Climate change is impacting the way people live around the world¶

::: {.cell .markdown}

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 Rapid City, SD.

Important

Read about some of the effects of – and solutions for – climate change on the Pine Ridge Reservation southeast of Rapid City.

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.

No description has been provided for this image

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.

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.
  1. Open reproducible science is about making research, it's methods and outcomes, available to anyone. This includes that the research can be repeated, the data used is available to the public, and there is transparency around the methods and results.
  2. GitHub supports open and reproducible science because it allows a space where findings can be shared easily with the public and other researchers. With GitHub's functionality of codespaces and the ability to fork code, it allows for others to take your code, use it, add onto it, and continue to progress research.

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

This Jupyter notebook doesn't have a machine-readable name because it has spaces and an exclimation point in the name.

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

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

NOTE: **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.

In [1]:
# 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.

In [2]:
# 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 Rapid City, CO (station ID USC00396947).

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).

The data is taken from 30 different sources at climate stations around the world. Over 25,000 stations are regularly updated with observations from within roughly the last month. The dataset includes observations from World Meteorological Organization, Cooperative, and CoCoRaHS networks. NOAA's National Centers for Environmental Information houses this data. The maximum temperature units is tenths of degrees C. The dataset is also routinely reconstructed (usually every week) from its roughly 30 data sources to ensure that GHCN-Daily is generally in sync with its growing list of constituent sources. The data is collected from climate stations around the world.

Citation: Menne, Matthew J., Imke Durre, Bryant Korzeniewski, Shelley McNeill, Kristy Thomas, Xungang Yin, Steven Anthony, Ron Ray, Russell S. Vose, Byron E.Gleason, and Tamara G. Houston (2012): Global Historical Climatology Network - Daily (GHCN-Daily), Version 3. [indicate subset used]. NOAA National Climatic Data Center. doi:10.7289/V5D21VHZ [May 13, 2024].

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.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.
In [3]:
# Getting data from NCEI for Rapid City, CO
rapidcityurl = (
    'https://www.ncei.noaa.gov/access/services/data/v1?'
    'dataset=daily-summaries'
    '&dataTypes=TOBS,PRCP'
    '&stations=USC00396947'
    '&startDate=1949-10-01'
    '&endDate=2024-05-03'
    '&includeStationName=true'
    '&includeStationLocation=1'
    '&units=standard')
rapidcityurl
Out[3]:
'https://www.ncei.noaa.gov/access/services/data/v1?dataset=daily-summaries&dataTypes=TOBS,PRCP&stations=USC00396947&startDate=1949-10-01&endDate=2024-05-03&includeStationName=true&includeStationLocation=1&units=standard'
In [4]:
# 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.

In [5]:
# creating a data frame for Rapid City
rapidcity_df = pd.read_csv(
  rapidcityurl,
  index_col='DATE',
  parse_dates=True,
  na_values=['NaN'])
rapidcity_df
Out[5]:
STATION NAME LATITUDE LONGITUDE ELEVATION PRCP TOBS
DATE
1949-10-01 USC00396947 RAPID CITY 4 NW, SD US 44.12055 -103.28417 1060.4 0.00 51.0
1949-10-02 USC00396947 RAPID CITY 4 NW, SD US 44.12055 -103.28417 1060.4 0.00 51.0
1949-10-03 USC00396947 RAPID CITY 4 NW, SD US 44.12055 -103.28417 1060.4 0.00 52.0
1949-10-04 USC00396947 RAPID CITY 4 NW, SD US 44.12055 -103.28417 1060.4 0.00 45.0
1949-10-05 USC00396947 RAPID CITY 4 NW, SD US 44.12055 -103.28417 1060.4 0.00 50.0
... ... ... ... ... ... ... ...
2024-04-28 USC00396947 RAPID CITY 4 NW, SD US 44.12055 -103.28417 1060.4 0.00 NaN
2024-04-29 USC00396947 RAPID CITY 4 NW, SD US 44.12055 -103.28417 1060.4 0.37 30.0
2024-04-30 USC00396947 RAPID CITY 4 NW, SD US 44.12055 -103.28417 1060.4 0.00 44.0
2024-05-01 USC00396947 RAPID CITY 4 NW, SD US 44.12055 -103.28417 1060.4 0.00 33.0
2024-05-02 USC00396947 RAPID CITY 4 NW, SD US 44.12055 -103.28417 1060.4 0.35 39.0

26109 rows × 7 columns

In [6]:
# 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.')

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

✅ Great work! You called a DataFrame.
You earned 1 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

In [7]:
# Check that the data was imported into a pandas DataFrame
type(rapidcity_df)
Out[7]:
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.

In [8]:
# Checking column names to know all columns in data
rapidcity_df.columns
Out[8]:
Index(['STATION', 'NAME', 'LATITUDE', 'LONGITUDE', 'ELEVATION', 'PRCP',
       'TOBS'],
      dtype='object')
In [9]:
# Rewriting data frame to only have precipitations and TOBS data
rapidcity_df = rapidcity_df[['PRCP', 'TOBS']]
rapidcity_df
Out[9]:
PRCP TOBS
DATE
1949-10-01 0.00 51.0
1949-10-02 0.00 51.0
1949-10-03 0.00 52.0
1949-10-04 0.00 45.0
1949-10-05 0.00 50.0
... ... ...
2024-04-28 0.00 NaN
2024-04-29 0.37 30.0
2024-04-30 0.00 44.0
2024-05-01 0.00 33.0
2024-05-02 0.35 39.0

26109 rows × 2 columns

In [10]:
# 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 += 4
    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))

❌ Oops - your data are not correct.
You earned 0 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:

In [11]:
# Plotting Rapid City PRCP and TOBS 
rapidcity_df.plot()
Out[11]:
<Axes: xlabel='DATE'>
No description has been provided for this image

****Label and describe your plots****

Source: https://xkcd.com/833
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

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').

In [12]:
# Plotting Daily Observed Temperature for Rapid City from 1944-2024
rapidcity_df.plot(
    y='TOBS',
    title='Rapid City Daily Observed Temperature 1944-2024',
    xlabel='Date',
    ylabel='Temperature (F)',
    legend=False,
    color='blue',
    figsize=(10,5),
    fontsize=14)
Out[12]:
<Axes: title={'center': 'Rapid City Daily Observed Temperature 1944-2024'}, xlabel='Date', ylabel='Temperature (F)'>
No description has been provided for this image

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?

In [13]:
# Convert to celcius
rapidcity_df.loc[:,'TCel'] = (rapidcity_df['TOBS'] - 32) * 5 / 9
rapidcity_df

/tmp/ipykernel_6497/1789627478.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
  rapidcity_df.loc[:,'TCel'] = (rapidcity_df['TOBS'] - 32) * 5 / 9
Out[13]:
PRCP TOBS TCel
DATE
1949-10-01 0.00 51.0 10.555556
1949-10-02 0.00 51.0 10.555556
1949-10-03 0.00 52.0 11.111111
1949-10-04 0.00 45.0 7.222222
1949-10-05 0.00 50.0 10.000000
... ... ... ...
2024-04-28 0.00 NaN NaN
2024-04-29 0.37 30.0 -1.111111
2024-04-30 0.00 44.0 6.666667
2024-05-01 0.00 33.0 0.555556
2024-05-02 0.35 39.0 3.888889

26109 rows × 3 columns

In [14]:
# 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.
❌ Oops - your data are not correct.
You earned 1 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.
In [15]:
# Creating a function to convert from fahrenheit to celsius
def to_celsius(fahrenheit):
    """Convert temperature to Celsius"""
    return (fahrenheit - 32) * 5 / 9

# Displaying dataframe with new column, celsius
rapidcity_df['celsius'] = rapidcity_df['TOBS'].apply(to_celsius)
rapidcity_df

/tmp/ipykernel_6497/2859914288.py:7: 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
  rapidcity_df['celsius'] = rapidcity_df['TOBS'].apply(to_celsius)
Out[15]:
PRCP TOBS TCel celsius
DATE
1949-10-01 0.00 51.0 10.555556 10.555556
1949-10-02 0.00 51.0 10.555556 10.555556
1949-10-03 0.00 52.0 11.111111 11.111111
1949-10-04 0.00 45.0 7.222222 7.222222
1949-10-05 0.00 50.0 10.000000 10.000000
... ... ... ... ...
2024-04-28 0.00 NaN NaN NaN
2024-04-29 0.37 30.0 -1.111111 -1.111111
2024-04-30 0.00 44.0 6.666667 6.666667
2024-05-01 0.00 33.0 0.555556 0.555556
2024-05-02 0.35 39.0 3.888889 3.888889

26109 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
In [16]:
# Subset the data
rapidcitysubset = rapidcity_df.loc['1983':'2023']
rapidcitysubset
Out[16]:
PRCP TOBS TCel celsius
DATE
1983-01-01 0.00 30.0 -1.111111 -1.111111
1983-01-02 0.00 29.0 -1.666667 -1.666667
1983-01-03 0.00 40.0 4.444444 4.444444
1983-01-04 0.00 33.0 0.555556 0.555556
1983-01-05 0.00 43.0 6.111111 6.111111
... ... ... ... ...
2023-12-27 0.31 32.0 0.000000 0.000000
2023-12-28 0.00 17.0 -8.333333 -8.333333
2023-12-29 0.00 28.0 -2.222222 -2.222222
2023-12-30 0.00 NaN NaN NaN
2023-12-31 0.00 NaN NaN NaN

13939 rows × 4 columns

In [17]:
# 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
In [18]:
# Resample the data to look at yearly mean values
rapidyearly = rapidcitysubset.resample('YE').mean()
rapidyearly
Out[18]:
PRCP TOBS TCel celsius
DATE
1983-12-31 0.038849 59.302632 15.168129 15.168129
1984-12-31 0.026145 54.458182 12.476768 12.476768
1985-12-31 0.039091 50.691667 10.384259 10.384259
1986-12-31 0.069551 53.672673 12.040374 12.040374
1987-12-31 0.039011 56.988950 13.882750 13.882750
1988-12-31 0.028017 56.983240 13.879578 13.879578
1989-12-31 0.056359 38.072829 3.373794 3.373794
1990-12-31 0.039068 40.363112 4.646174 4.646174
1991-12-31 0.056875 39.945869 4.414372 4.414372
1992-12-31 0.036714 39.525862 4.181034 4.181034
1993-12-31 0.055881 35.522581 1.956989 1.956989
1994-12-31 0.034540 39.479769 4.155427 4.155427
1995-12-31 0.063609 39.150568 3.972538 3.972538
1996-12-31 0.058785 36.547486 2.526381 2.526381
1997-12-31 0.057634 38.825073 3.791707 3.791707
1998-12-31 0.068343 40.563739 4.757633 4.757633
1999-12-31 0.073104 41.688202 5.382335 5.382335
2000-12-31 0.050771 39.750751 4.305973 4.305973
2001-12-31 0.049639 43.371134 6.317297 6.317297
2002-12-31 0.036126 33.482143 0.823413 0.823413
2003-12-31 0.039186 40.455253 4.697363 4.697363
2004-12-31 0.030242 38.877828 3.821016 3.821016
2005-12-31 0.044620 40.627119 4.792844 4.792844
2006-12-31 0.042870 40.873278 4.929599 4.929599
2007-12-31 0.038515 34.806931 1.559406 1.559406
2008-12-31 0.025892 34.204969 1.224983 1.224983
2009-12-31 0.053828 35.871324 2.150735 2.150735
2010-12-31 0.056767 39.012384 3.895769 3.895769
2011-12-31 0.060282 40.313846 4.618803 4.618803
2012-12-31 0.019341 42.008746 5.560415 5.560415
2013-12-31 0.060685 38.392638 3.551466 3.551466
2014-12-31 0.057726 39.211310 4.006283 4.006283
2015-12-31 0.057260 41.351275 5.195153 5.195153
2016-12-31 0.039508 42.161644 5.645358 5.645358
2017-12-31 0.034082 41.013889 5.007716 5.007716
2018-12-31 0.057335 36.670732 2.594851 2.594851
2019-12-31 0.085056 36.159544 2.310858 2.310858
2020-12-31 0.044006 41.023438 5.013021 5.013021
2021-12-31 0.032225 40.363248 4.646249 4.646249
2022-12-31 0.028421 39.331395 4.072997 4.072997
2023-12-31 0.046313 40.144578 4.524766 4.524766
In [19]:
# 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

In [20]:
# Plot mean annual temperature values from 1983 to 2023
rapidyearly.plot(
    y='TOBS',
    title='Rapid City Annual Mean Temperatures 1983-2023',
    xlabel='Year',
    ylabel='Temperature (F)',
    legend=False,
    color='blue',
    figsize=(10,5),
    fontsize=14
)
Out[20]:
<Axes: title={'center': 'Rapid City Annual Mean Temperatures 1983-2023'}, xlabel='Year', ylabel='Temperature (F)'>
No description has been provided for this image

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 Rapid City, 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 Rapid City temperature that are more of a starting point for a presentation or conversation are:

  • Rapid City, SD experienced cooler than average temperatures in 1995
  • Temperatures in Rapid City, SD appear to be on the rise over the past 40 years
  • Maximum annual temperatures in Rapid City, 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.

**Rapid City, CO colder than 40 years ago and temperatures continue to shift! ** 📰 🗞️ 📻¶

In the late eighties, Rapid City, Colorado experienced a drastic decrease in average annual temperature of almost 20 degrees fahrenheit. Today the annual mean remains low and is having more stark changes year to year.

Writing bear
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!

No description has been provided for this image

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 Rapid City, 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¶

No description has been provided for this image

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.
  1. 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.

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

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!

In [21]:
#Recreating with Rainier Paradise Ranger Station in Mount Rainier
#USC00456898  46.7858 -121.7425 1654.1 WA RAINIER PARADISE RS   
rainierurl = (
    'https://www.ncei.noaa.gov/access/services/data/v1?'
    'dataset=daily-summaries'
    '&dataTypes=TOBS,PRCP'
    '&stations=USC00456898'
    '&startDate=1916-12-01'
    '&endDate=2024-05-12'
    '&includeStationName=true'
    '&includeStationLocation=1'
    '&units=standard')
rainierurl
Out[21]:
'https://www.ncei.noaa.gov/access/services/data/v1?dataset=daily-summaries&dataTypes=TOBS,PRCP&stations=USC00456898&startDate=1916-12-01&endDate=2024-05-12&includeStationName=true&includeStationLocation=1&units=standard'
In [22]:
# creating a data frame for Rainier
rainier_df = pd.read_csv(
  rainierurl,
  index_col='DATE',
  parse_dates=True,
  na_values=['NaN'])
rainier_df
Out[22]:
STATION NAME LATITUDE LONGITUDE ELEVATION PRCP TOBS
DATE
1916-12-01 USC00456898 RAINIER PARADISE RANGER STATION, WA US 46.78639 -121.74222 1650.5 NaN 29.0
1916-12-02 USC00456898 RAINIER PARADISE RANGER STATION, WA US 46.78639 -121.74222 1650.5 NaN 28.0
1916-12-03 USC00456898 RAINIER PARADISE RANGER STATION, WA US 46.78639 -121.74222 1650.5 NaN 21.0
1916-12-04 USC00456898 RAINIER PARADISE RANGER STATION, WA US 46.78639 -121.74222 1650.5 NaN 23.0
1916-12-05 USC00456898 RAINIER PARADISE RANGER STATION, WA US 46.78639 -121.74222 1650.5 NaN 22.0
... ... ... ... ... ... ... ...
2024-05-08 USC00456898 RAINIER PARADISE RANGER STATION, WA US 46.78639 -121.74222 1650.5 NaN NaN
2024-05-09 USC00456898 RAINIER PARADISE RANGER STATION, WA US 46.78639 -121.74222 1650.5 NaN 54.0
2024-05-10 USC00456898 RAINIER PARADISE RANGER STATION, WA US 46.78639 -121.74222 1650.5 0.0 61.0
2024-05-11 USC00456898 RAINIER PARADISE RANGER STATION, WA US 46.78639 -121.74222 1650.5 0.0 59.0
2024-05-12 USC00456898 RAINIER PARADISE RANGER STATION, WA US 46.78639 -121.74222 1650.5 0.0 54.0

35888 rows × 7 columns

In [23]:
# Keeping only precipitation and TOBS columns
rainier_df = rainier_df[['PRCP', 'TOBS']]
rainier_df
Out[23]:
PRCP TOBS
DATE
1916-12-01 NaN 29.0
1916-12-02 NaN 28.0
1916-12-03 NaN 21.0
1916-12-04 NaN 23.0
1916-12-05 NaN 22.0
... ... ...
2024-05-08 NaN NaN
2024-05-09 NaN 54.0
2024-05-10 0.0 61.0
2024-05-11 0.0 59.0
2024-05-12 0.0 54.0

35888 rows × 2 columns

In [24]:
# Plotting Rainier Daily Observed Temperature
rainier_df.plot(
    y='TOBS',
    title='Rainier Paradise Ranger Station City Daily Observed Temperature 1916-2024',
    xlabel='Date',
    ylabel='Temperature (F)',
    legend=False,
    color='blue',
    figsize=(10,5),
    fontsize=14)
Out[24]:
<Axes: title={'center': 'Rainier Paradise Ranger Station City Daily Observed Temperature 1916-2024'}, xlabel='Date', ylabel='Temperature (F)'>
No description has been provided for this image
In [25]:
# Convert to celcius
rainier_df.loc[:,'TCel'] = (rainier_df['TOBS'] - 32) * 5 / 9
rainier_df

/tmp/ipykernel_6497/1784484435.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
  rainier_df.loc[:,'TCel'] = (rainier_df['TOBS'] - 32) * 5 / 9
Out[25]:
PRCP TOBS TCel
DATE
1916-12-01 NaN 29.0 -1.666667
1916-12-02 NaN 28.0 -2.222222
1916-12-03 NaN 21.0 -6.111111
1916-12-04 NaN 23.0 -5.000000
1916-12-05 NaN 22.0 -5.555556
... ... ... ...
2024-05-08 NaN NaN NaN
2024-05-09 NaN 54.0 12.222222
2024-05-10 0.0 61.0 16.111111
2024-05-11 0.0 59.0 15.000000
2024-05-12 0.0 54.0 12.222222

35888 rows × 3 columns

In [26]:
# creating subset from 1980 to 2023
rainiersubset = rainier_df.loc['1980':'2023']
rainiersubset
Out[26]:
PRCP TOBS TCel
DATE
1980-01-01 1.29 29.0 -1.666667
1980-01-02 0.08 31.0 -0.555556
1980-01-03 0.74 18.0 -7.777778
1980-01-04 0.10 21.0 -6.111111
1980-01-05 1.58 18.0 -7.777778
... ... ... ...
2023-12-27 NaN NaN NaN
2023-12-28 NaN 36.0 2.222222
2023-12-29 0.00 40.0 4.444444
2023-12-30 0.20 34.0 1.111111
2023-12-31 0.39 32.0 0.000000

15280 rows × 3 columns

In [27]:
# Resampling to get only mean yearly values 
rainieryearly = rainiersubset.resample('YE').mean()
rainieryearly
Out[27]:
PRCP TOBS TCel
DATE
1980-12-31 0.340847 34.983516 1.657509
1981-12-31 0.337315 36.912088 2.728938
1982-12-31 0.316418 34.668524 1.482513
1983-12-31 0.355205 35.173077 1.762821
1984-12-31 0.337637 33.704918 0.947177
1985-12-31 0.228000 35.640110 2.022283
1986-12-31 0.310932 38.142466 3.412481
1987-12-31 0.199331 39.781818 4.323232
1988-12-31 0.328297 37.186301 2.881279
1989-12-31 0.274848 35.632597 2.018109
1990-12-31 0.405439 35.107042 1.726135
1991-12-31 0.347582 36.430137 2.461187
1992-12-31 0.272932 37.699454 3.166363
1993-12-31 0.218822 33.498630 0.832572
1994-12-31 0.384620 35.877483 2.154157
1995-12-31 0.382644 35.853968 2.141093
1996-12-31 0.354772 36.933824 2.741013
1997-12-31 0.398710 35.944984 2.191658
1998-12-31 0.330840 36.889205 2.716225
1999-12-31 0.342132 35.739394 2.077441
2000-12-31 0.261652 33.385382 0.769657
2001-12-31 0.332028 33.288401 0.715778
2002-12-31 0.289858 37.241379 2.911877
2003-12-31 0.357840 35.927419 2.181900
2004-12-31 0.301335 40.064706 4.480392
2005-12-31 0.297229 38.962536 3.868076
2006-12-31 0.378636 38.075758 3.375421
2007-12-31 0.299623 37.391975 2.995542
2008-12-31 0.318908 35.789272 2.105151
2009-12-31 0.264627 36.655052 2.586140
2010-12-31 0.276537 38.596330 3.664628
2011-12-31 0.370717 34.744681 1.524823
2012-12-31 0.339748 36.509434 2.505241
2013-12-31 0.286890 38.206294 3.447941
2014-12-31 0.345017 38.678457 3.710254
2015-12-31 0.321603 41.861314 5.478508
2016-12-31 0.375076 38.674658 3.708143
2017-12-31 0.297438 40.143791 4.524328
2018-12-31 0.385768 38.235880 3.464378
2019-12-31 0.256429 38.764331 3.757962
2020-12-31 0.488738 37.295276 2.941820
2021-12-31 0.340103 40.833333 4.907407
2022-12-31 0.227745 41.918301 5.510167
2023-12-31 0.232778 43.404669 6.335927
In [28]:
# Plot mean annual temperature values for Rainier from 1980 to 2023
rainieryearly.plot(
    y='TOBS',
    title='Rainier Paradise Ranger Station Annual Mean Temperatures 1980-2023',
    xlabel='Year',
    ylabel='Temperature (F)',
    legend=False,
    color='blue',
    figsize=(10,5),
    fontsize=14
)
Out[28]:
<Axes: title={'center': 'Rainier Paradise Ranger Station Annual Mean Temperatures 1980-2023'}, xlabel='Year', ylabel='Temperature (F)'>
No description has been provided for this image

**Temperatures on the rise in Mount Rainier, WA over the last 40 years! ** 📰 🗞️ 📻¶

Over the last 40 years, Mount Rainier has experienced many drastic changes in temperature. Today we can see that both the extreme lows and extreme highs are increasing.

Writing bear
Writing bear

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

In [29]:
%%capture
%%bash
jupyter nbconvert *.ipynb --to markdown
In [ ]:
%%capture
%%bash
jupyter nbconvert *.ipynb --to html