00:00Hello everyone. In this video, we will talk about advanced python data visualization.
00:09So, we will talk about the count plot and cat plot.
00:17First, we will talk about the count plot.
00:20So, the count plot is the count of categories.
00:25We will talk about both categorical and numeric data.
00:29So, we will talk about the particular value of the occurrence.
00:33How do we create?
00:35We will talk about the count plot.
00:37So, in the count plot, we will provide the species species.
00:40So, we have three species.
00:42So, we will provide the exact species.
00:44And, we will talk about the df of species.
00:50So, that is the value counts.
00:54If we check it, we will see the list of each and every class.
00:58Chetosa, vertical and virginica.
01:01So, they will show us a 50-50 class.
01:03So, we will show you the count.
01:05So, if we look at the count plot,
01:07we will show each and every class.
01:09So, we can show you the count of 50.
01:1150. That will show it.
01:13Now, let's check the different columns in various different columns.
01:17So, that's the sepal length.
01:26So, let's see the sepal length in x axis.
01:30So, let's see the variation between x axis.
01:33So, let's increase the fixed size.
01:36So, plot.figure.
01:38Here, fix size, width and height provide.
01:43So, fix size equal to
01:47practically
01:5010, 10.
01:53So, after overtapped, you can stop.
01:59Then, you can see the sepal length.
02:01So, the sepal length is 4.3.
02:06Then, the sepal length is 4.3.
02:08Then, the sepal length is 3.
02:10So, let's analyze the sepal length.
02:12So, the sepal length is basically
02:144.2.
02:15So, we have to analyze the sepal length.
02:17So, we can see each and every sepal length is plus to count.
02:21So, each and every separate length of the class
02:23to count, we will analyze.
02:25So, in every column, we will separate
02:27and analyze both categorical and numeric data.
02:31So, we will pass this in case of count plot.
02:38Next, we will take cat plot.
02:40So, cat plot is used to support everyone.
02:43Perhaps, we will support it.
02:45So, now, sns.cat plot
02:47we will provide the exact list of species.
02:48So, three different species
02:50we will analyze the sepulant.
02:53So, data is equal to dm.
02:55So, we will pass this.
02:57Now, we will create a graph.
02:59So, we will provide the kind equal to box.
03:01So, we will use box plot
03:03for different species with variation
03:05of sepulant.
03:08So, we will analyze the outliers.
03:11So, this is one way.
03:13So, we will support all kinds of graphs
03:15in this cat plot.
03:16So, now, we will take the default
03:18to strip plot.
03:21So, we will provide the kind equal to box.
03:24So, we will provide the strip plot.
03:26So, we will reply the strip plot.
03:27So, basically, we will reply the strip plot.
03:30So, we will reply each and every class.
03:33Like, sns.cat plot
03:35x equal to petal n y-axis species
03:37and then data is called dm plot.
03:39So, we will show each and every species
03:40to petal n thoda variations.
03:42We will show the strip plot.
03:46So, we will show the strip plot.
03:46So, we will show the strip plot.
03:49Okay.
03:50So, we will try to complete variation
03:50from each and every category data.
03:53category data. So that is the histogram, then bar chart, count plot.
03:59We will be able to analyze the charts using cat plot.
04:04We will be able to analyze the data from x and y.
04:07We will be able to create any graphs.
04:09We will be able to create a kind.
04:12This is the purpose of cat plot.
04:15This is advanced python data visualization count plot and cat plot.
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