Python Numpy array initialization and basic operations, pythonnumpy

Python Numpy array initialization and basic operations, pythonnumpy

Python is an advanced, dynamic, and multi-generic programming language. Python code often looks like pseudo code, so you can use a few lines of highly readable code to implement a very powerful idea.

I. Basics:

The main data type of Numpy is ndarray, which is a multi-dimensional array. It has the following attributes:

Ndarray. ndim: dimension of the array
Ndarray. shape: the size of each dimension of the array.
Ndarray. size: Number of all elements in the array
Ndarray. dtype: type of elements in the array (numpy. int32, numpy. int16, and numpy. float64)
Ndarray. itemsize: Each element occupies several bytes.

Example:

>>> import numpy as np>>> a = np.arange(15).reshape(3, 5)>>> aarray([[ 0, 1, 2, 3, 4],    [ 5, 6, 7, 8, 9],    [10, 11, 12, 13, 14]])>>> a.shape(3, 5)>>> a.ndim2>>> a.dtype.name'int64'>>> a.itemsize8>>> a.size15>>> type(a)<type 'numpy.ndarray'>>>> b = np.array([6, 7, 8])>>> barray([6, 7, 8])>>> type(b)<type 'numpy.ndarray'>

2. Create an array:

Use the array Function to convert tuple and list to array:

>>> import numpy as np>>> a = np.array([2,3,4])>>> aarray([2, 3, 4])>>> a.dtypedtype('int64')>>> b = np.array([1.2, 3.5, 5.1])>>> b.dtypedtype('float64')

Multi-dimensional array:

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

Specify the type when generating the array:

>>> c = np.array( [ [1,2], [3,4] ], dtype=complex )>>> carray([[ 1.+0.j, 2.+0.j],    [ 3.+0.j, 4.+0.j]])

Generate an array and assign it a special value:

Ones: full 1
Zeros: All 0
Empty: Random Number, depending on memory

>>> np.zeros( (3,4) )array([[ 0., 0., 0., 0.],    [ 0., 0., 0., 0.],    [ 0., 0., 0., 0.]])>>> np.ones( (2,3,4), dtype=np.int16 )        # dtype can also be specifiedarray([[[ 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)>>> np.empty( (2,3) )                 # uninitialized, output may varyarray([[ 3.73603959e-262,  6.02658058e-154,  6.55490914e-260],    [ 5.30498948e-313,  3.14673309e-307,  1.00000000e+000]])

Generate an even array:

Arange (minimum value, maximum value, step size) (Left closed and right open)
Linspace (minimum, maximum, number of elements)

>>> np.arange( 10, 30, 5 )array([10, 15, 20, 25])>>> np.arange( 0, 2, 0.3 )         # it accepts float argumentsarray([ 0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8])>>> np.linspace( 0, 2, 9 )         # 9 numbers from 0 to 2array([ 0. , 0.25, 0.5 , 0.75, 1. , 1.25, 1.5 , 1.75, 2. ])>>> x = np.linspace( 0, 2*pi, 100 )    # useful to evaluate function at lots of points

III. Basic operations:

The entire array is involved in the operation in order:

>>> a = np.array( [20,30,40,50] )>>> b = np.arange( 4 )>>> barray([0, 1, 2, 3])>>> c = a-b>>> carray([20, 29, 38, 47])>>> b**2array([0, 1, 4, 9])>>> 10*np.sin(a)array([ 9.12945251, -9.88031624, 7.4511316 , -2.62374854])>>> a<35array([ True, True, False, False], dtype=bool)

The two dimensions still use the * symbol to multiply by position one by one. To represent matrix multiplication, use dot:

>>> A = np.array( [[1,1],...       [0,1]] )>>> B = np.array( [[2,0],...       [3,4]] )>>> A*B             # elementwise productarray([[2, 0],    [0, 4]])>>> A.dot(B)          # matrix productarray([[5, 4],    [3, 4]])>>> np.dot(A, B)        # another matrix productarray([[5, 4],    [3, 4]])

Built-in functions (min, max, sum), and axis can be used to specify which dimension to perform operations:

>>> b = np.arange(12).reshape(3,4)>>> barray([[ 0, 1, 2, 3],    [ 4, 5, 6, 7],    [ 8, 9, 10, 11]])>>>>>> b.sum(axis=0)              # sum of each columnarray([12, 15, 18, 21])>>>>>> b.min(axis=1)              # min of each rowarray([0, 4, 8])>>>>>> b.cumsum(axis=1)             # cumulative sum along each rowarray([[ 0, 1, 3, 6],    [ 4, 9, 15, 22],    [ 8, 17, 27, 38]])

Numpy also provides many global functions

>>> B = np.arange(3)>>> Barray([0, 1, 2])>>> np.exp(B)array([ 1.    , 2.71828183, 7.3890561 ])>>> np.sqrt(B)array([ 0.    , 1.    , 1.41421356])>>> C = np.array([2., -1., 4.])>>> np.add(B, C)array([ 2., 0., 6.])

4. Addressing, indexing and traversing:

The traversal Syntax of one-dimensional arrays is similar to that of python list:

>>> a = np.arange(10)**3>>> aarray([ 0,  1,  8, 27, 64, 125, 216, 343, 512, 729])>>> a[2]8>>> a[2:5]array([ 8, 27, 64])>>> a[:6:2] = -1000  # equivalent to a[0:6:2] = -1000; from start to position 6, exclusive, set every 2nd element to -1000>>> aarray([-1000,   1, -1000,  27, -1000,  125,  216,  343,  512,  729])>>> a[ : :-1]                 # reversed aarray([ 729,  512,  343,  216,  125, -1000,  27, -1000,   1, -1000])>>> for i in a:...   print(i**(1/3.))...nan1.0nan3.0nan5.06.07.08.09.0

Multi-dimensional arrays are accessed by specifying an index for each dimension. The order is first high-dimensional and then low-dimensional:

>>> def f(x,y):...   return 10*x+y...>>> b = np.fromfunction(f,(5,4),dtype=int)>>> barray([[ 0, 1, 2, 3],    [10, 11, 12, 13],    [20, 21, 22, 23],    [30, 31, 32, 33],    [40, 41, 42, 43]])>>> b[2,3]23>>> b[0:5, 1]            # each row in the second column of barray([ 1, 11, 21, 31, 41])>>> b[ : ,1]            # equivalent to the previous examplearray([ 1, 11, 21, 31, 41])>>> b[1:3, : ]           # each column in the second and third row of barray([[10, 11, 12, 13],    [20, 21, 22, 23]])When fewer indices are provided than the number of axes, the missing indices are considered complete slices:>>>>>> b[-1]                 # the last row. Equivalent to b[-1,:]array([40, 41, 42, 43])

... Symbol indicates that all dimensions with unspecified indexes are assigned:, which indicates all elements of the dimension in python:

>>> c = np.array( [[[ 0, 1, 2],        # a 3D array (two stacked 2D arrays)...         [ 10, 12, 13]],...        [[100,101,102],...         [110,112,113]]])>>> c.shape(2, 2, 3)>>> c[1,...]                  # same as c[1,:,:] or c[1]array([[100, 101, 102],    [110, 112, 113]])>>> c[...,2]                  # same as c[:,:,2]array([[ 2, 13],    [102, 113]])

Traversal:

If you only want to traverse the entire array, you can directly use:

>>> for row in b:...   print(row)...[0 1 2 3][10 11 12 13][20 21 22 23][30 31 32 33][40 41 42 43]

However, if you want to operate on each element, you need to use the flat attribute, which is an iterator for traversing the entire array.

>>> for element in b.flat:...   print(element)...

Summary

The above section describes the initialization and basic operations of the Python Numpy array. I hope it will help you. If you have any questions, please leave a message and I will reply to you in a timely manner. Thank you very much for your support for the help House website!