AnalystReps

Python practice: Series & DataFrame Anatomy

Chapter 19 · 8 questions · included in full access

  1. Question 1 · easy

    Load sales dataset and print shape

    Import pandas as pd. Load "sales.csv" into sales using pd.read_csv("sales.csv"). Print sales.shape:

    Solve it in your browser

  2. Question 2 · easy

    Inspect first rows with .head()

    Load "sales.csv". Print the row count of sales.head(3) using len(sales.head(3)):

    Solve it in your browser

  3. Question 3 · easy

    Select single column as Series

    Load "sales.csv". Select the "Units" column into units_col and print its sum using units_col.sum():

    Solve it in your browser

  4. Question 4 · easy

    Select multiple columns as DataFrame

    Load "sales.csv". Select ["OrderID", "Rep", "Revenue"] into sub_df and print list(sub_df.columns):

    Solve it in your browser

  5. Question 5 · easy

    Count distinct regions with nunique

    Load "sales.csv". Print the number of unique regions using sales["Region"].nunique():

    Solve it in your browser

  6. Question 6 · easy

    Summary statistics with describe

    Load "sales.csv". Compute summary statistics on "Revenue": print round(sales["Revenue"].mean(), 2):

    Solve it in your browser

  7. Question 7 · easy

    Value counts of categories

    Load "sales.csv". Get order frequency per category using sales["Category"].value_counts(). Print counts["Sheets"]:

    Solve it in your browser

  8. Question 8 · easy

    DataFrame concept check

    In Pandas, what is the key difference between a DataFrame and a Series?

    Solve it in your browser

These questions are part of full access.

The first chapters of every track are free. Full access is a one-time payment.

See pricing