pandas 在python pandas的数据框中为具有选定列的每行数据创建哈希值
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Create hash value for each row of data with selected columns in dataframe in python pandas
提问by lokheart
I have asked similar questionin R about creating hash value for each row of data. I know that I can use something like hashlib.md5(b'Hello World').hexdigest()to hash a string, but how about a row in a dataframe?
我在 R 中问过关于为每行数据创建哈希值的类似问题。我知道我可以使用诸如hashlib.md5(b'Hello World').hexdigest()散列字符串之类的东西，但是数据帧中的一行呢？
update 01
更新 01
I have drafted my code as below:
我已经起草了我的代码如下：
for index, row in course_staff_df.iterrows():
temp_df.loc[index,'hash'] = hashlib.md5(str(row[['cola','colb']].values)).hexdigest()
It seems not very pythonic to me, any better solution?
对我来说似乎不是很pythonic，有什么更好的解决方案吗？
回答by cwharland
Or simply:
或者干脆：
df.apply(lambda x: hash(tuple(x)), axis = 1)
As an example:
举个例子：
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.rand(3,5))
print df
df.apply(lambda x: hash(tuple(x)), axis = 1)
0 1 2 3 4
0 0.728046 0.542013 0.672425 0.374253 0.718211
1 0.875581 0.512513 0.826147 0.748880 0.835621
2 0.451142 0.178005 0.002384 0.060760 0.098650
0 5024405147753823273
1 -798936807792898628
2 -8745618293760919309
回答by Aaron Hall
Create hash value for each row of data with selected columns in dataframe in python pandas
在python pandas的数据框中为具有选定列的每行数据创建哈希值
These solutions work for the life of the Python process.
这些解决方案适用于 Python 进程的整个生命周期。
If order matters, one method would be to coerce the row (a Series object) to a tuple:
如果顺序很重要，一种方法是将行（一个 Series 对象）强制转换为元组：
>>> hash(tuple(df.irow(1)))
-4901655572611365671
This demonstrates order matters for tuple hashing:
这演示了元组散列的顺序问题：
>>> hash((1,2,3))
2528502973977326415
>>> hash((3,2,1))
5050909583595644743
To do so for every row, appended as a column would look like this:
要对每一行执行此操作，附加为列将如下所示：
>>> df = df.drop('hash', 1) # lose the old hash
>>> df['hash'] = pd.Series((hash(tuple(row)) for _, row in df.iterrows()))
>>> df
y x0 hash
0 11.624345 10 -7519341396217622291
1 10.388244 11 -6224388738743104050
2 11.471828 12 -4278475798199948732
3 11.927031 13 -1086800262788974363
4 14.865408 14 4065918964297112768
5 12.698461 15 8870116070367064431
6 17.744812 16 -2001582243795030948
7 16.238793 17 4683560048732242225
8 18.319039 18 -4288960467160144170
9 18.750630 19 7149535252257157079
[10 rows x 3 columns]
If order does not matter, use the hash of frozensets instead of tuples:
如果顺序无关紧要，请使用frozensets 的散列而不是元组：
>>> hash(frozenset((3,2,1)))
-272375401224217160
>>> hash(frozenset((1,2,3)))
-272375401224217160
Avoid summing the hashes of all of the elements in the row, as this could be cryptographically insecure and lead to hashes that fall outside the range of the original.
避免对行中所有元素的散列求和，因为这可能在密码学上不安全并导致散列超出原始范围。
(You could use modulo to constrain the range, but this amounts to rolling your own hash function, and the best practice is notto.)
（您可以使用模数来限制范围，但这相当于滚动您自己的哈希函数，而最佳做法是不要这样做。）
You can make permanent cryptographic quality hashes, for example using sha256, as well using the hashlibmodule.
您可以永久的使用密码散列质量，例如使用SHA256，以及使用该hashlib模块。
There is some discussion of the API for cryptographic hash functions in PEP 452.
PEP 452 中有一些关于加密散列函数的 API 的讨论。
Thanks to users Jamie Marshal and Discrete Lizard for their comments.
感谢用户 Jamie Marshal 和 Discrete Lizard 的评论。
回答by Neal Fultz
This is now available in pandas.util.hash_pandas_object:
现在可以在pandas.util.hash_pandas_object：
pandas.util.hash_pandas_object(df)
回答by Wesley Batista
I've came up with this adaption from the code provided on the question:
我从问题提供的代码中提出了这种改编：
new_df2 = df.copy()
key_combination = ['col1', 'col2', 'col3', 'col4']
new_df2.index = list(map(lambda x: hashlib.sha1('-'.join([col_value for col_value in x]).encode('utf-8')).hexdigest(), new_df2[key_combination].values))
