Charts

Agate offers two kinds of built in charting: very simple text bar charts and SVG charting via leather. 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:

table.limit(10).print_bars('State Name', 'TOTAL', width=80)
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

Table.print_bars() can be combined with Table.pivot() or Table.bins() to produce fast histograms:

table.bins('TOTAL', start=0, end=100000).print_bars('TOTAL', width=80)
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

table.limit(10).bar_chart('State Name', 'TOTAL', 'docs/images/bar_chart.svg')
../_images/bar_chart.svg

SVG column chart

table.limit(10).column_chart('State Name', 'TOTAL', 'docs/images/column_chart.svg')
../_images/column_chart.svg

SVG line chart

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')
../_images/line_chart.svg

SVG dots chart

table.scatterplot('exonerated', 'age', 'docs/images/dots_chart.svg')
../_images/dots_chart.svg

SVG lattice chart

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')
../_images/lattice.svg

Using matplotlib

If you need to make more complex charts, you can always use agate with matplotlib.

Here is an example of how you might generate a line chart:

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()