Tag: Python
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Python’s __radd__: How reflected operators handle 5 + x
If you tell Python it’ll run x.__add__(5), which handles the int 5. But what about int.__add__ doesn’t know how to handle x. So it returns NotImplemented — and Python turns it around, calling: That’s the “reverse add” magic method!
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Python `__add__`: Return a new instance, not an int
If your Python class implements __add__, it should usually return a new instance of your class, not an int:
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Python operator overloading: Implementing __add__ for +
How can your Python object support +? Implement __add__:
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Python operator overloading: Why x + y and y + x give different errors
Operators in Python are turned into “magic” method calls: x + y # becomes x.__add__(y) So: Different errors from different methods!
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Mutable default arguments in Python: why `__defaults__` bites
Because Python functions’ defaults are kept in __defaults__, avoid mutable defaults: add1.__defaults__ # ([1, 1, 1],)
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Python generator frames: Inspecting locals with gi_frame
When a Python generator yields, its stack frame remains — inspect it for the current line and local variables:
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AI seminars — just one of the big changes at LernerPython
July and August are often when people take a break or go on vacation. But for me, this summer has been super busy, full of writing and improving LernerPython based on feedback I’ve gotten from people around the world. And so, I’m here with some big changes I’m making at LernerPython World Headquarters: 1. AI…
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PyArrow dtypes in Pandas aren’t always faster than NumPy
Using PyArrow dtypes in Python Pandas isn’t always faster: %timeit s_pyarr.mean() # 21.5ms%timeit s_np.mean() # 47.3ms %timeit s_pyarr.nlargest(10) # 667ms%timeit s_np.nlargest(10) # 663ms
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PyArrow strings in pandas: Faster and smaller than object dtype
PyArrow strings in Python Pandas are smaller than Python strings. But they’re also far faster: %timeit s_pyarr.str.len() # 106 µs%timeit s_py.str.len() # 1.6 ms In many examples, PyArrow was far faster.
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PyArrow dtypes in pandas: Reading CSVs with dtype_backend
Want PyArrow dtypes in your Python Pandas data frame? The dtypes are double[pyarrow], int64[pyarrow], and string[pyarrow], not the normal NumPy ones. Note: This is still experimental… but it’s also the future.