NumPy: numpy.arange() function
The numpy.arange() function is used to generate an array with evenly spaced values within a specified interval. The function returns a one-dimensional array of type numpy.ndarray.
numpy.arange([start, ]stop, [step, ]dtype=None)
|start||Start of interval. The interval includes this value. The default start value is 0.||Optional|
|stop||End of interval. The interval does not include this value, except in some cases where step is not an integer and floating point round-off affects the length of out.||Required|
|step||Spacing between values. For any output out, this is the distance between two adjacent values, out[i+1] - out[i]. The default step size is 1. If step is specified as a position argument, start must also be given.||Optional|
|dtytpe||The type of the output array. If dtype is not given, infer the data type from the other input arguments.||Optional|
arange : ndarray - Array of evenly spaced values.
For floating point arguments, the length of the result is ceil((stop - start)/step). Because of floating point overflow, this rule may result in the last element of out being greater than stop.
Example: arange() function in NumPy.
>>> import numpy as np >>> np.arange(5) array([0, 1, 2, 3, 4]) >>> np.arange(5.0) array([ 0., 1., 2., 3., 4.])
In the above example the first line of the code creates an array of integers from 0 to 4 using np.arange(5). The arange() function takes only one argument, which is the stop value, and defaults to start value 0 and step size of 1.
The second line of the code creates an array of floating-point numbers from 0.0 to 4.0 using np.arange(5.0). Here, 5.0 is provided as the stop value, indicating that the range should go up to (but not include) 5.0. Since floating-point numbers are used, the resulting array contains floating-point values.
Both arrays have the same length and contain evenly spaced values.
Example: Numpy arange function with start, stop, and step arguments
>>> import numpy as np >>> np.arange(5,9) array([5, 6, 7, 8]) >>> np.arange(5,9,3) array([5, 8])
In the above code 'np.arange(5,9)' creates an array of integers from 5 to 8 (inclusive).
In the second example, 'np.arange(5,9,3)' creates an array of integers from 5 to 8 (inclusive) with a step size of 3. Since there is no integer between 5 and 8 that is evenly divisible by 3, the resulting array only contains two values, 5 and 8.
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