Python - Seaborn Basic Plots Line, Scatter | Python Courses in Tamil | Skillfloor - video Dailymotion

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Transcript

00:00Hello everyone. In this video, we are going to talk about Seaborn Basic Plots.

00:06So, our Line and Scatter Plot is going to be created by everyone.

00:13So, in our Seaborn Visualization, we are going to use the Director of Seaborn inbuilt data set, Iris data set.

00:19So, in Iris data set, there are basically 4 columns.

00:21That is, Supple Length, Supple Width, Petal Length and Petal Width.

00:24So, based on that flower, we will decide what type of species.

00:27That is, Setosa, Versinica, Virginica, Versicolera.

00:32In the 3 different categories, we are going to base in 4 columns.

00:36So, first, we are going to use Line Plot.

00:40So, Line Plot is normally used in Matplot.

00:42We are going to provide DFO of Sepulet.

00:45Now, in X-axis and Y-axis, we are going to separate code.

00:50So, we are going to create the Sns.Line Plot.

00:55So, we are going to create the Sns.Line Plot.

00:59The first argument is data.

01:01We are going to pass the data frame.

01:03Then, X is equal to df.Index.

01:06The index is, we have 150 flowers.

01:09Each and every categories.

01:11We have 50-50 flowers.

01:12So, in over flower, we have a Seaborn Length.

01:16So, in the pointed phase, we will draw lines.

01:20This is the first flower, the second flower is the Seaborn Length.

01:23So, we will join.

01:25So, in this case, we have Seaborn Length.

01:27So, in the flower, Seaborn Length variations, we will easily understand.

01:31This is a univariate analysis.

01:35So, in this case, we will do bivariate analysis.

01:38So, in this case, we have two columns.

01:40So, first,

01:42SNS.Line Plot.

01:44Data go to df.

01:45This is the Seaborn Length and Seaborn Length we will consider.

01:48So, based on X and Y coordinates,

01:50we will create a line plot.

01:52Now, we have another argument.

01:54Ci equal to none.

01:55Ci is the conference interval.

01:58Ci equal to none,

02:00we will provide a shaded region.

02:02The shaded region is basically,

02:04and the flower,

02:06the flower is the Y-axis.

02:08We will represent the same range.

02:11This particular flower,

02:13we will analyze the Seaborn Length and Seaborn Width.

02:16So, the Seaborn Length is 4.9.

02:20And the Seaborn Width is 3.

02:23Now, we will consider this shaded part.

02:26This is the range.

02:29Horizontal and vertical axis.

02:33So, the value is 2.7.

02:36Now, the value is 3.3.

02:38So, in this particular flower,

02:40the Seaborn Length is 2.7,

02:42and the 3.3.

02:43So, we will analyze the range.

02:45So, we will analyze this.

02:47We will analyze this.

02:48ConfidentlyTravel equal to none.

02:50We will provide a clear plot.

02:56We will provide a clear plot.

02:59We will remove the Shaded region.

03:01Next, we will analyze the Seaborn Length and Seaborn Width.

03:06We will analyze the X-axis and Y-axis.

03:09We will analyze the Bivariate analysis.

03:10In this case, we will view the entire plot.

03:13We will do separate plots.

03:15We have three different categories.

03:17So, we analyze the separate plots.

03:19We will analyze the Sepul Length and Sepul Width.

03:21We will analyze the hue.

03:23We will provide a categorical column.

03:25We will provide a categorical column.

03:27If we have any categories,

03:28we will analyze the base,

03:29the X-axis, Y-axis plot.

03:30We will analyze the separate plot.

03:32That is, the set-offs,

03:33the versical, the origin,

03:35the origin.

03:36So, if we can represent the set-offs and versical,

03:38the origin.

03:39We will provide a box.

03:40Now, what do we say?

03:42Legend.

03:43That's what we say.

03:44Next.

03:46Next.

03:47If we can visualize the range in this line plot,

03:49we can visualize the range directly.

03:52We can do this further analysis.

03:56So, in the SNS.line plot,

03:58X-axis is the Sepul Length.

03:59And Y-axis is the species.

04:02So, in the Y-axis,

04:04each and every species,

04:05the sepul length varies every time.

04:07We can specify the specific length.

04:09Here, the hue parameter is provided.

04:11That is the variation.

04:13We can choose one line.

04:14Then, data i equal to df,

04:17c i equal to none provide.

04:18So, if we do this,

04:20the set-offs are the sepul length

04:23of the range.

04:24So,

04:27if we analyze the line plot,

04:29we have 4.3 length,

04:325.7 length range.

04:36So, this is the specific analysis.

04:39So, bivariate analysis, univariate analysis,

04:42other specific hue parameter,

04:44we do this.

04:45Next, we will see the plot.

04:48Scatter plot.

04:49So,

04:50Scatter plot,

04:51we provide SNS.scatter plot.

04:53Basically,

04:54we show two columns

04:55relationship.

04:56We show two columns.

04:57We show two columns.

04:59That is the x-axis,

05:00petal length and y-axis

05:01petal width.

05:02So,

05:03here is the hue parameter.

05:04Each and every species.

05:05Separate.

05:06We analyze.

05:07Here,

05:08we increase the petal width.

05:10We increase the petal width.

05:12We increase the petal width.

05:14So,

05:15in the graph,

05:16we have a positive relationship.

05:18So,

05:19we separate each and every species.

05:20So,

05:21we separate each and every species.

05:22So,

05:23petal length and petal width analysis.

05:24It is separate.

05:25It is mixed up.

05:26So,

05:27it is mixed up.

05:28So,

05:29in the data,

05:30it is scattered.

05:31So,

05:32in this position,

05:33it is scattered.

05:34And,

05:35in the data,

05:36it is confined.

05:37Scatterness is less.

05:38Okay?

05:39Like,

05:40three plus,

05:41it is scattered.

05:42And,

05:43it is less than a square.

05:44It is less than a square.

05:45Okay?

05:46It is less than a square.

05:47So,

05:48we have to analyze this.

05:49We have to follow this.

05:51We have to analyze this.

05:52So,

05:53we have to analyze this.

05:54So,

05:55like,

05:5680 to

05:5790%.

05:58So,

05:59in the range,

06:00we have to analyze this.

06:01Now,

06:02we have to analyze this.

06:03Next,

06:04we have to analyze this.

06:05We have to analyze this.

06:06So,

06:07we have to analyze this.

06:08Like,

06:09for example,

06:10sepal length and petal width.

06:12So,

06:13sepal length and petal width.

06:15So,

06:16it is scattered.

06:18And,

06:19we have to compare this.

06:20We have to compare this.

06:22And,

06:23we have to compare this.

06:24We have to make positive relationship.

06:26And we have to compare this.

06:29It is very clear that the scatter is clear.

06:30Now,

06:31we can compare the plot and see the plot in the situation.

06:32And,

06:33this is ok.

06:34Actually, I said 80 to 90, but there is a 90 to 100.

06:38This is a 70 to 80.

06:40Precisely, we have to say exactly the value.

06:43Like the ranges, we have to say.

06:45So, we have to analyze the relationship in one column.

06:48So, we have to analyze the exact value.

06:51We have to analyze the correlation value.

06:53So, df of df.columns.

06:55We have to provide colon minus 1.

06:57So, the last column is species.

06:59We have to analyze a categorical column in a categorical column.

07:03So, we have to analyze the correlation value.

07:07So, we have to compare the plot to the length of the petal.

07:12Like I said 80 to 90 percent.

07:14Actually, that is 96 percent value.

07:16So, we have to analyze the exact relationship.

07:20We have to analyze the exact value.

07:23We have to use the exact value.

07:24We have to use the possibly correlated value.

07:27We have to analyze the character plot.

07:30We have to see the C-pond on the basic plots, line and scatter plot in the next video.

07:40Thank you!

Python - Seaborn Basic Plots Line, Scatter | Python Courses in Tamil | Skillfloor

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