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NumPy Array from Numerical Ranges

Chapters

In this article, we will see the functions that use numerical ranges to create a NumPy ndarray.

  • arange() function
  • linspace() function
  • logspace() function

arange() function

The arange() function of the NumPy library returns a ndarray having evenly spaced numerical values within a given range.

Syntax:

arange([start,] stop[, step,], dtype=None, *, like=None)

The arange() function takes the following arguments:

  • start: start of the interval(value included in the interval) and default is 0.
  • stop: end of the interval(value not included in the interval).
  • step: Spacing between two consecutive values in the interval and default is 1.
  • dtype: Sets the data type of elements in the array.

The stop is a mandatory parameter and start, step, dtype, and like are optional parameters in the arange() function.

Whenever step is specified it is required to provide start. Both integer and real values can be passed as input to start, stop and step arguments.

NumPy Array using arange() function:

We import the NumPy package using the import statement. Creating a ndarray using arange() function.

import numpy as np
np_arr = np.arange(10)
np_arr #other than Jupyter Notebook users, use print(np_arr)
#Output:
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

arange() function with start, step and dtype:

np_arr = np.arange(1.5, 5, 0.5, dtype = complex)
np_arr #other than Jupyter Notebook users, use print(np_arr)
#Output:
array([1.5+0.j, 2. +0.j, 2.5+0.j, 3. +0.j, 3.5+0.j, 4. +0.j, 4.5+0.j])

linspace() function

The linspace() function works similar to NumPy’s arange() function. It returns a ndarray having evenly spaced elements in a given interval. The linspace() function uses the number of samples whereas arange() function works on step size.

Syntax:

linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0)

The arguments of linspace() function are:

  • start, stop and dtype are same as mentioned in arange() function.
  • num: Number of evenly spaced values to generate and default is 50.
  • endpoint: If False, stop value not is included in the interval and default is True.
  • retstep: If True, returns the evenly spaced values with step size between conscecutive values in the interval, default is False.

The start and stop parameters are mandatory and others are optional in the linspace() function.

NumPy Array using linspace() function:

np_arr = np.linspace(0, 10, num=5)
np_arr #other than Jupyter Notebook users, use print(np_arr)
#Output:
array([ 0. ,  2.5,  5. ,  7.5, 10. ])

The output array consists of 5 evenly spaced elements as the number of sample values is given 5(num=5).

linspace() function with retstep and endpoint:

np_arr = np.linspace(2, 12, num=5, dtype=int, retstep=True, endpoint=False)
np_arr #other than Jupyter Notebook users, use print(np_arr)
#Output:
(array([ 2,  4,  6,  8, 10]), 2.0)

The output array does not have the stop value as element(endpoint=False) and 2.0 is the step size(difference between two values in the interval).

logspace() function

The logspace() function of NumPy returns a ndarray that has evenly spaced values on a log scale.

Syntax:

np.logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0)

The parameters of logspace() function are:

  • num, endpoint and dtype are same as mentioned in linspace() function.
  • base: base value, default is 10.
  • start: start point of the sequence is base(start).
  • stop: end of the sequence is base(stop).

NumPy Array using logspace() function:

np_arr = np.logspace(1, 3, num=5)
np_arr #other than Jupyter Notebook users, use print(np_arr)
#Output
array([  10.        ,   31.6227766 ,  100.        ,  316.22776602,
       1000.        ])

The output array has 5 even spaced values between 101 to 103.

logspace() function with base:

np_arr = np.logspace(0, 2, num=3, base=2)
np_arr #other than Jupyter Notebook users, use print(np_arr)
#Output
array([1., 2., 4.])

The output array has 3 even spaced values between 20 to 22.