Create hash value for each row of data with selected columns in dataframe in python pandas

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I have asked similar question in 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?

update 01

I have drafted my code as below:

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?

Or simply:

df.apply(lambda x: hash(tuple(x)), axis = 1)

As an example:

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

Hashing on Pandas DataFrame More Effectively, >10x faster way to hash column(s) than for loop We use hashing to protect sensitive data in multiple ways e.g. when want get Source DataFrame as sourcedf; concatenate value of column column name is start with Hash_ combine with all columns in column Python + Pandas + Google SpreadSheet. One example is if you want to execute some code using the values of each row as input. Also, if your dataframe is reasonably small (e.g. less than 1000 items), performance is not really an issue. – oulenz Oct 16 '19 at 8:53

Create hash value for each row of data with selected columns in dataframe in python pandas

These solutions work for the life of the Python process.

If order matters, one method would be to coerce the row (a Series object) to a tuple:

>>> hash(tuple(df.irow(1)))

This demonstrates order matters for tuple hashing:

>>> hash((1,2,3))
>>> hash((3,2,1))

To do so for every row, appended as a column would look like this:

>>> 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:

>>> hash(frozenset((3,2,1)))
>>> hash(frozenset((1,2,3)))

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.

(You could use modulo to constrain the range, but this amounts to rolling your own hash function, and the best practice is not to.)

You can make permanent cryptographic quality hashes, for example using sha256, as well using the hashlib module.

There is some discussion of the API for cryptographic hash functions in PEP 452.

Thanks to users Jamie Marshal and Discrete Lizard for their comments.

Hashing Pandas DataFrame Column with Nonce, Your dataset can commonly contain sensitive data in one or more columns. Here I share how to create a new column containing hashed strings… The same clear text would generate the same hash value from the same Select only few columns as d1 . Spark | Scala | Python | Pandas for Beginners� Step 3: Get the Average for each Column and Row in Pandas DataFrame. You can then apply the following syntax to get the average for each column:. df.mean(axis=0) For our example, this is the complete Python code to get the average commission earned for each employee over the 6 first months (average by column):

This is now available in pandas.util.hash_pandas_object:


Apply function to every row in a Pandas DataFrame, Python is a great language for performing data analysis tasks. It provides with a huge amount of Classes and function which help in analyzing� Pandas: Find maximum values & position in columns or rows of a Dataframe; Pandas Dataframe: Get minimum values in rows or columns & their index position; Python: Add column to dataframe in Pandas ( based on other column or list or default value) Python Pandas : How to drop rows in DataFrame by index labels

I've came up with this adaption from the code provided on the question:

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))

General functions — pandas 1.1.0 documentation, Return reshaped DataFrame organized by given index / column values. pivot_table (data[, values, index, columns, …]) Create a spreadsheet-style pivot table as a DataFrame. crosstab (index, columns[ Evaluate a Python expression as a string using various backends. Return a data hash of the Index/Series/ DataFrame. Note also that row with index 1 is the second row. Row with index 2 is the third row and so on. If you’re wondering, the first row of the dataframe has an index of 0. That’s just how indexing works in Python and pandas. Extracting a single cell from a pandas dataframe ¶ df2.loc["California","2013"]

df.set_index(pd.util.hash_pandas_object(df), drop=False, inplace=True)

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pandas.melt — pandas 1.1.0 documentation, pandas.eval � pandas.util.hash_array � pandas.util.hash_pandas_object � pandas.test This function is useful to massage a DataFrame into a format where one or more Create a spreadsheet-style pivot table as a DataFrame. DataFrame.pivot. Return reshaped DataFrame organized by given index / column values. Original Dataframe a b c 0 222 34 23 1 333 31 11 2 444 16 21 3 555 32 22 4 666 33 27 5 777 35 11 ***** Apply a lambda function to each row or each column in Dataframe ***** *** Apply a lambda function to each column in Dataframe *** Modified Dataframe by applying lambda function on each column: a b c 0 232 44 33 1 343 41 21 2 454 26 31 3 565 42

pandas.wide_to_long — pandas 1.1.0 documentation, Each row of these wide variables are assumed to be uniquely identified by i (can be a single column All remaining variables in the data frame are left intact. Here 5 is the number of rows and 3 is the number of columns. Pandas Count Values for each Column. We will use dataframe count() function to count the number of Non Null values in the dataframe. We will select axis =0 to count the values in each Column

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