# Are You a Tolkien Baby?

The Social Security Administration makes all its data on baby names available to the public, which as you can imagine leads to some fun analyses. I decided to look at the influence J.R.R. Tolkien, in particular the Lord of the Rings novels, has had on baby names over the decades since his books were published.

(One note before proceeding: This post was written entirely in an iPython, technically Jupyter, notebook, which is almost as cool as writing it in R Markdown… at least for me. I’m just warning readers that they’ll see a lot of Python code sprinkled throughout. You can find the entire, unaltered notebook on GitHub here.)

## The Data

You can download the entire data set from the Social Security Administration here. The data goes back as far as 1880. I was only concerned with 1954 to 2015 because ’54 was the year the first two books were published and ’15 is the latest year available.

The Social Security Administration notes:

To safeguard privacy, we restrict our list of names to those with at least 5 occurrences

So I’m not getting every person out there named after a Tolkien character, but it’s good enough for a blog post.

In [1]:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline

In [2]:
years = range(1954,2016)

In [3]:
pieces = []

In [4]:
columns =['name','sex','births']


After downloading and extracting the data into a folder, I ran a for loop over each year to pull all relevant data into one place.

In [5]:
for year in years:
path = 'data/yob%d.txt' % year
frame = pd.read_csv(path, names=columns)

frame['year'] = year
pieces.append(frame)

In [6]:
names = pd.concat(pieces, ignore_index=True)


Next, I compiled a list of all the main characters from the novels.

In [7]:
character_names = np.array(["Aragorn","Arwen","Bilbo","Boromir","Denethor","Elrond","Eomer","Eowyn","Faramir","Frodo","Galadriel","Gandalf","Gimli","Gollum","Haldir","Isildur","Legolas","Meriadoc","Peregrin","Samwise","Saruman","Theoden"])

In [8]:
print(character_names)

['Aragorn' 'Arwen' 'Bilbo' 'Boromir' 'Denethor' 'Elrond' 'Eomer' 'Eowyn'
'Faramir' 'Frodo' 'Galadriel' 'Gandalf' 'Gimli' 'Gollum' 'Haldir'
'Isildur' 'Legolas' 'Meriadoc' 'Peregrin' 'Samwise' 'Saruman' 'Theoden']


I then used that list to subset the dataframe with all names.

In [9]:
names_df = names[names['name'].isin(character_names)]


Then using the pandas pivot_table function, I looked at all births with names from a Tolkien character by year.

In [10]:
names_pivot = pd.pivot_table(names_df,index=['year'],aggfunc=sum)


## One Ring to Name Them All

Looking at the first five observations, it appears that the first year in which 5 or more births were recorded with names from the list was 1968.

In [11]:
print(names_pivot.head())

      births
year
1968       5
1969      10
1970      30
1971      23
1972      22


The first figure below shows all births with character names from the list, from 1968 to 2015. It appears that there was a slight uptick in the 70s, which increased toward the end of the decade — presumably because of the 1978 animated film by Ralph Bakshi. Then the number of births skyrockets after the release of Peter Jackson’s first film in the trilogy, 2001’s The Fellowship of the Ring.

In [12]:
fig,ax = plt.subplots(figsize=(9,6))
ax.plot(names_pivot)
# Hide the right and top spines
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# Only show ticks on the left and bottom spines
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
plt.xlim(1968,2015)
plt.title("Children Named After Tolkien Characters, 1968-2015\n", fontsize=20)
plt.xlabel("Years\n\n Source:Social Security Administration")
plt.ylabel("Births")

Out[12]:
<matplotlib.text.Text at 0x8f85dd0>

Next, I broke the births down by gender.

In [13]:
boys = names_df[names_df['sex']=="M"]

In [14]:
girls = names_df[names_df['sex']=="F"]


As you can see from the figure below, the trend is mostly girls. Considering the names for men in the Lord of the Rings novels, I can see why a name like Arwen is more popular than Gandalf. (Full disclosure: A former colleague of mine is named Arwen and I actually think it’s a cool name. Sadly I don’t know any Gandalfs).

In [15]:
fig,ax = plt.subplots(figsize=(10,6))
ax.plot(boys['year'],boys['births'],label="Boys")
ax.plot(girls['year'],girls['births'],label="Girls")
# Hide the right and top spines
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# Only show ticks on the left and bottom spines
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
plt.xlim(1968,2015)
plt.xlabel("Year\n\n Source:Social Security Administration")
plt.ylabel("Births")
plt.title("Children Named After Tolkien Characters, 1968-2015\n", fontsize=20)
ax.legend()

Out[15]:
<matplotlib.legend.Legend at 0x8d45d90>

So which names are the most popular by gender? First I looked at the boy names.

In [16]:
boys_names = boys.pivot_table(boys, index=['name'],aggfunc=np.sum)

In [17]:
boys_names = boys_names.drop('year',1)
boys_names.sort_values(['births'],ascending=False)

Out[17]:
births
name
Theoden 130
Samwise 49
Aragorn 43
Peregrin 29
Legolas 13
Gandalf 5

For boys, Theoden leads the very small pack, followed by Samwise, Aragorn and Peregrin. It looks like Legolas and Gandalf are the least-popular names.

If you know a Gandalf, congratulations! That’s pretty rare. Based on the most-recent monthly population estimates for the United States by the U.S. Census Bureau, Population Division, there were 312,418,820 people living in the U.S as of July 2015.[1] That means 0.00000160041% of the population is named Gandalf!

Samwise is also a tough name. Even if you knew someone named Samwise, he’d probably go by Sam. On the flipside, if someone is simply named “Sam,” it’s difficult to tell (short of interviewing the parents) whether the name was inspired by Samwise or not.

The figure below shows the number of children named “Sam” since the books were published, starting in 1954.

In [18]:
sams = names[names['name']=="Sam"]
sams_pivot = pd.pivot_table(sams,index=['year'],aggfunc=sum)
fig,ax = plt.subplots(figsize=(9,6))
ax.plot(sams_pivot)
# Hide the right and top spines
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# Only show ticks on the left and bottom spines
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
plt.xlim(1954,2015)
plt.title("Children Named 'Sam', 1954-2015\n", fontsize=20)
plt.xlabel("Years\n\n Source:Social Security Administration")
plt.ylabel("Births")

Out[18]:
<matplotlib.text.Text at 0x42b2b10>

There’s a peak in 1960, but who knows if the books were responsible for that. There are too many other variables to account for when it comes to a common name like “Sam.”

Next, I looked at the girl names.

In [19]:
girls_names = girls.pivot_table(girls, index=['name'],aggfunc=np.sum)

In [20]:
girls_names = girls_names.drop('year',1)
girls_names.sort_values(['births'],ascending=False)

Out[20]:
births
name
Arwen 1843
Eowyn 879
Galadriel 174

Arwen leads the pack for girls. In fact, considering the totals for each name, it’s more likely you know a girl or woman named after a LotR character than a boy or man.

Just don’t ask them how to get to Mordor. We have Google Maps for that!

[1] I went with July 2015 because the monthly estimates beginning with August 1, 2015 are short-term projections.