In particular, pandas conventions regarding division by zero differ from numpy in the following ways: 1) np.array([-1, 0, 1], dtype=dtype1) // np.array([0, 0, 0], dtype=dtype2) gives [nan, nan, nan] for most dtype combinations, and [0, 0, 0] for the remaining pairs (the remaining being dtype1==dtype2==intN and dtype==dtype2==uintN).
pandas convention is to return [-inf, nan, inf] for all dtype combinations.
Note: the numpy behavior described here is py3-specific.
np.array([-1, 0, 1], dtype=dtype1) % np.array([0, 0, 0], dtype=dtype2) gives precisely the same results as the // operation.
pandas convention is to return [nan, nan, nan] for all dtype combinations.
divmod behavior consistent with 1) and 2).
Missing data handling for arithmetic operations.
Missing data handling for arithmetic operations.
In particular, pandas conventions regarding division by zero differ from numpy in the following ways: 1) np.array([-1, 0, 1], dtype=dtype1) // np.array([0, 0, 0], dtype=dtype2) gives [nan, nan, nan] for most dtype combinations, and [0, 0, 0] for the remaining pairs (the remaining being dtype1==dtype2==intN and dtype==dtype2==uintN).
pandas convention is to return [-inf, nan, inf] for all dtype combinations.
Note: the numpy behavior described here is py3-specific.
np.array([-1, 0, 1], dtype=dtype1) % np.array([0, 0, 0], dtype=dtype2) gives precisely the same results as the // operation.
pandas convention is to return [nan, nan, nan] for all dtype combinations.
divmod behavior consistent with 1) and 2).
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them
