Difference between revisions of "Numpy"
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[[category:Python]] | [[category:Python]] | ||
Module easing handling large data sets. | Module easing handling large data sets. | ||
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+ | It seems to be common to <code>import numpy as np</code>. Therefor below np is used on this page. | ||
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+ | Numpy documentation for [https://docs.scipy.org/doc/numpy/reference/routines.math.html#mathematical-functions| mathematical functions]. | ||
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+ | At least some functions work on [[Python:DataTypes#list|lists]] too. | ||
=Array= | =Array= | ||
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;array1[array1 > 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. | Arrays are by default multi-dimensional. Basically this is a list of arrays. | ||
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:Multiply each element in all rows of mdarray with the corresponding element in array | :Multiply each element in all rows of mdarray with the corresponding element in array | ||
− | ; | + | ;np.sum(array) |
+ | ;array.sum() | ||
+ | :Return the addition of all values in array (prod, | ||
+ | |||
+ | ;np.mean(array) | ||
+ | ;array.mean() | ||
:Return the average of all values in array | :Return the average of all values in array | ||
− | ; | + | ;np.median(array) |
:Return the middle value of array(sorted) | :Return the middle value of array(sorted) | ||
− | ; | + | ;np.std(array) |
:Return the standard deviation in | :Return the standard deviation in | ||
− | ; | + | ;np.corrcoef(array[<nowiki>:</nowiki>,0],array[<nowiki>:</nowiki>,1]) |
:Return the correlation between 2 columns | :Return the correlation between 2 columns |
Revision as of 17:20, 1 December 2018
Module easing handling large data sets.
It seems to be common to import numpy as np
. Therefor below np is used on this page.
Numpy documentation for mathematical functions.
At least some functions work on lists too.
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
- np.sum(array)
- array.sum()
- Return the addition of all values in array (prod,
- np.mean(array)
- array.mean()
- Return the average of all values in array
- np.median(array)
- Return the middle value of array(sorted)
- np.std(array)
- Return the standard deviation in
- np.corrcoef(array[:,0],array[:,1])
- Return the correlation between 2 columns