seaborn画图初步¶

python中的也有一些不错的绘图包，例如seaborn。基础的matplotlib过于底层，想把一个分类变量映射到颜色上去都要费好几行代码。seaborn就类似于R中的lattice，画一些统计图形非常方便。下面是总结的几种常用图形画法。

加载包

In [1]:

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

设定参数

In [82]:

sns.set_palette("deep", desat=.6)
sns.set_context(rc={"figure.figsize": (8, 4)})
np.random.seed(9221999)

条形图

In [41]:

data = np.random.choice(['a','b','c'], size=100)
sns.barplot(data);

直方图

In [83]:

data = randn(100)
sns.distplot(data,bins=30);

线图

In [84]:

sns.tsplot(data);

密度曲线，配合分组

In [19]:

c1, c2 = sns.color_palette("Set1", 2)

dist1, dist2= stats.norm(0, 1).rvs((2, 100))
dist2 = pd.Series(dist2 + 2, name="dist2")

sns.kdeplot(dist1, shade=True, color=c1)
sns.kdeplot(dist2, shade=True, color=c2);

二维密度图

In [23]:

mpl.rc("figure", figsize=(8, 8))
data = np.random.multivariate_normal([0, 0], [[1, 2], [2, 20]], size=1000)
data = pd.DataFrame(data, columns=["X", "Y"])
sns.kdeplot(data.X, data.Y, shade=True);

双变量联合分布

In [10]:

x = stats.gamma(3).rvs(5000)
y = stats.gamma(5).rvs(5000)
with sns.axes_style("white"):
    sns.jointplot(x, y, kind="kde");

箱线图

In [85]:

data = [randn(100), randn(100) + 1]
sns.boxplot(data);

In [86]:

sns.violinplot(data, color="pastel");

散点图，配合分组回归

In [72]:

tips.head()

Out[72]:

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

In [79]:

tips = sns.load_dataset("tips")
sns.lmplot(x="total_bill", y="tip", hue='sex',col='sex',data=tips,fit_reg=True);

相关矩阵

In [61]:

titanic = sns.load_dataset("titanic").dropna()
attention = sns.load_dataset("attention")
sns.set_context(rc={"figure.figsize": (8, 8)})

In [62]:

sns.corrplot(titanic[[0,1,3,4,5,6]]);