《Python for Data Analysis》
柱狀圖
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
tips = pd.read_csv('examples/tips.csv')
party_counts = pd.crosstab(tips['day'], tips['size'])
# Not many 1- and 6-person parties
party_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')
散點圖
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})