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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

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import matplotlib.pyplot as plt

# Initalize a Figure and Axes
fig, ax = plt.subplots()

# Plot the CO2 variable in blue
ax.plot(climate_change.index, climate_change["co2"], color='blue')

# Create a twin Axes that shares the x-axis
ax2 = ax.twinx()

# Plot the relative temperature in red
ax2.plot(climate_change.index, climate_change["relative_temp"], color='red')

plt.show()

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.
fig, ax = plt.subplots()

# Plot a histogram of "Weight" for mens_rowing
ax.hist(mens_rowing["Weight"], label="Rowing", histtype='step', bins=5)

# Compare to histogram of "Weight" for mens_gymnastics
ax.hist(mens_gymnastics["Weight"], label="Gymnastics", histtype='step', bins=5)

ax.set_xlabel("Weight (kg)")
ax.set_ylabel("# of observations")

# Add the legend and show the Figure

plt.show()
fig, ax = plt.subplots()

# Add a bar for the rowing "Height" column mean/std
ax.bar("Rowing", mens_rowing["Height"].mean(), yerr=mens_rowing["Height"].std())

# Add a bar for the gymnastics "Height" column mean/std
ax.bar("Gymnastics", mens_gymnastics["Height"].mean(), yerr=mens_gymnastics["Height"].std())

# Label the y-axis
ax.set_ylabel("Height (cm)")

plt.show()
fig, ax = plt.subplots()

# Add Seattle temperature data in each month with error bars
ax.errorbar(seattle_weather["MONTH"], seattle_weather["MLY-TAVG-NORMAL"], yerr=seattle_weather["MLY-TAVG-STDDEV"])

# Add Austin temperature data in each month with error bars
ax.errorbar(austin_weather["MONTH"], austin_weather["MLY-TAVG-NORMAL"], yerr=austin_weather["MLY-TAVG-STDDEV"])


# Set the y-axis label
ax.set_ylabel("Temperature (Fahrenheit)")

plt.show()
fig, ax = plt.subplots()

# Add a boxplot for the "Height" column in the DataFrames
ax.boxplot([mens_rowing["Height"], mens_gymnastics["Height"]])

# Add x-axis tick labels:
ax.set_xticklabels(["Rowing", "Gymnastics"])

# Add a y-axis label
ax.set_yticklabels("Height (cm)")

plt.show()
fig, ax = plt.subplots()

# Add data: "co2", "relative_temp" as x-y, index as color
ax.scatter(climate_change["co2"], climate_change["relative_temp"], c=climate_change.index)

# Set the x-axis label to "CO2 (ppm)"
ax.set_xlabel("CO2 (ppm)")

# Set the y-axis label to "Relative temperature (C)"
ax.set_ylabel("Relative temperature (C)")

plt.show()