Difference between revisions of "Numpy"
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Class of iterable, mutable objects. Very much like a [[Python:Datatypes#list|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. | Class of iterable, mutable objects. Very much like a [[Python:Datatypes#list|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. | ||
− | [[Python:DataTypes#Slicing|Slicing]] works like in lists. | + | [[Python:DataTypes#Slicing|Slicing]] works like in [[Python:DataTypes#list|lists]]. |
Numpy provides automatic mapping of operations to the array elements. | Numpy provides automatic mapping of operations to the array elements. | ||
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:If the rows have different number of columns only the number of rows is returned (rows,) | :If the rows have different number of columns only the number of rows is returned (rows,) | ||
− | Slicing works too: | + | [[Python:DataTypes#Slicing|Slicing]] works for multi-dimensional arrays too: |
;mdarray[<nowiki>:</nowiki>,2] | ;mdarray[<nowiki>:</nowiki>,2] | ||
:Return the 3rd element of all rows | :Return the 3rd element of all rows |
Revision as of 15:57, 1 December 2018
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.
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 for multi-dimensional arrays 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