Python Bar Chart

4 Key excerpts on "Python Bar Chart"

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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)

  Here is an example of how to create a simple bar chart using Matplotlib in Python: import matplotlib.pyplot as plt #Sample data categories = ['A', 'B', 'C', 'D'] values = [1, 4, 2, 5] #Plot the data plt.bar(categories, values) #Add labels and title plt.xlabel('Categories') 108  Data Visualization with Python plt.ylabel('Values') plt.title('Bar Chart Example') #Show the plot plt.show() In this example, we create a sample data set of categories and values. We then use the bar() 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 5.7: A Bar Plot This will create a bar chart with the categories on the x-axis and the values on the y-axis. The x-axis will be labeled as "Categories" and the y-axis as "Values". The title of the plot will be "Bar chart example". Histogram A histogram is a type of graphical representation used to show the distribution of a dataset. It is an estimate of the probability distribution of a continuous variable, showing the number of observations that fall within specified ranges (or "bins") of values. A histogram is created by dividing the range of values of the dataset into a series of intervals, called bins. The height of each bar in the histogram represents the number of observations that fall within the corresponding bin. The following elements make up a histogram: Data Visualization with Matplotlib  109 • X-axis: The x-axis represents the range of values in the dataset, divided into bins. • Y-axis: The y-axis represents the frequency of observations in each bin. • Bars: The bars represent the number of observations in each bin. The height of each bar is proportional to the frequency of observations in that bin. • Bin width: The width of each bin represents the range of values that fall within that bin.

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  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?

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  eBook - ePub

  Hands on Data Science for Biologists Using 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. To view the graphs in the Jupyter Notebook, we have to use a Jupyter function “%matplotlib inline” in the notebook. The notebook of this chapter can be found with the supplementary files labeled as “Python for Data Visualization5.ipnyb”.

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  eBook - PDF

  Learning Scientific Programming with Python

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

    (Publisher)

  Horizontal bar charts are catered for either by setting orientation='horizontal' or by using the analogous ax.barh method. Example E7.9 The following program produces a bar chart of letter frequencies in the English language, estimated by analysis of the text of Moby-Dick. 7 The vertical bars are centered and labeled by letter (Figure 7.10). Listing 7.9 Letter frequencies in the text of Moby-Dick. # eg7-charfreq.py import numpy as np import matplotlib.pyplot as plt text_file = 'moby-dick.txt ' letters = ' ABCDEFGHIJKLMNOPQRSTUVWXYZ ' # Initialize the dictionary of letter counts: { ' A ' : 0, ' B ' : 0, ...}. lcount = dict ([(letter , 0) for letter in letters]) # Read in the text and count the letter occurrences. for letter in open (text_file).read(): try : lcount[letter.upper()] += 1 except KeyError: # Ignore characters that are not letters. 7 See, for example, www.gutenberg.org/ebooks/2701 for a free text file of this novel. 316 Matplotlib Figure 7.10 Letter frequencies in the novel Moby-Dick. pass # The total number of letters. norm = sum (lcount.values()) fig, ax = plt.subplots() # The bar chart, with letters along the horizontal axis and the calculated # letter frequencies as percentages as the bar height. x = range (26) ax.bar(x, [lcount[letter]/norm * 100 for letter in letters], width=0.8, color= 'g ', alpha=0.5, align= ' center ') ax.set_xticks(x) ax.set_xticklabels(letters) ax.tick_params(axis= 'x ', direction= ' out ') ax.set_xlim(-0.5, 25.5) ax.yaxis.grid(True) ax.set_ylabel( ' Letter frequency , % ') plt.show() For monochrome plots, it is sometimes preferable to distinguish bars by patterns. The hatch argument can be used to do this, using any of several predefined patterns (see Table 7.9) as illustrated in the example below.

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