def extract_index(data):
index = None
if len(data) == 0:
index = Index([])
elif len(data) > 0:
raw_lengths = []
indexes = []
have_raw_arrays = False
have_series = False
have_dicts = False
have_ordered = False
for val in data:
if isinstance(val, ABCSeries):
have_series = True
indexes.append(val.index)
elif isinstance(val, dict):
have_dicts = True
if isinstance(val, OrderedDict):
have_ordered = True
indexes.append(list(val.keys()))
elif is_list_like(val) and getattr(val, "ndim", 1) == 1:
have_raw_arrays = True
raw_lengths.append(len(val))
if not indexes and not raw_lengths:
raise ValueError(
"If using all scalar values, you must pass an index")
if have_series:
index = _union_indexes(indexes)
elif have_dicts:
index = _union_indexes(indexes,
sort=not (compat.PY36 or have_ordered))
if have_raw_arrays:
lengths = list(set(raw_lengths))
if len(lengths) > 1:
raise ValueError("arrays must all be same length")
if have_dicts:
raise ValueError(
"Mixing dicts with non-Series may lead to ambiguous ordering."
)
if have_series:
if lengths[0] != len(index):
msg = ("array length {length} does not match index "
"length {idx_len}".format(length=lengths[0],
idx_len=len(index)))
raise ValueError(msg)
else:
index = ibase.default_index(lengths[0])
return ensure_index(index)
def _init_dict(self, data, axes, dtype=None):
items, major, minor = axes
# prefilter if items passed
if items is not None:
items = _ensure_index(items)
data = dict((k, v) for k, v in data.iteritems() if k in items)
else:
items = Index(_try_sort(data.keys()))
for k, v in data.iteritems():
if not isinstance(v, DataFrame):
data[k] = DataFrame(v)
if major is None:
indexes = [v.index for v in data.values()]
major = _union_indexes(indexes)
if minor is None:
indexes = [v.columns for v in data.values()]
minor = _union_indexes(indexes)
axes = [items, major, minor]
reshaped_data = data.copy() # shallow
# homogenize
item_shape = (1, len(major), len(minor))
for k in items:
if k not in data:
values = np.empty(item_shape, dtype=dtype)
values.fill(np.nan)
reshaped_data[k] = values
else:
v = data[k]
v = v.reindex(index=major, columns=minor, copy=False)
if dtype is not None:
v = v.astype(dtype)
values = v.values
shape = values.shape
reshaped_data[k] = values.reshape((1,) + shape)
# segregates dtypes and forms blocks matching to columns
blocks = form_blocks(reshaped_data, axes)
mgr = BlockManager(blocks, axes).consolidate()
return mgr
def extract_index(data):
index = None
if len(data) == 0:
index = Index([])
elif len(data) > 0:
raw_lengths = []
indexes = []
have_raw_arrays = False
have_series = False
have_dicts = False
for val in data:
if isinstance(val, ABCSeries):
have_series = True
indexes.append(val.index)
elif isinstance(val, dict):
have_dicts = True
indexes.append(list(val.keys()))
elif is_list_like(val) and getattr(val, 'ndim', 1) == 1:
have_raw_arrays = True
raw_lengths.append(len(val))
if not indexes and not raw_lengths:
raise ValueError('If using all scalar values, you must pass'
' an index')
if have_series or have_dicts:
index = _union_indexes(indexes)
if have_raw_arrays:
lengths = list(set(raw_lengths))
if len(lengths) > 1:
raise ValueError('arrays must all be same length')
if have_dicts:
raise ValueError('Mixing dicts with non-Series may lead to '
'ambiguous ordering.')
if have_series:
if lengths[0] != len(index):
msg = ('array length {length} does not match index '
'length {idx_len}'
.format(length=lengths[0], idx_len=len(index)))
raise ValueError(msg)
else:
index = ibase.default_index(lengths[0])
return ensure_index(index)
def extract_index(data):
index = None
if len(data) == 0:
index = Index([])
elif len(data) > 0:
raw_lengths = []
indexes = []
have_raw_arrays = False
have_series = False
have_dicts = False
for v in data:
if isinstance(v, ABCSeries):
have_series = True
indexes.append(v.index)
elif isinstance(v, dict):
have_dicts = True
indexes.append(list(v.keys()))
elif is_list_like(v) and getattr(v, 'ndim', 1) == 1:
have_raw_arrays = True
raw_lengths.append(len(v))
if not indexes and not raw_lengths:
raise ValueError('If using all scalar values, you must pass'
' an index')
if have_series or have_dicts:
index = _union_indexes(indexes)
if have_raw_arrays:
lengths = list(set(raw_lengths))
if len(lengths) > 1:
raise ValueError('arrays must all be same length')
if have_dicts:
raise ValueError('Mixing dicts with non-Series may lead to '
'ambiguous ordering.')
if have_series:
if lengths[0] != len(index):
msg = ('array length %d does not match index length %d' %
(lengths[0], len(index)))
raise ValueError(msg)
else:
index = ibase.default_index(lengths[0])
return ensure_index(index)
