There are different display tools you can use to help you understand a plot the first time you see it. Legends and annotations serve this purpose. Legends identify different series of data within your plot. To access it, you call the matplotlib function legend () .
Annotations, on the other hand, help in identifying the important points in the plot. Annotations are called using the matplotlib function annotate() . An annotation must always have an arrow and a label, each of which could be described by different parameters. Because of this reason, you can use the help (annotate) function to get the best explanation.
Other display tools include labels, grids, and titles. A label will be present on both axes, but you can call them using the functions xlabel () and ylabel () for the x and y axis respectively. The title of your plot can be identified using the title () function, while the grid is identified using the grid () function. It is wise to note that you can turn the grid plot on or off where necessary.
In Matplotlib, you will be working with a lot of tools and functions that enhance manipulation and representation of the objects you work with, alongside any internal objects that might be present. By design, matplotlib is built into three layers as shown below:
● The scripting layer
This layer is also referred to as the pyplot . This is where functions and artist classes operate. The pyplot is an interface used in data visualization and analysis.
● The artist layer
This is an intermediate Matplotlib layer. All the elements in this layer are used in building charts, and include things like markers, titles, and labels assigned to the x and y axis.
● The backend layer
This is the lowest level in Matplotlib. All the APIs are found in this layer. At this point, graphic element implementation takes place, albeit at the lowest possible level. Each of these layers can only share communication with the layer beneath it, but not the one above it, hence the nature of communication in Matplotlib is unidirectional.
Having mentioned pyplot , you should also learn about pylab . Pylab is a unique module that is installed together with Matplotlib, while pyplot on the other hand runs as an internal package in Matplotlib. Your installation code for these two will look like this:
from pylab import *
and
import matplotlib.pyplot as plt
import numpy as np
Pylab allows you to enjoy the benefits of using pyplot and NumPy within the same namespace, without necessarily having to import NumPy as a separate package. If you already have pylab imported, you will not need to call the NumPy and pyplot functions because they are automatically called, in a process similar to what you experience in MATLAB as shown below:
Instead of having
plt.plot()
np.array([1,2,3,4]
You will have
plot(x,y)
array([1,2,3,4])
Essentially, the role of the pyplot package is to enable you to program in Python through the matplotlib library.
Annotations, on the other hand, help in identifying the important points in the plot. Annotations are called using the matplotlib function annotate() . An annotation must always have an arrow and a label, each of which could be described by different parameters. Because of this reason, you can use the help (annotate) function to get the best explanation.
Other display tools include labels, grids, and titles. A label will be present on both axes, but you can call them using the functions xlabel () and ylabel () for the x and y axis respectively. The title of your plot can be identified using the title () function, while the grid is identified using the grid () function. It is wise to note that you can turn the grid plot on or off where necessary.
In Matplotlib, you will be working with a lot of tools and functions that enhance manipulation and representation of the objects you work with, alongside any internal objects that might be present. By design, matplotlib is built into three layers as shown below:
● The scripting layer
This layer is also referred to as the pyplot . This is where functions and artist classes operate. The pyplot is an interface used in data visualization and analysis.
● The artist layer
This is an intermediate Matplotlib layer. All the elements in this layer are used in building charts, and include things like markers, titles, and labels assigned to the x and y axis.
● The backend layer
This is the lowest level in Matplotlib. All the APIs are found in this layer. At this point, graphic element implementation takes place, albeit at the lowest possible level. Each of these layers can only share communication with the layer beneath it, but not the one above it, hence the nature of communication in Matplotlib is unidirectional.
Having mentioned pyplot , you should also learn about pylab . Pylab is a unique module that is installed together with Matplotlib, while pyplot on the other hand runs as an internal package in Matplotlib. Your installation code for these two will look like this:
from pylab import *
and
import matplotlib.pyplot as plt
import numpy as np
Pylab allows you to enjoy the benefits of using pyplot and NumPy within the same namespace, without necessarily having to import NumPy as a separate package. If you already have pylab imported, you will not need to call the NumPy and pyplot functions because they are automatically called, in a process similar to what you experience in MATLAB as shown below:
Instead of having
plt.plot()
np.array([1,2,3,4]
You will have
plot(x,y)
array([1,2,3,4])
Essentially, the role of the pyplot package is to enable you to program in Python through the matplotlib library.
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