NumPy | 07 create an array from a range of values

numpy package using arange function to create and return a numerical range ndarray objects, functions in the following format:

numpy.arange(start, stop, step, dtype)

The start and stop step and the step to set the specified range to generate a ndarray.

parameter	description
`start`	Start value, default is`0`
`stop`	Termination value (no)
`step`	Step, default`1`
`dtype`	Return `ndarray`data type, if one is not provided use of the type of input data.

Example 1: generating an array of 0 to 5:

import numpy as np
 
x = np.arange(5)  
print (x)

Output:

[0  1  2 3 4]

Example 2: Bit Set return type float

import numpy as np
 
# Set the DTYPE 
X = np.arange (. 5, DTYPE = a float )  
 Print (X)

Output:

[0.  1.  2. 3. 4.]

Example 3: set the start value and the end value of the step size

import numpy as np

x = np.arange(10,20,2)  
print (x)

Output:

[10  12  14 16 18]

numpy.linspace function to create a one-dimensional array, the array is composed of an arithmetic sequence, the following format:

np.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None)

parameter	description
`start`	Sequence start value
`stop`	Termination sequence of values, if `endpoint`is `true`, the value is included in the number of column
`num`	Steps quantity of sample to be generated, the default is`50`
`endpoint`	This value is `ture`, the number of the column contains the `stop`values, whereas it does not, the default is True.
`retstep`	If True, the resulting array will appear in the pitch, the converse is not displayed.
`dtype`	`ndarray` Data types

Example 1: set to a start point, end point 10, the number of columns 10

import numpy as np

a = np.linspace(1,10,10)
print(a)

The output is:

[ 1.  2. 3. 4. 5. 6. 7. 8. 9. 10.]

Example 2: Set all elements of an arithmetic sequence

import numpy as np

a = np.linspace(1,1,10)
print(a)

The output is:

[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

Example 3: The endpoint is set to false, not including the stop value

import numpy as np
 
a = np.linspace(10, 20,  5, endpoint =  False)  
print(a)

The output is:

[10. 12. 14. 16. 18.]

If the endpoint is set to true, it will contain 20.

import numpy as np

a = np.linspace(10, 20, 5, endpoint = True)
print(a)

The output is:

[10.  12.5 15.  17.5 20. ]

Example 4: disposing interval

import numpy as np

a =np.linspace(1,10,10,retstep= True)
print(a)

# Expand Examples 
B = np.linspace (1,10,10) .reshape ([10,1 ])
 Print (B)

The output is:

(array([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.]), 1.0) [[ 1.] [ 2.] [ 3.] [ 4.] [ 5.] [ 6.] [ 7.] [ 8.] [ 9.] [10.]]

numpy.logspace 函数用于创建一个于等比数列。格式如下：

np.logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None)

参数	描述
`start`	序列的起始值为：base ** start
`stop`	序列的终止值为：base ** stop。如果`endpoint`为`true`，该值包含于数列中
`num`	要生成的等步长的样本数量，默认为`50`
`endpoint`	该值为 `ture` 时，数列中中包含`stop`值，反之不包含，默认是True。
`base`	对数 log 的底数。
`dtype`	`ndarray` 的数据类型

实例1

import numpy as np

# 默认底数是 10
a = np.logspace(1.0,  2.0, num =  10)  
print (a)

输出结果为：

[ 10.           12.91549665 16.68100537 21.5443469 27.82559402 35.93813664 46.41588834 59.94842503 77.42636827 100. ]

实例2: 将对数的底数设置为 2

import numpy as np

a = np.logspace(0,9,10,base=2)
print (a)

输出如下：

[  1.   2. 4. 8. 16. 32. 64. 128. 256. 512.]