python数据分析与机器学习实战-14.单变量分析绘图

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

sns.set(color_codes = True)
x = np.random.normal(size=100)
sns.distplot(x,kde=False)
/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.
  warnings.warn("The 'normed' kwarg is deprecated, and has been "





<matplotlib.axes._subplots.AxesSubplot at 0x1a1510af28>

sns.distplot(x,bins=20,kde=False)
/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.
  warnings.warn("The 'normed' kwarg is deprecated, and has been "





<matplotlib.axes._subplots.AxesSubplot at 0x1a1505f860>

x = np.random.gamma(6,size=200)
sns.distplot(x,kde=False,fit=stats.gamma)
/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.
  warnings.warn("The 'normed' kwarg is deprecated, and has been "





<matplotlib.axes._subplots.AxesSubplot at 0x1a153a0240>

mean,cov = [0,1],[(1,.5),(.5,1)]
data = np.random.multivariate_normal(mean,cov,200)
df = pd.DataFrame(data,columns=["x","y"])
df

	x	y
0	-0.174087	1.795256
1	-0.416757	0.638361
2	-1.188138	-0.331589
3	-0.609992	0.435072
4	-1.839588	1.710629
5	-0.636881	0.890326
6	-0.833977	0.792370
7	1.789158	2.060371
8	1.156777	0.663219
9	0.423476	0.673290
10	-0.412169	0.751990
11	-1.126475	0.535133
12	1.323243	1.533676
13	-0.023044	-0.599542
14	0.691180	0.879479
15	1.478605	2.477569
16	-1.637043	-0.499630
17	-0.094884	2.142044
18	-0.989239	1.218270
19	1.420987	1.013807
20	0.360396	2.465208
21	1.358501	1.184887
22	2.339145	1.285469
23	1.282484	2.062299
24	0.854708	0.706735
25	-0.223393	-0.172632
26	-0.513652	0.535946
27	1.207449	1.181039
28	-1.219564	-1.178302
29	-0.855500	0.167194
…	…	…
170	1.022425	1.816807
171	0.344980	0.766577
172	1.043292	0.955718
173	0.765037	2.321773
174	0.683540	0.501221
175	1.188854	0.022803
176	-0.287907	-0.166137
177	-0.778977	0.036364
178	-1.875389	0.689938
179	0.183550	1.683339
180	0.220839	1.396213
181	0.660952	1.921363
182	-2.357542	1.145418
183	0.521454	0.304456
184	-0.321098	-1.072025
185	-0.265391	1.166742
186	1.182235	2.652254
187	0.920143	1.772478
188	0.620841	1.806727
189	-0.892951	0.177710
190	-0.463518	2.034266
191	-0.031959	0.967620
192	-0.412957	0.324511
193	-0.021698	0.949692
194	1.897446	2.806375
195	-0.346429	0.899780
196	-0.320803	-0.144683
197	-0.332870	0.751261
198	-0.579072	0.318083
199	-0.889261	0.091241

200 rows × 2 columns

观察两个变量之间的分布关系最好用散点图

sns.jointplot(x="x",y="y",data=df)
/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.
  warnings.warn("The 'normed' kwarg is deprecated, and has been "
/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.
  warnings.warn("The 'normed' kwarg is deprecated, and has been "





<seaborn.axisgrid.JointGrid at 0x1a22fb1cf8>

x,y = np.random.multivariate_normal(mean,cov,1000).T
with sns.axes_style("white"):
    sns.jointplot(x=x,y=y,data=df,kind="hex",color='k')
/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.
  warnings.warn("The 'normed' kwarg is deprecated, and has been "
/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.
  warnings.warn("The 'normed' kwarg is deprecated, and has been "

iris = sns.load_dataset("iris")
sns.pairplot(iris)
<seaborn.axisgrid.PairGrid at 0x1a23d144e0>

# seaborn