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An overview of the numpy library. numpy is an abbreviation for numeric Python and is based around the ndarray class. The ndarray class is a datastructure which bridges the numeric datamodel previously explored in the int, float, bool and complex classes alongside the collection based datamodel previously explored in the tuple and list. The ndarray is the foundational data structure used for data science.

0:00:00 builtins datatypes
0:02:45 importing numpy
0:04:15 numpy library overview
0:06:32 ndarray overview
0:07:16 ndarray instantiation and attributes
0:13:28 ndarray constructors (array, empty, zeros, ones, full)
0:16:40 rows and columns
0:23:13 ndarray constructors from prototype array (empty_like, zeros_like, ones_like, full_like)
0:25:40 visualising higher dimensional arrays
0:29:55 constructing higher dimensional arrays
0:33:43 shape
0:35:37 flattening
0:38:58 indexing
0:42:32 collection based identifiers
0:49:47 broadcasting arrays (broadcast, broadcast_to)
0:58:30 sorting (sort, argsort)
1:06:02 ndarray constructors (arange, linspace and logspace)
1:08:17 avoiding naming conflicts with builtins
1:14:56 grids (ogrid and meshgrid)
1:18:55 numeric operators
1:25:35 datatypes, rolling over and overflow
1:28:44 boolean identifiers
1:33:45 set-like identifiers
1:37:11 statistical identifiers
1:57:32 vectorized math identifiers
2:09:25 numpy random module
2:18:39 datetime64 and timedelta64
2:24:50 numpy linalg module
2:32:00 scipy library api

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