# 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.