duration of the timedelta in seconds. See here • Period and PeriodIndex can handle multiplied freq like 3D, which corresponding to 3 days span. See here • Development installed versions of pandas will now freq='3D') 8 Chapter 1. What’s New pandas: powerful Python data analysis toolkit, Release 0.17.0 In [23]: p Out[23]: Period('2015-08-01', '3D') In [24]: p + 1 Out[24]: Period('2015-08-04', '3D') In 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') Out[27]: Timestamp('2015-08-03 00:00:00') You can

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433 9.3.1 From 3D ndarray with optional axis labels . . . . . . . . . . . . . . . . . . . . . . . . . . . 434 9.3.2 duration of the timedelta in seconds. See here • Period and PeriodIndex can handle multiplied freq like 3D, which corresponding to 3 days span. See here • Development installed versions of pandas will now Period('2015-08-01', freq='3D') In [23]: p Out[23]: 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()

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435 9.3.1 From 3D ndarray with optional axis labels . . . . . . . . . . . . . . . . . . . . . . . . . . . 435 9.3.2 duration of the timedelta in seconds. See here • Period and PeriodIndex can handle multiplied freq like 3D, which corresponding to 3 days span. See here • Development installed versions of pandas will now Period('2015-08-01', freq='3D') In [23]: p Out[23]: 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()

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481 8.3.1 From 3D ndarray with optional axis labels . . . . . . . . . . . . . . . . . . . . . . . . . . . 482 8.3.2 duration of the timedelta in seconds. See here • Period and PeriodIndex can handle multiplied freq like 3D, which corresponding to 3 days span. See here • Development installed versions of pandas will now = pd.Period('2015-08-01', freq='3D') In [23]: p Out[23]: Period('2015-08-01', '3D') In [24]: p + 1 \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\Out[24]: Period('2015-08-04', '3D') In [25]: p - 2 \\\\\\\\\\\\

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 479 8.3.1 From 3D ndarray with optional axis labels . . . . . . . . . . . . . . . . . . . . . . . . . . . 480 8.3.2 duration of the timedelta in seconds. See here • Period and PeriodIndex can handle multiplied freq like 3D, which corresponding to 3 days span. See here • Development installed versions of pandas will now = pd.Period('2015-08-01', freq='3D') In [23]: p Out[23]: Period('2015-08-01', '3D') In [24]: p + 1 \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\Out[24]: Period('2015-08-04', '3D') In [25]: p - 2 \\\\\\\\\\\\

resample method on TimeSeries and DataFrame objects (multiple time series). resample also works on panels (3D). Here is some code that resamples daily data to montly with scikits.timeseries: In [17]: import scikits labeled, size-mutable tabular structure with potentially heterogeneously-typed columns 3 Panel General 3D labeled, also size-mutable array 4.1.1 Why more than 1 data structure? The best way to think about expect: 7.3. Panel 123 pandas: powerful Python data analysis toolkit, Release 0.12.0 7.3.1 From 3D ndarray with optional axis labels In [104]: wp = Panel(randn(2, 5, 4), items=[’Item1’, ’Item2’],

columns of each of the DataFrames Construction of Panels works about like you would expect: From 3D ndarray with optional axis labels In [124]: wp = pd.Panel(np.random.randn(2, 5, 4), items=['Item1' frequency alias like below. Because freq represents a span of Period, it cannot be negative like “-3D”. In [321]: pd.Period('2012', freq='A-DEC') Out[321]: Period('2012', 'A-DEC') In [322]: pd.Period('2012-1-1' Notebook. 5.2.6 Plotly Plotly’s Python API enables interactive figures and web shareability. Maps, 2D, 3D, and live-streaming graphs are rendered with WebGL and D3.js. The library supports plotting directly

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 509 8.3.1 From 3D ndarray with optional axis labels . . . . . . . . . . . . . . . . . . . . . . . . . . . 510 8.3.2 duration of the timedelta in seconds. See here • Period and PeriodIndex can handle multiplied freq like 3D, which corresponding to 3 days span. See here • Development installed versions of pandas will now = pd.Period('2015-08-01', freq='3D') In [23]: p Out[23]: Period('2015-08-01', '3D') In [24]: p + 1 \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\Out[24]: Period('2015-08-04', '3D') In [25]: p - 2 \\\\\\\\\\\\

labeled, size-mutable tabular structure with potentially heterogeneously-typed columns 3 Panel General 3D labeled, also size-mutable array 4.1.1 Why more than 1 data structure? The best way to think about columns of each of the DataFrames Construction of Panels works about like you would expect: 5.3.1 From 3D ndarray with optional axis labels In [324]: wp = Panel(randn(2, 5, 4), items=[’Item1’, ’Item2’],

labeled, size-mutable tabular structure with potentially heterogeneously-typed columns 3 Panel General 3D labeled, also size-mutable array 4.1.1 Why more than 1 data structure? The best way to think about columns of each of the DataFrames Construction of Panels works about like you would expect: 5.3.1 From 3D ndarray with optional axis labels In [324]: wp = Panel(randn(2, 5, 4), items=[’Item1’, ’Item2’],