2000-01-04 -0.646 -1.051 -0.716 2000-01-05 0.613 0.501 -1.380 2000-01-06 0.624 0.790 0.818 2000-01-07 1.559 0.335 0.919 2000-01-10 -1.381 0.365 -1.811 2000-01-11 -0.673 -1.968 -0.401 2000-01-12 -0.583 553 -0.419 2000-01-04 0 -0.405 -0.070 2000-01-05 0 -0.112 -1.993 2000-01-06 0 0.166 0.195 2000-01-07 0 -1.224 -0.640 2000-01-10 0 1.746 -0.429 2000-01-11 0 -1.294 0.272 2000-01-12 0 -0.416 -0.046 553 -0.419 2000-01-04 0 -0.405 -0.070 2000-01-05 0 -0.112 -1.993 2000-01-06 0 0.166 0.195 2000-01-07 0 -1.224 -0.640 2000-01-10 0 1.746 -0.429 2000-01-11 0 -1.294 0.272 2000-01-12 0 -0.416 -0.046

2000-01-04 -0.646 -1.051 -0.716 2000-01-05 0.613 0.501 -1.380 2000-01-06 0.624 0.790 0.818 2000-01-07 1.559 0.335 0.919 2000-01-10 -1.381 0.365 -1.811 2000-01-11 -0.673 -1.968 -0.401 2000-01-12 -0.583 553 -0.419 2000-01-04 0 -0.405 -0.070 2000-01-05 0 -0.112 -1.993 2000-01-06 0 0.166 0.195 2000-01-07 0 -1.224 -0.640 2000-01-10 0 1.746 -0.429 2000-01-11 0 -1.294 0.272 2000-01-12 0 -0.416 -0.046 553 -0.419 2000-01-04 0 -0.405 -0.070 2000-01-05 0 -0.112 -1.993 2000-01-06 0 0.166 0.195 2000-01-07 0 -1.224 -0.640 2000-01-10 0 1.746 -0.429 2000-01-11 0 -1.294 0.272 2000-01-12 0 -0.416 -0.046

2000-01-04 -0.646 -1.051 -0.716 2000-01-05 0.613 0.501 -1.380 2000-01-06 0.624 0.790 0.818 2000-01-07 1.559 0.335 0.919 2000-01-10 -1.381 0.365 -1.811 2000-01-11 -0.673 -1.968 -0.401 2000-01-12 -0.583 553 -0.419 2000-01-04 0 -0.405 -0.070 2000-01-05 0 -0.112 -1.993 2000-01-06 0 0.166 0.195 2000-01-07 0 -1.224 -0.640 2000-01-10 0 1.746 -0.429 2000-01-11 0 -1.294 0.272 2000-01-12 0 -0.416 -0.046 553 -0.419 2000-01-04 0 -0.405 -0.070 2000-01-05 0 -0.112 -1.993 2000-01-06 0 0.166 0.195 2000-01-07 0 -1.224 -0.640 2000-01-10 0 1.746 -0.429 2000-01-11 0 -1.294 0.272 2000-01-12 0 -0.416 -0.046

powerful Python data analysis toolkit Release 0.20.3 Wes McKinney & PyData Development Team Jul 07, 2017 CONTENTS 1 What’s New 3 1.1 v0.20.3 (July 7, 2017) . . . . . . . . . . . . . . . . . . . pandas: powerful Python data analysis toolkit, Release 0.20.3 PDF Version Zipped HTML Date: Jul 07, 2017 Version: 0.20.3 Binary Installers: http://pypi.python.org/pypi/pandas Source Repository: http://github 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 2000-01-09 -1.197071 -1.066969 -0.303421 2000-01-10

In [46]: s1 Out[46]: 2013-01-02 1 2013-01-03 2 2013-01-04 3 2013-01-05 4 2013-01-06 5 2013-01-07 6 Freq: D, dtype: int64 In [47]: df['F'] = s1 Setting values by label: In [48]: df.at[dates[0] Release 0.25.3 (continued from previous page) In [113]: ts Out[113]: 2012-03-06 0.198218 2012-03-07 -0.649384 2012-03-08 0.743513 2012-03-09 -0.541289 2012-03-10 -0.594013 Freq: D, dtype: float64 ts_utc = ts.tz_localize('UTC') In [115]: ts_utc Out[115]: 2012-03-06 00:00:00+00:00 0.198218 2012-03-07 00:00:00+00:00 -0.649384 2012-03-08 00:00:00+00:00 0.743513 2012-03-09 00:00:00+00:00 -0.541289

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 2000-01-09 -1.197071 -1.066969 -0.303421 2000-01-10 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 0.901805 2.098877 1.171216 2.242573 0.520260 1.803042 2000-01-09 df.tail() Out[110]: bar A B foo 2016-04-05 0.640880 0.126205 2016-04-06 0.171465 0.737086 2016-04-07 0.127029 0.369650 2016-04-08 0.604334 0.103104 2016-04-09 0.802374 0.945553 1.2. v0.20.1 (May 5

-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 2000-01-09 -0.360596 1.412609 -0.398833 2000-01-10 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 1.667624 4.096925 1.619575 3.254838 0.948507 0.045502 2000-01-09 df.tail() Out[110]: bar A B foo 2016-04-05 0.640880 0.126205 2016-04-06 0.171465 0.737086 2016-04-07 0.127029 0.369650 2016-04-08 0.604334 0.103104 2016-04-09 0.802374 0.945553 Previous Behavior:

Period('2015-08-01', '3D') In [24]: p + 1 Out[24]: Period('2015-08-04', '3D') In [25]: p - 2 Out[25]: Period('2015-07-26', '3D') In [26]: p.to_timestamp() Out[26]: Timestamp('2015-08-01 00:00:00') In [27]: p.to_timestamp(how='E') 8-01', '2015-08-03', '2015-08-05', '2015-08-07'], dtype='int64', freq='2D') In [30]: idx + 1 Out[30]: PeriodIndex(['2015-08-03', '2015-08-05', '2015-08-07', '2015-08-09'], dtype='int64', freq='2D') Support Previous Behavior: In [2]: pd.to_datetime(['2009-07-31', 'asd']) Out[2]: array(['2009-07-31', 'asd'], dtype=object) New Behavior: In [3]: pd.to_datetime(['2009-07-31', 'asd']) ValueError: Unknown string format

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 analysis toolkit, Release 0.060336 -0.434942 2001-01-03 00:00:00 2001-01-06 -0.494305 0.737973 2001-01-03 00:00:00 2001-01-07 0.451632 0.334124 2001-01-03 00:00:00 # datetime64[ns] out of the box In [39]: df.get_dtype_counts() 643870 NaT 2001-01-05 NaN -0.434942 NaT 2001-01-06 -0.494305 0.737973 2001-01-03 00:00:00 2001-01-07 0.451632 0.334124 2001-01-03 00:00:00 Astype conversion on datetime64[ns] to object, implicity converts

Out[13]: group val group date 1 2016-01-03 1 5 2016-01-04 1 5 2016-01-05 1 5 2016-01-06 1 5 2016-01-07 1 5 2016-01-08 1 5 2016-01-09 1 5 ... ... ... 1.2. v0.18.1 (May 3, 2016) 45 pandas: powerful Python Out[14]: group val group date 1 2016-01-03 1 5 2016-01-04 1 5 2016-01-05 1 5 2016-01-06 1 5 2016-01-07 1 5 2016-01-08 1 5 2016-01-09 1 5 ... ... ... 2 2016-01-18 2 7 2016-01-19 2 7 2016-01-20 2 7 615396 0.075381 0.368824 2010-01-01 09:00:06 0.933140 0.651378 0.397203 0.788730 2010-01-01 09:00:07 0.316836 0.568099 0.869127 0.436173 2010-01-01 09:00:08 0.802148 0.143767 0.704261 0.704581 2010-01-01