'multithreading for data from dataframe pandas

I'm struggling to use multithreading for calculating relatedness between list of customers who have different shopping items on their baskets. So I have a pandas data frame consists of 1,000 customers, which means that I have to calculate the relatedness 1 million times and this takes too long to process

An example of the data frame looks like this:

  ID     Item       
    1    Banana    
    1    Apple     
    2    Orange    
    2    Banana    
    2    Tomato    
    3    Apple     
    3    Tomato    
    3    Orange

Here is the simplefied version of the code:

import pandas as pd

def relatedness (customer1, customer2):
    # do some calculations to measure the relation between the customers

data= pd.read_csv(data_file)
customers_list= list (set(data['ID']))

relatedness_matrix = pd.DataFrame(index=[customers_list], columns=[customers_list])
for i in customers_list:
    for j in customer_list:
        relatedness_matrix.loc[i,j] = relatedness (i,j)
pythonmultithreadingdataframebigdata


Solution 1:[1]

Was looking for same problem about having heavy calculations using pandas DataFrame and found

DASK http://dask.pydata.org/en/latest/

(from this SO https://datascience.stackexchange.com/questions/172/is-there-a-straightforward-way-to-run-pandas-dataframe-isin-in-parallel)

Hope this helps

Solution 2:[2]

Check out Modin: "Modin provides seamless integration and compatibility with existing pandas code. Even using the DataFrame constructor is identical." https://modin.readthedocs.io/en/latest/

Sources

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Source: Stack Overflow

Solution Source
Solution 1 Community
Solution 2 CyberPlayerOne

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