利用Python进行数据分析-----绘图和可视化
- 利用Python进行数据分析-----绘图和可视化
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- matplotlib API 入门
- 使用pandas和seaborn绘图
- 其它的Python可视化工具
利用Python进行数据分析-----绘图和可视化
import numpy as np
import pandas as pd
PREVIOUS_MAX_ROWS = pd.options.display.max_rows
pd.options.display.max_rows = 20
np.random.seed(12345)
import matplotlib.pyplot as plt
import matplotlib
plt.rc('figure', figsize=(10, 6))
np.set_printoptions(precision=4, suppress=True)
matplotlib API 入门




plt.plot(np.random.randn(50).cumsum(), 'k--')

_ = ax1.hist(np.random.randn(100), bins=20, color='k', alpha=0.3)
ax2.scatter(np.arange(30), np.arange(30) + 3 * np.random.randn(30))


fig, axes = plt.subplots(2, 2, sharex=True, sharey=True)
for i in range(2):
for j in range(2):
axes[i, j].hist(np.random.randn(500), bins=50, color='k', alpha=0.5)
plt.subplots_adjust(wspace=0, hspace=0)




data = np.random.randn(30).cumsum()
plt.plot(data, 'k--', label='Default')
plt.plot(data, 'k-', drawstyle='steps-post', label='steps-post')
plt.legend(loc='best')




from numpy.random import randn
fig = plt.figure(); ax = fig.add_subplot(1, 1, 1)
ax.plot(randn(1000).cumsum(), 'k', label='one')
ax.plot(randn(1000).cumsum(), 'k--', label='two')
ax.plot(randn(1000).cumsum(), 'k.', label='three')


from datetime import datetime
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
data = pd.read_csv('examples/spx.csv', index_col=0, parse_dates=True)
spx = data['SPX']
spx.plot(ax=ax, style='k-')
crisis_data = [
(datetime(2007, 10, 11), 'Peak of bull market'),
(datetime(2008, 3, 12), 'Bear Stearns Fails'),
(datetime(2008, 9, 15), 'Lehman Bankruptcy')
]
for date, label in crisis_data:
ax.annotate(label, xy=(date, spx.asof(date) + 75),
xytext=(date, spx.asof(date) + 225),
arrowprops=dict(facecolor='black', headwidth=4, width=2,
headlength=4),
horizontalalignment='left', verticalalignment='top')
ax.set_xlim(['1/1/2007', '1/1/2011'])
ax.set_ylim([600, 1800])
ax.set_title('Important dates in the 2008-2009 financial crisis')


fig = plt.figure(figsize=(12, 6)); ax = fig.add_subplot(1, 1, 1)
rect = plt.Rectangle((0.2, 0.75), 0.4, 0.15, color='k', alpha=0.3)
circ = plt.Circle((0.7, 0.2), 0.15, color='b', alpha=0.3)
pgon = plt.Polygon([[0.15, 0.15], [0.35, 0.4], [0.2, 0.6]],
color='g', alpha=0.5)
ax.add_patch(rect)
ax.add_patch(circ)
ax.add_patch(pgon)



使用pandas和seaborn绘图



s = pd.Series(np.random.randn(10).cumsum(), index=np.arange(0, 100, 10))
s.plot()


df = pd.DataFrame(np.random.randn(10, 4).cumsum(0),
columns=['A', 'B', 'C', 'D'],
index=np.arange(0, 100, 10))
df.plot()


fig, axes = plt.subplots(2, 1)
data = pd.Series(np.random.rand(16), index=list('abcdefghijklmnop'))
data.plot.bar(ax=axes[0], color='k', alpha=0.7)
data.plot.barh(ax=axes[1], color='k', alpha=0.7)

np.random.seed(12348)
df = pd.DataFrame(np.random.rand(6, 4),
index=['one', 'two', 'three', 'four', 'five', 'six'],
columns=pd.Index(['A', 'B', 'C', 'D'], name='Genus'))
df
df.plot.bar()


plt.close('all')
tips = pd.read_csv('examples/tips.csv')
party_counts = pd.crosstab(tips['day'], tips['size'])
party_counts
party_counts = party_counts.loc[:, 2:5]
party_pcts = party_counts.div(party_counts.sum(1), axis=0)
party_pcts
party_pcts.plot.bar()

plt.close('all')
import seaborn as sns
tips['tip_pct'] = tips['tip'] / (tips['total_bill'] - tips['tip'])
tips.head()
sns.barplot(x='tip_pct', y='day', data=tips, orient='h')

plt.close('all')
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')

macro = pd.read_csv('examples/macrodata.csv')
data = macro[['cpi', 'm1', 'tbilrate', 'unemp']]
trans_data = np.log(data).diff().dropna()
trans_data[-5:]


plt.figure()
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})

sns.factorplot(x='day', y='tip_pct', hue='time', col='smoker',
kind='bar', data=tips[tips.tip_pct < 1])

sns.factorplot(x='day', y='tip_pct', row='time',
col='smoker',
kind='bar', data=tips[tips.tip_pct < 1])

sns.factorplot(x='tip_pct', y='day', kind='box',
data=tips[tips.tip_pct < 0.5])

其它的Python可视化工具

