Np.empty trong python

All you need to do is pass a list to it; optionally, you can specify the data type.

import numpy as np

list = ['Python', 'Golang', 'PHP', 'Javascript']
arr = np.array(list)
print(arr)

Output

['Python' 'Golang' 'PHP' 'Javascript']

As you can see in the output, we have created a list of strings and then passed the list to the np.array() function, and as a result, it will create a numpy array.

np.empty

The np.empty(shape, dtype=float, order=’C’) is a numpy library function that returns a new array of given shapes and types without initializing entries. Numpy empty() function creates a new array of given shapes and types without initializing entries.

On the other side, it requires the user to manually set all the values in the array and be used with caution.

A Numpy array is a very diverse data structure from a list and is designed to be used differently.

Understanding Numpy array

To create an empty array in Numpy (e.g., a 2D array m*n to store), use the np.empty(shape=[0, n]).

import numpy as np

arr = np.empty([0, 2])
print(arr)

Output

[]

How to initialize an Efficiently numpy array

NumPy arrays are stored in the contiguous blocks of memory. Therefore, if you need to append rows or columns to an existing array, the entire array must be copied to the new memory block, creating gaps for storing new items. This is very inefficient if done repeatedly to create an array.

This is the best case in adding rows if you have to create the array as big as your dataset will eventually be and then insert the data row-by-row.

import numpy as np

arr = np.zeros([4, 3])
print(arr)

Output

[[0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]]

And then, you can add the data of row by row, which is how you initialize the array and then append the value to the numpy array.

See also

How to Create Empty Numpy Array in Python

How to Create Numpy Array in Python

How to Append to Numpy Array

Facebook

Twitter

Pinterest

WhatsApp

Previous articlePython max int: Maximum Integer Value in Python

Next articleDeque in C++: The Complete Guide

Krunal

https://appdividend.com/

Krunal Lathiya is an Information Technology Engineer. By profession, he is a web developer with knowledge of multiple back-end platforms (e.g., PHP, Node.js, Python) and frontend JavaScript frameworks (e.g., Angular, React, and Vue).

NumPy empty() array function in Python is used to create a new array of given shapes and types, without initializing entries. This function takes three arguments, we can customize the specific data type and order by passing these parameters. In this article, I will explain syntax and how to use the numpy.empty() function which returns an array of uninitialized data of the given shape, order, and datatype. Object arrays will be initialized to None.

1. Quick Examples of Empty Array

If you are in a hurry, below are some quick examples of how to use NumPy empty() array function in Python.

# Below are the quick examples

# Example 1: use empty() function with 1-dimensional array
arr = np.empty(4)

# Example 2: create numpy empty() function 
arr = np.empty(shape = 5)

# Example 3: use empty() function with 2-dimensional array
arr = np.empty([5, 3])

# Example 4: create numpy empty 3 x 4 matrix
arr =  np.empty(shape = [3,4]) 

# Example 5: use numpy empty() function with dtype=int 
arr = np.empty((4, 3), dtype=int)

# Example 6: use numpy.empty() function with dtype=float
arr = np.empty([3, 4], dtype=float)

2. Syntax of NumPy empty()

Following is the syntax to create numpy.empty() function.

# Syntax of  numpy.empty() 
numpy.empty(shape, dtype=float, order='C', *, like=None)

2.1 Parameters of empty()

Following are the parameters of empty().

• shape – It defines the shape of the empty array which is an int or tuple of int. The shape of the empty array, e.g., (3, 4) or 2.

• 
  # Syntax of  numpy.empty() 
  numpy.empty(shape, dtype=float, order='C', *, like=None)
  

  0 – It is an optional parameter that desired output data-type for the array, e.g., numpy.int8. Default is numpy.float64.

• 
  # Syntax of  numpy.empty() 
  numpy.empty(shape, dtype=float, order='C', *, like=None)
  

  1 – {‘C’, ‘F’}, optional: To store multi-dimensional data in row-major (C) or column-major (F) order/pattern in the memory location.

• 
  # Syntax of  numpy.empty() 
  numpy.empty(shape, dtype=float, order='C', *, like=None)
  

  2 – value should be array_like, optional

2.2 Return value of empty()

It returns ndarray of the array of uninitialized data of the given shape, order, and datatype. Object arrays will be initialized to None.

3. Use empty() Function with 1- D Array

To create a one-dimensional Numpy array of shape 4 use the NumPy

# Syntax of  numpy.empty() 
numpy.empty(shape, dtype=float, order='C', *, like=None)

import numpy as np

# Use empty() function with 1-dimensional array
arr = np.empty(4)
print(arr)

# Output
# [2.12199579e-314 2.12199579e-314 1.10473078e-320 6.95272004e-310]

# create numpy empty() function 
arr = np.empty(shape = 5)
print(arr)

# Output
# [0.   0.25 0.5  0.75 1.  ]

4. Use empty() Function with 2- D Array

To create a two-dimensional array of empty use the shape of columns and rows as the value to shape parameter. We passed a list of numbers, [5,3] to the shape parameter. This indicates to numpy.empty() that we want to create an empty NumPy array with 5 rows and 3 columns.

# Use empty() function with 2-dimensional array
arr = np.empty([5, 3])
print(arr)

# Output
# [[6.23042070e-307 3.56043053e-307 1.60219306e-306]
#  [2.44763557e-307 1.69119330e-306 2.22522596e-306]
#  [6.23059386e-307 1.69119602e-306 1.78019082e-306]
#  [1.78020984e-306 6.23053954e-307 9.34609790e-307]
#  [2.22522868e-306 2.56765117e-312 5.97819431e-322]]

Alternate, follow the below examples to create NumPy empty 3 x 4 matrix using numpy.empty() function.

# create numpy empty 3 x 4 matrix
arr =  np.empty(shape = [3,4]) 
print(arr)

# Output
# [[6.23042070e-307 3.56043053e-307 1.60219306e-306 2.44763557e-307]
#  [1.69119330e-306 2.22522596e-306 6.23059386e-307 1.69119602e-306]
#  [1.78022342e-306 2.13620807e-306 1.78021119e-306 1.69120552e-306]]

5. Use NumPy empty() Function with dtype=int

If you want

# Syntax of  numpy.empty() 
numpy.empty(shape, dtype=float, order='C', *, like=None)

# Syntax of  numpy.empty() 
numpy.empty(shape, dtype=float, order='C', *, like=None)

# Syntax of  numpy.empty() 
numpy.empty(shape, dtype=float, order='C', *, like=None)

# use numpy empty() function with dtype=int 
arr = np.empty((4, 3), dtype=int)
print(arr)

# Output
# [[0 0 0]
#  [0 0 0]
#  [0 0 0]
#  [0 0 0]]

6. Use numpy.empty() Function with dtype=float

To create a Numpy array that’s empty with floating point numbers instead of integers use

# Syntax of  numpy.empty() 
numpy.empty(shape, dtype=float, order='C', *, like=None)

import numpy as np

# Use empty() function with 1-dimensional array
arr = np.empty(4)
print(arr)

# Output
# [2.12199579e-314 2.12199579e-314 1.10473078e-320 6.95272004e-310]

# create numpy empty() function 
arr = np.empty(shape = 5)
print(arr)

# Output
# [0.   0.25 0.5  0.75 1.  ]

# use numpy.empty() function with dtype=float
arr = np.empty([3, 4], dtype=float)
print(arr)

# Output
# [[6.23042070e-307 3.56043053e-307 1.60219306e-306 2.44763557e-307]
#  [1.69119330e-306 2.22522596e-306 6.23059386e-307 1.69119602e-306]
#  [1.78022342e-306 2.13620807e-306 1.78021119e-306 1.69120552e-306]]

7. Conclusion

In this article, I have explained NumPy empty() array function using how to create an array of uninitialized data of the given shape, order, and datatype with examples.