def test_replace_str_to_str_chain(self):
a = np.arange(1, 5)
astr = a.astype(str)
bstr = np.arange(2, 6).astype(str)
df = DataFrame({'a': astr})
with tm.assertRaisesRegexp(ValueError, "Replacement not allowed .+"):
df.replace({'a': dict(zip(astr, bstr))})
def test_time(self):
import matplotlib.pyplot as plt
plt.close("all")
t = datetime(1, 1, 1, 3, 30, 0)
deltas = np.random.randint(1, 20, 3).cumsum()
ts = np.array([(t + timedelta(minutes=int(x))).time() for x in deltas])
df = DataFrame({"a": np.random.randn(len(ts)), "b": np.random.randn(len(ts))}, index=ts)
ax = df.plot()
# verify tick labels
ticks = ax.get_xticks()
labels = ax.get_xticklabels()
for t, l in zip(ticks, labels):
m, s = divmod(int(t), 60)
h, m = divmod(m, 60)
xp = l.get_text()
if len(xp) > 0:
rs = time(h, m, s).strftime("%H:%M:%S")
self.assert_(xp, rs)
# change xlim
ax.set_xlim("1:30", "5:00")
# check tick labels again
ticks = ax.get_xticks()
labels = ax.get_xticklabels()
for t, l in zip(ticks, labels):
m, s = divmod(int(t), 60)
h, m = divmod(m, 60)
xp = l.get_text()
if len(xp) > 0:
rs = time(h, m, s).strftime("%H:%M:%S")
self.assert_(xp, rs)
def _check_colors(self, collections, linecolors=None, facecolors=None,
mapping=None):
"""
Check each artist has expected line colors and face colors
Parameters
----------
collections : list-like
list or collection of target artist
linecolors : list-like which has the same length as collections
list of expected line colors
facecolors : list-like which has the same length as collections
list of expected face colors
mapping : Series
Series used for color grouping key
used for andrew_curves, parallel_coordinates, radviz test
"""
from matplotlib.lines import Line2D
from matplotlib.collections import Collection, PolyCollection
conv = self.colorconverter
if linecolors is not None:
if mapping is not None:
linecolors = self._get_colors_mapped(mapping, linecolors)
linecolors = linecolors[:len(collections)]
self.assertEqual(len(collections), len(linecolors))
for patch, color in zip(collections, linecolors):
if isinstance(patch, Line2D):
result = patch.get_color()
# Line2D may contains string color expression
result = conv.to_rgba(result)
elif isinstance(patch, PolyCollection):
result = tuple(patch.get_edgecolor()[0])
else:
result = patch.get_edgecolor()
expected = conv.to_rgba(color)
self.assertEqual(result, expected)
if facecolors is not None:
if mapping is not None:
facecolors = self._get_colors_mapped(mapping, facecolors)
facecolors = facecolors[:len(collections)]
self.assertEqual(len(collections), len(facecolors))
for patch, color in zip(collections, facecolors):
if isinstance(patch, Collection):
# returned as list of np.array
result = patch.get_facecolor()[0]
else:
result = patch.get_facecolor()
if isinstance(result, np.ndarray):
result = tuple(result)
expected = conv.to_rgba(color)
self.assertEqual(result, expected)
def create_data():
""" create the pickle/msgpack data """
data = {
'A': [0., 1., 2., 3., np.nan],
'B': [0, 1, 0, 1, 0],
'C': ['foo1', 'foo2', 'foo3', 'foo4', 'foo5'],
'D': date_range('1/1/2009', periods=5),
'E': [0., 1, Timestamp('20100101'), 'foo', 2.]
}
index = dict(int=Index(np.arange(10)),
date=date_range('20130101', periods=10),
period=period_range('2013-01-01', freq='M', periods=10))
mi = dict(reg2=MultiIndex.from_tuples(tuple(zip(*[['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']])),
names=['first', 'second']))
series = dict(float=Series(data['A']),
int=Series(data['B']),
mixed=Series(data['E']),
ts=TimeSeries(np.arange(10).astype(np.int64), index=date_range('20130101',periods=10)),
mi=Series(np.arange(5).astype(np.float64),
index=MultiIndex.from_tuples(tuple(zip(*[[1, 1, 2, 2, 2], [3, 4, 3, 4, 5]])),
names=['one', 'two'])),
dup=Series(np.arange(5).astype(np.float64), index=['A', 'B', 'C', 'D', 'A']),
cat=Series(Categorical(['foo', 'bar', 'baz'])),
per=Series([Period('2000Q1')] * 5))
mixed_dup_df = DataFrame(data)
mixed_dup_df.columns = list("ABCDA")
frame = dict(float=DataFrame(dict(A=series['float'], B=series['float'] + 1)),
int=DataFrame(dict(A=series['int'], B=series['int'] + 1)),
mixed=DataFrame(dict([(k, data[k]) for k in ['A', 'B', 'C', 'D']])),
mi=DataFrame(dict(A=np.arange(5).astype(np.float64), B=np.arange(5).astype(np.int64)),
index=MultiIndex.from_tuples(tuple(zip(*[['bar', 'bar', 'baz', 'baz', 'baz'],
['one', 'two', 'one', 'two', 'three']])),
names=['first', 'second'])),
dup=DataFrame(np.arange(15).reshape(5, 3).astype(np.float64),
columns=['A', 'B', 'A']),
cat_onecol=DataFrame(dict(A=Categorical(['foo', 'bar']))),
cat_and_float=DataFrame(dict(A=Categorical(['foo', 'bar', 'baz']),
B=np.arange(3).astype(np.int64))),
mixed_dup=mixed_dup_df)
mixed_dup_panel = Panel(dict(ItemA=frame['float'], ItemB=frame['int']))
mixed_dup_panel.items = ['ItemA', 'ItemA']
panel = dict(float=Panel(dict(ItemA=frame['float'], ItemB=frame['float'] + 1)),
dup=Panel(np.arange(30).reshape(3, 5, 2).astype(np.float64),
items=['A', 'B', 'A']),
mixed_dup=mixed_dup_panel)
return dict(series=series,
frame=frame,
panel=panel,
index=index,
mi=mi,
sp_series=dict(float=_create_sp_series(),
ts=_create_sp_tsseries()),
sp_frame=dict(float=_create_sp_frame()))
def test_time(self):
t = datetime(1, 1, 1, 3, 30, 0)
deltas = np.random.randint(1, 20, 3).cumsum()
ts = np.array([(t + timedelta(minutes=int(x))).time() for x in deltas])
df = DataFrame({'a': np.random.randn(len(ts)),
'b': np.random.randn(len(ts))},
index=ts)
ax = df.plot()
# verify tick labels
ticks = ax.get_xticks()
labels = ax.get_xticklabels()
for t, l in zip(ticks, labels):
m, s = divmod(int(t), 60)
h, m = divmod(m, 60)
xp = l.get_text()
if len(xp) > 0:
rs = time(h, m, s).strftime('%H:%M:%S')
self.assertEqual(xp, rs)
# change xlim
ax.set_xlim('1:30', '5:00')
# check tick labels again
ticks = ax.get_xticks()
labels = ax.get_xticklabels()
for t, l in zip(ticks, labels):
m, s = divmod(int(t), 60)
h, m = divmod(m, 60)
xp = l.get_text()
if len(xp) > 0:
rs = time(h, m, s).strftime('%H:%M:%S')
self.assertEqual(xp, rs)
def _range_from_fields(year=None, month=None, quarter=None, day=None,
hour=None, minute=None, second=None, freq=None):
if hour is None:
hour = 0
if minute is None:
minute = 0
if second is None:
second = 0
if day is None:
day = 1
ordinals = []
if quarter is not None:
if freq is None:
freq = 'Q'
base = frequencies.FreqGroup.FR_QTR
else:
base, mult = frequencies.get_freq_code(freq)
if base != frequencies.FreqGroup.FR_QTR:
raise AssertionError("base must equal FR_QTR")
year, quarter = _make_field_arrays(year, quarter)
for y, q in compat.zip(year, quarter):
y, m = libperiod.quarter_to_myear(y, q, freq)
val = libperiod.period_ordinal(y, m, 1, 1, 1, 1, 0, 0, base)
ordinals.append(val)
else:
base, mult = frequencies.get_freq_code(freq)
arrays = _make_field_arrays(year, month, day, hour, minute, second)
for y, mth, d, h, mn, s in compat.zip(*arrays):
ordinals.append(libperiod.period_ordinal(
y, mth, d, h, mn, s, 0, 0, base))
return np.array(ordinals, dtype=np.int64), freq
def create_data():
""" create the pickle/msgpack data """
data = {
'A': [0., 1., 2., 3., np.nan],
'B': [0, 1, 0, 1, 0],
'C': ['foo1', 'foo2', 'foo3', 'foo4', 'foo5'],
'D': date_range('1/1/2009', periods=5),
'E': [0., 1, Timestamp('20100101'), 'foo', 2.]
}
scalars = dict(timestamp=Timestamp('20130101'))
if LooseVersion(pandas.__version__) >= '0.17.0':
scalars['period'] = Period('2012','M')
index = dict(int=Index(np.arange(10)),
date=date_range('20130101', periods=10),
period=period_range('2013-01-01', freq='M', periods=10))
mi = dict(reg2=MultiIndex.from_tuples(tuple(zip(*[['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']])),
names=['first', 'second']))
series = dict(float=Series(data['A']),
int=Series(data['B']),
mixed=Series(data['E']),
ts=Series(np.arange(10).astype(np.int64), index=date_range('20130101',periods=10)),
mi=Series(np.arange(5).astype(np.float64),
index=MultiIndex.from_tuples(tuple(zip(*[[1, 1, 2, 2, 2], [3, 4, 3, 4, 5]])),
names=['one', 'two'])),
dup=Series(np.arange(5).astype(np.float64), index=['A', 'B', 'C', 'D', 'A']),
<<<<<<< HEAD
<<<<<<< HEAD
cat=Series(Categorical(['foo', 'bar', 'baz'])))
if LooseVersion(pandas.__version__) >= '0.17.0':
series['period'] = Series([Period('2000Q1')] * 5)
def df2dbiter(dataframe, table_class, harmonize_cols_first=True, harmonize_rows_first=True,
parse_dates=None):
"""
Returns a generator of of ORM model instances (reflecting the rows database table mapped by
table_class) from the given dataframe. The returned generator can be used in loops and each
element can be e.g. added to the database by means of sqlAlchemy `session.add` method.
NOTE: Only the dataframe columns whose name match the table_class columns will be used.
Therefore, it is safe to append to dataframe any column whose name is not in table_class
columns. Some iterations might return None according to the parameters (see below)
:param table_class: the CLASS of the table whose rows are instantiated and returned
:param dataframe: the input dataframe
:param harmonize_cols_first: if True (default when missing), the dataframe column types
are harmonized to reflect the table_class column types, and columns without a match
in table_class are filtered out. See `harmonize_columns(dataframe)`. If
`harmonize_cols_first` and `harmonize_rows_first` are both True, the harmonization is
executed in that order (first columns, then rows).
:param harmonize_rows_first: if True (default when missing), NA values are checked for
those table columns which have the property nullable set to False. In this case, the
generator **might return None** (the user should in case handle it, e.g. skipping
these model instances which are not writable to the db). If
`harmonize_cols_first` and `harmonize_rows_first` are both True, the harmonization is
executed in that order (first columns, then rows).
:param parse_dates: a list of strings denoting additional column names whose values should
be parsed as dates. Ignored if harmonize_cols_first is False
"""
if harmonize_cols_first:
colnames, dataframe = _harmonize_columns(table_class, dataframe, parse_dates)
else:
colnames = list(shared_colnames(table_class, dataframe))
# table_col_names = get_col_names(table_class)
# colnames = [c for c in dataframe.columns if c in table_col_names] # FIXME: optimize this?
new_df = dataframe[colnames]
if dataframe.empty:
for _ in len(dataframe):
yield None
return
valid_rows = cycle([True])
if harmonize_rows_first:
non_nullable_cols = list(shared_colnames(table_class, new_df, nullable=False))
if non_nullable_cols:
valid_rows = new_df[non_nullable_cols].notnull().all(axis=1).values
# df = ~pd_isnull(new_df[non_nullable_cols])
# valid_rows = df.apply(lambda row: row.all(), axis=1).values
cols, datalist = _insert_data(new_df)
# Note below: datalist is an array of N column, each of M rows (it would be nicer to return an
# array of N rows, each of them representing a table row. But we do not want to touch pandas
# code.
# See _insert_table below). Thus we zip it:
for is_ok, row_values in zip(valid_rows, zip(*datalist)):
if is_ok:
# we could make a single line statement, but two lines are more readable:
row_args_dict = dict(zip(cols, row_values))
yield table_class(**row_args_dict)
else:
yield None
def create_data():
""" create the pickle data """
import numpy as np
import pandas
from pandas import (Series,TimeSeries,DataFrame,Panel,
SparseSeries,SparseTimeSeries,SparseDataFrame,SparsePanel,
Index,MultiIndex,PeriodIndex,
date_range,period_range,bdate_range,Timestamp)
nan = np.nan
data = {
'A': [0., 1., 2., 3., np.nan],
'B': [0, 1, 0, 1, 0],
'C': ['foo1', 'foo2', 'foo3', 'foo4', 'foo5'],
'D': date_range('1/1/2009', periods=5),
'E' : [0., 1, Timestamp('20100101'),'foo',2.],
}
index = dict(int = Index(np.arange(10)),
date = date_range('20130101',periods=10),
period = period_range('2013-01-01', freq='M', periods=10))
mi = dict(reg2 = MultiIndex.from_tuples(tuple(zip(*[['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']])),
names=['first', 'second']))
series = dict(float = Series(data['A']),
int = Series(data['B']),
mixed = Series(data['E']),
ts = TimeSeries(np.arange(10).astype(np.int64),index=date_range('20130101',periods=10)),
mi = Series(np.arange(5).astype(np.float64),index=MultiIndex.from_tuples(tuple(zip(*[[1,1,2,2,2],
[3,4,3,4,5]])),
names=['one','two'])),
dup=Series(np.arange(5).astype(np.float64), index=['A', 'B', 'C', 'D', 'A']))
frame = dict(float = DataFrame(dict(A = series['float'], B = series['float'] + 1)),
int = DataFrame(dict(A = series['int'] , B = series['int'] + 1)),
mixed = DataFrame(dict([ (k,data[k]) for k in ['A','B','C','D']])),
mi = DataFrame(dict(A = np.arange(5).astype(np.float64), B = np.arange(5).astype(np.int64)),
index=MultiIndex.from_tuples(tuple(zip(*[['bar','bar','baz','baz','baz'],
['one','two','one','two','three']])),
names=['first','second'])),
dup = DataFrame(np.arange(15).reshape(5, 3).astype(np.float64),
columns=['A', 'B', 'A']))
panel = dict(float = Panel(dict(ItemA = frame['float'], ItemB = frame['float']+1)),
dup = Panel(np.arange(30).reshape(3, 5, 2).astype(np.float64),
items=['A', 'B', 'A']))
return dict( series = series,
frame = frame,
panel = panel,
index = index,
mi = mi,
sp_series = dict(float = _create_sp_series(),
ts = _create_sp_tsseries()),
sp_frame = dict(float = _create_sp_frame())
)
def _align_core(terms):
term_index = [i for i, term in enumerate(terms)
if hasattr(term.value, 'axes')]
term_dims = [terms[i].value.ndim for i in term_index]
ndims = pd.Series(dict(zip(term_index, term_dims)))
# initial axes are the axes of the largest-axis'd term
biggest = terms[ndims.idxmax()].value
typ = biggest._constructor
axes = biggest.axes
naxes = len(axes)
gt_than_one_axis = naxes > 1
for value in (terms[i].value for i in term_index):
is_series = isinstance(value, pd.Series)
is_series_and_gt_one_axis = is_series and gt_than_one_axis
for axis, items in enumerate(value.axes):
if is_series_and_gt_one_axis:
ax, itm = naxes - 1, value.index
else:
ax, itm = axis, items
if not axes[ax].is_(itm):
axes[ax] = axes[ax].join(itm, how='outer')
for i, ndim in compat.iteritems(ndims):
for axis, items in zip(range(ndim), axes):
ti = terms[i].value
if hasattr(ti, 'reindex_axis'):
transpose = isinstance(ti, pd.Series) and naxes > 1
reindexer = axes[naxes - 1] if transpose else items
term_axis_size = len(ti.axes[axis])
reindexer_size = len(reindexer)
ordm = np.log10(abs(reindexer_size - term_axis_size))
if ordm >= 1 and reindexer_size >= 10000:
warnings.warn('Alignment difference on axis {0} is larger '
'than an order of magnitude on term {1!r}, '
'by more than {2:.4g}; performance may '
'suffer'.format(axis, terms[i].name, ordm),
category=pd.io.common.PerformanceWarning,
stacklevel=6)
if transpose:
f = partial(ti.reindex, index=reindexer, copy=False)
else:
f = partial(ti.reindex_axis, reindexer, axis=axis,
copy=False)
terms[i].update(f())
terms[i].update(terms[i].value.values)
return typ, _zip_axes_from_type(typ, axes)
def group_mean(lat, lon, data):
indexer = np.lexsort((lon, lat))
lat = lat.take(indexer)
lon = lon.take(indexer)
sorted_data = data.take(indexer)
keys = 1000 * lat + lon
unique_keys = np.unique(keys)
result = ndi.mean(sorted_data, labels=keys, index=unique_keys)
decoder = keys.searchsorted(unique_keys)
return dict(zip(zip(lat.take(decoder), lon.take(decoder)), result))
def sequence_grids(layer_grids):
"""Go through the list of layer girds and perform the same thing as sequence_layers.
Parameters:
-----------
layer_grids: a list of two dimensional layer grids
"""
for grid1, grid2 in zip(layer_grids[:-1], layer_grids[1:]):
for row1, row2 in zip(grid1, grid2):
for layer1, layer2 in zip(row1, row2):
if layer2.data is None:
layer2.data = layer1.data
merge_aes(layer1, layer2)
return layer_grids
def test_constructors(self, closed, name):
left, right = Index([0, 1, 2, 3]), Index([1, 2, 3, 4])
ivs = [Interval(l, r, closed=closed) for l, r in zip(left, right)]
expected = IntervalIndex._simple_new(
left=left, right=right, closed=closed, name=name)
result = IntervalIndex(ivs, name=name)
tm.assert_index_equal(result, expected)
result = IntervalIndex.from_intervals(ivs, name=name)
tm.assert_index_equal(result, expected)
result = IntervalIndex.from_breaks(
np.arange(5), closed=closed, name=name)
tm.assert_index_equal(result, expected)
result = IntervalIndex.from_arrays(
left.values, right.values, closed=closed, name=name)
tm.assert_index_equal(result, expected)
result = IntervalIndex.from_tuples(
zip(left, right), closed=closed, name=name)
tm.assert_index_equal(result, expected)
result = Index(ivs, name=name)
assert isinstance(result, IntervalIndex)
tm.assert_index_equal(result, expected)
# idempotent
tm.assert_index_equal(Index(expected), expected)
tm.assert_index_equal(IntervalIndex(expected), expected)
result = IntervalIndex.from_intervals(
expected.values, name=expected.name)
tm.assert_index_equal(result, expected)
left, right = expected.left, expected.right
result = IntervalIndex.from_arrays(
left, right, closed=expected.closed, name=expected.name)
tm.assert_index_equal(result, expected)
result = IntervalIndex.from_tuples(
expected.to_tuples(), closed=expected.closed, name=expected.name)
tm.assert_index_equal(result, expected)
breaks = expected.left.tolist() + [expected.right[-1]]
result = IntervalIndex.from_breaks(
breaks, closed=expected.closed, name=expected.name)
tm.assert_index_equal(result, expected)
def insert(self):
ins = self.insert_statement()
data_list = []
# to avoid if check for every row
keys = self.frame.columns
if self.index is not None:
for t in self.frame.itertuples():
data = dict((k, self.maybe_asscalar(v)) for k, v in zip(keys, t[1:]))
data[self.index] = self.maybe_asscalar(t[0])
data_list.append(data)
else:
for t in self.frame.itertuples():
data = dict((k, self.maybe_asscalar(v)) for k, v in zip(keys, t[1:]))
data_list.append(data)
self.pd_sql.execute(ins, data_list)
def groupby1(lat, lon, data):
indexer = np.lexsort((lon, lat))
lat = lat.take(indexer)
lon = lon.take(indexer)
sorted_data = data.take(indexer)
keys = 1000.0 * lat + lon
unique_keys = np.unique(keys)
bounds = keys.searchsorted(unique_keys)
result = group_agg(sorted_data, bounds, lambda x: x.mean())
decoder = keys.searchsorted(unique_keys)
return dict(zip(zip(lat.take(decoder), lon.take(decoder)), result))
def _combineFrame(self, other, func, axis=0):
index, columns = self._get_plane_axes(axis)
axis = self._get_axis_number(axis)
other = other.reindex(index=index, columns=columns)
if axis == 0:
new_values = func(self.values, other.values)
elif axis == 1:
new_values = func(self.values.swapaxes(0, 1), other.values.T)
new_values = new_values.swapaxes(0, 1)
elif axis == 2:
new_values = func(self.values.swapaxes(0, 2), other.values)
new_values = new_values.swapaxes(0, 2)
# TODO: make faster!
new_frames = {}
for item, item_slice in zip(self.items, new_values):
old_frame = self[item]
ofv = old_frame.default_fill_value
ok = old_frame.default_kind
new_frames[item] = SparseDataFrame(item_slice,
index=self.major_axis,
columns=self.minor_axis,
default_fill_value=ofv,
default_kind=ok)
return self._new_like(new_frames)
def _create_table_statement(self):
"Return a CREATE TABLE statement to suit the contents of a DataFrame."
columns = list(map(str, self.frame.columns))
pat = re.compile('\s+')
if any(map(pat.search, columns)):
warnings.warn(_SAFE_NAMES_WARNING)
column_types = [self._sql_type_name(typ) for typ in self.frame.dtypes]
if self.index is not None:
for i, idx_label in enumerate(self.index[::-1]):
columns.insert(0, idx_label)
column_types.insert(0, self._sql_type_name(self.frame.index.get_level_values(i).dtype))
flv = self.pd_sql.flavor
br_l = _SQL_SYMB[flv]['br_l'] # left val quote char
br_r = _SQL_SYMB[flv]['br_r'] # right val quote char
col_template = br_l + '%s' + br_r + ' %s'
columns = ',\n '.join(col_template %
x for x in zip(columns, column_types))
template = """CREATE TABLE %(name)s (
%(columns)s
)"""
create_statement = template % {'name': self.name, 'columns': columns}
return create_statement
def _init_data(self, data, copy, dtype, **kwargs):
"""
Generate ND initialization; axes are passed
as required objects to __init__
"""
if data is None:
data = {}
if dtype is not None:
dtype = self._validate_dtype(dtype)
passed_axes = [kwargs.get(a) for a in self._AXIS_ORDERS]
axes = None
if isinstance(data, BlockManager):
if any(x is not None for x in passed_axes):
axes = [x if x is not None else y
for x, y in zip(passed_axes, data.axes)]
mgr = data
elif isinstance(data, dict):
mgr = self._init_dict(data, passed_axes, dtype=dtype)
copy = False
dtype = None
elif isinstance(data, (np.ndarray, list)):
mgr = self._init_matrix(data, passed_axes, dtype=dtype, copy=copy)
copy = False
dtype = None
else: # pragma: no cover
raise PandasError('Panel constructor not properly called!')
NDFrame.__init__(self, mgr, axes=axes, copy=copy, dtype=dtype)
def __unicode__(self):
"""
Return a string representation for a particular Panel
Invoked by unicode(df) in py2 only.
Yields a Unicode String in both py2/py3.
"""
class_name = str(self.__class__)
shape = self.shape
dims = u('Dimensions: %s') % ' x '.join(
["%d (%s)" % (s, a) for a, s in zip(self._AXIS_ORDERS, shape)])
def axis_pretty(a):
v = getattr(self, a)
if len(v) > 0:
return u('%s axis: %s to %s') % (a.capitalize(),
com.pprint_thing(v[0]),
com.pprint_thing(v[-1]))
else:
return u('%s axis: None') % a.capitalize()
output = '\n'.join(
[class_name, dims] + [axis_pretty(a) for a in self._AXIS_ORDERS])
return output
def _get_join_indexers(left_keys, right_keys, sort=False, how='inner'):
"""
Parameters
----------
Returns
-------
"""
from functools import partial
assert len(left_keys) == len(right_keys), \
'left_key and right_keys must be the same length'
# bind `sort` arg. of _factorize_keys
fkeys = partial(_factorize_keys, sort=sort)
# get left & right join labels and num. of levels at each location
llab, rlab, shape = map(list, zip(* map(fkeys, left_keys, right_keys)))
# get flat i8 keys from label lists
lkey, rkey = _get_join_keys(llab, rlab, shape, sort)
# factorize keys to a dense i8 space
# `count` is the num. of unique keys
# set(lkey) | set(rkey) == range(count)
lkey, rkey, count = fkeys(lkey, rkey)
# preserve left frame order if how == 'left' and sort == False
kwargs = {'sort': sort} if how == 'left' else {}
join_func = _join_functions[how]
return join_func(lkey, rkey, count, **kwargs)
def get_data_famafrench(name):
# path of zip files
zip_file_path = '{0}/{1}.zip'.format(_FAMAFRENCH_URL, name)
with urlopen(zip_file_path) as url:
raw = url.read()
with tempfile.TemporaryFile() as tmpf:
tmpf.write(raw)
with ZipFile(tmpf, 'r') as zf:
data = zf.open(zf.namelist()[0]).readlines()
line_lengths = np.array(lmap(len, data))
file_edges = np.where(line_lengths == 2)[0]
datasets = {}
edges = zip(file_edges + 1, file_edges[1:])
for i, (left_edge, right_edge) in enumerate(edges):
dataset = [d.split() for d in data[left_edge:right_edge]]
if len(dataset) > 10:
ncol_raw = np.array(lmap(len, dataset))
ncol = np.median(ncol_raw)
header_index = np.where(ncol_raw == ncol - 1)[0][-1]
header = dataset[header_index]
ds_header = dataset[header_index + 1:]
# to ensure the header is unique
header = ['{0} {1}'.format(j, hj) for j, hj in enumerate(header,
start=1)]
index = np.array([d[0] for d in ds_header], dtype=int)
dataset = np.array([d[1:] for d in ds_header], dtype=float)
datasets[i] = DataFrame(dataset, index, columns=header)
return datasets
def __init__(self, values, index, level=-1, value_columns=None):
if values.ndim == 1:
values = values[:, np.newaxis]
self.values = values
self.value_columns = value_columns
if value_columns is None and values.shape[1] != 1: # pragma: no cover
raise ValueError('must pass column labels for multi-column data')
self.index = index
self.level = self.index._get_level_number(level)
levels = index.levels
labels = index.labels
def _make_index(lev,lab):
i = lev.__class__(_make_index_array_level(lev.values,lab))
i.name = lev.name
return i
self.new_index_levels = list([ _make_index(lev,lab) for lev,lab in zip(levels,labels) ])
self.new_index_names = list(index.names)
self.removed_name = self.new_index_names.pop(self.level)
self.removed_level = self.new_index_levels.pop(self.level)
self._make_sorted_values_labels()
self._make_selectors()
def test_convert_r_dataframe():
is_na = robj.baseenv.get("is.na")
seriesd = _test.getSeriesData()
frame = pd.DataFrame(seriesd, columns=['D', 'C', 'B', 'A'])
# Null data
frame["E"] = [np.nan for item in frame["A"]]
# Some mixed type data
frame["F"] = ["text" if item % 2 == 0 else np.nan for item in range(30)]
r_dataframe = convert_to_r_dataframe(frame)
assert np.array_equal(convert_robj(r_dataframe.rownames), frame.index)
assert np.array_equal(convert_robj(r_dataframe.colnames), frame.columns)
assert all(is_na(item) for item in r_dataframe.rx2("E"))
for column in frame[["A", "B", "C", "D"]]:
coldata = r_dataframe.rx2(column)
original_data = frame[column]
assert np.array_equal(convert_robj(coldata), original_data)
for column in frame[["D", "E"]]:
for original, converted in zip(frame[column],
r_dataframe.rx2(column)):
if pd.isnull(original):
assert is_na(converted)
else:
assert original == converted
def test_format_timedelta_ticks_wide(self):
if is_platform_mac():
pytest.skip("skip on mac for precision display issue on older mpl")
expected_labels = [
'00:00:00',
'1 days 03:46:40',
'2 days 07:33:20',
'3 days 11:20:00',
'4 days 15:06:40',
'5 days 18:53:20',
'6 days 22:40:00',
'8 days 02:26:40',
''
]
rng = timedelta_range('0', periods=10, freq='1 d')
df = DataFrame(np.random.randn(len(rng), 3), rng)
ax = df.plot(fontsize=2)
fig = ax.get_figure()
fig.canvas.draw()
labels = ax.get_xticklabels()
self.assertEqual(len(labels), len(expected_labels))
for l, l_expected in zip(labels, expected_labels):
self.assertEqual(l.get_text(), l_expected)
def _get_multiindex_indexer(join_keys, index, sort):
from functools import partial
# bind `sort` argument
fkeys = partial(_factorize_keys, sort=sort)
# left & right join labels and num. of levels at each location
rlab, llab, shape = map(list, zip(* map(fkeys, index.levels, join_keys)))
if sort:
rlab = list(map(np.take, rlab, index.labels))
else:
i8copy = lambda a: a.astype('i8', subok=False, copy=True)
rlab = list(map(i8copy, index.labels))
# fix right labels if there were any nulls
for i in range(len(join_keys)):
mask = index.labels[i] == -1
if mask.any():
# check if there already was any nulls at this location
# if there was, it is factorized to `shape[i] - 1`
a = join_keys[i][llab[i] == shape[i] - 1]
if a.size == 0 or not a[0] != a[0]:
shape[i] += 1
rlab[i][mask] = shape[i] - 1
# get flat i8 join keys
lkey, rkey = _get_join_keys(llab, rlab, shape, sort)
# factorize keys to a dense i8 space
lkey, rkey, count = fkeys(lkey, rkey)
return _algos.left_outer_join(lkey, rkey, count, sort=sort)
def _get_new_axes(self):
ndim = self._get_result_dim()
new_axes = [None] * ndim
if self.join_axes is None:
for i in range(ndim):
if i == self.axis:
continue
new_axes[i] = self._get_comb_axis(i)
else:
if len(self.join_axes) != ndim - 1:
raise AssertionError("length of join_axes must not be " "equal to {0}".format(ndim - 1))
# ufff...
indices = lrange(ndim)
indices.remove(self.axis)
for i, ax in zip(indices, self.join_axes):
new_axes[i] = ax
if self.ignore_index:
concat_axis = None
else:
concat_axis = self._get_concat_axis()
new_axes[self.axis] = concat_axis
return new_axes
def _get_expiry_dates_and_links(self):
"""
Gets available expiry dates.
Returns
-------
Tuple of:
List of datetime.date objects
Dict of datetime.date objects as keys and corresponding links
"""
url = self._OPTIONS_BASE_URL.format(sym=self.symbol)
root = self._parse_url(url)
try:
links = root.xpath('//*[@id="options_menu"]/form/select/option')
except IndexError:
raise RemoteDataError('Expiry dates not available')
expiry_dates = [dt.datetime.strptime(element.text, "%B %d, %Y").date() for element in links]
links = [element.attrib['data-selectbox-link'] for element in links]
expiry_links = dict(zip(expiry_dates, links))
self._expiry_links = expiry_links
self._expiry_dates = expiry_dates
return expiry_dates, expiry_links
def test_time_musec(self):
t = datetime(1, 1, 1, 3, 30, 0)
deltas = np.random.randint(1, 20, 3).cumsum()
ts = np.array([(t + timedelta(microseconds=int(x))).time()
for x in deltas])
df = DataFrame({'a': np.random.randn(len(ts)),
'b': np.random.randn(len(ts))},
index=ts)
_, ax = self.plt.subplots()
ax = df.plot(ax=ax)
# verify tick labels
ticks = ax.get_xticks()
labels = ax.get_xticklabels()
for t, l in zip(ticks, labels):
m, s = divmod(int(t), 60)
# TODO: unused?
# us = int((t - int(t)) * 1e6)
h, m = divmod(m, 60)
xp = l.get_text()
if len(xp) > 0:
rs = time(h, m, s).strftime('%H:%M:%S.%f')
assert xp == rs
def get_data_fred(name, start=dt.datetime(2010, 1, 1), end=dt.datetime.today()):
"""
Get data for the given name from the St. Louis FED (FRED).
Date format is datetime
Returns a DataFrame.
If multiple names are passed for "series" then the index of the
DataFrame is the outer join of the indicies of each series.
"""
start, end = _sanitize_dates(start, end)
if not is_list_like(name):
names = [name]
else:
names = name
urls = [_FRED_URL + "%s" % n + "/downloaddata/%s" % n + ".csv" for n in names]
def fetch_data(url, name):
with urlopen(url) as resp:
data = read_csv(
resp, index_col=0, parse_dates=True, header=None, skiprows=1, names=["DATE", name], na_values="."
)
try:
return data.truncate(start, end)
except KeyError:
if data.ix[3].name[7:12] == "Error":
raise IOError("Failed to get the data. Check that {0!r} is " "a valid FRED series.".format(name))
raise
df = concat([fetch_data(url, n) for url, n in zip(urls, names)], axis=1, join="outer")
return df
def _save_chunk(self, start_i, end_i):
data_index = self.data_index
# create the data for a chunk
slicer = slice(start_i, end_i)
for i in range(len(self.blocks)):
b = self.blocks[i]
d = b.to_native_types(slicer=slicer, na_rep=self.na_rep,
float_format=self.float_format,
decimal=self.decimal,
date_format=self.date_format,
quoting=self.quoting)
for col_loc, col in zip(b.mgr_locs, d):
# self.data is a preallocated list
self.data[col_loc] = col
ix = data_index.to_native_types(slicer=slicer, na_rep=self.na_rep,
float_format=self.float_format,
decimal=self.decimal,
date_format=self.date_format,
quoting=self.quoting)
libwriters.write_csv_rows(self.data, ix, self.nlevels,
self.cols, self.writer)
def _get_merge_keys(self):
"""
Note: has side effects (copy/delete key columns)
Parameters
----------
left
right
on
Returns
-------
left_keys, right_keys
"""
self._validate_specification()
left_keys = []
right_keys = []
join_names = []
right_drop = []
left_drop = []
left, right = self.left, self.right
is_lkey = lambda x: isinstance(
x, (np.ndarray, ABCSeries)) and len(x) == len(left)
is_rkey = lambda x: isinstance(
x, (np.ndarray, ABCSeries)) and len(x) == len(right)
# ugh, spaghetti re #733
if _any(self.left_on) and _any(self.right_on):
for lk, rk in zip(self.left_on, self.right_on):
if is_lkey(lk):
left_keys.append(lk)
if is_rkey(rk):
right_keys.append(rk)
join_names.append(None) # what to do?
else:
right_keys.append(right[rk]._values)
join_names.append(rk)
else:
if not is_rkey(rk):
right_keys.append(right[rk]._values)
if lk == rk:
# avoid key upcast in corner case (length-0)
if len(left) > 0:
right_drop.append(rk)
else:
left_drop.append(lk)
else:
right_keys.append(rk)
left_keys.append(left[lk]._values)
join_names.append(lk)
elif _any(self.left_on):
for k in self.left_on:
if is_lkey(k):
left_keys.append(k)
join_names.append(None)
else:
left_keys.append(left[k]._values)
join_names.append(k)
if isinstance(self.right.index, MultiIndex):
right_keys = [lev._values.take(lab)
for lev, lab in zip(self.right.index.levels,
self.right.index.labels)]
else:
right_keys = [self.right.index.values]
elif _any(self.right_on):
for k in self.right_on:
if is_rkey(k):
right_keys.append(k)
join_names.append(None)
else:
right_keys.append(right[k]._values)
join_names.append(k)
if isinstance(self.left.index, MultiIndex):
left_keys = [lev._values.take(lab)
for lev, lab in zip(self.left.index.levels,
self.left.index.labels)]
else:
left_keys = [self.left.index.values]
if left_drop:
self.left = self.left.drop(left_drop, axis=1)
if right_drop:
self.right = self.right.drop(right_drop, axis=1)
return left_keys, right_keys, join_names
def __init__(self, objs, axis=0, join='outer', join_axes=None,
keys=None, levels=None, names=None,
ignore_index=False, verify_integrity=False, copy=True):
if isinstance(objs, (NDFrame, compat.string_types)):
raise TypeError('first argument must be an iterable of pandas '
'objects, you passed an object of type '
'"{0}"'.format(type(objs).__name__))
if join == 'outer':
self.intersect = False
elif join == 'inner':
self.intersect = True
else: # pragma: no cover
raise ValueError('Only can inner (intersect) or outer (union) '
'join the other axis')
if isinstance(objs, dict):
if keys is None:
keys = sorted(objs)
objs = [objs[k] for k in keys]
else:
objs = list(objs)
if len(objs) == 0:
raise ValueError('No objects to concatenate')
if keys is None:
objs = [obj for obj in objs if obj is not None]
else:
# #1649
clean_keys = []
clean_objs = []
for k, v in zip(keys, objs):
if v is None:
continue
clean_keys.append(k)
clean_objs.append(v)
objs = clean_objs
keys = clean_keys
if len(objs) == 0:
raise ValueError('All objects passed were None')
# consolidate data & figure out what our result ndim is going to be
ndims = set()
for obj in objs:
if not isinstance(obj, NDFrame):
raise TypeError("cannot concatenate a non-NDFrame object")
# consolidate
obj.consolidate(inplace=True)
ndims.add(obj.ndim)
# get the sample
# want the higest ndim that we have, and must be non-empty
# unless all objs are empty
sample = None
if len(ndims) > 1:
max_ndim = max(ndims)
for obj in objs:
if obj.ndim == max_ndim and np.sum(obj.shape):
sample = obj
break
else:
# filter out the empties if we have not multi-index possibiltes
# note to keep empty Series as it affect to result columns / name
non_empties = [obj for obj in objs
if sum(obj.shape) > 0 or isinstance(obj, Series)]
if (len(non_empties) and (keys is None and names is None and
levels is None and join_axes is None)):
objs = non_empties
sample = objs[0]
if sample is None:
sample = objs[0]
self.objs = objs
# Need to flip BlockManager axis in the DataFrame special case
self._is_frame = isinstance(sample, DataFrame)
if self._is_frame:
axis = 1 if axis == 0 else 0
self._is_series = isinstance(sample, ABCSeries)
if not 0 <= axis <= sample.ndim:
raise AssertionError("axis must be between 0 and {0}, "
"input was {1}".format(sample.ndim, axis))
# if we have mixed ndims, then convert to highest ndim
# creating column numbers as needed
if len(ndims) > 1:
current_column = 0
max_ndim = sample.ndim
self.objs, objs = [], self.objs
for obj in objs:
ndim = obj.ndim
if ndim == max_ndim:
pass
elif ndim != max_ndim - 1:
raise ValueError("cannot concatenate unaligned mixed "
"dimensional NDFrame objects")
else:
name = getattr(obj, 'name', None)
if ignore_index or name is None:
name = current_column
current_column += 1
# doing a row-wise concatenation so need everything
# to line up
if self._is_frame and axis == 1:
name = 0
obj = sample._constructor({name: obj})
self.objs.append(obj)
# note: this is the BlockManager axis (since DataFrame is transposed)
self.axis = axis
self.join_axes = join_axes
self.keys = keys
self.names = names
self.levels = levels
self.ignore_index = ignore_index
self.verify_integrity = verify_integrity
self.copy = copy
self.new_axes = self._get_new_axes()
def groupings(self):
from pandas.core.groupby.grouper import Grouping
return [
Grouping(lvl, lvl, in_axis=False, level=None, name=name)
for lvl, name in zip(self.levels, self.names)
]
def _get_colors_mapped(self, series, colors):
unique = series.unique()
# unique and colors length can be differed
# depending on slice value
mapped = dict(zip(unique, colors))
return [mapped[v] for v in series.values]
def _maybe_add_join_keys(self, result, left_indexer, right_indexer):
left_has_missing = None
right_has_missing = None
keys = zip(self.join_names, self.left_on, self.right_on)
for i, (name, lname, rname) in enumerate(keys):
if not _should_fill(lname, rname):
continue
take_left, take_right = None, None
if name in result:
if left_indexer is not None and right_indexer is not None:
if name in self.left:
if left_has_missing is None:
left_has_missing = any(left_indexer == -1)
if left_has_missing:
take_right = self.right_join_keys[i]
if not com.is_dtype_equal(result[name].dtype,
self.left[name].dtype):
take_left = self.left[name]._values
elif name in self.right:
if right_has_missing is None:
right_has_missing = any(right_indexer == -1)
if right_has_missing:
take_left = self.left_join_keys[i]
if not com.is_dtype_equal(result[name].dtype,
self.right[name].dtype):
take_right = self.right[name]._values
elif left_indexer is not None \
and isinstance(self.left_join_keys[i], np.ndarray):
take_left = self.left_join_keys[i]
take_right = self.right_join_keys[i]
if take_left is not None or take_right is not None:
if take_left is None:
lvals = result[name]._values
else:
lfill = na_value_for_dtype(take_left.dtype)
lvals = algos.take_1d(take_left, left_indexer,
fill_value=lfill)
if take_right is None:
rvals = result[name]._values
else:
rfill = na_value_for_dtype(take_right.dtype)
rvals = algos.take_1d(take_right, right_indexer,
fill_value=rfill)
# if we have an all missing left_indexer
# make sure to just use the right values
mask = left_indexer == -1
if mask.all():
key_col = rvals
else:
key_col = Index(lvals).where(~mask, rvals)
if name in result:
result[name] = key_col
else:
result.insert(i, name or 'key_%d' % i, key_col)
def _check_colors(self,
collections,
linecolors=None,
facecolors=None,
mapping=None):
"""
Check each artist has expected line colors and face colors
Parameters
----------
collections : list-like
list or collection of target artist
linecolors : list-like which has the same length as collections
list of expected line colors
facecolors : list-like which has the same length as collections
list of expected face colors
mapping : Series
Series used for color grouping key
used for andrew_curves, parallel_coordinates, radviz test
"""
from matplotlib.lines import Line2D
from matplotlib.collections import (Collection, PolyCollection,
LineCollection)
conv = self.colorconverter
if linecolors is not None:
if mapping is not None:
linecolors = self._get_colors_mapped(mapping, linecolors)
linecolors = linecolors[:len(collections)]
self.assertEqual(len(collections), len(linecolors))
for patch, color in zip(collections, linecolors):
if isinstance(patch, Line2D):
result = patch.get_color()
# Line2D may contains string color expression
result = conv.to_rgba(result)
elif isinstance(patch, (PolyCollection, LineCollection)):
result = tuple(patch.get_edgecolor()[0])
else:
result = patch.get_edgecolor()
expected = conv.to_rgba(color)
self.assertEqual(result, expected)
if facecolors is not None:
if mapping is not None:
facecolors = self._get_colors_mapped(mapping, facecolors)
facecolors = facecolors[:len(collections)]
self.assertEqual(len(collections), len(facecolors))
for patch, color in zip(collections, facecolors):
if isinstance(patch, Collection):
# returned as list of np.array
result = patch.get_facecolor()[0]
else:
result = patch.get_facecolor()
if isinstance(result, np.ndarray):
result = tuple(result)
expected = conv.to_rgba(color)
self.assertEqual(result, expected)
def _make_concat_multiindex(indexes, keys, levels=None, names=None):
if ((levels is None and isinstance(keys[0], tuple)) or
(levels is not None and len(levels) > 1)):
zipped = lzip(*keys)
if names is None:
names = [None] * len(zipped)
if levels is None:
levels = [Categorical.from_array(
zp, ordered=True).categories for zp in zipped]
else:
levels = [_ensure_index(x) for x in levels]
else:
zipped = [keys]
if names is None:
names = [None]
if levels is None:
levels = [_ensure_index(keys)]
else:
levels = [_ensure_index(x) for x in levels]
if not _all_indexes_same(indexes):
label_list = []
# things are potentially different sizes, so compute the exact labels
# for each level and pass those to MultiIndex.from_arrays
for hlevel, level in zip(zipped, levels):
to_concat = []
for key, index in zip(hlevel, indexes):
try:
i = level.get_loc(key)
except KeyError:
raise ValueError('Key %s not in level %s'
% (str(key), str(level)))
to_concat.append(np.repeat(i, len(index)))
label_list.append(np.concatenate(to_concat))
concat_index = _concat_indexes(indexes)
# these go at the end
if isinstance(concat_index, MultiIndex):
levels.extend(concat_index.levels)
label_list.extend(concat_index.labels)
else:
factor = Categorical.from_array(concat_index, ordered=True)
levels.append(factor.categories)
label_list.append(factor.codes)
if len(names) == len(levels):
names = list(names)
else:
# make sure that all of the passed indices have the same nlevels
if not len(set([idx.nlevels for idx in indexes])) == 1:
raise AssertionError("Cannot concat indices that do"
" not have the same number of levels")
# also copies
names = names + _get_consensus_names(indexes)
return MultiIndex(levels=levels, labels=label_list, names=names,
verify_integrity=False)
new_index = indexes[0]
n = len(new_index)
kpieces = len(indexes)
# also copies
new_names = list(names)
new_levels = list(levels)
# construct labels
new_labels = []
# do something a bit more speedy
for hlevel, level in zip(zipped, levels):
hlevel = _ensure_index(hlevel)
mapped = level.get_indexer(hlevel)
mask = mapped == -1
if mask.any():
raise ValueError('Values not found in passed level: %s'
% str(hlevel[mask]))
new_labels.append(np.repeat(mapped, n))
if isinstance(new_index, MultiIndex):
new_levels.extend(new_index.levels)
new_labels.extend([np.tile(lab, kpieces) for lab in new_index.labels])
else:
new_levels.append(new_index)
new_labels.append(np.tile(np.arange(n), kpieces))
if len(new_names) < len(new_levels):
new_names.extend(new_index.names)
return MultiIndex(levels=new_levels, labels=new_labels, names=new_names,
verify_integrity=False)
def _stack_multi_columns(frame, level_num=-1, dropna=True):
def _convert_level_number(level_num, columns):
"""
Logic for converting the level number to something we can safely pass
to swaplevel:
We generally want to convert the level number into a level name, except
when columns do not have names, in which case we must leave as a level
number
"""
if level_num in columns.names:
return columns.names[level_num]
else:
if columns.names[level_num] is None:
return level_num
else:
return columns.names[level_num]
this = frame.copy()
# this makes life much simpler
if level_num != frame.columns.nlevels - 1:
# roll levels to put selected level at end
roll_columns = this.columns
for i in range(level_num, frame.columns.nlevels - 1):
# Need to check if the ints conflict with level names
lev1 = _convert_level_number(i, roll_columns)
lev2 = _convert_level_number(i + 1, roll_columns)
roll_columns = roll_columns.swaplevel(lev1, lev2)
this.columns = roll_columns
if not this.columns.is_lexsorted():
# Workaround the edge case where 0 is one of the column names,
# which interferes with trying to sort based on the first
# level
level_to_sort = _convert_level_number(0, this.columns)
this = this.sort_index(level=level_to_sort, axis=1)
# tuple list excluding level for grouping columns
if len(frame.columns.levels) > 2:
tuples = list(
zip(*[
lev.take(lab) for lev, lab in zip(this.columns.levels[:-1],
this.columns.labels[:-1])
]))
unique_groups = [key for key, _ in itertools.groupby(tuples)]
new_names = this.columns.names[:-1]
new_columns = MultiIndex.from_tuples(unique_groups, names=new_names)
else:
new_columns = unique_groups = this.columns.levels[0]
# time to ravel the values
new_data = {}
level_vals = this.columns.levels[-1]
level_labels = sorted(set(this.columns.labels[-1]))
level_vals_used = level_vals[level_labels]
levsize = len(level_labels)
drop_cols = []
for key in unique_groups:
loc = this.columns.get_loc(key)
# can make more efficient?
# we almost always return a slice
# but if unsorted can get a boolean
# indexer
if not isinstance(loc, slice):
slice_len = len(loc)
else:
slice_len = loc.stop - loc.start
if slice_len == 0:
drop_cols.append(key)
continue
elif slice_len != levsize:
chunk = this.loc[:, this.columns[loc]]
chunk.columns = level_vals.take(chunk.columns.labels[-1])
value_slice = chunk.reindex(columns=level_vals_used).values
else:
if frame._is_mixed_type:
value_slice = this.loc[:, this.columns[loc]].values
else:
value_slice = this.values[:, loc]
new_data[key] = value_slice.ravel()
if len(drop_cols) > 0:
new_columns = new_columns.difference(drop_cols)
N = len(this)
if isinstance(this.index, MultiIndex):
new_levels = list(this.index.levels)
new_names = list(this.index.names)
new_labels = [lab.repeat(levsize) for lab in this.index.labels]
else:
new_levels = [this.index]
new_labels = [np.arange(N).repeat(levsize)]
new_names = [this.index.name] # something better?
new_levels.append(level_vals)
new_labels.append(np.tile(level_labels, N))
new_names.append(frame.columns.names[level_num])
new_index = MultiIndex(levels=new_levels,
labels=new_labels,
names=new_names,
verify_integrity=False)
result = DataFrame(new_data, index=new_index, columns=new_columns)
# more efficient way to go about this? can do the whole masking biz but
# will only save a small amount of time...
if dropna:
result = result.dropna(axis=0, how='all')
return result
def create_data(constructor):
return dict(zip((constructor(x) for x in dates_as_str), values))
def _get_dummies_1d(data,
prefix,
prefix_sep='_',
dummy_na=False,
sparse=False,
drop_first=False,
dtype=None):
# Series avoids inconsistent NaN handling
codes, levels = _factorize_from_iterable(Series(data))
if dtype is None:
dtype = np.uint8
dtype = np.dtype(dtype)
if is_object_dtype(dtype):
raise ValueError("dtype=object is not a valid dtype for get_dummies")
def get_empty_Frame(data, sparse):
if isinstance(data, Series):
index = data.index
else:
index = np.arange(len(data))
if not sparse:
return DataFrame(index=index)
else:
return SparseDataFrame(index=index, default_fill_value=0)
# if all NaN
if not dummy_na and len(levels) == 0:
return get_empty_Frame(data, sparse)
codes = codes.copy()
if dummy_na:
codes[codes == -1] = len(levels)
levels = np.append(levels, np.nan)
# if dummy_na, we just fake a nan level. drop_first will drop it again
if drop_first and len(levels) == 1:
return get_empty_Frame(data, sparse)
number_of_cols = len(levels)
if prefix is not None:
dummy_strs = [
u'{prefix}{sep}{level}'
if isinstance(v, text_type) else '{prefix}{sep}{level}'
for v in levels
]
dummy_cols = [
dummy_str.format(prefix=prefix, sep=prefix_sep, level=v)
for dummy_str, v in zip(dummy_strs, levels)
]
else:
dummy_cols = levels
if isinstance(data, Series):
index = data.index
else:
index = None
if sparse:
sparse_series = {}
N = len(data)
sp_indices = [[] for _ in range(len(dummy_cols))]
for ndx, code in enumerate(codes):
if code == -1:
# Blank entries if not dummy_na and code == -1, #GH4446
continue
sp_indices[code].append(ndx)
if drop_first:
# remove first categorical level to avoid perfect collinearity
# GH12042
sp_indices = sp_indices[1:]
dummy_cols = dummy_cols[1:]
for col, ixs in zip(dummy_cols, sp_indices):
sarr = SparseArray(np.ones(len(ixs), dtype=dtype),
sparse_index=IntIndex(N, ixs),
fill_value=0,
dtype=dtype)
sparse_series[col] = SparseSeries(data=sarr, index=index)
out = SparseDataFrame(sparse_series,
index=index,
columns=dummy_cols,
default_fill_value=0,
dtype=dtype)
return out
else:
dummy_mat = np.eye(number_of_cols, dtype=dtype).take(codes, axis=0)
if not dummy_na:
# reset NaN GH4446
dummy_mat[codes == -1] = 0
if drop_first:
# remove first GH12042
dummy_mat = dummy_mat[:, 1:]
dummy_cols = dummy_cols[1:]
return DataFrame(dummy_mat, index=index, columns=dummy_cols)
def get_dummies(data,
prefix=None,
prefix_sep='_',
dummy_na=False,
columns=None,
sparse=False,
drop_first=False):
"""
Convert categorical variable into dummy/indicator variables
Parameters
----------
data : array-like, Series, or DataFrame
prefix : string, list of strings, or dict of strings, default None
String to append DataFrame column names
Pass a list with length equal to the number of columns
when calling get_dummies on a DataFrame. Alternatively, `prefix`
can be a dictionary mapping column names to prefixes.
prefix_sep : string, default '_'
If appending prefix, separator/delimiter to use. Or pass a
list or dictionary as with `prefix.`
dummy_na : bool, default False
Add a column to indicate NaNs, if False NaNs are ignored.
columns : list-like, default None
Column names in the DataFrame to be encoded.
If `columns` is None then all the columns with
`object` or `category` dtype will be converted.
sparse : bool, default False
Whether the dummy columns should be sparse or not. Returns
SparseDataFrame if `data` is a Series or if all columns are included.
Otherwise returns a DataFrame with some SparseBlocks.
drop_first : bool, default False
Whether to get k-1 dummies out of k categorical levels by removing the
first level.
.. versionadded:: 0.18.0
Returns
-------
dummies : DataFrame or SparseDataFrame
Examples
--------
>>> import pandas as pd
>>> s = pd.Series(list('abca'))
>>> pd.get_dummies(s)
a b c
0 1 0 0
1 0 1 0
2 0 0 1
3 1 0 0
>>> s1 = ['a', 'b', np.nan]
>>> pd.get_dummies(s1)
a b
0 1 0
1 0 1
2 0 0
>>> pd.get_dummies(s1, dummy_na=True)
a b NaN
0 1 0 0
1 0 1 0
2 0 0 1
>>> df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'],
... 'C': [1, 2, 3]})
>>> pd.get_dummies(df, prefix=['col1', 'col2'])
C col1_a col1_b col2_a col2_b col2_c
0 1 1 0 0 1 0
1 2 0 1 1 0 0
2 3 1 0 0 0 1
>>> pd.get_dummies(pd.Series(list('abcaa')))
a b c
0 1 0 0
1 0 1 0
2 0 0 1
3 1 0 0
4 1 0 0
>>> pd.get_dummies(pd.Series(list('abcaa')), drop_first=True)
b c
0 0 0
1 1 0
2 0 1
3 0 0
4 0 0
See Also
--------
Series.str.get_dummies
"""
from pandas.core.reshape.concat import concat
from itertools import cycle
if isinstance(data, DataFrame):
# determine columns being encoded
if columns is None:
columns_to_encode = data.select_dtypes(
include=['object', 'category']).columns
else:
columns_to_encode = columns
# validate prefixes and separator to avoid silently dropping cols
def check_len(item, name):
len_msg = ("Length of '{name}' ({len_item}) did not match the "
"length of the columns being encoded ({len_enc}).")
if is_list_like(item):
if not len(item) == len(columns_to_encode):
len_msg = len_msg.format(name=name,
len_item=len(item),
len_enc=len(columns_to_encode))
raise ValueError(len_msg)
check_len(prefix, 'prefix')
check_len(prefix_sep, 'prefix_sep')
if isinstance(prefix, compat.string_types):
prefix = cycle([prefix])
if isinstance(prefix, dict):
prefix = [prefix[col] for col in columns_to_encode]
if prefix is None:
prefix = columns_to_encode
# validate separators
if isinstance(prefix_sep, compat.string_types):
prefix_sep = cycle([prefix_sep])
elif isinstance(prefix_sep, dict):
prefix_sep = [prefix_sep[col] for col in columns_to_encode]
if set(columns_to_encode) == set(data.columns):
with_dummies = []
else:
with_dummies = [data.drop(columns_to_encode, axis=1)]
for (col, pre, sep) in zip(columns_to_encode, prefix, prefix_sep):
dummy = _get_dummies_1d(data[col],
prefix=pre,
prefix_sep=sep,
dummy_na=dummy_na,
sparse=sparse,
drop_first=drop_first)
with_dummies.append(dummy)
result = concat(with_dummies, axis=1)
else:
result = _get_dummies_1d(data,
prefix,
prefix_sep,
dummy_na,
sparse=sparse,
drop_first=drop_first)
return result