Plot in Python
In Python, a plot refers to a graphical representation of data using libraries such as Matplotlib or Seaborn. Plots can be used to visualize trends, patterns, and relationships within the data, making it easier for programmers and data analysts to interpret and communicate their findings. Python's plotting capabilities are widely used in data visualization and analysis tasks.
5 Key excerpts on "Plot in Python"
- Yasha Hasija, Rajkumar Chakraborty(Authors)
- 2021(Publication Date)
- CRC Press(Publisher)
...5 Python for Data Visualization Introduction “Data science” is a buzzword in today’s age of high throughput biology. When we say data science, we handle enormous amounts of data and arrive at insights into biological findings. Up until this point, we have learned how to handle large datasets and how to do an efficient calculation on these. Data visualization is another way to derive insights from data through visualizations by using elements like graphs (e.g. scatterplots, histograms, etc), maps, or charts that allow for the understanding of complexities within the data by identifying local trends or patterns, forming clusters, locating outliers, and more. Data visualization is the preliminary step after loading the data to view the distribution of values. Cleaning the data, checking the quality of data, doing exploratory data analysis, and presenting data and results are some of the necessary tasks that a data scientist needs to do before applying any Machine Learning or statistical model on the data. In this chapter, we will describe one of the primary data visualization libraries of Python called “Matplotlib” and draw a few basic graphics. Next, we will browse through a library called “Seaborn” which provides a high-level interface for drawing beautiful and informative statistical graphs. Lastly, we will learn about interactive and geographical data plotting. Matplotlib Matplotlib is the most popular plotting library in the Python community. It gives us control over almost every aspect of a figure or plot. Its design is familiar with Matlab, which is another programming language with its own graphical plotting capabilities. The primary goal of this section is to go through the basics of plotting using Matplotlib. If we have Anaconda distribution, then we have acquired Matplotlib installed by default, or else we have to install it using a “pip” installer. Matplotlib is imported as “plt”, similar to “np” for NumPy and “pd” for pandas...
eBook - ePubR and Python for Oceanographers
A Practical Guide with Applications
- Hakan Alyuruk(Author)
- 2019(Publication Date)
- Elsevier(Publisher)
...Chapter 3 Plotting Abstract In R and Python, it is possible to produce many types of plots like histogram, bar, line, scatter, box, and contour plots. In this chapter, basic introductions to plotting in R and Python are given with examples. Keywords R; Python; Plotting; Plotting commands; Scientific plots 3.1 Plots in R In R, there are many possibilities with a wide number of built-in plotting functions to visualize your data. Plotting functions are classified as high-level and low-level functions. High-level functions are used to create new plots with automatically generated axes, titles, labels, and other plot properties. Low-level functions provide the ability to add more information to an existing plot, such as texts, lines, and points. Besides these functions, it is also possible to customize plots by changing graphical parameters. In general, data and graphical parameters are introduced as arguments in these functions to generate a plot. 3.1.1 High-level plotting functions In R, high-level plotting functions are plot(), qqplot(), qqnorm(), qqline(), hist(), dotchart(), image(), contour() and persp()...
eBook - ePubData Analysis for Corporate Finance
Building financial models using SQL, Python, and MS PowerBI
- Mariano F. Scandizzo CFA CQF(Author)
- 2021(Publication Date)
- Fulton Books, Inc.(Publisher)
...Chapter 8 Matplotlib Introduction Matplotlib is undeniably the fundamental stone when it comes to charts and visualizations in Python. Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy and Scipy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits such as Tkinter, wxPython, Qt, or GTK+. The structure of this chapter will resemble more of a cheat sheet than a typical book chapter. Why? You will notice as you get familiar with Python data science that while the analytical portion could be reduced to a reasonable number of lines of code, a professional, well-detailed visualization (chart), on the other hand, can take a considerable amount of time to produce. This could certainly make the visualization portion of your analysis quite burdensome, tedious, and time consuming. Thus, I have a two-fold recommendation: If you will use Matplotlib, Plotly, Seaborn, bokeh, or similar library, work on a series of standard templates ready to deploy. Follow your company brand guidelines, take the time to do a very granular customization and polishing of your charts, and then, never, never touch them again. Do not get me wrong, you might need to add new versions here and there. Nevertheless, the point is, if you do not standardize the visualizations to minimize the time it takes to make changes every time you need to do a new analysis, you will notice that 80 percent of the total time you spent building a report will go toward creating charts. My personal preference is the following: get your data from multiple sources and tables, preprocess and analyze your data with Python, SQL, MS Excel, or a combination of all of them, depending on the case, get output numbers, and then leverage platforms such as MS PowerBI or Google Data Studio...
eBook - ePub- Phillip D. Brooker(Author)
- 2019(Publication Date)
- SAGE Publications Ltd(Publisher)
...To that end, we’ll explore how to build a line graph and a bar chart using the XML dataset we looked at in Chapter 12, on the topic of the 10 leading causes of death across different US states from 1999 to 2016 (which is useful for thinking about things like healthcare inequalities across different states and across time). These are the most basic things you can do in Matplotlib, but will stand you in good stead as you develop your skills further and start to envision (and construct) more complex and sophisticated visualisations in Python to suit your analytic needs. Indeed, part of what this chapter is intending to do is emphasise the idea that as a Python programmer you are now in a position where you have a great deal of power and control over how you construct your analyses and findings – the creative application of a library like matplotlib can assist in that in many different ways, and you might go on to develop some really innovative and new ways of looking at social phenomena through Python programming. But, as with anything, we have to start by learning the basics and work from there – so, let’s do exactly that! Line graphs in Matplotlib One of the simplest things we can do in Matplotlib is look at numerical data on a line graph. All we mean by a line graph is a plot with an x - and y -axis (i.e. two lines starting from a single point of origin, one going from left to right/horizontal, and one going from bottom to top/vertical), onto which we can place various “datapoints” (i.e. individual bits of data) and see if there are any patterns we might identify by looking at this collection of datapoints as an object of analysis...
eBook - ePubBiostatistical Design and Analysis Using R
A Practical Guide
- Murray Logan(Author)
- 2011(Publication Date)
- Wiley-Blackwell(Publisher)
...5 Graphical data presentation Graphical summaries provide three very important rolls in data analyses. Firstly, they are an important part of the initial exploratory data analyses that should precede any formal statistical analyses. Secondly, they provide visual representations of the patterns and trends revealed in complex statistical analyses. Finally, in some instances (such as regression trees and ordination plots), graphical representations are the primary result of the analyses. R accommodates many of the standard exploratory data analyses via specific plotting functions. Many of these functions require little user input and produce very rudimentary plots – although the quality of such exploratory data analyses is rarely of great importance (as they are typically only for the researcher). Nevertheless, the plotting functionality within R is also highly customizable in order to produce rich, publication quality graphical and analytical summaries. Typically, a graphic begins with a high-level plotting function that defines the coarse structure of the graphic including its dimensions, axes scales, plotting symbol types and titles before creating a new plotting region on the graphics device. The most frequently used high-level plotting function is the plot() function which is a generic, overloaded a function that produces different plots depending on the class of object passed as its first argument. A range of the graphics produced by plot were illustrated on page 36. Other commonly used high-level plotting functions include hist(), boxplot(), scatterplot() and pairs(). Additional elements (such as text and lines) are added using the rich set of low-level graphical functions available. Common low-level plotting functions include lines(), points(), text() and axis()...
