由 Timestamp 对象组成的 Pandas 系列的 min() 和 max() 方法的意外结果
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Unexpected results of min() and max() methods of Pandas series made of Timestamp objects
提问by LukaszJ
I encountered this behaviour when doing basic data munging, like in this example:
我在进行基本数据处理时遇到了这种行为，就像在这个例子中一样：
In [55]: import pandas as pd
In [56]: import numpy as np
In [57]: rng = pd.date_range('1/1/2000', periods=10, freq='4h')
In [58]: lvls = ['A','A','A','B','B','B','C','C','C','C']
In [59]: df = pd.DataFrame({'TS': rng, 'V' : np.random.randn(len(rng)), 'L' : lvls})
In [60]: df
Out[60]:
L TS V
0 A 2000-01-01 00:00:00 -1.152371
1 A 2000-01-01 04:00:00 -2.035737
2 A 2000-01-01 08:00:00 -0.493008
3 B 2000-01-01 12:00:00 -0.279055
4 B 2000-01-01 16:00:00 -0.132386
5 B 2000-01-01 20:00:00 0.584091
6 C 2000-01-02 00:00:00 -0.297270
7 C 2000-01-02 04:00:00 -0.949525
8 C 2000-01-02 08:00:00 0.517305
9 C 2000-01-02 12:00:00 -1.142195
the problem:
问题：
In [61]: df['TS'].min()
Out[61]: 31969-04-01 00:00:00
In [62]: df['TS'].max()
Out[62]: 31973-05-10 00:00:00
while this looks ok:
虽然这看起来没问题：
In [63]: df['V'].max()
Out[63]: 0.58409076701429163
In [64]: min(df['TS'])
Out[64]: <Timestamp: 2000-01-01 00:00:00>
when aggregating after groupby:
在 groupby 后聚合时：
In [65]: df.groupby('L').min()
Out[65]:
TS V
L
A 9.466848e+17 -2.035737
B 9.467280e+17 -0.279055
C 9.467712e+17 -1.142195
In [81]: val = df.groupby('L').agg('min')['TS']['A']
In [82]: type(val)
Out[82]: numpy.float64
Apparently in this particular case it has something to do with using frequency datetime index as argument of pd.Series function:
显然，在这种特殊情况下，它与使用频率日期时间索引作为 pd.Series 函数的参数有关：
In [76]: rng.min()
Out[76]: <Timestamp: 2000-01-01 00:00:00>
In [77]: ts = pd.Series(rng)
In [78]: ts.min()
Out[78]: 31969-04-01 00:00:00
In [79]: type(ts.min())
Out[79]: numpy.datetime64
However, my initial problem was with min/max of Timestamp series parsed from strings via pd.read_csv()
但是，我最初的问题是通过 pd.read_csv() 从字符串解析的最小/最大时间戳系列
What am I doing wrong?
我究竟做错了什么？
采纳答案by Chang She
As @meteore points out, it's a problem with the string repr of the np.datetime64 type in NumPy 1.6.x. The underlying data, should still be correct. To workaround this problem, you can do something like:
正如@meteore 指出的那样，这是 NumPy 1.6.x 中 np.datetime64 类型的字符串 repr 的问题。基础数据，应该仍然是正确的。要解决此问题，您可以执行以下操作：
In [15]: df
Out[15]:
L TS V
0 A 2000-01-01 00:00:00 0.752035
1 A 2000-01-01 04:00:00 -1.047444
2 A 2000-01-01 08:00:00 1.177557
3 B 2000-01-01 12:00:00 0.394590
4 B 2000-01-01 16:00:00 1.835067
5 B 2000-01-01 20:00:00 -0.768274
6 C 2000-01-02 00:00:00 -0.564037
7 C 2000-01-02 04:00:00 -2.644367
8 C 2000-01-02 08:00:00 -0.571187
9 C 2000-01-02 12:00:00 1.618557
In [16]: df.TS.astype(object).min()
Out[16]: datetime.datetime(2000, 1, 1, 0, 0)
In [17]: df.TS.astype(object).max()
Out[17]: datetime.datetime(2000, 1, 2, 12, 0)
![pandas.read_csv 中 parse_date=[0] 和 parse_date=True 的区别](/res/img/loading.gif)