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### NumPy basics

Array handling is provided to Python by the optional package NumPy. NumPy derives from the older module Numeric which documentation is available at http://nume ric.scipy.org/numpydoc/numdoc.htm. You will have to pay for the NumPy documentation4, but the Numeric one may be sufficient to begin handling arrays with Python. You can also find an extensive list of the NumPy functions, methods and attributes at http://www.scipy.org/Numpy_Example_List_With_Doc .
The type ndarray provided by NumPy module has a large set of attributes and methods which can help to deal with these objects. This module also brings many useful functions.

 ```>>> from numpy import array, reshape >>> a = reshape(array(range(1,25)),(2,3,4)) >>> # integers from 1 to 24 rearranged in a 2x3x4 data cube >>> a array([[[ 1, 2, 3, 4], [ 5, 6, 7, 8], [ 9, 10, 11, 12]], [[13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23, 24]]]) >>> type(a) ```

Basic array elements access may be summarized as follows:

 ```>>> a[0] # First subarray through first dimension array([[ 1, 2, 3, 4], [ 5, 6, 7, 8], [ 9, 10, 11, 12]]) >>> a[0][1] # Second subarray through first dimension of subarray 'a[0]' array([5, 6, 7, 8]) >>> a[0][1][2] 7 >>> a[0,1,2] # Same as above 7 >>> j = (0,1,2) >>> a[j] # Tuples are valid indices 7 ```
 ```>>> a[:] # All subarrays through first dimension, thus 'a' itself array([[[ 1, 2, 3, 4], [ 5, 6, 7, 8], [ 9, 10, 11, 12]], [[13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23, 24]]]) ```
 ```>>> a[0:1] # lower limit is included, upper limit excluded, # thus only a[0] array([[[ 1, 2, 3, 4], [ 5, 6, 7, 8], [ 9, 10, 11, 12]]]) >>> a[:,1:,:2] # All elements through 1st dimension, # all elements except 1st one (0) through 2nd dimension, # 1st two elements (0 and 1) through 3rd dimension: array([[[ 5, 6], [ 9, 10]], [[17, 18], [21, 22]]]) ```

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