Interactive Session

🎬 The Whole Game, Part 1: Import to Aggregate

You are about to run a complete data science workflow. You will start with a raw file on the internet and finish with a monthly climate summary, all before you know how any single piece works. Today you run through all of it once. Over the next eight days you will learn how each part was built.

Don’t panic πŸš€

You are not expected to understand every line today. Your job is to copy code, run it, and change one thing at a time. Our job is to show you what a workflow looks like. By next Friday you will know how all of it works.

We will use real Arctic weather data from the Arctic LTER station at Toolik Lake, Alaska: daily measurements from 1988 onward. This session covers the first half of the workflow: Import, Explore, Group, and Aggregate. We skip Clean along the way, for a reason you will see. Session 1d finishes the job with Visualize and Export, and gives the whole workflow its name.

Getting Started

Set up your notebook with the usual ritual:

  1. Create a new notebook (+ β†’ Notebook β†’ eds217_2026).
  2. Rename it to Session_1C_Whole_Game_1.ipynb.
  3. Add a title cell (Markdown), updating the date:
# Day 1: Session 1C - The Whole Game, Part 1

[Session Webpage](https://eds-217-essential-python.github.io/course-materials/interactive-sessions/1c_whole_game_1.html)

Date: 08/31/2026
Caution

Save often (Ctrl+S / Cmd+S).

How today’s steps work

You are going to run code you cannot fully read yet. To make that manageable, every step of the workflow follows the same small pattern. We chose this pattern for Day 1 to help you build confidence before you build understanding.

Each step has up to three parts:

  1. Run it. Copy the canonical cell and run it exactly. Everyone gets the same result, so we all begin each step from the same place.
  2. Change one thing. Swap a single item from a short menu, then run it again to see what moves. These cells always rebuild their result from df, so they leave your data alone. The next canonical cell brings everyone back together.
  3. Say it in a sentence. After some steps you will write one f-string that reports a number you just produced. f-strings are the thing you learned to write this morning, so this is where you practice writing for yourself.

The goal on Day 1 is to see the whole workflow and get a feel for each step. Mastering the syntax comes later. Running working code shows you where each step lands. Changing one thing gives you a safe way to experiment. Writing a sentence keeps you using the one skill you already own.

Step 1: Import πŸ“‚

Import means loading your tools and then loading your data.

βœ… Canonical cell. Copy and run it exactly:

Code 
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)

The import ... as ... lines load Python toolkits under short nicknames. You will see three of them all course long: pd for pandas (data), plt for matplotlib (plots), and np for numpy (math). pd.read_csv(url) reads a CSV straight from the web into a DataFrame, a table, which we store in df.

Note

Today read_csv() needs only one thing: the url of a CSV file. It can do much more, and you will learn that on Day 2.

πŸ” Change one thing

read_csv builds a table, and you can give that table any name you like. Load the same data into a new variable, using one name from this menu:

  • climate = pd.read_csv(url)
  • toolik = pd.read_csv(url)
  • weather = pd.read_csv(url)

Your original df stays exactly as it was. You have simply made a second copy under a new name.

Code 
# πŸ” Example: same data under a new name. Reads url again; leaves df alone.
climate = pd.read_csv(url)
✍️ Say it in a sentence (optional)

Write an f-string that names what you loaded. For example, set dataset = "Toolik weather" and then print f"I loaded the {dataset} dataset."

Step 2: Explore πŸ”

Explore means getting to know the table before you analyze it.

βœ… Canonical cell. Preview the first rows:

Code 
df.head()
Year Month Date LTER_Site Station Daily_AirTemp_Mean_C Flag_Daily_AirTemp_Mean_C Daily_AirTemp_AbsMax_C Flag_Daily_AirTemp_AbsMax_C Daily_AirTemp_AbsMin_C ... Daily_Precip_Total_mm Flag_Daily_Precip_Total_mm Daily_windsp_mean_msec FLAG_Daily_windsp_mean_msec Daily_Windspeed_AbsMax_m_s Daily_globalrad_total_jcm2 FLAG_Daily_globalrad_total_mjm2 Moss Soil20cm Comments
0 1988 6 19880601 ARC TLKMAIN 8.4 E NaN NaN NaN ... 0.0 E NaN NaN NaN NaN NaN NaN NaN Air temp 1 & 5 meter estimated from regressio...
1 1988 6 19880602 ARC TLKMAIN 6.0 E NaN NaN NaN ... 0.0 E NaN NaN NaN NaN NaN NaN NaN Air temp 1 & 5 meter estimated from regressio...
2 1988 6 19880603 ARC TLKMAIN 5.8 E NaN NaN NaN ... 0.0 E NaN NaN NaN NaN NaN NaN NaN Air temp 1 & 5 meter estimated from regressio...
3 1988 6 19880604 ARC TLKMAIN 1.8 E NaN NaN NaN ... 0.0 E NaN NaN NaN NaN NaN NaN NaN Air temp 1 & 5 meter estimated from regressio...
4 1988 6 19880605 ARC TLKMAIN 6.8 E NaN NaN NaN ... 2.5 E NaN NaN NaN NaN NaN NaN NaN Air temp 1 & 5 meter estimated from regressio...

5 rows Γ— 21 columns

πŸ” Change one thing

Swap .head() for one other no-argument view of the same table:

  • df.info() shows the column names, types, and how many values are present.
  • df.describe() shows summary statistics for the number columns.

Each one looks at df without changing it.

Code 
# πŸ” Example: a structural overview instead of a row preview. Reads df; leaves it alone.
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 11171 entries, 0 to 11170
Data columns (total 21 columns):
 #   Column                           Non-Null Count  Dtype  
---  ------                           --------------  -----  
 0   Year                             11171 non-null  int64  
 1   Month                            11171 non-null  int64  
 2   Date                             11171 non-null  int64  
 3   LTER_Site                        11171 non-null  object 
 4   Station                          11171 non-null  object 
 5   Daily_AirTemp_Mean_C             11171 non-null  float64
 6   Flag_Daily_AirTemp_Mean_C        1310 non-null   object 
 7   Daily_AirTemp_AbsMax_C           11001 non-null  float64
 8   Flag_Daily_AirTemp_AbsMax_C      1020 non-null   object 
 9   Daily_AirTemp_AbsMin_C           10967 non-null  float64
 10  Flag_Daily_AirTemp_AbsMin_C      1236 non-null   object 
 11  Daily_Precip_Total_mm            10751 non-null  float64
 12  Flag_Daily_Precip_Total_mm       3169 non-null   object 
 13  Daily_windsp_mean_msec           10345 non-null  float64
 14  FLAG_Daily_windsp_mean_msec      1 non-null      object 
 15  Daily_Windspeed_AbsMax_m_s       10325 non-null  float64
 16  Daily_globalrad_total_jcm2       4118 non-null   float64
 17  FLAG_Daily_globalrad_total_mjm2  15 non-null     object 
 18  Moss                             10333 non-null  float64
 19  Soil20cm                         10351 non-null  float64
 20  Comments                         8924 non-null   object 
dtypes: float64(9), int64(3), object(9)
memory usage: 1.8+ MB
Code 
# πŸ” Example: summary statistics. Reads df; leaves it alone.
df.describe()
Year Month Date Daily_AirTemp_Mean_C Daily_AirTemp_AbsMax_C Daily_AirTemp_AbsMin_C Daily_Precip_Total_mm Daily_windsp_mean_msec Daily_Windspeed_AbsMax_m_s Daily_globalrad_total_jcm2 Moss Soil20cm
count 11171.000000 11171.000000 1.117100e+04 11171.000000 11001.000000 10967.000000 10751.000000 10345.000000 10325.000000 4118.000000 10333.000000 10351.000000
mean 2003.203384 6.570674 2.003271e+07 -8.196437 -3.447132 -13.295924 0.950953 2.995592 6.321288 1324.307188 -1.471402 -2.055647
std 8.831548 3.443637 8.830795e+04 14.831314 14.805298 14.727318 2.749060 1.553574 2.878432 866.670139 7.354649 5.555551
min 1988.000000 1.000000 1.988060e+07 -55.600000 -53.600000 -57.600000 0.000000 0.000000 0.000000 -2.000000 -24.000000 -21.000000
25% 1996.000000 4.000000 1.996012e+07 -19.700000 -14.200000 -25.600000 0.000000 2.000000 4.300000 591.000000 -7.000000 -6.000000
50% 2003.000000 7.000000 2.003092e+07 -7.100000 -2.900000 -12.300000 0.000000 2.800000 5.900000 1268.000000 -2.000000 -1.000000
75% 2011.000000 10.000000 2.011051e+07 4.400000 8.900000 -0.100000 0.400000 3.600000 7.800000 2026.000000 4.000000 2.000000
max 2018.000000 12.000000 2.018123e+07 20.700000 28.200000 14.900000 64.500000 12.500000 27.000000 3205.000000 19.000000 11.000000

The health check

Before you trust a dataset, run a quick health check. This is a fixed phrase you will reuse all course long. It counts the missing values in every column:

Code 
df.isnull().sum()
Year                                   0
Month                                  0
Date                                   0
LTER_Site                              0
Station                                0
Daily_AirTemp_Mean_C                   0
Flag_Daily_AirTemp_Mean_C           9861
Daily_AirTemp_AbsMax_C               170
Flag_Daily_AirTemp_AbsMax_C        10151
Daily_AirTemp_AbsMin_C               204
Flag_Daily_AirTemp_AbsMin_C         9935
Daily_Precip_Total_mm                420
Flag_Daily_Precip_Total_mm          8002
Daily_windsp_mean_msec               826
FLAG_Daily_windsp_mean_msec        11170
Daily_Windspeed_AbsMax_m_s           846
Daily_globalrad_total_jcm2          7053
FLAG_Daily_globalrad_total_mjm2    11156
Moss                                 838
Soil20cm                             820
Comments                            2247
dtype: int64
Important

Look at Daily_AirTemp_Mean_C. It has zero missing values, so the column we are about to analyze is complete. A few other columns do have gaps, which is normal. We only need our column to be clean.

✍️ Say it in a sentence (required)

df.info() reported how many rows the dataset has. Read that number, then report it in your own f-string. For example:

n_rows = 11171          # the number info() showed you
print(f"The Toolik dataset has {n_rows} daily records.")

Now write a second sentence of your own, maybe about how many columns there are.

Step 3: Clean 🧹 (skipped today, and here is why)

The next step in a normal workflow is Clean: fixing missing values, wrong types, and duplicate rows. Our health check already showed that Daily_AirTemp_Mean_C has no missing values, so there is nothing to clean for today’s analysis, and we can go straight on.

Note

Enjoy this while it lasts. Most real datasets need real cleaning, and it is often the biggest part of a project. You will spend all of Day 4 on it. Today the data came ready to use.

Step 4: Group πŸ‘₯

Group means splitting the rows into buckets by some key. Here we bucket every daily record by its Month, so all the Januaries sit together, all the Februaries, and so on.

βœ… Canonical cell. Copy and run it exactly:

Code 
monthly = df.groupby('Month')
πŸ” Change one thing

Change only the key you group by. Pick one from this menu:

  • by_year = df.groupby('Year') buckets records by year, which gives many groups.
  • by_station = df.groupby('Station') gives a single bucket here, because every row is the same station.

Both read df without changing it, and neither touches your monthly grouping.

Code 
# πŸ” Example: group by Year instead of Month. Reads df; leaves df and `monthly` alone.
by_year = df.groupby('Year')

Step 5: Aggregate πŸ“ˆ

Aggregate means collapsing each bucket down to a single number. Here we take the mean temperature within each month. Notice the two-step shape: first group, then pick a column and average it.

βœ… Canonical cell. Copy and run it exactly:

Code 
monthly_means = monthly['Daily_AirTemp_Mean_C'].mean()
monthly_means
Month
1    -22.889032
2    -20.700945
3    -20.692366
4    -11.762556
5     -0.795161
6      8.589892
7     11.222060
8      7.234860
9     -0.110753
10   -10.544225
11   -18.339355
12   -21.442560
Name: Daily_AirTemp_Mean_C, dtype: float64

That gives twelve numbers, one average temperature per month, summarizing thousands of daily records into a clear seasonal pattern.

πŸ” Change one thing

Keep the grouping and change only the column you average. Pick one from this menu:

  • monthly['Daily_Precip_Total_mm'].mean() gives average daily precipitation.
  • monthly['Daily_AirTemp_AbsMax_C'].mean() gives the average daily high temperature.

Store the result under a new name so your monthly_means stays put.

Code 
# πŸ” Example: average a different column. Reads `monthly`; leaves monthly_means alone.
monthly_precip = monthly['Daily_Precip_Total_mm'].mean()
monthly_precip
Month
1     0.316590
2     0.490909
3     0.264937
4     0.317931
5     0.604227
6     1.537217
7     2.674922
8     2.041935
9     1.249828
10    0.671121
11    0.506000
12    0.425243
Name: Daily_Precip_Total_mm, dtype: float64
✍️ Say it in a sentence (required)

Look at your monthly_means, pick one month’s value, read it off, and report it in an f-string. For example:

july_temp = 11.2        # July's value, read from monthly_means
print(f"At Toolik, the average July temperature is {july_temp} degrees Celsius.")

Now write one more sentence for the month you find most surprising.

πŸ§ͺ Sandbox (5 minutes)

Now poke at it yourself. Try a different column, a different grouping key, or a deliberate typo. Errors are expected today, and they are welcome. A red error message is Python telling you something. See what you can make happen.

β†©οΈŽοΈ Canonical restart. Run this to rejoin the group

When the five minutes are up, run this cell to reload the data and rebuild the canonical results. Everyone returns to the same state, ready for Part 2:

Code 
# 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()

Key Points

  • You ran a real workflow from start to finish: Import, Explore, Group, and Aggregate. You skipped Clean only because the health check proved our column was complete.
  • pd.read_csv(url) loads a CSV from the web into a DataFrame. .head(), .info(), and .describe() are your no-argument ways to look at it.
  • df.isnull().sum() is the health check for counting missing values.
  • Grouping and then averaging turns thousands of rows into a short summary. You did it in two steps: first monthly = df.groupby('Month'), then monthly['Daily_AirTemp_Mean_C'].mean().
  • The change-one-thing cells always re-read df, so your data stays put and the group can always reconverge.

Resources

➑️ Next: In The Whole Game, Part 2 you will visualize these monthly means, save your results, and see the full ten-step workflow named.

End interactive session 1C