Data Visualization Tutorial For Beginners | Big Data Analytics Tutorial | Simplilearn Video Lecture | Taming the Big Data with HAdoop and MapReduce - Software Development

[Music]
let's now start this lesson by defining
what data visualization is data
visualization is the technique to
present the data in a pictorial or
graphical format it enables stakeholders
and decision-makers to analyze data
visually the data in graphical formats
allows them to identify new trends and
patterns easily well you might think why
data visualization is important let's
explain with an example you are a sales
manager in a leading global organization
the organization plans to study the sale
details of each product across all
regions and countries this is to
identify the product which has the
highest sales in a particular region and
up the production this research will
enable the organization to increase the
manufacturing of that product in the
particular region the data involved for
this research might be huge and complex
the research on this large numeric data
is difficult and time-consuming when it
is performed manually when this numeric
data is plotted on a graph or converted
to charts it's easy to identify the
patterns and predict the result
accurately the main benefits of data
visualization are as follows it
simplifies the complex quantitative
information it helps analyze and explore
Big Data easily it identifies the areas
that need attention or improvement it
identifies the relationship between data
points and variables it explores new
patterns and reveals hidden patterns in
the data there are three major
considerations for data visualization
they are clarity accuracy and efficiency
first ensure that data set is complete
and relevant this enables the data
scientist to use the new patterns yield
from the data in the relevant places
second ensure using appropriate
graphical representation to convey the
right message third use efficient
visualization technique which highlights
all the data points there are some basic
factors that one would need to be aware
of before visualizing the data visual
effect coordination system data types
and scale informative interpretation
visual effect includes the usage of
appropriate shapes colors and size to
represent the analyzed data
the coordinate system helps to organize
the data points within the provided
coordinates the data types and scale
choose the type of data such as numeric
or categorical the informative
interpretation helps create visuals in
an effective and easily interpretable
manner using labels title legends and
pointers so far you have learned what
data visualization is and how it helps
interpret results with large and complex
data with the help of the Python
programming language you can perform
this data visualization you'll learn
more about how to visualize data using
the Python programming language in the
subsequent screens many new Python data
visualization libraries are introduced
recently such as mat plot library Vista
Boca Seabourn Heigl folium and networks
the mat plot library has emerged as the
main data visualization library let's
now learn about this mat plot library in
detail
Matt plot library there's a Python
two-dimensional plotting library for
data visualization and creating
interactive graphics or plots using
pythons mat plot library the data
visualization of large and complex data
becomes easy there are several
advantages of using mat plot library to
visualize data they are as follows it's
a multi-platform
data visualization tool built on the
numpy and slide PI framework therefore
it's fast and efficient it's as essence
the ability to work well with many
operating systems and graphic backends
it possesses high quality graphics in
plots to print in view for a range of
graphs such as histograms bar charts pie
charts scatter plots and heat maps with
jupiter notebook integration the
developers have been free to spend their
time implementing features rather than
struggling with cross-platform
compatibility it has large community
support and cross-platform support as it
is an open source tool it has full
control over graph or plot styles such
as line properties fonts and axis
properties let's now try
to understand a plot a plot is a
graphical representation of data which
shows relationship between two variables
or the distribution of data look at the
example shown on the screen this is a
2-dimensional line plot of the random
numbers on the y axis and the range on
the x axis the background of the plot is
called grid the text first plot denotes
the title of the plot and text line 1
denotes the legend
you can create a plot using four simple
steps to import the required libraries
define or import the required data set
set the plot parameters display the
created plot
let's consider the
example plot used earlier follow the
steps below to obtain this plot the
first step is to import the required
libraries here we have imported numpy
and pi plot and style from mat plot
library numpy is used to generate the
random numbers and the PI plot which is
built in Python library is used to plot
numbers in style class is used for
setting the grid style mat plot library
in line is required to display the plot
within Jupiter notebook the second step
is to define or import the required data
set here we have defined the data set
random number using numpy random method
note that the range is 10 we have used
the print method to view the created
random numbers the third step is to set
the plot parameters in this step we set
the style of the plot labels of the
coordinates title of the plot the legend
and the line width in this example we
have used ggplot as the plot style the
plot method is used to plot the graph
against the random numbers and the plot
method the word G denotes the plot line
color of green label denotes the legend
label and it's named as line 1 also the
line width is set to 2 note that we have
labeled the x-axis as range and the
y-axis as labels and set the title as
first plot the last step is to display
the created plot use the legend method
to plot the graph based on the set
conditions and the show method to
display the created plot let's now learn
how to create a two dimensional plot
consider the following example a new
tree worldwide firm wants to know how
many people visit its website at a
particular time this analysis helps it
control and monitor the website traffic
this example involves two variables
namely users and time therefore this is
a 2-dimensional or 2d plot take a look
at the program that creates a 2d plot
object web customers is a list on the
number of users and time hours indicates
the time from this we understand that
there are 123 customers on the website
at 7 a.m.
645 customers on the website at 8 a.m.
and so on the ggplot is used to set the
grid style and the plot method is used
to plot the website customers against
time don't forget to mat plot library in
line to display or view the plot on the
jupiter notebook the website traffic
curve is plotted and the graph is shown
on the screen it's also possible to
change the line style of the plot to
change the line style of the plot use
define the line style as dashed in the
plot method observe the output graph
changes to a dashed line also note that
the color is defined as blue using that
plot library it's also possible to set
the desired access to interpret the
required result use the axis method to
set the axis in this example shown on
the screen the x-axis is set to range
from 6.5 to 17.5 and the y-axis is set
to range from 50 to 2000 let's now
understand how to set the transparency
level of the line and to annotate a plot
alpha is an attribute which controls the
transperancy of the line lower the Alpha
value more transparent the line here the
alpha value is defined as 0.4 the
annotate method is used to annotate the
graph the syntax for annotate method is
shown on the screen the keyword max is
the attribute that denotes the
annotation text H a indicates the
horizontal alignment V a indicates the
vertical alignment XY text indicates the
text position and XY indicates the arrow
position the keyword arrow prompts
indicates the properties of the arrow in
this example the arrow property is
defined as the green color the output
graph is shown on the screen so far
you've learned how to set line width
title x-axis and y-axis label
the plot legend line color and annotate
the graph for a single plot the plot we
created for website traffic in the
previous screens it's for only one day
let's now learn how to create multiple
plots say for three days using the same
example the data set number of user for
Monday Tuesday and Wednesday is defined
with respect to its time distribution
use different color and linewidth for
each day to distinguish the plot in this
example we have used red for Monday
green for Tuesday and blue for Wednesday
the output graph is shown on the screen
a subplot is used to display multiple
plots in the same window with a subplot
you can arrange plots in a regular grid
all you need to do is specify the number
of rows columns and plot the syntax for
subplot is shown on the screen it
divides the current window into an M by
n grid and creates an axis for a subplot
in the position specified by P for
example subplot two one two creates two
sub plots which are stacked vertically
on a grid if you want to plot four
graphs in one window then the syntax use
should be subplot two one for layout and
spacing adjustments are two important
factors to be considered while creating
subplots use PLT subplots adjust method
with the parameters h space and w space
to adjust the distances between the
subplots and move them around on the
grid
this demo you can see how to create two
subplots that will display side by side
and a single frame two subplots stacked
one on top of the other or vertically
split in a single frame and four
subplots displayed in a single frame
first import matplotlib pipe lot and
style
type percentage matplotlib inline to
view the plot in jupiter notebook
define the parameters such as
temperature wind humidity precipitation
data and a time data
you can see the data being tight here
next to create two subplots to be
displayed its side by side in a given
frame for one to one and 1/2 to specify
the figure size subplot space title the
color for time and temperature data
which is blue here and line style and
wit
you
similarly specify the color for wind
which is red it's line style and width
you
you can see the temperature and wind sub
flight charts displayed side by side in
a given frame here to create subplots
for 2 1 1 & 2 1 to specify the
parameters
you
this will create two subplot
top of the other or vertically split in
a given frame let's use humidity and
precipitation data to plot the graph
specify the title color line style and
line width for both the graphs
you
you can see the two subplots
on top of the other with two different
colors indicating precipitation and
humidity here the two graphs are
separate
finally let's draw four subplots 4 2 2 1
2 2 2 2 2 3 and 2 to 4 that will display
in a given frame
you
specify the title subplot data color
line style and line width for all four
subplots
you
you can see the four subplots displayed
in a single frame
demo you learned how to create sub flies
displayed side-by-side vertically split
subplots and four subplots displayed in
a single frame using Matt plot live you
can create different types of plots
using Matt plot library histogram
scatter plot heat map high chart error
bar histograms histograms are graphical
representations of a probability
distribution in fact a histogram is a
kind of bar chart using Matt plot
library and it's bar chart function you
can create histogram charts a histogram
chart has several advantages some of
them are as follows
it displays the number of values within
a specified interval it's suitable for
large data sets as they can be grouped
within the intervals scatter plots a
scatter plot is used to graphically
display the relationship between
variables a basic plot can be created
using the plot method however if you
need more control of a plot it's
recommended that you use the scatter
method provided by Matt plot library it
has several advantages it shows the
correlation between variables it's
suitable for large data sets it's easy
to find clusters it's possible to
represent each piece of data as a point
on the plot in this demo you'll learn
how to generate a histogram and scatter
plot using Matt plot live
let's import a dataset called Boston
dataset which we will use to create the
histogram and scatterplot from the site
KITT learn library
let's import matplotlib pipeline
you
type percentage matplotlib inline to
view the plot in jupiter notebook
let's use the data in Boston real estate
data set to create the histogram and
scatterplot lo2 this data
you can view this data by using the
print command
now define the X ax
for the data which is Boston real state
data
likewise define the y-axis for the data
which is Boston real estate data with
the target extension
specify the plot style figure style
number of bins and labels of the x-axis
and y-axis use the show method to
display the histogram created by you
you
specify the style size datasets and
labels of the scatterplot that you want
to create use the show method to display
the scatterplot created by you
you
heatmaps a heatmap is a better
to visualize two-dimensional data using
heat maps you can gain deeper and
quicker insight into data than those
afforded by other types of plots it has
several advantages it draws attention to
the risky prone area it uses the entire
data set to draw bigger and more
meaningful insights it's used for
cluster analysis and can deal with large
data sets
in this demonstration you'll learn how
to generate a heat map for a data set
using matplotlib
let's import the required library
matplotlib pipe lot and seaboard
titin percentage matplotlib in line to
view the plot in jupiter notebook
let's load the flights data set from the
built-in data sets of Seaborn library
use head to view the top five records of
the data set
we have to arrange the columns to
generate the heat map let's use the
pivot method to arrange the columns
month year and passengers
let's view the flight data set that's
now
generate the heatmap
let's use the heatmap method and pass
light data as an argument
this will generate the heat map which
you can see here in this demo you
learned how to create and display a heat
map pie charts pie charts are typically
used to show percentage or proportional
data note that usually the percentage
represented by each category is provided
next to the corresponding slice of the
pie
mat plot library provides the pie method
to make pie charts it has several
advantages it summarizes a large data
set in visual form it displays the
relative proportions of multiple classes
of data the size of the circle is made
proportional to the total quantity
in this demonstration you'll learn how
to create a pie chart and display it
first
Ford matplotlib pipeline
type percentage Matt plot live in line
to view the plot in Jupiter notebook
type the job data within parentheses
using single quotes separated by commas
specify the labels as IT finance
marketing admin HR and operations
you
specify the slice IT to explode
you
use the show method to display the pie
chart
you can see the pie chart with the
slices labels and I T the largest slice
error bars in error bars used to show
the graphical representation of the
variability of data it's used mainly to
point out errors it builds confidence
about the data analysis by unleashing
the statistical differences between the
two groups of data it has several
advantages
it shows the variability in data and
indicates the errors it depicts the
precision in the data analysis it
demonstrates how well a function and
model are used in the data analysis it
defines the underlying data
Seabourn is a Python visualization
library based on mat plot library it
provides a high level interface for
drawing attractive statistical graphics
it was originally developed at Stanford
University and is widely used for
plotting and visualizing data there are
several advantages it possesses built-in
themes for better visualizations it has
tools built-in statistical functions
which reveal hidden patterns in the data
set it has functions to visualize
matrices of data which become very
important when visualizing large data
sets
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