# Creating a Plot Charts in Python with Matplotlib

In this article, we created plots in Python with the matplotlib library. We discussed the concepts you need to know to understand how Matplotlib works, and set about creating and customizing real plots.

In this article, we created plots in Python with the matplotlib library. We discussed the concepts you need to know to understand how Matplotlib works, and set about creating and customizing real plots.

You generate a huge amount of data on a daily basis. A critical part of data analysis is visualization. A variety of graphing tools have developed over the past few years. Given the popularity of Python as a language for data analysis, this tutorial focuses on creating graphs using a popular Python libraryMatplotlib.

Matplotlib is a huge library, which can be a bit overwhelming for a beginner — even if one is fairly comfortable with Python. While it is easy to generate a plot using a few lines of code, it may be difficult to comprehend what actually goes on in the back-end of this library. This tutorial explains the core concepts of Matplotlib so that one can explore its full potential.

Let’s get started!

Prerequisites

The library that we will use in this tutorial to create graphs is Python’s `matplotlib`. This post assumes you are using version `3.0.3`. To install it, run the following `pip` command in the terminal.

``````pip install matplotlib==3.0.3

``````

To verify the version of the library that you have installed, run the following commands in the Python interpreter.

``````>>> import matplotlib
>>> print(matplotlib.__version__)
'3.0.3'
``````

If you are using Jupyter notebooks, you can display Matplotlib graphs inline using the following magic command.

``````%matplotlib inline

``````

### Pyplot and Pylab: A Note

During the initial phases of its development, Mathworks’ MATLAB influenced John Hunter, the creator of Matplotlib. There is one key difference between the use of commands in MATLAB and Python. In MATLAB, all functions are available at the top level. Essentially, if you imported everthing from `matplotlib.pylab`, functions such as `plot()` would be available to use.

This feature was convenient for those who were accustomed to MATLAB. In Python, though, this could potentially create a conflict with other functions.

Therefore, it is a good practice to use the `pyplot` source.

``````from matplotlib import pyplot as plt

``````

All functions such as `plot()` are available within `pyplot`. You can use the same `plot()` function using `plt.plot()` after the import earlier.

Dissecting a Matplotlib Plot

The Matplotlib documentation describes the anatomy of a plot, which is essential in building an understanding of various features of the library.

The major parts of a Matplotlib plot are as follows:

• Figure: The container of the full plot and its parts
• Title: The title of the plot
• Axes: The X and Y axis (some plots may have a third axis too!)
• Legend: Contains the labels of each plot

Each element of a plot can be manipulated in Matplotlib’s, as we will see later.

Without further delay, let’s create our first plot!

Create a Plot

Creating a plot is not a difficult task. First, import the `pyplot` module. Although there is no convention, it is generally imported as a shorter form &mdash `plt`. Use the `.plot()` method and provide a list of numbers to create a plot. Then, use the `.show()` method to display the plot.

``````from matplotlib import pyplot as plt
plt.plot([0,1,2,3,4])
plt.show()
``````

Notice that Matplotlib creates a line plot by default. The numbers provided to the `.plot()` method are interpreted as the y-values to create the plot. Here is the documentation of the `[.plot()](https://matplotlib.org/3.1.0/api/_as_gen/matplotlib.pyplot.plot.html)` method for you to further explore.

Now that you have successfully created your first plot, let us explore various ways to customize your plots in Matplotlib.

Customize Plot

Let us discuss the most popular customizations in your Matplotlib plot. Each of the options discussed here are methods of `pyplot` that you can invoke to set the parameters.

• `title`: Sets the title of the chart, which is passed as an argument.
• `ylabel`: Sets the label of the Y axis. `xlabel` can be used to set the label of the X axis.
• `yticks`: Sets which ticks to show on the Y axis. `xticks` is the corresponding option for showing ticks on the X axis.
• `legend`: Displays the legend on the plot. The `loc` argument of the `.legend()` method sets the position of the legend on the graph. The `best` option for the `loc` arguments lets Matplotlib decide the least intrusive position of the legend on the figure.

Let us use these options in our plot.

``````plt.plot([0,1,2,3,4], label='y = x')
plt.title('Y = X Straight Line')
plt.ylabel('Y Axis')
plt.yticks([1,2,3,4])
plt.legend(loc = 'best')
plt.show()
``````

Here is the output of the code above. Notice that a title has appeared in the figure, the Y axis is labelled, the number of ticks on the Y axis are lesser than those in the X axis and a legend is shown on the top left corner.

After tinkering with the basic options of a plot, let’s create multiple plots in same figure. Let us try to create two straight lines in our plot.

To achieve this, use the `.plot()` method twice with different data sets. You can set the label for each line plot using the `label` argument of the `.plot()` method to make the code shorter.

``````plt.plot([0,1,2,3,4], label='y = x')
plt.plot([0,2,4,6,8], label='y = 2x')
plt.title('Two Straight Lines')
plt.legend(loc = 'best')
plt.show()
``````

Next, let’s try to create a different type of plot. To create a scatter plot of points on the XY plane, use the `.scatter()` method.

``````plt.scatter([1,2,3,4], [5,1,4,2])
plt.show()
``````

Here is what the scatter plot looks like.

A number of other plots can be created on Matplotlib. You can use the `.hist()` method to create a histogram. You can add multiple plots to a figure using the `.subplot()` method. You can even create a vector path using the `[path](https://matplotlib.org/3.1.0/api/path_api.html#module-matplotlib.path)` module of `[pyplot](https://matplotlib.org/3.1.0/api/path_api.html#module-matplotlib.path)`.

Export Plots with Matplotlib

After exploring various options while creating plots with Matplotlib, the next step is to export the plots that you have created. To save a figure as an image, you can use the `.savefig()` method. The filename with the filepath should be provided as an argument to this method.

``````plt.savefig('my_figure.png')

``````

While the documentation for `[savefig](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html)` lists various arguments, the two most important ones are listed below:

• `dpi`: This argument is used to set the resolution of the resulting image in DPI (dots per inch).
• `transparent`: if set to True, the background of the figure is transparent.

While the code above saves a single figure, you may need to save multiple figures in a same file. Matplotlib allows you to save multiple figures to a single PDF file using the `PdfPages` class. The steps to create a PDF file with multiple plots are listed below:

• First, import the `PdfPages` class from `matplotlib.backends.backend_pdf` and initialize it to an empty PDF file.
• Initialize a figure object using the `.figure()` class and create the plot. Once the plot is created, use the `.savefig()` method of the `PdfPages` class to save the figure.
• Once all figures have been added, close the PDF file using the `.close()` method.

To summarize the process, the following code snippet creates a PDF with the two figures that we created above.

``````from matplotlib.backends.backend_pdf import PdfPages
pdf = PdfPages('multipage.pdf')

fig1 = plt.figure()
plt.plot([0,1,2,3,4])
plt.close()
pdf.savefig(fig1)

fig2 = plt.figure()
plt.plot([0,2,4,6,8])
plt.close()
pdf.savefig(fig2)

pdf.close()
``````
Conclusion

In this tutorial, we created plots in Python with the `matplotlib` library. We discussed the concepts you need to know to understand how Matplotlib works, and set about creating and customizing real plots. And we showed you how to export your plots for use in real-world scenarios, like reports and presentations.

How do you create plots with Python? Let us know in the comments below.

## Python GUI Programming Projects using Tkinter and Python 3

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## Tutorial How to write MatLab functions in Python

A tutorial on writing MatLab-like functions using the Python language and the NumPy library.

Overview

Recently in my work, I was re-writing algorithms developed in MatLab to Python, some functions are not so simple to adapt, especially the array functions that are called Cell Arrays.

MatLab has an API where you can call MatLab functions via Python. The idea, however, was not to use MatLab, but the same algorithm works the same way using only Python and NumPy, and the GNU Octave also has an API similar to that of MatLab.

To maintain compatibility, I have created functions with the same name that are used in MatLab that is encapsulated in a class called Precision.

1. Testing

Make the repository clone and follow the instructions in the README file:

Below I will show some examples, these are contained in the unit tests.

### 1.1 Start Stopwatch Time

Measuring the time spent in processing.

``````from precision import Precision

p = Precision()
p.tic()
for i in range(0, 1000): print(i)
p.toc()

``````

The output will look something like this:

``````: > Elapsed time is 0:0:2 secounds.

``````

### 1.2 Percentiles of a Data Set

This is used to get a percentile. In the example below, we are creating a range of ordinal dates by cutting 5% from the left and 5% from the right.

``````from datetime import datetime
from precision import Precision

p = Precision()
d = [i for i in p.dtrange(datetime(2018, 6, 12),
datetime(2059, 12, 12),
{'days':1, 'hours':2})]
x = [p.datenum(i.date()) for i in d]

x1 = p.prctile(x, 5)
x2 = p.prctile(x, 95)
r = (x2 - x1)

``````

The output will look something like this:

``````5% lower: 737980.1
5% higher: 751621.9
delta: 13641.800000000047

``````

### 1.3 Cell Array (cell2mat)

This converts a cell array to an ordinary array of the underlying data type.

``````from precision import Precision

p = Precision()
p.cell2mat([[1, 2], [3, 4]])
p.cell2mat('1 2; 3 4')

``````

The output will look something like this:

``````matrix([[1, 2],
[3, 4]])

``````

### 1.4 Cell Array (num2cell)

Convert array to cell array with consistently sized cells.

``````import numpy
from precision import Precision

p = Precision()
x = numpy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], numpy.int64)
p.num2cell(x)

``````

The output will look something like this:

``````[[1, 2, 3], [4, 5, 6], [7, 8, 9]]

``````

### 1.5 Concatenate Strings (strcat)

This concatenates strings horizontally using strcat.

``````import pandas
from precision import Precision

p = Precision()
df = pandas.DataFrame(data={'A': [1, 2], 'B': [3, 4]}, dtype=numpy.int8)
p.strcat(df, 'B')

``````

The output will look something like this:

``````['3', '4']

``````

### 1.6 Histogram (histc)

This counts the number of values in x that are within each specified bin range. The input, binranges, determines the endpoints for each bin. The output, bincounts, contains the number of elements from x in each bin.

``````import numpy
from precision import Precision

p = Precision()
v = numpy.array([[1.5, 2.0, 3], [4, 5.9, 6]], numpy.int64)
p.histc(v, numpy.amax(v) + 1)

``````

The output will look something like this:

``````(array([1, 1, 1, 0, 1, 1, 1]), array([1., 1.71428571, 2.42857143,
3.14285714, 3.85714286, 4.57142857, 5.28571429, 6.]))

``````

### 1.7 Unique

Looking for unique values in an array and returning the indexes, inverse, and counts.

``````import numpy
from precision import Precision

p = Precision()
x = [0, 1, 1, 2, 3, 4, 4, 5, 5, 6, 7, 7, 7]
p.unique(numpy.array([x]))

``````

The output will look something like this:

``````array([[array([0, 1, 2, 3, 4, 5, 6, 7]),
array([[ 0,  1,  3,  4,  5,  7,  9, 10]]),
array([0, 1, 1, 2, 3, 4, 4, 5, 5, 6, 7, 7, 7]),
array([1, 2, 1, 1, 2, 2, 1, 3])]], dtype=object)

``````

### 1.8 Overlaps

Looking for the overlays between two arrays returning the index.

``````import numpy
from precision import Precision

p = Precision()
x, y = p.overlap2d(numpy.array(['A','B','B','C']),
numpy.array(['C','A','B','C','D']))

``````

The output will look something like this:

``````(array([0, 1, 2, 3]), array([1, 2, 0, 3]))

``````
Considerations

There are functions that are not exactly MatLab but will serve as support, I hope it can help someone. There is an interesting article in NumPy for users who are migrating from MatLab to Python.

MATLAB vs Python: Why and How to Make the Switch

Creating a Plot Charts in Python with Matplotlib

Python Tutorial - Python GUI Programming - Python GUI Examples (Tkinter Tutorial)

Essential Python 3 code for lists