《利用Python进行数据分析》笔记---第10章时间序列

写在前面的话：

实例中的所有数据都是在GitHub上下载的，打包下载即可。
地址是：http://github.com/pydata/pydata-book

还有一定要说明的：

我使用的是Python2.7，书中的代码有一些有错误，我使用自己的2.7版本调通。

# coding: utf-8
from __future__ import division
from pandas import Series, DataFrame
import pandas as pd
from numpy.random import randn
import numpy as np
import matplotlib.pyplot as plt

pd.options.display.max_rows = 12
np.set_printoptions(precision=4, suppress=True)
plt.rc('figure', figsize=(12, 4))


from datetime import datetime
now = datetime.now()
now

now.year, now.month, now.day

delta = datetime(2011, 1, 7) - datetime(2008, 6, 24, 8, 15)
delta
delta.days
delta.seconds

from datetime import timedelta
start = datetime(2011, 1, 7)
start + timedelta(12)
start - 2 * timedelta(12)

stamp = datetime(2011, 1, 3)
str(stamp)
stamp.strftime('%Y-%m-%d')

value = '2011-01-03'
datetime.strptime(value, '%Y-%m-%d')
datestrs = ['7/6/2011', '8/6/2011']
[datetime.strptime(x, '%m/%d/%Y') for x in datestrs]

from dateutil.parser import parse
parse('2011-01-03')
parse('Jan 31, 1997 10:45 PM')
parse('6/12/2011', dayfirst=True)
datestrs

pd.to_datetime(datestrs)
idx = pd.to_datetime(datestrs + [None])
idx
idx[2]
pd.isnull(idx)

from datetime import datetime
dates = [datetime(2011, 1, 2), datetime(2011, 1, 5), datetime(2011, 1, 7),
         datetime(2011, 1, 8), datetime(2011, 1, 10), datetime(2011, 1, 12)]
ts = Series(np.random.randn(6), index=dates)
ts
type(ts)
ts.index
ts + ts[::2]
ts.index.dtype
stamp = ts.index[0]
stamp

stamp = ts.index[2]
ts[stamp]
ts['1/10/2011']
ts['20110110']
longer_ts = Series(np.random.randn(1000),index=pd.date_range('1/1/2000', periods=1000))
longer_ts
longer_ts['2001']
longer_ts['2001-05']
ts[datetime(2011, 1, 7):]
ts
ts['1/6/2011':'1/11/2011']
ts.truncate(after='1/9/2011')

dates = pd.date_range('1/1/2000', periods=100, freq='W-WED')
long_df = DataFrame(np.random.randn(100, 4),index=dates,columns=['Colorado', 'Texas', 'New York', 'Ohio'])
long_df.ix['5-2001']
dates = pd.DatetimeIndex(['1/1/2000', '1/2/2000', '1/2/2000', '1/2/2000', '1/3/2000'])
dup_ts = Series(np.arange(5), index=dates)
dup_ts
dup_ts.index.is_unique
dup_ts['1/3/2000']
dup_ts['1/2/2000']
grouped = dup_ts.groupby(level=0)
grouped.mean()
grouped.count()

ts
ts.resample('D')
index = pd.date_range('4/1/2012', '6/1/2012')
index
pd.date_range(start='4/1/2012', periods=20)
pd.date_range(end='6/1/2012', periods=20)
pd.date_range('1/1/2000', '12/1/2000', freq='BM')
pd.date_range('5/2/2012 12:56:31', periods=5)
pd.date_range('5/2/2012 12:56:31', periods=5, normalize=True)

from pandas.tseries.offsets import Hour, Minute
hour = Hour()
hour
four_hours = Hour(4)
four_hours
pd.date_range('1/1/2000', '1/3/2000 23:59', freq='4h')
Hour(2) + Minute(30)
pd.date_range('1/1/2000', periods=10, freq='1h30min')
rng = pd.date_range('1/1/2012', '9/1/2012', freq='WOM-3FRI')
list(rng)
ts = Series(np.random.randn(4),index=pd.date_range('1/1/2000', periods=4, freq='M'))
ts
ts.shift(2)
ts.shift(-2)
ts / ts.shift(1) - 1
ts.shift(2, freq='M')
ts.shift(3, freq='D')
ts.shift(1, freq='3D')
ts.shift(1, freq='90T')

from pandas.tseries.offsets import Day, MonthEnd
now = datetime(2011, 11, 17)
now + 3 * Day()
now + MonthEnd()
now + MonthEnd(2)
offset = MonthEnd()
offset.rollforward(now)
offset.rollback(now)
ts = Series(np.random.randn(20),index=pd.date_range('1/15/2000', periods=20, freq='4d'))
ts.groupby(offset.rollforward).mean()
ts.resample('M', how='mean')

import pytz
pytz.common_timezones[-5:]
tz = pytz.timezone('US/Eastern')
tz
rng = pd.date_range('3/9/2012 9:30', periods=6, freq='D')
ts = Series(np.random.randn(len(rng)), index=rng)
print(ts.index.tz)
pd.date_range('3/9/2012 9:30', periods=10, freq='D', tz='UTC')
ts_utc = ts.tz_localize('UTC')
ts_utc
ts_utc.index
ts_utc.tz_convert('US/Eastern')
ts_eastern = ts.tz_localize('US/Eastern')
ts_eastern.tz_convert('UTC')
ts_eastern.tz_convert('Europe/Berlin')
ts.index.tz_localize('Asia/Shanghai')

stamp = pd.Timestamp('2011-03-12 04:00')
stamp_utc = stamp.tz_localize('utc')
stamp_utc.tz_convert('US/Eastern')
stamp_moscow = pd.Timestamp('2011-03-12 04:00', tz='Europe/Moscow')
stamp_moscow
stamp_utc.value
stamp_utc.tz_convert('US/Eastern').value

from pandas.tseries.offsets import Hour
stamp = pd.Timestamp('2012-03-12 01:30', tz='US/Eastern')
stamp
stamp + Hour()
stamp = pd.Timestamp('2012-11-04 00:30', tz='US/Eastern')
stamp
stamp + 2 * Hour()

rng = pd.date_range('3/7/2012 9:30', periods=10, freq='B')
ts = Series(np.random.randn(len(rng)), index=rng)
ts
ts1 = ts[:7].tz_localize('Europe/London')
ts2 = ts1[2:].tz_convert('Europe/Moscow')
result = ts1 + ts2
result.index
p = pd.Period(2007, freq='A-DEC')
p
p + 5
p - 2
pd.Period('2014', freq='A-DEC') - p
rng = pd.period_range('1/1/2000', '6/30/2000', freq='M')
rng

Series(np.random.randn(6), index=rng)
values = ['2001Q3', '2002Q2', '2003Q1']
index = pd.PeriodIndex(values, freq='Q-DEC')
index
p = pd.Period('2007', freq='A-DEC')
p.asfreq('M', how='start')
p.asfreq('M', how='end')
p = pd.Period('2007', freq='A-JUN')
p.asfreq('M', 'start')
p.asfreq('M', 'end')
p = pd.Period('Aug-2007', 'M')
p.asfreq('A-JUN')
rng = pd.period_range('2006', '2009', freq='A-DEC')
ts = Series(np.random.randn(len(rng)), index=rng)
ts
ts.asfreq('M', how='start')
ts.asfreq('B', how='end')
p = pd.Period('2012Q4', freq='Q-JAN')
p
p.asfreq('D', 'start')
p.asfreq('D', 'end')
p4pm = (p.asfreq('B', 'e') - 1).asfreq('T', 's') + 16 * 60
p4pm
p4pm.to_timestamp()

rng = pd.period_range('2011Q3', '2012Q4', freq='Q-JAN')
ts = Series(np.arange(len(rng)), index=rng)
ts
new_rng = (rng.asfreq('B', 'e') - 1).asfreq('T', 's') + 16 * 60
ts.index = new_rng.to_timestamp()
ts
rng = pd.date_range('1/1/2000', periods=3, freq='M')
ts = Series(randn(3), index=rng)
pts = ts.to_period()
ts
pts
rng = pd.date_range('1/29/2000', periods=6, freq='D')
ts2 = Series(randn(6), index=rng)
ts2.to_period('M')

pts = ts.to_period()
pts
pts.to_timestamp(how='end')

data = pd.read_csv('D:\Source Code\pydata-book-master\ch08\macrodata.csv')
data.year
data.quarter

index = pd.PeriodIndex(year=data.year, quarter=data.quarter, freq='Q-DEC')
index
data.index = index
data.infl

rng = pd.date_range('1/1/2000', periods=100, freq='D')
ts = Series(randn(len(rng)), index=rng)
ts.resample('M', how='mean')
ts.resample('M', how='mean', kind='period')

rng = pd.date_range('1/1/2000', periods=12, freq='T')
ts = Series(np.arange(12), index=rng)
ts
ts.resample('5min', how='sum')
ts.resample('5min', how='sum', closed='left')
ts.resample('5min', how='sum', closed='left', label='left')
ts.resample('5min', how='sum', loffset='-1s')
ts.resample('5min', how='ohlc')
rng = pd.date_range('1/1/2000', periods=100, freq='D')
ts = Series(np.arange(100), index=rng)
ts.groupby(lambda x: x.month).mean()
ts.groupby(lambda x: x.weekday).mean()
frame = DataFrame(np.random.randn(2, 4), index=pd.date_range('1/1/2000', periods=2, freq='W-WED'), columns=['Colorado', 'Texas', 'New York', 'Ohio'])
frame
df_daily = frame.resample('D')
df_daily
frame.resample('D', fill_method='ffill')
frame.resample('D', fill_method='ffill', limit=2)
frame.resample('W-THU', fill_method='ffill')

frame = DataFrame(np.random.randn(24, 4),index=pd.period_range('1-2000', '12-2001', freq='M'),columns=['Colorado', 'Texas', 'New York', 'Ohio'])
frame[:5]
annual_frame = frame.resample('A-DEC', how='mean')
annual_frame
annual_frame.resample('Q-DEC', fill_method='ffill')
annual_frame.resample('Q-DEC', fill_method='ffill', convention='start')
annual_frame.resample('Q-MAR', fill_method='ffill')
close_px_all = pd.read_csv('D:\Source Code\pydata-book-master\ch09\stock_px.csv', parse_dates=True, index_col=0)
close_px = close_px_all[['AAPL', 'MSFT', 'XOM']]
close_px = close_px.resample('B', fill_method='ffill')
close_px.info()
close_px['AAPL'].plot()
close_px.ix['2009'].plot()
close_px['AAPL'].ix['01-2011':'03-2011'].plot()
appl_q = close_px['AAPL'].resample('Q-DEC', fill_method='ffill')
appl_q.ix['2009':].plot()
close_px = close_px.asfreq('B').fillna(method='ffill')
close_px.AAPL.plot()
pd.rolling_mean(close_px.AAPL, 250).plot()
plt.figure()
appl_std250 = pd.rolling_std(close_px.AAPL, 250, min_periods=10)
appl_std250[5:12]
appl_std250.plot()