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- python - What does . shape [] do in for i in range (Y. shape [0 . . .
The shape attribute for numpy arrays returns the dimensions of the array If Y has n rows and m columns, then Y shape is (n,m) So Y shape[0] is n
- 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 For a 2D array, the shape would be (n,m) where n is the number of rows and m is the number of
- python - x. shape [0] vs x [0]. shape in NumPy - Stack Overflow
x[0] shape will give the Length of 1st row of an array x shape[0] will give the number of rows in an array In your case it will give output 10 If you will type x shape[1], it will print out the number of columns i e 1024 If you would type x shape[2], it will give an error, since we are working on a 2-d array and we are out of index Let me explain you all the uses of 'shape' with a simple example by taking a 2-d array of zeros of dimension 3x4
- How to use . format in python to print out the data shape
I'm trying to print the shape of the dataframe using format in python Tried using print statement incorporating format and shape commands df_shape = df shape print ("The data has Rows {}, Col
- What does shape[0] and shape[1] do in python? - Stack Overflow
3 In python shape [0] returns the dimension but in this code it is returning total number of set Please can someone tell me work of shape [0] and shape [1]? Code:
- python - What does range (y. shape [1]) mean in for i in range . . .
I'm trying to find out how this above-mentioned piece of code works in a layman sense? for context, this code contains Numpy, Seaborn, Pandas and matplotlib below is the line of code: dataset2 = d
- python - Why dataframe. shape [0] prints an integer, but dataframe . . .
train shape[0] python returned 1467 - an integer Curious how Pandas handles these two different inputs, and why they are different Is this a specific feature, or just a quirk?
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