In Part 1 you turned a raw file into a twelve-number monthly climate summary. Now you will finish the game. You will visualize those numbers, save your results, and put a name to the ten-step workflow you just played through.
This is still a preview, so keep working the same way: copy a cell, run it, then change one thing. You will learn how visualization really works on Day 7. Today, just enjoy turning numbers into a picture.
Set up your notebook with the usual ritual:
+ β Notebook β eds217_2026).Session_1D_Whole_Game_2.ipynb.# Day 1: Session 1D - The Whole Game, Part 2
[Session Webpage](https://eds-217-essential-python.github.io/course-materials/interactive-sessions/1d_whole_game_2.html)
Date: 08/31/2026We are in a fresh notebook, so first re-run the workflow from Part 1 to rebuild df and monthly_means.
β Canonical cell. Copy and run it exactly:
import pandas as pd
import matplotlib.pyplot as plt
url = "https://eds-217-essential-python.github.io/data/toolik_weather.csv"
df = pd.read_csv(url)
monthly = df.groupby('Month')
monthly_means = monthly['Daily_AirTemp_Mean_C'].mean()Visualize means turning numbers into a picture you can read quickly. The simplest plot is a line plot with plt.plot().
β Canonical cell. Copy and run it exactly:
plt.plot(monthly_means)
plt.title("Toolik Monthly Temperatures")
plt.xlabel("Month")
plt.ylabel("Temperature (Β°C)")
plt.show()
The same twelve numbers now show a clear seasonal cycle. We can also draw them as bars with plt.bar(). Copy the first line exactly. The month numbers for the x-axis come straight from your monthly_means.
plt.bar(monthly_means.index, monthly_means)
plt.title("Toolik Monthly Temperatures")
plt.xlabel("Month")
plt.ylabel("Temperature (Β°C)")
plt.show()
Change only a label. The data stays exactly the same. Pick one new title from this menu and re-run:
plt.title("Average Temperature by Month")plt.title("Arctic Seasonality at Toolik")plt.title("Monthly Mean Air Temperature, 1988 to 2018")You could reword the plt.ylabel(...) text instead. Either way, change just one thing.
# π Example: same plot, one new label. The data (monthly_means) stays the same.
plt.bar(monthly_means.index, monthly_means)
plt.title("Arctic Seasonality at Toolik")
plt.xlabel("Month")
plt.ylabel("Temperature (Β°C)")
plt.show()
Read the coldest monthβs value off your chart, then report it in an f-string. For example:
coldest = -22.9 # January's value, read from the chart
print(f"Toolik's coldest month averages about {coldest} degrees Celsius.")Now write one more sentence comparing it to the warmest month.
Export means saving your results so you or a collaborator can use them later. This is the foundation of reproducible science. monthly_means.to_csv(...) writes your summary to a file.
β Canonical cell. Copy and run it exactly (in your notebook):
monthly_means.to_csv("monthly_means.csv", header=True)Open the Explorer in Positron and you will find monthly_means.csv. It looks like this:
Month,Daily_AirTemp_Mean_C
1,-22.89
2,-20.70
3,-20.69
4,-11.76
5,-0.80
6,8.59Change only the file name. Pick one and re-save:
monthly_means.to_csv("monthly_temps.csv", header=True)monthly_means.to_csv("toolik_monthly.csv", header=True)monthly_means.to_csv("my_results.csv", header=True)Same data, new file. Your monthly_means in memory stays the same.
Name what you saved. For example, set filename = "monthly_means.csv" and then print f"I saved my results to {filename}."
You just did it. Here is the workflow you played through, named in full. Every analysis in this course, and in your career, is some path through these ten steps:
flowchart LR
A["1. Import π"] --> B["2. Explore π"] --> C["3. Clean π§Ή"]
C --> D["4. Filter π―"] --> E["5. Sort π"] --> F["6. Transform π§"]
F --> G["7. Group π₯"] --> H["8. Aggregate π"] --> I["9. Join / Reshape π"]
I --> J["10. Visualize π"]
Today you traveled Import β Explore β Group β Aggregate β Visualize β Export, skipping the steps we have not taught yet. That is normal. Few analyses use every step, and they always happen in this order.
π Read more: The Data Science Workflow gives a one-sentence description of each step and shows which day you will learn it.
Every βcode strangerβ you met today becomes a friend on a specific day:
| Step | What it does | Youβll learn it on |
|---|---|---|
| 1. Import | Load data into a DataFrame | Day 2 |
| 2. Explore | Get to know the table | Day 2 |
| 3. Clean | Fix missing values, types, duplicates | Day 4 |
| 4. Filter | Keep only the rows you want | Day 3 |
| 5. Sort | Order rows by a column | Day 3 |
| 6. Transform | Build new columns | Day 4 |
| 7. Group | Split rows into buckets by a key | Day 5 |
| 8. Aggregate | Collapse each bucket to a number | Day 5 |
| 9. Join / Reshape | Combine tables; add a time dimension | Day 6 |
| 10. Visualize | Turn numbers into pictures | Day 7 |
Play. Re-title a plot, swap plt.plot for plt.bar, save under a silly file name, or break something on purpose. Errors are expected and welcome today. They are how you find the edges.
When time is up, run this to reload the data and rebuild your results from scratch:
# Restart: reload df and rebuild the canonical results
df = pd.read_csv(url)
monthly = df.groupby('Month')
monthly_means = monthly['Daily_AirTemp_Mean_C'].mean()plt.plot() and plt.bar(). Label with plt.title(), plt.xlabel(), and plt.ylabel(). Today you change labels only..to_csv("filename.csv") so your work is saved and reproducible.π That is the whole game. You ran a complete Python data science workflow today.
End interactive session 1D