datascience.tables.Table.hist

Table.hist(select=None, overlay=True, bins=None, counts=None, unit=None, **vargs)

Plots one histogram for each column in the table.

Every column must be numerical.

Kwargs:

overlay (bool): If True, plots 1 chart with all the histograms

overlaid on top of each other (instead of the default behavior of one histogram for each column in the table). Also adds a legend that matches each bar color to its column.

bins (column name or list): Lower bound for each bin in the

histogram. If None, bins will be chosen automatically.

counts (column name or column): A column of counted values.

All other columns are treated as counts of these values. If None, each value in each row is assigned a count of 1.

vargs: Additional arguments that get passed into :func:plt.hist.

See http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.hist for additional arguments that can be passed into vargs. These include: range, normed, cumulative, and orientation, to name a few.

>>> t = Table().with_columns([
...     'count',  [9, 3, 3, 1],
...     'points', [1, 2, 2, 10]])
>>> t
count | points
9     | 1
3     | 2
3     | 2
1     | 10
>>> t.hist() 
<histogram of values in count>
<histogram of values in points>

>>> t = Table().with_columns([
...     'value',      [101, 102, 103],
...     'proportion', [0.25, 0.5, 0.25]])
>>> t.hist(counts='value') 
<histogram of values in prop weighted by corresponding values in value>