def make_column_dummies(data, column, prefix=False, prefix_sep="_"):
from pandas import Factor
factor = Factor.from_array(data[column].values)
dummy_mat = np.eye(len(factor.levels)).take(factor.labels, axis=0)
if prefix:
dummy_cols = ["%s%s%s" % (column, prefix_sep, str(v)) for v in factor.levels]
else:
dummy_cols = factor.levels
dummies = DataFrame(dummy_mat, index=data.index, columns=dummy_cols)
return dummies
def make_column_dummies(data, column, prefix=False, prefix_sep='_'):
from pandas import Factor
factor = Factor.from_array(data[column].values)
dummy_mat = np.eye(len(factor.levels)).take(factor.labels, axis=0)
if prefix:
dummy_cols = [
'%s%s%s' % (column, prefix_sep, str(v)) for v in factor.levels
]
else:
dummy_cols = factor.levels
dummies = DataFrame(dummy_mat, index=data.index, columns=dummy_cols)
return dummies
def make_axis_dummies(frame, axis='minor', transform=None):
"""
Construct 1-0 dummy variables corresponding to designated axis
labels
Parameters
----------
axis : {'major', 'minor'}, default 'minor'
transform : function, default None
Function to apply to axis labels first. For example, to
get "day of week" dummies in a time series regression you might
call:
make_axis_dummies(panel, axis='major',
transform=lambda d: d.weekday())
Returns
-------
dummies : DataFrame
Column names taken from chosen axis
"""
from pandas import Factor
numbers = {
'major' : 0,
'minor' : 1
}
num = numbers.get(axis, axis)
items = frame.index.levels[num]
labels = frame.index.labels[num]
if transform is not None:
mapped_items = items.map(transform)
factor = Factor.from_array(mapped_items.take(labels))
labels = factor.labels
items = factor.levels
values = np.eye(len(items), dtype=float)
values = values.take(labels, axis=0)
return DataFrame(values, columns=items, index=frame.index)
def make_axis_dummies(frame, axis='minor', transform=None):
"""
Construct 1-0 dummy variables corresponding to designated axis
labels
Parameters
----------
axis : {'major', 'minor'}, default 'minor'
transform : function, default None
Function to apply to axis labels first. For example, to
get "day of week" dummies in a time series regression you might
call:
make_axis_dummies(panel, axis='major',
transform=lambda d: d.weekday())
Returns
-------
dummies : DataFrame
Column names taken from chosen axis
"""
from pandas import Factor
numbers = {'major': 0, 'minor': 1}
num = numbers.get(axis, axis)
items = frame.index.levels[num]
labels = frame.index.labels[num]
if transform is not None:
mapped_items = items.map(transform)
factor = Factor.from_array(mapped_items.take(labels))
labels = factor.labels
items = factor.levels
values = np.eye(len(items), dtype=float)
values = values.take(labels, axis=0)
return DataFrame(values, columns=items, index=frame.index)