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 21 pandas: powerful Python data analysis toolkit, Release 0.7.1 PyData uses a shared copyright model. Each contributor maintains copyright over their contributions to PyData. However, it is important 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 21 pandas: powerful Python data analysis toolkit, Release 0.7.2 PyData uses a shared copyright model. Each contributor maintains copyright over their contributions to PyData. However, it is important 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 25 pandas: powerful Python data analysis toolkit, Release 0.7.3 PyData uses a shared copyright model. Each contributor maintains copyright over their contributions to PyData. However, it is important 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’],

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 \\\\\\\\\\\\

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 \\\\\\\\\\\\

============================================================================== Dep. Variable: hr R-squared: 0.685 Model: OLS Adj. R-squared: 0.665 Method: Least Squares F-statistic: 34.28 Date: Fri, 25 Jan 2019 Prob 16:28:07 Log-Likelihood: -205.92 No. Observations: 68 AIC: 421.8 Df Residuals: 63 BIC: 432.9 Df Model: 4 Covariance Type: nonrobust =============================================================================== 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'