Plot in Python

8 Key excerpts on "Plot in Python"

• 

  eBook - ePub

  Applying Math with Python

  Over 70 practical recipes for solving real-world computational math problems, 2nd Edition

  • Sam Morley(Author)
  • 2022(Publication Date)
  • Packt Publishing

    (Publisher)

  2

  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.

  Plotting is one of the most important aspects covered in this book. Plotting data, functions, or solutions can often help you gain an understanding of a problem that can really help to reason about your methods. We will see plotting again in every chapter of this book.

  In this chapter, we will cover the following recipes:

  • Basic plotting with Matplotlib
  • Adding subplots
  • Plotting with error bars
  • Saving Matplotlib figures
  • Surface and contour plots
  • Customizing three-dimensional plots
  • Plotting vector fields with quiver plots

  Technical requirements

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

  python3.10 -m pip install matplotlib

  This will install the most recent version of Matplotlib, which, at the time of writing this book, is version 3.5.2.

  Matplotlib contains numerous sub-packages, but the main user interface (UI ) is the matplotlib.pyplot package, which, by convention, is imported under the plt alias. This is achieved using the following import statement:

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  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.

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  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)

  Line plot A line plot (also known as a line chart or line graph) is a type of graphical representation used to display the relationship between two variables. It plots individual data points as points on a two-dimensional coordinate system and then connects the points with a line. Line plots are useful for exploring trends and patterns in a dataset over time or across categories. They can be used to show the following: • Trends: A line plot can show whether the values of a variable are increasing, decreasing, or remaining constant over time. • Patterns: A line plot can reveal patterns in the data, such as seasonal trends, fluctuations, or spikes. • Comparison: Line plots can be used to compare different variables, showing how they change over time or across categories. • Outliers: Outliers, or data points that fall outside the expected range of values, can be easily identified on a line plot. Line plots are a useful tool for exploring the relationship between two variables and are often used in exploratory data analysis. They can be created using the plot function in the Matplotlib library in Python. 106  Data Visualization with Python Here is an example of how to create a simple line plot using Matplotlib in Python: import matplotlib.pyplot as plt import numpy as np #Sample data x=np.linspace(10,20,100) y=np.cos(x) #Plot the data plt.plot(x,y) #Add labels and title plt.xlabel('Time (s)') plt.ylabel('Amplitude') plt.title('Cosine wave') #Show the plot plt.show() In this example, we generate a sample data set x and y using the linspace() and sin() functions from the NumPy library. We then use the plot() function to plot the data, and add labels and a title to the plot using the xlabel(), ylabel(), and title() functions. Finally, we use the show() function to display the plot: Figure 5.6: A line plot Data Visualization with Matplotlib  107 This will create a line plot of the sine wave with the x-axis labeled as "Time (s)" and the y-axis labeled as "Amplitude".

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  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.

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  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 

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  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.

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  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

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  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.

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