Plotting in Python

11 Key excerpts on "Plotting in Python"

• 

  eBook - ePub

  Applying Math with Python

  Practical recipes for solving computational math problems using Python programming and its libraries

  • Sam Morley(Author)
  • 2020(Publication Date)
  • Packt Publishing

    (Publisher)

  Mathematical Plotting with Matplotlib

  Plotting is a fundamental tool in all of mathematics. A good plot can reveal hidden details, suggest future directions, verify results, or reinforce an argument. It is no surprise, then, that the scientific Python stack features a powerful and flexible plotting library called Matplotlib.

  In this chapter, we will plot functions and data in a variety of styles and create figures that are fully labeled and annotated. We will create three-dimensional plots, customize the appearance of figures, create figures that contain multiple plots using subplots, and save figures directly to files for applications that are not running in an interactive environment.

  In this chapter, we will cover the following recipes:

  • Basic plotting with Matplotlib
  • Changing the plotting style
  • Adding labels and legends to plots
  • Adding subplots
  • Saving Matplotlib figures
  • Surface and contour plots
  • Customizing three-dimensional plots

  Technical requirements

  The main plotting package for Python is Matplotlib, which can be installed using your favorite package manager, such as pip:

  python3.8 -m pip install matplotlib

  This will install the most recent version of Matplotlib, which, at the time of writing this book, is version 3.2.1.

  Matplotlib contains numerous sub-packages, but the main user interface is the matplotlib.pyplotpackage, which, by convention, is imported under the pltalias. This is achieved using the following import statement:

  import matplotlib.pyplot as plt Many of the recipes in this chapter also require NumPy, which, as usual, is imported under the npalias.

  The code for this chapter can be found in the Chapter 02 folder of the GitHub repository at https://github.com/PacktPublishing/Applying-Math-with-Python/tree/master/Chapter%2002 .

  Check out the following video to see the Code in Action: https://bit.ly/2ZOSuhs .

  Basic plotting with Matplotlib

  Plotting is an important part of understanding behavior. So much can be learned by simply plotting a function or data that would otherwise be hidden. In this recipe, we will walk through how to plot a simple function or data using Matplotlib.

  Sign up to read

  Learn more about book
• 

  eBook - PDF

  Data Visualization with Python

  Exploring Matplotlib, Seaborn, and Bokeh for Interactive Visualizations (English Edition)

  • Dr. Pooja(Author)
  • 2023(Publication Date)
  • BPB Publications

    (Publisher)

  This chapter presents various functions available in the matplotlib library of python which are used for presenting data in graphical representations. Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. With this library you can create and design from basic plots to three dimension (3D) plots. However, before plotting you need to understand the dataset, we will work on exploratory data analysis in the last chapter of this book, wherein we will learn to find the insights of data for better understanding. 94  Data Visualization with Python In this chapter, you will learn ways to make use of matplotlib library to its best for plotting various plots/charts as mentioned in the previous chapters of this book. For better illustrations, we will try to create synthetic datasets for plotting or we will utilize datasets available at various sources for research purposes. Whichever way, the links/datasets will be made available to you at the end of this chapter. Also steps to install python are available in the annexure at the end. Structure In this chapter, we will discuss the following topics: • Introduction • Importing Matplotlib and its documentation • Understanding Figure and subplots • Basic plots • Advanced plots • Saving the plot Objectives In this chapter, Matplotlib library is discussed in detail, which is used for plotting the data. Our objective is to introduce the generally used 'plot styles' and 'features' of the Matplotlib library, which are required for plotting the outcomes acquired by the simulations or visualizing the data during various applications based on machine learning, data analytics, business analytics and so on. Introduction Data visualization means graphical or photographic depiction of the data utilizing graphs, charts, and so on. The function of outlining information is to imagine variant or program connections between variables.

  Sign up to read

  Learn more about book
• 

  eBook - PDF

  The Python Book

  • Rob Mastrodomenico(Author)
  • 2022(Publication Date)
  • Wiley

    (Publisher)

  159 17 Plotting Plotting is a key component when looking to work with data, and Python has a number of plotting libraries. In this chapter, we cover plotting in great detail starting with creating basic plots directly from Pandas DataFrames to getting more control using matplotlib all the way through to using Seaborn. The chapter is fully example driven with code snippets alongside the graphics they produce. The aim of including both is to give you the ability to see what the code produces but also act as a reference for when you want to produce graphs. The examples are based on datasets that come from with Python with many being taken from the documentation with the idea of giving them greater explanation to help you understand what is happening. The packages that we will look to use in this chapter are imported below. >>> import matplotlib.pyplot as plt >>> import numpy as np >>> import pandas as pd >>> import matplotlib >>> import seaborn as sns 17.1 Pandas Initially, we will look at plotting methods on Series and DataFrame objects in pandas. One of the great things about pandas is that we have inbuilt plotting methods that we can call and produce plots from. This allows very fast visual presentation of datasets that we want to analyse. >>> iris = sns . load_dataset( 'iris' ) >>> iris . sepal_length . plot() < matplotlib . axes . _subplots . AxesSubplot object at 0x113e62290> >>> plt . show() >>> iris . sepal_length . plot() < matplotlib . axes . _subplots . AxesSubplot object at 0x113e62290> >>> plt . savefig( '/path/to/file/file_name.pdf' ) The Python Book , First Edition. Rob Mastrodomenico. © 2022 John Wiley & Sons Ltd. Published 2022 by John Wiley & Sons Ltd. 160 17 Plotting 0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 20 40 60 80 100 120 140 Figure 17.1 Line plot of sepal length. The previous code snippets and associated plot show how we can produce a simple line plot of a Series.

  Sign up to read

  Learn more about book
• 

  eBook - ePub

  Scientific Computing with Python

  • Claus Fuhrer, Olivier Verdier, Jan Erik Solem(Authors)
  • 2021(Publication Date)
  • Packt Publishing

    (Publisher)

  Plotting

  Plotting in Python can be done with the  pyplot  part of the module Matplotlib. With matplotlib,  you can create high-quality figures and graphics and also plot and visualize your results. Matplotlib is open source and freely available software. The Matplotlib website also contains excellent documentation with examples, see 35 . In this section, we will show you how to use the most common features. The examples in the upcoming sections assume that you have imported the module as:

  from matplotlib.pyplot import *

  In case you want to use the plotting commands in IPython, it is recommended that you run the magic command   %matplotlib  directly after starting the IPython shell. This prepares IPython for interactive plotting.

  6.1 Making plots with basic plotting commands

  In this section, we will create plots by means of basic commands. It is the entry point for studying how to make graphical representations of mathematical objects and data using Python.

  6.1.1 Using the plot command and some of its variants

  The standard plotting function is  plot. Calling  plot(x,y)  creates a figure window with a plot of  as a function of . The input arguments are arrays (or lists) of equal length. It is also possible to use  plot(y), in which case the values in  will be plotted against their index, that is,  plot(y)  is a short form of  plot(range(len(y)),y).

  Here is an example that shows how to plot  using 200 sample points and with markers at every fourth point:

  # plot sin(x) for some interval x = linspace(-2*pi,2*pi,200) plot(x,sin(x)) # plot marker for every 4th point samples = x[::4] plot(samples,sin(samples),'r*') # add title and grid lines title('Function sin(x) and some points plotted') grid()

  The result is shown in the following figure (Figure 6.1 ):

  Figure 6.1: A plot of the function sin(x) with grid lines shown

  As you can see, the standard plot is a solid  blue  curve. Each axis gets automatically scaled to fit the values, but can also be set manually. Color and plot options can be given after the first two input arguments. Here,  r*  indicates red star-shaped markers. Formatting is covered in more detail in the next section. The command  title 

  Sign up to read

  Learn more about book
• 

  eBook - ePub

  Pandas 1.x Cookbook

  Practical recipes for scientific computing, time series analysis, and exploratory data analysis using Python, 2nd Edition

  • Matt Harrison, Theodore Petrou(Authors)
  • 2020(Publication Date)
  • Packt Publishing

    (Publisher)

  13

  Visualization with Matplotlib, Pandas, and Seaborn

  Introduction

  Visualization is a critical component in exploratory data analysis, as well as presentations and applications. During exploratory data analysis, you are usually working alone or in small groups and need to create plots quickly to help you better understand your data. It can help you identify outliers and missing data, or it can spark other questions of interest that will lead to further analysis and more visualizations. This type of visualization is usually not done with the end user in mind. It is strictly to help you better your current understanding. The plots do not have to be perfect.

  When preparing visualizations for a report or application, a different approach must be used. You should pay attention to small details. Also, you usually will have to narrow down all possible visualizations to only the select few that best represent your data. Good data visualizations have the viewer enjoying the experience of extracting information. Almost like movies that make viewers get lost in them, good visualizations will have lots of information that really sparks interest.

  The primary data visualization library in Python is matplotlib, a project begun in the early 2000s, that was built to mimic the plotting capabilities from Matlab. Matplotlib is enormously capable of plotting most things you can imagine, and it gives its users tremendous power to control every aspect of the plotting surface.

  That said, it is not the friendliest library for beginners to grasp. Thankfully, pandas makes visualizing data very easy for us and usually plots what we want with a single call to the plot method. pandas does no plotting on its own. It internally calls matplotlib functions to create the plots.

  Seaborn is also a visualization library that wraps matplotlib and does not do any actual plotting itself. Seaborn makes beautiful plots and has many types of plots that are not available from matplotlib or pandas. Seaborn works with tidy (long) data, while pandas works best with aggregated (wide) data. Seaborn also accepts pandas DataFrame objects in its plotting functions.

  Sign up to read

  Learn more about book
• 

  No longer available |Learn more

  Python 3 Data Visualization Using ChatGPT / GPT-4

  • Oswald Campesato(Author)
  • 2023(Publication Date)
  • Mercury Learning and Information

    (Publisher)

  Data visualization refers to presenting data in a graphical manner, such as bar charts, line graphs, and heat maps. As you probably know, Big Data comprises massive amounts of data, which leverages data visualization tools to assist in making better decisions.

  Data visualization is a crucial aspect of data analysis, providing an intuitive understanding of the underlying patterns and relationships. With tools like Matplotlib and Seaborn, Python has become an efficient platform for crafting compelling visuals. As always, the key lies in practice and exploration.

  Visual representation of data enables viewers to quickly grasp complex data insights. Python, with its rich ecosystem of visualization libraries, provides tools to render data in meaningful ways. This chapter focuses on various visualization tasks using libraries such as Matplotlib and Seaborn.

  Python is a versatile and powerful programming language that can be used for various applications ranging from Web development to data analysis. In this chapter, we will explore solutions to common programming tasks using Python. Each task will have a detailed explanation and solution.

  Advanced visualization techniques offer deeper insights and more comprehensive narratives from data. Tools like Matplotlib and Seaborn make these tasks more accessible. While these visuals are powerful, it is essential to ensure clarity and avoid misrepresentation. Continue experimenting and refining your visualization skills to tell more compelling data stories.

  In the realm of data visualization, the journey from novice to expert is marked by the adoption of advanced techniques that can extract and depict deeper insights from data. In this chapter, we will explore some of these advanced visualization techniques and understand how they can be applied to various data scenarios.

  Effective data visualization is about conveying complex data narratives in an intuitive manner. As we continue our exploration, this chapter unfolds more advanced visualization techniques, enabling us to represent data in a multitude of insightful ways.

  Sign up to read

  Learn more about book
• 

  eBook - PDF

  Learning Scientific Programming with Python

  • Christian Hill(Author)
  • 2020(Publication Date)
  • Cambridge University Press

    (Publisher)

  7 Matplotlib Matplotlib is probably the most popular Python package for plotting data. It can be used through the procedural interface pyplot in very quick scripts to produce simple visualizations of data (see Chapter 3) but, as described in this chapter, with care it can also produce high-quality figures for journal articles, books and other publications. Although there is some limited functionality for producing three-dimensional plots (see Section 7.6), it is primarily a two-dimensional plotting library. 7.1 Line Plots and Scatter Plots Matplotlib is a large package organized in a hierarchy: at the highest level is the matplotlib.pyplot module. This provides a “state-machine environment” with a similar interface to MATLAB and allows the user to add plot elements (data points, lines, annotations, etc.) through simple function calls. This is the interface introduced in Chapter 3. At a lower level, which allows more advanced and customizable use, Matplotlib has an object-oriented interface that allows one to create a figure object to which one or more axes objects are attached. Most plotting, annotation and customization then occurs through these axes objects. This is this approach adopted in this chapter. To use Matplotlib in this way, we use the following recommended imports: import matplotlib.pyplot as plt import numpy as np 7.1.1 Plotting on a Single Axes Object The top-level object, containing all the elements of a plot is called Figure. To create a figure object, call plt.figure. No arguments are necessary, but optional customization can be specified by setting the values described in Table 7.1. For example, In [x]: # a default figure , with title "Figure 1" In [x]: fig = plt.figure() In [x]: # a small (4.5" x 2") figure with red background In [x]: fig = plt.figure( ' Population density ', figsize=(4.5, 2.), ....: facecolor= ' red ') 294

  Sign up to read

  Learn more about book
• 

  eBook - ePub

  Python for Data Science For Dummies

  • John Paul Mueller, Luca Massaron(Authors)
  • 2019(Publication Date)
  • For Dummies

    (Publisher)

  Part 3

  Visualizing Information

  IN THIS PART … Installing and using MatPlotLib. Defining parts of a graphic output. Creating and using various kinds of data presentations. Working with geographical data.

  Passage contains an image Chapter 10

  Getting a Crash Course in MatPlotLib

  IN THIS CHAPTER

  Creating a basic graph

  Adding measurement lines to your graph

  Dressing your graph up with styles and color

  Documenting your graph with labels, annotations, and legends

  Most people visualize information better when they see it in graphic, versus textual, format. Graphics help people see relationships and make comparisons with greater ease. Even if you can deal with the abstraction of textual data with ease, performing data analysis is all about communication. Unless you can communicate your ideas to other people, the act of obtaining, shaping, and analyzing the data has little value beyond your own personal needs. Fortunately, Python makes the task of converting your textual data into graphics relatively easy using MatPlotLib, which is actually a simulation of the MATLAB application. You can see a comparison of the two at https://pyzo.org/python_vs_matlab.html .

  If you already know how to use MATLAB (see MATLAB For Dummies, by John Paul Mueller [Wiley]), if you’d like to learn), moving over to MatPlotLib is relatively easy because they both use the same sort of state machine to perform tasks and they have a similar method of defining graphic elements. A number of people feel that MatPlotLib is superior to MATLAB because you can do things like perform tasks using less code when working with MatPlotLib than when using MATLAB (see https://phillipmfeldman.org/Python/Advantages_of_Python_Over_Matlab.html ). Others have noted that the transition from MATLAB to MatPlotLib is relatively straightforward (see https://vnoel.wordpress.com/2008/05/03/bye-matlab-hello-python-thanks-sage/

  Sign up to read

  Learn more about book
• 

  eBook - ePub

  Hands-On Data Analysis with Pandas

  A Python data science handbook for data collection, wrangling, analysis, and visualization, 2nd Edition

  • Stefanie Molin(Author)
  • 2021(Publication Date)
  • Packt Publishing

    (Publisher)

  Chapter 5 : Visualizing Data with Pandas and Matplotlib

  So far, we have been working with data strictly in a tabular format. However, the human brain excels at picking out visual patterns; hence, our natural next step is learning how to visualize our data. Visualizations make it much easier to spot aberrations in our data and explain our findings to others. However, we should not reserve data visualizations exclusively for those we present our conclusions to, as visualizations will be crucial in helping us understand our data quickly and more completely in our exploratory data analysis.

  There are numerous types of visualizations that go way beyond what we may have seen in the past. In this chapter, we will cover the most common plot types, such as line plots, histograms, scatter plots, and bar plots, along with several other plot types that build upon these. We won't be covering pie charts—they are notorious for being difficult to read properly, and there are better ways to get our point across.

  Python has many libraries for creating visualizations, but the main one for data analysis (and other purposes) is matplotlib . The matplotlib library can be a little tricky to learn at first, but thankfully, pandas has its own wrappers around some of the matplotlib functionality, allowing us to create many different types of visualizations without needing to write a single line with matplotlib (or, at least, very few). For more complicated plot types that aren't built into pandas or matplotlib , we have the seaborn library, which we will discuss in the next chapter. With these three at our disposal, we should be able to create most (if not all) of the visualizations we desire. Animations and interactive plots are beyond the scope of this book, but you can check out the Further reading

  Sign up to read

  Learn more about book
• 

  No longer available |Learn more

  Python 3 and Data Visualization

  • Oswald Campesato(Author)
  • 2023(Publication Date)
  • Mercury Learning and Information

    (Publisher)

  5

  MATPLOTLIB FOR DATA VISUALIZATION

  T his chapter introduces data visualization, along with a collection of Python -based code samples that use Matplotlib to render charts and graphs. In addition, this chapter contains visualization code samples that combine Pandas and Matplotlib .

  The first part of this chapter briefly discusses data visualization, with a short list of some data visualization tools, and a list of various types of visualization (bar graphs, pie charts, and so forth).

  The first part of this chapter contains a very short introduction to Matplotlib , followed by short code samples that display the available styles in colors in Matplotlib .

  The second part of this chapter contains an assortment of Python code samples that render horizontal lines, slanted lines, and parallel lines. This section also contains a set of code samples that show you how to render a grid of points in several ways.

  The third part of this chapter shows you how to load images, display a checkerboard pattern, and plotting trigonometric function in Matplotlib . The fourth section contains examples of rendering charts and graphs in Matplotlib , which includes histograms, bar charts, pie charts, and heat maps.

  The fourth section contains code samples for rendering 3D charts, financial data, and data from a sqlite3 database. The fifth section is optional because it requires some knowledge of working with time series datasets.

  WHAT IS DATA VISUALIZATION?

  Data visualization refers to presenting data in a graphical manner, such as bar charts, line graphs, heat maps, and many other specialized representations. As you probably know, big data comprises massive amounts of data, which leverages data visualization tools to assist in making better decisions.

  A key role for good data visualization is to tell a meaningful story, which in turn focuses on useful information that resides in datasets that can contain many data points (i.e., billions of rows of data). Another aspect of data visualization is its effectiveness: how well does it convey the trends that might exist in the dataset?

  Sign up to read

  Learn more about book
• 

  eBook - ePub

  Mastering Python

  • Rick van Hattem(Author)
  • 2022(Publication Date)
  • Packt Publishing

    (Publisher)

  np.square(b) .

  If you are familiar with the mathematics behind vectors and matrices, this library might feel very natural to you. At the very least, it can be a slightly more obvious way to declare how your data interacts.

  Plotting, graphing, and charting

  Being able to read, process, and write data is important, of course, but to understand the meaning of data it is often far more convenient to create a plot, graph, or chart. As the old adage goes: “A picture is worth a thousand words.”

  If you have experience with any of the libraries mentioned earlier in this chapter, you may know that many of them have options for graphical output. In (almost?) all cases, however, this is not really a built-in feature but a convenient shortcut to an external library such as matplotlib .

  As is the case with several of the libraries mentioned in this chapter, there are multiple libraries with similar features and possibilities, so this is certainly not an exhaustive list. To make visual plotting easier, for these examples we will mostly rely on jupyter-notebook with the use of the ipywidgets to create interactive samples. As always, the code (in these cases, the jupyter-notebooks ) can be found on GitHub at https://github.com/mastering-python/code_2 .

  Matplotlib

  The matplotlib library is the reliable standard for plotting and is supported by many of the scientific libraries in this chapter.

  Most of the libraries mentioned earlier in this chapter either explain how matplotlib can be used with the library, or even have utility functions to facilitate plotting with matplotlib .

  Does this mean that the matplotlib library is the gold standard for plotting? As usual, it depends. While matplotlib

  Sign up to read

  Learn more about book