Python - Advanced Python Data Visualizations Relplot, Heatmap | Python Courses in Tamil | Skillfloor - video Dailymotion

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Transcript

00:00Hello everyone. In this video, we will talk about advanced python data visualization.

00:07So, we will talk about a relationship. Then, we will talk about a heat map.

00:16So, we will create a relationship plot.

00:19So, SNS.reddit plot. First, we will pass data frame.

00:23We have all columns in x-axis and y-axis.

00:26So, we have 4 numeric data and 1 categorical data.

00:31So, we have x-axis petal length and y-axis petal width.

00:35Then, hue with respect to species.

00:37So, we have 3-plask based.

00:39We have petal length and petal width adjust.

00:41Then, we provide style equal to species.

00:44So, each and every species.

00:46We provide separate color.

00:48Then, size equal to sepal length.

00:50So, sepal length.

00:52So, this is actually vertical.

00:57So, this is actually vertical.

01:01So, this is vertical.

01:02So, this is vertical size.

01:03So, we will compare and compare.

01:06So, we have kind equal to scatter.

01:08So, we will create scatter plot.

01:10If we we cut out data.

01:11If we are in scatter plot.

01:12So, it is olan to share,

01:14we have to represent the shape and shape.

01:16We have to represent the class.

01:18We are to represent the size.

01:19So, we have to represent the size.

01:20And, when we are to go to size.

01:22Then, we have to look at the length.

01:23And we will compare it to the length.

01:24And we will analyze.

01:25So, this one is maximum.

01:28So, actually, we can choose a particular point.

01:31So, this will actually be vertical.

01:33So, we will check go to vertical size.

01:35Let's check the first plot and compare it to the first plot.

01:44Let's compare it to the separate length, 4.8.

01:50Let's compare it to the maximum size.

01:55Let's compare it and quit.

01:58This is the relationship plot.

02:00Let's compare it to the positive directions.

02:07Let's compare it to the 90 to 98% correlated.

02:12This is the relationship plot.

02:14Let's compare it to the advanced version of the scatter plot.

02:19Let's compare it to the line chart.

02:21Let's compare it to the data.

02:23Let's compare it to the species based on the sepulant.

02:26The x-axis is the species, y-axis is the sepulant.

02:29Then, the kind is provided to the line chart.

02:31Let's compare it to the aggregation.

02:33Let's compare the mean value.

02:35Let's compare the mean value to each and every.

02:38Let's compare the markers to the mark.

02:41Let's compare the set.

02:43The set is the mean sepulant.

02:45The diversity color is the sepulant.

02:48The 5.8.

02:50The average is the 6.655.

02:55So, we analyze it.

02:59So, we use the advanced version of scatter plot.

03:02Then, we use the line plot with estimate r equal to mean.

03:06Then, confidence interval.

03:07We show the error part.

03:10Then, the marker equal to the mean parts.

03:14Let's highlight it.

03:15Then, species.

03:16Due with respective species,

03:18we show the result of three different color variations.

03:21Next, heat map.

03:24Next, heat map.

03:29Heat map is basically.

03:31We use the graphical representation of data.

03:35Like color palette.

03:36So, color palette variations.

03:38We use the correlation value.

03:41So, what do we see in sns?

03:44Actually, we use the correlation among the columns.

03:47So, correlation is basically.

03:49Numeric columns.

03:50So, tc variable is df of df.columns.

03:54So, last column is species.

03:55So, it is categorical column.

03:56So, we drop it.

03:58Then, we analyze the correlation.

03:59We analyze the result.

04:01So, in the data.

04:02There are actually values.

04:04So, in the color visualization.

04:06We can use heat map.

04:08So, we can use heat map.

04:09So, sns.heatmap.

04:10We pass the tc.

04:12So, there is correlation value among the numerical variables.

04:15So, when we pass it.

04:16Unet equal to 2.

04:17So, in the color palette.

04:19Along with values.

04:20We show it.

04:21So, in the color palette.

04:23Here, we base it.

04:25We have higher values.

04:27If it is 100 percent correlated.

04:29And, if we compare the values.

04:30If we compare the color.

04:31If we compare the color.

04:32Like.

04:33Complete.

04:34Standard.

04:35So, here.

04:36Most of the diagonal elements.

04:38Because.

04:39And, we compare the color.

04:41And with the same column, we will compare it to 100% Correlation.

04:46So, most of all, we will diagonal it to 100% Correlator.

04:49Then, we will check the color variation.

04:51So, we will check this color variation.

04:54So, we will check this color variation.

04:58Then, we will match exactly this color.

05:03So, this is 82%.

05:06That's why we will show this value.

05:08So, we will check the color variation.

05:11So, we will check the color variation.

05:13We will check the number of independent variables.

05:15That are the numerical data.

05:17So, if we have the outcome of the numerical data,

05:21we will check the number of the column.

05:23We will check the number of the target column.

05:26We will analyze it.

05:29So, this is the heat map.

05:32This is advanced.

05:37Python data visualization.

05:40Next video.

Python - Advanced Python Data Visualizations Relplot, Heatmap | Python Courses in Tamil | Skillfloor

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