# Emulate Excel¶

One of agate’s most powerful assets is that instead of a wimpy “formula” language, you have the entire Python language at your disposal. Here are examples of how to translate a few common Excel operations.

## Simple formulas¶

If you need to simulate a simple Excel formula you can use the Formula class to apply an arbitrary function.

Excel:

=(\$A1 + \$B1) / \$C1

agate:

def f(row):
return (row['a'] + row['b']) / row['c']

new_table = table.compute([
('new_column', Formula(f))
])

If this still isn’t enough flexibility, you can also create your own subclass of Computation.

## SUM¶

number_type = agate.Number()

def five_year_total(row):
columns = ('2009', '2010', '2011', '2012', '2013')

return sum(tuple(row[c] for c in columns)]

formula = agate.Formula(number_type, five_year_total)

new_table = table.compute([
('five_year_total', formula)
])

## TRIM¶

new_table = table.compute([
('name_stripped', Formula(text_type, lambda r: r['name'].strip()))
])

## CONCATENATE¶

new_table = table.compute([
('full_name', Formula(text_type, lambda r: '%(first_name)s %(middle_name)s %(last_name)s' % r))
])

## IF¶

new_table = table.compute([
('mvp_candidate', Formula(boolean_type, lambda r: row['batting_average'] > 0.3))
])

## VLOOKUP¶

states = {
'AL': 'Alabama',
'AZ': 'Arizona',
...
}

new_table = table.compute([
('mvp_candidate', Formula(text_type, lambda r: states[row['state_abbr']]))
])

## Pivot tables¶

You can emulate most of the functionality of Excel’s pivot tables using the TableSet.aggregate() method.

jobs = employees.group_by('job_title')
summary = jobs.aggregate([
('employee_count', agate.Length())
('salary_mean', agate.Mean('salary')),
('salary_median', agate.Median('salary'))
])

The resulting summary table will have four columns: job_title, employee_count, salary_mean and salary_median.