Get started with open reproducible science! (API version)

It’s another ESIIL Earth Data Science Workflow

This notebook contains your next environmental data science (EDS) coding challenge! Before we get started, make sure to read or review the guidelines below. These will help make sure that your code is readable and reproducible.

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Don’t get caught by these interactive coding notebook gotchas

Image source: https://alaskausfws.medium.com/whats-big-and-brown-and-loves-salmon-e1803579ee36

These are the most common issues that will keep you from getting started and delay your code review:

Run your code in the right environment to avoid import errors

We’ve created a coding environment for you to use that already has all the software and libraries you will need! When you try to run some code, you may be prompted to select a kernel. The kernel refers to the version of Python you are using. You should use the base kernel, which should be the default option.

Always run your code start to finish before submitting

Before you commit your work, make sure it runs reproducibly by clicking:

1. Restart (this button won’t appear until you’ve run some code), then
2. Run All

Check your code to make sure it’s clean and easy to read

• Format all cells prior to submitting (right click on your code).

• Use expressive names for variables so you or the reader knows what they are.

• Use comments to explain your code – e.g.

  # This is a comment, it starts with a hash sign
  

Label and describe your plots

Source: https://xkcd.com/833

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

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Get started with open reproducible science!

Open reproducible science makes scientific methods, data and outcomes available to everyone. That means that everyone who wants should be able to find, read, understand, and run your workflows for themselves.

Image from https://www.earthdata.nasa.gov/esds/open-science/oss-for-eso-workshops

Few if any science projects are 100% open and reproducible (yet!). However, members of the open science community have developed open source tools and practices that can help you move toward that goal. You will learn about many of those tools in the Intro to Earth Data Science textbook. Don’t worry about learning all the tools at once – we’ve picked a few for you to get started with.

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Further reading

Read our textbook chapter about open reproducible science.

What does open reproducible science mean to you?

Create a new Markdown cell below this one using the + Markdown button in the upper left.

In the new cell, answer the following questions using a numbered list in Markdown:

1. In 1-2 sentences, define open reproducible science.
2. In 1-2 sentences, choose one of the open source tools that you have learned about (i.e. Shell, Git/GitHub, Jupyter Notebook, Python) and explain how it supports open reproducible science.

Human-readable and Machine-readable

Create a new Markdown cell below this one using the ESC + b keyboard shortcut.

In the new cell, answer the following question in a Markdown quote:

1. In 1-2 sentences, does this Jupyter Notebook file have a machine-readable name? Explain your answer.

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Readable, well-documented scientific workflows are easier to reproduce

As the comic below suggests, code that is hard to read is also hard to get working. We refer to code that is easy to read as clean code.

And because if you just leave it there, it’s going to start contaminating things downstream even if no one touches it directly. (from [XKCD](https://xkcd.com/2138/))

In the prompt below, list 3 things you can do to write clean code, and then list 3 more advantages of doing so.

• Edit the text below. You may have to double click.
• You can use examples from the textbook, or come up with your own.
• Use Markdown to format your list.

I can write clean code by: YOUR ANSWER HERE

Advantages of clean code include: YOUR ANSWER HERE

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What the fork?! Who wrote this?

Below is a scientific Python workflow. 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! The most important thing is to identify problems with the code and write high-quality GitHub Issues

At the end, you’ll repeat the workflow for a location and measurement of your choosing.

Alright! Let’s clean up this code. First things first…

Machine-readable file names

Rename this notebook if necessary with an expressive and machine-readable file name

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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.

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.

Your task

1. Correct the typo below to properly import the pandas package under its alias pd.
2. Run the cell to import pandas

# Import pandas
import pandas as pd

Once you have run the cell above and imported pandas, run the cell below. It is a test cell that will tell you if you completed the task successfully. If a test cell isn’t working the way you expect, check that you ran your code immediately before running the test.

# DO NOT MODIFY THIS TEST CELL
points = 0
try:
    pd.DataFrame()
    points += 5
    print('\u2705 Great work! You correctly imported the pandas library.')
except:
    print('\u274C Oops - pandas was not imported correctly.')
print('You earned {} of 5 points for importing pandas'.format(points))

✅ Great work! You correctly imported the pandas library.
You earned 5 of 5 points for importing pandas

---

There are more Earth Observation data online than any one person could ever look at

NASA’s Earth Observing System Data and Information System (EOSDIS) alone manages over 9PB of data. 1 PB is roughly 100 times the entire Library of Congress (a good approximation of all the books available in the US). It’s all available to you once you learn how to download what you want.

Here we’re using the NOAA National Centers for Environmental Information (NCEI) Access Data Service application progamming interface (API) to request data from their web servers. We will be using data collected as part of the Global Historical Climatology Network daily (GHCNd) from their Climate Data Online library program at NOAA.

For this example we’re requesting daily summary data in Boulder, CO (station ID USC00050848) located on the NOAA Campus (39.99282°, -105.26683°).

Your task:

1. Research the Global Historical Climatology Network - Daily data source.
2. In the cell below, write a 2-3 sentence description of the data source. You should describe:
   • who takes the data
   • where the data were taken
   • what the maximum temperature units are
   • how the data are collected
3. Include a citation of the data (HINT: See the ‘Data Citation’ tab on the GHCNd overview page).

YOUR DATA DESCRIPTION AND CITATION HERE 🛎️

You can access NCEI GHCNd Data from the internet using its API 🖥️ 📡 🖥️

The cell below contains the URL for the data you will use in this part of the notebook. We created this URL by generating what is called an API endpoint using the NCEI API documentation.

Note

An application programming interface (API) is a way for two or more computer programs or components to communicate with each other. It is a type of software interface, offering a service to other pieces of software (Wikipedia).

However, we still have a problem - we can’t get the URL 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).

Read more

Check out the textbook section on variables

Your task

1. Pick an expressive variable name for the URL

   HINT: click on the Variables button up top to see all your variables. Your new url variable will not be there until you define it and run the code

2. Reformat the URL so that it adheres to the 79-character PEP-8 line limit

   HINT: You should see two vertical lines in each cell - don’t let your code go past the second line

3. At the end of the cell where you define your url variable, call your variable (type out its name) so it can be tested.

# Request API endpoint
boulder_url = ('https://www.ncei.noaa.gov/access/services/data/v1?'
               'dataset=daily-summaries'
               '&dataTypes=TOBS,PRCP'
               '&stations=USC00050848'
               '&startDate=1893-10-01'
               '&endDate=2024-02-18'
               '&includeStationName=true'
               '&includeStationLocation=1'
               '&units=standard')
boulder_url

'https://www.ncei.noaa.gov/access/services/data/v1?dataset=daily-summaries&dataTypes=TOBS,PRCP&stations=USC00050848&startDate=1893-10-01&endDate=2024-02-18&includeStationName=true&includeStationLocation=1&units=standard'

# DO NOT MODIFY THIS TEST CELL
resp_url = _
points = 0

if type(resp_url)==str:
    points += 3
    print('\u2705 Great work! You correctly called your url variable.')
else:
    print('\u274C Oops - your url variable was not called correctly.')

if len(resp_url)==218:
    points += 3
    print('\u2705 Great work! Your url is the correct length.')
else:
    print('\u274C Oops - your url variable is not the correct length.')

print('You earned {} of 6 points for defining a url variable'.format(points))

✅ Great work! You correctly called your url variable.
✅ Great work! Your url is the correct length.
You earned 6 of 6 points for defining a url variable

---

Download and get started working with NCEI data

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…

You’re ready to fix some code!

Your task is to:

1. Leave a space between the # and text in the comment and try making the comment more informative

2. Make any changes needed to get this code to run. HINT: The my_url variable doesn’t exist - you need to replace it with the variable name you chose.

3. Modify the .read_csv() statement to include the following parameters:

   • 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

4. Clean up the code by using expressive variable names, expressive column names, PEP-8 compliant code, and descriptive comments

Make sure to call your DataFrame by typing it’s name as the last line of your code cell Then, you will be able to run the test cell below and find out if your answer is correct.

boulder_df = pd.read_csv(
  boulder_url,
  index_col='DATE',
  parse_dates=True,
  na_values=['NaN'])
boulder_df

	STATION	NAME	LATITUDE	LONGITUDE	ELEVATION	PRCP	TOBS
DATE
1893-10-01	USC00050848	BOULDER, CO US	39.99282	-105.26683	1673.0	0.94	NaN
1893-10-02	USC00050848	BOULDER, CO US	39.99282	-105.26683	1673.0	0.00	NaN
1893-10-03	USC00050848	BOULDER, CO US	39.99282	-105.26683	1673.0	0.00	NaN
1893-10-04	USC00050848	BOULDER, CO US	39.99282	-105.26683	1673.0	0.04	NaN
1893-10-05	USC00050848	BOULDER, CO US	39.99282	-105.26683	1673.0	0.00	NaN
...	...	...	...	...	...	...	...
2024-02-14	USC00050848	BOULDER, CO US	39.99282	-105.26683	1673.0	0.00	41.0
2024-02-15	USC00050848	BOULDER, CO US	39.99282	-105.26683	1673.0	0.00	39.0
2024-02-16	USC00050848	BOULDER, CO US	39.99282	-105.26683	1673.0	0.20	23.0
2024-02-17	USC00050848	BOULDER, CO US	39.99282	-105.26683	1673.0	0.22	23.0
2024-02-18	USC00050848	BOULDER, CO US	39.99282	-105.26683	1673.0	0.00	42.0

46112 rows × 7 columns

# DO NOT MODIFY THIS TEST CELL
tmax_df_resp = _
points = 0

if isinstance(tmax_df_resp, pd.DataFrame):
    points += 2
    print('\u2705 Great work! You called a DataFrame.')
else:
    print('\u274C Oops - make sure to call your DataFrame for testing.')

print('You earned {} of 2 points for downloading data'.format(points))

✅ Great work! You called a DataFrame.
You earned 2 of 2 points for downloading data

HINT: Check out the type() function below - you can use it to check that your data is now in DataFrame type object

# Check that the data was imported into a pandas DataFrame
type(boulder_df)

pandas.core.frame.DataFrame

Clean up your DataFrame

Use double brackets to only select the columns you want in your DataFrame

Make sure to call your DataFrame by typing it’s name as the last line of your code cell Then, you will be able to run the test cell below and find out if your answer is correct.

# Cleaning up the data frame
boulder_df = boulder_df[['PRCP','TOBS']]
boulder_df

	PRCP	TOBS
DATE
1893-10-01	0.94	NaN
1893-10-02	0.00	NaN
1893-10-03	0.00	NaN
1893-10-04	0.04	NaN
1893-10-05	0.00	NaN
...	...	...
2024-02-14	0.00	41.0
2024-02-15	0.00	39.0
2024-02-16	0.20	23.0
2024-02-17	0.22	23.0
2024-02-18	0.00	42.0

46112 rows × 2 columns

# DO NOT MODIFY THIS TEST CELL
tmax_df_resp = _
points = 0

summary = [round(val, 2) for val in tmax_df_resp.mean().values]
if summary == [0.05, 54.53]:
    points += 5
    print('\u2705 Great work! You correctly downloaded data.')
else:
    print('\u274C Oops - your data are not correct.')
print('You earned {} of 5 points for downloading data'.format(points))

✅ Great work! You correctly downloaded data.
You earned 5 of 5 points for downloading data

---

Plot the precpitation column (PRCP) vs time to explore the data

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

boulder_df.plot()

<Axes: xlabel='DATE'>

You’ll always need to add some instructions on labels and how you want your plot to look.

Your task:

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

HINT: 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='TOBS').

# Plot the data using .plot
boulder_df.plot(
    y='TOBS',
    title='Temperature (F) at Boulder, CO\n1893-2024',
    xlabel='Year',
    ylabel='Temperature (F)',
    legend=False,
    figsize=(10,6))

<Axes: title={'center': 'Temperature (F) at Boulder, CO\n1893-2024'}, xlabel='Year', ylabel='Temperature (F)'>

Want 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!

---

Convert units

Modify the code below to add a column that includes temperature in Celsius. The code below was written by your colleague. Can you fix this so that it correctly calculates temperature in Celsius and adds a new column?

# Convert to celcius
boulder_df['TCel'] = (boulder_df['TOBS'] - 32) * 5 / 9
boulder_df

/tmp/ipykernel_9647/860760448.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  boulder_df['TCel'] = (boulder_df['TOBS'] - 32) * 5 / 9

	PRCP	TOBS	TCel
DATE
1893-10-01	0.94	NaN	NaN
1893-10-02	0.00	NaN	NaN
1893-10-03	0.00	NaN	NaN
1893-10-04	0.04	NaN	NaN
1893-10-05	0.00	NaN	NaN
...	...	...	...
2024-02-14	0.00	41.0	5.000000
2024-02-15	0.00	39.0	3.888889
2024-02-16	0.20	23.0	-5.000000
2024-02-17	0.22	23.0	-5.000000
2024-02-18	0.00	42.0	5.555556

46112 rows × 3 columns

# DO NOT MODIFY THIS TEST CELL
tmax_df_resp = _
points = 0

if isinstance(tmax_df_resp, pd.DataFrame):
    points += 1
    print('\u2705 Great work! You called a DataFrame.')
else:
    print('\u274C Oops - make sure to call your DataFrame for testing.')

summary = [round(val, 2) for val in tmax_df_resp.mean().values]
if summary == [0.05, 54.53, 12.52]:
    points += 4
    print('\u2705 Great work! You correctly converted to Celcius.')
else:
    print('\u274C Oops - your data are not correct.')
print('You earned {} of 5 points for converting to Celcius'.format(points))

✅ Great work! You called a DataFrame.
✅ Great work! You correctly converted to Celcius.
You earned 5 of 5 points for converting to Celcius

Want an EXTRA CHALLENGE?

1. As you did above, rewrite the code to be more expressive
2. Using the code below as a framework, write and apply a function that converts to Celcius. > Functions let you reuse code you have already written
3. You should also rewrite this function and parameter names to be more expressive.

# Write a function to convert Fahrenheit to Celsius
def fah_to_cel(fah):
    """Convert temperature to Celcius"""
    return (fah-32)*5/9 # Put your equation in here

boulder_df['celcius_column'] = boulder_df['TOBS'].apply(fah_to_cel)
boulder_df

/tmp/ipykernel_9647/2628125120.py:6: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  boulder_df['celcius_column'] = boulder_df['TOBS'].apply(fah_to_cel)

	PRCP	TOBS	TCel	celcius_column
DATE
1893-10-01	0.94	NaN	NaN	NaN
1893-10-02	0.00	NaN	NaN	NaN
1893-10-03	0.00	NaN	NaN	NaN
1893-10-04	0.04	NaN	NaN	NaN
1893-10-05	0.00	NaN	NaN	NaN
...	...	...	...	...
2024-02-14	0.00	41.0	5.000000	5.000000
2024-02-15	0.00	39.0	3.888889	3.888889
2024-02-16	0.20	23.0	-5.000000	-5.000000
2024-02-17	0.22	23.0	-5.000000	-5.000000
2024-02-18	0.00	42.0	5.555556	5.555556

46112 rows × 4 columns

Subsetting and Resampling

Often when working with time-series data you may want to focus on a shorter window of time, or look at weekly, monthly, or annual summaries to help make the analysis more manageable.

Read more

Read more about subsetting and resampling time-series data in our Learning Portal.

For this demonstration, we will look at the last 40 years worth of data and resample to explore a summary from each year that data were recorded.

Your task

1. Replace start-year and end-year with 1983 and 2023
2. Replace dataframe with the name of your data
3. Replace new_dataframe with something more expressive
4. Call your new variable
5. Run the cell

# Subset the data 1983 - 2023
boulder_1983_2023 = boulder_df['1983':'2023']
boulder_1983_2023

	PRCP	TOBS	TCel	celcius_column
DATE
1983-01-01	0.0	NaN	NaN	NaN
1983-01-02	0.0	NaN	NaN	NaN
1983-01-03	0.0	NaN	NaN	NaN
1983-01-04	0.0	NaN	NaN	NaN
1983-01-05	0.0	NaN	NaN	NaN
...	...	...	...	...
2023-12-27	0.0	41.0	5.000000	5.000000
2023-12-28	0.0	NaN	NaN	NaN
2023-12-29	0.0	39.0	3.888889	3.888889
2023-12-30	0.0	38.0	3.333333	3.333333
2023-12-31	0.0	33.0	0.555556	0.555556

14629 rows × 4 columns

# DO NOT MODIFY THIS TEST CELL
df_resp = _
points = 0

if isinstance(df_resp, pd.DataFrame):
    points += 1
    print('\u2705 Great work! You called a DataFrame.')
else:
    print('\u274C Oops - make sure to call your DataFrame for testing.')

summary = [round(val, 2) for val in df_resp.mean().values]
if summary == [0.06, 55.67, 13.15]:
    points += 5
    print('\u2705 Great work! You correctly converted to Celcius.')
else:
    print('\u274C Oops - your data are not correct.')
print('You earned {} of 5 points for subsetting'.format(points))

✅ Great work! You called a DataFrame.
❌ Oops - your data are not correct.
You earned 1 of 5 points for subsetting

Now we are ready to calculate annual statistics

Here you will resample the 1983-2023 data to look the annual mean values.

Resample your data

1. Replace new_dataframe with the variable you created in the cell above where you subset the data
2. Replace 'TIME' with a 'W', 'M', or 'Y' depending on whether you’re doing a weekly, monthly, or yearly summary
3. Replace STAT with a sum, min, max, or mean depending on what kind of statistic you’re interested in calculating.
4. Replace resampled_data with a more expressive variable name
5. Call your new variable
6. Run the cell

# Resample the data to look at yearly mean values
boulder_yearly_mean = boulder_1983_2023.resample('Y').mean()
boulder_yearly_mean

/tmp/ipykernel_9647/2796985930.py:2: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead.
  boulder_yearly_mean = boulder_1983_2023.resample('Y').mean()

	PRCP	TOBS	TCel	celcius_column
DATE
1983-12-31	0.068588	53.319749	11.844305	11.844305
1984-12-31	0.050656	50.601093	10.333940	10.333940
1985-12-31	0.047781	52.354571	11.308095	11.308095
1986-12-31	0.058493	55.616438	13.120244	13.120244
1987-12-31	0.070740	54.205479	12.336377	12.336377
1988-12-31	0.046311	54.650273	12.583485	12.583485
1989-12-31	0.058585	55.400943	13.000524	13.000524
1990-12-31	0.053782	59.463504	15.257502	15.257502
1991-12-31	0.058000	54.498623	12.499235	12.499235
1992-12-31	0.047486	54.556164	12.531202	12.531202
1993-12-31	0.062365	50.829341	10.460745	10.460745
1994-12-31	0.046000	38.715789	3.730994	3.730994
1995-12-31	0.080630	54.792818	12.662676	12.662676
1996-12-31	0.059235	55.233516	12.907509	12.907509
1997-12-31	0.078055	54.274725	12.374847	12.374847
1998-12-31	0.061068	55.931507	13.295282	13.295282
1999-12-31	0.071099	56.079452	13.377473	13.377473
2000-12-31	0.043434	56.719780	13.733211	13.733211
2001-12-31	0.049863	56.457534	13.587519	13.587519
2002-12-31	0.038027	56.638356	13.687976	13.687976
2003-12-31	0.060329	57.230137	14.016743	14.016743
2004-12-31	0.074235	55.420765	13.011536	13.011536
2005-12-31	0.047726	56.871233	13.817352	13.817352
2006-12-31	0.052904	57.772603	14.318113	14.318113
2007-12-31	0.047205	56.616438	13.675799	13.675799
2008-12-31	0.046503	56.175342	13.430746	13.430746
2009-12-31	0.057216	54.212575	12.340319	12.340319
2010-12-31	0.055644	55.854795	13.252664	13.252664
2011-12-31	0.061068	55.975275	13.319597	13.319597
2012-12-31	0.042760	59.857534	15.476408	15.476408
2013-12-31	0.093562	55.454795	13.030441	13.030441
2014-12-31	0.064575	55.367123	12.981735	12.981735
2015-12-31	0.073753	56.710744	13.728191	13.728191
2016-12-31	0.047131	57.836066	14.353370	14.353370
2017-12-31	0.061617	60.129129	15.627294	15.627294
2018-12-31	0.052740	57.005479	13.891933	13.891933
2019-12-31	0.057644	54.426997	12.459443	12.459443
2020-12-31	0.046721	57.691460	14.273033	14.273033
2021-12-31	0.056658	57.538462	14.188034	14.188034
2022-12-31	0.051479	56.139726	13.410959	13.410959
2023-12-31	0.062740	55.694215	13.163453	13.163453

# DO NOT MODIFY THIS TEST CELL
df_resp = _
points = 0

if isinstance(df_resp, pd.DataFrame):
    points += 1
    print('\u2705 Great work! You called a DataFrame.')
else:
    print('\u274C Oops - make sure to call your DataFrame for testing.')

summary = [round(val, 2) for val in df_resp.mean().values]
if summary == [0.06, 55.37, 12.99]:
    points += 5
    print('\u2705 Great work! You correctly converted to Celcius.')
else:
    print('\u274C Oops - your data are not correct.')
print('You earned {} of 5 points for resampling'.format(points))

✅ Great work! You called a DataFrame.
❌ Oops - your data are not correct.
You earned 1 of 5 points for resampling

Plot your resampled data

# Plot mean annual temperature values
boulder_yearly_mean.plot(y='TOBS')

<Axes: xlabel='DATE'>

boulder_df_reset = boulder_yearly_mean.reset_index()
boulder_df_reset

	DATE	PRCP	TOBS	TCel	celcius_column
0	1983-12-31	0.068588	53.319749	11.844305	11.844305
1	1984-12-31	0.050656	50.601093	10.333940	10.333940
2	1985-12-31	0.047781	52.354571	11.308095	11.308095
3	1986-12-31	0.058493	55.616438	13.120244	13.120244
4	1987-12-31	0.070740	54.205479	12.336377	12.336377
5	1988-12-31	0.046311	54.650273	12.583485	12.583485
6	1989-12-31	0.058585	55.400943	13.000524	13.000524
7	1990-12-31	0.053782	59.463504	15.257502	15.257502
8	1991-12-31	0.058000	54.498623	12.499235	12.499235
9	1992-12-31	0.047486	54.556164	12.531202	12.531202
10	1993-12-31	0.062365	50.829341	10.460745	10.460745
11	1994-12-31	0.046000	38.715789	3.730994	3.730994
12	1995-12-31	0.080630	54.792818	12.662676	12.662676
13	1996-12-31	0.059235	55.233516	12.907509	12.907509
14	1997-12-31	0.078055	54.274725	12.374847	12.374847
15	1998-12-31	0.061068	55.931507	13.295282	13.295282
16	1999-12-31	0.071099	56.079452	13.377473	13.377473
17	2000-12-31	0.043434	56.719780	13.733211	13.733211
18	2001-12-31	0.049863	56.457534	13.587519	13.587519
19	2002-12-31	0.038027	56.638356	13.687976	13.687976
20	2003-12-31	0.060329	57.230137	14.016743	14.016743
21	2004-12-31	0.074235	55.420765	13.011536	13.011536
22	2005-12-31	0.047726	56.871233	13.817352	13.817352
23	2006-12-31	0.052904	57.772603	14.318113	14.318113
24	2007-12-31	0.047205	56.616438	13.675799	13.675799
25	2008-12-31	0.046503	56.175342	13.430746	13.430746
26	2009-12-31	0.057216	54.212575	12.340319	12.340319
27	2010-12-31	0.055644	55.854795	13.252664	13.252664
28	2011-12-31	0.061068	55.975275	13.319597	13.319597
29	2012-12-31	0.042760	59.857534	15.476408	15.476408
30	2013-12-31	0.093562	55.454795	13.030441	13.030441
31	2014-12-31	0.064575	55.367123	12.981735	12.981735
32	2015-12-31	0.073753	56.710744	13.728191	13.728191
33	2016-12-31	0.047131	57.836066	14.353370	14.353370
34	2017-12-31	0.061617	60.129129	15.627294	15.627294
35	2018-12-31	0.052740	57.005479	13.891933	13.891933
36	2019-12-31	0.057644	54.426997	12.459443	12.459443
37	2020-12-31	0.046721	57.691460	14.273033	14.273033
38	2021-12-31	0.056658	57.538462	14.188034	14.188034
39	2022-12-31	0.051479	56.139726	13.410959	13.410959
40	2023-12-31	0.062740	55.694215	13.163453	13.163453

boulder_df_reset['year'] = boulder_df_reset['DATE'].dt.year
boulder_df_reset

	DATE	PRCP	TOBS	TCel	celcius_column	year
0	1983-12-31	0.068588	53.319749	11.844305	11.844305	1983
1	1984-12-31	0.050656	50.601093	10.333940	10.333940	1984
2	1985-12-31	0.047781	52.354571	11.308095	11.308095	1985
3	1986-12-31	0.058493	55.616438	13.120244	13.120244	1986
4	1987-12-31	0.070740	54.205479	12.336377	12.336377	1987
5	1988-12-31	0.046311	54.650273	12.583485	12.583485	1988
6	1989-12-31	0.058585	55.400943	13.000524	13.000524	1989
7	1990-12-31	0.053782	59.463504	15.257502	15.257502	1990
8	1991-12-31	0.058000	54.498623	12.499235	12.499235	1991
9	1992-12-31	0.047486	54.556164	12.531202	12.531202	1992
10	1993-12-31	0.062365	50.829341	10.460745	10.460745	1993
11	1994-12-31	0.046000	38.715789	3.730994	3.730994	1994
12	1995-12-31	0.080630	54.792818	12.662676	12.662676	1995
13	1996-12-31	0.059235	55.233516	12.907509	12.907509	1996
14	1997-12-31	0.078055	54.274725	12.374847	12.374847	1997
15	1998-12-31	0.061068	55.931507	13.295282	13.295282	1998
16	1999-12-31	0.071099	56.079452	13.377473	13.377473	1999
17	2000-12-31	0.043434	56.719780	13.733211	13.733211	2000
18	2001-12-31	0.049863	56.457534	13.587519	13.587519	2001
19	2002-12-31	0.038027	56.638356	13.687976	13.687976	2002
20	2003-12-31	0.060329	57.230137	14.016743	14.016743	2003
21	2004-12-31	0.074235	55.420765	13.011536	13.011536	2004
22	2005-12-31	0.047726	56.871233	13.817352	13.817352	2005
23	2006-12-31	0.052904	57.772603	14.318113	14.318113	2006
24	2007-12-31	0.047205	56.616438	13.675799	13.675799	2007
25	2008-12-31	0.046503	56.175342	13.430746	13.430746	2008
26	2009-12-31	0.057216	54.212575	12.340319	12.340319	2009
27	2010-12-31	0.055644	55.854795	13.252664	13.252664	2010
28	2011-12-31	0.061068	55.975275	13.319597	13.319597	2011
29	2012-12-31	0.042760	59.857534	15.476408	15.476408	2012
30	2013-12-31	0.093562	55.454795	13.030441	13.030441	2013
31	2014-12-31	0.064575	55.367123	12.981735	12.981735	2014
32	2015-12-31	0.073753	56.710744	13.728191	13.728191	2015
33	2016-12-31	0.047131	57.836066	14.353370	14.353370	2016
34	2017-12-31	0.061617	60.129129	15.627294	15.627294	2017
35	2018-12-31	0.052740	57.005479	13.891933	13.891933	2018
36	2019-12-31	0.057644	54.426997	12.459443	12.459443	2019
37	2020-12-31	0.046721	57.691460	14.273033	14.273033	2020
38	2021-12-31	0.056658	57.538462	14.188034	14.188034	2021
39	2022-12-31	0.051479	56.139726	13.410959	13.410959	2022
40	2023-12-31	0.062740	55.694215	13.163453	13.163453	2023

boulder_df_reset.set_index('year', inplace=True)
boulder_df_reset

	DATE	PRCP	TOBS	TCel	celcius_column
year
1983	1983-12-31	0.068588	53.319749	11.844305	11.844305
1984	1984-12-31	0.050656	50.601093	10.333940	10.333940
1985	1985-12-31	0.047781	52.354571	11.308095	11.308095
1986	1986-12-31	0.058493	55.616438	13.120244	13.120244
1987	1987-12-31	0.070740	54.205479	12.336377	12.336377
1988	1988-12-31	0.046311	54.650273	12.583485	12.583485
1989	1989-12-31	0.058585	55.400943	13.000524	13.000524
1990	1990-12-31	0.053782	59.463504	15.257502	15.257502
1991	1991-12-31	0.058000	54.498623	12.499235	12.499235
1992	1992-12-31	0.047486	54.556164	12.531202	12.531202
1993	1993-12-31	0.062365	50.829341	10.460745	10.460745
1994	1994-12-31	0.046000	38.715789	3.730994	3.730994
1995	1995-12-31	0.080630	54.792818	12.662676	12.662676
1996	1996-12-31	0.059235	55.233516	12.907509	12.907509
1997	1997-12-31	0.078055	54.274725	12.374847	12.374847
1998	1998-12-31	0.061068	55.931507	13.295282	13.295282
1999	1999-12-31	0.071099	56.079452	13.377473	13.377473
2000	2000-12-31	0.043434	56.719780	13.733211	13.733211
2001	2001-12-31	0.049863	56.457534	13.587519	13.587519
2002	2002-12-31	0.038027	56.638356	13.687976	13.687976
2003	2003-12-31	0.060329	57.230137	14.016743	14.016743
2004	2004-12-31	0.074235	55.420765	13.011536	13.011536
2005	2005-12-31	0.047726	56.871233	13.817352	13.817352
2006	2006-12-31	0.052904	57.772603	14.318113	14.318113
2007	2007-12-31	0.047205	56.616438	13.675799	13.675799
2008	2008-12-31	0.046503	56.175342	13.430746	13.430746
2009	2009-12-31	0.057216	54.212575	12.340319	12.340319
2010	2010-12-31	0.055644	55.854795	13.252664	13.252664
2011	2011-12-31	0.061068	55.975275	13.319597	13.319597
2012	2012-12-31	0.042760	59.857534	15.476408	15.476408
2013	2013-12-31	0.093562	55.454795	13.030441	13.030441
2014	2014-12-31	0.064575	55.367123	12.981735	12.981735
2015	2015-12-31	0.073753	56.710744	13.728191	13.728191
2016	2016-12-31	0.047131	57.836066	14.353370	14.353370
2017	2017-12-31	0.061617	60.129129	15.627294	15.627294
2018	2018-12-31	0.052740	57.005479	13.891933	13.891933
2019	2019-12-31	0.057644	54.426997	12.459443	12.459443
2020	2020-12-31	0.046721	57.691460	14.273033	14.273033
2021	2021-12-31	0.056658	57.538462	14.188034	14.188034
2022	2022-12-31	0.051479	56.139726	13.410959	13.410959
2023	2023-12-31	0.062740	55.694215	13.163453	13.163453

# Using ChatGPT to help add a trendline to the plot

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np

fig, ax = plt.subplots(figsize=(5,3))

x_values = boulder_df_reset.index


ax.scatter(x_values,
           boulder_df_reset["TCel"],
           label="Temperature Data",
           color='white',
           edgecolor='black')

# Fit a linear regression model
slope, intercept = np.polyfit(x_values, boulder_df_reset["TCel"], 1)
trendline = slope * x_values + intercept

ax.plot(x_values, trendline, color='red', label="Trendline")
ax.plot(x_values, boulder_df_reset["TCel"], color='grey', markersize=0.3)


# Add text box with slope value (From ChatGPT)
slope_text = f"Slope: {slope:.3f} °C/yr"
ax.text(0.5, 0.6, slope_text, transform=ax.transAxes, fontsize=10,
        verticalalignment='top', bbox=dict(boxstyle='round', facecolor='whitesmoke', alpha=0.9))



ax.set(title="Mean Annual Temperature\nBoulder, CO (1983-2023)",
       ylabel="Temperature (°C)")

ax.legend()  # Add legend to display labels

plt.show()

# Create interactive map using folium
import folium

# Weather station info
station_lat = 39.99282
station_long = -105.26683
station_location_name = 'NOAA Weather Station [GHCND:USC00050848] - Boulder, CO'

# Create a Folium map object
m = folium.Map(location=[station_lat,station_long], zoom_start=10)

# Add marker for river gage
folium.Marker(
    location=[station_lat, station_long],
    popup=station_location_name,
    icon=folium.Icon(color='black')
).add_to(m)


# Save the map as an HTML file or display it inline
m.save('map.html')
# OR
m

Make this Notebook Trusted to load map: File -> Trust Notebook

---

Describe your plot

We like to use an approach called “Assertion-Evidence” for presenting scientific results. There’s a lot of video tutorials and example talks available on the Assertion-Evidence web page. The main thing you need to do now is to practice writing a message or headline rather than descriptions or topic sentences for the plot you just made (what they refer to as “visual evidence”).

For example, it would be tempting to write something like “A plot of maximum annual temperature in Boulder, Colorado over time (1983-2023)”. However, this doesn’t give the reader anything to look at, or explain why we made this particular plot (we know, you made this one because we told you to)

Some alternatives for different plots of Boulder temperature that are more of a starting point for a presentation or conversation are:

• Boulder, CO experienced cooler than average temperatures in 1995
• Temperatures in Bouler, CO appear to be on the rise over the past 40 years
• Maximum annual temperatures in Boulder, CO are becoming more variable over the previous 40 years

We could back up some of these claims with further analysis included later on, but we want to make sure that our audience has some guidance on what to look for in the plot.

---

YOUR BOULDER PLOT HEADLINE HERE 📰 🗞️ 📻

Describe your plot in this cell in 2-3 sentences

Writing bear

Image credit: https://www.craiyon.com/image/OAbZtyelSoS7FdGko6hvQg

---

THIS ISN’T THE END! 😄

Don’t forget to reproduce your analysis in a new location or time!

Image source: https://www.independent.co.uk/climate-change/news/by-the-left-quick-march-the-emperor-penguins-migration-1212420.html

---

Your turn: pick a new location and/or measurement to plot 🌏 📈

Below (or in a new notebook!), recreate the workflow you just did in a place that interests you OR with a different measurement. See the instructions above to adapt the URL that we created for Boulder, CO using the NCEI API. You will need to make your own new Markdown and Code cells below this one, or create a new notebook.

---

Congratulations, you’re almost done with this coding challenge 🤩 – now make sure that your code is reproducible

Image source: https://dfwurbanwildlife.com/2018/03/25/chris-jacksons-dfw-urban-wildlife/snow-geese-galore/

Your task

1. If you didn’t already, go back to the code you modified about and write more descriptive comments so the next person to use this code knows what it does.

2. Make sure to Restart and Run all up at the top of your notebook. This will clear all your variables and make sure that your code runs in the correct order. It will also export your work in Markdown format, which you can put on your website.

---

BONUS: Create a shareable Markdown of your work

Below is some code that you can run that will save a Markdown file of your work that is easily shareable and can be uploaded to GitHub Pages. You can use it as a starting point for writing your portfolio post!

%%capture
%%bash
jupyter nbconvert *.ipynb --to html