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modin-project/modin | modin/pandas/base.py | BasePandasDataset.all | 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:
... | python | 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:
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.apply | def apply(
self,
func,
axis=0,
broadcast=None,
raw=False,
reduce=None,
result_type=None,
convert_dtype=True,
args=(),
**kwds
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"""Apply a function along input axis of DataFrame.
Args:
func: T... | python | def apply(
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func,
axis=0,
broadcast=None,
raw=False,
reduce=None,
result_type=None,
convert_dtype=True,
args=(),
**kwds
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Args:
func: T... | [
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.bfill | 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
) | python | 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
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.bool | 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
... | python | 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
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.copy | 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()) | python | 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()) | [
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.count | 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.
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"""Get the count of non-null objects in the DataFrame.
Arguments:
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.cummax | 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... | python | 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.
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.cumprod | 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... | python | 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.
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.describe | 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):
... | python | 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):
... | [
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.diff | 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
"""
... | python | 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
"""
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.drop | 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... | python | def drop(
self,
labels=None,
axis=0,
index=None,
columns=None,
level=None,
inplace=False,
errors="raise",
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.dropna | 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... | python | 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.
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.drop_duplicates | 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
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.eq | 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.
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axis: The axis to perform the eq over.
level: The Multilevel index level to apply eq over.
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.fillna | def fillna(
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axis=None,
inplace=False,
limit=None,
downcast=None,
**kwargs
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.filter | def filter(self, items=None, like=None, regex=None, axis=None):
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.floordiv | def floordiv(self, other, axis="columns", level=None, fill_value=None):
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.ge | def ge(self, other, axis="columns", level=None):
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.get_dtype_counts | def get_dtype_counts(self):
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Returns:
The counts of dtypes in this object.
"""
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Returns:
The counts of dtypes in this object.
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.get_ftype_counts | def get_ftype_counts(self):
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.head | 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):
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re... | python | def head(self, n=5):
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Args:
n (int): The number of rows to return.
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A new DataFrame with the first n rows of the DataFrame.
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.idxmax | def idxmax(self, axis=0, skipna=True, *args, **kwargs):
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axis (int): Identify the max over the rows (1) or columns (0).
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.isin | def isin(self, values):
"""Fill a DataFrame with booleans for cells contained in values.
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values (iterable, DataFrame, Series, or dict): The values to find.
Returns:
A new DataFrame with booleans representing whether or not a cell
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... | python | def isin(self, values):
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.le | def le(self, other, axis="columns", level=None):
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.lt | 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.
... | python | def lt(self, other, axis="columns", level=None):
"""Checks element-wise that this is less than other.
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axis: The axis to perform the lt over.
level: The Multilevel index level to apply lt over.
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.mean | def mean(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
"""Computes mean across the DataFrame.
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axis (int): The axis to take the mean on.
skipna (bool): True to skip NA values, false otherwise.
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.median | 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.
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axis (int): The axis to take the median on.
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.memory_usage | def memory_usage(self, index=True, deep=False):
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index (bool): Whether to include the memory usage of the DataFrame's
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.min | 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.
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axis (int): The axis to take the min on.
skipna (bool): True to skip NA values, false otherwise.
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.mod | def mod(self, other, axis="columns", level=None, fill_value=None):
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other: The object to use to apply the mod against this.
axis: The axis to mod over.
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.mode | def mode(self, axis=0, numeric_only=False, dropna=True):
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axis (int): The axis to take the mode on.
numeric_only (bool): if True, only apply to numeric columns.
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axis (int): The axis to take the mode on.
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.mul | def mul(self, other, axis="columns", level=None, fill_value=None):
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Args:
other: The object to use to apply the multiply against this.
axis: The axis to multiply over.
level: The Multilevel in... | python | def mul(self, other, axis="columns", level=None, fill_value=None):
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other: The object to use to apply the multiply against this.
axis: The axis to multiply over.
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.ne | 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.
... | python | def ne(self, other, axis="columns", level=None):
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axis: The axis to perform the ne over.
level: The Multilevel index level to apply ne over.
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.nunique | 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
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"""Return Series with number of distinct
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Args:
axis : {0 or 'index', 1 or 'columns'}, default 0
dropna : boolean, default True
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nunique : Series
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.pow | def pow(self, other, axis="columns", level=None, fill_value=None):
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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... | python | def pow(self, other, axis="columns", level=None, fill_value=None):
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.prod | def prod(
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axis=None,
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Args:
axis : {index (0), columns (1)}
skipna : bool... | python | def prod(
self,
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level=None,
numeric_only=None,
min_count=0,
**kwargs
):
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axis : {index (0), columns (1)}
skipna : bool... | [
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.quantile | 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,
... | python | def quantile(self, q=0.5, axis=0, numeric_only=True, interpolation="linear"):
"""Return values at the given quantile over requested axis,
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.rank | def rank(
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.reset_index | def reset_index(
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.rmod | def rmod(self, other, axis="columns", level=None, fill_value=None):
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.round | 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__(
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"""Round each element in the DataFrame.
Args:
decimals: The number of decimals to round to.
Returns:
A new DataFrame.
"""
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.rpow | def rpow(self, other, axis="columns", level=None, fill_value=None):
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.rsub | def rsub(self, other, axis="columns", level=None, fill_value=None):
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other: The object to use to apply the subtraction to this.
axis: The axis to apply the subtraction over.
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.rtruediv | def rtruediv(self, other, axis="columns", level=None, fill_value=None):
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other: The object to use to apply the div against this.
axis: The axis to div over.
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.sample | def sample(
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n=None,
frac=None,
replace=False,
weights=None,
random_state=None,
axis=None,
):
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self,
n=None,
frac=None,
replace=False,
weights=None,
random_state=None,
axis=None,
):
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.set_axis | def set_axis(self, labels, axis=0, inplace=None):
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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.
... | python | def set_axis(self, labels, axis=0, inplace=None):
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labels (pandas.Index or list-like): The Index to assign.
axis (string or int): The axis to reassign.
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.sort_index | def sort_index(
self,
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level=None,
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kind="quicksort",
na_position="last",
sort_remaining=True,
by=None,
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... | python | def sort_index(
self,
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level=None,
ascending=True,
inplace=False,
kind="quicksort",
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sort_remaining=True,
by=None,
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.sort_values | def sort_values(
self,
by,
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.sub | def sub(self, other, axis="columns", level=None, fill_value=None):
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.truediv | def truediv(self, other, axis="columns", level=None, fill_value=None):
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.var | def var(
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modin-project/modin | modin/pandas/base.py | BasePandasDataset.size | def size(self):
"""Get the number of elements in the DataFrame.
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return len(self._query_compiler.index) * len(self._query_compiler.columns) | python | def size(self):
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The number of elements in the DataFrame.
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modin-project/modin | modin/engines/python/pandas_on_python/frame/partition.py | PandasOnPythonFramePartition.get | def get(self):
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"""Flushes the call_queue and returns the data.
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modin-project/modin | modin/engines/python/pandas_on_python/frame/partition.py | PandasOnPythonFramePartition.apply | def apply(self, func, **kwargs):
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modin-project/modin | modin/engines/dask/pandas_on_dask_delayed/frame/partition.py | DaskFramePartition.apply | def apply(self, func, **kwargs):
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modin-project/modin | modin/engines/dask/pandas_on_dask_delayed/frame/partition.py | DaskFramePartition.add_to_apply_calls | def add_to_apply_calls(self, func, **kwargs):
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self.delayed_cal... | python | def add_to_apply_calls(self, func, **kwargs):
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modin-project/modin | modin/experimental/engines/pyarrow_on_ray/io.py | _read_csv_with_offset_pyarrow_on_ray | def _read_csv_with_offset_pyarrow_on_ray(
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modin-project/modin | modin/data_management/utils.py | compute_chunksize | 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... | python | def compute_chunksize(df, num_splits, default_block_size=32, axis=None):
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modin-project/modin | modin/data_management/utils.py | _get_nan_block_id | def _get_nan_block_id(partition_class, n_row=1, n_col=1, transpose=False):
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Args:
partition_class (BaseFramePartition): The class to use to put the object
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n_row(int): The number of rows.
n_col(int): The n... | python | def _get_nan_block_id(partition_class, n_row=1, n_col=1, transpose=False):
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partition_class (BaseFramePartition): The class to use to put the object
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modin-project/modin | modin/data_management/utils.py | split_result_of_axis_func_pandas | 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 ... | python | def split_result_of_axis_func_pandas(axis, num_splits, result, length_list=None):
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axis: The axis to split across.
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modin-project/modin | modin/pandas/indexing.py | _parse_tuple | 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
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row_loc, col_loc = slice(None), slice(None)
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row_loc = tup[0]
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"""Unpack the user input for getitem and setitem and compute ndim
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loc[[a,b],] -> ([a,b], :),
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modin-project/modin | modin/pandas/indexing.py | _is_enlargement | def _is_enlargement(locator, global_index):
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"""
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modin-project/modin | modin/pandas/indexing.py | _compute_ndim | def _compute_ndim(row_loc, col_loc):
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col_scaler = is_scalar(col_loc)
if row_scaler and col_scaler:
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else:
ndim = 2
return ndim | python | 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)
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modin-project/modin | modin/pandas/indexing.py | _LocationIndexerBase._broadcast_item | def _broadcast_item(self, row_lookup, col_lookup, item, to_shape):
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Notes:
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modin-project/modin | modin/pandas/indexing.py | _LocationIndexerBase._write_items | def _write_items(self, row_lookup, col_lookup, item):
"""Perform remote write and replace blocks.
"""
self.qc.write_items(row_lookup, col_lookup, item) | python | def _write_items(self, row_lookup, col_lookup, item):
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modin-project/modin | modin/pandas/indexing.py | _LocIndexer._handle_enlargement | 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(
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"""Handle Enlargement (if there is one).
Returns:
None
"""
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modin-project/modin | modin/pandas/indexing.py | _LocIndexer._compute_enlarge_labels | 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
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modin-project/modin | modin/engines/ray/pandas_on_ray/io.py | _split_result_for_readers | def _split_result_for_readers(axis, num_splits, df): # pragma: no cover
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Args:
axis: Which axis to split over.
num_splits: The number of splits to create.
df: The DataFrame after it has been read.
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axis: Which axis to split over.
num_splits: The number of splits to create.
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modin-project/modin | modin/engines/ray/pandas_on_ray/io.py | _read_parquet_columns | def _read_parquet_columns(path, columns, num_splits, kwargs): # pragma: no cover
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modin-project/modin | modin/engines/ray/pandas_on_ray/io.py | _read_csv_with_offset_pandas_on_ray | def _read_csv_with_offset_pandas_on_ray(
fname, num_splits, start, end, kwargs, header
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"""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)
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fname: The filename of the file to ope... | python | def _read_csv_with_offset_pandas_on_ray(
fname, num_splits, start, end, kwargs, header
): # pragma: no cover
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modin-project/modin | modin/engines/ray/pandas_on_ray/io.py | _read_hdf_columns | 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 ... | python | 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.
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path_or_buf: The path of the HDF5 file.
columns: The list ... | [
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modin-project/modin | modin/engines/ray/pandas_on_ray/io.py | _read_feather_columns | def _read_feather_columns(path, columns, num_splits): # pragma: no cover
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path: The path of the Feather file.
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modin-project/modin | modin/engines/ray/pandas_on_ray/io.py | _read_sql_with_limit_offset | 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... | python | 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)
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modin-project/modin | modin/engines/ray/generic/io.py | get_index | 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 | python | 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
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modin-project/modin | modin/engines/ray/generic/io.py | RayIO.read_parquet | 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.
... | python | 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.
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modin-project/modin | modin/engines/ray/generic/io.py | RayIO._read_csv_from_file_pandas_on_ray | 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_... | python | 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.
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modin-project/modin | modin/engines/ray/generic/io.py | RayIO._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 ... | python | 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
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modin-project/modin | modin/engines/ray/generic/io.py | RayIO.read_hdf | 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... | python | def read_hdf(cls, path_or_buf, **kwargs):
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path_or_buf: string, buffer or path object
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modin-project/modin | modin/engines/ray/generic/io.py | RayIO.read_feather | 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... | python | 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.
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modin-project/modin | modin/engines/ray/generic/io.py | RayIO.to_sql | 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... | python | 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)
"""
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modin-project/modin | modin/engines/ray/generic/io.py | RayIO.read_sql | 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... | python | 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
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modin-project/modin | modin/pandas/datetimes.py | to_datetime | 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... | python | 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,
):
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modin-project/modin | modin/experimental/pandas/io_exp.py | read_sql | 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:
... | python | 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:
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modin-project/modin | modin/engines/ray/generic/frame/partition_manager.py | RayFrameManager.block_lengths | 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... | python | 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:
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modin-project/modin | modin/engines/ray/generic/frame/partition_manager.py | RayFrameManager.block_widths | 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 ... | python | 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:
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modin-project/modin | modin/engines/ray/pandas_on_ray/frame/partition.py | deploy_ray_func | 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... | python | 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.
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modin-project/modin | modin/engines/ray/pandas_on_ray/frame/partition.py | PandasOnRayFramePartition.get | 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:
... | python | 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)
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modin-project/modin | modin/engines/base/frame/partition_manager.py | BaseFrameManager.block_lengths | 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... | python | 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:
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modin-project/modin | modin/engines/base/frame/partition_manager.py | BaseFrameManager.block_widths | 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... | python | 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:
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modin-project/modin | modin/engines/base/frame/partition_manager.py | BaseFrameManager.map_across_blocks | 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... | python | 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... | [
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modin-project/modin | modin/engines/base/frame/partition_manager.py | BaseFrameManager.copartition_datasets | 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... | python | 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.
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modin-project/modin | modin/engines/base/frame/partition_manager.py | BaseFrameManager.map_across_full_axis | 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... | python | 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
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modin-project/modin | modin/engines/base/frame/partition_manager.py | BaseFrameManager.take | 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... | python | 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:
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modin-project/modin | modin/engines/base/frame/partition_manager.py | BaseFrameManager.concat | 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... | python | 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... | [
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modin-project/modin | modin/engines/base/frame/partition_manager.py | BaseFrameManager.to_pandas | 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... | python | 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... | [
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is_transposed: A flag for telling this object that the external
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modin-project/modin | modin/engines/base/frame/partition_manager.py | BaseFrameManager.get_indices | 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... | python | 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
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modin-project/modin | modin/engines/base/frame/partition_manager.py | BaseFrameManager._get_blocks_containing_index | 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... | python | 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
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modin-project/modin | modin/engines/base/frame/partition_manager.py | BaseFrameManager._get_dict_of_block_index | 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
`_... | python | 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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