Pandas加入/合并“仅对唯一值索引重新索引有效”

2024-10-02 20:43:48 发布

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男 | 程序猿一只，喜欢编程写python代码。

我在连接操作中得到一个非常健壮的错误。我尝试了一次合并（左索引，右索引），结果也是一样的

索引完全相同（按设计），由index.is_unique（TRUE）和index.get_duplicates（）（空）检查

基本版本：

df1.join(series)
merge(df1, series_as_df,

print tempres.index

<；类“pandas.tseries.index.DatetimeIndex”>； [2013-01-14 17:04:45, ..., 2013-01-14 16:53:05] 长度：89，频率：无，时区：无

奇怪的是打印值：打印tempres.index.value [1970-01-16 121:04:45 1970-01-16 121:04:35 1970-01-16 121:04:25 1970-01-16 121:04:15 1970-01-16 121:04:05 1970-01-16 121:03:55 1970-01-16 121:03:45 1970-01-16 121:03:35 1970-01-16 121:03:25 1970-01-16 121:03:15 1970-01-16 121:03:05 1970-01-16 121:02:55 1970-01-16 121:02:45 1970-01-16 121:02:35 1970-01-16 121:02:25 1970-01-16 121:02:15 1970-01-16 121:02:05 1970-01-16 121:01:55 1970-01-16 121:01:45 1970-01-16 121:01:35 1970-01-16 121:01:25 …]

如果需要的话，我可以加上腌制的丝线和df

使用最新版本0.10.x

谢谢

卢克

我的代码（从较大的代码中剪切）

XYTparams (existing dataframe)
prep_functions[funcname] = [list of values, same length as XYTparams]

iSeries = Series(prep_functions[funcname], index = XYTparams.index, name = funcname)
XYTparams = XYTparams.join(iSeries)

回顾我的问题：

我在基本数据帧上使用合并和连续连接。在尝试下一次合并/加入时，我开始出现错误。我无法在一个简单的测试中重现这一点，但我在问题开始之前保存了数据帧

我找不到问题所在

base_df = load('SPOparams.pic')
lookup_df = load('lookup.pic')

print base_df
print lookup_df

print base_df.count()

print base_df['VKCSKEY1']
print lookup_df['traf_key']

# reset index does not change a thing
base_df = base_df.reset_index(drop=True)

print base_df.index
print base_df.index.get_duplicates()
print lookup_df.index
print lookup_df.index.get_duplicates()


# checking value matches
for k in lookup_df['traf_key']:
    print k, k in  base_df['VKCSKEY1'].values

# why does this merge is unsuccesfull ???
# in any combination of the parameters
df_result =merge(base_df, lookup_df, 
             how='left', 
             #how = 'outer',
             left_on ='VKCSKEY1', 
             right_on ='traf_key',
             #left_index=True, 
             #right_index = True,
             #sort=True, 
             #suffixes=('', '.m'), copy=True
             )
print df_result

输出：

1.6.1
0.10.1
<class 'pandas.core.frame.DataFrame'>
Int64Index: 89 entries, 0 to 88
Data columns:
T                        89  non-null values
X                        89  non-null values
Y                        89  non-null values
precip_quantity_1hour    89  non-null values
pressure                 89  non-null values
rel_humidity             89  non-null values
temp                     89  non-null values
temp_max                 0  non-null values
temp_min                 0  non-null values
wind_direction           89  non-null values
wind_speed               89  non-null values
BC_TRAF                  89  non-null values
closest                  89  non-null values
closest.m                89  non-null values
AGGP.P50_ID              89  non-null values
AGGP.FUNC_CLASS          89  non-null values
AGGP.SPEED_CAT           89  non-null values
LINK_ID                  89  non-null values
FUNC_CLASS               89  non-null values
SPEED_CAT                89  non-null values
AR_AUTO                  89  non-null values
AR_BUS                   89  non-null values
AR_TAXIS                 89  non-null values
AR_CARPOOL               89  non-null values
AR_PEDEST                89  non-null values
AR_TRUCKS                89  non-null values
STCA20_PCT               89  non-null values
VKC_LINKNR               89  non-null values
TRVIC150R1               89  non-null values
closest.m                89  non-null values
closest.m.m              89  non-null values
VKCP.LINK_ID             89  non-null values
VKCP.FUNC_CLASS          89  non-null values
VKCP.SPEED               89  non-null values
VKCP.LINKNR              89  non-null values
VKCP.TWIN_ID             89  non-null values
VKCSKEY1                 89  non-null values
dtypes: datetime64[ns](1), float64(13), int64(9), object(14)
<class 'pandas.core.frame.DataFrame'>
Index: 30 entries, (60744, 0) to (58314, 0)
Data columns:
traf_key      30  non-null values
weekday_nr    30  non-null values
linknr        30  non-null values
 weekday      30  non-null values
vr0           30  non-null values
vr1           30  non-null values
vr2           30  non-null values
vr3           30  non-null values
vr4           30  non-null values
vr5           30  non-null values
vr6           30  non-null values
vr7           30  non-null values
vr8           30  non-null values
vr9           30  non-null values
vr10          30  non-null values
vr11          30  non-null values
vr12          30  non-null values
vr13          30  non-null values
vr14          30  non-null values
vr15          30  non-null values
vr16          30  non-null values
vr17          30  non-null values
vr18          30  non-null values
vr19          30  non-null values
vr20          30  non-null values
vr21          30  non-null values
vr22          30  non-null values
vr23          30  non-null values
au0           30  non-null values
au1           30  non-null values
au2           30  non-null values
au3           30  non-null values
au4           30  non-null values
au5           30  non-null values
au6           30  non-null values
au7           30  non-null values
au8           30  non-null values
au9           30  non-null values
au10          30  non-null values
au11          30  non-null values
au12          30  non-null values
au13          30  non-null values
au14          30  non-null values
au15          30  non-null values
au16          30  non-null values
au17          30  non-null values
au18          30  non-null values
au19          30  non-null values
au20          30  non-null values
au21          30  non-null values
au22          30  non-null values
au23          30  non-null values
sn0           30  non-null values
sn1           30  non-null values
sn2           30  non-null values
sn3           30  non-null values
sn4           30  non-null values
sn5           30  non-null values
sn6           30  non-null values
sn7           30  non-null values
sn8           30  non-null values
sn9           30  non-null values
sn10          30  non-null values
sn11          30  non-null values
sn12          30  non-null values
sn13          30  non-null values
sn14          30  non-null values
sn15          30  non-null values
sn16          30  non-null values
sn17          30  non-null values
sn18          30  non-null values
sn19          30  non-null values
sn20          30  non-null values
sn21          30  non-null values
sn22          30  non-null values
sn23          30  non-null values
dtypes: float64(24), int64(50), object(2)
T                        89
X                        89
Y                        89
precip_quantity_1hour    89
pressure                 89
rel_humidity             89
temp                     89
temp_max                  0
temp_min                  0
wind_direction           89
wind_speed               89
BC_TRAF                  89
closest                  89
closest.m                89
AGGP.P50_ID              89
AGGP.FUNC_CLASS          89
AGGP.SPEED_CAT           89
LINK_ID                  89
FUNC_CLASS               89
SPEED_CAT                89
AR_AUTO                  89
AR_BUS                   89
AR_TAXIS                 89
AR_CARPOOL               89
AR_PEDEST                89
AR_TRUCKS                89
STCA20_PCT               89
VKC_LINKNR               89
TRVIC150R1               89
closest.m                89
closest.m.m              89
VKCP.LINK_ID             89
VKCP.FUNC_CLASS          89
VKCP.SPEED               89
VKCP.LINKNR              89
VKCP.TWIN_ID             89
VKCSKEY1                 89
0     (60744, 0)
1     (60744, 0)
2     (60744, 0)
3     (60750, 0)
4     (60768, 0)
5     (60768, 0)
6     (60758, 0)
7     (60758, 0)
8     (69223, 0)
9     (69223, 0)
10    (69223, 0)
11    (64265, 0)
12    (64265, 0)
13    (64265, 0)
14    (64265, 0)
15    (64265, 0)
16    (64265, 0)
17    (64265, 0)
18    (64265, 0)
19    (64265, 0)
20    (64216, 0)
21    (64216, 0)
22    (64216, 0)
23    (64216, 0)
24    (64216, 0)
25    (64216, 0)
26    (64216, 0)
27    (64216, 0)
28    (64216, 0)
29    (57085, 0)
30    (57085, 0)
31    (57085, 0)
32    (57085, 0)
33    (57085, 0)
34    (57085, 0)
35    (57014, 0)
36    (57033, 0)
37    (57033, 0)
38    (64065, 0)
39    (64065, 0)
40    (64065, 0)
41    (64065, 0)
42    (64065, 0)
43    (57070, 0)
44    (64062, 0)
45    (64062, 0)
46    (64062, 0)
47    (64062, 0)
48    (57070, 0)
49    (64061, 0)
50    (64061, 0)
51    (64061, 0)
52    (64061, 0)
53    (59849, 0)
54    (59415, 0)
55    (58487, 0)
56    (58054, 0)
57    (58054, 0)
58    (58054, 0)
59    (52551, 0)
60    (58054, 0)
61    (58054, 0)
62    (58054, 0)
63    (58054, 0)
64    (52551, 0)
65    (58054, 0)
66    (58488, 0)
67    (58488, 0)
68    (58028, 0)
69    (58464, 0)
70    (58028, 0)
71    (57989, 0)
72    (58595, 0)
73    (58027, 0)
74    (57989, 0)
75    (58595, 0)
76    (58595, 0)
77    (58019, 0)
78    (58595, 0)
79    (58595, 0)
80    (58019, 0)
81    (58595, 0)
82    (58595, 0)
83    (66715, 0)
84    (58595, 0)
85    (59295, 0)
86    (67614, 0)
87    (58314, 0)
88    (58314, 0)
Name: VKCSKEY1, Length: 89
VKCSKEY1
(60744, 0)    (60744, 0)
(60750, 0)    (60750, 0)
(60768, 0)    (60768, 0)
(60758, 0)    (60758, 0)
(69223, 0)    (69223, 0)
(64265, 0)    (64265, 0)
(64216, 0)    (64216, 0)
(57085, 0)    (57085, 0)
(57014, 0)    (57014, 0)
(57033, 0)    (57033, 0)
(64065, 0)    (64065, 0)
(57070, 0)    (57070, 0)
(64062, 0)    (64062, 0)
(64061, 0)    (64061, 0)
(59849, 0)    (59849, 0)
(59415, 0)    (59415, 0)
(58487, 0)    (58487, 0)
(58054, 0)    (58054, 0)
(52551, 0)    (52551, 0)
(58488, 0)    (58488, 0)
(58028, 0)    (58028, 0)
(58464, 0)    (58464, 0)
(57989, 0)    (57989, 0)
(58595, 0)    (58595, 0)
(58027, 0)    (58027, 0)
(58019, 0)    (58019, 0)
(66715, 0)    (66715, 0)
(59295, 0)    (59295, 0)
(67614, 0)    (67614, 0)
(58314, 0)    (58314, 0)
Name: traf_key
Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88], dtype=int64)
[]
Index([(60744, 0), (60750, 0), (60768, 0), (60758, 0), (69223, 0), (64265, 0), (64216, 0), (57085, 0), (57014, 0), (57033, 0), (64065, 0), (57070, 0), (64062, 0), (64061, 0), (59849, 0), (59415, 0), (58487, 0), (58054, 0), (52551, 0), (58488, 0), (58028, 0), (58464, 0), (57989, 0), (58595, 0), (58027, 0), (58019, 0), (66715, 0), (59295, 0), (67614, 0), (58314, 0)], dtype=object)
[]
(60744, 0) True
(60750, 0) True
(60768, 0) True
(60758, 0) True
(69223, 0) True
(64265, 0) True
(64216, 0) True
(57085, 0) True
(57014, 0) True
(57033, 0) True
(64065, 0) True
(57070, 0) True
(64062, 0) True
(64061, 0) True
(59849, 0) True
(59415, 0) True
(58487, 0) True
(58054, 0) True
(52551, 0) True
(58488, 0) True
(58028, 0) True
(58464, 0) True
(57989, 0) True
(58595, 0) True
(58027, 0) True
(58019, 0) True
(66715, 0) True
(59295, 0) True
(67614, 0) True
(58314, 0) True
Traceback (most recent call last):
  File "L:\temp\pandas_join_bug.py", line 43, in <module>
    right_on ='traf_key',
  File "C:\Python27\lib\site-packages\pandas\tools\merge.py", line 36, in merge
    return op.get_result()
  File "C:\Python27\lib\site-packages\pandas\tools\merge.py", line 185, in get_result
    ldata, rdata = self._get_merge_data()
  File "C:\Python27\lib\site-packages\pandas\tools\merge.py", line 277, in _get_merge_data
    copydata=False)
  File "C:\Python27\lib\site-packages\pandas\core\internals.py", line 1194, in _maybe_rename_join
    to_rename = self.items.intersection(other.items)
  File "C:\Python27\lib\site-packages\pandas\core\index.py", line 666, in intersection
    indexer = self.get_indexer(other.values)
  File "C:\Python27\lib\site-packages\pandas\core\index.py", line 812, in get_indexer
    raise Exception('Reindexing only valid with uniquely valued Index '
Exception: Reindexing only valid with uniquely valued Index objects

一旦发生错误，我就无法使任何merge或join语句成功。起初，我没有看到错误与重复的合并/连接操作有关。最新集合的任何单个合并/联接现在都可以工作。当我需要另一个合并/加入时，我得到了相同的错误

Tags： in true pandas df base get index merge

1条回答

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1楼 · 发布于 2024-10-02 20:43:48

重复的列名将导致此错误，请尝试消除重复的列名

Pandas加入/合并“仅对唯一值索引重新索引有效”

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