Time Zone Handling



In [1]:

    
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
import pytz
pytz.common_timezones[-5:]









    Out[1]:





['US/Eastern', 'US/Hawaii', 'US/Mountain', 'US/Pacific', 'UTC']



In [2]:

    
tz = pytz.timezone('US/Eastern')
tz









    Out[2]:





<DstTzInfo 'US/Eastern' LMT-1 day, 19:04:00 STD>

Localization and Conversion



In [3]:

    
rng = pd.date_range('3/9/2012 9:30', periods=6, freq='D')
ts = Series(np.random.randn(len(rng)), index=rng)



In [4]:

    
print(ts.index.tz)









    



None



In [5]:

    
pd.date_range('3/9/2012 9:30', periods=10, freq='D', tz='UTC')









    Out[5]:





DatetimeIndex(['2012-03-09 09:30:00+00:00', '2012-03-10 09:30:00+00:00',
               '2012-03-11 09:30:00+00:00', '2012-03-12 09:30:00+00:00',
               '2012-03-13 09:30:00+00:00', '2012-03-14 09:30:00+00:00',
               '2012-03-15 09:30:00+00:00', '2012-03-16 09:30:00+00:00',
               '2012-03-17 09:30:00+00:00', '2012-03-18 09:30:00+00:00'],
              dtype='datetime64[ns, UTC]', freq='D')



In [6]:

    
ts_utc = ts.tz_localize('UTC')
ts_utc









    Out[6]:





2012-03-09 09:30:00+00:00    0.396522
2012-03-10 09:30:00+00:00    0.658530
2012-03-11 09:30:00+00:00    0.155084
2012-03-12 09:30:00+00:00   -0.045595
2012-03-13 09:30:00+00:00   -0.465521
2012-03-14 09:30:00+00:00    1.047965
Freq: D, dtype: float64



In [7]:

    
ts_utc.index









    Out[7]:





DatetimeIndex(['2012-03-09 09:30:00+00:00', '2012-03-10 09:30:00+00:00',
               '2012-03-11 09:30:00+00:00', '2012-03-12 09:30:00+00:00',
               '2012-03-13 09:30:00+00:00', '2012-03-14 09:30:00+00:00'],
              dtype='datetime64[ns, UTC]', freq='D')



In [8]:

    
ts_utc.tz_convert('US/Eastern')









    Out[8]:





2012-03-09 04:30:00-05:00    0.396522
2012-03-10 04:30:00-05:00    0.658530
2012-03-11 05:30:00-04:00    0.155084
2012-03-12 05:30:00-04:00   -0.045595
2012-03-13 05:30:00-04:00   -0.465521
2012-03-14 05:30:00-04:00    1.047965
Freq: D, dtype: float64



In [9]:

    
ts_eastern = ts.tz_localize('US/Eastern')
ts_eastern.tz_convert('UTC')









    Out[9]:





2012-03-09 14:30:00+00:00    0.396522
2012-03-10 14:30:00+00:00    0.658530
2012-03-11 13:30:00+00:00    0.155084
2012-03-12 13:30:00+00:00   -0.045595
2012-03-13 13:30:00+00:00   -0.465521
2012-03-14 13:30:00+00:00    1.047965
Freq: D, dtype: float64



In [10]:

    
ts_eastern.tz_convert('Europe/Berlin')









    Out[10]:





2012-03-09 15:30:00+01:00    0.396522
2012-03-10 15:30:00+01:00    0.658530
2012-03-11 14:30:00+01:00    0.155084
2012-03-12 14:30:00+01:00   -0.045595
2012-03-13 14:30:00+01:00   -0.465521
2012-03-14 14:30:00+01:00    1.047965
Freq: D, dtype: float64



In [11]:

    
ts.index.tz_localize('Asia/Shanghai')









    Out[11]:





DatetimeIndex(['2012-03-09 09:30:00+08:00', '2012-03-10 09:30:00+08:00',
               '2012-03-11 09:30:00+08:00', '2012-03-12 09:30:00+08:00',
               '2012-03-13 09:30:00+08:00', '2012-03-14 09:30:00+08:00'],
              dtype='datetime64[ns, Asia/Shanghai]', freq='D')

Operations with time zone-aware Timestamp objects



In [12]:

    
stamp = pd.Timestamp('2011-03-12 04:00')
stamp_utc = stamp.tz_localize('utc')
stamp_utc.tz_convert('US/Eastern')









    Out[12]:





Timestamp('2011-03-11 23:00:00-0500', tz='US/Eastern')



In [13]:

    
stamp_moscow = pd.Timestamp('2011-03-12 04:00', tz='Europe/Moscow')
stamp_moscow









    Out[13]:





Timestamp('2011-03-12 04:00:00+0300', tz='Europe/Moscow')



In [14]:

    
stamp_utc.value









    Out[14]:





1299902400000000000



In [15]:

    
stamp_utc.tz_convert('US/Eastern').value









    Out[15]:





1299902400000000000



In [16]:

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









    Out[16]:





Timestamp('2012-03-12 01:30:00-0400', tz='US/Eastern')



In [17]:

    
stamp + Hour()









    Out[17]:





Timestamp('2012-03-12 02:30:00-0400', tz='US/Eastern')



In [18]:

    
stamp = pd.Timestamp('2012-11-04 00:30', tz='US/Eastern')
stamp









    Out[18]:





Timestamp('2012-11-04 00:30:00-0400', tz='US/Eastern')



In [19]:

    
stamp + 2 * Hour()









    Out[19]:





Timestamp('2012-11-04 01:30:00-0500', tz='US/Eastern')

Operations between different time zones



In [20]:

    
rng = pd.date_range('3/7/2012 9:30', periods=10, freq='B')
ts = Series(np.random.randn(len(rng)), index=rng)
ts









    Out[20]:





2012-03-07 09:30:00    0.155215
2012-03-08 09:30:00    0.088318
2012-03-09 09:30:00   -0.305385
2012-03-12 09:30:00    0.028647
2012-03-13 09:30:00    0.638672
2012-03-14 09:30:00    0.136588
2012-03-15 09:30:00    0.494592
2012-03-16 09:30:00   -1.676338
2012-03-19 09:30:00   -1.282896
2012-03-20 09:30:00   -0.283933
Freq: B, dtype: float64



In [21]:

    
ts1 = ts[:7].tz_localize('Europe/London')
ts2 = ts1[2:].tz_convert('Europe/Moscow')
result = ts1 + ts2
result.index









    Out[21]:





DatetimeIndex(['2012-03-07 09:30:00+00:00', '2012-03-08 09:30:00+00:00',
               '2012-03-09 09:30:00+00:00', '2012-03-12 09:30:00+00:00',
               '2012-03-13 09:30:00+00:00', '2012-03-14 09:30:00+00:00',
               '2012-03-15 09:30:00+00:00'],
              dtype='datetime64[ns, UTC]', freq='B')

Periods and Period Arithmetic



In [22]:

    
p = pd.Period(2007, freq='A-DEC')
p









    Out[22]:





Period('2007', 'A-DEC')



In [23]:

    
p + 5









    Out[23]:





Period('2012', 'A-DEC')



In [24]:

    
p - 2









    Out[24]:





Period('2005', 'A-DEC')



In [25]:

    
pd.Period('2014', freq='A-DEC') - p









    Out[25]:





7



In [26]:

    
rng = pd.period_range('1/1/2000', '6/30/2000', freq='M')
rng









    Out[26]:





PeriodIndex(['2000-01', '2000-02', '2000-03', '2000-04', '2000-05', '2000-06'], dtype='period[M]', freq='M')



In [27]:

    
Series(np.random.randn(6), index=rng)









    Out[27]:





2000-01    0.974588
2000-02    0.464866
2000-03   -1.313586
2000-04   -0.367646
2000-05   -0.456358
2000-06    0.101885
Freq: M, dtype: float64



In [28]:

    
values = ['2001Q3', '2002Q2', '2003Q1']
index = pd.PeriodIndex(values, freq='Q-DEC')
index









    Out[28]:





PeriodIndex(['2001Q3', '2002Q2', '2003Q1'], dtype='period[Q-DEC]', freq='Q-DEC')

Period Frequency Conversion



In [30]:

    
p = pd.Period('2007', freq='A-DEC')
p.asfreq('M', how='start')









    Out[30]:





Period('2007-01', 'M')



In [31]:

    
p.asfreq('M', how='end')









    Out[31]:





Period('2007-12', 'M')



In [32]:

    
p = pd.Period('2007', freq='A-JUN')
p.asfreq('M', 'start')









    Out[32]:





Period('2006-07', 'M')



In [33]:

    
p.asfreq('M', 'end')









    Out[33]:





Period('2007-06', 'M')



In [34]:

    
p = pd.Period('Aug-2007', 'M')
p.asfreq('A-JUN')









    Out[34]:





Period('2008', 'A-JUN')



In [35]:

    
rng = pd.period_range('2006', '2009', freq='A-DEC')
ts = Series(np.random.randn(len(rng)), index=rng)
ts









    Out[35]:





2006   -0.111317
2007   -0.783856
2008    0.228831
2009    0.833668
Freq: A-DEC, dtype: float64



In [36]:

    
ts.asfreq('M', how='start')









    Out[36]:





2006-01   -0.111317
2007-01   -0.783856
2008-01    0.228831
2009-01    0.833668
Freq: M, dtype: float64



In [37]:

    
ts.asfreq('B', how='end')









    Out[37]:





2006-12-29   -0.111317
2007-12-31   -0.783856
2008-12-31    0.228831
2009-12-31    0.833668
Freq: B, dtype: float64

Quarterly period frequencies



In [38]:

    
p = pd.Period('2012Q4', freq='Q-JAN')
p









    Out[38]:





Period('2012Q4', 'Q-JAN')



In [39]:

    
p.asfreq('D', 'start')









    Out[39]:





Period('2011-11-01', 'D')



In [40]:

    
p.asfreq('D', 'end')









    Out[40]:





Period('2012-01-31', 'D')



In [41]:

    
p4pm = (p.asfreq('B', 'e') - 1).asfreq('T', 's') + 16 * 60
p4pm









    Out[41]:





Period('2012-01-30 16:00', 'T')



In [42]:

    
p4pm.to_timestamp()









    Out[42]:





Timestamp('2012-01-30 16:00:00')



In [43]:

    
rng = pd.period_range('2011Q3', '2012Q4', freq='Q-JAN')
ts = Series(np.arange(len(rng)), index=rng)
ts









    Out[43]:





2011Q3    0
2011Q4    1
2012Q1    2
2012Q2    3
2012Q3    4
2012Q4    5
Freq: Q-JAN, dtype: int64



In [44]:

    
new_rng = (rng.asfreq('B', 'e') - 1).asfreq('T', 's') + 16 * 60
ts.index = new_rng.to_timestamp()
ts









    Out[44]:





2010-10-28 16:00:00    0
2011-01-28 16:00:00    1
2011-04-28 16:00:00    2
2011-07-28 16:00:00    3
2011-10-28 16:00:00    4
2012-01-30 16:00:00    5
dtype: int64

Converting Timestamps to Periods (and back)



In [46]:

    
rng = pd.date_range('1/1/2000', periods=3, freq='M')
ts = Series(np.random.randn(3), index=rng)
pts = ts.to_period()
ts









    Out[46]:





2000-01-31    0.055936
2000-02-29    0.105876
2000-03-31    1.180710
Freq: M, dtype: float64



In [47]:

    
pts









    Out[47]:





2000-01    0.055936
2000-02    0.105876
2000-03    1.180710
Freq: M, dtype: float64



In [49]:

    
rng = pd.date_range('1/29/2000', periods=6, freq='D')
ts2 = Series(np.random.randn(6), index=rng)
ts2.to_period('M')









    Out[49]:





2000-01    0.314051
2000-01    0.617664
2000-01    1.300151
2000-02   -0.441530
2000-02   -1.872125
2000-02    1.055898
Freq: M, dtype: float64



In [50]:

    
pts = ts.to_period()
pts









    Out[50]:





2000-01    0.055936
2000-02    0.105876
2000-03    1.180710
Freq: M, dtype: float64



In [51]:

    
pts.to_timestamp(how='end')









    Out[51]:





2000-01-31    0.055936
2000-02-29    0.105876
2000-03-31    1.180710
Freq: M, dtype: float64

Creating a PeriodIndex from arrays



In [52]:

    
data = pd.read_csv('macrodata.csv')
data.year









    Out[52]:





0      1959.0
1      1959.0
2      1959.0
3      1959.0
4      1960.0
5      1960.0
6      1960.0
7      1960.0
8      1961.0
9      1961.0
10     1961.0
11     1961.0
12     1962.0
13     1962.0
14     1962.0
15     1962.0
16     1963.0
17     1963.0
18     1963.0
19     1963.0
20     1964.0
21     1964.0
22     1964.0
23     1964.0
24     1965.0
25     1965.0
26     1965.0
27     1965.0
28     1966.0
29     1966.0
        ...  
173    2002.0
174    2002.0
175    2002.0
176    2003.0
177    2003.0
178    2003.0
179    2003.0
180    2004.0
181    2004.0
182    2004.0
183    2004.0
184    2005.0
185    2005.0
186    2005.0
187    2005.0
188    2006.0
189    2006.0
190    2006.0
191    2006.0
192    2007.0
193    2007.0
194    2007.0
195    2007.0
196    2008.0
197    2008.0
198    2008.0
199    2008.0
200    2009.0
201    2009.0
202    2009.0
Name: year, dtype: float64



In [53]:

    
data.quarter









    Out[53]:





0      1.0
1      2.0
2      3.0
3      4.0
4      1.0
5      2.0
6      3.0
7      4.0
8      1.0
9      2.0
10     3.0
11     4.0
12     1.0
13     2.0
14     3.0
15     4.0
16     1.0
17     2.0
18     3.0
19     4.0
20     1.0
21     2.0
22     3.0
23     4.0
24     1.0
25     2.0
26     3.0
27     4.0
28     1.0
29     2.0
      ... 
173    2.0
174    3.0
175    4.0
176    1.0
177    2.0
178    3.0
179    4.0
180    1.0
181    2.0
182    3.0
183    4.0
184    1.0
185    2.0
186    3.0
187    4.0
188    1.0
189    2.0
190    3.0
191    4.0
192    1.0
193    2.0
194    3.0
195    4.0
196    1.0
197    2.0
198    3.0
199    4.0
200    1.0
201    2.0
202    3.0
Name: quarter, dtype: float64



In [54]:

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









    Out[54]:





PeriodIndex(['1959Q1', '1959Q2', '1959Q3', '1959Q4', '1960Q1', '1960Q2',
             '1960Q3', '1960Q4', '1961Q1', '1961Q2',
             ...
             '2007Q2', '2007Q3', '2007Q4', '2008Q1', '2008Q2', '2008Q3',
             '2008Q4', '2009Q1', '2009Q2', '2009Q3'],
            dtype='period[Q-DEC]', length=203, freq='Q-DEC')



In [55]:

    
data.index = index
data.infl









    Out[55]:





1959Q1    0.00
1959Q2    2.34
1959Q3    2.74
1959Q4    0.27
1960Q1    2.31
1960Q2    0.14
1960Q3    2.70
1960Q4    1.21
1961Q1   -0.40
1961Q2    1.47
1961Q3    0.80
1961Q4    0.80
1962Q1    2.26
1962Q2    0.13
1962Q3    2.11
1962Q4    0.79
1963Q1    0.53
1963Q2    2.75
1963Q3    0.78
1963Q4    2.46
1964Q1    0.13
1964Q2    0.90
1964Q3    1.29
1964Q4    2.05
1965Q1    1.28
1965Q2    2.54
1965Q3    0.89
1965Q4    2.90
1966Q1    4.99
1966Q2    2.10
          ... 
2002Q2    1.56
2002Q3    2.66
2002Q4    3.08
2003Q1    1.31
2003Q2    1.09
2003Q3    2.60
2003Q4    3.02
2004Q1    2.35
2004Q2    3.61
2004Q3    3.58
2004Q4    2.09
2005Q1    4.15
2005Q2    1.85
2005Q3    9.14
2005Q4    0.40
2006Q1    2.60
2006Q2    3.97
2006Q3   -1.58
2006Q4    3.30
2007Q1    4.58
2007Q2    2.75
2007Q3    3.45
2007Q4    6.38
2008Q1    2.82
2008Q2    8.53
2008Q3   -3.16
2008Q4   -8.79
2009Q1    0.94
2009Q2    3.37
2009Q3    3.56
Freq: Q-DEC, Name: infl, dtype: float64

Resampling and Frequency Conversion



In [57]:

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









    Out[57]:





2000-01-31   -0.145038
2000-02-29   -0.048047
2000-03-31   -0.079677
2000-04-30   -0.222866
Freq: M, dtype: float64



In [58]:

    
ts.resample('M', kind='period').mean()









    Out[58]:





2000-01   -0.145038
2000-02   -0.048047
2000-03   -0.079677
2000-04   -0.222866
Freq: M, dtype: float64

Downsampling



In [59]:

    
rng = pd.date_range('1/1/2000', periods=12, freq='T')
ts = Series(np.arange(12), index=rng)
ts









    Out[59]:





2000-01-01 00:00:00     0
2000-01-01 00:01:00     1
2000-01-01 00:02:00     2
2000-01-01 00:03:00     3
2000-01-01 00:04:00     4
2000-01-01 00:05:00     5
2000-01-01 00:06:00     6
2000-01-01 00:07:00     7
2000-01-01 00:08:00     8
2000-01-01 00:09:00     9
2000-01-01 00:10:00    10
2000-01-01 00:11:00    11
Freq: T, dtype: int64



In [61]:

    
ts.resample('5min').sum()









    Out[61]:





2000-01-01 00:00:00    10
2000-01-01 00:05:00    35
2000-01-01 00:10:00    21
Freq: 5T, dtype: int64



In [62]:

    
ts.resample('5min', closed='left').sum()









    Out[62]:





2000-01-01 00:00:00    10
2000-01-01 00:05:00    35
2000-01-01 00:10:00    21
Freq: 5T, dtype: int64



In [63]:

    
ts.resample('5min', closed='left', label='left').sum()









    Out[63]:





2000-01-01 00:00:00    10
2000-01-01 00:05:00    35
2000-01-01 00:10:00    21
Freq: 5T, dtype: int64



In [64]:

    
ts.resample('5min', loffset='-1s').sum()









    Out[64]:





1999-12-31 23:59:59    10
2000-01-01 00:04:59    35
2000-01-01 00:09:59    21
Freq: 5T, dtype: int64

Open-High-Low-Close (OHLC) resampling



In [66]:

    
ts.resample('5min').ohlc()









    Out[66]:






  
    
      
      open
      high
      low
      close
    
  
  
    
      2000-01-01 00:00:00
      0
      4
      0
      4
    
    
      2000-01-01 00:05:00
      5
      9
      5
      9
    
    
      2000-01-01 00:10:00
      10
      11
      10
      11

Resampling with GroupBy



In [67]:

    
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()









    Out[67]:





1    15
2    45
3    75
4    95
dtype: int64



In [68]:

    
ts.groupby(lambda x: x.weekday).mean()









    Out[68]:





0    47.5
1    48.5
2    49.5
3    50.5
4    51.5
5    49.0
6    50.0
dtype: float64

Upsampling and interpolation



In [70]:

    
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



In [71]:

    
df_daily = frame.resample('D')
df_daily









    Out[71]:





DatetimeIndexResampler [freq=<Day>, axis=0, closed=left, label=left, convention=start, base=0]



In [73]:

    
frame.resample('D').ffill()



In [74]:

    
frame.resample('D').ffill(limit=2)



In [75]:

    
frame.resample('W-THU').ffill()

Resampling with periods



In [76]:

    
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]



In [78]:

    
annual_frame = frame.resample('A-DEC').mean()
annual_frame



In [79]:

    
annual_frame.resample('Q-DEC').ffill()



In [80]:

    
annual_frame.resample('Q-DEC', convention='start').ffill()



In [81]:

    
annual_frame.resample('Q-MAR').ffill()

Time series plotting



In [83]:

    
close_px_all = pd.read_csv('stock_px.csv', parse_dates=True, index_col=0)
close_px = close_px_all[['AAPL', 'MSFT', 'XOM']]
close_px = close_px.resample('B').ffill()
close_px.info()









    



<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2292 entries, 2003-01-02 to 2011-10-14
Freq: B
Data columns (total 3 columns):
AAPL    2292 non-null float64
MSFT    2292 non-null float64
XOM     2292 non-null float64
dtypes: float64(3)
memory usage: 71.6 KB



In [84]:

    
%matplotlib inline



In [85]:

    
close_px['AAPL'].plot()









    Out[85]:





<matplotlib.axes._subplots.AxesSubplot at 0x11659b9b0>



In [86]:

    
close_px.ix['2009'].plot()









    Out[86]:





<matplotlib.axes._subplots.AxesSubplot at 0x1166ec7f0>



In [87]:

    
close_px['AAPL'].ix['01-2011':'03-2011'].plot()









    Out[87]:





<matplotlib.axes._subplots.AxesSubplot at 0x11690ef60>



In [88]:

    
appl_q = close_px['AAPL'].resample('Q-DEC').ffill()
appl_q.ix['2009':].plot()









    Out[88]:





<matplotlib.axes._subplots.AxesSubplot at 0x116aa0fd0>

Moving window functions



In [89]:

    
close_px = close_px.asfreq('B').fillna(method='ffill')



In [97]:

    
close_px.AAPL.plot()
pd.rolling_mean(close_px.AAPL, 250).plot()









    



/Users/alexkirnas/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:2: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with 
	Series.rolling(window=250,center=False).mean()
  from ipykernel import kernelapp as app






    Out[97]:





<matplotlib.axes._subplots.AxesSubplot at 0x116edd908>



In [92]:

    
from matplotlib import pyplot as plt
plt.figure()









    Out[92]:





<matplotlib.figure.Figure at 0x116bcd630>






    





<matplotlib.figure.Figure at 0x116bcd630>



In [96]:

    
appl_std250 = pd.rolling_std(close_px.AAPL, 250, min_periods=10)
appl_std250[5:12]









    



/Users/alexkirnas/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:1: FutureWarning: pd.rolling_std is deprecated for Series and will be removed in a future version, replace with 
	Series.rolling(window=250,min_periods=10,center=False).std()
  if __name__ == '__main__':






    Out[96]:





2003-01-09         NaN
2003-01-10         NaN
2003-01-13         NaN
2003-01-14         NaN
2003-01-15    0.077496
2003-01-16    0.074760
2003-01-17    0.112368
Freq: B, Name: AAPL, dtype: float64



In [98]:

    
appl_std250.plot()









    Out[98]:





<matplotlib.axes._subplots.AxesSubplot at 0x116bf66a0>



In [100]:

    
expanding_mean = lambda x: rolling_mean(x, len(x), min_periods=1)
pd.rolling_mean(close_px, 60).plot(logy=True)









    



/Users/alexkirnas/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:2: FutureWarning: pd.rolling_mean is deprecated for DataFrame and will be removed in a future version, replace with 
	DataFrame.rolling(window=60,center=False).mean()
  from ipykernel import kernelapp as app






    Out[100]:





<matplotlib.axes._subplots.AxesSubplot at 0x117095320>



In [101]:

    
plt.close('all')

Exponentially-weighted functions



In [102]:

    
fig, axes = plt.subplots(nrows=2, ncols=1, sharex=True, sharey=True,
                         figsize=(12, 7))

aapl_px = close_px.AAPL['2005':'2009']

ma60 = pd.rolling_mean(aapl_px, 60, min_periods=50)
ewma60 = pd.ewma(aapl_px, span=60)

aapl_px.plot(style='k-', ax=axes[0])
ma60.plot(style='k--', ax=axes[0])
aapl_px.plot(style='k-', ax=axes[1])
ewma60.plot(style='k--', ax=axes[1])
axes[0].set_title('Simple MA')
axes[1].set_title('Exponentially-weighted MA')









    



/Users/alexkirnas/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:6: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with 
	Series.rolling(window=60,min_periods=50,center=False).mean()
/Users/alexkirnas/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:7: FutureWarning: pd.ewm_mean is deprecated for Series and will be removed in a future version, replace with 
	Series.ewm(span=60,min_periods=0,adjust=True,ignore_na=False).mean()






    Out[102]:





<matplotlib.text.Text at 0x1173c26a0>

Binary moving window functions



In [103]:

    
close_px
spx_px = close_px_all['SPX']



In [106]:

    
spx_rets = spx_px / spx_px.shift(1) - 1
returns = close_px.pct_change()
corr = pd.rolling_corr(returns.AAPL, spx_rets, 125, min_periods=100)
corr.plot()









    



/Users/alexkirnas/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:3: FutureWarning: pd.rolling_corr is deprecated for Series and will be removed in a future version, replace with 
	Series.rolling(window=125,min_periods=100).corr(other=<Series>)
  app.launch_new_instance()






    Out[106]:





<matplotlib.axes._subplots.AxesSubplot at 0x11758f080>



In [107]:

    
corr = pd.rolling_corr(returns, spx_rets, 125, min_periods=100)
corr.plot()









    



/Users/alexkirnas/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:1: FutureWarning: pd.rolling_corr is deprecated for DataFrame and will be removed in a future version, replace with 
	DataFrame.rolling(window=125,min_periods=100).corr(other=<Series>)
  if __name__ == '__main__':






    Out[107]:





<matplotlib.axes._subplots.AxesSubplot at 0x117a026a0>

User-defined moving window functions



In [108]:

    
from scipy.stats import percentileofscore
score_at_2percent = lambda x: percentileofscore(x, 0.02)
result = pd.rolling_apply(returns.AAPL, 250, score_at_2percent)
result.plot()









    



/Users/alexkirnas/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:3: FutureWarning: pd.rolling_apply is deprecated for Series and will be removed in a future version, replace with 
	Series.rolling(window=250,center=False).apply(func=<function>,args=<tuple>,kwargs=<dict>)
  app.launch_new_instance()






    Out[108]:





<matplotlib.axes._subplots.AxesSubplot at 0x119222198>

Performance and Memory Usage Notes



In [110]:

    
rng = pd.date_range('1/1/2000', periods=10000000, freq='10ms')
ts = Series(np.random.randn(len(rng)), index=rng)
ts









    Out[110]:





2000-01-01 00:00:00.000   -0.261426
2000-01-01 00:00:00.010    1.447974
2000-01-01 00:00:00.020    1.282878
2000-01-01 00:00:00.030   -0.743761
2000-01-01 00:00:00.040   -0.233302
2000-01-01 00:00:00.050   -0.073649
2000-01-01 00:00:00.060    0.416794
2000-01-01 00:00:00.070    3.401725
2000-01-01 00:00:00.080    1.999578
2000-01-01 00:00:00.090    1.831277
2000-01-01 00:00:00.100   -2.666666
2000-01-01 00:00:00.110   -0.117959
2000-01-01 00:00:00.120   -1.252543
2000-01-01 00:00:00.130    1.352375
2000-01-01 00:00:00.140   -0.279760
2000-01-01 00:00:00.150   -1.250280
2000-01-01 00:00:00.160    0.366356
2000-01-01 00:00:00.170    1.402564
2000-01-01 00:00:00.180   -0.183119
2000-01-01 00:00:00.190    0.767696
2000-01-01 00:00:00.200    0.763645
2000-01-01 00:00:00.210   -0.570289
2000-01-01 00:00:00.220   -1.304732
2000-01-01 00:00:00.230   -0.740207
2000-01-01 00:00:00.240    1.190865
2000-01-01 00:00:00.250   -0.467620
2000-01-01 00:00:00.260    1.351668
2000-01-01 00:00:00.270    0.996540
2000-01-01 00:00:00.280    0.510814
2000-01-01 00:00:00.290    1.415786
                             ...   
2000-01-02 03:46:39.700   -2.141464
2000-01-02 03:46:39.710   -0.414348
2000-01-02 03:46:39.720   -1.203861
2000-01-02 03:46:39.730    0.532026
2000-01-02 03:46:39.740    0.088419
2000-01-02 03:46:39.750    0.453416
2000-01-02 03:46:39.760    0.698670
2000-01-02 03:46:39.770    0.269576
2000-01-02 03:46:39.780   -0.199871
2000-01-02 03:46:39.790   -0.333376
2000-01-02 03:46:39.800   -0.787558
2000-01-02 03:46:39.810    1.954365
2000-01-02 03:46:39.820   -0.972755
2000-01-02 03:46:39.830    0.716378
2000-01-02 03:46:39.840    0.567826
2000-01-02 03:46:39.850   -0.238453
2000-01-02 03:46:39.860    0.096672
2000-01-02 03:46:39.870   -0.051460
2000-01-02 03:46:39.880    0.351672
2000-01-02 03:46:39.890    0.769636
2000-01-02 03:46:39.900   -1.209181
2000-01-02 03:46:39.910   -0.645599
2000-01-02 03:46:39.920   -1.039245
2000-01-02 03:46:39.930   -0.524349
2000-01-02 03:46:39.940   -1.805458
2000-01-02 03:46:39.950    0.146402
2000-01-02 03:46:39.960    0.898881
2000-01-02 03:46:39.970   -0.357954
2000-01-02 03:46:39.980    0.572494
2000-01-02 03:46:39.990    0.464001
Freq: 10L, dtype: float64



In [111]:

    
ts.resample('15min', how='ohlc').info()









    



<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 112 entries, 2000-01-01 00:00:00 to 2000-01-02 03:45:00
Freq: 15T
Data columns (total 4 columns):
open     112 non-null float64
high     112 non-null float64
low      112 non-null float64
close    112 non-null float64
dtypes: float64(4)
memory usage: 4.4 KB






    



/Users/alexkirnas/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:1: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...).ohlc()
  if __name__ == '__main__':



In [113]:

    
%timeit ts.resample('15min').ohlc()









    



10 loops, best of 3: 105 ms per loop



In [114]:

    
rng = pd.date_range('1/1/2000', periods=10000000, freq='1s')
ts = Series(np.random.randn(len(rng)), index=rng)
%timeit ts.resample('15s').ohlc()









    



1 loop, best of 3: 134 ms per loop



In [ ]:

	Colorado	Texas	New York	Ohio
2000-01-05	-1.406035	-1.478801	0.392373	-0.657052
2000-01-12	1.500412	0.348431	0.290324	2.157489

	Colorado	Texas	New York	Ohio
2000-01-05	-1.406035	-1.478801	0.392373	-0.657052
2000-01-06	-1.406035	-1.478801	0.392373	-0.657052
2000-01-07	-1.406035	-1.478801	0.392373	-0.657052
2000-01-08	-1.406035	-1.478801	0.392373	-0.657052
2000-01-09	-1.406035	-1.478801	0.392373	-0.657052
2000-01-10	-1.406035	-1.478801	0.392373	-0.657052
2000-01-11	-1.406035	-1.478801	0.392373	-0.657052
2000-01-12	1.500412	0.348431	0.290324	2.157489

	Colorado	Texas	New York	Ohio
2000-01-05	-1.406035	-1.478801	0.392373	-0.657052
2000-01-06	-1.406035	-1.478801	0.392373	-0.657052
2000-01-07	-1.406035	-1.478801	0.392373	-0.657052
2000-01-08	NaN	NaN	NaN	NaN
2000-01-09	NaN	NaN	NaN	NaN
2000-01-10	NaN	NaN	NaN	NaN
2000-01-11	NaN	NaN	NaN	NaN
2000-01-12	1.500412	0.348431	0.290324	2.157489

	Colorado	Texas	New York	Ohio
2000-01-06	-1.406035	-1.478801	0.392373	-0.657052
2000-01-13	1.500412	0.348431	0.290324	2.157489

	Colorado	Texas	New York	Ohio
2000-01	2.208283	0.473251	0.684872	-0.408695
2000-02	-0.583126	-0.655351	-0.289822	0.392892
2000-03	0.388789	1.221921	2.001440	0.783503
2000-04	0.754020	-0.986099	0.183046	-0.741675
2000-05	-0.518199	-0.941237	-0.182994	0.533555

	open	high	low	close
2000-01-01 00:00:00	0	4	0	4
2000-01-01 00:05:00	5	9	5	9
2000-01-01 00:10:00	10	11	10	11

	Colorado	Texas	New York	Ohio
2000	-0.175203	0.198798	0.365745	-0.084970
2001	-0.008988	-0.127339	0.138357	-0.213611

	Colorado	Texas	New York	Ohio
2000Q1	-0.175203	0.198798	0.365745	-0.084970
2000Q2	-0.175203	0.198798	0.365745	-0.084970
2000Q3	-0.175203	0.198798	0.365745	-0.084970
2000Q4	-0.175203	0.198798	0.365745	-0.084970
2001Q1	-0.008988	-0.127339	0.138357	-0.213611
2001Q2	-0.008988	-0.127339	0.138357	-0.213611
2001Q3	-0.008988	-0.127339	0.138357	-0.213611
2001Q4	-0.008988	-0.127339	0.138357	-0.213611