The graphic display of statistical information is known as data visualization. Programming data visualization tools offer a straightforward approach to examining and comprehending trends, outliers, and patterns in data by utilizing visual components like charts, graphs, and maps.
The data visualization tools Python has available and their related technologies are crucial to evaluating vast volumes of data and making data-driven choices in the age of big data. This article will focus on what data visualization is, its advantages, and why Python is best for the said application.
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What Is the importance of programming data visualization?
Businesses need data visualization to help them rapidly see data patterns, which would otherwise be difficult. Analysts can see ideas and novel patterns thanks to the graphical depiction of data sets. In addition, making sense of the quintillion bytes of data generated daily is hard without data visualization.
Data visualization expands to all industries where data is present since every professional field benefits from knowing their data. Information is the most critical leverage for every organization. One may make arguments and use knowledge by using visualization. To visualize and comprehend data, one can use a dashboard, graph, infographics, map, chart, video presentation, etc. In addition, decision-makers may use data visualization to interrelate data to gain more significant insights and benefit from its relevance.
How can One benefit from data visualization?
Business stakeholders can concentrate on the areas that need attention by analyzing reports. The visual mediums aid analysts in comprehending the crucial information required for their line of work. Whether it’s a business report or a business model, a graphic depiction of the data helps businesses make better analyses and decisions that enhance revenues.
People analyze images more quickly than they do long, laborious analytical forms or reports. Decision-makers may move fast based on fresh data insights if the data is well-communicated, speeding both decision-making and corporate growth.
Business users may utilize data visualization to understand their massive data sets. They gain by spotting fresh patterns and mistakes. They can focus on locations that show red flags or progress. The business advances as a result of this procedure.
Why use Python for programming data visualization graphs and content?
By putting data in a visual context and attempting to comprehend it, data visualization seeks to reveal patterns, trends, and connections that would not otherwise be visible. Python has a number of excellent graphing packages with a wide range of functionality. Python provides a great library for you whether you want to make interactive or fully customized plots.
A few well-known plotting libraries in Python include Matplotlib – low-level and flexible, Pandas Visualization – easy-to-use interface based on Matplotlib, Seaborn – high-level interface and great default styles, plotnine – based on R’s ggplot2 and uses Grammar of Graphics, Plotly – which can create interactive plots and Bokeh.
Which library Is the most popular for Python data visualization?
A large number of available modules may be intimidating if you’re new to Python visualization. Additionally, specific libraries could be better suited for a given circumstance than others. You should be able to distinguish between the varied features of each library and make an informed decision.
How to use Matplotlib as a graph tool for Python
Matplotlib is the most well-liked and outstanding Python library for data visualization to date. Almost everyone involved in data science has at some point used Matplotlib. Delphi may be used to create a simple yet effective user interface for this advanced data visualization engine.
NumPy arrays are the foundation of the user-friendly, low-level data visualization package of the Matplotlib library. It includes a number of visualizations, including scatter plots, line plots, and histograms.
Enter the following command in the terminal to install this.
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pip install matplotlib |
The matplotlib library uses various plots for data visualization. These include scatter plots, line charts, bar charts, and histograms. For example, to plot a histogram, the following code lines can be used:
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import pandas as pd import matplotlib.pyplot as plt # reading the database data = pd.read_csv("tips.csv") # Scatter plot with day against tip plt.scatter(data['day'], data['tip'], c=data['size'], s=data['total_bill']) # Adding Title to the Plot plt.title("Scatter Plot") # Setting the X and Y labels plt.xlabel('Day') plt.ylabel('Tip') plt.colorbar() plt.show() |
The colorbar() function is included in the code to demonstrate the flexibility of using the library and make the output more appealing.
Seaborn for Python is great for programming data visualization
On top of Matplotlib, Seaborn is a high-level interface. It offers stunning design themes and color schemes to create more appealing graphs.
Enter the following command in the terminal to install Seaborn.
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pip install seaborn |
Seaborn is developed on top of the Matplotlib library. Therefore, it is relatively easy to use Seaborn and Matplotlib together. To use the customization feature of Matplotlib, we simply need to use the Seaborn Plotting function as usual. Just like Matplotlib, the Seaborn library uses different plots. For example, scatter plots, line plots, bar plots, and histograms.
An example code to a histogram:
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# importing packages import seaborn as sns import matplotlib.pyplot as plt import pandas as pd # reading the database data = pd.read_csv("tips.csv") sns.histplot(x='total_bill', data=data, kde=True, hue='sex') plt.show() |
Use Bokeh as a graph tool with Python
The third library on our list is Bokeh. The primary reason for Bokeh’s fame is its dynamic chart presentation. Bokeh produces its plots using HTML and JavaScript, which employs contemporary web browsers, to present attractive, succinct designs of innovative visuals with high-level interaction. The installation command for this library is
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pip install bokeh |
Just like the other two libraries, this library also uses plots like scatter plots, line charts, and bar charts. The additional interactivity feature to the plots is one of Bokeh’s primary characteristics. The legend is interactive, thanks to the click policy parameter. Interactivity comes in two flavors. Below is an example of a plot using the Bokeh library:
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# importing the modules from bokeh.plotting import figure, output_file, show import pandas as pd # instantiating the figure object graph = figure(title = "Bokeh Bar Chart") # reading the database data = pd.read_csv("tips.csv") # plotting the graph graph.vbar(data['total_bill'], top=data['tip'], legend_label = "Bill VS Tips", color='green') graph.vbar(data['tip'], top=data['size'], legend_label = "Tips VS Size", color='red') graph.legend.click_policy = "hide" # displaying the model show(graph) |
Python has several charting libraries, including Matplotlib, Seaborn, and many additional data visualization tools with various capabilities for building educational, unique, and visually appealing charts to show data most simply and powerfully.
So, what are you waiting for? Click here to begin programming data visualization using Python.
