For people interested in learning about data visualization and data analytics in detail, Matplotlib is possibly the most effective tool out there.
Data visualization can easily be termed as the process of converting numbers, text, or massive data sets into various graphs such as maps, plots, bar plots, histograms, pie charts, and so on. To create these visually appealing graphical plots, we need some efficient tools. A tool that can effectively convert data into plots, Matplotlib is one of Python’s most powerful data visualization packages.
This blog will show you how to use numerous graphs and charts to quickly and easily communicate your data to others.
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Matplotlib: A Detailed Understanding
Matplotlib is a cross-platform charting and data visualization toolkit and package for Python and its numerical extension, NumPy. As a result, it acts as an open-source replacement for MATLAB.
Matplotlib’s APIs can also be used to incorporate charts in more than one graphical user interface. We can even do 3D graphing and plotting using this library.
In most cases, a Python matplotlib code is developed in such a way that only a few lines of code are required to generate an extensive visual data plot that satisfactorily conveys the required information. The matplotlib coding layer traces two API paths:
- An OO API collection: The flexibility of arranging these plots with real-time object values is easier and less cumbersome than pyplot. This API serves the backend servers of matplot0ib and allows you to use Matplotlib’s backend layers directly.
- The pyplot API: It is a hierarchy of several coding elements of Python. This list is topped by matplotlib.pyplot.
We need to understand how to work with plots primarily:
- matplotlib.pyplot.figure: The top-level container is a figure. Everything visualized in a plot, including one or more Axes, is included.
- Matplotlib.pyplot.axes: Axes contain most of the elements in a plot like text, axis, tick, Line2D, etc. It also takes part in setting the coordinates. This is where the data gets plotted. The X-Axis, Y-Axis, and perhaps a Z-Axis are examples of axes.
Matplotlib and its integrations can be downloaded from the Python Package Index (PyPI) as a binary (pre-compiled) package. The PIP command can be used to install Matplotlib. !pip install matplotlib
Next, we need to know how to install Matplotlib in Google-Colab. Colab Notebooks are almost the same as Jupyter Notebooks, except that they are cloud-based. This feature makes it easier to use and makes it far more accessible. It’s also linked to our Google Drive, making it much easier to access our Colab notebooks at any time, from any location, and on any device.
If you get an error like “no module named” and a module name when importing matplotlib, it means you need to install that module too. A common problem is that people often don’t have the module named “six”. This implies you’ll need to install six pip packages.
You can also go to Matplotlib.org and install it by going to the downloads area and selecting your proper version. Remember that just because you have a 64-bit operating system doesn’t mean you have a 64-bit Python version. Unless you tried to upgrade to 64 bit, you probably have 32 bit. Open IDLE and go to the top of the page.
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What does each icon mean?
When you open the application on your machine, the window will look like something like this:
This is a matplotlib window, which allows us to navigate, observe, and modify our graph. You can usually see the coordinates in the bottom right corner of the graph if you hover over it. The buttons can also be used for interacting with the application. These buttons can be found in a variety of places, but in the image above, they’re in the lower-left corner.
Here, each icon is a separate entity and helps you explore the application further.
Usage of Matplotlib Function
Relation with Numpy:
Numpy is a scientific computing software. Matplotlib requires Numpy, which employs NumPy functions for numerical data and multi-dimensional arrays, as illustrated in the following code snippet of a NumPy array
import numpy as nmp
from matplotlib import pyplot as pplt
# forming an ndarray on x axis using the numpy range() function:
x = nmp.arange(2,13)
# Store equation values on y axis:
y = 5*x-1
pplt.title(“Plot of numpy array”)
# Plot values using x,y coordinates:
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Line plots in Matplotlib+
A line plot is used to see how the x and y axes are related in the function provided to us by the user.
The plot() function in the Pyplot module of the Matplotlib package is used to plot the coordinates x and y in a 2D hexagonal plot.
plot() accepts several arguments, including plot(x, y, scalex, scaley, data, kwargs). [kwargs specific properties such as line label, linewidth, marker, color, and so on.]
x, y are the horizontal and vertical axis coordinates, with x values being optional and range(len(y) being the default value.
The scalex and scaley options are used to autoscale the x- and y-axes, respectively, with the default value of true.
Code snippet for a line plot, as per analyticsvidhya.com
#this line will create array of numbers between 1 to 10 of length 100
x1 = np.linspace(0, 10, 100) #line plot
pplt.plot(x1, np.sin(x1), ‘-‘,color=’orange’)
pplt.plot(x1, np.cos(x1), ‘–‘,color=’b’)
#give the name of the x and y axis
#also give the title of the plot
Line plot for the provided code snippet
Pie plot in Matplotlib
Here is an example of a pie plot code snippet on matplotlib:
import matplotlib.pyplot as pplt
# Data labels, sizes, and colors are defined:
labels = ‘Broccoli’, ‘Chocolate Cake’, ‘Blueberries’, ‘Raspberries’
sizes = [30, 330, 245, 210]
colors = [‘green’, ‘brown’, ‘blue’, ‘red’]
# Data is plotted:
pplt.pie(sizes, labels=labels, colors=colors)
This code results in a pie chart like:
Multiple plots can also be created by using this library function. A code snippet from geeksforgeeks.org shows a proper example.
# Python program to show pyplot module
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
# Creating a new figure with width = 5 inches
# and height = 4 inches
fig = plt.figure(figsize =(5, 4))
# Creating first axes for the figure
ax1 = fig.add_axes([1, 1, 1, 1])
# Creating second axes for the figure
ax2 = fig.add_axes([1, 0.5, 0.5, 0.5])
# Adding the data to be plotted
ax1.plot([2, 3, 4, 5, 5, 6, 6], [5, 7, 1, 3, 4, 6 ,8])
ax2.plot([1, 2, 3, 4, 5], [2, 3, 4, 5, 6])
The result of the above code is shown in the image above.
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Matplotlib is an extremely valuable library function. It has all the facilities of turning small, easy code snippets into graphs of all types. You can try plotting different datasets and mathematical functions now that you know the basics of plotting and charting.
For someone who can code in Python or similar languages, matplotlib acts as a catalyst to enhance their knowledge. It broadens one’s understanding and helps prepare easily understandable graphs. These graphs help in the preparation of a conformed analysis of data.
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What is the function of the Back/Forward Buttons?
These buttons work similarly to your browser's forward and back buttons. You can use these to go back or ahead to the previous spot you were at.
What is the function of the Home Button?
Once you've started navigating your chart, the home button will come in handy. You may click on this to return to the original view at any time. If you click this before you've traversed your graph, nothing will happen.
What is the use of the Zoom button and Axis Pan?
You can use the zoom button to select a specific square to zoom into by clicking and dragging it. A left-click and drag will be required to zoom in. The Axis Pan is a cross-looking button, which you use to click and drag your graph around.