# How can I estimate unique occurrence counts from a random sampling of data?

Let's say I have a large set of $S$ values which sometimes repeat. I wish to estimate the total number of unique values in the large set.

If I take a random sample of $T$ values, and determine that it contains $T_u$ unique values, can I use this to estimate the number of unique values in the large set?

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• Can you also keep count of the number of copies of each unique value in the sample? Strikes me that might help. – onestop Nov 27 '11 at 19:40
• @onestop, yes I could do that – sanity Nov 30 '11 at 16:25

Here is a whole paper about the problem, with a summary of various approaches. It's called Distinct Value Estimation in the literature.

If I had to do this myself, without having read fancy papers, I'd do this. In building language models, one often has to estimate the probability of observing a previously unknown word, given a bunch of text. A pretty good approach at solving this problem for language models in particular is to use the number of words that occurred exactly once, divided by the total number of tokens. It's called the Good Turing Estimate.

Let u1 be the number of values that occurred exactly once in a sample of m items.

P[new item next] ~= u1 / m.


Let u be the number of unique items in your sample of size m.

If you mistakenly assume that the 'new item next' rate didn't decrease as you got more data, then using Good Turing, you'll have

total uniq set of size s ~= u + u1 / m * (s - m)


This has some nasty behavior as u1 becomes really small, but that might not be a problem for you in practice.

• what is s in this case? the total number of 'words'? – Nathan Dec 10 '17 at 4:16
• Indeed, s occurs twice in this, both on the left and right hand size? – PascalVKooten Sep 16 at 13:32

The simulation strategy

Collect m random samples of size n from the set S. For each of the m samples, compute the number u of unique values and divide by n to normalize. From the simulated distribution of normalized u, compute summary statistics of interest (e.g., mean, variance, interquartile range). Multiply the simulated mean of normalized u by the cardinality of S to estimate the number of unique values.

The greater are m and n, the more closely your simulated mean will match the true number of unique values.

• Isn't this solution kind of lame? It doesn't take into account saturation effects at all. – rrenaud Nov 28 '11 at 21:36
• @rrenaud Compared to your solution, I agree that mine appears inferior. – Brash Equilibrium Nov 28 '11 at 22:21
• @rrenaud I do still advocate a simulation strategy whereby you calculate the probability of unique items using the GTFE on as many-as-feasible large-as-feasible samples to get some sense of sampling error for the probability of unique items. Or is there an explicit formula to calculate all of the moments? I wouldn't think it is the negative binomial since the binomial distribution, according to the Wikipedia reference, does not characterize the distribution of the number of unique items. But awesome! I'll file this away for later. – Brash Equilibrium Nov 28 '11 at 22:31

Here's an implementation for pandas:

import math
import numpy as np
from collections import Counter

def estimate_uniqueness(df, col, r=10000, n=None):
""" Draws a sample of size r from column col from dataframe df and
returns an estimate for the number of unique values given a
population size of n """
n = n or df.shape
sample = df[col][np.random.randint(0, n, r)]
counts = sample.value_counts()
fis = Counter(counts)
estimate = math.sqrt(n / r) * fis + sum([fis[x] for x in fis if x > 1])
return estimate


Relies on Section 2 and 4 of this paper: http://ftp.cse.buffalo.edu/users/azhang/disc/disc01/cd1/out/papers/pods/towardsestimatimosur.pdf