)[:10]:

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  2. Question 2 · easy

    Extract calendar year with .dt.year

    Load "sales.csv". Convert Date to datetime, extract sales["Year"] = sales["Date"].dt.year, and print int(sales["Year"].iloc[0]):

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  3. Question 3 · easy

    Extract month name with .dt.month_name

    Load "sales.csv". Convert Date to datetime, extract month name using sales["Date"].dt.month_name(). Print the first row value:

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  4. Question 4 · easy

    Extract weekday name with .dt.day_name

    Load "sales.csv". Convert Date to datetime, extract weekday with sales["Date"].dt.day_name(). Print row 0 weekday:

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  5. Question 5 · easy

    Extract fiscal quarter with .dt.quarter

    Load "sales.csv". Convert Date to datetime, extract quarter with sales["Date"].dt.quarter. Print row 0 quarter:

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  6. Question 6 · easy

    Filter orders by date boundary

    Load "sales.csv". Convert Date to datetime. Filter orders placed on or after "2026-02-01". Print matching order count:

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  7. Question 7 · medium

    Calculate invoice lead time days

    Given df = pd.DataFrame({"Order": pd.to_datetime(["2026-04-01"]), "Ship": pd.to_datetime(["2026-04-06"])}), calculate lead_days = (df["Ship"] - df["Order"]).dt.days. Print int(lead_days.iloc[0]):

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  8. Question 8 · medium

    Tally orders by day of week

    Load "sales.csv". Convert Date to datetime, compute sales["Date"].dt.day_name().value_counts(), and print the Wednesday order count:

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