seaborn的facetgrid函数的使用及热力图的绘制

%matplotlib inline
import numpy as np
import pandas as pd
import seaborn as sns
from scipy import stats
import matplotlib as mpl
import matplotlib.pyplot as plt

sns.set(style="ticks")
np.random.seed(sum(map(ord, "axis_grids")))

tips = sns.load_dataset("tips")
tips.head()

image.png

g = sns.FacetGrid(tips, col="time")

output_2_0.png

g = sns.FacetGrid(tips, col="time")
g.map(plt.hist, "tip");   #hist条形图

output_3_0.png

g = sns.FacetGrid(tips, col="sex", hue="smoker")
g.map(plt.scatter, "total_bill", "tip", alpha=.7)  #alpha颜色的透明度
g.add_legend();

output_4_0.png

g = sns.FacetGrid(tips, row="smoker", col="time", margin_titles=True)
g.map(sns.regplot, "size", "total_bill", color=".1", fit_reg=False, x_jitter=.1);

output_5_0.png

g = sns.FacetGrid(tips, col="day", size=4, aspect=.5)
g.map(sns.barplot, "sex", "total_bill");

output_6_0.png

from pandas import Categorical
ordered_days = tips.day.value_counts().index
print (ordered_days)
ordered_days = Categorical(['Thur', 'Fri', 'Sat', 'Sun'])
g = sns.FacetGrid(tips, row="day", row_order=ordered_days,
                  size=1.7, aspect=4,)
g.map(sns.boxplot, "total_bill");

CategoricalIndex(['Sat', 'Sun', 'Thur', 'Fri'], categories=['Thur', 'Fri', 'Sat', 'Sun'], ordered=False, dtype='category')

output_7_1.png

pal = dict(Lunch="seagreen", Dinner="gray")  #字典数据赋值
g = sns.FacetGrid(tips, hue="time", palette=pal, size=5)   #palette调色板设定
g.map(plt.scatter, "total_bill", "tip", s=50, alpha=.7, linewidth=.5, edgecolor="white")
g.add_legend();

output_8_0.png

g = sns.FacetGrid(tips, hue="sex", palette="Set1", size=5, hue_kws={"marker": ["^", "v"]})
g.map(plt.scatter, "total_bill", "tip", s=100, linewidth=.5, edgecolor="white")
g.add_legend();

output_9_0.png

with sns.axes_style("white"):
    g = sns.FacetGrid(tips, row="sex", col="smoker", margin_titles=True, size=2.5)
g.map(plt.scatter, "total_bill", "tip", color="#334488", edgecolor="white", lw=.5);
g.set_axis_labels("Total bill (US Dollars)", "Tip");
g.set(xticks=[10, 30, 50], yticks=[2, 6, 10]);
g.fig.subplots_adjust(wspace=.02, hspace=.02);
#g.fig.subplots_adjust(left  = 0.125,right = 0.5,bottom = 0.1,top = 0.9, wspace=.02, hspace=.02)

output_10_0.png

iris = sns.load_dataset("iris")
g = sns.PairGrid(iris)
g.map(plt.scatter);

output_11_0.png

g = sns.PairGrid(iris)
g.map_diag(plt.hist)
g.map_offdiag(plt.scatter);

output_12_0.png

g = sns.PairGrid(iris, hue="species")
g.map_diag(plt.hist)
g.map_offdiag(plt.scatter)
g.add_legend();

output_13_0.png

g = sns.PairGrid(iris, vars=["sepal_length", "sepal_width"], hue="species")
g.map(plt.scatter);

output_14_0.png

g = sns.PairGrid(tips, hue="size", palette="GnBu_d")
g.map(plt.scatter, s=50, edgecolor="white")
g.add_legend();

output_15_0.png

热力图的绘制

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np; 
np.random.seed(0)
import seaborn as sns;
sns.set()

uniform_data = np.random.rand(3, 3)
print (uniform_data)
heatmap = sns.heatmap(uniform_data)

[[ 0.0187898   0.6176355   0.61209572]
 [ 0.616934    0.94374808  0.6818203 ]
 [ 0.3595079   0.43703195  0.6976312 ]]

output_1_1.png

ax = sns.heatmap(uniform_data, vmin=0.2, vmax=0.5)

output_2_0.png

normal_data = np.random.randn(3, 3)
print (normal_data)
ax = sns.heatmap(normal_data, center=0)

[[ 0.3113635  -0.77602047 -0.30736481]
 [-0.36652394  1.11971196 -0.45792242]
 [ 0.4253934  -0.02797118  1.47598983]]

output_3_1.png

flights = sns.load_dataset("flights")
flights.head()

image.png

flights = flights.pivot("month", "year", "passengers")
print (flights)
ax = sns.heatmap(flights)

year       1949  1950  1951  1952  1953  1954  1955  1956  1957  1958  1959  \
month                                                                         
January     112   115   145   171   196   204   242   284   315   340   360   
February    118   126   150   180   196   188   233   277   301   318   342   
March       132   141   178   193   236   235   267   317   356   362   406   
April       129   135   163   181   235   227   269   313   348   348   396   
May         121   125   172   183   229   234   270   318   355   363   420   
June        135   149   178   218   243   264   315   374   422   435   472   
July        148   170   199   230   264   302   364   413   465   491   548   
August      148   170   199   242   272   293   347   405   467   505   559   
September   136   158   184   209   237   259   312   355   404   404   463   
October     119   133   162   191   211   229   274   306   347   359   407   
November    104   114   146   172   180   203   237   271   305   310   362   
December    118   140   166   194   201   229   278   306   336   337   405   

year       1960  
month            
January     417  
February    391  
March       419  
April       461  
May         472  
June        535  
July        622  
August      606  
September   508  
October     461  
November    390  
December    432  

output_5_1.png

ax = sns.heatmap(flights, annot=True,fmt="d")

output_6_0.png

ax = sns.heatmap(flights, linewidths=.5)

output_7_0.png

ax = sns.heatmap(flights, cmap="YlGnBu")

output_8_0.png

ax = sns.heatmap(flights, cbar=False)

output_9_0.png