In [2]:
import agate
table = agate.Table.from_csv('examples/realdata/ks_1033_data.csv')
print(table)
In [3]:
kansas_city = table.where(lambda r: r['county'] in ('JACKSON', 'CLAY', 'CASS', 'PLATTE'))
print(len(table.rows))
print(len(kansas_city.rows))
We can then print the Sum of the costs of all those rows. (The cost column is named total_cost.)
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print('$%d' % kansas_city.aggregate(agate.Sum('total_cost')))
In [5]:
# Group by county
counties = table.group_by('county')
print(counties.keys())
We then use the aggregate function to sum the total_cost column for each table in the group. The resulting values are collapsed into a new table, totals, which has a row for each county and a column named total_cost_sum containing the new total.
In [6]:
# Aggregate totals for all counties
totals = counties.aggregate([
('total_cost_sum', agate.Sum('total_cost'),)
])
print(totals.column_names)
Finally, we sort the counties by their total cost, limit the results to the top 10 and then print the results as a text bar chart.
In [7]:
totals.order_by('total_cost_sum', reverse=True).limit(20).print_bars('county', 'total_cost_sum', width=100)
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