1. Учиться
    2. /
  2. Courses
    3. /
  3. Introduction to Data Visualization with Matplotlib
Connected

Exercise

Plotting time-series: putting it all together

In this exercise, you will plot two time-series with different scales on the same Axes, and annotate the data from one of these series.

The CO2/temperatures data is provided as a DataFrame called climate_change. You should also use the function that we have defined before, called plot_timeseries, which takes an Axes object (as the axes argument) plots a time-series (provided as x and y arguments), sets the labels for the x-axis and y-axis and sets the color for the data, and for the y tick/axis labels:

plot_timeseries(axes, x, y, color, xlabel, ylabel)

Then, you will annotate with text an important time-point in the data: on 2015-10-06, when the temperature first rose to above 1 degree over the average.

Инструкции

100 XP
  • Use the plot_timeseries function to plot CO2 levels against time. Set xlabel to "Time (years)" ylabel to "CO2 levels" and color to 'blue'.
  • Create ax2, as a twin of the first Axes.
  • In ax2, plot temperature against time, setting the color ylabel to "Relative temp (Celsius)" and color to 'red'.
  • Annotate the data using the ax2.annotate method. Place the text ">1 degree" in x=pd.Timestamp('2008-10-06'), y=-0.2 pointing with a gray thin arrow to x=pd.Timestamp('2015-10-06'), y = 1.