Pandas DataFrames如何看起来相同但是失败等于()？

Question

为了证实我明白大熊猫df.groupby()和df.reset_index()做什么,我试图做从数据帧的往返相同的数据和背部的分组版本.在往返之后,列和行必须再次排序,因为groupby()影响行顺序并reset_index()影响列顺序,但经过两次快速操作将列和索引按顺序排列后,数据帧看起来相同:

• 相同的列名列表.
• 每列都有相同的dtypes.
• 相应的索引值严格相等.
• 相应的数据值严格相等.

然而,在所有这些检查成功后,df1.equals(df5)返回惊人的价值False.

这些数据帧之间有什么区别,equals()发现我还没弄明白如何检查自己？

测试代码:

csv_text = """\
Title,Year,Director
North by Northwest,1959,Alfred Hitchcock
Notorious,1946,Alfred Hitchcock
The Philadelphia Story,1940,George Cukor
To Catch a Thief,1955,Alfred Hitchcock
His Girl Friday,1940,Howard Hawks
"""

import pandas as pd

df1 = pd.read_csv('sample.csv')
df1.columns = map(str.lower, df1.columns)
print(df1)

df2 = df1.groupby(['director', df1.index]).first()
df3 = df2.reset_index('director')
df4 = df3[['title', 'year', 'director']]
df5 = df4.sort_index()
print(df5)

print()
print(repr(df1.columns))
print(repr(df5.columns))
print()
print(df1.dtypes)
print(df5.dtypes)
print()
print(df1 == df5)
print()
print(df1.index == df5.index)
print()
print(df1.equals(df5))

我运行脚本时收到的输出是:

                    title  year          director
0      North by Northwest  1959  Alfred Hitchcock
1               Notorious  1946  Alfred Hitchcock
2  The Philadelphia Story  1940      George Cukor
3        To Catch a Thief  1955  Alfred Hitchcock
4         His Girl Friday  1940      Howard Hawks
                    title  year          director
0      North by Northwest  1959  Alfred Hitchcock
1               Notorious  1946  Alfred Hitchcock
2  The Philadelphia Story  1940      George Cukor
3        To Catch a Thief  1955  Alfred Hitchcock
4         His Girl Friday  1940      Howard Hawks

Index(['title', 'year', 'director'], dtype='object')
Index(['title', 'year', 'director'], dtype='object')

title       object
year         int64
director    object
dtype: object
title       object
year         int64
director    object
dtype: object

  title  year director
0  True  True     True
1  True  True     True
2  True  True     True
3  True  True     True
4  True  True     True

[ True  True  True  True  True]

False

谢谢你的帮助!

Answer 1

这对我来说感觉像个错误,但可能只是因为我误解了一些东西.块以不同的顺序列出:

>>> df1._data
BlockManager
Items: Index(['title', 'year', 'director'], dtype='object')
Axis 1: Int64Index([0, 1, 2, 3, 4], dtype='int64')
IntBlock: slice(1, 2, 1), 1 x 5, dtype: int64
ObjectBlock: slice(0, 4, 2), 2 x 5, dtype: object
>>> df5._data
BlockManager
Items: Index(['title', 'year', 'director'], dtype='object')
Axis 1: Int64Index([0, 1, 2, 3, 4], dtype='int64')
ObjectBlock: slice(0, 4, 2), 2 x 5, dtype: object
IntBlock: slice(1, 2, 1), 1 x 5, dtype: int64

在core/internals.py,我们有BlockManager方法

def equals(self, other):
    self_axes, other_axes = self.axes, other.axes
    if len(self_axes) != len(other_axes):
        return False
    if not all (ax1.equals(ax2) for ax1, ax2 in zip(self_axes, other_axes)):
        return False
    self._consolidate_inplace()
    other._consolidate_inplace()
    return all(block.equals(oblock) for block, oblock in
               zip(self.blocks, other.blocks))

并且最后all假定块中self和other对应.但如果我们print在它之前添加一些调用,我们会看到:

>>> df1.equals(df5)
blocks self: (IntBlock: slice(1, 2, 1), 1 x 5, dtype: int64, ObjectBlock: slice(0, 4, 2), 2 x 5, dtype: object)
blocks other: (ObjectBlock: slice(0, 4, 2), 2 x 5, dtype: object, IntBlock: slice(1, 2, 1), 1 x 5, dtype: int64)
False

所以我们比较错误的东西.我不知道这是否是一个错误的原因是因为我不知道是否equals是意味着是这个挑剔与否.如果是这样,我认为至少有一个doc bug,因为equals应该大声说它并不意味着用于你可能会认为它来自名称和文档字符串.