Previously in this chapter, you learned how to create your figure and axis objects using the subplots () function from pyplot (which you imported using the alias plt ): fig, ax. Save figure as an image file (e.g.png format). Customize the labels, colors and look of your matplotlib plot. Observed_values. Use matplotlib to create scatter, line and bar plots. Observed_values = # these are the temperatures that go on the X axis Paris_stamps = # This list is a list of datetimes that compose the X axis This results in a spline that has fewer knots than the number of data points, and hence is no longer strictly an interpolating spline, but rather a smoothing. # Code that imports the CSV, changes the times to the other city. This is the code around my application of matplotlib. Then you can change tick labels to corresponding categories as you want. I want the indices i/j/k of Lattice to be the coordinate axes and the value contained in the tensor element to be plotted as a colored dot, making a 3D mesh. You could use numerical data, range (0, len (observedvalues)), to finish the plot first. Everything works, except, the scatter chart is showing a vertical line of dots beginning at the center of the x axis, instead of a dot above each listed date on the x axis. Essentially, you are trying to plot categorical data on a scatter plot, which is designed for numerical data.
The app I am writing parses data in a CSV of datetimes and temperature observation pairs to the same time in another city then writes a report and a scatter chart.
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