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- python - What does . shape [] do in for i in range (Y. shape [0 . . .
shape is a tuple that gives you an indication of the number of dimensions in the array So in your case, since the index value of Y shape[0] is 0, your are working along the first dimension of your array
- tensorflow placeholder - understanding `shape= [None,`
You can think of a placeholder in TensorFlow as an operation specifying the shape and type of data that will be fed into the graph placeholder X defines that an unspecified number of rows of shape (128, 128, 3) of type float32 will be fed into the graph a Placeholder does not hold state and merely defines the type and shape of the data to flow
- arrays - what does numpy ndarray shape do? - Stack Overflow
yourarray shape or np shape() or np ma shape() returns the shape of your ndarray as a tuple; And you can get the (number of) dimensions of your array using yourarray ndim or np ndim() (i e it gives the n of the ndarray since all arrays in NumPy are just n-dimensional arrays (shortly called as ndarray s)) For a 1D array, the shape would be (n,) where n is the number of elements in your array
- I want to know what it means (height,width)=img. shape [:2]
The shape property of the img object evidently is a list which contains some image data, the first two elements of which are here being copied into variables height and width
- python - x. shape [0] vs x [0]. shape in NumPy - Stack Overflow
On the other hand, x shape is a 2-tuple which represents the shape of x, which in this case is (10, 1024) x shape[0] gives the first element in that tuple, which is 10 Here's a demo with some smaller numbers, which should hopefully be easier to understand
- python - shape vs len for numpy array - Stack Overflow
Still, performance-wise, the difference should be negligible except for a giant giant 2D dataframe So in line with the previous answers, df shape is good if you need both dimensions, for a single dimension, len() seems more appropriate conceptually Looking at property vs method answers, it all points to usability and readability of code
- python - ValueError: shape mismatch: objects cannot be broadcast to a . . .
ValueError: shape mismatch: objects cannot be broadcast to a single shape It computes the first two (I am running several thousand of these tests in a loop) and then dies
- How to find the size or shape of a DataFrame in PySpark?
Why doesn't Pyspark Dataframe simply store the shape values like pandas dataframe does with shape? Having to call count seems incredibly resource-intensive for such a common and simple operation
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