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```{index} single: Python; SciPy
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```
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In addition to what’s in Anaconda, this lecture will need the following libraries:
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```{code-cell} ipython3
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:tags: [hide-output]
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!pip install --upgrade quantecon
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```
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We use the following imports.
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```{code-cell} ipython3
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import numpy as np
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import quantecon as qe
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```
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## Overview
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[SciPy](https://scipy.org/) builds on top of NumPy to provide common tools for scientific programming such as
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SciPy is a package that contains various tools that are built on top of NumPy, using its array data type and related functionality.
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In fact, when we import SciPy we also get NumPy, as can be seen from this excerpt the SciPy initialization file:
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````{note}
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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:
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```{code-cell} python3
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# Import numpy symbols to scipy namespace
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```python
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from numpy import *
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from numpy.random import rand, randn
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from numpy.fft import fft, ifft
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from numpy.lib.scimath import *
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```
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However, it's more common and better practice to use NumPy functionality explicitly.
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However, it is better practice to use NumPy functionality explicitly.
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```{code-cell} python3
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```python
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import numpy as np
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import quantecon as qe
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a = np.identity(3)
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```
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More recent versions of SciPy (1.15+) no longer automatically import NumPy symbols.
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````
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What is useful in SciPy is the functionality in its sub-packages
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*`scipy.optimize`, `scipy.integrate`, `scipy.stats`, etc.
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