复制或分组pandas DataFrame时如何保持主要顺序？

Question

如何按顺序使用或操作（猴子补丁）pandas，以便在复制和 groupby 聚合的结果对象上始终保持相同的主要顺序？

我使用pandas.DataFrame 作为业务应用程序（风险模型）中的数据结构，需要快速聚合多维数据。与 pandas 的聚合主要取决于底层 numpy 数组上使用的主要排序方案。

不幸的是，当我创建副本或使用 groupby 和 sum 执行聚合时，pandas（版本 0.23.4）会更改底层 numpy 数组的主要顺序。

影响是：

案例 1：17.2 秒

案例 2：5 分 46 秒

在 DataFrame 及其具有 45023 行和 100000 列的副本上。对索引执行聚合。索引是pd.MultiIndex，有 15 个级别。聚合保持三个级别，并导致大约 239 个组。

我通常在具有 45000 行和 100000 列的 DataFrame 上工作。在这一行，我有一个 pandas.MultiIndex，大约有 15 个级别。要计算各种层次结构节点的统计信息，我需要在索引维度上聚合（总和）。

如果底层 numpy 数组是 c_contiguous，那么聚合速度很快，因此以列优先顺序（C 顺序）保持。如果是f_contiguous，则非常慢，因此是行主要顺序（F 顺序）。

不幸的是，pandas 将主要顺序从 C 更改为 F

• 创建一个DataFrame 的副本，甚至在什么时候，

• 通过 grouby 进行聚合，然后对 grouper 求和。因此，生成的 DataFrame 具有不同的主要顺序 (!)

当然，我可以坚持使用另一个“数据模型”，只需将 MultiIndex 保留在列上。那么当前的熊猫版本总是对我有利。但这是不行的。我认为，可以预期，对于正在考虑的两个操作（groupby-sum 和 copy），不应更改主要顺序。

import numpy as np
import pandas as pd

print("pandas version: ", pd.__version__)

array = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
array.flags
print("Numpy array is C-contiguous: ", data.flags.c_contiguous)

dataframe = pd.DataFrame(array, index = pd.MultiIndex.from_tuples([('A', 'U'), ('A', 'V'), ('B', 'W')], names=['dim_one', 'dim_two']))
print("DataFrame is C-contiguous: ", dataframe.values.flags.c_contiguous)

dataframe_copy = dataframe.copy()
print("Copy of DataFrame is C-contiguous: ", dataframe_copy.values.flags.c_contiguous)

aggregated_dataframe = dataframe.groupby('dim_one').sum()
print("Aggregated DataFrame is C-contiguous: ", aggregated_dataframe.values.flags.c_contiguous)


## Output in Jupyter Notebook
# pandas version:  0.23.4
# Numpy array is C-contiguous:  True
# DataFrame is C-contiguous:  True
# Copy of DataFrame is C-contiguous:  False
# Aggregated DataFrame is C-contiguous:  False

应保留数据的主要顺序。如果 pandas 喜欢切换到隐式偏好，那么它应该允许覆盖它。 Numpy 允许在创建副本时输入顺序。

pandas 的补丁版本应该会导致

## Output in Jupyter Notebook
# pandas version:  0.23.4
# Numpy array is C-contiguous:  True
# DataFrame is C-contiguous:  True
# Copy of DataFrame is C-contiguous:  True
# Aggregated DataFrame is C-contiguous:  True

上面截取的示例代码。

Answer 1

熊猫猴子补丁（0.23.4 可能还有其他版本）

我创建了一个补丁，我想与你分享。它导致上述问题中提到的性能提升。

它适用于熊猫版本 0.23.4。对于其他版本，您需要尝试它是否仍然有效。

需要以下两个模块，您可以根据放置它​​们的位置调整导入。

memory_layout.py   
memory.py

要修补您的代码，您只需在程序或笔记本的最开始导入以下内容并设置内存布局参数。它会对 pandas 进行修补，并确保 DataFrames 的副本具有所要求的布局。

from memory_layout import memory_layout
# memory_layout.order = 'F'  # assert F-order on copy
# memory_layout.order = 'K'  # Keep given layout on copy 
memory_layout.order = 'C'  # assert C-order on copy

memory_layout.py

使用以下内容创建文件 memory_layout.py。

import numpy as np
from pandas.core.internals import Block
from memory import memory_layout

# memory_layout.order = 'F'  # set memory layout order to 'F' for np.ndarrays in DataFrame copies (fortran/row order)
# memory_layout.order = 'K'  # keep memory layout order for np.ndarrays in DataFrame copies (order out is order in)
memory_layout.order = 'C'  # set memory layout order to 'C' for np.ndarrays in DataFrame copies (C/column order)


def copy(self, deep=True, mgr=None):
    """
    Copy patch on Blocks to set or keep the memory layout
    on copies.

    :param self: `pandas.core.internals.Block`
    :param deep: `bool`
    :param mgr: `BlockManager`
    :return: copy of `pandas.core.internals.Block`
    """
    values = self.values
    if deep:
        if isinstance(values, np.ndarray):
memory_layout))
            values = memory_layout.copy_transposed(values)
memory_layout))
        else:
            values = values.copy()
    return self.make_block_same_class(values)


Block.copy = copy  # Block for pandas 0.23.4: in pandas.core.internals.Block

memory.py

创建文件memory.py，内容如下。

"""
Implements MemoryLayout copy factory to change memory layout
of `numpy.ndarrays`.
Depending on the use case, operations on DataFrames can be much
faster if the appropriate memory layout is set and preserved.

The implementation allows for changing the desired layout. Changes apply when
copies or new objects are created, as for example, when slicing or aggregating
via groupby ...

This implementation tries to solve the issue raised on GitHub
https://github.com/pandas-dev/pandas/issues/26502

"""
import numpy as np

_DEFAULT_MEMORY_LAYOUT = 'K'


class MemoryLayout(object):
    """
    Memory layout management for numpy.ndarrays.

    Singleton implementation.

    Example:
    >>> from memory import memory_layout
    >>> memory_layout.order = 'K'  #
    >>> # K ... keep array layout from input
    >>> # C ... set to c-contiguous / column order
    >>> # F ... set to f-contiguous / row order
    >>> array = memory_layout.apply(array)
    >>> array = memory_layout.apply(array, 'C')
    >>> array = memory_layout.copy(array)
    >>> array = memory_layout.apply_on_transpose(array)

    """

    _order = _DEFAULT_MEMORY_LAYOUT
    _instance = None

    @property
    def order(self):
        """
        Return memory layout ordering.

        :return: `str`
        """
        if self.__class__._order is None:
            raise AssertionError("Array layout order not set.")
        return self.__class__._order

    @order.setter
    def order(self, order):
        """
        Set memory layout order.
        Allowed values are 'C', 'F', and 'K'. Raises AssertionError
        when trying to set other values.

        :param order: `str`
        :return: `None`
        """
        assert order in ['C', 'F', 'K'], "Only 'C', 'F' and 'K' supported."
        self.__class__._order = order

    def __new__(cls):
        """
        Create only one instance throughout the lifetime of this process.

        :return: `MemoryLayout` instance as singleton
        """
        if cls._instance is None:
            cls._instance = super(MemoryLayout, cls).__new__(MemoryLayout)
        return cls._instance

    @staticmethod
    def get_from(array):
        """
        Get memory layout from array

        Possible values:
           'C' ... only C-contiguous or column order
           'F' ... only F-contiguous or row order
           'O' ... other: both, C- and F-contiguous or both
           not C- or F-contiguous (as on empty arrays).

        :param array: `numpy.ndarray`
        :return: `str`
        """
        if array.flags.c_contiguous == array.flags.f_contiguous:
            return 'O'
        return {True: 'C', False: 'F'}[array.flags.c_contiguous]

    def apply(self, array, order=None):
        """
        Apply the order set or the order given as input on the array
        given as input.

        Possible values:
           'C' ... apply C-contiguous layout or column order
           'F' ... apply F-contiguous layout or row order
           'K' ... keep the given layout

        :param array: `numpy.ndarray`
        :param order: `str`
        :return: `np.ndarray`
        """
        order = self.__class__._order if order is None else order

        if order == 'K':
            return array

        array_order = MemoryLayout.get_from(array)
        if array_order == order:
            return array

        return np.reshape(np.ravel(array), array.shape, order=order)

    def copy(self, array, order=None):
        """
        Return a copy of the input array with the memory layout set.
        Layout set:
           'C' ... return C-contiguous copy
           'F' ... return F-contiguous copy
           'K' ... return copy with same layout as
           given by the input array.

        :param array: `np.ndarray`
        :return: `np.ndarray`
        """
        order = order if order is not None else self.__class__._order
        return array.copy(order=self.get_from(array)) if order == 'K' \
            else array.copy(order=order)

    def copy_transposed(self, array):
        """
        Return a copy of the input array in order that its transpose
        has the memory layout set.

        Note: numpy simply changes the memory layout from row to column
        order instead of reshuffling the data in memory.

        Layout set:
           'C' ... return F-contiguous copy
           'F' ... return C-contiguous copy
           'K' ... return copy with oposite (C versus F) layout as
           given by the input array.

        :param array: `np.ndarray`
        :return: `np.ndarray`

        :param array:
        :return:
        """
        if self.__class__._order == 'K':
            return array.copy(
                order={'C': 'C', 'F': 'F', 'O': None}[self.get_from(array)])
        else:
            return array.copy(
                order={'C': 'F', 'F': 'C'}[self.__class__._order])

    def __str__(self):
        return str(self.__class__._order)


memory_layout = MemoryLayout()  # Singleton