| Current File : //usr/local/lib64/python3.6/site-packages/pandas/core/internals/construction.py |
"""
Functions for preparing various inputs passed to the DataFrame or Series
constructors before passing them to a BlockManager.
"""
from collections import abc
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import numpy.ma as ma
from pandas._libs import lib
from pandas._typing import Axis, DtypeObj, Scalar
from pandas.core.dtypes.cast import (
construct_1d_arraylike_from_scalar,
maybe_cast_to_datetime,
maybe_convert_platform,
maybe_infer_to_datetimelike,
maybe_upcast,
)
from pandas.core.dtypes.common import (
is_categorical_dtype,
is_datetime64tz_dtype,
is_dtype_equal,
is_extension_array_dtype,
is_integer_dtype,
is_list_like,
is_object_dtype,
)
from pandas.core.dtypes.generic import (
ABCDataFrame,
ABCDatetimeIndex,
ABCIndexClass,
ABCSeries,
ABCTimedeltaIndex,
)
from pandas.core import algorithms, common as com
from pandas.core.arrays import Categorical
from pandas.core.construction import extract_array, sanitize_array
from pandas.core.indexes import base as ibase
from pandas.core.indexes.api import (
Index,
ensure_index,
get_objs_combined_axis,
union_indexes,
)
from pandas.core.internals.managers import (
create_block_manager_from_arrays,
create_block_manager_from_blocks,
)
if TYPE_CHECKING:
from pandas import Series # noqa:F401
# ---------------------------------------------------------------------
# BlockManager Interface
def arrays_to_mgr(
arrays,
arr_names,
index,
columns,
dtype: Optional[DtypeObj] = None,
verify_integrity: bool = True,
):
"""
Segregate Series based on type and coerce into matrices.
Needs to handle a lot of exceptional cases.
"""
arr_names = ensure_index(arr_names)
if verify_integrity:
# figure out the index, if necessary
if index is None:
index = extract_index(arrays)
else:
index = ensure_index(index)
# don't force copy because getting jammed in an ndarray anyway
arrays = _homogenize(arrays, index, dtype)
columns = ensure_index(columns)
else:
columns = ensure_index(columns)
index = ensure_index(index)
# from BlockManager perspective
axes = [columns, index]
return create_block_manager_from_arrays(arrays, arr_names, axes)
def masked_rec_array_to_mgr(
data, index, columns, dtype: Optional[DtypeObj], copy: bool
):
"""
Extract from a masked rec array and create the manager.
"""
# essentially process a record array then fill it
fill_value = data.fill_value
fdata = ma.getdata(data)
if index is None:
index = get_names_from_index(fdata)
if index is None:
index = ibase.default_index(len(data))
index = ensure_index(index)
if columns is not None:
columns = ensure_index(columns)
arrays, arr_columns = to_arrays(fdata, columns)
# fill if needed
new_arrays = []
for fv, arr, col in zip(fill_value, arrays, arr_columns):
# TODO: numpy docs suggest fv must be scalar, but could it be
# non-scalar for object dtype?
assert lib.is_scalar(fv), fv
mask = ma.getmaskarray(data[col])
if mask.any():
arr, fv = maybe_upcast(arr, fill_value=fv, copy=True)
arr[mask] = fv
new_arrays.append(arr)
# create the manager
arrays, arr_columns = reorder_arrays(new_arrays, arr_columns, columns)
if columns is None:
columns = arr_columns
mgr = arrays_to_mgr(arrays, arr_columns, index, columns, dtype)
if copy:
mgr = mgr.copy()
return mgr
# ---------------------------------------------------------------------
# DataFrame Constructor Interface
def init_ndarray(values, index, columns, dtype: Optional[DtypeObj], copy: bool):
# input must be a ndarray, list, Series, index
if isinstance(values, ABCSeries):
if columns is None:
if values.name is not None:
columns = [values.name]
if index is None:
index = values.index
else:
values = values.reindex(index)
# zero len case (GH #2234)
if not len(values) and columns is not None and len(columns):
values = np.empty((0, 1), dtype=object)
# we could have a categorical type passed or coerced to 'category'
# recast this to an arrays_to_mgr
if is_categorical_dtype(getattr(values, "dtype", None)) or is_categorical_dtype(
dtype
):
if not hasattr(values, "dtype"):
values = _prep_ndarray(values, copy=copy)
values = values.ravel()
elif copy:
values = values.copy()
index, columns = _get_axes(len(values), 1, index, columns)
return arrays_to_mgr([values], columns, index, columns, dtype=dtype)
elif is_extension_array_dtype(values) or is_extension_array_dtype(dtype):
# GH#19157
if isinstance(values, np.ndarray) and values.ndim > 1:
# GH#12513 a EA dtype passed with a 2D array, split into
# multiple EAs that view the values
values = [values[:, n] for n in range(values.shape[1])]
else:
values = [values]
if columns is None:
columns = Index(range(len(values)))
return arrays_to_mgr(values, columns, index, columns, dtype=dtype)
# by definition an array here
# the dtypes will be coerced to a single dtype
values = _prep_ndarray(values, copy=copy)
if dtype is not None:
if not is_dtype_equal(values.dtype, dtype):
try:
values = values.astype(dtype)
except Exception as orig:
# e.g. ValueError when trying to cast object dtype to float64
raise ValueError(
f"failed to cast to '{dtype}' (Exception was: {orig})"
) from orig
# _prep_ndarray ensures that values.ndim == 2 at this point
index, columns = _get_axes(
values.shape[0], values.shape[1], index=index, columns=columns
)
values = values.T
# if we don't have a dtype specified, then try to convert objects
# on the entire block; this is to convert if we have datetimelike's
# embedded in an object type
if dtype is None and is_object_dtype(values):
if values.ndim == 2 and values.shape[0] != 1:
# transpose and separate blocks
dvals_list = [maybe_infer_to_datetimelike(row) for row in values]
for n in range(len(dvals_list)):
if isinstance(dvals_list[n], np.ndarray):
dvals_list[n] = dvals_list[n].reshape(1, -1)
from pandas.core.internals.blocks import make_block
# TODO: What about re-joining object columns?
block_values = [
make_block(dvals_list[n], placement=[n]) for n in range(len(dvals_list))
]
else:
datelike_vals = maybe_infer_to_datetimelike(values)
block_values = [datelike_vals]
else:
block_values = [values]
return create_block_manager_from_blocks(block_values, [columns, index])
def init_dict(data: Dict, index, columns, dtype: Optional[DtypeObj] = None):
"""
Segregate Series based on type and coerce into matrices.
Needs to handle a lot of exceptional cases.
"""
arrays: Union[Sequence[Any], "Series"]
if columns is not None:
from pandas.core.series import Series # noqa:F811
arrays = Series(data, index=columns, dtype=object)
data_names = arrays.index
missing = arrays.isna()
if index is None:
# GH10856
# raise ValueError if only scalars in dict
index = extract_index(arrays[~missing])
else:
index = ensure_index(index)
# no obvious "empty" int column
if missing.any() and not is_integer_dtype(dtype):
if dtype is None or (
not is_extension_array_dtype(dtype)
and np.issubdtype(dtype, np.flexible)
):
# GH#1783
nan_dtype = np.dtype(object)
else:
nan_dtype = dtype
val = construct_1d_arraylike_from_scalar(np.nan, len(index), nan_dtype)
arrays.loc[missing] = [val] * missing.sum()
else:
keys = list(data.keys())
columns = data_names = Index(keys)
arrays = [com.maybe_iterable_to_list(data[k]) for k in keys]
# GH#24096 need copy to be deep for datetime64tz case
# TODO: See if we can avoid these copies
arrays = [
arr if not isinstance(arr, ABCIndexClass) else arr._data for arr in arrays
]
arrays = [
arr if not is_datetime64tz_dtype(arr) else arr.copy() for arr in arrays
]
return arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
# ---------------------------------------------------------------------
def _prep_ndarray(values, copy: bool = True) -> np.ndarray:
if not isinstance(values, (np.ndarray, ABCSeries, Index)):
if len(values) == 0:
return np.empty((0, 0), dtype=object)
elif isinstance(values, range):
arr = np.arange(values.start, values.stop, values.step, dtype="int64")
return arr[..., np.newaxis]
def convert(v):
return maybe_convert_platform(v)
# we could have a 1-dim or 2-dim list here
# this is equiv of np.asarray, but does object conversion
# and platform dtype preservation
try:
if is_list_like(values[0]) or hasattr(values[0], "len"):
values = np.array([convert(v) for v in values])
elif isinstance(values[0], np.ndarray) and values[0].ndim == 0:
# GH#21861
values = np.array([convert(v) for v in values])
else:
values = convert(values)
except (ValueError, TypeError):
values = convert(values)
else:
# drop subclass info, do not copy data
values = np.asarray(values)
if copy:
values = values.copy()
if values.ndim == 1:
values = values.reshape((values.shape[0], 1))
elif values.ndim != 2:
raise ValueError(f"Must pass 2-d input. shape={values.shape}")
return values
def _homogenize(data, index, dtype: Optional[DtypeObj]):
oindex = None
homogenized = []
for val in data:
if isinstance(val, ABCSeries):
if dtype is not None:
val = val.astype(dtype)
if val.index is not index:
# Forces alignment. No need to copy data since we
# are putting it into an ndarray later
val = val.reindex(index, copy=False)
else:
if isinstance(val, dict):
if oindex is None:
oindex = index.astype("O")
if isinstance(index, (ABCDatetimeIndex, ABCTimedeltaIndex)):
val = com.dict_compat(val)
else:
val = dict(val)
val = lib.fast_multiget(val, oindex._values, default=np.nan)
val = sanitize_array(
val, index, dtype=dtype, copy=False, raise_cast_failure=False
)
homogenized.append(val)
return homogenized
def extract_index(data) -> Index:
"""
Try to infer an Index from the passed data, raise ValueError on failure.
"""
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:
index = union_indexes(indexes)
elif have_dicts:
index = union_indexes(indexes, sort=False)
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:
assert index is not None # for mypy
if lengths[0] != len(index):
msg = (
f"array length {lengths[0]} does not match index "
f"length {len(index)}"
)
raise ValueError(msg)
else:
index = ibase.default_index(lengths[0])
return ensure_index(index)
def reorder_arrays(arrays, arr_columns, columns):
# reorder according to the columns
if (
columns is not None
and len(columns)
and arr_columns is not None
and len(arr_columns)
):
indexer = ensure_index(arr_columns).get_indexer(columns)
arr_columns = ensure_index([arr_columns[i] for i in indexer])
arrays = [arrays[i] for i in indexer]
return arrays, arr_columns
def get_names_from_index(data):
has_some_name = any(getattr(s, "name", None) is not None for s in data)
if not has_some_name:
return ibase.default_index(len(data))
index = list(range(len(data)))
count = 0
for i, s in enumerate(data):
n = getattr(s, "name", None)
if n is not None:
index[i] = n
else:
index[i] = f"Unnamed {count}"
count += 1
return index
def _get_axes(N, K, index, columns) -> Tuple[Index, Index]:
# helper to create the axes as indexes
# return axes or defaults
if index is None:
index = ibase.default_index(N)
else:
index = ensure_index(index)
if columns is None:
columns = ibase.default_index(K)
else:
columns = ensure_index(columns)
return index, columns
def dataclasses_to_dicts(data):
"""
Converts a list of dataclass instances to a list of dictionaries.
Parameters
----------
data : List[Type[dataclass]]
Returns
--------
list_dict : List[dict]
Examples
--------
>>> @dataclass
>>> class Point:
... x: int
... y: int
>>> dataclasses_to_dicts([Point(1,2), Point(2,3)])
[{"x":1,"y":2},{"x":2,"y":3}]
"""
from dataclasses import asdict
return list(map(asdict, data))
# ---------------------------------------------------------------------
# Conversion of Inputs to Arrays
def to_arrays(
data, columns, coerce_float: bool = False, dtype: Optional[DtypeObj] = None
):
"""
Return list of arrays, columns.
"""
if isinstance(data, ABCDataFrame):
if columns is not None:
arrays = [
data._ixs(i, axis=1).values
for i, col in enumerate(data.columns)
if col in columns
]
else:
columns = data.columns
arrays = [data._ixs(i, axis=1).values for i in range(len(columns))]
return arrays, columns
if not len(data):
if isinstance(data, np.ndarray):
columns = data.dtype.names
if columns is not None:
return [[]] * len(columns), columns
return [], [] # columns if columns is not None else []
if isinstance(data[0], (list, tuple)):
return _list_to_arrays(data, columns, coerce_float=coerce_float, dtype=dtype)
elif isinstance(data[0], abc.Mapping):
return _list_of_dict_to_arrays(
data, columns, coerce_float=coerce_float, dtype=dtype
)
elif isinstance(data[0], ABCSeries):
return _list_of_series_to_arrays(
data, columns, coerce_float=coerce_float, dtype=dtype
)
elif isinstance(data[0], Categorical):
if columns is None:
columns = ibase.default_index(len(data))
return data, columns
elif (
isinstance(data, (np.ndarray, ABCSeries, Index))
and data.dtype.names is not None
):
columns = list(data.dtype.names)
arrays = [data[k] for k in columns]
return arrays, columns
else:
# last ditch effort
data = [tuple(x) for x in data]
return _list_to_arrays(data, columns, coerce_float=coerce_float, dtype=dtype)
def _list_to_arrays(
data: List[Scalar],
columns: Union[Index, List],
coerce_float: bool = False,
dtype: Optional[DtypeObj] = None,
) -> Tuple[List[Scalar], Union[Index, List[Axis]]]:
if len(data) > 0 and isinstance(data[0], tuple):
content = list(lib.to_object_array_tuples(data).T)
else:
# list of lists
content = list(lib.to_object_array(data).T)
# gh-26429 do not raise user-facing AssertionError
try:
columns = _validate_or_indexify_columns(content, columns)
result = _convert_object_array(content, dtype=dtype, coerce_float=coerce_float)
except AssertionError as e:
raise ValueError(e) from e
return result, columns
def _list_of_series_to_arrays(
data: List,
columns: Union[Index, List],
coerce_float: bool = False,
dtype: Optional[DtypeObj] = None,
) -> Tuple[List[Scalar], Union[Index, List[Axis]]]:
if columns is None:
# We know pass_data is non-empty because data[0] is a Series
pass_data = [x for x in data if isinstance(x, (ABCSeries, ABCDataFrame))]
columns = get_objs_combined_axis(pass_data, sort=False)
indexer_cache: Dict[int, Scalar] = {}
aligned_values = []
for s in data:
index = getattr(s, "index", None)
if index is None:
index = ibase.default_index(len(s))
if id(index) in indexer_cache:
indexer = indexer_cache[id(index)]
else:
indexer = indexer_cache[id(index)] = index.get_indexer(columns)
values = extract_array(s, extract_numpy=True)
aligned_values.append(algorithms.take_1d(values, indexer))
values = np.vstack(aligned_values)
if values.dtype == np.object_:
content = list(values.T)
columns = _validate_or_indexify_columns(content, columns)
content = _convert_object_array(content, dtype=dtype, coerce_float=coerce_float)
return content, columns
else:
return values.T, columns
def _list_of_dict_to_arrays(
data: List,
columns: Union[Index, List],
coerce_float: bool = False,
dtype: Optional[DtypeObj] = None,
) -> Tuple[List[Scalar], Union[Index, List[Axis]]]:
"""
Convert list of dicts to numpy arrays
if `columns` is not passed, column names are inferred from the records
- for OrderedDict and dicts, the column names match
the key insertion-order from the first record to the last.
- For other kinds of dict-likes, the keys are lexically sorted.
Parameters
----------
data : iterable
collection of records (OrderedDict, dict)
columns: iterables or None
coerce_float : bool
dtype : np.dtype
Returns
-------
tuple
arrays, columns
"""
if columns is None:
gen = (list(x.keys()) for x in data)
sort = not any(isinstance(d, dict) for d in data)
columns = lib.fast_unique_multiple_list_gen(gen, sort=sort)
# assure that they are of the base dict class and not of derived
# classes
data = [(type(d) is dict) and d or dict(d) for d in data]
content = list(lib.dicts_to_array(data, list(columns)).T)
columns = _validate_or_indexify_columns(content, columns)
content = _convert_object_array(content, dtype=dtype, coerce_float=coerce_float)
return content, columns
def _validate_or_indexify_columns(
content: List, columns: Optional[Union[Index, List]]
) -> Union[Index, List[Axis]]:
"""
If columns is None, make numbers as column names; Otherwise, validate that
columns have valid length.
Parameters
----------
content: list of data
columns: Iterable or None
Returns
-------
columns: If columns is Iterable, return as is; If columns is None, assign
positional column index value as columns.
Raises
------
1. AssertionError when content is not composed of list of lists, and if
length of columns is not equal to length of content.
2. ValueError when content is list of lists, but length of each sub-list
is not equal
3. ValueError when content is list of lists, but length of sub-list is
not equal to length of content
"""
if columns is None:
columns = ibase.default_index(len(content))
else:
# Add mask for data which is composed of list of lists
is_mi_list = isinstance(columns, list) and all(
isinstance(col, list) for col in columns
)
if not is_mi_list and len(columns) != len(content): # pragma: no cover
# caller's responsibility to check for this...
raise AssertionError(
f"{len(columns)} columns passed, passed data had "
f"{len(content)} columns"
)
elif is_mi_list:
# check if nested list column, length of each sub-list should be equal
if len({len(col) for col in columns}) > 1:
raise ValueError(
"Length of columns passed for MultiIndex columns is different"
)
# if columns is not empty and length of sublist is not equal to content
elif columns and len(columns[0]) != len(content):
raise ValueError(
f"{len(columns[0])} columns passed, passed data had "
f"{len(content)} columns"
)
return columns
def _convert_object_array(
content: List[Scalar], coerce_float: bool = False, dtype: Optional[DtypeObj] = None
) -> List[Scalar]:
"""
Internal function ot convert object array.
Parameters
----------
content: list of processed data records
coerce_float: bool, to coerce floats or not, default is False
dtype: np.dtype, default is None
Returns
-------
arrays: casted content if not object dtype, otherwise return as is in list.
"""
# provide soft conversion of object dtypes
def convert(arr):
if dtype != np.dtype("O"):
arr = lib.maybe_convert_objects(arr, try_float=coerce_float)
arr = maybe_cast_to_datetime(arr, dtype)
return arr
arrays = [convert(arr) for arr in content]
return arrays
# ---------------------------------------------------------------------
# Series-Based
def sanitize_index(data, index: Index):
"""
Sanitize an index type to return an ndarray of the underlying, pass
through a non-Index.
"""
if len(data) != len(index):
raise ValueError(
"Length of values "
f"({len(data)}) "
"does not match length of index "
f"({len(index)})"
)
if isinstance(data, np.ndarray):
# coerce datetimelike types
if data.dtype.kind in ["M", "m"]:
data = sanitize_array(data, index, copy=False)
return data