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def all(self, axis=0, bool_only=None, skipna=True, level=None, **kwargs):
"""Return whether all elements are True over requested axis
Note:
If axis=None or axis=0, this call applies df.all(axis=1)
to the transpose of df.
"""
if axis is not None:
... |
def apply(
self,
func,
axis=0,
broadcast=None,
raw=False,
reduce=None,
result_type=None,
convert_dtype=True,
args=(),
**kwds
):
"""Apply a function along input axis of DataFrame.
Args:
func: T... |
def bfill(self, axis=None, inplace=False, limit=None, downcast=None):
"""Synonym for DataFrame.fillna(method='bfill')"""
return self.fillna(
method="bfill", axis=axis, limit=limit, downcast=downcast, inplace=inplace
) |
def bool(self):
"""Return the bool of a single element PandasObject.
This must be a boolean scalar value, either True or False. Raise a
ValueError if the PandasObject does not have exactly 1 element, or that
element is not boolean
"""
shape = self.shape
... |
def copy(self, deep=True):
"""Creates a shallow copy of the DataFrame.
Returns:
A new DataFrame pointing to the same partitions as this one.
"""
return self.__constructor__(query_compiler=self._query_compiler.copy()) |
def count(self, axis=0, level=None, numeric_only=False):
"""Get the count of non-null objects in the DataFrame.
Arguments:
axis: 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
level: If the axis is a MultiIndex (hierarchical), count along a
part... |
def cummax(self, axis=None, skipna=True, *args, **kwargs):
"""Perform a cumulative maximum across the DataFrame.
Args:
axis (int): The axis to take maximum on.
skipna (bool): True to skip NA values, false otherwise.
Returns:
The cumulative maximum of... |
def cumprod(self, axis=None, skipna=True, *args, **kwargs):
"""Perform a cumulative product across the DataFrame.
Args:
axis (int): The axis to take product on.
skipna (bool): True to skip NA values, false otherwise.
Returns:
The cumulative product o... |
def describe(self, percentiles=None, include=None, exclude=None):
"""
Generates descriptive statistics that summarize the central tendency,
dispersion and shape of a dataset's distribution, excluding NaN values.
Args:
percentiles (list-like of numbers, optional):
... |
def diff(self, periods=1, axis=0):
"""Finds the difference between elements on the axis requested
Args:
periods: Periods to shift for forming difference
axis: Take difference over rows or columns
Returns:
DataFrame with the diff applied
"""
... |
def drop(
self,
labels=None,
axis=0,
index=None,
columns=None,
level=None,
inplace=False,
errors="raise",
):
"""Return new object with labels in requested axis removed.
Args:
labels: Index or column labels to dro... |
def dropna(self, axis=0, how="any", thresh=None, subset=None, inplace=False):
"""Create a new DataFrame from the removed NA values from this one.
Args:
axis (int, tuple, or list): The axis to apply the drop.
how (str): How to drop the NA values.
'all': drop... |
def drop_duplicates(self, keep="first", inplace=False, **kwargs):
"""Return DataFrame with duplicate rows removed, optionally only considering certain columns
Args:
subset : column label or sequence of labels, optional
Only consider certain columns for ident... |
def eq(self, other, axis="columns", level=None):
"""Checks element-wise that this is equal to other.
Args:
other: A DataFrame or Series or scalar to compare to.
axis: The axis to perform the eq over.
level: The Multilevel index level to apply eq over.
... |
def fillna(
self,
value=None,
method=None,
axis=None,
inplace=False,
limit=None,
downcast=None,
**kwargs
):
"""Fill NA/NaN values using the specified method.
Args:
value: Value to use to fill holes. This value ... |
def filter(self, items=None, like=None, regex=None, axis=None):
"""Subset rows or columns based on their labels
Args:
items (list): list of labels to subset
like (string): retain labels where `arg in label == True`
regex (string): retain labels matching regex i... |
def floordiv(self, other, axis="columns", level=None, fill_value=None):
"""Divides this DataFrame against another DataFrame/Series/scalar.
Args:
other: The object to use to apply the divide against this.
axis: The axis to divide over.
level: The Multilevel inde... |
def ge(self, other, axis="columns", level=None):
"""Checks element-wise that this is greater than or equal to other.
Args:
other: A DataFrame or Series or scalar to compare to.
axis: The axis to perform the gt over.
level: The Multilevel index level to apply gt... |
def get_dtype_counts(self):
"""Get the counts of dtypes in this object.
Returns:
The counts of dtypes in this object.
"""
if hasattr(self, "dtype"):
return pandas.Series({str(self.dtype): 1})
result = self.dtypes.value_counts()
result.ind... |
def get_ftype_counts(self):
"""Get the counts of ftypes in this object.
Returns:
The counts of ftypes in this object.
"""
if hasattr(self, "ftype"):
return pandas.Series({self.ftype: 1})
return self.ftypes.value_counts().sort_index() |
def gt(self, other, axis="columns", level=None):
"""Checks element-wise that this is greater than other.
Args:
other: A DataFrame or Series or scalar to compare to.
axis: The axis to perform the gt over.
level: The Multilevel index level to apply gt over.
... |
def head(self, n=5):
"""Get the first n rows of the DataFrame.
Args:
n (int): The number of rows to return.
Returns:
A new DataFrame with the first n rows of the DataFrame.
"""
if n >= len(self.index):
return self.copy()
re... |
def idxmax(self, axis=0, skipna=True, *args, **kwargs):
"""Get the index of the first occurrence of the max value of the axis.
Args:
axis (int): Identify the max over the rows (1) or columns (0).
skipna (bool): Whether or not to skip NA values.
Returns:
... |
def isin(self, values):
"""Fill a DataFrame with booleans for cells contained in values.
Args:
values (iterable, DataFrame, Series, or dict): The values to find.
Returns:
A new DataFrame with booleans representing whether or not a cell
is in values.
... |
def le(self, other, axis="columns", level=None):
"""Checks element-wise that this is less than or equal to other.
Args:
other: A DataFrame or Series or scalar to compare to.
axis: The axis to perform the le over.
level: The Multilevel index level to apply le ov... |
def lt(self, other, axis="columns", level=None):
"""Checks element-wise that this is less than other.
Args:
other: A DataFrame or Series or scalar to compare to.
axis: The axis to perform the lt over.
level: The Multilevel index level to apply lt over.
... |
def mean(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
"""Computes mean across the DataFrame.
Args:
axis (int): The axis to take the mean on.
skipna (bool): True to skip NA values, false otherwise.
Returns:
The mean of the D... |
def median(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
"""Computes median across the DataFrame.
Args:
axis (int): The axis to take the median on.
skipna (bool): True to skip NA values, false otherwise.
Returns:
The median ... |
def memory_usage(self, index=True, deep=False):
"""Returns the memory usage of each column in bytes
Args:
index (bool): Whether to include the memory usage of the DataFrame's
index in returned Series. Defaults to True
deep (bool): If True, introspect the da... |
def min(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
"""Perform min across the DataFrame.
Args:
axis (int): The axis to take the min on.
skipna (bool): True to skip NA values, false otherwise.
Returns:
The min of the DataFr... |
def mod(self, other, axis="columns", level=None, fill_value=None):
"""Mods this DataFrame against another DataFrame/Series/scalar.
Args:
other: The object to use to apply the mod against this.
axis: The axis to mod over.
level: The Multilevel index level to app... |
def mode(self, axis=0, numeric_only=False, dropna=True):
"""Perform mode across the DataFrame.
Args:
axis (int): The axis to take the mode on.
numeric_only (bool): if True, only apply to numeric columns.
Returns:
DataFrame: The mode of the DataFrame.... |
def mul(self, other, axis="columns", level=None, fill_value=None):
"""Multiplies this DataFrame against another DataFrame/Series/scalar.
Args:
other: The object to use to apply the multiply against this.
axis: The axis to multiply over.
level: The Multilevel in... |
def ne(self, other, axis="columns", level=None):
"""Checks element-wise that this is not equal to other.
Args:
other: A DataFrame or Series or scalar to compare to.
axis: The axis to perform the ne over.
level: The Multilevel index level to apply ne over.
... |
def nunique(self, axis=0, dropna=True):
"""Return Series with number of distinct
observations over requested axis.
Args:
axis : {0 or 'index', 1 or 'columns'}, default 0
dropna : boolean, default True
Returns:
nunique : Series
""... |
def pow(self, other, axis="columns", level=None, fill_value=None):
"""Pow this DataFrame against another DataFrame/Series/scalar.
Args:
other: The object to use to apply the pow against this.
axis: The axis to pow over.
level: The Multilevel index level to appl... |
def prod(
self,
axis=None,
skipna=None,
level=None,
numeric_only=None,
min_count=0,
**kwargs
):
"""Return the product of the values for the requested axis
Args:
axis : {index (0), columns (1)}
skipna : bool... |
def quantile(self, q=0.5, axis=0, numeric_only=True, interpolation="linear"):
"""Return values at the given quantile over requested axis,
a la numpy.percentile.
Args:
q (float): 0 <= q <= 1, the quantile(s) to compute
axis (int): 0 or 'index' for row-wise,
... |
def rank(
self,
axis=0,
method="average",
numeric_only=None,
na_option="keep",
ascending=True,
pct=False,
):
"""
Compute numerical data ranks (1 through n) along axis.
Equal values are assigned a rank that is the [method] of
... |
def reset_index(
self, level=None, drop=False, inplace=False, col_level=0, col_fill=""
):
"""Reset this index to default and create column from current index.
Args:
level: Only remove the given levels from the index. Removes all
levels by default
... |
def rmod(self, other, axis="columns", level=None, fill_value=None):
"""Mod this DataFrame against another DataFrame/Series/scalar.
Args:
other: The object to use to apply the div against this.
axis: The axis to div over.
level: The Multilevel index level to app... |
def round(self, decimals=0, *args, **kwargs):
"""Round each element in the DataFrame.
Args:
decimals: The number of decimals to round to.
Returns:
A new DataFrame.
"""
return self.__constructor__(
query_compiler=self._query_compile... |
def rpow(self, other, axis="columns", level=None, fill_value=None):
"""Pow this DataFrame against another DataFrame/Series/scalar.
Args:
other: The object to use to apply the pow against this.
axis: The axis to pow over.
level: The Multilevel index level to app... |
def rsub(self, other, axis="columns", level=None, fill_value=None):
"""Subtract a DataFrame/Series/scalar from this DataFrame.
Args:
other: The object to use to apply the subtraction to this.
axis: The axis to apply the subtraction over.
level: Mutlilevel index... |
def rtruediv(self, other, axis="columns", level=None, fill_value=None):
"""Div this DataFrame against another DataFrame/Series/scalar.
Args:
other: The object to use to apply the div against this.
axis: The axis to div over.
level: The Multilevel index level to... |
def sample(
self,
n=None,
frac=None,
replace=False,
weights=None,
random_state=None,
axis=None,
):
"""Returns a random sample of items from an axis of object.
Args:
n: Number of items from axis to return. Cannot be used... |
def set_axis(self, labels, axis=0, inplace=None):
"""Assign desired index to given axis.
Args:
labels (pandas.Index or list-like): The Index to assign.
axis (string or int): The axis to reassign.
inplace (bool): Whether to make these modifications inplace.
... |
def sort_index(
self,
axis=0,
level=None,
ascending=True,
inplace=False,
kind="quicksort",
na_position="last",
sort_remaining=True,
by=None,
):
"""Sort a DataFrame by one of the indices (columns or index).
Args:
... |
def sort_values(
self,
by,
axis=0,
ascending=True,
inplace=False,
kind="quicksort",
na_position="last",
):
"""Sorts by a column/row or list of columns/rows.
Args:
by: A list of labels for the axis to sort over.
... |
def sub(self, other, axis="columns", level=None, fill_value=None):
"""Subtract a DataFrame/Series/scalar from this DataFrame.
Args:
other: The object to use to apply the subtraction to this.
axis: The axis to apply the subtraction over.
level: Mutlilevel index ... |
def to_numpy(self, dtype=None, copy=False):
"""Convert the DataFrame to a NumPy array.
Args:
dtype: The dtype to pass to numpy.asarray()
copy: Whether to ensure that the returned value is a not a view on another
array.
Returns:
A num... |
def truediv(self, other, axis="columns", level=None, fill_value=None):
"""Divides this DataFrame against another DataFrame/Series/scalar.
Args:
other: The object to use to apply the divide against this.
axis: The axis to divide over.
level: The Multilevel index... |
def var(
self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs
):
"""Computes variance across the DataFrame.
Args:
axis (int): The axis to take the variance on.
skipna (bool): True to skip NA values, false otherwise.
ddof (... |
def size(self):
"""Get the number of elements in the DataFrame.
Returns:
The number of elements in the DataFrame.
"""
return len(self._query_compiler.index) * len(self._query_compiler.columns) |
def get(self):
"""Flushes the call_queue and returns the data.
Note: Since this object is a simple wrapper, just return the data.
Returns:
The object that was `put`.
"""
if self.call_queue:
return self.apply(lambda df: df).data
else:
... |
def apply(self, func, **kwargs):
"""Apply some callable function to the data in this partition.
Note: It is up to the implementation how kwargs are handled. They are
an important part of many implementations. As of right now, they
are not serialized.
Args:
f... |
def apply(self, func, **kwargs):
"""Apply some callable function to the data in this partition.
Note: It is up to the implementation how kwargs are handled. They are
an important part of many implementations. As of right now, they
are not serialized.
Args:
f... |
def add_to_apply_calls(self, func, **kwargs):
"""Add the function to the apply function call stack.
This function will be executed when apply is called. It will be executed
in the order inserted; apply's func operates the last and return
"""
import dask
self.delayed_cal... |
def _read_csv_with_offset_pyarrow_on_ray(
fname, num_splits, start, end, kwargs, header
): # pragma: no cover
"""Use a Ray task to read a chunk of a CSV into a pyarrow Table.
Note: Ray functions are not detected by codecov (thus pragma: no cover)
Args:
fname: The filename of the file to open.... |
def compute_chunksize(df, num_splits, default_block_size=32, axis=None):
"""Computes the number of rows and/or columns to include in each partition.
Args:
df: The DataFrame to split.
num_splits: The maximum number of splits to separate the DataFrame into.
default_block_size: Minimum num... |
def _get_nan_block_id(partition_class, n_row=1, n_col=1, transpose=False):
"""A memory efficient way to get a block of NaNs.
Args:
partition_class (BaseFramePartition): The class to use to put the object
in the remote format.
n_row(int): The number of rows.
n_col(int): The n... |
def split_result_of_axis_func_pandas(axis, num_splits, result, length_list=None):
"""Split the Pandas result evenly based on the provided number of splits.
Args:
axis: The axis to split across.
num_splits: The number of even splits to create.
result: The result of the computation. This ... |
def _parse_tuple(tup):
"""Unpack the user input for getitem and setitem and compute ndim
loc[a] -> ([a], :), 1D
loc[[a,b],] -> ([a,b], :),
loc[a,b] -> ([a], [b]), 0D
"""
row_loc, col_loc = slice(None), slice(None)
if is_tuple(tup):
row_loc = tup[0]
if len(tup) == 2:
... |
def _is_enlargement(locator, global_index):
"""Determine if a locator will enlarge the global index.
Enlargement happens when you trying to locate using labels isn't in the
original index. In other words, enlargement == adding NaNs !
"""
if (
is_list_like(locator)
and not is_slice(l... |
def _compute_ndim(row_loc, col_loc):
"""Compute the ndim of result from locators
"""
row_scaler = is_scalar(row_loc)
col_scaler = is_scalar(col_loc)
if row_scaler and col_scaler:
ndim = 0
elif row_scaler ^ col_scaler:
ndim = 1
else:
ndim = 2
return ndim |
def _broadcast_item(self, row_lookup, col_lookup, item, to_shape):
"""Use numpy to broadcast or reshape item.
Notes:
- Numpy is memory efficient, there shouldn't be performance issue.
"""
# It is valid to pass a DataFrame or Series to __setitem__ that is larger than
... |
def _write_items(self, row_lookup, col_lookup, item):
"""Perform remote write and replace blocks.
"""
self.qc.write_items(row_lookup, col_lookup, item) |
def _handle_enlargement(self, row_loc, col_loc):
"""Handle Enlargement (if there is one).
Returns:
None
"""
if _is_enlargement(row_loc, self.qc.index) or _is_enlargement(
col_loc, self.qc.columns
):
_warn_enlargement()
self.qc.enla... |
def _compute_enlarge_labels(self, locator, base_index):
"""Helper for _enlarge_axis, compute common labels and extra labels.
Returns:
nan_labels: The labels needs to be added
"""
# base_index_type can be pd.Index or pd.DatetimeIndex
# depending on user input and pan... |
def _split_result_for_readers(axis, num_splits, df): # pragma: no cover
"""Splits the DataFrame read into smaller DataFrames and handles all edge cases.
Args:
axis: Which axis to split over.
num_splits: The number of splits to create.
df: The DataFrame after it has been read.
Retu... |
def _read_parquet_columns(path, columns, num_splits, kwargs): # pragma: no cover
"""Use a Ray task to read columns from Parquet into a Pandas DataFrame.
Note: Ray functions are not detected by codecov (thus pragma: no cover)
Args:
path: The path of the Parquet file.
columns: The list of c... |
def _read_csv_with_offset_pandas_on_ray(
fname, num_splits, start, end, kwargs, header
): # pragma: no cover
"""Use a Ray task to read a chunk of a CSV into a Pandas DataFrame.
Note: Ray functions are not detected by codecov (thus pragma: no cover)
Args:
fname: The filename of the file to ope... |
def _read_hdf_columns(path_or_buf, columns, num_splits, kwargs): # pragma: no cover
"""Use a Ray task to read columns from HDF5 into a Pandas DataFrame.
Note: Ray functions are not detected by codecov (thus pragma: no cover)
Args:
path_or_buf: The path of the HDF5 file.
columns: The list ... |
def _read_feather_columns(path, columns, num_splits): # pragma: no cover
"""Use a Ray task to read columns from Feather into a Pandas DataFrame.
Note: Ray functions are not detected by codecov (thus pragma: no cover)
Args:
path: The path of the Feather file.
columns: The list of column na... |
def _read_sql_with_limit_offset(
num_splits, sql, con, index_col, kwargs
): # pragma: no cover
"""Use a Ray task to read a chunk of SQL source.
Note: Ray functions are not detected by codecov (thus pragma: no cover)
"""
pandas_df = pandas.read_sql(sql, con, index_col=index_col, **kwargs)
if in... |
def get_index(index_name, *partition_indices): # pragma: no cover
"""Get the index from the indices returned by the workers.
Note: Ray functions are not detected by codecov (thus pragma: no cover)"""
index = partition_indices[0].append(partition_indices[1:])
index.names = index_name
return index |
def read_parquet(cls, path, engine, columns, **kwargs):
"""Load a parquet object from the file path, returning a DataFrame.
Ray DataFrame only supports pyarrow engine for now.
Args:
path: The filepath of the parquet file.
We only support local files for now.
... |
def _read_csv_from_file_pandas_on_ray(cls, filepath, kwargs={}):
"""Constructs a DataFrame from a CSV file.
Args:
filepath (str): path to the CSV file.
npartitions (int): number of partitions for the DataFrame.
kwargs (dict): args excluding filepath provided to read_... |
def _read(cls, filepath_or_buffer, **kwargs):
"""Read csv file from local disk.
Args:
filepath_or_buffer:
The filepath of the csv file.
We only support local files for now.
kwargs: Keyword arguments in pandas.read_csv
"""
# The ... |
def read_hdf(cls, path_or_buf, **kwargs):
"""Load a h5 file from the file path or buffer, returning a DataFrame.
Args:
path_or_buf: string, buffer or path object
Path to the file to open, or an open :class:`pandas.HDFStore` object.
kwargs: Pass into pandas.read_h... |
def read_feather(cls, path, columns=None, use_threads=True):
"""Read a pandas.DataFrame from Feather format.
Ray DataFrame only supports pyarrow engine for now.
Args:
path: The filepath of the feather file.
We only support local files for now.
mu... |
def to_sql(cls, qc, **kwargs):
"""Write records stored in a DataFrame to a SQL database.
Args:
qc: the query compiler of the DF that we want to run to_sql on
kwargs: parameters for pandas.to_sql(**kwargs)
"""
# we first insert an empty DF in order to create the fu... |
def read_sql(cls, sql, con, index_col=None, **kwargs):
"""Reads a SQL query or database table into a DataFrame.
Args:
sql: string or SQLAlchemy Selectable (select or text object) SQL query to be
executed or a table name.
con: SQLAlchemy connectable (engine/connect... |
def to_datetime(
arg,
errors="raise",
dayfirst=False,
yearfirst=False,
utc=None,
box=True,
format=None,
exact=True,
unit=None,
infer_datetime_format=False,
origin="unix",
cache=False,
):
"""Convert the arg to datetime format. If not Ray DataFrame, this falls
ba... |
def read_sql(
sql,
con,
index_col=None,
coerce_float=True,
params=None,
parse_dates=None,
columns=None,
chunksize=None,
partition_column=None,
lower_bound=None,
upper_bound=None,
max_sessions=None,
):
""" Read SQL query or database table into a DataFrame.
Args:
... |
def block_lengths(self):
"""Gets the lengths of the blocks.
Note: This works with the property structure `_lengths_cache` to avoid
having to recompute these values each time they are needed.
"""
if self._lengths_cache is None:
try:
# The first col... |
def block_widths(self):
"""Gets the widths of the blocks.
Note: This works with the property structure `_widths_cache` to avoid
having to recompute these values each time they are needed.
"""
if self._widths_cache is None:
try:
# The first column ... |
def deploy_ray_func(func, partition, kwargs): # pragma: no cover
"""Deploy a function to a partition in Ray.
Note: Ray functions are not detected by codecov (thus pragma: no cover)
Args:
func: The function to apply.
partition: The partition to apply the function to.
kwargs: A dict... |
def get(self):
"""Gets the object out of the plasma store.
Returns:
The object from the plasma store.
"""
if len(self.call_queue):
return self.apply(lambda x: x).get()
try:
return ray.get(self.oid)
except RayTaskError as e:
... |
def block_lengths(self):
"""Gets the lengths of the blocks.
Note: This works with the property structure `_lengths_cache` to avoid
having to recompute these values each time they are needed.
"""
if self._lengths_cache is None:
# The first column will have the cor... |
def block_widths(self):
"""Gets the widths of the blocks.
Note: This works with the property structure `_widths_cache` to avoid
having to recompute these values each time they are needed.
"""
if self._widths_cache is None:
# The first column will have the correct... |
def map_across_blocks(self, map_func):
"""Applies `map_func` to every partition.
Args:
map_func: The function to apply.
Returns:
A new BaseFrameManager object, the type of object that called this.
"""
preprocessed_map_func = self.preprocess_func(map_func... |
def copartition_datasets(self, axis, other, left_func, right_func):
"""Copartition two BlockPartitions objects.
Args:
axis: The axis to copartition.
other: The other BlockPartitions object to copartition with.
left_func: The function to apply to left. If None, just u... |
def map_across_full_axis(self, axis, map_func):
"""Applies `map_func` to every partition.
Note: This method should be used in the case that `map_func` relies on
some global information about the axis.
Args:
axis: The axis to perform the map across (0 - index, 1 - column... |
def take(self, axis, n):
"""Take the first (or last) n rows or columns from the blocks
Note: Axis = 0 will be equivalent to `head` or `tail`
Axis = 1 will be equivalent to `front` or `back`
Args:
axis: The axis to extract (0 for extracting rows, 1 for extracting colum... |
def concat(self, axis, other_blocks):
"""Concatenate the blocks with another set of blocks.
Note: Assumes that the blocks are already the same shape on the
dimension being concatenated. A ValueError will be thrown if this
condition is not met.
Args:
axis: Th... |
def to_pandas(self, is_transposed=False):
"""Convert this object into a Pandas DataFrame from the partitions.
Args:
is_transposed: A flag for telling this object that the external
representation is transposed, but not the internal.
Returns:
A Pandas Data... |
def get_indices(self, axis=0, index_func=None, old_blocks=None):
"""This gets the internal indices stored in the partitions.
Note: These are the global indices of the object. This is mostly useful
when you have deleted rows/columns internally, but do not know
which ones were del... |
def _get_blocks_containing_index(self, axis, index):
"""Convert a global index to a block index and local index.
Note: This method is primarily used to convert a global index into a
partition index (along the axis provided) and local index (useful
for `iloc` or similar operation... |
def _get_dict_of_block_index(self, axis, indices, ordered=False):
"""Convert indices to a dict of block index to internal index mapping.
Note: See `_get_blocks_containing_index` for primary usage. This method
accepts a list of indices rather than just a single value, and uses
`_... |
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