Numpy - Revision history

Hdridder: /* Array */

2022-03-05T15:53:09Z

Array

← Older revision		Revision as of 15:53, 5 March 2022
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	:Create a 1 dimensional array		:Create a 1 dimensional array
	;array1 = np.array([[1,2,3],[2,5,9]])		;array1 = np.array([[1,2,3],[2,5,9]])
	:Create a 2 dimensional array		:Create a 2 dimensional array (1,2,3, is row 0, 2,5,9 row 1)

	;array1 / array2		;array1 / array2
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	[[Python:DataTypes#Slicing\|Slicing]] works for multi-dimensional arrays 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 (the 3rd column)
	;mdarray[2,4<nowiki>:</nowiki>6]		;mdarray[2,4<nowiki>:</nowiki>6]
	:Return from the 3rd row the the elements 4 and 5 (5th and 6th)		:Return from the 3rd row the the elements 4 and 5 (5th and 6th)
			;mdarray[2,<nowiki>:</nowiki>]
			:Return the 3rd row of a 2 dimensional array


	Operations are still applied to all elements on all rows		Operations are still applied to all elements on all rows

Hdridder at 10:37, 7 November 2021

2021-11-07T10:37:23Z

← Older revision		Revision as of 10:37, 7 November 2021
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	;np.nan		;np.nan
	:Not a number, can be used to fill in an unknown value in a series (see [[Pandas]])		:Not a number, can be used to fill in an unknown value in a series (see [[Pandas]])

			=Installation problem=
			On raspberry pi numpy installed by pip may fail to load reporting a lot ending with:
			Original error was: libf77blas.so.3: cannot open shared object file: No such file or directory

			Solution is to install libatlas-base-dev
			apt install libatlas-base-dev

Hdridder at 08:26, 25 September 2019

2019-09-25T08:26:49Z

← Older revision		Revision as of 08:26, 25 September 2019
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	Numpy documentation for [https://docs.scipy.org/doc/numpy/reference/routines.math.html#mathematical-functions\| mathematical functions].		Numpy documentation for [https://docs.scipy.org/doc/numpy/reference/routines.math.html#mathematical-functions\| mathematical functions].

	At least some functions work on [[Python:DataTypes#list\|lists]] too.		At least some functions work on [[Python:DataTypes#list\|lists]] too. Don't do that as this will be much slower than using the python functions.

	=Array=		=Array=

Hdridder at 08:23, 25 September 2019

2019-09-25T08:23:56Z

← Older revision		Revision as of 08:23, 25 September 2019
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	[[category:Python]]		[[category:Python]]
	Module easing handling large data sets.		Module easing handling large data sets. It is very fast as all elements in an numpy array are of the same datatype.

	It seems to be common to <code>import numpy as np</code>. Therefor below np is used on this page.		It seems to be common to <code>import numpy as np</code>. Therefor below np is used on this page.

Hdridder: /* Array */

2019-05-26T16:20:09Z

Array

← Older revision		Revision as of 16:20, 26 May 2019
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	[[Python:DataTypes#Slicing\|Slicing]] works like in [[Python:DataTypes#list\|lists]].		[[Python:DataTypes#Slicing\|Slicing]] works like in [[Python:DataTypes#list\|lists]].

			Arrays are by default multi-dimensional. Basically this is a list of arrays. A matrix is a multi-dimensional array where all elements (rows) are of the same size. Using the numpy matrix class is [https://docs.scipy.org/doc/numpy/reference/generated/numpy.matrix.html discouraged]

	Numpy provides automatic mapping of operations to the array elements.		Numpy provides automatic mapping of operations to the array elements.

	;~~<code>~~array1 / array2~~</code>~~		;array1 = np.array([1,2,3])
			:Create a 1 dimensional array
			;array1 = np.array([[1,2,3],[2,5,9]])
			:Create a 2 dimensional array

			;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.		: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.

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	:Return all elements of array1 > x. Can be used with different arrays too providing they are the same size.		: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. A matrix is a multi-dimensional array where all elements (rows) are of the same size. Using the numpy matrix class is [https://docs.scipy.org/doc/numpy/reference/generated/numpy.matrix.html discouraged]
	;mdarray[0][2]		;mdarray[0][2]
	;mdarray[0,2] (preferred)		;mdarray[0,2] (preferred)
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	Operations are still applied to all elements on all rows		Operations are still applied to all elements on all rows
	;mdarray1 * 2		;mdarray1 * 2
	:Operate on all elements		:Operate on all elements, return the array with all elements multiplied by 2.

	;mdarray1 * array1(1row)		;mdarray1 * array1(1row)

Hdridder: /* Array */

2019-05-26T14:20:45Z

Array

← Older revision		Revision as of 14:20, 26 May 2019
Line 9:		Line 9:

	=Array=		=Array=
	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 [[Python:DataTypes#list\|lists]].		[[Python:DataTypes#Slicing\|Slicing]] works like in [[Python:DataTypes#list\|lists]].

Hdridder: /* Array */

2019-05-26T14:18:02Z

Array

← Older revision		Revision as of 14:18, 26 May 2019
Line 24:		Line 24:
	:Return all elements of array1 > x. Can be used with different arrays too providing they are the same size.		: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. A matrix is a multi-dimensional array where all elements (rows) are of the same size. Using the numpy matrix class is [https://docs.scipy.org/doc/numpy/reference/generated/numpy.matrix.html discouraged]
	;mdarray[0][2]		;mdarray[0][2]
	;mdarray[0,2] (preferred)		;mdarray[0,2] (preferred)

Hdridder: /* Array */

2019-04-28T10:49:49Z

Array

← Older revision		Revision as of 10:49, 28 April 2019
Line 65:		Line 65:

	;<span id=linspace>np.linspace(start,stop,num)</span>		;<span id=linspace>np.linspace(start,stop,num)</span>
	:Create an array of num element evenly distributed from start to stop. 50 is the default for num.		:Create an array of num element evenly distributed from start to stop. 50 is the default for num. Sort of floating point [[Python:DataTypes#range\|range]].

	;np.where(array1 == a)		;np.where(array1 == a)

Hdridder: /* Array */

2019-03-06T21:11:41Z

Array

← Older revision		Revision as of 21:11, 6 March 2019
Line 68:		Line 68:

	;np.where(array1 == a)		;np.where(array1 == a)
			:Return a [[Python:DataTypes#tuple\|tuple]] of arrays of indexnumbers (not the index) in array1 that match the condition
	;np.where(array1 == a)[0][0]		;np.where(array1 == a)[0][0]
	~~:return a [[Python:DataTypes#tuple\|tuple] of arrays of indexnumbers in array1 that match the condition~~		:Return the first indexnumber (not the index) in array1 that matches the condition
	:Return the first index in array1 that matches the condition

Hdridder: /* Array */

2018-12-27T21:40:09Z

Array

← Older revision		Revision as of 21:40, 27 December 2018
Line 41:		Line 41:

	Operations are still applied to all elements on all rows		Operations are still applied to all elements on all rows
	;~~mdarray~~ * 2		;mdarray1 * 2
	:Operate on all elements		:Operate on all elements

	;~~mdarray~~ * ~~array~~(1row)		;mdarray1 * array1(1row)
	:Multiply each element in all rows of ~~mdarray~~ with the corresponding element in ~~array~~		:Multiply each element in all rows of mdarray1 with the corresponding element in array1

	;np.sum(~~array~~)		;np.sum(array1)
	;~~array~~.sum()		;array1.sum()
	:Return the addition of all values in ~~array~~ (prod,		:Return the addition of all values in array1 (prod works too)

	;np.mean(~~array~~)		;np.mean(array1)
	;~~array~~.mean()		;array1.mean()
	:Return the average of all values in ~~array~~		:Return the average of all values in array1

	;np.median(~~array~~)		;np.median(array1)
	:Return the middle value of ~~array~~(sorted)		:Return the middle value of array1(sorted)

	;np.std(~~array~~)		;np.std(array1)
	:Return the standard deviation in		:Return the standard deviation in array1

	;np.corrcoef(array[<nowiki>:</nowiki>,0],array[<nowiki>:</nowiki>,1])		;np.corrcoef(array[<nowiki>:</nowiki>,0],array[<nowiki>:</nowiki>,1])
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	;<span id=linspace>np.linspace(start,stop,num)</span>		;<span id=linspace>np.linspace(start,stop,num)</span>
	:Create an array of num element evenly distributed from start to stop. 50 is the default for num.		:Create an array of num element evenly distributed from start to stop. 50 is the default for num.

			;np.where(array1 == a)
			;np.where(array1 == a)[0][0]
			:return a [[Python:DataTypes#tuple\|tuple] of arrays of indexnumbers in array1 that match the condition
			:Return the first index in array1 that matches the condition


	==Randomness==		==Randomness==