Find a string from a column of one table in another table

[visedwings049]

I am using Python 3.9

I have created two pandas data frames from csv files
product and supplier


1. I have created a product table that splits a description out into multiple columns.

2. I have created a supplier table that has the supplier product. Many times a supplier product code is in the description of a product.

3. I want to populate the product.supplier code column with any string that is contained in the supplier.product column. In this example we would have found the code widget in column 4 in the supplier table and returned the word widget in the supplier code table.

4. So i want to do this on a loop for column 0 then move on to column 1 and so on.

There will never be two examples of a supplier code in the same string, so i am not worried about overwriting a first instance with a second.

I have tried the str.contains function but this just returns true or false.

[deanhystad]

What is a table? Is it a spreadsheet? Is it a table in a PDF? Is it a CSV file? Is it a pandas DataFrame, Is it a table in a database?

[visedwings049]

I am using Python 3.9

1. I have created a product table that splits a description out into multiple columns.

2. I have created a supplier table that has the supplier product. Many times a supplier product code is in the description of a product.

3. I want to populate the product.supplier code column with any string that is contained in the supplier.product column. In this example we would have found the code widget in column 4 in the supplier table and returned the word widget in the supplier code table.

4. So i want to do this on a loop for column 0 then move on to column 1 and so on.

There will never be two examples of a supplier code in the same string, so i am not worried about overwriting a first instance with a second.

I have tried the str.contains function but this just returns true or false.

I apologize as this is my first post but they are both being read as pandas dataframes from CSV.

[deanhystad]

Something like this maybe?

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

import pandas as pd from string import ascii_letters as letters from random import choice, choices, randint     def find_supplier(description):     """Return word if word in description matches a supplier code, else None."""     intersection = set(description.split()) & suppliers     return list(intersection)[0] if intersection else None     # Make some random table thing that we can use to search for words in the description # that match a supplier code. product_table = pd.DataFrame(     [         {             "Product": i,             "Supplier Code": choice("ABCDE"),             "Description": " ".join(choices(letters, k=randint(5, 10))),         }         for i in range(100, 120)     ] )   # Get set of suppliers. suppliers = set(product_table["Supplier Code"].values)   # Make supplier table.  Supplier table contains rows from product_table # where one of the words in the description matches a supplier code. supplier_table = product_table[["Description"]] supplier_table["Product"] = supplier_table["Description"].map(find_supplier) supplier_table = supplier_table[~supplier_table["Product"].isna()][     ["Product", "Description"] ] print(supplier_table)

Output:
   Product          Description
1        E    T u V x Z E a k s
2        A  K H a K P G z m l A
3        E          Q L H E q J
5        B  N X x b i B q D F M
8        D      d U q K Y W I D
10       C    U V H C f F n N z
14       C            C o X u J
15       E          D F e E Q u
18       B          o f B P x O

This is easy to break up into individual supplier tables.

1 2 3 4 5 6 7

for supplier in suppliers:     print(         supplier,         supplier_table[supplier_table["Product"] == supplier].reset_index(drop=True),         sep="\n",         end="\n\n",     )

Output:
A
  Product        Description
0       A    d j A c S o F U
1       A          o A w I W
2       A          z s e j A
3       A  c P R w Z M A V b

D
  Product          Description
0       D  t u P r p R v G D O
1       D    j P w D h v o m w

C
  Product    Description
0       C  n j r C r R T

B
  Product        Description
0       B  H O P B A c r C n
1       B          B g Z r z
2       B    r o y g u l B A

E
  Product          Description
0       E            P E m t Z
1       E  S E Y m F a K h Z T

[visedwings049]

Wow that is an amazing way to do it. This is very helpful i am going to play with this code and see if i can duplicate it with my data set. Thank you so much for this example i had not done anything with defining a function prior to this and that is extremely cool.

[visedwings049]

I am using Python 3.9

1. I have created a product table that splits a description out into multiple columns.

2. I have created a supplier table that has the supplier product. Many times a supplier product code is in the description of a product.

3. I want to populate the product.supplier code column with any string that is contained in the supplier.product column. In this example we would have found the code widget in column 4 in the supplier table and returned the word widget in the supplier code table.

4. So i want to do this on a loop for column 0 then move on to column 1 and so on.

There will never be two examples of a supplier code in the same string, so i am not worried about overwriting a first instance with a second.

I have tried the str.contains function but this just returns true or false.

I apologize as this is my first post but they are both being read as pandas dataframes from CSV.

Worked beautifully and its way faster than sending everything to a different column. Had no idea you could do this in python. I was able to compare thousands of rows of data in less than 30 seconds. Thanks Again!!

[visedwings049]

Something like this maybe?

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

import pandas as pd from string import ascii_letters as letters from random import choice, choices, randint     def find_supplier(description):     """Return word if word in description matches a supplier code, else None."""     intersection = set(description.split()) & suppliers     return list(intersection)[0] if intersection else None     # Make some random table thing that we can use to search for words in the description # that match a supplier code. product_table = pd.DataFrame(     [         {             "Product": i,             "Supplier Code": choice("ABCDE"),             "Description": " ".join(choices(letters, k=randint(5, 10))),         }         for i in range(100, 120)     ] )   # Get set of suppliers. suppliers = set(product_table["Supplier Code"].values)   # Make supplier table.  Supplier table contains rows from product_table # where one of the words in the description matches a supplier code. supplier_table = product_table[["Description"]] supplier_table["Product"] = supplier_table["Description"].map(find_supplier) supplier_table = supplier_table[~supplier_table["Product"].isna()][     ["Product", "Description"] ] print(supplier_table)

Output:
   Product          Description
1        E    T u V x Z E a k s
2        A  K H a K P G z m l A
3        E          Q L H E q J
5        B  N X x b i B q D F M
8        D      d U q K Y W I D
10       C    U V H C f F n N z
14       C            C o X u J
15       E          D F e E Q u
18       B          o f B P x O

This is easy to break up into individual supplier tables.

1 2 3 4 5 6 7

for supplier in suppliers:     print(         supplier,         supplier_table[supplier_table["Product"] == supplier].reset_index(drop=True),         sep="\n",         end="\n\n",     )

Output:
A
  Product        Description
0       A    d j A c S o F U
1       A          o A w I W
2       A          z s e j A
3       A  c P R w Z M A V b

D
  Product          Description
0       D  t u P r p R v G D O
1       D    j P w D h v o m w

C
  Product    Description
0       C  n j r C r R T

B
  Product        Description
0       B  H O P B A c r C n
1       B          B g Z r z
2       B    r o y g u l B A

E
  Product          Description
0       E            P E m t Z
1       E  S E Y m F a K h Z T

Worked beautifully and its way faster than sending everything to a different column. Had no idea you could do this in python. I was able to compare thousands of rows of data in less than 30 seconds. Thanks Again!!

[deanhystad]

Thousands of rows in 30 seconds is really slow. I modified my code to process 100,000 products and it did that in 0.1 seconds. So I doubled the number of suppliers, and that only increased the time about about 10% (0.11 seconds). Next I tripled the length of the description, and that doubled the time (0.22 seconds).

What could be making your program run so slow?

[deanhystad]

I was getting a warning that I ignored in my code. It doesn't cause any problems in my example, but it has potential for causing very confusing behaviors.

In my example I did this to make a new dataframe for suppliers:

1	supplier_table = product_table[["Description"]]

This does not create a new dataframe. It creates a slice of the product_table dataframe. What I should have done is make a copy of that slice so that supplier_table and product_table are independent.

1	supplier_table = product_table[["Description"]].copy()