what does numpy ndarray shape do

Question

I have a simple question about the  shape function  which confused me a lot    a   np array  1  2  3       Create a rank 1 array print type a                Prints   lt class  numpy ndarray  gt   print a shape               Prints   3     b   np array   1 2 3   4 5 6         Create a rank 2 array print b shape                        Prints   2  3     What did the  shape exactly do  count how many rows  how many columns  then the a shape suppose to be   1 3   one row three columns  right

User · Answer

array   1  2  3    4  5  6    7  8  9

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Unlike it s most popular commercial competitor  numpy pretty much from the outset is about  arbitrary-dimensional  arrays  that s why the core class is called ndarray  You can check the dimensionality of a numpy array using the  ndim property  The  shape property is a tuple of length  ndim containing the length of each dimensions  Currently  numpy can handle up to 32 dimensions   a   np ones 32  1    a   array                                  1                                   a shape    1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  a ndim   32   If a numpy array happens to be 2d like your second example  then it s appropriate to think about it in terms of rows and columns  But a 1d array in numpy is truly 1d  no rows or columns   If you want something like a row or column vector you can achieve this by creating a 2d array with one of its dimensions equal to 1   a   np array   1 2 3      a  row vector  b   np array   1   2   3      a  column vector    or if you don t want to type so many brackets  b   np array   1 2 3    T

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shape   gives the actual shape of your array in terms of no of elements in it  No of rows No of Columns  The answer you get is in the form of tuples   For Example  1D ARRAY   d np array  1 2 3 4   print d   1     Output   4   ie the number4 denotes the no of elements in the 1D Array   2D Array   e np array   1 2 3   4 5 6       print e   2 3    Output   2 3  ie the number of rows and the number of columns   The number of elements in the final output will depend on the number of rows in the Array    it goes on increasing gradually

User · Answer

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 ndarrays    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 columns in your array   Please note that in 1D case  the shape would simply be  n    instead of what you said as either  1  n  or  n  1  for row and column vectors respectively   This is to follow the convention that   For 1D array  return a shape tuple with only 1 element  nbsp   i e   n    For 2D array  return a shape tuple with only 2 elements  i e   n m   For 3D array  return a shape tuple with only 3 elements  i e   n m k   For 4D array  return a shape tuple with only 4 elements  i e   n m k j    and so on     Also  please see the example below to see how np shape   or np ma shape   behaves with 1D arrays and scalars     sample array In  10   u   np arange 10     get its shape In  11   np shape u       u shape Out 11    10      get array dimension using  np ndim  In  12   np ndim u  Out 12   1  In  13   np shape np mean u   Out 13              empty tuple  to indicate that a scalar is a 0D array      check using  numpy ndim  In  14   np ndim np mean u   Out 14   0   P S   So  the shape tuple is consistent with our understanding of dimensions of space  at least mathematically

[arrays] what does numpy ndarray shape do?

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