You will be working on this project with our Replit starter code.
We are still developing the interactive instructional part of the Python curriculum. For now, here are some videos on the freeCodeCamp.org YouTube channel that will teach you everything you need to know to complete this project:
You will analyze a dataset of the global average sea level change since 1880. You will use the data to predict the sea level change through year 2050.
Use the data to complete the following tasks:
- Use Pandas to import the data from
epa-sea-level.csv. - Use matplotlib to create a scatter plot using the
Yearcolumn as the x-axis and theCSIRO Adjusted SeaLevel column as the y-axix. - Use the
linregressfunction fromscipy.statsto get the slope and y-intercept of the line of best fit. Plot the line of best fit over the top of the scatter plot. Make the line go through the year 2050 to predict the sea level rise in 2050. - Plot a new line of best fit just using the data from year 2000 through the most recent year in the dataset. Make the line also go through the year 2050 to predict the sea level rise in 2050 if the rate of rise continues as it has since the year 2000.
- The x label should be
Year, the y label should beSea Level (inches), and the title should beRise in Sea Level.
Unit tests are written for you under test_module.py.
The boilerplate also includes commands to save and return the image.
For development, you can use main.py to test your functions. Click the "run" button and main.py will run.
We imported the tests from test_module.py to main.py for your convenience. The tests will run automatically whenever you hit the "run" button.
Solution link: Replit
Global Average Absolute Sea Level Change, 1880-2014 from the US Environmental Protection Agency using data from CSIRO, 2015; NOAA, 2015.
The contents of this repository are covered under the MIT License.