Think Java, but interactive

Think Java, but interactive

The online version of Think Java now features interactive widgets that run the code examples in the browser. For readers, it means you can try out the examples, modify them, and test your code. And for us it means we can publish an interactive version of the book without providing servers.

The widgets use java-runner, an in-browser Java editor and interpreter developed by Think Java co-author Chris Mayfield.

java-runner implements a subset of Java with the features needed for most programming classes, plus some safety features — like a maximum instruction count to stop infinite loops. It provides a code editor and a REPL.

Here’s an example of the code editor, from Section 6.1:

And here’s an example of the REPL, from Section 6.6::

We’re aware of one example where the behavior of java-runner differs from the Java language specification — so we’re working on that.

Greatest GOAT of All Time?

Greatest GOAT of All Time?

A recent post claims that the “most statistically dominant athlete” of all time was cricketer Don Bradman. It’s a bold claim – let’s see if it holds up.

In Chapter 4 of Probably Overthinking It, I listed a few examples of athletes who are considered the Greatest Of All Time (GOAT), and noted that in many cases they are not just a little better than the second-best, but much better. That is, they are outliers among outliers.

And I suggested that part of the explanation for this phenomenon is that the distribution of accomplishment in many fields (at least, the ones where accomplishment can be quantified) follows a lognormal distribution. That matters because the lognormal distribution has a long tail, which means that extreme values can be much farther from the mean than we would see in a Gaussian (normal) distribution.

So I was intrigued by this post about Don Bradman:

The most statistically dominant athlete ever isn’t Jordan or Messi — and you’ve probably never heard of him. 🏏

I plotted the career batting averages of the greatest Test cricketers of all time. They make a clean bell curve. In cricket, averaging 50 makes you an immortal legend, and the all-time record holders top out around 60 — about 2 standard deviations above the mean.

Then there’s Don Bradman: 99.94. More than 6 standard deviations out. The gap between him and the SECOND best player is bigger than the gap between #2 and the entire average. By the math, a batsman this good should appear about once in 7 billion.

To be this far ahead in basketball, Michael Jordan would’ve had to average 43 points in every single game he ever played. No one in any sport — not Jordan, not Gretzky, not Messi — is this far from their peers.

Not just the greatest. The single biggest outlier in the history of sport.

Let’s see if that’s true. In particular, I’ll investigate the “clean bell curve” – it implies a Gaussian model of the data, which is where that “once in 7 billion” comes from.

To give away the ending, here’s what I found:

  • The data don’t fit a Gaussian particularly well, especially in the tail, so the “one in 7 billion” might not be right.
  • I thought they might fit a lognormal better, but I was wrong.
  • It turns out that the data fit a Weibull distribution very well, and from that we can get a revised estimate of how much of a GOAT Bradman was.

As it turns out, he was a pretty goaty GOAT – but not quite one in 7 billion.

Load the data

I got the data from Kaggle (runs_of_batsmen.csv).

DATA_PATH = "runs_of_batsmen.csv"

df = pd.read_csv(DATA_PATH)

# Convert to numeric, coercing errors to NaN
df['Batting Average'] = pd.to_numeric(df['Batting Average'], errors='coerce')
df['Innings'] = pd.to_numeric(df['Innings'], errors='coerce')

I select only batters with 10 or more innings.

df = df.query('Innings >= 10')

Here’s what the distribution of batting averages looks like.

import seaborn as sns

sns.kdeplot(df['Batting Average'])
decorate()
_images/9e6e3e754fa48807f6d4f5b72552714ed01e3d2dec5cfb8bb37abb5ad845d7aa.png

That’s not much of a bell curve. It’s pretty clearly skewed to the right, even if we leave Bradman out of it. But let’s fit a Gaussian to it anyway.

The Gaussian Model

There are a lot of ways to fit a model to data. The one I like for applications like this is percentile matching – that is, finding a model that minimizes the average vertical distance between the empirical CDF of the data and the CDF of the model. That’s what fit_normal does.

avg_series = df["Batting Average"].dropna()
gaussian_model = fit_normal(avg_series)

Here’s what that looks like, plotting the tail distribution, which is the complement of the CDF. The shaded area shows the differences between the data and the model.

tail_data = TailDist.from_seq(avg_series)
plot_fit_with_area(tail_data, gaussian_model, kind="tail")
decorate(xlabel="Batting Average", title='Gaussian model')
_images/4b2c95fa3fda1563ca2714d6361e97b106ed1984db9479b168798ca548a9d940.png

This is not a great fit. There are clear differences in the shapes of the tail distributions.

And here’s the average error, which is related to the area between the curves.

mae_normal = average_error(avg_series, gaussian_model)
mae_normal

0.017745997600540485

When we plot the tail distributions on linear scales, it looks like the model might be good enough. The problem is that we can’t see what’s happening in the tail. For that, it’s useful to plot the tail distributions on a log-y scale.

n = len(avg_series)
qs = np.linspace(tail_data.qs.min(), tail_data.qs.max(), 200)
tail_model = TailDist(gaussian_model.sf(qs), index=qs)

plot_model_bounds(gaussian_model, n=n, qs=qs, kind="tail", color="gray", alpha=0.2)
tail_model.plot(label="model")
tail_data.plot(label="data")
decorate(yscale="log", title='Gaussian model (log-y scale)')
_images/b8bccf875d3216eb826852be8de578e37cbfb0909184ce61df747e79bef1db5e.png

The model fits the left side of the distribution well enough, but after that, it diverges badly. Around 60, the difference between the model and the data is about an order of magnitude.

Nevertheless, we can use the Gaussian model to compute the probability of a batting average as high as Bradman’s 99.94.

BRADMAN = 99.94


def probability_of_exceeding(model, x, n):
    p = model.cdf(x)
    one_in = 1 / (1 - p)
    in_sample = 1 / (1 - p ** n)
    return one_in, in_sample

In the fitted distribution, the probability of a single batter achieving Bradman’s average is about one per 5.7 trillion.

one_in, in_sample = probability_of_exceeding(gaussian_model, BRADMAN, n)
one_in / 1e12

5.71160383940456

And even if we account for the sample size, the probability that one of 3438 batsman reaches that level is one per 1.7 billion. So, according to the Gaussian model, this outcome is basically impossible.

n, in_sample / 1e9

(3438, 1.6613158346144736)

But the Gaussian model doesn’t fit the data well – and there’s no reason it should. To see why not, let’s think about the process that generates the distribution of batting averages.

As a simplifying assumption, suppose a batsman has the same probability of getting out at any time; in that case, the number of runs in an inning might follow a negative binomial (NB) distribution. As we add up innings (or average over them), the total would eventually converge to a normal distribution, but most batsmans don’t have enough innings to converge. So for each batsman, the distribution of runs follows something between NB and normal. And when we combine them, we get a mixture of those hybrids. There’s no obvious reason the results should fit a simple mathematical model.

But it turns out that they do.

The Weibull Model

The Weibull distribution is not the first thing I thought of – I tried a lognormal distribution first. But a Weibull distribution fits the data really, really well. The fit_scipy_dist is a more general version of fit_normal that works with any of the SciPy distributions.

weibull_model = fit_scipy_dist(avg_series, weibull_min)
weibull_model.args

(1.8667199063293203, 0.9939717502596944, 22.486446489455503)

Here’s the result. Again, the gray area shows the difference between the data and the model.

plot_fit_with_area(tail_data, weibull_model, kind="tail")
decorate(xlabel="Batting Average", title="Weibull model")
_images/2c9b224b40833eea766b5d0cd386fc20182601b3ae5245f8f68f7ffca163a4cf.png

The gray area is not visible. Here’s the average error.

mae_weibull = average_error(avg_series, weibull_model)
mae_weibull

0.002555441381799216

The average error of the Gaussian model is about 7x bigger.

mae_normal / mae_weibull

6.944396270223196

Here’s what the tail looks like on a log-y axis.

tail_model = TailDist(weibull_model.sf(qs), index=qs)

plot_model_bounds(weibull_model, n=n, qs=qs, kind="tail", color="gray", alpha=0.2)
tail_model.plot(label="model")
tail_data.plot(label="data")
decorate(yscale="log", title="Weibull model (log-y scale)")
_images/b42367fb1469d102aa278bd0b6f4c1a339ea7f78a8919fd2da3d27b61c2e3fc3.png

The model fits the data well – within the bounds of variability we expect for this sample size – except for Bradman, who is still an outlier.

Again, we can compute the probability that any batter exceeds Bradman’s level:

one_in, in_sample = probability_of_exceeding(weibull_model, BRADMAN, n)
one_in / 1e6

7.980438255436601

It’s about one per 8 million. And in a sample of 3438 batsmen, the chance that any one of them reaches that level is one per 2,322.

in_sample / 1e3

2.3217442928624186

So Bradman is still an outlier among outliers, but not quite the statistical anomaly that he would be in a normal distribution.

Finally, let’s think about why a Weibull distribution fits this dataset so well. In the data-generating process I suggested earlier, if a batsman has the same probability of getting out at any time, the number of runs in an inning might follow a negative binomial (NB) distribution. If we think of the run-generating process as continuous, the distribution of runs before an out would be exponential. And if we relax the assumption that the hazard rate is constant, the distribution of runs per inning would be Weibull.

I think that’s an intriguing first step, but at best it explains the distribution of runs per inning – but not the distribution of batting averages across batsmen with, presumably, different hazard rates.

Appendix: The Lognormal Model

Just for completeness, here’s the lognormal model.

log_series = np.log10(avg_series)
log_model = fit_normal(log_series, x0=norm.fit(log_series))
tail_log_data = TailDist.from_seq(log_series)

plot_fit_with_area(tail_log_data, log_model, kind="tail")
decorate(xlabel="log10(Batting Average)", title="Lognormal model")
_images/6344bb082e43613d7cc8910ef8f34769e420045404b8d576f8382bc64b0c8bc2.png

The average error is slightly worse than the Gaussian model.

mae_lognormal = average_error(log_series, log_model)
mae_lognormal

0.01885918996726559

And it doesn’t fit the tail well at all.

log_qs = np.log10(qs)
tail_model = TailDist(log_model.sf(log_qs), index=log_qs)

plot_model_bounds(log_model, n=n, qs=log_qs, kind="tail", color="gray", alpha=0.2)
tail_model.plot(label="model")
tail_log_data.plot(label="data")
decorate(xlabel="log10(Batting Average)", yscale="log", title="Lognormal model (log-y scale)")
_images/5d0319f985ddb2ca9f541683b89a467e19b0e12c468c4c00b5a969111401be6b.png

The Frog Puzzle

The Frog Puzzle

Here’s a probability puzzle from a TED-Ed video called Can you solve the frog riddle? by Derek Abbott. It came up recently in this Reddit thread:

You’re stranded in a rainforest after accidentally eating a poisonous mushroom. To survive the poison, you need to lick a certain species of frog. Only female frogs produce the antidote. Male and female frogs occur in equal numbers and look identical, but male frogs have a distinctive croak.

You see one frog alone on a tree stump. In another direction, you hear the croak of a male frog coming from a clearing with two frogs. You can’t tell which one made the sound.

You only have time to go to one place. What are your chances of survival if you go to the clearing and lick both frogs? What if you go to the lone frog?

The second question is relatively easy: if we assume that you are equally likely to see a male or female frog, the probability is 50% that the lone frog is female.

The first question depends on how we interpret the puzzle. In particular, it hinges on the word “distinctive” – does that mean:

  • Only male frogs croak, and the sound is distinguishable from background noises, or
  • Both male and female frogs croak, but the male croak is distinguishable from the female croak.

Based on the answer presented in the video, the first meaning is intended. So we’ll start by solving that version.

But the second meaning makes the problem a little harder, so we’ll solve that one, too.

Only Male Frogs Croak

To solve the intended version of the puzzle, we’ll assume

  • Only male frogs croak, and
  • When two frogs appear together, their sexes are independent.

So we’ll start with a prior where all two-frog combinations are equally likely.

from sympy import Rational

hypo = ['FF', 'FM', 'MF', 'MM']
prior = Rational(1)

Now let’s think about the likelihood of the data under each scenario. In the video, the solution is based on these assumptions:

  • If both frogs are female, the probability of hearing the male croak is 0.
  • If either frog is male, the probability that one of them croaks is 1.
likelihood = [0, 1, 1, 1]

I’ll use a BayesTable to compute the posterior probability for each scenario.

import pandas as pd
import numpy as np

class BayesTable(pd.DataFrame):
    def __init__(self, hypo, prior=1, **options):
        columns = ['prior', 'likelihood', 'unnorm', 'posterior']
        super().__init__(index=hypo, columns=columns, **options)
        self.prior = prior
    
    def update(self, likelihood):
        self.likelihood = likelihood
        self.unnorm = self.prior * self.likelihood
        nc = self.unnorm.sum()
        self.posterior = self.unnorm / nc

table = BayesTable(hypo, prior)
table.update(likelihood)
table
priorlikelihoodunnormposterior
FF1000
FM1111/3
MF1111/3
MM1111/3

From the table, we can extract the posterior probability that both frogs are male.

from sympy import init_printing
init_printing(use_latex=False)

table.posterior['MM']

1/3

With these assumptions, the probability 1/3 that both frogs are male (and you die), so the probability is 2/3 that at least one is female (and you live).

And that’s the answer in the video.

Poisson (not Poison) Frogs

But is that the right likelihood? Suppose frogs are equally likely to croak at any instant in time, so their croaks follow a Poisson process. If we assume that these croaking processes are independent, two frogs would be more likely to croak, during a given interval, than one.

If the interval is much longer than the average time between croaks, the probability that either frog croaks approaches 1, which is consistent with the previous solution.

But if the interval is short – as it might be if you were deciding whether to approach the first frog – the probability of hearing a croak would be double if there are two male frogs rather than one.

In that case, the likelihood of the data would be:

half = Rational(1, 2)
likelihood = [0, half, half, 1]

And here are the posterior probabilities:

table = BayesTable(hypo, prior)
table.update(likelihood)
table
priorlikelihoodunnormposterior
FF1000
FM11/21/21/4
MF11/21/21/4
MM1111/2

With Poisson frogs and a short interval, the probability of two male frogs is 1/2, so it doesn’t matter whether you approach the lone frog or the pair of frogs.

Female Frogs Croak, Too

Now let’s think about the other interpretation of the puzzle: suppose both male and female frogs croak, but we can distinguish one from the other. And suppose male and female frogs croak at different rates, but they are still independent.

Assume that male frogs croak at a rate of 1 per time unit, and female frogs at a rate of r per time unit. In that case, if we start listening at a random time, the probability that we hear a male frog first is 1 / (r+1) if there’s only one male frog, and 1 if there are two male frogs.

So the likelihood in this case is:

from sympy import symbols

r = symbols('r')
likelihood = [0, 1 / (r+1), 1 / (r+1), 1]

And here are the posteriors

table = BayesTable(hypo, prior)
table.update(likelihood)
table
priorlikelihoodunnormposterior
FF1000
FM11/(r + 1)1/(r + 1)1/((1 + 2/(r + 1))*(r + 1))
MF11/(r + 1)1/(r + 1)1/((1 + 2/(r + 1))*(r + 1))
MM1111/(1 + 2/(r + 1))

In this scenario, here’s the probability you die.

prob_die = table.posterior['MM']
prob_die.simplify()

r + 1
─────
r + 3

If female frogs don’t croak, we get the same answer as in the first scenario.

prob_die.subs({r: 0})

1/3

If male and female frogs croak at the same rate, the probability that both frogs are male is 1/2.

prob_die.subs({r: 1})

1/2

But if female frogs croak much more often, the fact that a male croaked first is strong evidence that both are male, so the posterior probability is close to 1.

prob_die.subs({r: 1000}).evalf()

0.998005982053839

Assortative Mating

Now suppose that when we see two frogs together, their sexes are not independent; specifically, let’s assume that the probability of a same-sex pair is p, so the probability of a mixed-sex pair is 1-p. In this scenario, the priors (before we hear the croak) are not equal.

p = symbols('p')
prior = [p, 1-p, 1-p, p]

Here are the posterior probabilities, assuming again that both male and female frogs, possibly at different rates.

likelihood = [0, 1 / (r+1), 1 / (r+1), 1]
table = BayesTable(hypo, prior)
table.update(likelihood)
table
priorlikelihoodunnormposterior
FFp000
FM1 – p1/(r + 1)(1 – p)/(r + 1)(1 – p)/((p + 2*(1 – p)/(r + 1))*(r + 1))
MF1 – p1/(r + 1)(1 – p)/(r + 1)(1 – p)/((p + 2*(1 – p)/(r + 1))*(r + 1))
MMp1pp/(p + 2*(1 – p)/(r + 1))
table.posterior['MM'].simplify()

 p⋅(r + 1) 
───────────
p⋅r - p + 2

If p=1/2, this simplifies to the previous scenario.

table.posterior['MM'].subs({p: half}).simplify()

r + 1
─────
r + 3

And if r=0 (female frogs don’t croak), we get the answer presented in the video.

table.posterior['MM'].subs({p: half, r: 0}).simplify()

1/3

But depending on the assumptions, the probability can be as low as 0

table.posterior['MM'].subs({p: 0, r: 1}).simplify()

0

Or as high as 1.

table.posterior['MM'].subs({p: 1, r: 0}).simplify()

1

Or anything in between. As is often the case with problems like these, the answer depends on a precise specification of the data-generating process.

Discussion

If all of this seems like more trouble than it’s worth, let me suggest a metacognitive shortcut for solving puzzles like this.

  1. Notice that in all probability puzzles, the answer is either 1/2 or 1/3.
  2. Also, the answer is always counterintuitive; otherwise it wouldn’t be a puzzle.
  3. Therefore, if your intuition says the answer is 1/2, it’s actually 1/3, and vice versa.

That might save you some time.

This notebook uses methods and materials from Think Bayes, second edition. If you like this sort of thing, you can read the whole book, and more examples, at allendowney.github.io/ThinkBayes2/.



Planning for your midlife crisis

Planning for your midlife crisis

Yesterday I presented a talk at ODSC East 2026, called “Counterfactual Analysis with Bayesian Models: What Drives the Life Expectancy Gap?” Here’s the abstract

Across nearly every country in the world, women live longer than men—but the size of this gap varies from about two years in some countries to more than twelve in others. What explains these differences, and how much of the gap can be closed?

In this talk, I present a practical approach to counterfactual analysis using Bayesian regression models. Using publicly available mortality data, we build a model that relates the life expectancy gap between men and women to differences in cause-specific death rates, including homicide, drug overdoses, traffic fatalities, smoking-related disease, and chronic illness.

The model generates posterior simulations that answer “what-if” questions. For example: How much smaller would the U.S. life expectancy gap be if homicide rates matched those in Western Europe?

The talk presents the workflow—from assembling global datasets to fitting interpretable Bayesian models with PyMC and generating counterfactual simulations. Attendees will learn how Bayesian models can support explainable modeling and analysis under uncertainty.

I think the talk went well, and we got some good questions at the end. There’s no recording, unfortunately, but my slides are here. And if you want to know more, I have a series of blog posts on Substack

The fifth and final post is on the way. In the meantime, here’s a quick post on a related topic.

Are you middle-aged?

Here’s a question from Reddit’s Stupid Questions forum:

I always thought middle age was in your 40s but since life expectancy is around 75 or so, wouldn’t it be about 35?

If life expectancy is 75, you might think the midpoint is half that, which is 37.5. But if 75 is life expectancy at birth and you survive to age 37.5, your life expectancy at that age is higher than 75. So 37.5 is not halfway!

If we really want to find the midpoint – and it wouldn’t be Probably Overthinking It if we didn’t – we have to find the age where your expected remaining lifetime equals your current age.

Let’s do it.

Data

From the Human Mortality Database I downloaded life tables for the United States, combined and broken down for men and women. The following function reads and cleans a table.

def read_life_table(filename):
    lt = pd.read_fwf(filename, skiprows=2, infer_nrows=200)
    lt['Age'] = lt['Age'].str.replace('+', '', regex=False).astype(int)
    return lt

Here are the first few rows of the combined table (see notes below for details).

blt = read_life_table('../data/bltper_1x1.txt')
blt.head()
YearAgemxqxaxlxdxLxTxex
0193300.061290.058610.25100000586195624608960960.90
1193310.009460.009410.509413988693696599398563.67
2193320.004350.004340.509325340593050590028963.27
3193330.003100.003100.509284828892704580723962.55
4193340.002390.002380.509256022192450571453561.74

We’ll also read the female and male tables.

flt = read_life_table('../data/fltper_1x1.txt')

mlt = read_life_table('../data/mltper_1x1.txt')

The tables include data from 1933 to 2024, so we’ll select the most recent data.

year = blt['Year'].unique()[-1]
table = blt.query('Year == @year').set_index('Age')

The column we’ll use is ex, which is life expectancy as a function of age.

age = table.index.to_series()
ex = table['ex']

Life expectancy at birth is 79 years, so the naive midpoint is 39.5.

ex[0], ex[0] / 2

(79.08, 39.54)

But at age 40, expected remaining lifetime is 41.1, so 39.5 is not the midpoint.

ex[39], ex[40]

(42.04, 41.12)

This plot shows life expectancy at each age, compared to age.

ex.plot(label='Remaining life expectancy')
age.plot(label='Age')
decorate(ylabel='Years',
        title='Remaining life expectancy vs age, United States 2024')
_images/a55e9cbde25b36d9c42e2ede2a78a827460d537f230b5cc4e7dfcb519a44bbc6.png

“Middle age” is where the lines cross, which we can compute by linear interpolation.

from scipy.interpolate import interp1d

inverse = interp1d(ex - age, age)
inverse(0)

array(40.58638743)

So the overall midpoint is 40.6 years. But as you might expect, it’s different for men and women. Let’s put the analysis we did in a function.

def get_midpoint(filename):
    lt = read_life_table(filename)
    year = lt['Year'].unique()[-1]
    table = lt.query('Year == @year').set_index('Age')

    age = pd.Series(table.index)
    ex = table['ex']

    inverse = interp1d(ex - age, age)
    return inverse(0)

And run it for men.

get_midpoint('../data/mltper_1x1.txt')

array(39.57142857)

And women.

get_midpoint('../data/fltper_1x1.txt')

array(41.56185567)

Men hit middle age at 39.6, women at 41.6.

The Gender Gap and Age

Finally, let’s see how the gender gap in life expectancy changes as a function of age.

ex_male = mlt.query('Year == @year').set_index('Age')['ex']
ex_female = flt.query('Year == @year').set_index('Age')['ex']
gap = ex_female - ex_male

gap.plot(label='')
decorate(ylabel='Years',
         title='Life expectancy gender gap vs age')
_images/2cf48be5acc971ad8f6973506c224767a25ef2e38bcf53c5b12b0f895c26ed11.png

At birth the life expectancy gap is close to five years. At age 100, it is close to zero.

But just looking at the gap might be misleading. For a more complete picture let’s also look at the ratio.

ratio = ex_female / ex_male
ratio.plot(label='')

decorate(ylabel='Ratio',
         title='Life expectancy gender ratio (female / male)')
_images/1a514d22895c3e18a767184700d6edf467975abb4b302afe044fd42c002ae21c.png

The life expectancy ratio tells a more complicated story.

  • At birth, the ratio is 1.06, which means female babies live 6% longer, on average.
  • Around age 80, the ratio peaks at nearly 1.14 – so between female and male octogenarians, we expect the women to live 14% longer.
  • At advanced ages, the ratio declines steeply and actually crosses over after age 100 – although the crossover is minimal and might not be statistically valid.

To interpret these results, we can think about the causes of death that contribute to age-specific death rates at different stages of life.

  • In young adulthood, the causes of death that contribute most to gender gaps include road traffic, homicide, accidental injury, drug use disorders.
  • In advanced adulthood, they include cancer, cardiovascular disease, respiratory disease, liver disease, diabetes, and suicide.

The causes that affect younger people have large gender gaps, but relatively low death rates. As people get older, these low-rate causes contribute less to age-specific death rates, and the higher-rate causes contribute more.

I think that’s a plausible explanation for the increasing ratio from age 0 to 80. For the decline that follows, I can only speculate that there is a selection effect: people who get to these advanced ages are likely to have better-than-average lifestyle histories (less smoking and drinking, better diet, more exercise) – and among people with better lifestyles, the gender gap is small.

Notes

Data credit: HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at [www.mortality.org].

Here are the columns of the 1×1 Period Life Tables:

  • Year: Calendar year to which the period life table refers.
  • Age: Exact age (x), in years, at the beginning of the interval ([x, x+1)).
  • mx: Central death rate at age (x):
  • qx: Probability of dying between ages (x) and (x+1):
  • ax: Average fraction of the interval lived by those who die in ([x, x+1)). Typically around 0.5 for most ages, lower for infants (reflecting higher early mortality within the year).
  • lx: Number of survivors at exact age (x), out of a radix (usually 100,000 births).
  • dx: Number of deaths between ages (x) and (x+1):
  • Lx: Person-years lived between ages (x) and (x+1), approximately
  • Tx: Total person-years remaining above age (x):
  • ex: Life expectancy at age (x):

The details are in this Jupyter notebook.