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Introduction to Data Visualization with Matplotlib

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.mfe-app-workspace-kj242g{position:absolute;top:-8px;}.mfe-app-workspace-11ezf91{display:inline-block;}.mfe-app-workspace-11ezf91:hover .Anchor__copyLink{visibility:visible;}Introduction to Data Visualization with Matplotlib

Run the hidden code cell below to import the data used in this course.

Take Notes

Add notes about the concepts you've learned and code cells with code you want to keep.

ax2 = ax.twinx() Don't forget the x after twin, since it is the x axis we're twinning.

`.mfe-app-workspace-jfrv3u{font-size:13px;line-height:20px;font-family:JetBrainsMonoNL,Menlo,Monaco,'Courier New',monospace;}`# Add your code snippets here``

Explore Datasets

Use the DataFrames imported in the first cell to explore the data and practice your skills!

• Using `austin_weather` and `seattle_weather`, create a Figure with an array of two Axes objects that share a y-axis range (`MONTHS` in this case). Plot Seattle's and Austin's `MLY-TAVG-NORMAL` (for average temperature) in the top Axes and plot their `MLY-PRCP-NORMAL` (for average precipitation) in the bottom axes. The cities should have different colors and the line style should be different between precipitation and temperature. Make sure to label your viz!
• Using `climate_change`, create a twin Axes object with the shared x-axis as time. There should be two lines of different colors not sharing a y-axis: `co2` and `relative_temp`. Only include dates from the 2000s and annotate the first date at which `co2` exceeded 400.
• Create a scatter plot from `medals` comparing the number of Gold medals vs the number of Silver medals with each point labeled with the country name.
• Explore if the distribution of `Age` varies in different sports by creating histograms from `summer_2016`.
• Try out the different Matplotlib styles available and save your visualizations as a PNG file.
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