我想我碰到了一只大熊猫。我希望得到一些帮助，以验证错误或帮助我弄清楚逻辑错误在代码中的位置。
我的代码如下：
import pandas, numpy, StringIO
def sq_fixer(sr):
sr = sr.where(sr != '20200229')
ranks = sr.argsort().astype(float)
ranks[ranks == -1] = numpy.nan
return ','.join(ranks.astype(numpy.str))
def correct_date(sr):
date_fixer = lambda x: pandas.datetime(x.year -100, x.month, x.day) if x > pandas.datetime.now() else x
sr = pandas.to_datetime(sr).apply(date_fixer).astype(pandas.datetime)
return sr
txt = '''ID,RUN_START_DATE,PUSHUP_START_DATE,SITUP_START_DATE,PULLUP_START_DATE
1,2013-01-24,2013-01-02,,2013-02-03
2,2013-01-30,2013-01-21,2013-01-13,2013-01-06
3,2013-01-29,2013-01-28,2013-01-01,2013-01-29
4,2013-02-16,2013-02-12,2013-01-04,2013-02-11
5,2013-01-06,2013-02-07,2013-02-25,2013-02-12
6,2013-01-26,2013-01-28,2013-02-12,2013-01-10
7,2013-01-26,,2013-01-12,2013-01-30
8,2013-01-03,2013-01-24,2013-01-19,2013-01-02
9,2013-01-22,2013-01-13,2013-02-03,
10,2013-02-06,2013-01-16,2013-02-07,2013-01-11
3347,,2008-02-27,2008-04-10,2008-02-13
3588,2004-09-12,,2004-11-06,2004-09-06
3784,2003-02-22,,2003-06-21,2003-02-19
593,2009-04-03,,2009-06-01,2009-04-01
4148,2003-03-21,2002-09-20,2003-04-01,2003-01-01
4299,2004-05-24,2004-07-23,,2004-04-22
4590,2005-05-05,2005-12-05,2005-04-05,
4830,2001-06-12,2000-10-12,2001-07-28,2001-01-28
4941,2006-11-08,2006-12-19,2006-07-19,2007-02-24
1416,2004-04-03,2004-05-19,2004-02-06,
1580,2008-12-20,,2009-03-19,2008-12-19
1661,2005-10-03,2005-10-26,2005-09-12,2006-02-19
1759,2001-10-18,,2002-01-17,2001-10-17
1858,2003-04-14,2003-05-17,,2002-12-17
1972,2003-06-01,2003-07-14,2002-12-14,
5905,2000-11-18,2001-01-13,,2000-11-04
2052,2002-06-11,,2002-08-23,2001-12-12
2165,2006-10-01,,2007-02-27,2006-09-30
2218,2007-09-19,,2008-02-06,2007-09-09
2350,2000-08-08,,2000-09-22,2000-01-08
2432,2001-08-22,,2001-09-25,2000-12-16
2611,2005-05-07,,2005-06-05,2005-03-26
2612,2005-05-06,,2005-05-26,2005-04-11
7378,2009-08-07,2009-01-30,2010-01-20,2009-06-08
7550,2006-04-08,,2006-06-01,2006-04-01 '''
df = pandas.read_csv(StringIO.StringIO(txt))
sequence_array = ['RUN_START_DATE', 'PUSHUP_START_DATE', 'SITUP_START_DATE', 'PULLUP_START_DATE']
xsequence_array = ['X_RUN_START_DATE', 'X_PUSHUP_START_DATE', 'X_SITUP_START_DATE', 'X_PULLUP_START_DATE']
df[sequence_array] = df[sequence_array].apply(correct_date, axis=1)
fix_day = lambda x: x if x > 0 else 29
fix_month = lambda x: x if x > 0 else 02
fix_year = lambda x: x if x > 0 else 2020
for col in sequence_array:
xcol = 'X_{0}'.format(col)
df[xcol] = ['{0:04d}{1:02d}{2:02d}'.format(fix_year(c.year), fix_month(c.month), fix_day(c.day)) for c in df[col]]
df['X_AS_SEQUENCE'] = df[xsequence_array].apply(sq_fixer, axis=1)
当我运行代码时，大多数结果都是正确的。以索引6为例：
In [31]: df.ix[6]
Out[31]:
ID 7
RUN_START_DATE 2013-01-26 00:00:00
PUSHUP_START_DATE NaN
SITUP_START_DATE 2013-01-12 00:00:00
PULLUP_START_DATE 2013-01-30 00:00:00
X_RUN_START_DATE 20130126
X_PUSHUP_START_DATE 20200229
X_SITUP_START_DATE 20130112
X_PULLUP_START_DATE 20130130
X_AS_SEQUENCE 1.0,nan,0.0,2.0
但是，某些索引似乎将pandas.argsort（）抛出循环。以索引10为例：
In [32]: df.ix[10]
Out[32]:
ID 3347
RUN_START_DATE NaN
PUSHUP_START_DATE 2008-02-27 00:00:00
SITUP_START_DATE 2008-04-10 00:00:00
PULLUP_START_DATE 2008-02-13 00:00:00
X_RUN_START_DATE 20200229
X_PUSHUP_START_DATE 20080227
X_SITUP_START_DATE 20080410
X_PULLUP_START_DATE 20080213
X_AS_SEQUENCE nan,2.0,0.0,1.0
argsort应该返回
nan,1.0,2.0,0.0而不是nan,2.0,0.0,1.0。我已经做了三天了。在这一点上，我不确定是我还是错误。我不确定如何回溯它以获得答案。非常感激任何的帮助！
最佳答案
您可能错误地解释了argsort的结果。 argsort不给出值的排名。如果要对值进行排名，请使用rank方法。argsort返回的Series中的值在删除NaN之后给出原始值的相应位置。在您的情况下，由于将20200229转换为NaN，因此您正在对NaN, 20080227, 20080410, 20080213进行argsorting。非NaN值为
nonnan = [20080227, 20080410, 20080213]
结果
NaN, 2, 0, 1说：argsort sorted values
NaN NaN
2 nonnan[2] = 20080213
0 nonnan[0] = 20080227
1 nonnan[1] = 20080410
所以对我来说看起来还可以。
关于python - Pandas argsort的有趣结果，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/15630302/