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Definitely in the data science stack though. For example: how pandas behaves with slicing, and copy vs view semantics are totally different from vanilla python

Definitely in the data science stack though. For example: how pandas behaves with slicing, and copy vs view semantics are totally different from vanilla python, and are confusing enough coming from vanilla python. God help you if you learn pandas and python at the same time.

vram22 9y ago
>how pandas behaves with slicing, and copy vs view semantics are totally different from vanilla python, and are confusing enough coming from vanilla python Can you give a few examples of this?
- paultopia 9y ago
  Sure thing. For slicing, see Jake VanderPlas's book: https://jakevdp.github.io/PythonDataScienceHandbook/03.02-data-indexing-and-selection.html https://jakevdp.github.io/PythonDataScienceHandbook/03.02-da...) that describes the complexities -- some kinds of pandas slicing include the last index, some kinds don't. View vs copy is even worse. There are some situations where indexing on a DataFrame yields a view, some situations where it yields a copy, and they are effectively indeterminate in a lot of situations. From the Pandas documentation https://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy https://pandas.pydata.org/pandas-docs/stable/indexing.html#i...: See that __getitem__ in there? Outside of simple cases, it’s very hard to predict whether it will return a view or a copy (it depends on the memory layout of the array, about which pandas makes no guarantees), and therefore whether the __setitem__ will modify dfmi or a temporary object that gets thrown out immediately afterward. By contrast, the vanilla python rules are much simpler: slicing always leaves off the last index, and the rules for view vs copy of mutable data structures like lists are at least determinate...
  - vram22 9y ago
    Thanks for the reply. I can see how it can be confusing, and more so for that indeterminate part.

vram22 9y ago

>how pandas behaves with slicing, and copy vs view semantics are totally different from vanilla python, and are confusing enough coming from vanilla python Can you give a few examples of this?

paultopia 9y ago
Sure thing. For slicing, see Jake VanderPlas's book: https://jakevdp.github.io/PythonDataScienceHandbook/03.02-data-indexing-and-selection.html https://jakevdp.github.io/PythonDataScienceHandbook/03.02-da...) that describes the complexities -- some kinds of pandas slicing include the last index, some kinds don't. View vs copy is even worse. There are some situations where indexing on a DataFrame yields a view, some situations where it yields a copy, and they are effectively indeterminate in a lot of situations. From the Pandas documentation https://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy https://pandas.pydata.org/pandas-docs/stable/indexing.html#i...: See that __getitem__ in there? Outside of simple cases, it’s very hard to predict whether it will return a view or a copy (it depends on the memory layout of the array, about which pandas makes no guarantees), and therefore whether the __setitem__ will modify dfmi or a temporary object that gets thrown out immediately afterward. By contrast, the vanilla python rules are much simpler: slicing always leaves off the last index, and the rules for view vs copy of mutable data structures like lists are at least determinate...
- vram22 9y ago
  Thanks for the reply. I can see how it can be confusing, and more so for that indeterminate part.