Lecture 9: Intro to Python

What is R?

• R is a programming language specifically designed for statistics and data analysis
  • Objects for storing data, and functions for interacting with data, are fundamental
  • R is very good at graphics and visualization
  • R is easily extended. Users can write and share their own functions and packages
• We can interact with R through IDEs like RStudio

Who uses R?

• R is widely used in statistical research and academia
• R is also widely used in applications of statistics, such as
  • Biology and bioinformatics
  • Ecology, forestry, and environmental science
  • Psychology
  • Sociology
• R is used in a variety of areas outside research, by government agencies, pharmaceutical companies, insurance companies, etc.

What other options exist?

• SAS
• Stata
• SPSS
• Excel
• Python
• Julia
• Matlab
• Many others…

What is Python

• Python is a general-purpose programming language
• Like R, python has a wide range of packages to extend functionality
• Certain Python packages allow for sophisticated data analysis and modeling
  • SciPy, NumPy
  • scikit-learn, statsmodels, pytorch
  • pandas
  • matplotlib

R vs. Python

My own, personal, preferences:

R is good for

• Data visualization and wrangling
• Classical statistics
• Statistical inference
• New statistical methods

Python is good for

• General-purpose programming
• Challenging data types (e.g. images)
• Prediction and machine learning

Why do we teach R?

• Excellent support for the material covered in a statistics degree
• Benefits to using a consistent language across courses
• Valuable for a wide variety of future careers
• The primary research tool for most (if not all) the faculty

A taste of Python

Recall our code from the first class:

M <- 10 # number of people at the party
hats <- 1:M # numbered hats
nsim <- 10000 # number of simulations
results <- rep(0, nsim) # vector to store the results

for(i in 1:nsim){
  randomized_hats <- sample(hats, M, replace = FALSE)
  results[i] <- sum(randomized_hats == hats) > 0
}

mean(results)

[1] 0.6296

A taste of Python

Here is the same code, written in Python

import numpy as np

M = 10 # number of people at the party
hats = np.arange(M) # numbered hats
nsim = 10000 # number of simulations
results = np.zeros(nsim) # to store the results

for i in range(nsim):
    randomized_hats = np.random.choice(hats, M, replace = False)
    results[i] = np.sum(randomized_hats == hats) > 0

np.mean(results)

0.634

What similarities and differences do you notice?

Step 1: representing the hats

import numpy as np

M = 10 # number of people at the party
hats = np.arange(M) # numbered hats

hats

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

hats[0]

0

hats[1]

1

• hats is a 1-dimensional array (similar to a vector in R)
• Python is 0-indexed: the first entry is hats[0]

Step 2: everyone draws a random hat

import numpy as np

M = 10 # number of people at the party
hats = np.arange(M) # numbered hats

randomized_hats = np.random.choice(hats, M, replace = False)

randomized_hats

array([6, 8, 5, 2, 1, 0, 4, 7, 9, 3])

• np.random.choice works like R’s sample function
• Booleans in Python are True and False (as opposed to TRUE and FALSE, or T and F)

Step 3: check who got their original hat

import numpy as np

M = 10 # number of people at the party
hats = np.arange(M) # numbered hats

randomized_hats = np.random.choice(hats, M, replace = False)
randomized_hats

array([5, 6, 0, 3, 1, 8, 9, 7, 2, 4])

randomized_hats == hats

array([False, False, False,  True, False, False, False,  True, False,
       False])

np.sum(randomized_hats == hats)

2

• NumPy arrays allow for “vectorized” operations, like in R

Step 4: iteration

import numpy as np

M = 10 # number of people at the party
hats = np.arange(M) # numbered hats
nsim = 10000 # number of simulations
results = np.zeros(nsim) # to store the results

for i in range(nsim):
    randomized_hats = np.random.choice(hats, M, replace = False)
    results[i] = np.sum(randomized_hats == hats) > 0

np.mean(results)

0.6282

• range(nsim) is similar to 1:nsim in R
• We don’t use the curly braces { }. Instead we use whitespace (four spaces is standard, you just have to be consistent)

Using Python through RStudio

• You can make Python chunks in Quarto documents, just like R chunks:

```{python}

```

Class activity

https://sta279-f23.github.io/class_activities/ca_lecture_9.html