numpy.empty
Функция empty() возвращает новый массив заданной формы и типа без инициированных записей.
Параметры: shape — целое число, список или кортеж целых чисел Задает размеры необходимого массива — целое число или кортеж целых чисел. dtype — тип данных NumPy (необязательный) Определяет тип данных выходного массива. order — ‘C’ или ‘F’ (необязательный) Этот параметр определяет в каком порядке массивы должны храниться в памяти: строчном C-стиле или столбчатом стиле Fortran. Возвращает: результат — массив NumPy Массив неициированных (случайных) значений, указанной формы, типа и порядка.
Замечание
Функция empty в отличие от таких функций как zeros или ones не устанавливает элементы массива в какое-то определенное значение и работает немного быстрее. В результате работы функции empty все элементы приобретают случайное значение, которое зависит от состояния памяти, однако, использовать эту функцию в качестве генератора псевдослучайных чисел настоятельно не рекомендуется.
Функция может оказаться крайне полезной, если в вашем коде приходится очень часто создавать временные массивы.
numpy.empty#
Return a new array of given shape and type, without initializing entries.
Parameters : shape int or tuple of int
Shape of the empty array, e.g., (2, 3) or 2 .
dtype data-type, optional
Desired output data-type for the array, e.g, numpy.int8 . Default is numpy.float64 .
order <‘C’, ‘F’>, optional, default: ‘C’
Whether to store multi-dimensional data in row-major (C-style) or column-major (Fortran-style) order in memory.
like array_like, optional
Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as like supports the __array_function__ protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument.
New in version 1.20.0.
Array of uninitialized (arbitrary) data of the given shape, dtype, and order. Object arrays will be initialized to None.
Return an empty array with shape and type of input.
Return a new array setting values to one.
Return a new array setting values to zero.
Return a new array of given shape filled with value.
empty , unlike zeros , does not set the array values to zero, and may therefore be marginally faster. On the other hand, it requires the user to manually set all the values in the array, and should be used with caution.
Create an empty Numpy Array of given length or shape & data type in Python
In this article we will discuss different ways to create an empty 1D,2D or 3D Numpy array and of different data types like int or string etc.
Python’s numpy module provides a function empty() to create new arrays,
- It accepts shape and data type as arguments.
- Returns a new array of given shape and data type but without initializing entries. It means the returned numpy array will contain garbage values.
- If data type argument is not provided then the default data type of all entries in the returned numpy array will be float.
Let’s use this empty() function to create an empty numpy array of different shape and data types.
Create an empty 1D Numpy array of given length
To create an 1D Numpy array of length 5, we need pass a the integer 5 as shape argument to the empty() function,
It returned an empty array of 5 floats with garbage values.
Read More:
Create an empty Numpy array of given shape using numpy.empty()
In the previous example, we create an empty 1D numpy array. Let’s see how to create 2D and 3D empty Numpy array using empty() function,
Create an empty 2D Numpy array using numpy.empty()
To create an empty 2D Numpy array we can pass the shape of the 2D array ( i.e. row & column count) as a tuple to the empty() function.
Let’s create a empty 2D Numpy array with 5 rows and 3 columns,
It returned an empty 2D Numpy Array of 5 rows and 3 columns but all values in this 2D numpy array were not initialized.
As we did not provided the data type argument (dtype), so by default all entries will be float.
Create an empty 3D Numpy array using numpy.empty()
To create an empty 3D Numpy array we can pass the shape of the 3D array as a tuple to the empty() function.
Let’s create a empty 3D Numpy array with 2 matrix of 3 rows and 3 columns,
It returned an empty 3D Numpy Array with 2 matrices of 3 rows and 3 columns, but all values in this 3D numpy array were not initialized.
In all the above examples, we didn’t provide any data type argument. Therefore by default float data type was used and all elements were of float data type. But it might be possible that in some scenarios you want to create empty numpy arrays of other data types. Let’s see how to do that,
Create an empty Numpy array with custom data type
To create an empty numpy array of some specific data type, we can pass that data type as a dtype argument in the empty() function.
Let’s understand with some examples,
Create an empty Numpy array of 5 Integers
To create an empty numpy array of 5 integers, we need to pass int as dtype argument in the numpy.empty() function,
Create an empty Numpy array of 5 Complex Numbers
To create an empty numpy array of 5 complex numbers, we need to pass complex as dtype argument in the numpy.empty() function,
Create an empty Numpy array of 5 strings
To create an empty numpy array of 5 strings (with size 3), we need to pass ‘S3’ as dtype argument in the numpy.empty() function,
The complete example is as follows,
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How do I create an empty array and then append to it in NumPy?
I want to create an empty array and append items to it, one at a time.
Can I use this list-style notation with NumPy arrays?
15 Answers 15
That is the wrong mental model for using NumPy efficiently. NumPy arrays are stored in contiguous blocks of memory. To append rows or columns to an existing array, the entire array needs to be copied to a new block of memory, creating gaps for the new elements to be stored. This is very inefficient if done repeatedly.
Instead of appending rows, allocate a suitably sized array, and then assign to it row-by-row:
A NumPy array is a very different data structure from a list and is designed to be used in different ways. Your use of hstack is potentially very inefficient. every time you call it, all the data in the existing array is copied into a new one. (The append function will have the same issue.) If you want to build up your matrix one column at a time, you might be best off to keep it in a list until it is finished, and only then convert it into an array.
item can be a list, an array or any iterable, as long as each item has the same number of elements.
In this particular case ( data is some iterable holding the matrix columns) you can simply use
(Also note that using list as a variable name is probably not good practice since it masks the built-in type by that name, which can lead to bugs.)