A Comprehensive Guide On Data Visualization In Python

This article was published as a part of the Data Science Blogathon

Overview

• Data visualization is the process of finding, interpreting, and comparing data so that it can communicate more clearly complex ideas, thus making it easier to identify once analysis of logical patterns.
• Data visualization is important for many analytical tasks including data summaries, test data analysis, and model output analysis. One of the easiest ways to connect with other people is to see good.
• Fortunately, Python has many libraries that provide useful tools for extracting data from data. The most popular of these are Matplotlib, Seaborn, Bokeh, Altair, etc.

Introduction

 

Table of Contents

1. What is Data Visualization?
2. Importance of data visualization
3. Data Visualization Process
4. Basic principles for data visualization
5. Data visualization formats
6. Data Visualization in Python
7. Color Schemes for Visualization of Data in Python
8. Other tools for data visualization
9. Conclusion
10. End Notes

What is Data Visualization?

 

Image 2

Importance of Data Visualization

Data Visualization Process

Basic principles for data visualization

1. Preview: This ensures that viewers have more data comprehension, as their starting point for checking. This means giving them a visual summary of different types of data, describing their relationship at the same time. This strategy helps us to visualize the process of data, in all its different levels, simultaneously.

2. Zoom in and filter: The second step involves inserting the first so that viewers can understand the data basement. Zoom in / out enables us to select available data subsets that meet certain methods while maintaining the concept of position and context.

Data visualization formats

1. Bar Charts

 

2. Histograms

Histograms represent flexibility in the form of bars, where the face of each bar is equal to the number of values ​​represented. They offer an overview of demographic or sample distribution with a particular aspect. The two differences in the histogram are:

1. Standing columns
2. Horizontal columns

 

3. Pie charts

The pie chart contains a circle divided into categories, each representing a portion of the theme. They can be divided into no more than five data groups. They can be useful for comparing different or continuous data.

The two differences in the pie chart are:

1. Standard: Used to show relationships between components.
2. Donut: A variation of style that facilitates the inclusion of a whole value or design element in the center.

4. Scatter Plot

Scatter plots sites use a point spread over the Cartesian integration plane to show the relationship between the two variables. They also help us determine whether the different data groups are related or not.

5. Heat Maps

Temperature maps represent individual values ​​from a set of data in the matrix using color variation or color intensity. They often use color to help viewers compare and contrast data at two distinct categories. They are useful for viewing web pages, where the areas most users encounter are represented by “hot” colors, and pages that receive the fewest clicks are displayed in “cold” colors.

6. Line Plot

 

Color Schemes for Data Visualization in Python

Color is one of the most powerful data resources visual acuity, and it is important if we are to understand the details correctly. Color can be used to separate elements, balance or represents values, and interacts with cultural symbols associated with a particular color. It rules our understanding again so that we can analyze it, we must first understand its three types:

Hue: This is what we usually think of when we upload a photo color. There is no order of colors; they can only be distinguished by their characteristics (blue, red, yellow, etc.).

Brightness: This is an average measure that describes the amount of light reflected in an object with another. Light is measured on a scale, and we can talk about bright and dark values ​​in one color.

 

Data Visualization in Python

There are two temporal.csv and map.csv files. The first one we will use in most studies includes data on the popularity of three words over time (from 2004 to now, 2020). In addition, I have added category variables (singular and zero) to show the functionality of charts that vary by category.

 

In the descriptive command, we will see how the data is distributed, size, minimum, mean.

df.describe()

df.info()

Data Visualization in Python using Matplotlib

import matplotlib.pyplot as plt
plt.plot(df['Mes'], df['data science'], label='data science')

We can make graphs of many variations on the same graph and compare them.

plt.plot(df['Mes'], df['data science'], label='data science')
plt.plot(df['Mes'], df['machine learning'], label='machine learning')
plt.plot(df['Mes'], df['deep learning'], label='deep learning')
plt.xlabel('Date')
plt.ylabel('Popularity')
plt.title('Popularity of AI terms by date')
plt.grid(True)
plt.legend()

If you are working with Python from a terminal or script, after explaining the graph of the functions listed above use plt.show (). If working from Jupyter notebook, add% matplotlib to the queue at the beginning of the file and run it before creating a chart.

fig, axes = plt.subplots(2,2)
axes[0, 0].hist(df['data science'])
axes[0, 1].scatter(df['Mes'], df['data science'])
axes[1, 0].plot(df['Mes'], df['machine learning'])
axes[1, 1].plot(df['Mes'], df['deep learning'])

We can draw a graph with different styles of different points for each:

plt.plot(df['Mes'], df['data science'], 'r-')
plt.plot(df['Mes'], df['data science']*2, 'bs')
plt.plot(df['Mes'], df['data science']*3, 'g^')

plt.scatter(df['data science'], df['machine learning'])

With Bar chart:

plt.bar(df['Mes'], df['machine learning'], width=20)

With Histogram:

plt.hist(df['deep learning'], bins=15)

Data Visualization in Python using Seaborn

import seaborn as sns
sns.set()
sns.scatterplot(df['Mes'], df['data science'])

sns.relplot(x='Mes', y='deep learning', hue='data science', size='machine learning', col='categorical', data=df)

 

One of the most popular drawings provided by Seaborn is the heatmap. It is very common to use it to show all connections between variables in the dataset:

sns.heatmap(df.corr(), annot=True, fmt='.2f')

sns.pairplot(df)

sns.pairplot(df, hue='categorical')

sns.jointplot(x='data science', y='machine learning', data=df)

sns.catplot(x='categorical', y='data science', kind='violin', data=df)

Data Visualization in Python using Bokeh

from bokeh.plotting import figure, output_file, save
output_file('data_science_popularity.html')

We draw what we want and save it to a file:

p = figure(title='data science', x_axis_label='Mes', y_axis_label='data science')
p.line(df['Mes'], df['data science'], legend='popularity', line_width=2)
save(p)

Other Tools for Data Visualization

Some data visualization tools help in visualizing the data effectively and faster than the traditional python coding method. These are some of the examples:

Databox

Zoho Analytics is probably one of the most popular BI tools on this list. One thing you can be sure of is that with Zoho analytics, you can upload your data securely. Additionally, you can use a variety of charts, tables, and objects to transform your data concisely.

If you want to easily visualize and visualize data, then Tableau is a tool for visualizing your data. It helps you to create charts, maps, and all other technical graphics. To improve your visual presentation, you can also get a desktop app.

You can check out my article on Analytics Vidhya for more information on trending Data Visualization Tools. Top 10 Data Visualization Tools.

Conclusion

End Notes

In this article, we discussed Data Visualization. Some basic formats of data visualization and some practical implementation of python libraries for data visualization. Finally, we concluded with some tools which can perform the data visualization in python effectively.

If you have any doubt or any feedback feel free to share with us in the comments below. You can check out more articles on Data Visualization on Analytics Vidhya Blog.

Thanks For Reading!

About Me:

Hey, I am Sharvari Raut. I love to write!

Technical Writer | AI Developer | Avid Reader | Data Science | Open Source Contributor

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Sharvari Santosh 02 Sep 2022

I am Sharvari Raut. I love to write. I am a final year student in Computer Science and Engineering from NCER Pune. I have worked as a freelance technical writer for few startups and companies. Having 2 yrs of experience in Technical Writing I have written over 100+ technical articles which are published till now. Writing for Analytics Vidhya is one of my favourite things to do.