Seaborn
1 import numpy as np 2 import pandas as pd 3 import matplotlib.pyplot as plt 4 import seaborn as sns 5 6 %matplotlib notebook
1 np.random.seed(1234) 2 3 v1 = pd.Series(np.random.normal(0,10,1000), name='v1') 4 v2 = pd.Series(2*v1 + np.random.normal(60,15,1000), name='v2')
1 plt.figure() 2 plt.hist(v1, alpha=0.7, bins=np.arange(-50,150,5), label='v1'); 3 plt.hist(v2, alpha=0.7, bins=np.arange(-50,150,5), label='v2'); 4 plt.legend();
1 # plot a kernel density estimation over a stacked barchart 2 plt.figure() 3 plt.hist([v1, v2], histtype='barstacked', normed=True); 4 v3 = np.concatenate((v1,v2)) 5 sns.kdeplot(v3);
1 plt.figure() 2 # we can pass keyword arguments for each individual component of the plot 3 sns.distplot(v3, hist_kws={'color': 'Teal'}, kde_kws={'color': 'Navy'});
1 sns.jointplot(v1, v2, alpha=0.4);
1 grid = sns.jointplot(v1, v2, alpha=0.4); 2 grid.ax_joint.set_aspect('equal')
1 sns.jointplot(v1, v2, kind='hex');
1 # set the seaborn style for all the following plots 2 sns.set_style('white') 3 4 sns.jointplot(v1, v2, kind='kde', space=0);
1 iris = pd.read_csv('iris.csv') 2 iris.head()
1 sns.pairplot(iris, hue='Name', diag_kind='kde', size=2);
1 plt.figure(figsize=(8,6)) 2 plt.subplot(121) 3 sns.swarmplot('Name', 'PetalLength', data=iris); 4 plt.subplot(122) 5 sns.violinplot('Name', 'PetalLength', data=iris);优质内容筛选与推荐>>
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