Array creation#
See also
Introduction#
There are 6 general mechanisms for creating arrays:
Conversion from other Python structures (i.e. lists and tuples)
Intrinsic NumPy array creation functions (e.g. arange, ones, zeros, etc.)
Replicating, joining, or mutating existing arrays
Reading arrays from disk, either from standard or custom formats
Creating arrays from raw bytes through the use of strings or buffers
Use of special library functions (e.g., random)
You can use these methods to create ndarrays or Structured arrays. This document will cover general methods for ndarray creation.
1) Converting Python sequences to NumPy arrays#
NumPy arrays can be defined using Python sequences such as lists and
tuples. Lists and tuples are defined using [...] and (...),
respectively. Lists and tuples can define ndarray creation:
a list of numbers will create a 1D array,
a list of lists will create a 2D array,
further nested lists will create higher-dimensional arrays. In general, any array object is called an ndarray in NumPy.
>>> import numpy as np
>>> a1D = np.array([1, 2, 3, 4])
>>> a2D = np.array([[1, 2], [3, 4]])
>>> a3D = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
When you use numpy.array to define a new array, you should
consider the dtype of the elements in the array,
which can be specified explicitly. This feature gives you
more control over the underlying data structures and how the elements
are handled in C/C++ functions.
When values do not fit and you are using a dtype, NumPy may raise an
error:
>>> import numpy as np
>>> np.array([127, 128, 129], dtype=np.int8)
Traceback (most recent call last):
...
OverflowError: Python integer 128 out of bounds for int8
An 8-bit signed integer represents integers from -128 to 127.
Assigning the int8 array to integers outside of this range results
in overflow. This feature can often be misunderstood. If you
perform calculations with mismatching dtypes, you can get unwanted
results, for example:
>>> import numpy as np
>>> a = np.array([2, 3, 4], dtype=np.uint32)
>>> b = np.array([5, 6, 7], dtype=np.uint32)
>>> c_unsigned32 = a - b
>>> print('unsigned c:', c_unsigned32, c_unsigned32.dtype)
unsigned c: [4294967293 4294967293 4294967293] uint32
>>> c_signed32 = a - b.astype(np.int32)
>>> print('signed c:', c_signed32, c_signed32.dtype)
signed c: [-3 -3 -3] int64
Notice when you perform operations with two arrays of the same
dtype: uint32, the resulting array is the same type. When you
perform operations with different dtype, NumPy will
assign a new type that satisfies all of the array elements involved in
the computation, here uint32 and int32 can both be represented in
as int64.
The default NumPy behavior is to create arrays in either 32 or 64-bit signed
integers (platform dependent and matches C long size) or double precision
floating point numbers. If you expect your
integer arrays to be a specific type, then you need to specify the dtype while
you create the array.
2) Intrinsic NumPy array creation functions#
NumPy has over 40 built-in functions for creating arrays as laid out in the Array creation routines. These functions can be split into roughly three categories, based on the dimension of the array they create:
1D arrays
2D arrays
ndarrays
1 - 1D array creation functions#
The 1D array creation functions e.g. numpy.linspace and
numpy.arange generally need at least two inputs, start and
stop.
numpy.arange creates arrays with regularly incrementing values.
Check the documentation for complete information and examples. A few
examples are shown:
>>> import numpy as np
>>> np.arange(10)
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.arange(2, 10, dtype=float)
array([2., 3., 4., 5., 6., 7., 8., 9.])
>>> np.arange(2, 3, 0.1)
array([2. , 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9])
Note: best practice for numpy.arange is to use integer start, end, and
step values. There are some subtleties regarding dtype. In the second
example, the dtype is defined. In the third example, the array is
dtype=float to accommodate the step size of 0.1. Due to roundoff error,
the stop value is sometimes included.
numpy.linspace will create arrays with a specified number of elements, and
spaced equally between the specified beginning and end values. For
example:
>>> import numpy as np
>>> np.linspace(1., 4., 6)
array([1. , 1.6, 2.2, 2.8, 3.4, 4. ])
The advantage of this creation function is that you guarantee the
number of elements and the starting and end point. The previous
arange(start, stop, step) will not include the value stop.
2 - 2D array creation functions#
The 2D array creation functions e.g. numpy.eye, numpy.diag, and numpy.vander
define properties of special matrices represented as 2D arrays.
np.eye(n, m) defines a 2D identity matrix. The elements where i=j (row index and column index are equal) are 1
and the rest are 0, as such:
>>> import numpy as np
>>> np.eye(3)
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])
>>> np.eye(3, 5)
array([[1., 0., 0., 0., 0.],
[0., 1., 0., 0., 0.],
[0., 0., 1., 0., 0.]])
numpy.diag can define either a square 2D array with given values along
the diagonal or if given a 2D array returns a 1D array that is
only the diagonal elements. The two array creation functions can be helpful while
doing linear algebra, as such:
>>> import numpy as np
>>> np.diag([1, 2, 3])
array([[1, 0, 0],
[0, 2, 0],
[0, 0, 3]])
>>> np.diag([1, 2, 3], 1)
array([[0, 1, 0, 0],
[0, 0, 2, 0],
[0, 0, 0, 3],
[0, 0, 0, 0]])
>>> a = np.array([[1, 2], [3, 4]])
>>> np.diag(a)
array([1, 4])
vander(x, n) defines a Vandermonde matrix as a 2D NumPy array. Each column
of the Vandermonde matrix is a decreasing power of the input 1D array or
list or tuple,
x where the highest polynomial order is n-1. This array creation
routine is helpful in generating linear least squares models, as such:
>>> import numpy as np
>>> np.vander(np.linspace(0, 2, 5), 2)
array([[0. , 1. ],
[0.5, 1. ],
[1. , 1. ],
[1.5, 1. ],
[2. , 1. ]])
>>> np.vander([1, 2, 3, 4], 2)
array([[1, 1],
[2, 1],
[3, 1],
[4, 1]])
>>> np.vander((1, 2, 3, 4), 4)
array([[ 1, 1, 1, 1],
[ 8, 4, 2, 1],
[27, 9, 3, 1],
[64, 16, 4, 1]])
3 - general ndarray creation functions#
The ndarray creation functions e.g. numpy.ones,
numpy.zeros, and random define
arrays based upon the desired shape. The ndarray creation functions
can create arrays with any dimension by specifying how many dimensions
and length along that dimension in a tuple or list.
numpy.zeros will create an array filled with 0 values with the
specified shape. The default dtype is float64:
>>> import numpy as np
>>> np.zeros((2, 3))
array([[0., 0., 0.],
[0., 0., 0.]])
>>> np.zeros((2, 3, 2))
array([[[0., 0.],
[0., 0.],
[0., 0.]],
[[0., 0.],
[0., 0.],
[0., 0.]]])
numpy.ones will create an array filled with 1 values. It is identical to
zeros in all other respects as such:
>>> import numpy as np
>>> np.ones((2, 3))
array([[1., 1., 1.],
[1., 1., 1.]])
>>> np.ones((2, 3, 2))
array([[[1., 1.],
[1., 1.],
[1., 1.]],
[[1., 1.],
[1., 1.],
[1., 1.]]])
The random method of the result of
default_rng will create an array filled with random
values between 0 and 1. It is included with the numpy.random
library. Below, two arrays are created with shapes (2,3) and (2,3,2),
respectively. The seed is set to 42 so you can reproduce these
pseudorandom numbers:
>>> import numpy as np
>>> from numpy.random import default_rng
>>> default_rng(42).random((2,3))
array([[0.77395605, 0.43887844, 0.85859792],
[0.69736803, 0.09417735, 0.97562235]])
>>> default_rng(42).random((2,3,2))
array([[[0.77395605, 0.43887844],
[0.85859792, 0.69736803],
[0.09417735, 0.97562235]],
[[0.7611397 , 0.78606431],
[0.12811363, 0.45038594],
[0.37079802, 0.92676499]]])
numpy.indices will create a set of arrays (stacked as a one-higher
dimensioned array), one per dimension with each representing variation in that
dimension:
>>> import numpy as np
>>> np.indices((3,3))
array([[[0, 0, 0],
[1, 1, 1],
[2, 2, 2]],
[[0, 1, 2],
[0, 1, 2],
[0, 1, 2]]])
This is particularly useful for evaluating functions of multiple dimensions on a regular grid.
3) Replicating, joining, or mutating existing arrays#
Once you have created arrays, you can replicate, join, or mutate those
existing arrays to create new arrays. When you assign an array or its
elements to a new variable, you have to explicitly numpy.copy the array,
otherwise the variable is a view into the original array. Consider the
following example:
>>> import numpy as np
>>> a = np.array([1, 2, 3, 4, 5, 6])
>>> b = a[:2]
>>> b += 1
>>> print('a =', a, '; b =', b)
a = [2 3 3 4 5 6] ; b = [2 3]
In this example, you did not create a new array. You created a variable,
b that viewed the first 2 elements of a. When you added 1 to b you
would get the same result by adding 1 to a[:2]. If you want to create a
new array, use the numpy.copy array creation routine as such:
>>> import numpy as np
>>> a = np.array([1, 2, 3, 4])
>>> b = a[:2].copy()
>>> b += 1
>>> print('a = ', a, 'b = ', b)
a = [1 2 3 4] b = [2 3]
For more information and examples look at Copies and Views.
There are a number of routines to join existing arrays e.g. numpy.vstack,
numpy.hstack, and numpy.block. Here is an example of joining four 2-by-2
arrays into a 4-by-4 array using block:
>>> import numpy as np
>>> A = np.ones((2, 2))
>>> B = np.eye(2, 2)
>>> C = np.zeros((2, 2))
>>> D = np.diag((-3, -4))
>>> np.block([[A, B], [C, D]])
array([[ 1., 1., 1., 0.],
[ 1., 1., 0., 1.],
[ 0., 0., -3., 0.],
[ 0., 0., 0., -4.]])
Other routines use similar syntax to join ndarrays. Check the routine’s documentation for further examples and syntax.
4) Reading arrays from disk, either from standard or custom formats#
This is the most common case of large array creation. The details depend greatly on the format of data on disk. This section gives general pointers on how to handle various formats. For more detailed examples of IO look at How to Read and Write files.
Standard binary formats#
Various fields have standard formats for array data. The following lists the ones with known Python libraries to read them and return NumPy arrays (there may be others for which it is possible to read and convert to NumPy arrays so check the last section as well)
HDF5: h5py
FITS: Astropy
Examples of formats that cannot be read directly but for which it is not hard to convert are those formats supported by libraries like PIL (able to read and write many image formats such as jpg, png, etc).
Common ASCII formats#
Delimited files such as comma separated value (csv) and tab separated
value (tsv) files are used for programs like Excel and LabView. Python
functions can read and parse these files line-by-line. NumPy has two
standard routines for importing a file with delimited data numpy.loadtxt
and numpy.genfromtxt. These functions have more involved use cases in
Reading and writing files. A simple example given a simple.csv:
$ cat simple.csv
x, y
0, 0
1, 1
2, 4
3, 9
Importing simple.csv is accomplished using numpy.loadtxt:
>>> import numpy as np
>>> np.loadtxt('simple.csv', delimiter = ',', skiprows = 1)
array([[0., 0.],
[1., 1.],
[2., 4.],
[3., 9.]])
More generic ASCII files can be read using scipy.io and Pandas.
5) Creating arrays from raw bytes through the use of strings or buffers#
There are a variety of approaches one can use. If the file has a relatively
simple format then one can write a simple I/O library and use the NumPy
fromfile() function and .tofile() method to read and write NumPy arrays
directly (mind your byteorder though!) If a good C or C++ library exists that
read the data, one can wrap that library with a variety of techniques though
that certainly is much more work and requires significantly more advanced
knowledge to interface with C or C++.
6) Use of special library functions (e.g., SciPy, pandas, and OpenCV)#
NumPy is the fundamental library for array containers in the Python Scientific Computing stack. Many Python libraries, including SciPy, Pandas, and OpenCV, use NumPy ndarrays as the common format for data exchange, These libraries can create, operate on, and work with NumPy arrays.