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Tentative NumPy Tutorial

Please do not hesitate to click the edit button. You will need to create a User Account first.

1. Prerequisites

2. The Basics

1. An example

2. Array Creation

3. Printing Arrays

4. Basic Operations

5. Universal Functions

6. Indexing, Slicing and Iterating

3. Shape Manipulation

1. Changing the shape of an array

2. Stacking together different arrays

3. Splitting one array into several smaller ones

4. Copies and Views

1. No Copy at All

2. View or Shallow Copy

3. Deep Copy

4. Functions and Methods Overview

5. Less Basic

1. Broadcasting rules

6. Fancy indexing and index tricks

1. Indexing with Arrays of Indices

2. Indexing with Boolean Arrays

3. The ix_() function

4. Indexing with strings

7. Linear Algebra

1. Simple Array Operations

2. The Matrix Class

3. Indexing: Comparing Matrices and 2D Arrays

8. Tricks and Tips

1. "Automatic" Reshaping

2. Vector Stacking

3. Histograms

9. References

Prerequisites

Before reading this tutorial you should know a bit of Python. If you would like to refresh your memory, take a look at the Python tutorial.

If you wish to work the examples in this tutorial, you must also have some software installed on your computer. Minimally:

 Python

 NumPy

These you may find useful:

 ipython is an enhanced interactive Python shell which is very convenient for exploring NumPy's features

 matplotlib w ill enable you to plot graphics

 SciPy provides a lot of scientific routines that w ork on top of NumPy

The Basics

NumPy's main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same t ype, indexed

by a tuple of positive integers. In Numpy dimensions are called axes. The number of axes is rank.

For example, the coordinates of a point in 3D space [1, 2, 1] is an array of rank 1, because it has one axis. That axis has a length of 3.

In example pictured below, the array has rank 2 (it is 2-dimensional). The first dimension (axis) has a length of 2, the second dimension

has a length of 3.

[[ 1., 0., 0.],

[ 0., 1., 2.]]

Numpy's array class is called ndarray. It is also known by the alias array. Note that numpy.array is not the same as the Standard

Python Library class array.array, which only handles one-dimensional arrays and offers less functionality. The more important attributes

of an ndarray object are:

ndarray.ndim

the number of axes (dimensions) of the array. In the Python world, the number of dimensions is referred to as rank.

ndarray.shape

the dimensions of the array. This is a tuple of integers indicating the size of the array in each dimension. For a matrix with n rows

and m columns, shape w ill be (n,m). The length of the shape tuple is therefore the rank, or number of dimensions, ndim.

ndarray.size

the total number of elements of the array. This is equal to the product of the elements of shape.

ndarray.dtype

an object describing the type of the elements in the array. One can create or specify dtype's using standard Python types.

Additionally NumPy provides types of its own. numpy.int32, numpy.int16, and numpy.float64 are some examples.

ndarray.itemsize

the size in bytes of each element of the array. For example, an array of elements of type float64 has itemsize 8 (=64/8), while

one of type complex32 has itemsize 4 (=32/8). It is equivalent to ndarray.dtype.itemsize.

ndarray.data

the buffer containing the actual elements of the array. Normally, we won't need to use this attribute because we will access the

elements in an array using indexing facilities.

An example

>>> from numpy import *

>>> a = arange(10).reshape(2,5)

>>> a

array([[0, 1, 2, 3, 4],

[5, 6, 7, 8, 9]])

>>> a.shape

(2, 5)

>>> a.ndim

>>> a.dtype.name

'int32'

>>> a.itemsize

>>> a.size

>>> type(a)

numpy.ndarray

>>> b = numpy.array([6, 7, 8])

>>> b

array([6, 7, 8])

>>> type(b)

numpy.ndarray

Array Creation

There are several ways to create arrays.

For example, you can create an array from a regular Python list or tuple using the array function. The type of the resulting array is

deduced from the type of the elements in the sequences.

>>> from numpy import *

>>> a = array( [2,3,4] )

>>> a

array([2, 3, 4])

>>> a.dtype

dtype('int32')

>>> b = array([1.2, 3.5, 5.1])

>>> b.dtype

dtype('float64')

A frequent error consists in calling array with multiple numeric arguments, rather than providing a single list of numbers as an argument.

>>> a = array(1,2,3,4) # WRONG

>>> a = array([1,2,3,4]) # RIGHT

array transforms sequences of sequences into two-dimensional arrays, sequences of sequences of sequences into three-dimensional

arrays, and so on.

>>> b = array( [ (1.5,2,3), (4,5,6) ] )

>>> b

array([[ 1.5, 2. , 3. ],

[ 4. , 5. , 6. ]])

The type of the array can also be explicitly specified at creation time:

>>> c = array( [ [1,2], [3,4] ], dtype=complex )

>>> c

array([[ 1.+0.j, 2.+0.j],

[ 3.+0.j, 4.+0.j]])

Often, the elements of an array are originally unknown, but its size is known. Hence, NumPy offers several functions to create arrays with

initial placeholder content. These minimize the necessity of growing arrays, an expensive operation.

The function zeros creates an array full of zeros, the function ones creates an array full of ones, and the function empty creates an array

whose initial content is random and depends on the state of the memory. By default, the dtype of the created array is float64.

>>> zeros( (3,4) )

array([[0., 0., 0., 0.],

[0., 0., 0., 0.],

[0., 0., 0., 0.]])

>>> ones( (2,3,4), dtype=int16 ) # dtype can also be specified

array([[[ 1, 1, 1, 1],

[ 1, 1, 1, 1],

[ 1, 1, 1, 1]],

[[ 1, 1, 1, 1],

[ 1, 1, 1, 1],

[ 1, 1, 1, 1]]], dtype=int16)

>>> empty( (2,3) )

array([[ 3.73603959e-262, 6.02658058e-154, 6.55490914e-260],

[ 5.30498948e-313, 3.14673309e-307, 1.00000000e+000]])

To create sequences of numbers, NumPy provides a function analogous to range that returns arrays instead of lists

>>> arange( 10, 30, 5 )

array([10, 15, 20, 25])

>>> arange( 0, 2, 0.3 ) # it accepts float arguments

array([ 0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8])

When arange is used with floating point arguments, it is generally not possible to predict the number of elements obtained, due to the

finite floating point precision. For this reason, it is usually better to use the function linspace that receives as an argument the number of

elements that we want, instead of the step:

>>> linspace( 0, 2, 9 ) # 9 numbers from 0 to 2

array([ 0. , 0.25, 0.5 , 0.75, 1. , 1.25, 1.5 , 1.75, 2. ])

>>> x = linspace( 0, 2*pi, 100 ) # useful to evaluate function at lots of points

>>> f = sin(x)

See also

array, zeros, zeros_like, ones, ones_like, empty, empty_like, arange, linspace, rand, randn, fromfunction, fromfile

Printing Arrays

When you print an array, NumPy displays it in a similar way to nested lists, but with the following layout:

 the last axis is printed from left to right,

 the second-to-last is printed from top to bottom,

 the rest are also printed from top to bottom, with each slice separated from the next by an empty line.

One-dimensional arrays are then printed as rows, bidimensionals as matrices and tridimensionals as lists of matrices.

>>> a = arange(6) # 1d array

>>> print a

[0 1 2 3 4 5]

>>>

>>> b = arange(12).reshape(4,3) # 2d array

>>> print b

[[ 0 1 2]

[ 3 4 5]

[ 6 7 8]

[ 9 10 11]]

>>>

>>> c = arange(24).reshape(2,3,4) # 3d array

>>> print c

[[[ 0 1 2 3]

[ 4 5 6 7]

[ 8 9 10 11]]

[[12 13 14 15]

[16 17 18 19]

[20 21 22 23]]]

See below to get more details on reshape.

If an array is too large to be printed, NumPy automatically skips the central part of the array and only prints the corners:

>>> print arange(10000)

[ 0 1 2 ..., 9997 9998 9999]

>>>

>>> print arange(10000).reshape(100,100)

[[ 0 1 2 ..., 97 98 99]

[ 100 101 102 ..., 197 198 199]

[ 200 201 202 ..., 297 298 299]

...,

[9700 9701 9702 ..., 9797 9798 9799]

[9800 9801 9802 ..., 9897 9898 9899]

[9900 9901 9902 ..., 9997 9998 9999]]

To disable this behaviour and force NumPy to print the entire array, you can change the printing options using set_printoptions.

>>> set_printoptions(threshold='nan')

Basic Operations

Arithmetic operators on arrays apply elementwise. A new array is created and filled with the result.

>>> a = array( [20,30,40,50] )

>>> b = arange( 4 )

>>> b

array([0, 1, 2, 3])

>>> c = a-b

>>> c

array([20, 29, 38, 47])

>>> b**2

array([0, 1, 4, 9])

>>> 10*sin(a)

array([ 9.12945251, -9.88031624, 7.4511316 , -2.62374854])

>>> a<35

array([True, True, False, False], dtype=bool)

Unlike in many matrix languages, the product operator * operates elementwise in NumPy arrays. The matrix product can be performed

using the dot function or creating matrix objects ( see matrix section of this tutorial ).

>>> A = array( [[1,1],

... [0,1]] )

>>> B = array( [[2,0],

... [3,4]] )

>>> A*B # elementwise product

array([[2, 0],

[0, 4]])

>>> dot(A,B) # matrix product

array([[5, 4],

[3, 4]])

Some operations, such as += and *=, act in place to modify an existing array rather than create a new one.

>>> a = ones((2,3), dtype=int)

>>> b = random.random((2,3))

>>> a *= 3

>>> a

array([[3, 3, 3],

[3, 3, 3]])

>>> b += a

>>> b

array([[ 3.69092703, 3.8324276 , 3.0114541 ],

[ 3.18679111, 3.3039349 , 3.37600289]])

>>> a += b # b is converted to integer type

>>> a

array([[6, 6, 6],

[6, 6, 6]])

When operating with arrays of different types, the type of the resulting array corresponds to the more general or precise one (a behavior

known as upcasting).

>>> a = ones(3, dtype=int32)

>>> b = linspace(0,pi,3)

>>> b.dtype.name

'float64'

评论收藏

内容反馈

初出茅庐yy

2016-08-17

正是我想要的，谢谢！
yxtiankong

2014-08-28

跟python配套的，直接安装就行，感谢分享
whjtc

2015-04-22

很好，正是我想要的，谢谢！
wanghuichn1234

2013-11-19

虽然改到gsl了，还是感谢分享
omama1016

2012-05-15

NUMY的内容丰富，很好！也很清晰！

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