Activity 1: Readiness self-check¶

A short diagnostic before the course starts: run a ready-made Python analysis of the Palmer Penguins data, build two Tableau charts on the Superstore sample, answer five questions, and submit one combined PDF on GAP.

Adapted from the DATA 300 GAP course, Pre-In Class Module, Activity 1. Written in our own words; follow GAP for the official wording.

1. What and why¶

You do not write new code for the Python part. The aim is to run code correctly, read its output and describe what you see.

2. Route A: Try JupyterLab and Tableau (as on GAP)¶

Part 1: Open a notebook¶

Open Try JupyterLab, start a Python (Pyodide) notebook and rename it with the file name GAP gives (your last name, the course code and "Self_Check").

Part 2: Run the script¶

Paste GAP's single code block into one cell and run it with Shift + Enter. In outline, the script:

1. installs seaborn into the browser session with %pip;
2. imports pandas, seaborn and matplotlib, and open_url from pyodide.http;
3. loads the penguins CSV from the seaborn-data repository on GitHub;
4. shows the first five rows and the summary statistics table (describe());
5. draws a scatter plot of body mass against flipper length, coloured by species;
6. draws a box plot of body mass by island, split by sex.

If the data load hangs, see Known pitfalls below.

Part 3: Answer the three Python questions¶

GAP asks about the summary statistics table (a value from the body mass column), the overall pattern in the scatter plot, and which species sits highest in it. Read each answer from your own output, write it in a sentence, and say which table or chart it comes from.

Part 4: Export the notebook¶

Check that both tables and both charts are visible, then File → Print → Save as PDF with Background graphics ticked.

Tableau: claim the licence and build two sheets¶

1. Claim the student licence, install Tableau Desktop and activate it (setup).
2. Open Sample – Superstore from Saved Data Sources.
3. Sheet 1: a horizontal bar chart of total Sales for each Category (Category on Rows, Sales on Columns). Sorting the bars is optional but helps.
4. Sheet 2: a map of Profit by state (double-click the state field, then drag Profit to Color on the Marks card). Blue means profit, orange means a loss.
5. Answer GAP's two Tableau questions: the category with the highest sales, and at least one state making a loss.
6. File → Print to PDF, Entire workbook, so both sheets are included.

3. Route B: offline in Anaconda JupyterLab¶

Use this route if Try JupyterLab will not load the data, or if you prefer to work on your own laptop. It follows the same steps as GAP's script but loads the bundled copy of the data, so there is nothing to install or download. Create a notebook inside the DATA300_GAP folder (next to data), paste this into one cell and run it. The questions and the Tableau part are exactly the same as in Route A.

# Route B for Activity 1: run offline in Anaconda JupyterLab.
# The penguins file comes from the course folder, so nothing is installed or downloaded.
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

pd.set_option("display.max_columns", None)   # show every column when printing

# Read the bundled penguins table
df = pd.read_csv("data/penguins.csv")
print("Rows and columns:", df.shape)

# A first look: five rows, then the numeric summary
print("\nFirst five rows:")
print(df.head())
print("\nSummary of the numeric columns:")
print(df.describe())

# Chart 1: scatter of flipper length (x) and body mass (y), coloured by species
plt.figure(figsize=(8, 5))
sns.scatterplot(data=df, x="flipper_length_mm", y="body_mass_g",
                hue="species", palette="deep")
plt.title("Flipper length and body mass of each penguin, by species")
plt.xlabel("Flipper length (mm)")
plt.ylabel("Body mass (g)")
plt.show()

# Chart 2: box plot of body mass on each island, one box per sex
plt.figure(figsize=(8, 5))
sns.boxplot(data=df, x="island", y="body_mass_g", hue="sex", palette="pastel")
plt.title("Spread of body mass on each island, female and male")
plt.xlabel("Island")
plt.ylabel("Body mass (g)")
plt.show()

In JupyterLab you may replace the print(df.head()) and print(df.describe()) lines with display(df.head()) and display(df.describe()) for nicer tables; the numbers are the same. Export with Kernel → Restart Kernel and Run All Cells, then File → Print → Save as PDF.

4. Checks you can make¶

These tell you whether your output is right without giving any answers away.

• The data has 344 rows and 7 columns: species, island, three body measurements, body_mass_g and sex.
• In the summary table, the count row is lower than 344 for the measurement columns. Some penguins have missing measurements, and describe() quietly leaves them out. That is expected.
• describe() lists only the four numeric columns. Species, island and sex do not appear, because they are text.
• The scatter plot has three colours, one per species, and a legend.
• The box plot has three islands along the bottom and two boxes (male and female) on each where both sexes were measured.
• Your answers say which table or chart each one comes from, and the body mass answer has its unit (grams).
• In Tableau, Sheet 1 has three bars (one per category) and Sheet 2 shows a map of the United States with the states coloured on a blue–orange scale.

5. Known pitfalls and fixes¶

6. Beyond the activity¶

Optional, not graded. Try these in the same notebook if you finish early:

1. Count the missing values in every column with df.isnull().sum(). Which column has the most, and why might sex be harder to record than a measurement?
2. Make a table of how many penguins of each species were measured on each island with pd.crosstab(df["island"], df["species"]). Does every species live on every island? What does that mean for comparing islands in the box plot?
3. Plot bill length against bill depth, first without colour, then with hue="species". Describe how the overall pattern changes once species is shown. (This is a preview of confounding, from Day 1.)
4. In Tableau, add Profit to the colour of the Sales by Category bars. Is the category with the most sales also the most profitable?

7. Submission¶

1. Combine the three PDFs offline with Word, macOS Preview or Acrobat, as described in Combining PDFs without internet.
2. Open the combined file and check that all three parts are present and readable.
3. Upload it to Activity 1 on GAP and submit before Monday 9 November, 23:59 China Standard Time (how to submit).