Question

我想我已经遇到了大熊猫的一个错误。我希望得到一些帮助来验证错误或帮助我找出我的逻辑错误在我的代码中的位置。

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

然而，某些索引似乎抛出pandas.argsort（）作为循环。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。

我已经在这三天了。在这一点上，我不确定这是我还是一个bug。我不知道如何回溯它以获得答案。任何帮助都将非常感激！

Answer 1

您可能错误地解释了argsort的结果。 argsort没有给出值的排名。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后给出原始值的相应位置。在您的情况下，由于您将20200229转换为NaN，因此您需要NaN, 20080227, 20080410, 20080213。非NaN值是

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

所以我看起来还不错。

Answer 2

如果要对系列进行排序，只需使用sort_values（）或rank（）函数：

In [2]: a=pd.Series([3,2,1])

In [3]: a
Out[3]:
0    3
1    2
2    1
dtype: int64
In [4]: a.sort_values()
Out[4]:
2    1
1    2
0    3
dtype: int64

如果使用argsort（），这将为您提供排序系列中每个元素的位置，在这种情况下，1应该在0位置，2应该在1位置，3应该在2位置

In [5]: a.argsort()
Out[5]:
0    2
1    1
2    0
dtype: int64

与熊猫argsort有趣的结果

2 个答案: