Python - Data Visualization Using Matplotlib Part 2 | 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 2.

00:10So we will see how to create the scatterplot.

00:16So scatterplot is basically to analyze the relationship with the scatterplot.

00:22So in scatterplot, first plot.scatter.

00:25In the scatterplot, here in the x-axis, sepul and y-axis sepul width.

00:30Then here in the scene, we will see normal in the x-axis, y-axis, sepul and sepul width.

00:38So the data points will normal in the scatterplot.

00:42There is one color, one view.

00:44Now we will add the in-or parameter.

00:47That is the df of species.

00:49So in species, we have flowers, setosa, versical and virginica.

00:52So that we will map back to integers.

00:55So integers, we will convert.

00:57We will base the sepul length and sepul width variation.

01:01Next, we will plot.title.scatterplot, sepul length versus sepul width.

01:08Then x-label, y-label, x-axis, y-axis provide.

01:12Now we have plot.colorbar.colorbar.

01:15So colorbar activate.

01:17This label is equal to species based.

01:19This label is equal to color palette.

01:21Then plot.show.scatterplot analysis.

01:26So this is the first name.

01:30In the plot.

01:32In the plot.

01:33In the part one actually.

01:35Referring to.

01:37It is one value in color palette.

01:40So color palette value 0.

01:430 actually setosa.

01:46Correct?

01:47Next.

01:48In the yellow color actually 2.

01:51So yellow color part one.

01:53Versical or virginica.

01:57So yellow color.

01:58So yellow color.

01:59Color palette.

02:00In the color palette.

02:01In the color palette.

02:02We have nearest to 2.

02:03So this is virginica.

02:04Sepul length and sepul width analysis.

02:06Then.

02:07This is navy green.

02:08So they come under versical.

02:10Okay.

02:11That value is almost 0.8 or 0.75.

02:14One kit.

02:15Okay.

02:16So this comes under versical.

02:18Okay.

02:19That value is almost 0.8 or 0.75.

02:21One kit.

02:22Okay.

02:23So this comes under versical.

02:26This comes under versical.

02:28This comes under versical.

02:29This comes under versical.

02:30Data revolved.

02:31Here is the spread.

02:32So that doesn't matter.

02:33So the idea of sepul length and sepul width.

02:34This becomes more than relationship.

02:36Is it enough.

02:37Okay.

02:38Sepul length and sepul width.

02:39Normal relationship length.

02:40Knowledge.

02:41Analysis.

02:42This is X.

02:43To see the data.

02:44Scatter.

02:45If you follow the pattern.

02:46Okay.

02:47This relationship is less than.

02:48Next.

02:49It's again.

02:50Two column.

02:51So the pattern.

02:52That's the pattern.

02:53If we have the pattern.

02:54Petal Length and Petal Width

02:58So better, df.columns edit

03:02That you just copy paste

03:04Petal Length

03:06Petal Width

03:14That's how we change

03:24So this is how we change the x-axis and y-axis

03:31Now you just understand how it is

03:33Relationship

03:38So actually, we have the Petal Length and Petal Width

03:41So we change the x-label and y-label

03:44So x-axis and y-axis change

03:54With respect to species base

03:56So we change the color variation

03:58So the other way is

04:00Setosa, Versical and Virgin

04:03A normal pattern is

04:05That is increasing trend

04:07And data is on the path

04:09So almost 0.87

04:120.87

04:14Like 0.7

04:160.85

04:180.85

04:19So that

04:2070% to

04:2185%

04:22Relationship

04:23So

04:24Related

04:25So

04:26In this analysis

04:27Scat up.base

04:29Create

04:30Next

04:31If

04:33Correct

04:34Analyze

04:35Check

04:36Df

04:37Df.columns

04:38Df.columns

04:39Normal

04:40Show

04:41Last column

04:42Because

04:43Correlation

04:44Numeric

04:45Data

04:46So

04:47Last column

04:48Species

04:49Correlation

04:50Analyze

04:51So

04:52Petal Length

04:53With respect to Petal Width

04:54So Petal Length

04:55Petal Width

04:56That is 0.96

04:57Okay

04:58Okay

04:59So

05:00So

05:01So

05:02This

05:03Related

05:04Confirm

05:05Okay

05:06Next

05:07Histogram

05:08So

05:09Histogram

05:10Basically

05:11Continuous

05:12Data

05:13Count

05:14Count

05:15So

05:16For

05:17For

05:18Example

05:19Seppal Length

05:20So

05:21Seppal Length

05:22Seppal Length

05:23X-axis

05:24Y-axis

05:25Count

05:26Seppal Length

05:27Range

05:28Like

05:294.5

05:305.0

05:315.56

05:32Valu

05:34Is

05:35Is

05:36So

05:37There is a

05:38So this is the range of data that we will group 10 and split it up.

05:44If this is 20, we will analyze 20 division.

05:48So this is the bin scene.

05:50We will analyze the sepple length and continuous data.

05:52So we will analyze the histogram plot.

05:54So we will activate the grid.

05:56It will activate the edge of the color.

05:59So it will activate the difference.

06:02So sepple length between range.

06:06Now this is 4.6.

06:10This is 4.4.

06:12That is the sepple length.

06:144.4 to 4.6.

06:16Almost 9 plus.

06:18Next 4.6 to 5.05.

06:22Almost 22 plus.

06:26So we will analyze the count plot.

06:30Using histograms.

06:32So this comes at.

06:34We will analyze the same column.

06:36Correct?

06:38So this comes at univariate analysis.

06:42Univariate analysis.

06:442 columns analyze the bivariate analysis.

06:46The color of the histogram we provide.

06:48That is orange.

06:49Alpha is equal to 0.7.

06:50In the bar is transparency.

06:52That is why we cut the highlight.

06:54Like 0.9.

06:56That is the transparency is medium.

06:58So 0.7.

07:00Then we show grid lines.

07:02And the bins.

07:03And the bins.

07:04Separation.

07:05And the black color lines.

07:07We activate.

07:08H color equal to black.

07:09Then.

07:10Separated title.

07:11X label.

07:12Y label.

07:13Grid.

07:14We activate.

07:15And show.

07:16Histogram.

07:17Show.

07:18It is.

07:19Separate.

07:20Each and every individual.

07:21Column.

07:22Analyze.

07:30Data visualization.

07:31Using.

07:32Matplot.

07:33Part 2.

07:36So.

07:38Then.

07:39One.

07:40From.

07:41adrenaline.

07:42They are capable ofádhering.

07:43They don't gain with no.

07:45You.

07:46Sheesh.

07:47They are capable ofádhering.

07:48Weigh we occupy.

07:49They are capable ofádhering.

07:50Cook.

07:51You can go out.

07:52If people can go to the audience.

07:54One part.

07:55You can hit the audience.

07:56They have a bomb.

07:57They don't mett Echo.

07:58Our top.

07:59So.

08:00They do more disk.

08:01My diagram is irrelevant.

08:02They have Mottock.

08:03And the green dog.

Python - Data Visualization Using Matplotlib Part 2 | Python Courses in Tamil | Skillfloor

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