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def time_logger(name):
"""This logs the time usage of a code block"""
start_time = time.time()
yield
end_time = time.time()
total_time = end_time - start_time
logging.info("%s; time: %ss", name, total_time) |
def initialize_ray():
"""Initializes ray based on environment variables and internal defaults."""
if threading.current_thread().name == "MainThread":
plasma_directory = None
object_store_memory = os.environ.get("MODIN_MEMORY", None)
if os.environ.get("MODIN_OUT_OF_CORE", "False").title()... |
def apply(
self,
func,
num_splits=None,
other_axis_partition=None,
maintain_partitioning=True,
**kwargs
):
"""Applies func to the object.
See notes in Parent class about this method.
Args:
func: The function to apply.
... |
def get_dummies(
data,
prefix=None,
prefix_sep="_",
dummy_na=False,
columns=None,
sparse=False,
drop_first=False,
dtype=None,
):
"""Convert categorical variable into indicator variables.
Args:
data (array-like, Series, or DataFrame): data to encode.
prefix (strin... |
def apply(
self,
func,
num_splits=None,
other_axis_partition=None,
maintain_partitioning=True,
**kwargs
):
"""Applies func to the object in the plasma store.
See notes in Parent class about this method.
Args:
func: The function to... |
def shuffle(self, func, lengths, **kwargs):
"""Shuffle the order of the data in this axis based on the `lengths`.
Extends `BaseFrameAxisPartition.shuffle`.
Args:
func: The function to apply before splitting.
lengths: The list of partition lengths to split the result int... |
def deploy_axis_func(
cls, axis, func, num_splits, kwargs, maintain_partitioning, *partitions
):
"""Deploy a function along a full axis in Ray.
Args:
axis: The axis to perform the function along.
func: The function to perform.
num_splits: ... |
def deploy_func_between_two_axis_partitions(
cls, axis, func, num_splits, len_of_left, kwargs, *partitions
):
"""Deploy a function along a full axis between two data sets in Ray.
Args:
axis: The axis to perform the function along.
func: The function to perform.
... |
def query(self, expr, **kwargs):
"""Query columns of the DataManager with a boolean expression.
Args:
expr: Boolean expression to query the columns with.
Returns:
DataManager containing the rows where the boolean expression is satisfied.
"""
d... |
def to_pandas(self):
"""Converts Modin DataFrame to Pandas DataFrame.
Returns:
Pandas DataFrame of the DataManager.
"""
df = self.data.to_pandas(is_transposed=self._is_transposed)
if df.empty:
dtype_dict = {
col_name: pandas.Serie... |
def deploy_ray_axis_func(axis, func, num_splits, kwargs, *partitions):
"""Deploy a function along a full axis in Ray.
Args:
axis: The axis to perform the function along.
func: The function to perform.
num_splits: The number of splits to return
(see `split_result_of_axis_func... |
def deploy_ray_func_between_two_axis_partitions(
axis, func, num_splits, len_of_left, kwargs, *partitions
):
"""Deploy a function along a full axis between two data sets in Ray.
Args:
axis: The axis to perform the function along.
func: The function to perform.
num_splits: The number... |
def apply(self, func, num_splits=None, other_axis_partition=None, **kwargs):
"""Applies func to the object in the plasma store.
See notes in Parent class about this method.
Args:
func: The function to apply.
num_splits: The number of times to split the result object.
... |
def shuffle(self, func, num_splits=None, **kwargs):
"""Shuffle the order of the data in this axis based on the `func`.
Extends `BaseFrameAxisPartition.shuffle`.
:param func:
:param num_splits:
:param kwargs:
:return:
"""
if num_splits is None:
... |
def deploy_ray_func(func, partition, kwargs):
"""Deploy a function to a partition in Ray.
Args:
func: The function to apply.
partition: The partition to apply the function to.
kwargs: A dictionary of keyword arguments for the function.
Returns:
The result of the function.
... |
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()
return ray.get(self.oid) |
def apply(self, func, **kwargs):
"""Apply a function to the object stored in this partition.
Note: It does not matter if func is callable or an ObjectID. Ray will
handle it correctly either way. The keyword arguments are sent as a
dictionary.
Args:
func: The... |
def to_pandas(self):
"""Convert the object stored in this partition to a Pandas DataFrame.
Returns:
A Pandas DataFrame.
"""
dataframe = self.get().to_pandas()
assert type(dataframe) is pandas.DataFrame or type(dataframe) is pandas.Series
return dataframe |
def put(cls, obj):
"""Put an object in the Plasma store and wrap it in this object.
Args:
obj: The object to be put.
Returns:
A `RayRemotePartition` object.
"""
return PyarrowOnRayFramePartition(ray.put(pyarrow.Table.from_pandas(obj))) |
def isna(obj):
"""
Detect missing values for an array-like object.
Args:
obj: Object to check for null or missing values.
Returns:
bool or array-like of bool
"""
if isinstance(obj, BasePandasDataset):
return obj.isna()
else:
return pandas.isna(obj) |
def merge(
left,
right,
how="inner",
on=None,
left_on=None,
right_on=None,
left_index=False,
right_index=False,
sort=False,
suffixes=("_x", "_y"),
copy=True,
indicator=False,
validate=None,
):
"""Database style join, where common columns in "on" are merged.
A... |
def is_distributed(partition_column, lower_bound, upper_bound):
""" Check if is possible distribute a query given that args
Args:
partition_column: column used to share the data between the workers
lower_bound: the minimum value to be requested from the partition_column
upper_bound: the... |
def is_table(engine, sql):
""" Check with the given sql arg is query or table
Args:
engine: SQLAlchemy connection engine
sql: SQL query or table name
Returns:
True for table or False if not
"""
if engine.dialect.has_table(engine, sql):
return True
return False |
def get_table_metadata(engine, table):
""" Extract all useful infos from the given table
Args:
engine: SQLAlchemy connection engine
table: table name
Returns:
Dictionary of infos
"""
metadata = MetaData()
metadata.reflect(bind=engine, only=[table])
table_metadata = ... |
def get_table_columns(metadata):
""" Extract columns names and python typos from metadata
Args:
metadata: Table metadata
Returns:
dict with columns names and python types
"""
cols = OrderedDict()
for col in metadata.c:
name = str(col).rpartition(".")[2]
cols[nam... |
def check_query(query):
""" Check query sanity
Args:
query: query string
Returns:
None
"""
q = query.lower()
if "select " not in q:
raise InvalidQuery("SELECT word not found in the query: {0}".format(query))
if " from " not in q:
raise InvalidQuery("FROM wor... |
def get_query_columns(engine, query):
""" Extract columns names and python typos from query
Args:
engine: SQLAlchemy connection engine
query: SQL query
Returns:
dict with columns names and python types
"""
con = engine.connect()
result = con.execute(query).fetchone()
... |
def check_partition_column(partition_column, cols):
""" Check partition_column existence and type
Args:
partition_column: partition_column name
cols: dict with columns names and python types
Returns:
None
"""
for k, v in cols.items():
if k == partition_column:
... |
def get_query_info(sql, con, partition_column):
""" Return a columns name list and the query string
Args:
sql: SQL query or table name
con: database connection or url string
partition_column: column used to share the data between the workers
Returns:
Columns name list and q... |
def query_put_bounders(query, partition_column, start, end):
""" Put bounders in the query
Args:
query: SQL query string
partition_column: partition_column name
start: lower_bound
end: upper_bound
Returns:
Query with bounders
"""
where = " WHERE TMP_TABLE.{0... |
def compute_index(self, axis, data_object, compute_diff=True):
"""Computes the index after a number of rows have been removed.
Note: In order for this to be used properly, the indexes must not be
changed before you compute this.
Args:
axis: The axis to extract the index... |
def _prepare_method(self, pandas_func, **kwargs):
"""Prepares methods given various metadata.
Args:
pandas_func: The function to prepare.
Returns
Helper function which handles potential transpose.
"""
if self._is_transposed:
def helper(df, in... |
def numeric_columns(self, include_bool=True):
"""Returns the numeric columns of the Manager.
Returns:
List of index names.
"""
columns = []
for col, dtype in zip(self.columns, self.dtypes):
if is_numeric_dtype(dtype) and (
include_bool or ... |
def numeric_function_clean_dataframe(self, axis):
"""Preprocesses numeric functions to clean dataframe and pick numeric indices.
Args:
axis: '0' if columns and '1' if rows.
Returns:
Tuple with return value(if any), indices to apply func to & cleaned Manager.
"""... |
def _join_index_objects(self, axis, other_index, how, sort=True):
"""Joins a pair of index objects (columns or rows) by a given strategy.
Args:
axis: The axis index object to join (0 for columns, 1 for index).
other_index: The other_index to join on.
how: The type of... |
def join(self, other, **kwargs):
"""Joins a list or two objects together.
Args:
other: The other object(s) to join on.
Returns:
Joined objects.
"""
if not isinstance(other, list):
other = [other]
return self._join_list_of_managers(oth... |
def concat(self, axis, other, **kwargs):
"""Concatenates two objects together.
Args:
axis: The axis index object to join (0 for columns, 1 for index).
other: The other_index to concat with.
Returns:
Concatenated objects.
"""
return self._appe... |
def copartition(self, axis, other, how_to_join, sort, force_repartition=False):
"""Copartition two QueryCompiler objects.
Args:
axis: The axis to copartition along.
other: The other Query Compiler(s) to copartition against.
how_to_join: How to manage joining the inde... |
def to_pandas(self):
"""Converts Modin DataFrame to Pandas DataFrame.
Returns:
Pandas DataFrame of the DataManager.
"""
df = self.data.to_pandas(is_transposed=self._is_transposed)
if df.empty:
if len(self.columns) != 0:
df = pandas.DataFra... |
def from_pandas(cls, df, block_partitions_cls):
"""Improve simple Pandas DataFrame to an advanced and superior Modin DataFrame.
Args:
cls: DataManger object to convert the DataFrame to.
df: Pandas DataFrame object.
block_partitions_cls: BlockParitions object to store... |
def _inter_manager_operations(self, other, how_to_join, func):
"""Inter-data operations (e.g. add, sub).
Args:
other: The other Manager for the operation.
how_to_join: The type of join to join to make (e.g. right, outer).
Returns:
New DataManager with new da... |
def _inter_df_op_handler(self, func, other, **kwargs):
"""Helper method for inter-manager and scalar operations.
Args:
func: The function to use on the Manager/scalar.
other: The other Manager/scalar.
Returns:
New DataManager with new data and index.
... |
def binary_op(self, op, other, **kwargs):
"""Perform an operation between two objects.
Note: The list of operations is as follows:
- add
- eq
- floordiv
- ge
- gt
- le
- lt
- mod
- mul
... |
def update(self, other, **kwargs):
"""Uses other manager to update corresponding values in this manager.
Args:
other: The other manager.
Returns:
New DataManager with updated data and index.
"""
assert isinstance(
other, type(self)
),... |
def where(self, cond, other, **kwargs):
"""Gets values from this manager where cond is true else from other.
Args:
cond: Condition on which to evaluate values.
Returns:
New DataManager with updated data and index.
"""
assert isinstance(
cond... |
def _scalar_operations(self, axis, scalar, func):
"""Handler for mapping scalar operations across a Manager.
Args:
axis: The axis index object to execute the function on.
scalar: The scalar value to map.
func: The function to use on the Manager with the scalar.
... |
def reindex(self, axis, labels, **kwargs):
"""Fits a new index for this Manger.
Args:
axis: The axis index object to target the reindex on.
labels: New labels to conform 'axis' on to.
Returns:
A new QueryCompiler with updated data and new index.
"""
... |
def reset_index(self, **kwargs):
"""Removes all levels from index and sets a default level_0 index.
Returns:
A new QueryCompiler with updated data and reset index.
"""
drop = kwargs.get("drop", False)
new_index = pandas.RangeIndex(len(self.index))
if not drop... |
def transpose(self, *args, **kwargs):
"""Transposes this DataManager.
Returns:
Transposed new DataManager.
"""
new_data = self.data.transpose(*args, **kwargs)
# Switch the index and columns and transpose the
new_manager = self.__constructor__(new_data, self.c... |
def _full_reduce(self, axis, map_func, reduce_func=None):
"""Apply function that will reduce the data to a Pandas Series.
Args:
axis: 0 for columns and 1 for rows. Default is 0.
map_func: Callable function to map the dataframe.
reduce_func: Callable function to reduc... |
def count(self, **kwargs):
"""Counts the number of non-NaN objects for each column or row.
Return:
A new QueryCompiler object containing counts of non-NaN objects from each
column or row.
"""
if self._is_transposed:
kwargs["axis"] = kwargs.get("axis",... |
def mean(self, **kwargs):
"""Returns the mean for each numerical column or row.
Return:
A new QueryCompiler object containing the mean from each numerical column or
row.
"""
if self._is_transposed:
kwargs["axis"] = kwargs.get("axis", 0) ^ 1
... |
def min(self, **kwargs):
"""Returns the minimum from each column or row.
Return:
A new QueryCompiler object with the minimum value from each column or row.
"""
if self._is_transposed:
kwargs["axis"] = kwargs.get("axis", 0) ^ 1
return self.transpose().... |
def _process_sum_prod(self, func, **kwargs):
"""Calculates the sum or product of the DataFrame.
Args:
func: Pandas func to apply to DataFrame.
ignore_axis: Whether to ignore axis when raising TypeError
Return:
A new QueryCompiler object with sum or prod of th... |
def prod(self, **kwargs):
"""Returns the product of each numerical column or row.
Return:
A new QueryCompiler object with the product of each numerical column or row.
"""
if self._is_transposed:
kwargs["axis"] = kwargs.get("axis", 0) ^ 1
return self.t... |
def _process_all_any(self, func, **kwargs):
"""Calculates if any or all the values are true.
Return:
A new QueryCompiler object containing boolean values or boolean.
"""
axis = kwargs.get("axis", 0)
axis = 0 if axis is None else axis
kwargs["axis"] = axis
... |
def all(self, **kwargs):
"""Returns whether all the elements are true, potentially over an axis.
Return:
A new QueryCompiler object containing boolean values or boolean.
"""
if self._is_transposed:
# Pandas ignores on axis=1
kwargs["bool_only"] = Fals... |
def astype(self, col_dtypes, **kwargs):
"""Converts columns dtypes to given dtypes.
Args:
col_dtypes: Dictionary of {col: dtype,...} where col is the column
name and dtype is a numpy dtype.
Returns:
DataFrame with updated dtypes.
"""
# Gr... |
def _full_axis_reduce(self, axis, func, alternate_index=None):
"""Applies map that reduce Manager to series but require knowledge of full axis.
Args:
func: Function to reduce the Manager by. This function takes in a Manager.
axis: axis to apply the function to.
alter... |
def first_valid_index(self):
"""Returns index of first non-NaN/NULL value.
Return:
Scalar of index name.
"""
# It may be possible to incrementally check each partition, but this
# computation is fairly cheap.
def first_valid_index_builder(df):
df.... |
def idxmax(self, **kwargs):
"""Returns the first occurrence of the maximum over requested axis.
Returns:
A new QueryCompiler object containing the maximum of each column or axis.
"""
if self._is_transposed:
kwargs["axis"] = kwargs.get("axis", 0) ^ 1
r... |
def idxmin(self, **kwargs):
"""Returns the first occurrence of the minimum over requested axis.
Returns:
A new QueryCompiler object containing the minimum of each column or axis.
"""
if self._is_transposed:
kwargs["axis"] = kwargs.get("axis", 0) ^ 1
r... |
def last_valid_index(self):
"""Returns index of last non-NaN/NULL value.
Return:
Scalar of index name.
"""
def last_valid_index_builder(df):
df.index = pandas.RangeIndex(len(df.index))
return df.apply(lambda df: df.last_valid_index())
func =... |
def median(self, **kwargs):
"""Returns median of each column or row.
Returns:
A new QueryCompiler object containing the median of each column or row.
"""
if self._is_transposed:
kwargs["axis"] = kwargs.get("axis", 0) ^ 1
return self.transpose().median... |
def memory_usage(self, **kwargs):
"""Returns the memory usage of each column.
Returns:
A new QueryCompiler object containing the memory usage of each column.
"""
def memory_usage_builder(df, **kwargs):
return df.memory_usage(**kwargs)
func = self._build... |
def quantile_for_single_value(self, **kwargs):
"""Returns quantile of each column or row.
Returns:
A new QueryCompiler object containing the quantile of each column or row.
"""
if self._is_transposed:
kwargs["axis"] = kwargs.get("axis", 0) ^ 1
return ... |
def _full_axis_reduce_along_select_indices(self, func, axis, index):
"""Reduce Manger along select indices using function that needs full axis.
Args:
func: Callable that reduces the dimension of the object and requires full
knowledge of the entire axis.
axis: 0 f... |
def describe(self, **kwargs):
"""Generates descriptive statistics.
Returns:
DataFrame object containing the descriptive statistics of the DataFrame.
"""
# Use pandas to calculate the correct columns
new_columns = (
pandas.DataFrame(columns=self.columns)
... |
def dropna(self, **kwargs):
"""Returns a new QueryCompiler with null values dropped along given axis.
Return:
a new DataManager
"""
axis = kwargs.get("axis", 0)
subset = kwargs.get("subset", None)
thresh = kwargs.get("thresh", None)
how = kwargs.get("h... |
def eval(self, expr, **kwargs):
"""Returns a new QueryCompiler with expr evaluated on columns.
Args:
expr: The string expression to evaluate.
Returns:
A new QueryCompiler with new columns after applying expr.
"""
columns = self.index if self._is_transpos... |
def mode(self, **kwargs):
"""Returns a new QueryCompiler with modes calculated for each label along given axis.
Returns:
A new QueryCompiler with modes calculated.
"""
axis = kwargs.get("axis", 0)
def mode_builder(df, **kwargs):
result = df.mode(**kwargs... |
def fillna(self, **kwargs):
"""Replaces NaN values with the method provided.
Returns:
A new QueryCompiler with null values filled.
"""
axis = kwargs.get("axis", 0)
value = kwargs.get("value")
if isinstance(value, dict):
value = kwargs.pop("value")... |
def query(self, expr, **kwargs):
"""Query columns of the DataManager with a boolean expression.
Args:
expr: Boolean expression to query the columns with.
Returns:
DataManager containing the rows where the boolean expression is satisfied.
"""
columns = se... |
def rank(self, **kwargs):
"""Computes numerical rank along axis. Equal values are set to the average.
Returns:
DataManager containing the ranks of the values along an axis.
"""
axis = kwargs.get("axis", 0)
numeric_only = True if axis else kwargs.get("numeric_only", F... |
def sort_index(self, **kwargs):
"""Sorts the data with respect to either the columns or the indices.
Returns:
DataManager containing the data sorted by columns or indices.
"""
axis = kwargs.pop("axis", 0)
index = self.columns if axis else self.index
# sort_i... |
def _map_across_full_axis_select_indices(
self, axis, func, indices, keep_remaining=False
):
"""Maps function to select indices along full axis.
Args:
axis: 0 for columns and 1 for rows.
func: Callable mapping function over the BlockParitions.
indices: in... |
def quantile_for_list_of_values(self, **kwargs):
"""Returns Manager containing quantiles along an axis for numeric columns.
Returns:
DataManager containing quantiles of original DataManager along an axis.
"""
if self._is_transposed:
kwargs["axis"] = kwargs.get("a... |
def tail(self, n):
"""Returns the last n rows.
Args:
n: Integer containing the number of rows to return.
Returns:
DataManager containing the last n rows of the original DataManager.
"""
# See head for an explanation of the transposed behavior
if ... |
def front(self, n):
"""Returns the first n columns.
Args:
n: Integer containing the number of columns to return.
Returns:
DataManager containing the first n columns of the original DataManager.
"""
new_dtypes = (
self._dtype_cache if self._dt... |
def getitem_column_array(self, key):
"""Get column data for target labels.
Args:
key: Target labels by which to retrieve data.
Returns:
A new QueryCompiler.
"""
# Convert to list for type checking
numeric_indices = list(self.columns.get_indexer_f... |
def getitem_row_array(self, key):
"""Get row data for target labels.
Args:
key: Target numeric indices by which to retrieve data.
Returns:
A new QueryCompiler.
"""
# Convert to list for type checking
key = list(key)
def getitem(df, inter... |
def setitem(self, axis, key, value):
"""Set the column defined by `key` to the `value` provided.
Args:
key: The column name to set.
value: The value to set the column to.
Returns:
A new QueryCompiler
"""
def setitem(df, internal_indices=[])... |
def drop(self, index=None, columns=None):
"""Remove row data for target index and columns.
Args:
index: Target index to drop.
columns: Target columns to drop.
Returns:
A new QueryCompiler.
"""
if self._is_transposed:
return self.t... |
def insert(self, loc, column, value):
"""Insert new column data.
Args:
loc: Insertion index.
column: Column labels to insert.
value: Dtype object values to insert.
Returns:
A new PandasQueryCompiler with new data inserted.
"""
if ... |
def apply(self, func, axis, *args, **kwargs):
"""Apply func across given axis.
Args:
func: The function to apply.
axis: Target axis to apply the function along.
Returns:
A new PandasQueryCompiler.
"""
if callable(func):
return sel... |
def _post_process_apply(self, result_data, axis, try_scale=True):
"""Recompute the index after applying function.
Args:
result_data: a BaseFrameManager object.
axis: Target axis along which function was applied.
Returns:
A new PandasQueryCompiler.
""... |
def _dict_func(self, func, axis, *args, **kwargs):
"""Apply function to certain indices across given axis.
Args:
func: The function to apply.
axis: Target axis to apply the function along.
Returns:
A new PandasQueryCompiler.
"""
if "axis" not... |
def _list_like_func(self, func, axis, *args, **kwargs):
"""Apply list-like function across given axis.
Args:
func: The function to apply.
axis: Target axis to apply the function along.
Returns:
A new PandasQueryCompiler.
"""
func_prepared = s... |
def _callable_func(self, func, axis, *args, **kwargs):
"""Apply callable functions across given axis.
Args:
func: The functions to apply.
axis: Target axis to apply the function along.
Returns:
A new PandasQueryCompiler.
"""
def callable_app... |
def _manual_repartition(self, axis, repartition_func, **kwargs):
"""This method applies all manual partitioning functions.
Args:
axis: The axis to shuffle data along.
repartition_func: The function used to repartition data.
Returns:
A `BaseFrameManager` obje... |
def get_dummies(self, columns, **kwargs):
"""Convert categorical variables to dummy variables for certain columns.
Args:
columns: The columns to convert.
Returns:
A new QueryCompiler.
"""
cls = type(self)
# `columns` as None does not mean all col... |
def global_idx_to_numeric_idx(self, axis, indices):
"""
Note: this function involves making copies of the index in memory.
Args:
axis: Axis to extract indices.
indices: Indices to convert to numerical.
Returns:
An Index object.
"""
as... |
def _get_data(self) -> BaseFrameManager:
"""Perform the map step
Returns:
A BaseFrameManager object.
"""
def iloc(partition, row_internal_indices, col_internal_indices):
return partition.iloc[row_internal_indices, col_internal_indices]
masked_data = sel... |
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 _update_inplace(self, new_query_compiler):
"""Updates the current DataFrame inplace.
Args:
new_query_compiler: The new QueryCompiler to use to manage the data
"""
old_query_compiler = self._query_compiler
self._query_compiler = new_query_compiler
... |
def _validate_other(
self,
other,
axis,
numeric_only=False,
numeric_or_time_only=False,
numeric_or_object_only=False,
comparison_dtypes_only=False,
):
"""Helper method to check validity of other in inter-df operations"""
axis = self._... |
def _default_to_pandas(self, op, *args, **kwargs):
"""Helper method to use default pandas function"""
empty_self_str = "" if not self.empty else " for empty DataFrame"
ErrorMessage.default_to_pandas(
"`{}.{}`{}".format(
self.__name__,
op if isins... |
def abs(self):
"""Apply an absolute value function to all numeric columns.
Returns:
A new DataFrame with the applied absolute value.
"""
self._validate_dtypes(numeric_only=True)
return self.__constructor__(query_compiler=self._query_compiler.abs()) |
def add(self, other, axis="columns", level=None, fill_value=None):
"""Add this DataFrame to another or a scalar/list.
Args:
other: What to add this this DataFrame.
axis: The axis to apply addition over. Only applicaable to Series
or list 'other'.
... |
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