seaborn Paint Overview

1.seaborn basic settings

The style declarations provided Seaborn codes  sns.set() placed in front of the drawing, the image pattern can be provided

seaborn.set(context='notebook', style='darkgrid', palette='deep', font='sans-serif', font_scale=1, color_codes=True, rc=None)

• context : the default parameter controls the size of the frame, respectively {paper, notebook, talk, poster } four values. Which, poster> talk> notebook> paper .
• style : style default parameter control, respectively {darkgrid, whitegrid, dark, white , ticks}, you can change yourself to see the difference between them.
• Palette : parameters as the default palette. Respectively {deep, muted, bright, pastel , dark, colorblind} and the like, you can change their own view different therebetween.
• font: used to set the font
• font_scale : Set the font size,
• color_codes : The use of the previous 'r' and the like is not used abbreviations color palette.

 

Classification 2.seaborn map

1. Association graph
   1. Interface replot (relational plots) class diagram of the relationship, in fact, two kinds of integrated FIG below, can draw the following two kind parameters specified by FIG.
   2. Scatterplot scatterplot
   3. Line chart lineplot
2. Category map
   1. Interface catplot classification chart is actually integrated, the following can be drawn by the eight parameters specified kind following eight kinds of graphs in FIG.
   2. Scatter Categories
      1. stripplot() (kind="strip")
      2. swarmplot() (kind="swarm")
   3. Category profile
      1. boxplot() (kind="box")
      2. violinplot() (kind="violin")
      3. boxenplot () (kind = "boxes")
   4. Classification estimation map
      1. pointplot() (kind="point")
      2. barplot() (kind="bar")
      3. countplot() (kind="count")
3. Distribution
   1. Univariate distribution
      1. Histogram, quality estimation map distplot ()
      2. Kernel Density Estimation FIG kdeplot ()
   2. Bivariate relationship diagram
      1. Dual variable diagram jointplot () 
      2. The relationship between variables Photos pairplot ()
      3. The data points plotted as data array rugplot axis ()
4. Regression Figure
   1. Regression Model Figure Implot ()
   2. Linear regression FIG regplot ()
   3. Linear regression residuals FIG residplot ()
5. Matrix composition FIG.
1. Thermal FIG HeatMap ()
2. FIG aggregation ClusterMap ()

 

 

 

references:

【1】seaborn.set — seaborn 0.9.0 documentation