of an array is ambiguous. Use a.empty, a.any() or a. ˓→all(). See gotchas for a more detailed discussion. Comparing if objects are equivalent Often you may find that there is more than one way to compute indexables. Data is ordered (on the disk) in terms of the indexables. Here’s a simple use case. You store panel-type data, with dates in the major_axis and ids in the minor_axis. The data is then interleaved like when using numpy ufuncs such as numpy.logical_and. See the this old issue for a more detailed discussion. {{ header }} 3.3. MultiIndex / advanced indexing 425 pandas: powerful Python data analysis toolkit

Supporting services Harbor Packaging Docker Kubernetes Cloud Foundry Deep dive Harbor through panel discussion! Q1: What other features harbor should provide? Q2: How does the Harbor architecture evolve

DataFrame column attribute access and IPython completion . . . . . . . . . . . . . . . . . 433 9.3 Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434 9.3.3 From DataFrame using to_panel method . . . . . . . . . . . . . . . . . . . . . . . . . . 435 9.3.4 Item selection / addition / Conversion to DataFrame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437 9.4 Panel4D and PanelND (Deprecated) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438

DataFrame column attribute access and IPython completion . . . . . . . . . . . . . . . . . 434 9.3 Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435 9.3.3 From DataFrame using to_panel method . . . . . . . . . . . . . . . . . . . . . . . . . . 437 9.3.4 Item selection / addition / Conversion to DataFrame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439 9.4 Panel4D and PanelND (Deprecated) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439

ix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 1.3.4.2 Deprecate Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 1.3.4.3 Deprecate groupby DataFrame column attribute access and IPython completion . . . . . . . . . . . . . . . . . 481 8.3 Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 482 8.3.3 From DataFrame using to_panel method . . . . . . . . . . . . . . . . . . . . . . . . . . 484 8.3.4 Item selection / addition /

ix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 1.2.4.2 Deprecate Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 1.2.4.3 Deprecate groupby DataFrame column attribute access and IPython completion . . . . . . . . . . . . . . . . . 479 8.3 Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480 8.3.3 From DataFrame using to_panel method . . . . . . . . . . . . . . . . . . . . . . . . . . 482 8.3.4 Item selection / addition /

data with missing values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1808 3.6 Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . of an array is ambiguous. Use a.empty, a.any() or a. ˓→all(). See gotchas for a more detailed discussion. Comparing if objects are equivalent Often you may find that there is more than one way to compute indexables. Data is ordered (on the disk) in terms of the indexables. Here’s a simple use case. You store panel-type data, with dates in the major_axis and ids in the minor_axis. The data is then interleaved like

data with missing values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1811 3.6 Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . of an array is ambiguous. Use a.empty, a.any() or a. ˓→all(). See gotchas for a more detailed discussion. Comparing if objects are equivalent Often you may find that there is more than one way to compute indexables. Data is ordered (on the disk) in terms of the indexables. Here’s a simple use case. You store panel-type data, with dates in the major_axis and ids in the minor_axis. The data is then interleaved like

data with missing values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1884 3.6 Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . of an array is ambiguous. Use a.empty, a.any() or a. ˓→all(). See gotchas for a more detailed discussion. Comparing if objects are equivalent Often you may find that there is more than one way to compute indexables. Data is ordered (on the disk) in terms of the indexables. Here’s a simple use case. You store panel-type data, with dates in the major_axis and ids in the minor_axis. The data is then interleaved like

data with missing values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1884 3.6 Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . of an array is ambiguous. Use a.empty, a.any() or a. ˓→all(). See gotchas for a more detailed discussion. Comparing if objects are equivalent Often you may find that there is more than one way to compute indexables. Data is ordered (on the disk) in terms of the indexables. Here’s a simple use case. You store panel-type data, with dates in the major_axis and ids in the minor_axis. The data is then interleaved like