Numpy

Module easing handling large data sets.

Array

Class of iterable, mutable objects. Very much like a list but can have elements of only one(1) type (booleans can be mixed with numeric types, True = 1, False = 0). Arrays have their own set of methods. Some things are similar to lists, others differ.

Slicing works like in lists.

Numpy provides automatic mapping of operations to the array elements.

array1 / array2

Returns an array of the results from the division of all elements of array1 by the corresponding element of array2. Array1 and array2 must have the same number of elements.

array1 > x

Returns a boolean array same size as aray1 with True for elements > x and False for elements <= x

array1[array1 > x]

Return all elements of array1 > x. Can be used with different arrays too providing they are the same size.

Arrays are by default multi-dimensional. Basically this is a list of arrays.

mdarray[0][2]

mdarray[0,2] (preferred)

Return the 3rd element from the first array (row 1)

mdarray.shape

np.shape(mdarray)

Return the array's shape as tuple (rows,colums)

If the rows have different number of columns only the number of rows is returned (rows,)

Slicing works too:

mdarray[:,2]

Return the 3rd element of all rows

mdarray[2,4:6]

Return from the 3rd row the the elements 4 and 5 (5th and 6th)

Operations are still applied to all elements on all rows

mdarray * 2

Operate on all elements

mdarray * array(1row)

Multiply each element in all rows of mdarray with the corresponding element in array

numpy.mean(array)

Return the average of all values in array

numpy.median(array)

Return the middle value of array(sorted)

numpy.std(array)

Return the standard deviation in

numpy.corrcoef(array[:,0],array[:,1])

Return the correlation between 2 columns