## Overview

This post is chapter 5 in continuation of my coverage of Data Science from Scratch by Joel Grus.

It should be noted upfront that everything covered in this post can be done more expediently and efficiently in libraries like NumPy as well as the statistics module in Python.

The primary value of this book, and by extension this post, in my opinion, is the emphasis on **learning** how Python primitives can be used to build tools from the ground up. Here's a visual preview:

Specifically, we'll examine how specific features of the Python language as well as functions we built in a previous post on linear algebra (see also Matrices) can be used to build tools used to *describe* data and relationships within data (aka **statistics**).

I think this is pretty cool. Hopefully you agree.

#### Example Data

This chapter continues the narrative of you as a newly hired data scientist at DataScienster, the social network for data scientists, and your job is to *describe* how many friends members in this social network has. We have two `lists`

of `float`

to work with. We'll work with `num_friends`

first, then `daily_minutes`

later.

I wanted this post to be self-contained, and in order to do that we'll have to read in a larger than average `list`

of `floats`

. The alternative would be to get the data directly from the book's github repo (statistics.py)

```
num_friends = [100.0,49,41,40,25,21,21,19,19,18,18,16,15,15,15,15,14,14,13,13,13,13,12,12,11,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,8,8,8,8,8,8,8,8,8,8,8,8,8,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
daily_minutes = [1,68.77,51.25,52.08,38.36,44.54,57.13,51.4,41.42,31.22,34.76,54.01,38.79,47.59,49.1,27.66,41.03,36.73,48.65,28.12,46.62,35.57,32.98,35,26.07,23.77,39.73,40.57,31.65,31.21,36.32,20.45,21.93,26.02,27.34,23.49,46.94,30.5,33.8,24.23,21.4,27.94,32.24,40.57,25.07,19.42,22.39,18.42,46.96,23.72,26.41,26.97,36.76,40.32,35.02,29.47,30.2,31,38.11,38.18,36.31,21.03,30.86,36.07,28.66,29.08,37.28,15.28,24.17,22.31,30.17,25.53,19.85,35.37,44.6,17.23,13.47,26.33,35.02,32.09,24.81,19.33,28.77,24.26,31.98,25.73,24.86,16.28,34.51,15.23,39.72,40.8,26.06,35.76,34.76,16.13,44.04,18.03,19.65,32.62,35.59,39.43,14.18,35.24,40.13,41.82,35.45,36.07,43.67,24.61,20.9,21.9,18.79,27.61,27.21,26.61,29.77,20.59,27.53,13.82,33.2,25,33.1,36.65,18.63,14.87,22.2,36.81,25.53,24.62,26.25,18.21,28.08,19.42,29.79,32.8,35.99,28.32,27.79,35.88,29.06,36.28,14.1,36.63,37.49,26.9,18.58,38.48,24.48,18.95,33.55,14.24,29.04,32.51,25.63,22.22,19,32.73,15.16,13.9,27.2,32.01,29.27,33,13.74,20.42,27.32,18.23,35.35,28.48,9.08,24.62,20.12,35.26,19.92,31.02,16.49,12.16,30.7,31.22,34.65,13.13,27.51,33.2,31.57,14.1,33.42,17.44,10.12,24.42,9.82,23.39,30.93,15.03,21.67,31.09,33.29,22.61,26.89,23.48,8.38,27.81,32.35,23.84]
daily_hours = [dm / 60 for dm in daily_minutes]
```

## Describing

The `num_friends`

list is a list of numbers representing "number of friends" a person has, so for example, one person has 100 friends. The first thing we do to describe the data is to create a bar chart plotting the number of people who have 100 friends, 49 friends, 41 friends, and so on.

We'll import `Counter`

from `collections`

and import `matplotlib.pyplot`

.

We'll use `Counter`

to turn `num_friends`

list into a `defaultdict(int)`

-like object mapping keys to counts. For more info, please refer to this previous post on the Counters.

Once we use the `Counter`

collection, a high-performance container datatype, we can use methods like `most_common`

to find the keys with the most common values. Here we see that the five most common *number of friends* are 6, 1, 4, 3 and 9, respectively.

```
from collections import Counter
import matplotlib.pyplot as plt
friend_counts = Counter(num_friends)
# the five most common values are: 6, 1, 4, 3 and 9 friends
# [(6, 22), (1, 22), (4, 20), (3, 20), (9, 18)]
friend_counts.most_common(5)
```

To proceed with plotting, we'll use `friend_counts`

to create a `list comprehension`

that will loop through `friends_count`

and for all **keys** from 0-101 (xs) and print a corresponding **value** (if it exists). This becomes the y-axis to `num_friends`

, which is the x-axis:

```
xs = range(101) # x-axis: largest num_friend value is 100
ys = [friend_counts[x] for x in xs] # y-axis
plt.bar(xs, ys)
plt.axis([0, 101, 0, 25])
plt.title("Histogram of Friend Counts")
plt.xlabel("# of friends")
plt.ylabel("# of people")
plt.show()
```

Here is the plot below. You can see one person with 100 friends.

You can also read more about data visualization here.

Alternatively, we could generate simple statistics to describe the data using built-in Python methods: `len`

, `min`

, `max`

and `sorted`

.

```
num_points = len(num_friends) # number of data points in num_friends: 204
largest_value = max(num_friends) # largest value in num_friends: 100
smallest_value = min(num_friends) # smallest value in num_friends: 1
sorted_values = sorted(num_friends) # sort the values in ascending order
second_largest_value = sorted_values[-2] # second largest value from the back: 49
```

### Central Tendencies

The most common way of describing a set of data is to find it's **mean**, which is the sum of all the values, divided by the number of values. *note* : we'll continue to use type annotations. In my opinion, it helps you be a more deliberate and mindful Python programmer.

```
from typing import List
def mean(xs: List[float]) -> float:
return sum(xs) / len(xs)
assert 7.3333 < mean(num_friends) < 7.3334
```

However, the mean is **notoriously sensitive to outliers** so statisticians often supplement with other measures of central tendencies like **median**. Because the median is the *middle-most value*, it matters whether there is an *even* or *odd* number of data points.

Here, we'll create two private functions for both situations - even and odd number of data points - in calculating the median. First, we'll sort the data values. Then, for *even number* values, we'll find the two middle values and split them. For *odd number* of values, we'll divide the *length* of the dataset by 2 (i.e., 50).

Our median function will return either of the private function `_median_even`

or `_median_odd`

conditionally depending on if the length of a list of numbers is divisible (%2==0) by 2.

```
def _median_even(xs: List[float]) -> float:
"""If len(xs) is even, it's the average of the middle two elements"""
sorted_xs = sorted(xs)
hi_midpoint = len(xs) // 2 # e.g. length 4 => hi_midpoint 2
return (sorted_xs[hi_midpoint - 1] + sorted_xs[hi_midpoint]) / 2
def _median_odd(xs: List[float]) -> float:
"""If len(xs) is odd, its the middle element"""
return sorted(xs)[len(xs) // 2]
def median(v: List[float]) -> float:
"""Finds the 'middle-most' value of v"""
return _median_even(v) if len(v) % 2 == 0 else _median_odd(v)
assert median([1,10,2,9,5]) == 5
assert median([1, 9, 2, 10]) == (2 + 9) / 2
```

Because the median is the *middle-most value*, it does not fully depend on every value in the data. For illustration, hypothetically if we have a another list `num_friends2`

where one person had 10,000 friends, the **mean** would be much more sensitive to that change than the **median** would be.

```
num_friends2 = [10000.0,49,41,40,25,21,21,19,19,18,18,16,15,15,15,15,14,14
,13,13,13,13,12,12,11,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,9,9,9,9
,9,9,9,9,9,9,9,9,9,9,9,9,9,9,8,8,8,8,8,8,8,8,8,8,8,8,8,7,7,7,7,7,7,7,7,7,7
,7,7,7,7,7,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,5,5,5,5,5,5,5,5,5,5
,5,5,5,5,5,5,5,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,3,3,3,3,3,3,3,3,3,3
,3,3,3,3,3,3,3,3,3,3,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1
,1,1,1,1,1,1,1,1,1,1,1,1]
mean(num_friends2) # more sensitive to outliers: 7.333 => 55.86274509803921
median(num_friends2) # less sensitive to outliers: 6.0 => 6.0
```

You may also used `quantiles`

to describe your data. Whenever you've heard "X percentile", that is a description of quantiles relative to 100. In fact, the median is the 50th percentile (where 50% of the data lies below this point and 50% lies above).

Because `quantile`

is a position from 0-100, the second argument is a float from 0.0 to 1.0. We'll use that float to multiply with the length of the list. Then we'll wrap in `int`

to create an integer index which we'll use on a sorted xs to find the quantile.

```
def quantile(xs: List[float], p: float) -> float:
"""Returns the pth-percentile value in x"""
p_index = int(p * len(xs))
return sorted(xs)[p_index]
assert quantile(num_friends, 0.10) == 1
assert quantile(num_friends, 0.25) == 3
assert quantile(num_friends, 0.75) == 9
assert quantile(num_friends, 0.90) == 13
```

Finally, we have the **mode**, which looks at the most common values. First, we use the `Counter`

method on our list parameter and since Counter is a subclass of `dict`

we have access to methods like `values()`

to find all the values and `items()`

to find key value pairs.

We define `max_count`

to find the max value (22), then the function returns a list comprehension which loops through `counts.items()`

to find the key associated with the max_count (22). That is 1 and 6, meaning twenty-two people (the **mode**) had one or six friends.

```
def mode(x: List[float]) -> List[float]:
"""Returns a list, since there might be more than one mode"""
counts = Counter(x)
max_count = max(counts.values())
return [x_i for x_i, count in counts.items() if count == max_count]
assert set(mode(num_friends)) == {1, 6}
```

Because we had already used Counter on `num_friends`

previously (see `friend_counts`

), we could have just called the `most_common(2)`

method to get the same results:

```
mode(num_friends) # [6, 1]
friend_counts.most_common(2) # [(6, 22), (1, 22)]
```

### Dispersion

Aside from our data's central tendencies, we'll also want to understand it's spread or dispersion. The tools to do this are `data_range`

, `variance`

, `standard deviation`

and `interquartile range`

.

Range is a straightforward max value minus min value.

Variance measures how far a set of numbers is from their average value. What's more interesting, for our purpose, is how we need to borrow the functions we had previously built in the vectors and matrices posts to create the variance function.

If you look at its wikipedia page, **variance** is the *squared deviation* of a variable from its mean.

First, we'll need to create the `de_mean`

function that takes a list of numbers and subtract from all numbers in the list, the mean value (this gives us the deviation from the mean).

Then, we'll `sum_of_squares`

all those deviations, which means we'll take all the values, multiply them with itself (square it), then add the values (and divide by length of the list minus one) to get the variance.

Recall that the `sum_of_squares`

is a special case of the `dot`

product function.

```
# variance
from typing import List
Vector = List[float]
# see vectors.py in chapter 4 for dot and sum_of_squares
def dot(v: Vector, w: Vector) -> float:
"""Computes v_1 * w_1 + ... + v_n * w_n"""
assert len(v) == len(w), "vectors must be the same length"
return sum(v_i * w_i for v_i, w_i in zip(v,w))
def sum_of_squares(v: Vector) -> float:
"""Returns v_1 * v_1 + ... + v_n * v_n"""
return dot(v,v)
def de_mean(xs: List[float]) -> List[float]:
"""Translate xs by subtracting its mean (so the result has mean 0)"""
x_bar = mean(xs)
return [x - x_bar for x in xs]
def variance(xs: List[float]) -> float:
"""Almost the average squared deviation from the mean"""
assert len(xs) >= 2, "variance requires at least two elements"
n = len(xs)
deviations = de_mean(xs)
return sum_of_squares(deviations) / (n - 1)
assert 81.54 < variance(num_friends) < 81.55
```

The **variance** is `sum_of_squares`

deviations, which can be tricky to interpret. For example, we have a `num_friends`

with values ranging from 0 to 100.

What does a variance of 81.54 mean?

A more common alternative is the **standard deviation**. Here we take the square root of the variance using Python's `math`

module.

With a standard deviation of 9.03, and we know the mean of `num_friends`

is 7.3, anything below 7 + 9 = 16 or 7 - 9 (0 friends) friends is still *within a standard deviation of the mean*. And we can check by running `friend_counts`

that most people are within a standard deviation of the mean.

On the other hand, we know that someone with 20 friends is **more than one standard deviation** away from the mean.

```
import math
def standard_deviation(xs: List[float]) -> float:
"""The standard deviation is the square root of the variance"""
return math.sqrt(variance(xs))
assert 9.02 < standard_deviation(num_friends) < 9.04
```

However, because the **standard deviation** builds on the **variance**, which is dependent on the **mean**, we know that just like the mean, it can be sensitive to outliers, we can use an alternative called the **interquartile range**, which is based on the **median** and less sensitive to outliers.

Specifically, the interquartile range can be used to examine `num_friends`

between the 25th and 75th percentile. A large chunk of people are going to have *around 6 friends*.

```
def interquartile_range(xs: List[float]) -> float:
"""Returns the difference between the 75%-ile and the 25%-ile"""
return quantile(xs, 0.75) - quantile(xs, 0.25)
assert interquartile_range(num_friends) == 6
```

Now that we describe a single list of data, we'll also want to look at potential relationship between two data sources. For example, we may have a hypothesis that the amount of time spent on the DataScienster social network is somehow related to the number of friends someone has.

We'll examine covariance and correlations next.

## Correlation

If variance is how much a *single* set of numbers deviates from its mean (i.e., see `de_mean`

above), then **covariance** measures how two sets of numbers vary from *their* means. With the idea that if they co-vary the same amount, then they could be related.

Here we'll borrow the `dot`

production function we developed in the vectors in python post.

Moreover, we'll examine if there's a relationship between `num_friends`

and `daily_minutes`

and `daily_hours`

(see above).

```
def covariance(xs: List[float], ys: List[float]) -> float:
assert len(xs) == len(ys), "xs and ys must have same number of elements"
return dot(de_mean(xs), de_mean(ys)) / (len(xs) - 1)
assert 22.42 < covariance(num_friends, daily_minutes) < 22.43
assert 22.42 / 60 < covariance(num_friends, daily_hours) < 22.43 / 60
```

As with variance, a similar critique can be made of **covariance**, you have to do extra steps to interpret it. For example, the covariance of `num_friends`

and `daily_minutes`

is 22.43.

What does that mean? Is that considered a strong relationship?

A more intuitive measure would be a **correlation**:

```
def correlation(xs: List[float], ys: List[float]) -> float:
"""Measures how much xs and ys vary in tandem about their means"""
stdev_x = standard_deviation(xs)
stdev_y = standard_deviation(ys)
if stdev_x > 0 and stdev_y > 0:
return covariance(xs,ys) / stdev_x / stdev_y
else:
return 0 # if no variation, correlation is zero
assert 0.24 < correlation(num_friends, daily_minutes) < 0.25
assert 0.24 < correlation(num_friends, daily_hours) < 0.25
```

By dividing out the standard deviation of both input variables, correlation is always between -1 (perfect (anti) correlation) and 1 (perfect correlation). A correlation of 0.24 is relatively weak correlation (although what is considered weak, moderate, strong depends on the context of the data).

One thing to keep in mind is **simpson's paradox** or when the relationship between two variables change when accounting for a third, **confounding** variable. Moreover, we should keep this cliché in mind (it's a cliché for a reason): **correlation does not imply causation**.

### Summary

We are just five chapters in and we can begin to see how we're building the tools *now*, that we'll use later on. Here's a visual summary of what we've covered in this post and how it connects to previous posts, namely vectors, matrices and the python crash course: see setup, functions, lists, dictionaries and more.

This post continues my coverage of this book:

For more content on data science, machine learning, R, Python, SQL and more, find me on Twitter.

## Discussion (0)