When working with
data, it can be challenging to understand your data when it’s only in tabular
form. To understand what our data is notifying us and to better clean it and
select appropriate models, we need to visualize or represent it in pictorial
form. This helps uncover patterns, correlations, and trends that cannot be
obtained from data in a spreadsheet or CSV file.
Data visualization is
finding trends and correlations in our data using a visual representation. To
conduct data visualization in Python,
we can use different python data visualization modules such as Matplotlib,
Seaborn, Plotly, etc. In this article, Complete Guide to Python Data Visualization, we will discuss how to work with some Python data visualization modules.
What are data visualizations?
Simply put, data
visualization allows people to explore data in many different ways and see
patterns and insights that wouldn’t be possible when looking at it in its raw
form. People crave storytelling, and visualizations allow us to pull the story out
of our data stores.
The phrase “A
picture value is a thousand words” is accurate when converting vast piles
of data into images from which the viewer can actually understand and derive
meaning. Children’s storybooks contain many ideas but very few words. As
children, we don’t know many words, but visuals allow us to quickly understand
a story.
In our modern digital
world, we have a vast amount of data. Data scientists and ML engineers get most
of the data they work with in a structured or unstructured format. However, it
is difficult for humans to understand and analyze. Data visualizations (or
graphical representations) are essential to understanding data. They help users
explore data through visuals such as tables, charts, graphs, maps, and other
visualizations.
Different types of exploratory data analysis
Each data set has
many variables (functions, input, or independent variables) and target/output
variables (labels, dependent variables, classes, or class labels). The
responsibility of a data scientist is to thoroughly understand each feature
individually and the relationship between the various features. The goal is to
prepare a dataset for the implementation of ML algorithms.
We have three ways
for exploratory data analysis:
Univariate analysis
In univariate
analysis, each variable is analyzed separately. This will give us complete
statistical data for each element. There are a variety of data visualization
techniques for univariate analysis, including Box Plot, Histogram, PDF, and
CDF.
Bivariate analysis
Bivariate analysis is
performed to find the relationship between each element and the target
variable. Data visualization techniques for bivariate analysis are Scatter
plots and Heatmap.
Multivariate analysis
As the name suggests,
multivariate analysis is performed to understand the relationship between
different features of a data set. The Pair Plot is one of the main techniques
for visualizing data with multiple variables.
The importance of data visualization
We are living in an
era of visual information, and visual content plays a vital role in every
moment of our lives. Research by SHIFT Disruptive Learning has shown that we
typically process images 60,000 times faster than tables or text and that our
brains remember them better in the future. The studies found that after three
days, the subjects analyzed maintained between 10% and 20% of written or spoken
data compared to 65% of visual communication.
The human brain can
grasp images in just 13 milliseconds and store information if it is associated
with a concept. Our eyes can catch 36,000 visual messages per hour. 40% of the
nerve fibers are connected to the retina.
This shows that
people are better at visual processing information stored in their long-term
memory. As an outcome, visual presentation using pictures is a more effective
way of communicating information than text or a table; and takes up very little
space. Data visibility is more attractive, easier to work with, and easier to
remember.
Data visualization process
Several fields are
involved in the data recognition process to facilitate or reveal existing
relationships or discover something new in a dataset.
1. Filtering and
processing.
Refining and refining
data turns it into information by analyzing, interpreting, summarizing,
comparing, and examining.
2. Translation and
visual representation.
Creating a visual
representation by describing image sources, language, context, and opening
word, all for the recipient.
3. Visualization and
interpretation.
Finally, visual
acuity is adequate if it has a cognitive impact on knowledge creation.
Data
visualization in Python
Python offers various
plotting libraries, namely Matplotlib, Seaborn, and many other data
visualization packages with multiple features to create informative,
customized, and attractive graphs for the easiest and most effective
presentation of data.
Matplotlib and Seaborn
Matplotlib and Seaborn are python libraries used for
data visualization. They have built-in modules for drawing various graphs.
While Matplotlib embeds graphs into applications, Seaborn primarily uses
statistical graphs.
But when should we
use one of the two? Let’s understand this with a comparative analysis. The
table below compares the well-known Python visualization packages, Matplotlib
and Seaborn.
Matplotlib
“ It is mainly used for statistical
visualization and can perform complex visualizations with fewer commands.
“ Works with entire datasets.
“ Matplotlib works productively with data arrays
and frames. It considers aces and pieces as objects.
“ Matplotlib is more personalized and pairs well
with Pandas and Numpy for exploratory data analysis.
Seaborn
“ It is used for basic plotting such as line
graphs, bar graphs, etc.
“ It mainly works with datasets and arrays.
“ Seaborn is much more organized and functional
than Matplotlib, treating the entire data set as a standalone unit.
“ Seaborn has multiple built-in themes and is
mainly used for statistical analysis.
1. Bar Charts
Bar charts are one of
the most famous ways to visualize data because they present quickly set data in
an easy-to-understand format that permits viewers to notice height and depth at
a glimpse.
They are
miscellaneous and often used to compare different categories, analyze changes
over time, or compare certain parts. The three types of a bar chart are:
“ Vertical
Column: Data is also used
chronologically, it should be in a left-to-right format.
“ Horizontal
column: Used to visualize
categories
“ Fully
Stacked Column: Used to
visualize types that add up to 100%
2. Histograms
Histograms present
flexibility in the form of bars, where the area of ??each bar is equal to the
number of values ??represented. They offer an overview of the demographic or
sample distribution with a specific aspect. The two distinctions in the
histogram are:
“ Standing columns
“ Horizontal columns
3. Pie charts
A pie chart contains
a circle divided into categories representing part of the topic. They can be
split into more than five data groups. They can help compare disparate or
continuous data.
The two differences
in a pie chart are:
“ Standard: Used to display relationships between
components.
“ Donut: A style variation that makes it easy to
include a full-size or design element in the center.
4. Scatter plots
Scatter plots use a
point spanning the Cartesian integration plane to show the relationship between
the two variables. They also help us determine whether different groups of data
are related or not.
5. Heat maps
Heatmaps represent
individual values ??from a dataset in a matrix using color variation or
intensity. They usually use color to help viewers compare and contrast data in
two distinct categories. They help display web pages where the areas most users
encounter are represented by “hot” colors, and the least clicked
pages are displayed in “cool” shades.
6. Line plot
This is used to show
changes or trends in data over time. They help establish relationships,
accelerations, decelerations, and instabilities in a data set.
Color schemes for data visualization in Python
Color is one of the
most useful data sources for visual acuity and is essential if we are to
properly understand detail. Color can separate elements, balance or represent
values, and interact with cultural symbols associated with a particular color.
Again, it guides our understanding, to analyze it, we must first comprehend its
types:
Hue:
This is what we frequently think of when we upload the color of a photo. There
is no color order; they can only be distinguished by their properties (blue,
red, yellow, etc.).
Brightness:
This is an average approximation that describes the amount of light reflected
from one object to another. Light is estimated on a scale, and we can discuss
light and dark values ??in one color.
Saturation:
indicates the intensity of the given color. It varies according to the light. Dark
colors are less saturated and approach gray when a color is less saturated. In
other words, it is close to a neutral (empty) color. The following diagram
provides a summary of color application.
Winding Up!
In our modern big
data world, data visualizations are essential. They can give direction and
vision to data scientists and business front-end users. This article just gives
you examples of different visualizations you can create in Python and code to
get you started.
We hope you will find
them easy to understand and implement. How data can be visualized are endless.
This is just the beginning. Python and R visualizations give you a lot of
options to explore. Just take the data and start experimenting. You will be
amazed at the beautiful and informative images you can create.
