*Accompanying code examples of the book "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python" by Sebastian Raschka. All code examples are released under the MIT license. If you find this content useful, please consider supporting the work by buying a copy of the book.*

Other code examples and content are available on GitHub. The PDF and ebook versions of the book are available through Leanpub.

```
In [1]:
```%load_ext watermark

```
In [2]:
```%watermark -a 'Sebastian Raschka' -p numpy

```
```

This appendix offers a quick tour of the NumPy library for working with multi-dimensional arrays in Python. NumPy (short for Numerical Python) was created by Travis Oliphant in 2005, by merging Numarray into Numeric. Since then, the open source NumPy library has evolved into an essential library for scientific computing in Python and has become a building block of many other scientific libraries, such as SciPy, scikit-learn, pandas, and others.
What makes NumPy so particularly attractive to the scientific community is that it provides a convenient Python interface for working with multi-dimensional array data structures efficiently; the NumPy array data structure is also called `ndarray`

, which is short for *n*-dimensional array.

In addition to being mostly implemented in C and using Python as glue, the main reason why NumPy is so efficient for numerical computations is that NumPy arrays use contiguous blocks of memory that can be efficiently cached by the CPU. In contrast, Python lists are arrays of pointers to objects in random locations in memory, which cannot be easily cached and come with a more expensive memory-look-up. However, the computational efficiency and low-memory footprint come at a cost: NumPy arrays have a fixed size and are homogenous, which means that all elements must have the same type. Homogenous `ndarray`

objects have the advantage that NumPy can carry out operations using efficient C loops and avoid expensive type checks and other overheads of the Python API. While adding and removing elements from the end of a Python list is very efficient, altering the size of a NumPy array is very expensive since it requires to create a new array and carry over the contents of the old array that we want to expand or shrink.

Besides being more efficient for numerical computations than native Python code, NumPy can also be more elegant and readable due to vectorized operations and broadcasting, which are features that we will explore in this appendix. While this appendix should be sufficient to follow the code examples in this book if you are new to NumPy, there are many advanced NumPy topics that are beyond the scope of this book. If you are interested in a more in-depth coverage of NumPy, I selected a few resources that could be useful to you:

- Rougier, N.P., 2016. From Python to Numpy.
- Oliphant, T.E., 2015. A Guide to NumPy: 2nd Edition. USA: Travis Oliphant, independent publishing.
- Varoquaux, G., Gouillart, E., Vahtras, O., Haenel, V., Rougier, N.P., Gommers, R., Pedregosa, F., Jędrzejewski-Szmek, Z., Virtanen, P., Combelles, C. and Pinte, D., 2015. Scipy Lecture Notes.
- The official NumPy documentation

NumPy is built around `ndarrays`

objects, which are high-performance multi-dimensional array data structures. Intuitively, we can think of a one-dimensional NumPy array as a data structure to represent a vector of elements -- you may think of it as a fixed-size Python list where all elements share the same type. Similarly, we can think of a two-dimensional array as a data structure to represent a matrix or a Python list of lists. While NumPy arrays can have up to 32 dimensions if it was compiled without alterations to the source code, we will focus on lower-dimensional arrays for the purpose of illustration in this introduction.

Now, let us get started with NumPy by calling the `array`

function to create a two-dimensional NumPy array, consisting of two rows and three columns, from a list of lists:

```
In [3]:
```import numpy as np
lst = [[1, 2, 3], [4, 5, 6]]
ary1d = np.array(lst)
ary1d

```
Out[3]:
```

`ndarray`

object `ary1d`

should be of type `int64`

on a 64-bit machine, which we can confirm by accessing the `dtype`

attribute:

```
In [4]:
```ary1d.dtype

```
Out[4]:
```

`dtype`

parameter of the `array`

function, for example `np.int32`

to create 32-bit arrays. For a full list of supported data types, please refer to the official NumPy documentation. Once an array has been constructed, we can downcast or recast its type via the `astype`

method as shown in the following example:

```
In [5]:
```float32_ary = ary1d.astype(np.float32)
float32_ary

```
Out[5]:
```

```
In [6]:
```float32_ary.dtype

```
Out[6]:
```

In the following sections we will cover many more aspects of NumPy arrays; however, to conclude this basic introduction to the `ndarray`

object, let us take a look at some of its handy attributes.

For instance, the `itemsize`

attribute returns the size of a single array element in bytes:

```
In [7]:
```ary2d = np.array([[1, 2, 3],
[4, 5, 6]], dtype='int64')
ary2d.itemsize

```
Out[7]:
```

The code snippet above returned `8`

, which means that each element in the array (remember that `ndarray`

s are homogeneous) takes up 8 bytes in memory. This result makes sense since the array `ary2d`

has type `int64`

(64-bit integer), which we determined earlier, and 8 bits equals 1 byte. (Note that `'int64'`

is just a shorthand for `np.int64`

.)

To return the number of elements in an array, we can use the `size`

attribute, as shown below:

```
In [8]:
```ary2d.size

```
Out[8]:
```

*dimensions* as the *rank* of a tensor) can be obtained via the `ndim`

attribute:

```
In [9]:
```ary2d.ndim

```
Out[9]:
```

*axes*), we can access the `shape`

attribute as shown below:

```
In [10]:
```ary2d.shape

```
Out[10]:
```

The `shape`

is always a tuple; in the code example above, the two-dimensional `ary`

object has two *rows* and *three* columns, `(2, 3)`

, if we think of it as a matrix representation.

Similarly, the `shape`

of the one-dimensional array only contains a single value:

```
In [11]:
```np.array([1, 2, 3]).shape

```
Out[11]:
```

`array`

function, we can also provide a single float or integer, which will construct a zero-dimensional array (for instance, a representation of a scalar):

```
In [12]:
```scalar = np.array(5)
scalar

```
Out[12]:
```

```
In [13]:
```scalar.ndim

```
Out[13]:
```

```
In [14]:
```scalar.shape

```
Out[14]:
```

`array`

function to construct NumPy arrays from Python objects that are sequences or nested sequences -- lists, tuples, nested lists, iterables, and so forth. While `array`

is often our go-to function for creating `ndarray`

objects, NumPy implements a variety of functions for constructing arrays that may come in handy in different contexts. In this section, we will take a quick peek at those that we use most commonly -- you can find a more comprehensive list in the official documentation.

`array`

function works with most iterables in Python, including lists, tuples, and `range`

objects; however, `array`

does not support generator expression. If we want parse generators directly, however, we can use the `fromiter`

function as demonstrated below:

```
In [15]:
```def generator():
for i in range(10):
if i % 2:
yield i
gen = generator()
np.fromiter(gen, dtype=int)

```
Out[15]:
```

```
In [16]:
```# using 'comprehensions' the following
# generator expression is equivalent to
# the `generator` above
generator_expression = (i for i in range(10) if i % 2)
np.fromiter(generator_expression, dtype=int)

```
Out[16]:
```

`ndarray`

s of consisting of either ones and zeros by only specifying the elements along each axes (here: three rows and three columns):

```
In [17]:
```np.ones((3, 3))

```
Out[17]:
```

```
In [18]:
```np.zeros((3, 3))

```
Out[18]:
```

`'0.'`

or `'1.'`

), there is also `numpy.empty`

, which follows the same syntax as `numpy.ones`

and `np.zeros`

. However, instead of filling the array with a particular value, the `empty`

function creates the array with non-sensical values from memory. We can think of `zeros`

as a function that creates the array via `empty`

and then sets all its values to `0.`

-- in practice, a difference in speed is not noticeable, though.

`ndarrays`

that can be useful in the context of linear algebra -- a topic that we will explore later in this appendix.

```
In [19]:
```np.eye(3)

```
Out[19]:
```

```
In [20]:
```np.diag((3, 3, 3))

```
Out[20]:
```

`arange`

and `linspace`

. NumPy's `arange`

function follows the same syntax as Python's `range`

objects: If two arguments are provided, the first argument represents the start value and the second value defines the stop value of a half-open interval:

```
In [21]:
```np.arange(4., 10.)

```
Out[21]:
```

`arange`

also performs type inference similar to the `array`

function. If we only provide a single function argument, the range object treats this number as the endpoint of the interval and starts at 0:

```
In [22]:
```np.arange(5)

```
Out[22]:
```

`range`

, a third argument can be provided to define the *step* (the default step size is 1). For example, we can obtain an array of all uneven values between one and ten as follows:

```
In [23]:
```np.arange(1., 11., 2)

```
Out[23]:
```

`linspace`

function is especially useful if we want to create a particular number of evenly spaced values in a specified half-open interval:

```
In [24]:
```np.linspace(0., 1., num=5)

```
Out[24]:
```

```
In [25]:
```ary = np.array([1, 2, 3])
ary[0]

```
Out[25]:
```

`ary`

:

```
In [26]:
```ary[:2] # equivalent to ary[0:2]

```
Out[26]:
```

```
In [27]:
```ary = np.array([[1, 2, 3],
[4, 5, 6]])
ary[0, 0] # upper left

```
Out[27]:
```

```
In [28]:
```ary[-1, -1] # lower right

```
Out[28]:
```

```
In [29]:
```ary[0, 1] # first row, second column

```
Out[29]:
```

```
In [30]:
```ary[0] # entire first row

```
Out[30]:
```

```
In [31]:
```ary[:, 0] # entire first column

```
Out[31]:
```

```
In [32]:
```ary[:, :2] # first two columns

```
Out[32]:
```

```
In [33]:
```ary[0, 0]

```
Out[33]:
```

In the previous sections, you learned how to create NumPy arrays and how to access different elements in an array. It is about time that we introduce one of the core features of NumPy that makes working with `ndarray`

so efficient and convenient: vectorization. While we typically use for-loops if we want to perform arithmetic operations on sequence-like objects, NumPy provides vectorized wrappers for performing element-wise operations implicitly via so-called *ufuncs* -- short for universal functions.

As of this writing, there are more than 60 ufuncs available in NumPy; ufuncs are implemented in compiled C code and very fast and efficient compared to vanilla Python. In this section, we will take a look at the most commonly used ufuncs, and I recommend you to check out the official documentation for a complete list.

To provide an example of a simple ufunc for element-wise addition, consider the following example, where we add a scalar (here: 1) to each element in a nested Python list:

```
In [34]:
```lst = [[1, 2, 3], [4, 5, 6]]
for row_idx, row_val in enumerate(lst):
for col_idx, col_val in enumerate(row_val):
lst[row_idx][col_idx] += 1
lst

```
Out[34]:
```

```
In [35]:
```lst = [[1, 2, 3], [4, 5, 6]]
[[cell + 1 for cell in row] for row in lst]

```
Out[35]:
```

We can accomplish the same using NumPy's ufunc for element-wise scalar addition as shown below:

```
In [36]:
```ary = np.array([[1, 2, 3], [4, 5, 6]])
ary = np.add(ary, 1)
ary

```
Out[36]:
```

`add`

, `subtract`

, `divide`

, `multiply`

, and `exp`

(exponential). However, NumPy uses operator overloading so that we can use mathematical operators (`+`

, `-`

, `/`

, `*`

, and `**`

) directly:

```
In [37]:
```ary + 1

```
Out[37]:
```

```
In [38]:
```ary**2

```
Out[38]:
```

Above, we have seen examples of *binary* ufuncs, which are ufuncs that take two arguments as an input. In addition, NumPy implements several useful *unary* ufuncs, such as `log`

(natural logarithm), `log10`

(base-10 logarithm), and `sqrt`

(square root).

Often, we want to compute the sum or product of array element along a given axis. For this purpose, we can use a ufunc's `reduce`

operation. By default, `reduce`

applies an operation along the first axis (`axis=0`

). In the case of a two-dimensional array, we can think of the first axis as the rows of a matrix. Thus, adding up elements along rows yields the column sums of that matrix as shown below:

```
In [39]:
```ary = np.array([[1, 2, 3],
[4, 5, 6]])
np.add.reduce(ary) # column sumns

```
Out[39]:
```

To compute the row sums of the array above, we can specify `axis=1`

:

```
In [40]:
```np.add.reduce(ary, axis=1) # row sums

```
Out[40]:
```

`reduce`

as a more general operation, NumPy also provides shorthands for specific operations such as `product`

and `sum`

. For example, `sum(axis=0)`

is equivalent to `add.reduce`

:

```
In [41]:
```ary.sum(axis=0) # column sums

```
Out[41]:
```

`product`

and `sum`

both compute the product or sum of the entire array if we do not specify an axis:

```
In [42]:
```ary.sum()

```
Out[42]:
```

Other useful unary ufuncs are:

- mean (computes arithmetic average)
- std (computes the standard deviation)
- var (computes variance)
- np.sort (sorts an array)
- np.argsort (returns indices that would sort an array)
- np.min (returns the minimum value of an array)
- np.max (returns the maximum value of an array)
- np.argmin (returns the index of the minimum value)
- np.argmax (returns the index of the maximum value)
- array_equal (checks if two arrays have the same shape and elements)

A topic we glanced over in the previous section is broadcasting. Broadcasting allows us to perform vectorized operations between two arrays even if their dimensions do not match by creating implicit multidimensional grids. You already learned about ufuncs in the previous section where we performed element-wise addition between a scalar and a multidimensional array, which is just one example of broadcasting.

Naturally, we can also perform element-wise operations between arrays of equal dimensions:

```
In [43]:
```ary1 = np.array([1, 2, 3])
ary2 = np.array([4, 5, 6])
ary1 + ary2

```
Out[43]:
```

`ary1`

:

```
In [44]:
```ary3 = np.array([[4, 5, 6],
[7, 8, 9]])
ary3 + ary1 # similarly, ary1 + ary3

```
Out[44]:
```

`ValueError`

, because NumPy attempts to add the elements from the first axis of the left array (2 elements) to the first axis of the right array (3 elements):

```
In [45]:
```try:
ary3 + np.array([1, 2])
except ValueError as e:
print('ValueError:', e)

```
```

`ary3`

, the 2-element array must have two elements along its *first* axis as well:

```
In [46]:
```ary3 + np.array([[1], [2]])

```
Out[46]:
```

```
In [47]:
```np.array([[1], [2]]) + ary3

```
Out[47]:
```

*views* of NumPy arrays in memory. Working with views can be highly desirable since it avoids making unnecessary copies of arrays to save memory resources. To illustrate the concept of memory views, let us walk through a simple example where we access the first row in an array, assign it to a variable, and modify that variable:

```
In [48]:
```ary = np.array([[1, 2, 3],
[4, 5, 6]])
first_row = ary[0]
first_row += 99
ary

```
Out[48]:
```

`first_row`

also affected the original array. The reason for this is that `ary[0]`

created a view of the first row in `ary`

, and its elements were then incremented by 99. The same concept applies to slicing operations:

```
In [49]:
```ary = np.array([[1, 2, 3],
[4, 5, 6]])
first_row = ary[:1]
first_row += 99
ary

```
Out[49]:
```

```
In [50]:
```ary = np.array([[1, 2, 3],
[4, 5, 6]])
center_col = ary[:, 1]
center_col += 99
ary

```
Out[50]:
```

`copy`

method as shown below:

```
In [51]:
```second_row = ary[1].copy()
second_row += 99
ary

```
Out[51]:
```

`may_share_memory`

function from NumPy. However, be aware that it uses a heuristic and can return false negatives or false positives. The code snippets shows an example of `may_share_memory`

applied to the view (`first_row`

) and copy (`second_row`

) of the array elements from `ary`

:

```
In [52]:
```ary = np.array([[1, 2, 3],
[4, 5, 6]])
first_row = ary[:1]
first_row += 99
ary

```
Out[52]:
```

```
In [53]:
```np.may_share_memory(first_row, ary)

```
Out[53]:
```

```
In [54]:
```second_row = ary[1].copy()
second_row += 99
ary

```
Out[54]:
```

```
In [55]:
```np.may_share_memory(second_row, ary)

```
Out[55]:
```

*fancy* indexing. Via fancy indexing, we can use tuple or list objects of non-contiguous integer indices to return desired array elements. Since fancy indexing can be performed with non-contiguous sequences, it cannot return a view -- a contiguous slice from memory. Thus, fancy indexing always returns a copy of an array -- it is important to keep that in mind. The following code snippets show some fancy indexing examples:

```
In [56]:
```ary = np.array([[1, 2, 3],
[4, 5, 6]])
ary[:, [0, 2]] # first and and last column

```
Out[56]:
```

```
In [57]:
```this_is_a_copy = ary[:, [0, 2]]
this_is_a_copy += 99
ary

```
Out[57]:
```

```
In [58]:
```ary[:, [2, 0]] # first and and last column

```
Out[58]:
```

`True`

and `False`

values. Consider the following example, where we return all values in the array that are greater than 3:

```
In [59]:
```ary = np.array([[1, 2, 3],
[4, 5, 6]])
greater3_mask = ary > 3
greater3_mask

```
Out[59]:
```

Using these masks, we can select elements given our desired criteria:

```
In [60]:
```ary[greater3_mask]

```
Out[60]:
```

*and* operator '&' or the logical *or* operator '|'. The example below demonstrates how we can select array elements that are greater than 3 and divisible by 2:

```
In [61]:
```ary[(ary > 3) & (ary % 2 == 0)]

```
Out[61]:
```

In machine learning and deep learning, we often have to generate arrays of random numbers -- for example, the initial values of our model parameters before optimization. NumPy has a `random`

subpackage to create random numbers and samples from a variety of distributions conveniently. Again, I encourage you to browse through the more comprehensive numpy.random documentation for a more comprehensive list of functions for random sampling.

To provide a brief overview of the pseudo-random number generators that we will use most commonly, let's start with drawing a random sample from a uniform distribution:

```
In [62]:
```np.random.seed(123)
np.random.rand(3)

```
Out[62]:
```

In the code snippet above, we first seeded NumPy's random number generator. Then, we drew three random samples from a uniform distribution via `random.rand`

in the half-open interval [0, 1). I highly recommend the seeding step in practical applications as well as in research projects, since it ensures that our results are reproducible. If we run our code sequentially -- for example, if we execute a Python script -- it should be sufficient to seed the random number generator only once at the beginning to enforce reproducible outcomes between different runs. However, it is often useful to create separate `RandomState`

objects for various parts of our code, so that we can test methods of functions reliably in unit tests. Working with multiple, separate `RandomState`

objects can also be useful if we run our code in non-sequential order -- for example if we are experimenting with our code in interactive sessions or Jupyter Notebook environments.

The example below shows how we can use a `RandomState`

object to create the same results that we obtained via np.random.rand in the previous code snippet:

```
In [63]:
```rng1 = np.random.RandomState(seed=123)
rng1.rand(3)

```
Out[63]:
```

`randn`

, which returns a random sample of floats from a standard normal distribution $N(\mu, \sigma^2)$, where the mean, ($\mu$) is zero and unit variance ($\sigma = 1$). The example below creates a two-dimensional array of such *z*-scores:

```
In [64]:
```rng2 = np.random.RandomState(seed=123)
z_scores = rng2.randn(100, 2)

`rand`

and `randn`

take an arbitrary number of integer arguments, where each integer argument specifies the number of elements along a given axis -- the `z_scores`

array should now refer to an array of 100 rows and two columns. Let us now visualize how our random sample looks like using matplotlib:

```
In [65]:
```%matplotlib inline
import matplotlib.pyplot as plt
plt.scatter(z_scores[:, 0], z_scores[:, 1])
#plt.savefig('images/numpy-intro/random_1.png', dpi=600)
plt.show()

```
```

```
In [66]:
```rng3 = np.random.RandomState(seed=123)
scores = 2. * rng3.randn(100, 2) + 5.
plt.scatter(scores[:, 0], scores[:, 1])
#plt.savefig('images/numpy-intro/random_2.png', dpi=600)
plt.show()

```
```

In practice, we often run into situations where existing arrays do not have the *right* shape to perform certain computations. As you might remember from the beginning of this appendix, the size of NumPy arrays is fixed. Fortunately, this does not mean that we have to create new arrays and copy values from the old array to the new one if we want arrays of different shapes -- the size is fixed, but the shape is not. NumPy provides a `reshape`

methods that allow us to obtain a view of an array with a different shape.

For example, we can reshape a one-dimensional array into a two-dimensional one using `reshape`

as follows:

```
In [67]:
```ary1d = np.array([1, 2, 3, 4, 5, 6])
ary2d_view = ary1d.reshape(2, 3)
ary2d_view

```
Out[67]:
```

```
In [68]:
```np.may_share_memory(ary2d_view, ary1d)

```
Out[68]:
```

`-1`

):

```
In [69]:
```ary1d.reshape(2, -1)

```
Out[69]:
```

```
In [70]:
```ary1d.reshape(-1, 2)

```
Out[70]:
```

We can, of course, also use `reshape`

to flatten an array:

```
In [71]:
```ary2d = np.array([[1, 2, 3],
[4, 5, 6]])
ary2d.reshape(-1)

```
Out[71]:
```

Note that NumPy also has a shorthand for that called `ravel`

:

```
In [72]:
```ary2d.ravel()

```
Out[72]:
```

A function related to `ravel`

is `flatten`

. In contrast to `ravel`

, `flatten`

returns a copy, though:

```
In [73]:
```np.may_share_memory(ary2d.flatten(), ary2d)

```
Out[73]:
```

```
In [74]:
```np.may_share_memory(ary2d.ravel(), ary2d)

```
Out[74]:
```

`concatenate`

function as shown in the following examples:

```
In [75]:
```ary = np.array([1, 2, 3])
# stack along the first axis
np.concatenate((ary, ary))

```
Out[75]:
```

```
In [76]:
```ary = np.array([[1, 2, 3]])
# stack along the first axis (here: rows)
np.concatenate((ary, ary), axis=0)

```
Out[76]:
```

```
In [77]:
```# stack along the second axis (here: column)
np.concatenate((ary, ary), axis=1)

```
Out[77]:
```

`matrix`

type in NumPy. NumPy `matrix`

objects are analogous to NumPy arrays but are restricted to two dimensions. Also, matrices define certain operations differently than arrays; for instance, the `*`

operator performs matrix multiplication instead of element-wise multiplication. However, NumPy `matrix`

is less popular in the science community compared to the more general array data structure. Since we are also going to work with arrays that have more than two dimensions (for example, when we are working with convolutional neural networks), we will not use NumPy matrix data structures in this book.

```
In [78]:
```row_vector = np.array([1, 2, 3])
row_vector

```
Out[78]:
```

Similarly, we can use two-dimensional arrays to create column vectors:

```
In [79]:
```column_vector = np.array([[1, 2, 3]]).reshape(-1, 1)
column_vector

```
Out[79]:
```

```
In [80]:
```row_vector[:, np.newaxis]

```
Out[80]:
```

Note that in this context, `np.newaxis`

behaves like `None`

:

```
In [81]:
```row_vector[:, None]

```
Out[81]:
```

All three approaches listed above, using `reshape(-1, 1)`

, `np.newaxis`

, or `None`

yield the same results -- all three approaches create views not copies of the `row_vector`

array.

As we remember from the Linear Algebra appendix, we can think of a column vector as a matrix consisting only of one column. To perform matrix multiplication between matrices, we learned that number of columns of the left matrix must match the number of rows of the matrix to the right. In NumPy, we can perform matrix multiplication via the `matmul`

function:

```
In [82]:
```matrix = np.array([[1, 2, 3],
[4, 5, 6]])

```
In [83]:
```np.matmul(matrix, column_vector)

```
Out[83]:
```

```
In [84]:
```np.matmul(matrix, row_vector)

```
Out[84]:
```

Similarly, we can compute the dot-product between two vectors (here: the vector norm)

```
In [85]:
```np.matmul(row_vector, row_vector)

```
Out[85]:
```

`dot`

function that behaves similar to `matmul`

on pairs of one- or two-dimensional arrays -- its underlying implementation is different though, and one or the other can be slightly faster on specific machines and versions of BLAS:

```
In [86]:
```np.dot(row_vector, row_vector)

```
Out[86]:
```

```
In [87]:
```np.dot(matrix, row_vector)

```
Out[87]:
```

```
In [88]:
```np.dot(matrix, column_vector)

```
Out[88]:
```

`matmul`

or `dot`

to multiply two matrices (here: two-dimensional arrays). In this context, NumPy arrays have a handy `transpose`

method to transpose matrices if necessary:

```
In [89]:
```matrix = np.array([[1, 2, 3],
[4, 5, 6]])
matrix.transpose()

```
Out[89]:
```

```
In [90]:
```np.matmul(matrix, matrix.transpose())

```
Out[90]:
```

`transpose`

can be annoyingly verbose for implementing linear algebra operations -- think of PEP8's *80 character per line* recommendation -- NumPy has a shorthand for that: `T`

:

```
In [91]:
```matrix.T

```
Out[91]:
```

`numpy.linalg`

. If you want to perform a particular linear algebra routine that is not implemented in NumPy, it is also worth consulting the `scipy.linalg`

documentation -- SciPy is a library for scientific computing built on top of NumPy.