Climate Coding Challenge

Climate change is impacting the way people live around the world

Learning Goals:

Analyze temperature data over time
Parse date information so that it is represented as a datetime type
Use operators to convert to different units
Resample time-series data to different frequencies

Part 1: Overview

Higher highs, lower lows, storms, and smoke – we’re all feeling the effects of climate change. In this workflow, you will take a look at trends in temperature over time in Boulder, CO.

Conversation Starter

In a bulleted list, how is climate change affecting your home?

What the fork?! Who wrote this?

For this challenge, you’ll be running a scientific workflow in Python. But something’s wrong – The code won’t run! Your task is to follow the instructions below to clean and debug the Python code below so that it runs.

Tip

Don’t worry if you can’t solve every bug right away. We’ll get there! If you are working on one bug for more than about 10 minutes, it’s time to ask for help.

Alright! Let’s clean up this code.

Check out our demo video!

Climate Coding Challenge Video 1 by Earth Lab

DEMO: Climate Part 2 (EDA) by Earth Lab

DEMO: Climate Part 3 (EDA) by Earth Lab

DEMO: Climate Part 2 (EDA) by Earth Lab

DEMO: Climate Part 3 (EDA) by Earth Lab

Wrangle your data

Python packages let you use code written by experts around the world

Because Python is open source, lots of different people and organizations can contribute (including you!). Many contributions are in the form of packages which do not come with a standard Python download.

Read More: Packages need to be installed and imported.

Learn more about using Python packages. How do you find and use packages? What is the difference between installing and importing packages? When do you need to do each one? This article on Python packages will walk you through the basics.

In the cell below, someone was trying to import the pandas package, which helps us to work with tabular data such as comma-separated value or csv files (e.g. data with rows and columns like a spreadsheet). But something’s wrong!

Try It: Import packages

Correct the typo below to properly import the pandas package under its alias pd.
Add a descriptive comment next to the pandas package explaining what it does. You can add comments using the # symbol, just like we did for you with the earthpy package.
Run the cell to import the libraries you’ll need for this workflow.

# Import libraries
import earthpy # Manage local data
import holoviews as hv # Save interactive plots
import hvplot.pandas # Make interactive plots
import pandsa as pd

STEP 1: Download the practice data

Next, lets download some climate data from Boulder, CO to practice with. The data will come in comma-separate value, or CSV format.

STEP 2: Import data to Python

The pandas library you imported can download data from the internet directly into a type of Python object called a DataFrame. In the code cell below, you can see an attempt to do just this. But there are some problems…

Try It: Fix some code!

Make any changes needed to get this code to run. HINT: The filename.csv isn’t correct - you need to replace it with the name of the file you downloaded! See if you can find where the data downloaded to.
The pd.read_csv() function isn’t formatting the data 100% correctly. Modify the code to include the following additional parameters, making sure to put a comma (,) in-between each parameter:
- index_col='DATE' – this sets the DATE column as the index. Needed for subsetting and resampling later on
- parse_dates=True – this lets python know that you are working with time-series data, and values in the indexed column are date time objects
- na_values=['NaN'] – this lets python know how to handle missing values
We can’t get the data back later on because it isn’t saved in a variable. In other words, we need to give the url a name so that we can request in from Python later (sadly, Python has no ‘hey what was that thingy I typed yesterday?’ function). Make sure to use an expressive variable name so you remember what it is later on!

	STATION	TOBS
1893-10-01	USC00050848	NaN
1893-10-02	USC00050848	NaN
1893-10-03	USC00050848	NaN
1893-10-04	USC00050848	NaN
1893-10-05	USC00050848	NaN
...	...	...
2023-09-26	USC00050848	74.0
2023-09-27	USC00050848	69.0
2023-09-28	USC00050848	73.0
2023-09-29	USC00050848	66.0
2023-09-30	USC00050848	78.0

STEP 3: Clean up your `DataFrame`

Try It: Get rid of unwanted columns

You can use double brackets ([[ and ]]) to select only the columns that you want from your DataFrame:

Change some_column_name to the Temperature column name.
Put quotes around your column name so Python interprets it as text and not a variable name.
Clean up the code by using descriptive comments.

climate_df = climate_df[[some_column_name]]
climate_df

	TOBS
DATE
1893-10-01	NaN
1893-10-02	NaN
1893-10-03	NaN
1893-10-04	NaN
1893-10-05	NaN
...	...
2023-09-26	74.0
2023-09-27	69.0
2023-09-28	73.0
2023-09-29	66.0
2023-09-30	78.0

45971 rows × 1 columns

Convert units

It’s important to keep track of the units of all your data. You don’t want to be like the NASA team who crashed a probe into Mars because different teams used different units)!

Use labels to keep track of units for you and your collaborators

One way to keep track of your data’s units is to include the unit in data labels. In the case of a DataFrame, that usually means the column names.

Try It: Add units to your column name

A big part of writing expressive code is descriptive labels. Let’s rename the columns of your dataframe to include units. Complete the following steps:

Replace dataframe with the name of your DataFrame, and dataframe_units with an expressive new name.
Check out the documentation for GCHNd data. We downloaded data with “standard” units; find out what that means for temperature.
Replace 'temperature-column-name' with the temperature column name in your data, and 'temp_unit' with a column name that includes the correct unit. For example, you could make a column called 'temperature_k' to note that your temperatures are in degrees Kelvin.

dataframe_units = dataframe.rename(columns={
    'temperature-column-name': 'temp_unit',
})

dataframe_units

	temp_f
DATE
1893-10-01	NaN
1893-10-02	NaN
1893-10-03	NaN
1893-10-04	NaN
1893-10-05	NaN
...	...
2023-09-26	74.0
2023-09-27	69.0
2023-09-28	73.0
2023-09-29	66.0
2023-09-30	78.0

45971 rows × 1 columns

For scientific applications, it is often useful to have values in metric units

In this case, we want to convert data from degrees Fahrenheit to degrees Celcius. The equation for converting Fahrenheit temperature to Celcius is:

\[T_C = (T_F - 32) * \frac{5}{9}\]

Try It: Convert units

The code below attempts to convert the data to Celcius, using Python mathematical operators, like +, -, *, and /. Mathematical operators in Python work just like a calculator, and that includes using parentheses to designate the order of operations.

This code is not well documented and doesn’t follow PEP-8 guidelines, which has caused the author to miss an important error!

Complete the following steps:

Replace dataframe with the name of your DataFrame.
Replace 'old_temperature' with the column name you used; Replace 'new_temperature' with an expressive column name.
THERE IS AN ERROR IN THE CONVERSION MATH - Fix it!

dataframe_units['new_temperature'] = (dataframe_units['old_temperature']-32*5/9)
dataframe_units

	temp_f	temp_c
DATE
1893-10-01	NaN	NaN
1893-10-02	NaN	NaN
1893-10-03	NaN	NaN
1893-10-04	NaN	NaN
1893-10-05	NaN	NaN
...	...	...
2023-09-26	74.0	23.333333
2023-09-27	69.0	20.555556
2023-09-28	73.0	22.777778
2023-09-29	66.0	18.888889
2023-09-30	78.0	25.555556

45971 rows × 2 columns

Looking for an Extra Challenge?

Using the code below as a framework, write and apply a function that converts to Celcius. You should also rewrite this function name and parameter names to be more expressive.

# Convert units with a function
def convert(temperature):
    """Convert Fahrenheit temperature to Celcius"""
    return temperature # Put your equation in here

dataframe['TEMP_C'] = (
    dataframe['TEMP_F'].apply(convert))

Plot your results

STEP 1: Plot the temperature column vs time to explore the data

Plotting in Python is easy, but not quite this easy:

climate_u_df.plot()

Looks like we have both temperature units on the same plot, and it’s hard to see what it is because it’s missing labels!

Label your plot

Make sure each plot has:

A title that explains where and when the data are from
x- and y- axis labels with units where appropriate
A legend where appropriate

When plotting in Python, you’ll always need to add some instructions on labels and how you want your plot to look.

Try It: Plot your data

Change dataframe to your DataFrame name.
Change y= to the name of your temperature column name.
Use the title, ylabel, and xlabel parameters to add key text to your plot.
Adjust the size of your figure using figsize=(x,y) where x is figure width and y is figure height

Tip

Labels have to be a type in Python called a string. You can make a string by putting quotes around your label, just like the column names in the sample code (eg y='temperature').

# Plot the data using .plot
climate_u_df.plot(
    y='the_temperature_column',
    title='Title Goes Here',
    xlabel='Horizontal Axis Label Goes Here',
    ylabel='Vertical Axis Label Goes Here')

Looking for an Extra Challenge?

There are many other things you can do to customize your plot. Take a look at the pandas plotting galleries and the documentation of plot to see if there’s other changes you want to make to your plot. Some possibilities include:

Remove the legend since there’s only one data series
Increase the figure size
Increase the font size
Change the colors
Use a bar graph instead (usually we use lines for time series, but since this is annual it could go either way)
Add a trend line

Not sure how to do any of these? Try searching the internet, or asking an AI!

STEP 2: Clean up time series plots by resampling

You may notice that your plot looks a little “fuzzy”. This happens when Python is trying to plot a value for every date, but the resolution of the image is too low to actually do that. You can address this issue by resampling the data, or summarizing it over a time period of your choice. In this case, we will resample annually, giving us one data point per year.

Try It: Resample

Set the frequency of your final data by replacing DT_OFFSETwith a Datetime Offset Code. Check out the table in the pandas datetime documentation to find the one you want (we recommend the start of the year).
Choose how to summarize each year of data by replacing agg_method_here with a method that will calculate the average annual value. Check out the pandas resampling documentation for a list of common built-in options.
Add descriptive comments to the code so the next person reading it knows what it is doing.

ann_climate_df = (
    climate_u_df
    .resample('DT_OFFSET')
    .agg_method_here()
)
ann_climate_df

	temp_f	temp_c
DATE
1893-01-01	NaN	NaN
1894-01-01	NaN	NaN
1895-01-01	NaN	NaN
1896-01-01	NaN	NaN
1897-01-01	NaN	NaN
...	...	...
2019-01-01	54.426997	12.459443
2020-01-01	57.691460	14.273033
2021-01-01	57.538462	14.188034
2022-01-01	56.139726	13.410959
2023-01-01	58.996337	14.997965

131 rows × 2 columns

Try It: Plot Annual Data

Try plotting your new DataFrame in the cell below. Can you see what is going on more clearly now? Don’t forget to adjust your labels!
If you write your code on one line, it will most likely be to long to read without scrolling. Make sure you are following PEP-8 style guidelines by keeping your lines less than 80 characters long. If you are working in GitHub Codespaces, we have set you up with a vertical guide that is between 79 and 80 characters – make sure your code doesn’t go past it!
PEP-8 also suggests aligning any function parameters that are too long. See some examples below for what to do and what not to do.

PEP-8 tips!

Following the PEP-8 style guide is important because it makes your code easy for you and other collaborators to read. When you are splitting function calls across multiple lines, your code should look like this:

my_dataframe.plot(
    y='column_name',
    title=f'My Fantastic Plot',
    xlabel='The x Axis',
    ylabel='The y Axis'
)

or maybe this:

my_dataframe.plot(y='column_name',
                  title=f'My Fantastic Plot',
                  xlabel='The x Axis',
                  ylabel='The y Axis')

Warning

Try to avoid these PEP-8 violations:

my_dataframe.plot(y='column_name', title=f'My Fantastic Plot', xlabel='The x Axis', ylabel='The y Axis')

my_dataframe.plot(
    y='column_name',
      title=f'My Fantastic Plot',
   xlabel='The x Axis',
   ylabel='The y Axis'
)

my_dataframe.plot(y='column_name',
    title=f'My Fantastic Plot',
    xlabel='The x Axis',
    ylabel='The y Axis'
)

# Plot the annual data

Reflect and Respond: Interpret your plot

Create a new Markdown cell below this one.
Using a bulleted list in Markdown, write down 2 things you notice about the data. What physical phenomena or data anomaly could be causing each one?

STEP 3: Check specific values with an interactive plot

You can use the .hvplot() method with similar arguments to create an interactive plot.

Try It: Interactive Plot

Copy your plotting code into the cell below.
Replace .plot in your code with .hvplot
Check that your code follows PEP-8 guidelines.

Now, you should be able to hover over data points and see their values!

# Plot the annual data interactively

Reflect and Respond: Explore the data

Create a new Markdown cell below this one.
Hover over the lowest point on your plot. What is the overall maximum annual average temperature?

BONUS: Save your work

You will need to save your analyses and plots to tell others about what you find.

Looking for an Extra Challenge?: Save Your Plot

Just like with any other type of object in Python, if you want to reuse your work, you need to give it a name.

Go back to your hvplot code, and give your plot a name by assigning it to a variable. HINT: if you still want your plot to display in your notebook, make sure to call its name at the end of the cell.
Replace my_plot with the name you gave to your plot.
Replace 'my_plot.html' with the name you want for your plot. If you change the file extension, .html, to .png, you will get an image instead of an interactive webpage, provided you have the necessary libraries installed.

Once you run the code, you should see your saved plot in your files – go ahead and open it up.

Warning

If you are working in GitHub Codespaces, right-click on your file and download it to view it after saving.

hv.save(my_plot, 'my_plot.html')

So, is the climate changing?

STEP 1: Quantify how fast the climate is changing with a trend line

Global climate change causes different effects in different places when we zoom in to a local area. However, you probably noticed when you looked at mean annual temperatures over time that they were rising. We can use a technique called Linear Ordinary Least Squares (OLS) Regression to determine how quickly temperatures are rising on average.

Before we get started, it’s important to consider that OLS regression is not always the right technique, because it makes some important assumptions about our data:

Random error: Variation in temperature can be caused by many things beyond global climate change. For example, temperatures often vary with patterns of ocean surface temperatures (teleconnections), the most famous of which are El Niño and La Niña. By using a linear OLS regression, we’re assuming that all the variation in temperature except for climate change is random.
Normally distributed error: If you have taken a statistics class, you probably learned a lot about the normal, or Gaussian distribution. For right now, what you need to know is that OLS regression is useful for identifying trends in average temperature, but wouldn’t be appropriate for looking at trends in daily precipitation (because most days have zero precipitation), or at maximum or minimum annual temperatures (because these are extreme values, and the normal distribution tends to underestimate the likelihood of large events).
Linearity: We’re assuming that temperatures are increasing or decreasing at a constant rate over time. We wouldn’t be able to look at rates that change over time. For example, many locations in the Arctic remained the same temperature for much longer than the rest of the world, because ice melt was absorbing all the extra heat. Linear OLS regression wouldn’t be able to identify when the temperature rise began on its own.
Stationarity: We’re assuming that variation in temperature caused by things other than global climate change (e.g. the random error) behaves the same over time. For example, the linear OLS regression can’t take increased variability from year to year into account, which is a common effect of climate change. We often see “global weirding”, or more extreme head and cold, in addition to overall increases. You can observe this most easily by looking at your daily data again. Does it seem to be fanning in or out over time?

It’s pretty rare to encounter a perfect statistical model where all the assumptions are met, but you want to be on the lookout for serious discrepancies, especially when making predictions. For example, ignoring assumptions about Gaussian error arguably led to the 2008 financial crash.

Reflect and Respond: Is linear OLS regression right for your data?

Take a look at your data. In the cell below, write a few sentences about ways your data does and does not meet the linear OLS regression assumptions.

Try It: Import Packages

The following cell contains package imports that you will need to calculate and plot an OLS Linear trend line. Make sure to run the cell before moving on, and if you have any additional packages you would like to use, add them here later on.

# Advanced options on matplotlib/seaborn/pandas plots
import matplotlib.pyplot as plt
# Common statistical plots for tabular data
import seaborn as sns
# Fit an OLS linear regression
from sklearn.linear_model import LinearRegression

Try It: Regression

To get sample code, ask ChatGPT how to fit a linear model to your data. If you’re new to using large language models, go ahead and check out our query
Copy code that uses the scikit-learn package to perform a OLS linear regression to the code cell below.
Check out your previous plot. Does it make sense to include all the data when calculating a trend line? Be sure to select out data that meets the OLS assumptions.

Note

We know that some computers, networks, and countries block LLM (large language model) sites, and that LLMs can sometimes perpetuate oppressive or offensive language and ideas. However, LLMs are increasingly standard tools for programming – according to GitHub many developers code 55% faster with LLM assistance. We also see in our classes that LLMs give students the ability to work on complex real-world problems earlier on. We feel it’s worth the trade-off, and at this point we would be doing you a disservice professionally to teach you to code without LLMs. If you can’t access them, don’t worry – we’ll present a variety of options for finding example code. For example, you can also search for an example on a site like StackOverflow (this is how we all learned to code, and with the right question it’s a fantastic resource for any coder to get access to up-to-date information from world experts quickly). You can also use our solutions as a starting point.

# Fit an OLS Linear Regression to the data

Slope: 0.13079071315632046 degrees per year

STEP 2: Plot your trend line

Trend lines are often used to help your audience understand and process a time-series plot. In this case, we’ve chosed mean temperature values rather than extremes, so we think OLS is an appropriate model to use to show a trend.

Is it ok to plot a trend line even if OLS isn’t an appropriate model?

This is a tricky issue. When it comes to a trend line, choosing a model that is technically more appropriate may require much more complex code without resulting in a noticeably different trend line.

We think an OLS trend line is an ok visual tool to indicate the approximate direction and size of a trend. If you are showing standard error, making predictions or inferences based on your model, or calculating probabilities (p-values) based on your model, or making statements about the statistical significance of a trend, we’d suggest reconsidering your choice of model.

Try It: Regression Plot

Add values for x (year) and y (temperature) to plot a regression plot. You will have to select out the year from the index values, just like you probably did when fitting your linear model above!
Label the axes of your plot with the title, xlabel, and ylabel parameters. We’ve gotten you started with an example that shows how to put in the degree symbol. Make sure your labels match what you’re plotting!
Can you figure out how to customize the colors and line style on your plot? Check out the seaborn documentation for ideas.

# Plot annual average temperature with a trend line
ax = sns.regplot(
    x=, 
    y=,
)
# Set plot labels
ax.set(
    title='',
    xlabel='',
    ylabel='Temperature ($^\circ$F)'
)
# Display the plot without extra text
plt.show()

Reflect and Respond: Interpret the trend

Create a new Markdown cell below this one.
Write a plot headline. Your headline should interpret your plot, unlike a caption which neutrally describes the image.
Is the climate changing? How much? Report the slope of your trend line.

Part 1: Overview

What the fork?! Who wrote this?

Wrangle your data

Python packages let you use code written by experts around the world

STEP 1: Download the practice data

STEP 2: Import data to Python

STEP 3: Clean up your DataFrame

Convert units

Use labels to keep track of units for you and your collaborators

For scientific applications, it is often useful to have values in metric units

Plot your results

STEP 1: Plot the temperature column vs time to explore the data

STEP 2: Clean up time series plots by resampling

STEP 3: Check specific values with an interactive plot

BONUS: Save your work

So, is the climate changing?

STEP 1: Quantify how fast the climate is changing with a trend line

STEP 2: Plot your trend line

STEP 3: Clean up your `DataFrame`