00:00In this video, we are going to show advanced python data which should have creations.
00:10So, I have to do boxplot and wagon plot on the fd create again.
00:17First, we are going to show boxplot fd create again.
00:20So, first, we are going to show Wiskar plot.
00:23So, boxplot basically fd create again.
00:25Now, we are going to show lower boundary and upper boundary.
00:30So, here is lower bound and upper bound.
00:33And here is 25 percentile, 50 percentile and 75 percentile.
00:39So, here is the entire portal range.
00:42So, that is middle 50 percentage of data.
00:45Now, we are going to show data is lesser than lower bound and data is greater than upper bound.
00:53So, we are going to show outliers.
00:55So, in the outliers dataset, we are going to show .10 to represent blackboard.
00:58So, this is the outliers.
01:00So, when we are going to show boxplot basic,
01:02we are going to show outliers data.
01:04We are going to show outliers.
01:05Now, we are going to show IQR method.
01:07So, IQR method, we are going to use quartiles to calculate lower bound and upper bound.
01:13So, in the lower bound formula,
01:15we are going to show Q1 minus 1.5 times IQR.
01:20Then, upper bound formula is Q3 plus 1.5 times IQR.
01:26So, in the formula, we will calculate lower bound and upper bound values.
01:31So, when we are going to read the boxplot,
01:33we will create one column.
01:35So, in the boxplot,
01:36we will select SNS.boxplot x equal to separate.
01:38We will pass data to data frame.
01:40So, when we are going to separate width,
01:43we will detect lower bound and upper bound.
01:46We will show lower bound and upper bound data.
01:49we will show outliers.
01:51So, here we go to the boxplot,
01:53for example,
01:542.25,
01:58lower bound,
01:59upper bound is exactly 4.
02:01So, in the boxplot,
02:03it is 4.1,
02:044.2,
02:054.4.
02:06Then, it is 2.01.
02:11So, in the boxplot,
02:13we are going to show lower bound and upper bound.
02:16So, in the boxplot,
02:18we are going to show lower bound and upper bound.
02:21Then,
02:22we will analyze different species.
02:25So, we will use bivariate analysis.
02:29So, in the SNS.boxplot,
02:31we will create species.
02:33So, in the boxplot,
02:34we will show the species.
02:37Actually,
02:38we have 4 outliers in the boxplot.
02:41The 4 outliers in the boxplot,
02:43we will show the species in the boxplot.
02:45So,
02:46if you want to show the species in the boxplot,
02:48you will show the species in the boxplot.
02:49Then,
02:50we will analyze the species in the boxplot.
02:52Then,
02:53we will analyze the species in the boxplot.
02:54So,
02:55we will analyze the species in the boxplot.
02:57So,
02:58we will analyze the species in the boxplot.
02:59So,
03:04were already nerds and the ¿!! emerge from his own boxplot.
03:08If we总 term the species in the boxplot in the boxplot,
03:11we will analyze the species in the boxplot.
03:12So,
03:13if significant number of species in the boxplot are rooted in the boxplot,
03:15we will analyze the species in the boxplot.
03:16So,
03:17it will compare itold by X and sizeplot,
03:19So, we will compare the petal and time petal to any other column.
03:24We have only four outliers.
03:27If we look at individual, we will see the specific category.
03:32So, if we use our entire data, we will look at the first column.
03:37So, this is boxplot.
03:39Next, we will say the advancement of boxplot.
03:44So, this is similar to boxplot.
03:49So, this is a higher advanced visualization tool.
03:53So, this is very easy to create.
03:56So, SNS.Violentplot.
03:58X-axis is provided.
04:00Y-axis is provided.
04:02This is the data.
04:04So, this is the create violent plot.
04:08So, this is the wider region.
04:10So, this is the inner boxplot.
04:14Then, we will provide a violent plot.
04:17So, this is the wider region.
04:20So, there is more frequency occurrence.
04:22So, you can see the particularãy current.
04:23So, the particular separator has gone from the same size.
04:25That's the same amount of ranging perception.
04:26And the specific regions in this region.
04:28So, if we receive a larger region,
04:30it's smaller than sepull with a small count.
04:33So, if you see the frequency of occurrence of data,
04:35we can analyze the data.
04:37So, let's look at the versicular.
04:40So, versicular is the same.
04:43Sepul with 3 is the same.
04:47Sepul with 2 is the same.
04:53So, the occurrence of class is the same.
04:57We use further analysis.
05:00So, here we detect outliers.
05:03So, there is a specific range.
05:05So, in the viscur plot, plus additionally,
05:09frequency of occurrence of particular data.
05:13This is actually violent plot.
05:17So, violent plot and box plot.
05:21How do we create the advanced python data visualization in the next video?
05:35I'm sure you're moving on.
05:36That's all.
05:37I'm not afraid.
05:38But, then you can figure out the idea of this.
05:39The other thing is, can we get into this?
05:41I'm not afraid of it.
05:43Do you want to...
05:45I'm just curious.
05:46That's what it's doing.
05:47We're not in the description.
05:48We're going to do this.
05:49But, I think the logic of this.
05:50So, I'm driving around here.
05:51You know, when you're moving, you're going to the next video.
05:52That's my fault.
05:52I'm not feeling the moment.
05:54I'm feeling the first time.
05:55We're going through this to you.
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