为了确认我理解Pandas df.groupby()
和df.reset_index()
所做的事情,我尝试从dataframe往返到相同数据的分组版本并返回。在往返之后,必须再次对列和行进行排序,因为groupby()
会影响行顺序,而reset_index()
会影响列顺序,但经过两次快速操作,将列和索引重新排序后,数据流看起来是相同的:
然而,在所有这些检查成功后,df1.equals(df5)
返回惊人的值False
。
equals()
发现的这些数据文件之间有什么区别,我还没有弄清楚如何自己检查呢?
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))
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!
发布于 2015-03-27 23:46:42
对我来说,这感觉就像一只虫子,但可能只是我误解了一些东西。这些区块按不同的顺序列出:
>>> 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
中,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))
最后一个all
假设self
和other
中的块对应。但是,如果在它之前添加一些print
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
所以我们在比较错误的东西。我不确定这是否是一个bug的原因是因为我不确定equals
是否意味着这么挑剔。如果是这样的话,我认为至少有一个文档错误,因为equals
应该说它不打算用于您可能认为的名称和docstring中的内容。
https://stackoverflow.com/questions/29311659
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