Are there functions to retrieve the histogram counts of a Series in pandas?

Question

There is a method to plot Series histograms, but is there a function to retrieve the histogram counts to do further calculations on top of it?

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Answer 1

If you know the number of bins you want, you can use pandas' cut function, which is now accessible via value_counts. Using the same random example:

s = pd.Series(np.random.randn(100))
s.value_counts(bins=5)

Out[55]: 
(-0.512, 0.311]     40
(0.311, 1.133]      25
(-1.335, -0.512]    14
(1.133, 1.956]      13
(-2.161, -1.335]     8

Answer 2

If your Series was discrete you could use value_counts:

In [11]: s = pd.Series([1, 1, 2, 1, 2, 2, 3])

In [12]: s.value_counts()
Out[12]:
2    3
1    3
3    1
dtype: int64

You can see that s.hist() is essentially equivalent to s.value_counts().plot().

If it was of floats an awful hacky solution could be to use groupby:

s.groupby(lambda i: np.floor(2*s[i]) / 2).count()

Answer 3

Since hist and value_counts don't use the Series' index, you may as well treat the Series like an ordinary array and use np.histogram directly. Then build a Series from the result.

In [4]: s = Series(randn(100))

In [5]: counts, bins = np.histogram(s)

In [6]: Series(counts, index=bins[:-1])
Out[6]: 
-2.968575     1
-2.355032     4
-1.741488     5
-1.127944    26
-0.514401    23
 0.099143    23
 0.712686    12
 1.326230     5
 1.939773     0
 2.553317     1
dtype: int32

This is a really convenient way to organize the result of a histogram for subsequent computation.

To index by the center of each bin instead of the left edge, you could use bins[:-1] + np.diff(bins)/2.