NumPy - Array Creation
The NumPy package provides a number of ways to create a new ndarray object. Below is the list of most commonly used functions for this purpose:
| Function | Description |
|---|---|
| empty() | Returns a new array of given shape and type, without initializing entries. |
| ones() | Returns a new array of given shape and type, filled with ones. |
| zeros() | Returns a new array of given shape and type, filled with zeros. |
Lets discuss these functions in detail:
numpy.empty() function
The NumPy empty() function returns a new array of specified shape and data type, with uninitialized entries.
Syntax
numpy.empty(shape, dtype=float, order='C')
Parameters
shape |
Required. Specify shape of the returned array in form of int or tuple of ints. |
dtype |
Optional. Specify the desired data-type for the array. Default: float |
order |
Optional. Specify whether to store multi-dimensional data in row-major (C-style) or column-major (Fortran-style) order in memory. Two possible values are: C (C-style) and F (Fortran-style). Default: 'C' |
Example
In the example below, empty() function is used to create a 2 dimensional array of uninitialized (arbitrary) data of specified shape.
#array of uninitialized (arbitrary) data import numpy as np Arr = np.empty((2,2)) print(Arr)
The output of the above code will be:
[[ 6.93360212e-310 6.93360212e-310] [ 6.93359915e-310 6.93359743e-310]]
numpy.zeros() function
The NumPy zeros() function returns a new array of specified shape and data type, filled with zeros.
Syntax
numpy.zeros(shape, dtype=float, order='C')
Parameters
shape |
Required. Specify shape of the returned array in form of int or tuple of ints. |
dtype |
Optional. Specify the desired data-type for the array. Default: float |
order |
Optional. Specify whether to store multi-dimensional data in row-major (C-style) or column-major (Fortran-style) order in memory. Two possible values are: C (C-style) and F (Fortran-style). Default: 'C' |
Example
In the example below, zeros() function is used to create a 2 dimensional array of zeros of specified shape.
#array of zeros import numpy as np Arr = np.zeros((2,3)) print(Arr)
The output of the above code will be:
[[ 0. 0. 0.] [ 0. 0. 0.]]
Example
The data type of the returned array can be provided using dtype parameter.
#dtype=complex import numpy as np Arr = np.zeros((2,2), dtype=complex) print(Arr)
The output of the above code will be:
[[ 0.+0.j 0.+0.j] [ 0.+0.j 0.+0.j]]
numpy.ones() function
The NumPy ones() function returns a new array of specified shape and data type, filled with ones.
Syntax
numpy.ones(shape, dtype=float, order='C')
Parameters
shape |
Required. Specify shape of the returned array in form of int or tuple of ints. |
dtype |
Optional. Specify the desired data-type for the array. Default: float |
order |
Optional. Specify whether to store multi-dimensional data in row-major (C-style) or column-major (Fortran-style) order in memory. Two possible values are: C (C-style) and F (Fortran-style). Default: 'C' |
Example
In the example below, ones() function is used to create a 2 dimensional array of ones of specified shape.
#array of ones import numpy as np Arr = np.ones((2,3)) print(Arr)
The output of the above code will be:
[[ 1. 1. 1.] [ 1. 1. 1.]]
Example
The custom data type can also be used using dtype parameter.
#custom data type
import numpy as np
Arr = np.ones((2,2), dtype=[('x', 'i4'), ('y', 'i4')])
print(Arr)
The above code gives the following output:
[[(1, 1) (1, 1)] [(1, 1) (1, 1)]]
