Python - Seaborn Basic Plots Histogram, Distplot | Python Courses in Tamil | Skillfloor - video Dailymotion

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Transcript

00:00Hello everyone! In this video, we will talk about C-Bond Basic Plots.

00:08We will talk about Histogram and Displot.

00:15Histogram is basically univariate analysis.

00:19We will analyze a single column.

00:22Univariate analysis.

00:24Basically, we have a continuous data.

00:27We will confirm the range.

00:30We will visualize the Histogram.

00:34We have a Numeric data.

00:37This is the Cepal length.

00:40In the Cepal length, we have a long range.

00:43Let's start 2.0.

00:46For example, 4.4.

00:54So, 4.4.

00:56If we provide specific ranges,

01:00In the first plot,

01:02In the ranges,

01:04We have 2.14.

01:06We have 2.14.

01:07The Cepal length is 1.

01:10Then, 2.14.

01:12That is this plot.

01:14In this range,

01:17It is 2.28.

01:19If we consider 2.14,

01:212.28.

01:23The Cepal length,

01:24Plus,

01:25Count,

01:26There are 3.

01:27So, in this case,

01:28In the x-axis,

01:29We show the continuous data.

01:32Then,

01:33In the y-axis,

01:34We show the frequency.

01:35That is the count values.

01:37We show the histogram.

01:39So, in this case,

01:40In the ranges,

01:41In the ranges,

01:42Bins.

01:43So,

01:44In the tool,

01:452.4.

01:46Then,

01:472.14.

01:482.28.

01:49Then,

01:50In the graph analysis,

01:52Bins format,

01:53We provide.

01:54Okay?

01:55This is separate of Bins.

01:57We control these bins.

01:59We control these bins.

02:01Then,

02:02So,

02:03We will confirm,

02:04We will confirm,

02:05We will confirm,

02:06We will confirm,

02:07In this range,

02:08We will actually,

02:0920 bins.

02:10We will create 20 bins.

02:12How do we create histograms?

02:14How do we create histograms?

02:15SNS.hisplot.

02:17We will get a single continuous variable.

02:20That is,

02:21To supply width.

02:23Then,

02:24Data is equal to,

02:25Df.

02:26Provide.

02:27Then,

02:28Bins.

02:29And the number of ranges,

02:30We will split.

02:32Then,

02:33KDE.

02:34KDE stands for,

02:35Colonel

02:36Density Estimator.

02:38Density Estimator.

02:39That is,

02:40Density Estimator.

02:44In the kernel density estimator,

02:45Basically,

02:46Distribution plot,

02:48Provide.

02:49That is,

02:50Normal distributed curve,

02:51Distributed curve.

02:52This is,

02:53This is,

02:54PDF.

02:55Probability density function curve.

02:58That is,

02:59So,

03:00In the data,

03:01Normal distributed,

03:02Skewed distributed,

03:03Specifically,

03:04Left skewed,

03:05Right skewed,

03:06So,

03:07We have to analyze,

03:08Analyze.

03:09Okay.

03:10So,

03:11That is,

03:12That is,

03:13That is,

03:14That is,

03:15That is,

03:16That is,

03:17That is,

03:18That is,

03:19That is,

03:20That is,

03:21Probability density function,

03:22So,

03:23That curve,

03:24That is,

03:25That is,

03:26That is,

03:27That is,

03:28Almost,

03:29So,

03:30Normal distributed curve.

03:31So,

03:32Type of distribution,

03:33We have to activate,

03:34So,

03:35This is,

03:36That is,

03:37Normal,

03:38A,

03:39His plot analysis.

03:40Next,

03:41We change,

03:42Then,

03:43Var column,

03:44Check.

03:45So,

03:46With that,

03:47Now,

03:48Petal with,

03:49Which we analyze,

03:50And,

03:51Number of bins,

03:52Now,

03:5310,

03:54Provide,

03:55Okay.

03:56Now,

03:57Actually,

03:58First,

04:00Run,

04:01So,

04:02We import,

04:03Then,

04:04We have to import,

04:05Iris data,

04:06So,

04:07Iris data,

04:08So,

04:09Load,

04:10So,

04:11Iris data,

04:12Load,

04:13The same,

04:14With that,

04:15We have to be able to add,

04:16appear

04:17Now,

04:18As the same,

04:19To the same,

04:20So,

04:21Here,

04:22In this case,

04:23Can we add,

04:24To the same,

04:25To the same,

04:26To the same,

04:27To that,

04:28To the same,

04:29This is an equation,

04:30To the same,

04:31So,

04:32To the same,

04:33To the same,

04:34That number,

04:35To the same,

04:36To the same,

04:37To the same,

04:38To the same,

04:39petal length is completely skewed data

04:51so in this case we use his plot

04:54we will confirm the data cell where we have a count

04:581 to 2.2

05:02we will add the petal length

05:06here is 37

05:09then here is 13

05:12so we will count the 50 plus

05:16we will see that

05:18separate plus

05:20separate plus

05:22separate and analyze the parameter

05:24usually we provide

05:26q

05:28sns.hisplot

05:30x equals petal length

05:32data equals df

05:34kd equal to 2

05:36activate

05:38petal width

05:39based

05:40one species

05:42so first

05:44plot

05:45this is actually

05:46petal width

05:48for setosa

05:49in the plot

05:51so here is the distribution curve

05:53we will continue

05:54and analyze the blue color line

05:56so here is completely left skewed

05:59or right skewed

06:01tail is right

06:05right tail

06:06next

06:07we will see versical

06:08virginica

06:09in the plot

06:11in the plot

06:12actually shaded

06:13like light orange

06:15this is the color

06:17so two

06:18virginica

06:19overlap

06:20in the plot

06:21show

06:22so here is the

06:23versical

06:24plot

06:25actually

06:26here is the graph

06:27so here is the graph

06:29so here is the left

06:30a

06:31so here is the individual species

06:33individual species

06:35petal width

06:36or distribution

06:37we will analyze

06:38q parameter

06:39provide

06:40next

06:43displot

06:44displot

06:45his plot

06:46almost

06:47same

06:48okay

06:49recent version

06:50we will use

06:51his plot

06:52so

06:53his plot

06:54and displot

06:55we will compare

06:56to this plot

06:57univariate analysis

06:58for categorical data

06:59we can provide

07:00categorical data

07:01we can provide

07:03that

07:04we can use

07:05continuous variable

07:07like

07:08q parameter

07:09support

07:10so customization

07:11we can compare

07:12histogram

07:13and we can

07:14displot

07:15and

07:16result

07:17is the same

07:18so

07:19sns.displot

07:20we can provide

07:22the color

07:23we can provide

07:24the same

07:25we can provide

07:26the same

07:27that is

07:28continuous data

07:29confine

07:30then

07:31over blocks

07:32we can convert

07:33bins

07:34we can provide

07:35bins

07:36we can provide

07:37bins

07:38then

07:39kd

07:40is

07:41isplot

07:42we can provide

07:44kd

07:45equal to true

07:46kd

07:47equal to true

07:48we can provide

07:49provide

07:51this is the difference

07:52but

07:53we can provide

07:54same result

07:55so

07:56we can check

07:57petal

07:58width

07:59same plot

08:01so

08:03we can tell

08:04right

08:05right

08:06right

08:07right

08:08skew data

08:09so

08:10almost

08:11same

08:12but

08:13customization

08:14we can

08:16we can

08:18in the

08:19histogram

08:20that

08:21we can

08:22see

08:23this is the

08:24histogram

08:25and

08:26this

08:27plot

08:28next

08:29video

08:30thank you

Python - Seaborn Basic Plots Histogram, Distplot | Python Courses in Tamil | Skillfloor

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