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======
Charts
======
Agate offers two kinds of built in charting: very simple text bar charts and SVG charting via `leather <https://leather.rtfd.io/>`_. Both are intended for efficiently exploring data, rather than producing publication-ready charts.
Text-based bar chart
====================
agate has a builtin text-based bar-chart generator:
.. code-block:: python
table.limit(10).print_bars('State Name', 'TOTAL', width=80)
.. code-block:: bash
State Name TOTAL
ALABAMA 19,582 ▓░░░░░░░░░░░░░
ALASKA 2,705 ▓░░
ARIZONA 46,743 ▓░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░
ARKANSAS 7,932 ▓░░░░░
CALIFORNIA 76,639 ▓░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░
COLORADO 21,485 ▓░░░░░░░░░░░░░░░
CONNECTICUT 4,350 ▓░░░
DELAWARE 1,904 ▓░
DIST. OF COLUMBIA 2,185 ▓░
FLORIDA 59,519 ▓░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░
+-------------+------------+------------+-------------+
0 20,000 40,000 60,000 80,000
Text-based histogram
====================
:meth:`.Table.print_bars` can be combined with :meth:`.Table.pivot` or :meth:`.Table.bins` to produce fast histograms:
.. code-block:: Python
table.bins('TOTAL', start=0, end=100000).print_bars('TOTAL', width=80)
.. code-block:: bash
TOTAL Count
[0 - 10,000) 30 ▓░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░
[10,000 - 20,000) 12 ▓░░░░░░░░░░░░░░░░░░░░░░
[20,000 - 30,000) 7 ▓░░░░░░░░░░░░░
[30,000 - 40,000) 1 ▓░░
[40,000 - 50,000) 2 ▓░░░░
[50,000 - 60,000) 1 ▓░░
[60,000 - 70,000) 1 ▓░░
[70,000 - 80,000) 1 ▓░░
[80,000 - 90,000) 0 ▓
[90,000 - 100,000] 0 ▓
+-------------+------------+------------+-------------+
0.0 7.5 15.0 22.5 30.0
SVG bar chart
=============
.. code-block:: Python
table.limit(10).bar_chart('State Name', 'TOTAL', 'docs/images/bar_chart.svg')
.. figure:: ../images/bar_chart.svg
SVG column chart
================
.. code-block:: Python
table.limit(10).column_chart('State Name', 'TOTAL', 'docs/images/column_chart.svg')
.. figure:: ../images/column_chart.svg
SVG line chart
==============
.. code-block:: Python
by_year_exonerated = table.group_by('exonerated')
counts = by_year_exonerated.aggregate([
('count', agate.Count())
])
counts.order_by('exonerated').line_chart('exonerated', 'count', 'docs/images/line_chart.svg')
.. figure:: ../images/line_chart.svg
SVG dots chart
==============
.. code-block:: Python
table.scatterplot('exonerated', 'age', 'docs/images/dots_chart.svg')
.. figure:: ../images/dots_chart.svg
SVG lattice chart
==================
.. code-block:: Python
top_crimes = table.group_by('crime').having([
('count', agate.Count())
], lambda t: t['count'] > 100)
by_year = top_crimes.group_by('exonerated')
counts = by_year.aggregate([
('count', agate.Count())
])
by_crime = counts.group_by('crime')
by_crime.order_by('exonerated').line_chart('exonerated', 'count', 'docs/images/lattice.svg')
.. figure:: ../images/lattice.svg
Using matplotlib
================
If you need to make more complex charts, you can always use agate with `matplotlib <https://matplotlib.org/>`_.
Here is an example of how you might generate a line chart:
.. code-block:: python
import pylab
pylab.plot(table.columns['homeruns'], table.columns['wins'])
pylab.xlabel('Homeruns')
pylab.ylabel('Wins')
pylab.title('How homeruns correlate to wins')
pylab.show()
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