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Introduction to Data Visualization with Matplotlib

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

1 hidden cell

Take Notes

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

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For data vosualization, when it comes to classic plots, you can add markers by adding a "marker" argument when caling th e "plot()" method, and you can change the appearance of the lines by adding a "linestyle" argument to the plot() method. You can also change the color of your graph by adding an argument "color".

# 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.