def _get_dummies_1d(data,
prefix,
prefix_sep='_',
dummy_na=False,
sparse=False,
drop_first=False):
# Series avoids inconsistent NaN handling
codes, levels = _factorize_from_iterable(Series(data))
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_cols = ['%s%s%s' % (prefix, prefix_sep, v) for v in 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=np.uint8),
sparse_index=IntIndex(N, ixs),
fill_value=0,
dtype=np.uint8)
sparse_series[col] = SparseSeries(data=sarr, index=index)
out = SparseDataFrame(sparse_series,
index=index,
columns=dummy_cols,
default_fill_value=0,
dtype=np.uint8)
return out
else:
dummy_mat = np.eye(number_of_cols, dtype=np.uint8).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_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 None:
dummy_cols = levels
else:
# PY2 embedded unicode, gh-22084
def _make_col_name(prefix, prefix_sep, level):
fstr = '{prefix}{prefix_sep}{level}'
if PY2 and (isinstance(prefix, text_type) or
isinstance(prefix_sep, text_type) or
isinstance(level, text_type)):
fstr = u(fstr)
return fstr.format(prefix=prefix,
prefix_sep=prefix_sep,
level=level)
dummy_cols = [_make_col_name(prefix, prefix_sep, level)
for level in 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))]
mask = codes != -1
codes = codes[mask]
n_idx = np.arange(N)[mask]
for ndx, code in zip(n_idx, codes):
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)