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What is Numpy?

Numpy is an open source scientific computing tool kit of Python and an advanced numerical programming tool.

Powerful N-dimensional array object: ndarray

You can operate on array structure data (without traversing loops)

It has the functions of random number, linear algebra, Fourier transform, etc.

How to install it? Install anaconda scientific computing environment

Anaconda is recommended, which integrates many commonly used libraries and is easier to use when configuring the environment.

Download address: https://www.anaconda.com/download/

Install Numpy

Method 1: after installing anaconda, numpy can be used directly without the need for secondary installation.

Method 2: do not install anaconda can be installed using pip install numpy.

Install jupyter notebooks (recommended)

Method 1: after installing anaconda, jupyter notebooks can be used directly without the need for secondary installation.

Method 2: do not install anaconda can be installed using pip install jupyter.

Import of Numpy basic data structures

From numpy import np is recommended

From numpy import * is not recommended because numpy contains a large number of functions with the same name as the Python built-in function.

Generate ndarray

You can use array to generate arrays

Take a chestnut:

Import numpy as np

Ar = np.array ([[1mem2d3re4], [1min2pd3re4]))

Print (ar, type (ar))

> > >

[[1 2 3 4]

[1 2 3 4]]

Besides np.array, there are other functions that can create new arrays. Here are a few commonly used ones:

Array version of arange # python range

Asarray # converts input to ndarray

Ones # generates an array of all 1s based on a given shape and type

Ones_like # generates an array of all 1s with the same shape from a given array

Zeros # generates an array of all zeros based on a given shape and type

Zeros_like # generates an array of all 1s with the same shape from a given array

Eye # generates a Numeric feature matrix (diagonal 1, the rest 0)

Linspance # returns num evenly spaced samples calculated on the interval [start, stop]

Here are the examples of zeros,zeros_like and linspance:

Arr = np.zeros ((3pm 5))

Print (arr)

> > >

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

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

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

S = np.array ([list (range (10)), list (range (10))]))

Print (s)

Print (np.zeros_like (s)

> > >

[[0 1 2 3 4 5 6 7 8 9]

[10 11 12 13 14 15 16 17 18 19]]

[[0 0 0]

[0 0 0]]

Print (np.linspace (10 ~ (10) 20 ~ (m) num = 21)) # generated between 10 ~ (th) and 21 ~ (th)

Print (np.linspace (10 and 20 endpoint num = 21, endpoint = False) # appointment defaults to True and does not include the value on the left when it is False

Print (np.linspace (10 and 20 retstep num = 21, retstep = True)) # restep display step

> > >

[10. 10.5 11. 11.5 12. 12.5 13. 13.5 14. 14.5 15. 15.5 16. 16.5

17. 17.5 18. 18.5 19. 19.5 20.]

[10. 10.47619048 10.95238095 11.42857143 11.9047619 12.38095238

12.85714286 13.33333333 13.80952381 14.28571429 14.76190476 15.23809524

15.71428571 16.19047619 16.66666667 17.14285714 17.61904762 18.0952381

18.57142857 19.04761905 19.52380952]

Array ([10., 10. 5, 11., 11. 5, 12., 12. 5, 13., 13. 5, 14., 14. 5, 15.

15.5, 16., 16.5, 17., 17.5, 18., 18.5, 19., 19.5, 20.]), 0.5)

In addition to a few commonly used functions for generating arrays, here are some commonly used methods:

Import numpy as np

Ar = np.array ([[1mem2d3re4], [1min2pd3re4]))

Print (ar, type (ar))

Print (ar.ndim) # returns the number of dimensions in the array

The dimension of the print (ar.shape) # array that returns several rows and columns

The number of elements in the print (ar.size) # array

Type of print (ar.dtype) # element

The size of the elements in the print (ar.itemsize) # array

> > >

[[1 2 3 4]

[1 2 3 4]]

two

(2, 4)

eight

Int64

eight

Numpy Universal function Array shape Transformation (.T / .reshape () / .resize ())

T is the transpose function, which has no effect on the one-dimensional array.

# .T

Import numpy as np

Ar1 = np.arange (10)

Ar2 = np.zeros ((2pm 5))

Print (ar1.T)

Print (ar2.T) # transpose function

> > >

[0 1 2 3 4 5 6 7 8 9]

[[0. 0.]

[0. 0.]

[0. 0.]

[0. 0.]

[0. 0.]]

.reshape (), directly change the shape of the array, but the number of array elements must be the same before and after the change

Ar1 = np.arange (10)

Print (ar1.reshape (2, 5))

Print (np.reshape (np.arange (16), (2Jing 8))

> > >

[[0 1 2 3 4]

[5 6 7 8 9]]

[[0 1 2 3 4 5 6 7]

[8 9 10 11 12 13 14 15]]

.resize ()

Print (np.resize (np.arange (16), (3Jing 5)) # resize iterates sequentially when the number of subsequent array elements is less than the number generated previously

Print (np.resize (np.arange (12), (3Jing 5)) # resize is randomly populated when the number of elements behind the array is greater than the number of elements generated before.

> > >

[[0 1 2 3 4]

[5 6 7 8 9]

[10 11 12 13 14]]

[[0 1 2 3 4]

[5 6 7 8 9]

[10 11 0 1 2]]

Replication of array

Similar to the deep and shallow copy in python: Python | the deep and shallow copy of Python learning

Type conversion of array

.astype () can convert the types of elements in an array. In numpy, there are several element types (if there are too many, not all of them):

Int8, uint8 # signed and unsigned 8-bit integers

Int16, uint16 # signed and unsigned 16-bit integers

Int32, uint32 # signed and unsigned 32-bit integer

Int64, uint64 # signed and unsigned 64-bit integer

Float16 # semi-precision

Float32 # single precision

Float64 # double precision

Bool # Boolean

.

Take a type-converted chestnut:

Ar1 = np.arange (10 dtypewritten float)

Ar2 = ar1.astype (np.int64)

Print (ar1,ar2)

> > >

[0. 1. 2. 3. 4. 5. 6. 7. 8. 9.] [0 1 2 3 4 5 6 7 8 9]

Stacking of arrays

The array is stacked with hstack (), vstack (), and stack (), with the following examples:

A = np.arange (10)

B = np.arange (100.20)

Print (ar1,ar2)

# horizontal links

Print (np.hstack ((aPermib)

# Vertical links

A = np.array ([[1], [2], [3]])

B = np.array ([['a'], ['b'], ['c']])

Print (np.vstack ((aPermib)

# arbitrary stacking

A = np.arange (10)

B = np.arange (100.20)

Print (np.stack ((aQuinine b), axis=1)) # Vertical stacking

Print (np.stack ((aPerm) # stacking horizontally

>

[0 12 3 4 5 6 7 8 9] [10 11 12 13 14 15 16 17 18 19]

[0 12 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]

[['1']

['2']

['3']

['a']

['b']

['c']]

[[0 10]

[1 11]

[2 12]

[3 13]

[4 14]

[5 15]

[6 16]

[7 17]

[8 18]

[9 19]]

[[0 1 2 3 4 5 6 7 8 9]

[10 11 12 13 14 15 16 17 18 19]]

Array split

Array split is also divided into horizontal split and vertical split.

# Array split

Ar = np.arange (16) .reshape (4jue 4)

Print (ar)

Print (np.hsplit (ar,2)) # Vertical split

Print (np.vsplit (ar,2)) # horizontal split

> > >

[[0 1 2 3]

[4 5 6 7]

[8 9 10 11]

[12 13 14 15]]

[array ([0,1]

[4, 5]

[8, 9]

[12, 13]), array ([2, 3]

[6, 7]

[10, 11]

[14, 15]])]

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

[4, 5, 6, 7]), array ([8,9,10,11]

[12, 13, 14, 15])]

Common calculation function

The computational functions here are used in the same way as those in Python, and will not be discussed too much here.

# Computing function

Np.mean () # find the average

Np.max () # maximum

Np.min () # minimum

Np.gtd () # standard deviation

Np.var () # Variance

Np.sum () # where the parameter axis=0 is summed by column and axis=1 by row

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