绘图和可视化（seaborn）

《Python for Data Analysis》

柱状图

import pandas as pd import numpy as npimport seaborn as snsimport matplotlib.pyplot as plt %matplotlib inlinetips = pd.read_csv('examples/tips.csv')party_counts = pd.crosstab(tips['day'], tips['size'])# Not many 1- and 6-person partiesparty_counts = party_counts.loc[:, 2:5]tips['tip_pct'] = tips['tip'] / (tips['total_bill'] - tips['tip'])sns.barplot(x='tip_pct', y='day', hue='time', data=tips, orient='h')

comp1 = np.random.normal(0, 1, size=200)comp2 = np.random.normal(10, 2, size=200)values = pd.Series(np.concatenate([comp1, comp2]))sns.distplot(values, bins=100, color='k')

散点图

macro = pd.read_csv('examples/macrodata.csv')data = macro[['cpi', 'm1', 'tbilrate', 'unemp']]trans_data = np.log(data).diff().dropna()trans_data[-5:]sns.regplot('m1', 'unemp', data=trans_data)plt.title('Changes in log %s versus log %s' % ('m1', 'unemp'))

sns.pairplot(trans_data, diag_kind='kde', plot_kws={'alpha': 0.2})