pandas Python熊猫插入长整数
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Python pandas insert long integer
提问by Tom
I'm trying to insert long integers in a Pandas Dataframe
我正在尝试在 Pandas Dataframe 中插入长整数
import numpy as np
from pandas import DataFrame
data_scores = [(6311132704823138710, 273), (2685045978526272070, 23), (8921811264899370420, 45), (17019687244989530680L, 270), (9930107427299601010L, 273)]
dtype = [('uid', 'u8'), ('score', 'u8')]
data = np.zeros((len(data_scores),),dtype=dtype)
data[:] = data_scores
df_crawls = DataFrame(data)
print df_crawls.head()
But when I look in the dataframe, last values which are long are now negative :
但是当我查看数据帧时，最后一个很长的值现在是负数：
uid score 0 6311132704823138710 273 1 2685045978526272070 23 2 8921811264899370420 45 3 -1427056828720020936 270 4 -8516636646409950606 273
uids are 64 bits unsigned int, so 'u8' should be the correct dtype ? Any ideas ?
uid 是 64 位无符号整数，所以 'u8' 应该是正确的 dtype 吗？有任何想法吗 ？
回答by Wes McKinney
Yes-- it's a present limitation of pandas-- we do plan to add support for unsigned integer dtypes in the future. An error message would be much better:
是的——这是 Pandas 目前的限制——我们确实计划在未来添加对无符号整数 dtypes 的支持。错误信息会好得多：
http://github.com/pydata/pandas/issues/2355
http://github.com/pydata/pandas/issues/2355
For now you can make the column dtype=objectas a workaround.
现在，您可以将该列dtype=object作为一种解决方法。
EDIT 2012-11-27
编辑 2012-11-27
Detecting overflows now, though will become dtype=object for now until DataFrame has better support for unsigned data types.
现在检测溢出，但现在将变为 dtype=object，直到 DataFrame 更好地支持无符号数据类型。
In [3]: df_crawls
Out[3]:
uid score
0 6311132704823138710 273
1 2685045978526272070 23
2 8921811264899370420 45
3 17019687244989530680 270
4 9930107427299601010 273
In [4]: df_crawls.dtypes
Out[4]:
uid object
score int64
回答by deinonychusaur
This won't tell you what to do, except try on a 64-bit computer or contact pandas developers (or patch the problem yourself...). But at any rate, this seems to be your problem:
这不会告诉你该怎么做，除非在 64 位计算机上尝试或联系 Pandas 开发人员（或自己修补问题......）。但无论如何，这似乎是你的问题：
The problem is that DataFramedoes not understand unsigned int 64 bit, at least on a 32-bit machine.
问题是DataFrame不理解 unsigned int 64 位，至少在 32 位机器上是这样。
I changed the values of your data_scoreto better be able to track what was happening:
我更改了您的值data_score以更好地跟踪正在发生的事情：
data_scores = [(2**31 + 1, 273), (2 ** 31 - 1, 23), (2 ** 32 + 1, 45), (2 ** 63 - 1, 270), (2 ** 63 + 1, 273)]
Then I tried:
然后我尝试：
In [92]: data.dtype
Out[92]: dtype([('uid', '<u8'), ('score', '<u8')])
In [93]: data
Out[93]:
array([(2147483649L, 273L), (2147483647L, 23L), (4294967297L, 45L),
(9223372036854775807L, 270L), (9223372036854775809L, 273L)],
dtype=[('uid', '<u8'), ('score', '<u8')])
In [94]: df = DataFrame(data, dtype='uint64')
In [95]: df.values
Out[95]:
array([[2147483649, 273],
[2147483647, 23],
[4294967297, 45],
[9223372036854775807, 270],
[-9223372036854775807, 273]], dtype=int64)
Notice how the dtypeof DataFramedoesn't match the one requested in row 94. Also as I wrote in the comment above, the numpy array works perfectly. Further, if you specify uint32in row 94 it still specifies a dtypeof int64for the DataFramevalues. However it doesn't give you negative overflows, probably because uint32fits inside the positive values of the int64.
请注意dtypeof如何与DataFrame第 94 行中请求的不匹配。另外，正如我在上面的评论中所写的那样，numpy 数组工作得很好。此外，如果您uint32在第 94 行中指定，它仍会为值指定一个dtypeof 。但是它不会给你负溢出，可能是因为适合.int64DataFrameuint32int64
