Python NumPy linspace + Examples

In this Python NumPy tutorial, we will discuss Python NumPy linspace and also cover the below examples:

  • Python NumPy linspace
  • Python NumPy linspace integer
  • Python NumPy linspace 2d
  • Python NumPy linspace example
  • Python NumPy linspace logarithmic
  • Python NumPy linspace datetime
  • Python NumPy linspace int
  • Python NumPy linspace vector
  • Python NumPy arange vs linspace
  • Python NumPy linspace float

Python NumPy linspace

  • In this section, we will learn about Python numpy linspace.
  • The linspace function always returns evenly spaced numbers based on a given interval..
  • The interval by default includes the first value and last value, but the ending value can be optionally excluded from the result.
  • To use the linspace method we will declare a new script with the NumPy library.
  • Next, we will use the linspace function using numpy.linspace().
  • Within the function, we will add two parameters.
  • The first parameter is the starting value of the sequence and the second parameter is the ending value of the sequence.

Syntax:

Here is the syntax of numpy linspace

np.linspace(
            start,
            stop,
            endpoint=True,
            dtype=None,
            axis=0
            )
  1. Start: The default start value is 0. So this is the optional value if you didn’t mention the start value it will take default value zero.
  2. Stop: It is also a number end of the interval that does not include this value so similar to the built-in range functions the stop is not included it is exclusive but in some cases like this special cases it will include that when the step is not an integer and floating-point round of effect the length of out.
  3. Dtype: stands for data type the type of the output array if d-type is not given in for the datatype from the other input arguments
  4. Returns: It will return ndarray.

Example:

import numpy as np

a = (np.linspace(1,5))
print(a)

Here is the Screenshot of following given code

Python numpy linspace
Python numpy linspace

Read: Python NumPy concatenate

Python NumPy linspace integer

  • In this section, we will learn about Python NumPy linspace integer.
  • In NumPy linspace the default value of the dtype argument in the function header is None.
  • If you want to manually specify the data type, you can use the dtype parameter.
  • In this case, when we set datatype = integer, the linspace function produces a numpy dimension array of objects.

Syntax:

Here is the syntax of numpy linspace integer

np.linspace(
            start,
            stop,
            dtype=int,
            axis=0
            )

Example:

import numpy as np

a = (np.linspace(1,10,num=5,dtype="int"))
print(a)

Here is the Screenshot of following given code

Python numpy linspace integer
Python numpy linspace integer

Read: Python sort NumPy array

Python NumPy linspace 2d

  • In this section, we will learn about Python NumPy linspace 2d.
  • Two Dimensional Numpy means the collection of homogenous data in lists of a list. It is also known as a matrix. In a 2D array, you have to use two square brackets that is why it said lists of lists.
  • For creating a 2d array, then the key change is to feed multiple starts and stops. Each first-last pair declares a new dimension in the array.

Syntax:

np.linspace(
            start,
            stop,
            dtype=None,
            axis=0
            )

Example:

The following code assigns a list of numbers to each of the starts and stops arguments. Because start and stop are fed a list with 2 elements then 2d arrays of numbers will be created.

import numpy as np

a = (np.linspace([2,3],[4,6],num=3))
print(a)

Here is the Screenshot of the following given code

PYthon numpy linspace 2d
Python numpy linspace 2d

Read: Python NumPy matrix + Examples

Python NumPy linspace example

  • In this section, we will learn about the Python NumPy linspace example.
  • This linspace function also creates a sequence of evenly spaced values within a defined interval.
  • It also gives values in the specified range and the values are evenly space like arange function.
  • The numpy.linspace function will return an iterable sequence of evenly spaced elements on that interval.
  • In this example, first, we create a function and pass arguments in which we specify the datatype, start and stop parameters.
  • It will take a parameter that is num number of samples to generate and the default value is 50

Syntax:

np.linspace(
            start,
            stop,
            dtype,
            axis=0
            )

Example:

import numpy as np

a = (np.linspace(4,8,num=3)) #start and stop
print(a)

b = (np.linspace(1,5,num=3,dtype="int")) #data type
print(b)

c = (np.linspace(3,9,num=4,endpoint=False))
print(c)

d = (np.linspace(4,8,num=3,retstep=True))
print(d)

Here is the Screenshot of following given code

Python numpy linspace examples
Python numpy linspace examples

Read: Python NumPy append

Python NumPy linspace logarithmic

  • In this section, we will learn about Python NumPy linspace logarithmic.
  • The logarithmic function np.log() is used to calculate the user to find the natural logarithm of x, where x belongs to all the input array elements.
  • It is the inverse of the exponential method as well as an element-wise real logarithm.
  • In linspace logarithmic, we can easily use the function np.logspace().
  • Logspace returns even spaced numbers on a log scale value. Logspace has the same arguments as numpy.linspace.
  • In logspace, the iterable sequence starts at the base to the power of start and ends with the base stop.

Syntax:

Here is the syntax of logspace()

np.logspace(
            start,
            stop,
            dtype,
            axis=0,
            base
            )

Example:

import numpy as np

a = (np.logspace(4,8,num=3,base=2)) #start and stop
print(a)

Here is the Screenshot of following given code

Python numpy linspace logarithmic
Python numpy linspace logarithmic

Python NumPy linspace datetime

  • In this section, we will learn about Python NumPy linspace datetime.
  • Numpy has core array datatypes that natively support datetime functionality. The dtype is called ‘timestamp’, so named because “datetime” is already available in the datetime library in Python.

Example:

import pandas as pd
import numpy as np
start = pd.Timestamp('2012-02-02')
end = pd.Timestamp('2012-04-04')
new_t = np.linspace(start.value, end.value, 10)
new_t = pd.to_datetime(new_t)
print(new_t)

Here is the Screenshot of following given code

Python numpy linspace datetime
Python numpy linspace datetime

Read: Python NumPy arange + Examples

Python NumPy linspace int

  • In this section, we will learn about Python NumPy linspace int.
  • In linspace integer, we can easily use the function np.linspace.
  • we have to specify the datatype in the parameter.

Syntax:

Here is the syntax of numpy linspace integer

np.linspace(
            start,
            stop,
            dtype=int,
            axis=0
            )

Example:

import numpy as np

a = (np.linspace(1,6,num=3,dtype="int"))
print(a)

Here is the Screenshot of the following given code

Python numpy linspace int
Python numpy linspace int

Python NumPy linspace vector

  • In this section, we will learn about the Python NumPy linspace vector.
  • Vectors refer to the process of operation in entire arrays, concisely and efficiently.
  • Numpy also prefers to vectorized operation as “numpy array”.
  • Create a vector of evenly spaced points in the interval.

Syntax:

Here is the syntax of numpy linspace integer

np.linspace(
            start,
            stop,
            dtype,
            axis=0
            )

Example:

import numpy as np

y = (np.linspace(-5,5,7))
print(y)

Here is the Screenshot of following given code

Python numpy linspace vector
Python numpy linspace vector

Read: Python NumPy Sum + Examples

Python NumPy arange vs linspace

  • In this section, we will learn about Python NumPy arange vs linspace.
  • The Numpy arange function generates a NumPy array with evenly spaced values based on the start and stops intervals specifiePython numpy arrange vs linspace upon declaration.
  • numpy.linspace() and numpy.arange() functions are the same because the linspace function also creates an iterable sequence of evenly spaced values within a defined interval.
  • It also gives values in the given range and the values are evenly spaced like arange method.
  • The np. linspace() function will return an iterable sequence of evenly spaced values on that interval.
  • The last point of the given interval can be excluded.

Syntax:

np.linspace(
            start,
            stop,
            endpoint=True,
            dtype=None,
            axis=0
            )

Example:

import numpy as np

a = np.arange(2,10,2)
b = np.linspace(0,10,5)
print(a)
print(b)

Here is the Screenshot of following given code.

Python numpy arange vs linspace
Python numpy arrange vs linspace

Python NumPy linspace float

  • In this section, we will learn about the Python NumPy linspace float.
  • In linspace the default value of the dtype argument is None.
  • In this case, when we set dtype = float, the linspace function produces an nd. array objects.

Syntax:

Here is the syntax of numpy linspace integer

np.linspace(
            start,
            stop,
            dtype=float,
            axis=0
            )

Example:

import numpy as np

b = (np.linspace(1,5,num=5,dtype="float"))
print(b)

Here is the Screenshot of following given code

Python numpy linspace float
Python numpy linspace float

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In this Python tutorial, we will discuss Python numpy linspace and also cover the below examples:

  • Python numpy linspace
  • Python numpy linspace integer
  • Python numpy linspace 2d
  • Python numpy linspace example
  • Python numpy linspace logarithmic
  • Python numpy linspace datetime
  • Python numpy linspace int
  • Python numpy linspace vector
  • Python numpy arange vs linspace