```{index} single: Python; SciPy

```

In addition to what’s in Anaconda, this lecture will need the following libraries:

```{code-cell} ipython3

:tags: [hide-output]

!pip install --upgrade quantecon

```

We use the following imports.

```{code-cell} ipython3

import numpy as np

import quantecon as qe

```

## Overview

[SciPy](https://scipy.org/) builds on top of NumPy to provide common tools for scientific programming such as

SciPy is a package that contains various tools that are built on top of NumPy, using its array data type and related functionality.

In fact, when we import SciPy we also get NumPy, as can be seen from this excerpt the SciPy initialization file:

````{note}

In older versions of SciPy (`scipy < 0.15.1`), importing the package would also import NumPy symbols into the global namespace, as can be seen from this excerpt the SciPy initialization file:

```{code-cell} python3

# Import numpy symbols to scipy namespace

```python

from numpy import *

from numpy.random import rand, randn

from numpy.fft import fft, ifft

from numpy.lib.scimath import *

```

However, it's more common and better practice to use NumPy functionality explicitly.

However, it is better practice to use NumPy functionality explicitly.

```{code-cell} python3

```python

import numpy as np

import quantecon as qe

a = np.identity(3)

```

More recent versions of SciPy (1.15+) no longer automatically import NumPy symbols.

````

What is useful in SciPy is the functionality in its sub-packages

* `scipy.optimize`, `scipy.integrate`, `scipy.stats`, etc.