YouTip

In this chapter we will learn how to create arrays from numerical ranges. ### numpy.arange numpy.arange function in the numpy package is used to create a numerical range and return an ndarray object, with the following function format: numpy.arange(start, stop, step, dtype) According to the range specified by start and stop and the step set by step, generate an ndarray. Parameter description: | Parameter | Description | | --- | --- | | `start` | Start value, default is `0` | | `stop` | Stop value (not included) | | `step` | Step, default is `1` | | `dtype` | Data type of the returned `ndarray`, if not provided, the input data type will be used. | ### Instance Generate an array with length 5 from 0 to 4: ## Instance import numpy as np x = np.arange(5) print(x) The output is as follows: Set the return type to float: ## Instance import numpy as np x = np.arange(5, dtype = float) print(x) The output is as follows: [0. 1. 2. 3. 4.] Set the start value, stop value and step: ## Instance import numpy as np x = np.arange(10,20,2) print(x) The output is as follows: ### numpy.linspace numpy.linspace function is used to create a one-dimensional array, the array is composed of an arithmetic progression, with the following format: np.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None) Parameter description: | Parameter | Description | | --- | --- | | `start` | Start value of the sequence | | `stop` | Stop value of the sequence, if `endpoint` is `true`, this value is included in the sequence | | `num` | Number of samples to generate with equal spacing, default is `50` | | `endpoint` | When this value is `true`, the `stop` value is included in the sequence, otherwise not included, default is True. | | `retstep` | If it is True, the spacing will be displayed in the generated array, otherwise not displayed. | | `dtype` | Data type of `ndarray` | The following instance uses three parameters, setting the start point to 1, stop point to 10, and the number of sequence elements to 10. ## Instance 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.] Set the arithmetic progression with all elements being 1: ## Instance 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.] Set endpoint to false, not including the stop value: ## Instance import numpy as np a = np.linspace(10, 20, 5, endpoint = False) print(a) The output is: [10. 12. 14. 16. 18.] If endpoint is set to true, it will include 20. The following instance sets the spacing. ## Instance import numpy as np a =np.linspace(1,10,10,retstep= True) print(a) 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 numpy.logspace function is used to create a one-dimensional array composed of a geometric progression. The format is as follows: np.logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None) The base parameter means the subscript of log when taking logarithm. | Parameter | Description | | --- | --- | | `start` | Start value of the sequence is: base ** start | | `stop` | Stop value of the sequence is: base ** stop. If `endpoint` is `true`, this value is included in the sequence | | `num` | Number of samples to generate with equal spacing, default is `50` | | `endpoint` | When this value is `true`, the `stop` value is included in the sequence, otherwise not included, default is True. | | `base` | Base of the logarithm log. | | `dtype` | Data type of `ndarray` | ## Instance import numpy as np a = np.logspace(1.0, 2.0, num = 10) print(a) The output is: [ 10. 12.91549665 16.68100537 21.5443469 27.82559402 35.938