# Binning is sinning

You should never bin your data, you hear people saying.
But what if you really really want to?

## let’s bin!

First, we create fake sinusoidal data

import numpy as np

t = np.linspace(0, 10, 1000)
y = 5*np.sin(t) + np.random.randn(t.size)


which we now want to bin in bins of 0.5.

width = 0.5
bins = np.arange(t.min(), t.max()+width, width)


scipy has a handy function for this:

from scipy.stats import binned_statistic


which can be used as

binned_y = binned_statistic(t, y, bins=bins)


Let’s plot the result

import matplotlib.pyplot as plt

plt.plot(t, y, alpha=0.5)
plt.plot(bins[1:] - width/2.,  binned_y, 'o-') ## more options

By default, binned_statistic calculates the mean of the data inside each bin. But we can calculate other statistics, as explained in the documentation.
The statistic optional argument can be:

statistic : string or callable, optional
The statistic to compute (default is 'mean'). The following statistics are available:
'mean'   : compute the mean of values for points within each bin.
'median' : compute the median of values for points within each bin.
'count'  : compute the count of points within each bin.
This is identical to an unweighted histogram.
'sum'    : compute the sum of values for points within each bin.
This is identical to a weighted histogram.
'min'    : compute the minimum of values for points within each bin.
'max'    : compute the maximum of values for point within each bin.
function : a user-defined function which takes a 1D array of values,
and outputs a single numerical statistic.
This function will be called on the values in each bin.
Empty bins will be represented by function([]), or NaN if this returns an error.


## wrap-up

That’s it, a simple handy function to bin data!

Let me know in the comments if this is helpful, wrong or super-duper cool.