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.
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