python - Plotting a histogram from pre-counted data in Matplotlib -

i'd use matplotlib plot histogram on data that's been pre-counted. example, have raw data

data = [1, 2, 2, 3, 4, 5, 5, 5, 5, 6, 10]

given data, can use

pylab.hist(data, bins=[...])

to plot histogram.

in case, data has been pre-counted , represented dictionary:

counted_data = {1: 1, 2: 2, 3: 1, 4: 1, 5: 4, 6: 1, 10: 1}

ideally, i'd pass pre-counted data histogram function lets me control bin widths, plot range, etc, if had passed raw data. workaround, i'm expanding counts raw data:

data = list(chain.from_iterable(repeat(value, count) (value, count) in counted_data.iteritems()))

this inefficient when counted_data contains counts millions of data points.

is there easier way use matplotlib produce histogram pre-counted data?

alternatively, if it's easiest bar-plot data that's been pre-binned, there convenience method "roll-up" per-item counts binned counts?

you can use weights keyword argument np.histgram (which plt.hist calls underneath)

val, weight = zip(*[(k, v) k,v in counted_data.items()]) plt.hist(val, weights=weight) 

assuming only have integers keys, can use bar directly:

min_bin = np.min(counted_data.keys()) max_bin = np.max(counted_data.keys())  bins = np.arange(min_bin, max_bin + 1) vals = np.zeros(max_bin - min_bin + 1)  k,v in counted_data.items():     vals[k - min_bin] = v  plt.bar(bins, vals, ...) 

where ... ever arguments want pass bar (doc)

if want re-bin data see histogram separate list denoting frequency