[6]: dates Out[6]: DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-06'], dtype='datetime64[ns]', freq='D') In [7]: df = pd.DataFrame(np.random.randn(6, 4) 2013-01-03 -1.323650 0.427355 0.835343 -0.000698 2013-01-04 0.509859 -2.769586 1.000521 -0.865748 2013-01-05 0.139488 -0.259328 1.082034 -0.902452 2013-01-06 -0.130327 -0.372906 1.072236 -0.424347 Creating 769586 1.000521 -0.865748 2013-01-05 0.139488 -0.259328 1.082034 -0.902452 In [14]: df.tail(3) Out[14]: A B C D 2013-01-04 0.509859 -2.769586 1.000521 -0.865748 2013-01-05 0.139488 -0.259328 1.082034 -0

0.781836 -1.071357 0.441153 2000-01-03 2.353925 0.583787 0.221471 2000-01-04 NaN NaN NaN 2000-01-05 NaN NaN NaN 2000-01-06 NaN NaN NaN 2000-01-07 NaN NaN NaN 2000-01-08 0.901805 1.171216 0.520260 2.353925 3.550996 0.583787 1.655143 0.221471 1.504252 2000-01-04 NaN NaN NaN NaN NaN NaN 2000-01-05 NaN NaN NaN NaN NaN NaN 2000-01-06 NaN NaN NaN NaN NaN NaN 2000-01-07 NaN NaN NaN NaN NaN NaN 2000-01-08 Timestamp('20160101', tz='US/Eastern'), pd.Timestamp('20160101', tz='US/Eastern')])) Out[6]: array(['2016-01-01T05:00:00.000000000'], dtype='datetime64[ns]') # Index In [7]: pd.Index([pd.Timestamp('20160101', tz='US/Eastern')

0.781836 -1.071357 0.441153 2000-01-03 2.353925 0.583787 0.221471 2000-01-04 NaN NaN NaN 2000-01-05 NaN NaN NaN 2000-01-06 NaN NaN NaN 2000-01-07 NaN NaN NaN 2000-01-08 0.901805 1.171216 0.520260 2.353925 3.550996 0.583787 1.655143 0.221471 1.504252 2000-01-04 NaN NaN NaN NaN NaN NaN 2000-01-05 NaN NaN NaN NaN NaN NaN 2000-01-06 NaN NaN NaN NaN NaN NaN 2000-01-07 NaN NaN NaN NaN NaN NaN 2000-01-08 Timestamp('20160101', tz='US/Eastern'), pd.Timestamp('20160101', tz='US/Eastern')])) Out[6]: array(['2016-01-01T05:00:00.000000000'], dtype='datetime64[ns]') # Index In [7]: pd.Index([pd.Timestamp('20160101', tz='US/Eastern')

02:00 NaN NaN NaN NaN 2000-01-01 03:00 NaN NaN NaN NaN 2000-01-01 04:00 NaN NaN NaN NaN 2000-01-01 05:00 NaN NaN NaN NaN 2000-01-01 06:00 NaN NaN NaN NaN ... ... ... ... ... 2000-01-10 17:00 NaN NaN 603650 0.567011 -0.994009 2000-01-03 -2.230893 -1.635263 0.357573 2000-01-04 NaN NaN NaN 2000-01-05 NaN NaN NaN 2000-01-06 NaN NaN NaN 2000-01-07 NaN NaN NaN 2000-01-08 1.667624 1.619575 -0.948507 2.230893 0.198409 1.635263 0.000000 0.357573 1.351583 2000-01-04 NaN NaN NaN NaN NaN NaN 2000-01-05 NaN NaN NaN NaN NaN NaN 2000-01-06 NaN NaN NaN NaN NaN NaN 2000-01-07 NaN NaN NaN NaN NaN NaN 2000-01-08

quantity 0 2016-05-25 13:30:00.023 MSFT 51.95 75 1 2016-05-25 13:30:00.038 MSFT 51.95 155 2 2016-05-25 13:30:00.048 GOOG 720.77 100 3 2016-05-25 13:30:00.048 GOOG 720.92 100 4 2016-05-25 13:30:00.048 ask 0 2016-05-25 13:30:00.023 GOOG 720.50 720.93 1 2016-05-25 13:30:00.023 MSFT 51.95 51.96 2 2016-05-25 13:30:00.030 MSFT 51.97 51.98 3 2016-05-25 13:30:00.041 MSFT 51.99 52.00 4 2016-05-25 13:30:00 13:30:00.048 GOOG 720.50 720.93 5 2016-05-25 13:30:00.049 AAPL 97.99 98.01 6 2016-05-25 13:30:00.072 GOOG 720.50 720.88 7 2016-05-25 13:30:00.075 MSFT 52.01 52.03 An asof merge joins on the on, typically

quantity 0 2016-05-25 13:30:00.023 MSFT 51.95 75 1 2016-05-25 13:30:00.038 MSFT 51.95 155 2 2016-05-25 13:30:00.048 GOOG 720.77 100 3 2016-05-25 13:30:00.048 GOOG 720.92 100 4 2016-05-25 13:30:00.048 In [10]: quotes Out[10]: time ticker bid ask 0 2016-05-25 13:30:00.023 GOOG 720.50 720.93 1 2016-05-25 13:30:00.023 MSFT 51.95 51.96 2 2016-05-25 13:30:00.030 MSFT 51.97 51.98 6 Chapter 1. What’s 0 3 2016-05-25 13:30:00.041 MSFT 51.99 52.00 4 2016-05-25 13:30:00.048 GOOG 720.50 720.93 5 2016-05-25 13:30:00.049 AAPL 97.99 98.01 6 2016-05-25 13:30:00.072 GOOG 720.50 720.88 7 2016-05-25 13:30:00

Workers’ Day so let’s # add that for a couple of years In [43]: holidays = [’2012-05-01’, datetime(2013, 5, 1), np.datetime64(’2014-05-01’)] In [44]: bday_egypt = CustomBusinessDay(holidays=holidays, weekmask=weekmask_egypt) weekmask=weekmask_egypt) In [45]: dt = datetime(2013, 4, 30) In [46]: print dt + 2 * bday_egypt 2013-05-05 00:00:00 In [47]: dts = date_range(dt, periods=5, freq=bday_egypt).to_series() In [48]: print Series(dts dts).map(Series(’Mon Tue Wed Thu Fri Sat Sun’.split())) 2013-04-30 Tue 2013-05-02 Thu 2013-05-05 Sun 2013-05-06 Mon 2013-05-07 Tue dtype: object 10 Chapter 1. What’s New pandas: powerful Python data

station_london datetime 2019-05-07 02:00:00 NaN NaN 23.0 2019-05-07 03:00:00 50.5 25.0 19.0 2019-05-07 04:00:00 45.0 27.7 19.0 2019-05-07 05:00:00 NaN 50.4 16.0 2019-05-07 06:00:00 NaN 61.9 NaN Note: station_london datetime 2019-05-07 02:00:00 NaN NaN 23.0 2019-05-07 03:00:00 50.5 25.0 19.0 2019-05-07 04:00:00 45.0 27.7 19.0 2019-05-07 05:00:00 NaN 50.4 16.0 2019-05-07 06:00:00 NaN 61.9 NaN How ˓→ 2019-05-07 02:00:00 NaN NaN 23.0 43. ˓→286 2019-05-07 03:00:00 50.5 25.0 19.0 35. ˓→758 2019-05-07 04:00:00 45.0 27.7 19.0 35. ˓→758 2019-05-07 05:00:00 NaN 50.4 16.0 30. ˓→112 2019-05-07 06:00:00

station_london datetime 2019-05-07 02:00:00 NaN NaN 23.0 2019-05-07 03:00:00 50.5 25.0 19.0 2019-05-07 04:00:00 45.0 27.7 19.0 2019-05-07 05:00:00 NaN 50.4 16.0 2019-05-07 06:00:00 NaN 61.9 NaN Note: station_london datetime 2019-05-07 02:00:00 NaN NaN 23.0 2019-05-07 03:00:00 50.5 25.0 19.0 2019-05-07 04:00:00 45.0 27.7 19.0 2019-05-07 05:00:00 NaN 50.4 16.0 2019-05-07 06:00:00 NaN 61.9 NaN How ˓→ 2019-05-07 02:00:00 NaN NaN 23.0 43. ˓→286 2019-05-07 03:00:00 50.5 25.0 19.0 35. ˓→758 2019-05-07 04:00:00 45.0 27.7 19.0 35. ˓→758 2019-05-07 05:00:00 NaN 50.4 16.0 30. ˓→112 2019-05-07 06:00:00

0 2013-01-01 2013-01-01 00:00:00-05:00 2013-01-01 00:00:00+01:00 1 2013-01-02 2013-01-02 00:00:00-05:00 2013-01-02 00:00:00+01:00 2 2013-01-03 2013-01-03 00:00:00-05:00 2013-01-03 00:00:00+01:00 In [3]: datetime64[ns, CET] dtype: object In [4]: df.B Out[4]: 0 2013-01-01 00:00:00-05:00 1 2013-01-02 00:00:00-05:00 2 2013-01-03 00:00:00-05:00 Name: B, dtype: datetime64[ns, US/Eastern] In [5]: df.B.dt.tz_localize(None) periods=3,tz='US/Eastern') Out[1]: DatetimeIndex(['2013-01-01 00:00:00-05:00', '2013-01-02 00:00:00-05:00', '2013-01-03 00:00:00-05:00'], dtype='datetime64[ns]', freq='D', tz='US/Eastern') In [2]: pd.