Having tabular data can make it challenging to comprehend your data when working with it genuinely. Visualizing data or representing it in a pictorial form will enable us to understand better what the information means and how to clean and use it. Tables and CSV files can’t reveal patterns, correlations, or trends, but graphs can.
Visualizing data to find trends and correlations is referred to as Data Visualization. We can perform data visualization in Python using Matplotlib, Seaborn, etc. This article discusses how to use some Python modules for data visualization and covers the following topics in detail.
What is Data Visualization
In data analysis, data visualization refers to visualizing data. Inferences can be communicated graphically through the plotting of data. As part of the data delivery (DPA) discipline, data detection is also a feature of identifying, retrieving, managing, formatting, and efficiently delivering data.
In large data sets, data viewing helps identify patterns, styles, and vendors by easier identifying patterns, styles, and vendors. Diagrams, charts, information drawings, and visuals are all examples of this term.
Data viewing is an essential part of almost every job. Information-sharing managers and stakeholders can use it to demonstrate student test results, as well as artificial intelligence (AI) developers and computer science teachers.
It is also essential to big data projects. Early in the era of big data, businesses needed a way to quickly and easily view all of their data collections. They were naturally matched in terms of viewing tools.
The Importance of Data Visualization
By visualizing data, businesses can quickly identify trends that would otherwise prove challenging. Analysts can visualize new patterns and concepts through the pictorial representation of data sets. Data proliferation, including data visualization, is necessary to make sense of the quintillion bytes of data generated daily.
All professional fields benefit from a better understanding of their data, so data visualization is becoming increasingly popular. Almost every business relies on its information as its most valuable asset. It is through visualization that one can communicate ideas and make use of data.
Data can be visualized and understood using dashboards, graphs, infographics, maps, charts, videos, slides, etc. Data visualization enables decision-makers to interrelate data to find better insights and reap the benefits of data visualization.
Advantages of Data Visualization
The advantages of data visualization are listed below
Making Key Values Accessible
The first benefit of Data Visualization is that it allows massive data sets to be decoded and key values revealed. Especially when it comes to large amounts of data, it can be overwhelming to understand. Visualizing the data helps make key values of the data clear and easy to understand. As a result, everyone in the organization can easily understand and interpret it.
Our ability to visualize data enables us to recognize emerging trends and respond quickly based on what we see. Identifying strongly correlated parameters is easier when visuals and diagrams are used.
Certain relationships are apparent and others need to be recognized and clarified before we can concentrate on a particular data point that can positively impact our business.
Simple to Understand
Using graphic representations, which provide us with clear and coherent expressions of vast amounts of data, allows us to understand the data, reach conclusions, and see perspectives.
A data visualization tool makes it easy for managers and decision-makers to create and consume critical metrics quickly and easily. Any anomalies in these metrics – e.g., Sales are down significantly in one region – will allow decision-makers to promptly establish what operating conditions or decisions are at issue and how they react in response.
An Understanding of the Story
Dashboards are designed to tell stories. Visuals should be designed in such a way that they help the target audience quickly grasp the story. It would be best to convey the story in the simplest way possible without using excessively detailed visuals.
Represent Complex Relationships
Standard visuals, such as bar charts and line graphs, are often inadequate when presenting complex relationships.
It is virtually impossible to present a dataset with over a million distinct data points in a standard way, for example. An interactive hierarchical visual is a much better solution in that case. With Interactive Data Visualization, users can explore data in a way that fits their needs.
Visualizing Data With Python
Data visualization is probably one of Python’s most widely used features in data science today. Users can create highly customized, interactive plots with Python libraries using various features.
Several plotting libraries are included in Python, including Matplotlib, Seaborn, and other data visualization packages. For presenting information most simply and effectively, each has the unique features that allow it to construct informative, customized, and intriguing plots.
Visualization Packages for Python
Each of Python’s libraries has its features for visualizing data. This tutorial will cover four of these libraries. Each of these libraries can support a variety of graph types.
We are using the tips database from Kaggle to demonstrate the libraries
Let’s examine each library individually.
The Matplotlib package allows you to visualize 2D array plots in Python. To plot data, Matplotlib uses the NumPy library, which is also written in Python. As well as supporting Python and IPython shells, it is compatible with Jupyter notebooks and web application servers. With Matplotlib, we can investigate trends, behavioral patterns, and correlations using scatter, line, bar, and histogram plots, among others. In 2002, John Hunter launched it for the first time.
Dots are used to represent relationships between variables in scatter plots to observe relationships between variables. To draw a scatter plot, the matplotlib library provides the scatter() method.
# Importing the libraries import pandas as pda import matplotlib.pyplot as plt # Reading the dataset dataset = pda.read_csv("tips.csv") plt.scatter(dataset['total_bill'], dataset['size']) plt.title("Scatter Plot") plt.xlabel('Total_bill') plt.ylabel('size') plt.show()
To represent a data category, a bar plot or bar chart uses rectangular bars whose lengths and heights correspond to the data values they represent.
# Importing the libraries import pandas as pda import matplotlib.pyplot as plt # Reading the database data = pda.read_csv("tips.csv") plt.bar(data['total_bill'], data['day']) plt.title("Bar Chart") plt.xlabel('Day') plt.ylabel('Tip') plt.show()
With Seaborn, you can make statistical representations of datasets in Python. It is built on matplotlib and used to create a variety of visualizations. It integrates with pandas’ data structures. This library incorporates mapping and aggregation functions to create informative visuals. We recommend using the Jupyter/IPython interface with Matplotlib.
To plot a line plot in Seaborn, use the lineplot() method. It is also possible to pass just the data argument in this case.
# Importing the libraries import pandas as pda import seaborn as sn import matplotlib.pyplot as plt # Reading the database dataset = pda.read_csv("tips.csv") sn.lineplot(x='total_bill', y='day', data=dataset) plt.show()
The scatterplot() method is used to plot to scatter plots. It is similar to Matplotlib, but it requires additional argument data.
# Importing the libraries import seaborn as sn import matplotlib.pyplot as plt import pandas as pda # Reading the database data = pda.read_csv("tips.csv") sns.scatterplot(x='total_bill', y='tip', data=data,) plt.show()
Histograms can be plotted in Seaborn using histplot() method.
# Importing the libraries import seaborn as sn import matplotlib.pyplot as plt import pandas as pda # Reading the database data = pda.read_csv("tips.csv") sn.histplot(x='total_bill', data=data, kde=True, hue='sex') plt.show()
The plotly.py visualization library is an interactive, open-source, comprehensive, and declarative Python library. A wide variety of useful visualizations are available, such as scientific charts, 3D graphs, statistical charts, and financial charts. In addition to Jupyter notebooks and standalone HTML files, Plotly graphs can be viewed online. There are options for interacting and editing plots in the Plotly library. In both local and web browser modes, the robust API works perfectly.
Plotly’s scatter() method can be used to create scatter plots. It is also necessary to include an additional data argument, like Seaborn.
import pandas as pda import plotly.express # we are reading the csv dataset through pandas dataset = pda.read_csv("tips.csv") graph = plotly.express.scatter(dataset, x="total_bill", y="size", color='smoker') graph.show()
In Plotly, line plots are much more accessible and illustrious additions that assemble easy-to-style statistics from various data types. Each position of data is represented as a vertex with px. line
# importing the library import plotly.express as px import pandas as pda # we are reading the database data = pda.read_csv("tips.csv") # we are plotting the scatter chart fig = px.line(data, y='total_bill', color='sex') fig.show()
With plotly.express, you can create bar charts using the bar() method.
# importing the libraries import plotly.express as px import pandas as pd # reading the database data = pd.read_csv("tips.csv") fig = px.bar(data, x='day', y='total_bill', color='sex') # showing the plot fig.show()
To conclude, when you understand the use case and requirement, you can leverage many different libraries to their full potential. Various libraries have different syntaxes and semantics, so it’s imperative to understand the advantages and challenges of each. Let’s visualize!