# About Python

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```{epigraph}

"Python has gotten sufficiently weapons grade that we don’t descend into R

anymore. Sorry, R people. I used to be one of you but we no longer descend

Here's some example code that generates and plots a random graph, with node color determined by the shortest path length from a central node.

```{code-cell} ipython

%matplotlib inline

import networkx as nx

import matplotlib.pyplot as plt

plt.rcParams['figure.figsize'] = (10,6)

np.random.seed(1234)

# Generate a random graph

```{index} single: Debugging

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```{epigraph}

"Debugging is twice as hard as writing the code in the first place.

Therefore, if you write the code as cleverly as possible, you are, by definition,

We'll need the following imports

```{code-cell} ipython

%matplotlib inline

import numpy as np

import matplotlib.pyplot as plt

plt.rcParams['figure.figsize'] = (10,6)

```

(debug_magic)=

```{index} single: Python; User-defined functions

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## Overview

Functions are an extremely useful construct provided by almost all programming.

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## Overview

In this lecture, you will learn how to

```{code-cell} ipython

import numpy as np

import matplotlib.pyplot as plt

%matplotlib inline

plt.rcParams['figure.figsize'] = (10,6)

# Fixing random state for reproducibility

np.random.seed(19680801)

```{index} single: Python; Matplotlib

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## Overview

We've already generated quite a few figures in these lectures using [Matplotlib](http://matplotlib.org/).

Here's the kind of easy example you might find in introductory treatments

```{code-cell} ipython

%matplotlib inline

import matplotlib.pyplot as plt

plt.rcParams["figure.figsize"] = (10, 6) #set default figure size

import numpy as np

x = np.linspace(0, 10, 200)

# Python for Scientific Computing

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```{epigraph}

"We should forget about small efficiencies, say about 97% of the time:

premature optimization is the root of all evil." -- Donald Knuth

```{code-cell} ipython

import matplotlib.pyplot as plt

%matplotlib inline

from mpl_toolkits.mplot3d.axes3d import Axes3D

from matplotlib import cm

# Numba

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

```{code-cell} ipython3

Let's start with some imports:

```{code-cell} ipython3

%matplotlib inline

import numpy as np

import quantecon as qe

import matplotlib.pyplot as plt

plt.rcParams['figure.figsize'] = (10,6)

```

## Overview

```{index} single: Python; NumPy

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```{epigraph}

"Let's be clear: the work of science has nothing whatever to do with consensus. Consensus is the business of politics. Science, on the contrary, requires only one investigator who happens to be right, which means that he or she has results that are verifiable by reference to the real world. In science consensus is irrelevant. What is relevant is reproducible results." -- Michael Crichton

```

```{index} single: Python; Pandas

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

```{code-cell} ipython

place

```{code-cell} ipython

%matplotlib inline

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

plt.rcParams["figure.figsize"] = [10,8] # Set default figure size

import requests

```

# Parallelization

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

```{code-cell} ipython

Let's start with some imports:

```{code-cell} ipython

%matplotlib inline

import numpy as np

import quantecon as qe

import matplotlib.pyplot as plt

plt.rcParams['figure.figsize'] = (10,6)

```

## Types of Parallelization