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?

I keep using numpy's functions to do this and converting the result to a DataFrame or Series when I need this. It would be nice to stay with pandas objects the whole time.

Answer 1

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 2

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.