Python - Data Visualization Using Matplotlib in Python Part 4 | Python Courses in Tamil | Skillfloor - video Dailymotion

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Transcript

00:00Hello everyone, in the video we are going to talk about data visualization using matplot part 4.

00:09So we are going to look at the correlation plot.

00:15We are going to look at the correlation values in the form of tables.

00:19So we are going to create heatmap.

00:22So we are going to show the correlation values in the form of color palettes.

00:27So first, we will analyze the correlation values in the form of numeric data.

00:31In actual data set, we have sepulength, sepulwidth, petal length and petal width and species.

00:36So we have to drop the species in the form of categorical column.

00:39So we will drop the d after the highlock, we will add all rows and 4 columns.

00:44First 4 columns.

00:45We will analyze the correlation values in the form of independent features.

00:49So the values are like sepulength and sepulwidth.

00:52We will compare the same data.

00:54That is the second value.

00:55Sepulwidth with respect to sepulwidth.

00:57So we compare the same value to 100% relationship.

01:00This is the petal length with respect to petal width.

01:05We analyze the same value to petal width.

01:07It is 92.2% correlated.

01:09So we are going to visualize the color palette.

01:11We can use heatmap.

01:13So we can integrate the heatmap in the matplot.

01:16In the matplot, we can integrate the c-bone.

01:18In the c-bone, we have a heatmap.

01:21This is a color palette variation graph.

01:24So we can use it and analyze it.

01:26So import c-bone as sns.

01:28In the matplot, we can integrate the c-bone.

01:31So sns.heatmap.

01:33First, correlation value.

01:35That's why we pass this table.

01:38Anand equal to true value.

01:40Then color map equal to cool warm.

01:44So cool warm color.

01:46All the values are created.

01:47Then here is the title.

01:48Heatmap of the virus featured correlation.

01:50Here we can see the values.

01:51So here is the values.

01:52In the form of color palette visualization.

01:54So here is the color bar.

01:55So here is the color bar.

01:56In form of visualization, color palette visualization

01:58So, color bar, we activate the 3D

02:02So, there is a color palette

02:05Here is values

02:07Minus 1 is the one correlation value

02:10So, highly correlated

02:13Highly correlated value mostly red

02:16Very not red

02:18Then closer to red

02:20Highly correlated

02:22Then, 0.87

02:24This pattern is 0.87

02:26So, we analyze this

02:28We don't have values

02:30We don't have values

02:32If we activate the values

02:34If we activate the values

02:36If we show the values

02:37If we show the values

02:38If we add color palette

02:39Then, we will provide value

02:41So, we will match this color

02:44So, this

02:46Almost

02:48So, this

02:50Near as to minus 0.3

02:52So, we will analyze

02:54And that is equal to

02:55False

02:56Provide

02:57So, it is

02:58Analyzing the correlation

03:00Among the different columns

03:02Using a color palette

03:04That is why we make use of

03:06Heatmap

03:07We use

03:09Next

03:10Next

03:11Subplots

03:12So, we have 4

03:13Graphs

03:14Create

03:15That is equal number of rows

03:17And equal number of columns

03:18If we have row and columns

03:20We can move into

03:21We can move into

03:22Equal number

03:23If we say

03:2418 columns

03:25There is

03:263,8

03:279,2

03:28This is

03:29Easier

03:30Multiples

03:31We can move into

03:32Subplots

03:33Create

03:34Create

03:35So, first

03:36There is an outer boundary

03:38So, outer boundary

03:40We have 4 graph

03:42So, 2 row

03:44And 2 columns

03:45So, row index

03:460,1

03:47Column index

03:480,1

03:49So, this is

03:50Value

03:510,0

03:520,1

03:531,0

03:551,1

03:561,1

03:57Ok

03:58So, now

03:59In particular

04:00Plot

04:01We have a bar graph

04:02Scatter plot

04:03Create

04:04This position

04:05Call

04:06And we will fit

04:07So, first

04:09Outer boundary

04:10Create

04:11Width and height

04:12So, 10,10

04:13That is

04:14Fix size

04:15Provide

04:16Then, plot.subplots

04:17Plot.subplots

04:18Plot.subplots

04:19Of 2,2

04:20First

04:21Number of rows

04:22Second

04:23Number of columns

04:24That is

04:250,1

04:260,1

04:272 row

04:28And 2 columns

04:29Create

04:30Added

04:31So, this is

04:32Fix, axis

04:33Variable

04:34Store

04:35So, axis

04:36Is

04:370,0

04:380,1

04:391,0

04:401,1

04:411,1

04:42This is

04:43This is

04:44This is

04:45Hold

04:46So, first

04:48How to build

04:49In particular

04:50Plot

04:51Scatter plot

04:52Fit

04:53Here

04:54Actually

04:55Scatter plot

04:56Fit

04:57So,

04:58Graph

04:59We will

05:00acquire

05:01So, first

05:02We need to acquire

05:03This position

05:04We have to find

05:05Asks

05:060,0

05:07At

05:08That

05:09We need to create

05:10scatter plot create and we provide the color variation then we provide the title axis of 0,0.setTitle

05:20that means sepal length vs petal length and we create the scatter plot next bar graph in this position 0,1 plot

05:32so bar graph actually mean value that we take the mean value first group by and we store the variable

05:39average petal length so dot plot of kind equal to bar and we create the bar chart

05:460,1 axis then we provide the color variation and there are three plots

05:51there are three species so there are three colors then in the graph that is 0,1 axis

05:59title is average petal length by species then 1,0 is histogram so histogram

06:07how to plot the axis of 1,0 and the position is acquired and there is .his then this is univariate analysis

06:15so whatever column we pass to bin size and color provide

06:19then final 1,1 box plot create so df.boxplot what column? sepal length and by species so we have three boxplot analysis

06:29so we have to create the axis of 1,0 axis and grid equal to false so then we have to create the 4th plot

06:37here is box plot

06:38okay

06:39plot.tightlayout

06:40now we have four graph we have to fit in the graph so over graph that is the x axis y axis

06:44overlock

06:45so plot.tightlayout

06:47so tightlayout

06:48so tightlayout

06:49show

06:50so we have to create the first scatter plot

06:57then box plot

06:59then histogram

07:00then

07:01first

07:040,0 is scatter plot

07:06then

07:070,1 is bar chart

07:09then 1,0 is histogram

07:111,1 is box plot

07:13so we have to create the subplots

07:14so we have to create the subplots

07:17depending upon

07:18rows and columns are equal

07:20we have to divide

07:213,6

07:229,2

07:23and 18 plots

07:25so we have to create the subplots

07:27so this is the subplots

07:28power

07:29this is the data visualization using matplot

07:34part 4 is over

07:36next video

07:37thank you

07:38thank you

Python - Data Visualization Using Matplotlib in Python Part 4 | Python Courses in Tamil | Skillfloor

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