How do you make data visualizations the easiest way to use Python? Of course, matplotlib!.

01| Figure and subplot:

Matplotlib images are all in the figure object, like a canvas. Figsize is a set-size property of figure. A figure can have countless subplot to hold the coordinate system.

The beginning of the Python program with the beginning of the program plus "%matplotlib", you can display the figure, if you do not jump out the chart box. However, "%matplotlib inline" is directly represented in the programming interface, does not jump out of the frame again.

If you do not specify a drawing on an ax, matplotlib will default to the last ax, and if not, create a drawing. Give an example that is not specified:

We can create a number of frames at once, and then use them to implement the index. The fancy subplots (2,3) is a one-time creation of two rows and three columns of coordinates, however axes[0,1] indicates that the frame corresponding to the 2nd column of row No. 0 is used.

02| color, marking and Linetype:

Often used in the color, with its first letter in English to replace.

Markers are used to emphasize the actual number of points on a linear graph.

Linear can represent the shape of a line.

03| ticks, labels, and headings:

04| legend:

When you add subplot, it is possible to call Ax.legend () or plt.legend () after passing in the label parameter.

05| Pandas drawing:

Matplotlib is not a very advanced tool, and to successfully assemble a chart, you have to use a variety of components to implement it.

This is due to the fact that building a good chart must be used, but matplotlib must write many lines of code to implement this function, but pandas may be easily implemented with just a few lines of code!

How do you make data visualizations the easiest way to use Python? Of course, matplotlib!.