Learning Numpy - Simple Tutorial For Beginners - NumPy - Array From Numerical Ranges Part 7

numpy.arange

This function returns an ndarray object containing evenly spaced values within a given range. The format of the function is as follows −

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

Parameter	Description
start	The start of an interval. If omitted, defaults to 0
stop	The end of an interval (not including this number)
step	Spacing between values, default is 1
dtype	Data type of resulting ndarray. If not given, data type of input is used

Example 1

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

[0 1 2 3 4]

Example 2

import numpy as np
# dtype set 
x = np.arange(5, dtype = float)
print (x)

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

Example 3

# start and stop parameters set 
import numpy as np
x = np.arange(10,20,2)
print (x)

[10 12 14 16 18]

numpy.linspace

This function is similar to arange() function. In this function, instead of step size, the number of evenly spaced values between the interval is specified. The usage of this function is as follows −

numpy.linspace(start, stop, num, endpoint, retstep, dtype)

Parameter	Description
start	The starting value of the sequence
stop	The end value of the sequence, included in the sequence if endpoint set to true
num	The number of evenly spaced samples to be generated. Default is 50
endpoint	True by default, hence the stop value is included in the sequence. If false, it is not included
retstep	If true, returns samples and step between the consecutive numbers
dtype	Data type of output ndarray

Example 1

import numpy as np
x = np.linspace(10,20,5)
print (x)

[10.  12.5 15.  17.5 20. ]

Example 2

# endpoint set to false 
import numpy as np
x = np.linspace(10,20, 5, endpoint = False)
print (x)

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

Example 3

# find retstep value 
import numpy as np

x = np.linspace(1,2,5, retstep = True)
print (x)
# retstep here is 0.25

(array([1.  , 1.25, 1.5 , 1.75, 2.  ]), 0.25)

numpy.logspace

This function returns an ndarray object that contains the numbers that are evenly spaced on a log scale. Start and stop endpoints of the scale are indices of the base, usually 10.

numpy.logspace(start, stop, num, endpoint, base, dtype)

Following parameters determine the output of logspace function.

Parameter	Description
start	The starting point of the sequence is basestart
stop	The final value of sequence is basestop
num	The number of values between the range. Default is 50
endpoint	If true, stop is the last value in the range
base	Base of log space, default is 10
dtype	Data type of output array. If not given, it depends upon other input arguments

Example 1

import numpy as np
# default base is 10 
a = np.logspace(1.0, 2.0, num = 10)
print (a)

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

Example 2

# set base of log space to 2 
import numpy as np
a = np.logspace(1,10,num = 10, base = 2)
print (a)

[   2.    4.    8.   16.   32.   64.  128.  256.  512. 1024.]

References

• Learning Numpy - Array From Existing Data Part 6

• Learning Numpy - Array Creation Routines Part 5

• Learning Numpy - Array Attributes Part 4

• Learning Numpy - Data Types Part 3