# Lesson 3

## Importing Modules¶

This lesson covers:

• Module import

### Problem: Importing Modules¶

Python is a general-purpose programming language and is not specialized for numerical or statistical computation. The core modules that enable Python to store and access data efficiently and that provide statistical algorithms are located in modules. The most important are:

• NumPy (numpy) - provide the basic array block used throughout numerical Python
• pandas (pandas) - provides DataFrames which are used to store data in an easy-to-use format
• SciPy (scipy) - Basic statistics and random number generators. The most important submodule is scipy.stats
• matplotlib (matplotlib) - graphics. The most important submodule is matplotlib.pyplot.
• statsmodels (statsmodels) - statistical models such as OLS. The most important submodules are statsmodels.api and statsmodels.tsa.api.

Begin by importing the important modules.

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### Problem: Canonical Names¶

Use the as keyword to import the modules using their canonical names:

Module Canonical Name
numpy np
pandas pd
scipy sp
scipy.stats stats
matplotlib.pyplot plt
statsmodels.api sm
statsmodels.tsa.api tsa

Import the core modules using import module as canonical.

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### Problem: Importing individual functions¶

1. Import array, sqrt, log and exp from NumPy.
2. Import OLS from statsmodels.regression.linear_model
3. Import the stats module from scipy
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### Exercises¶

#### Exercise: Import det¶

The determinant function is located at numpy.linalg.det. Access this function using:

1. numpy
2. np
3. By importing linalg from numpy and accessing it from linalg
4. By directly importing the function

You can x in the setup code to call the function as func(x).

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# Setup: A simple 2 by 2 array to use with det
import numpy as np
x = np.array([[2,3],[1,2]])
print(x)

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