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Seaborn is a Python visualization library based on Matplotlib. It provides an advanced interface to draw attractive statistical graphs. Seaborn is actually a more advanced API package based on Matplotlib, which makes it much easier to draw and refine your drawings without a lot of tweaking.

Note: All code is implemented in IPython Notebook

Lmplot (regression diagram)

Lmplot is used to draw regression graph, through which we can intuitively overview the internal relationship of data

An overview of the Stripplot API:

seaborn.lmplot(x, y, data, hue=None, col=None, row=None, palette=None, col_wrap=None, size=5, aspect=1, markers=’o’, sharex=True, sharey=True, hue_order=None, col_order=None, row_order=None, legend=True, legend_out=True, x_estimator=None, x_bins=None, x_ci=’ci’, scatter=True, fit_reg=True, ci=95, n_boot=1000, units=None, order=1, logistic=False, lowess=False, robust=False, logx=False, x_partial=None, y_partial=None, truncate=False, x_jitter=None, y_jitter=None, scatter_kws=None, line_kws=None)

It can be seen that lmplot has a lot of parameters. Next, we will select some commonly used ones and talk about them. Some parameters will involve some knowledge of statistics.

Old routine, the pilot into the corresponding package:

Import seaborn as SNS %matplotlib inline sns.set(font_scale=1.5,style="white")
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The data set for this trial is Seaborn’s built-in Tips data set:

data=sns.load_dataset("tips")
data.head(5) 
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Let’s take a look at lmplot first

sns.lmplot(x="total_bill",y="tip",data=data)
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It can be seen that LMPLOT conducts unary linear regression on the selected data set, fitting an optimal straight line.

Let’s move on to a demonstration of specific parameters.

Col: Categorizes columns according to specified attributes

Row: Categorizes rows based on specified attributes

sns.lmplot(x="total_bill",y="tip",data=data,row="sex",col="smoker")
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In combination with our data set, we can see how these two parameters are used by looking at the horizontal and vertical coordinates above

Col_wrap: Specifies the number of columns per row, at most equal to the number of different categories for the COL argument

sns.lmplot(x="total_bill",y="tip",data=data,col="day",col_wrap=4) 
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sns.lmplot(x="total_bill",y="tip",data=data,col="day",col_wrap=2) 
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Aspect: Controls the aspect ratio of the diagram

sns.lmplot(x="total_bill",y="tip",data=data,aspect=1)  
 The ratio of length to width is equal to one to one, i.e. square
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sns.lmplot(x="total_bill",y="tip", data = data, aspect = 1.5)# Length over width is equal to 1:1.5, as you can see the horizontal axis is a bit longer
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Sharex: X scale (default True)

Sharey: sharey scale (default True)

sns.lmplot(x="total_bill",y="tip",data=data,row="sex",col="smoker",sharex=False)
 # You can see that when set to False, the 5# scale of the x axis is different for each subgraph
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Hue: Used for classification

sns.lmplot(x="total_bill",y="tip",data=data,hue="sex",palette="husl") 
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Ci: Confidence intervals for controlling regression (those of you who have studied statistics know this)

# show a confidence interval of 0.95
sns.lmplot(x="total_bill",y="tip",data=data,ci=95)
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X_jitter: Randomly add noise points to the X-axis

Y_jitter: Randomly add noise points to the Y-axis

Setting these two parameters does not affect the final regression line

sns.lmplot(x="size",y="tip",data=data,x_jitter=False) 
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sns.lmplot(x="size",y="tip",data=data,x_jitter=True)    
You can see that the data points in the previous column were randomly selected
# Scrambled, but does not affect the final regression line
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Order: controls the power of regression performed (more than one is polynomial regression)

sns.lmplot(x="total_bill",y="tip",data=data,order=1)  # Unary linear regression
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sns.lmplot(x="total_bill",y="tip",data=data,order=2) The maximum number of times is 2
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sns.lmplot(x="total_bill",y="tip",data=data,order=3) The maximum number of times is 3
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There are also some parameters involved in more in-depth statistical knowledge, here is not an introduction, interested in the official document can check!

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