Skip to main content

Is it possible to merge two tables in Power Query Editor (Power BI) with Python fuzzy matching?

Merge two tables in power query editor (Power BI) based on string similarity with Python

Consider the tables bellow:

Table1 Table1 | Name | ... | | ------ | --- | | Apple Fruit A11 | ... | | Banana Fruit B12 | ... | | ... | ... |

Table2 Table2 | Name | Value | | ------ | ----- | | Apple A11R/T | 40 | | B4n4n4 Fruit B12_T | 50 | | Berry A11 | 60 | | ... | ... |

I want to get the Value from Table2 into Table1. But for some reason when I use the built-in power query editor merge with fuzzy matching. It will match Apple Fruit A11 with Berry A11 instead of Apple A11 R/T. I've read the documentation, and it says that the built-in function works best with single words. I tried to remove spaces both from Table1[Name] and Table2[Name] but it didn't improve results.

I looked around trying to find a solution, but wasn't able to figure out yet. Is there a way to do this using python? Or is there a simpler solution?

The results that I am expecting:

Table1 Expected Result | Name | ... | Table2.Name | Table2.Value | | ------ | --- |---| --- | | Apple Fruit A11 | ... |Apple A11R/T |40 | | Banana Fruit B12 | ... |B4n4n4 Fruit B12_T |50 | | ... | ... |... |... |

--- For some reason the tables are not showing up like the preview, that's why there are also images for each table.



source https://stackoverflow.com/questions/76384972/is-it-possible-to-merge-two-tables-in-power-query-editor-power-bi-with-python

Labels

Labels:

Comments

Post a Comment

Popular posts from this blog

ValueError: X has 10 features, but LinearRegression is expecting 1 features as input

So, I am trying to predict the model but its throwing error like it has 10 features but it expacts only 1. So I am confused can anyone help me with it? more importantly its not working for me when my friend runs it. It works perfectly fine dose anyone know the reason about it? cv = KFold(n_splits = 10) all_loss = [] for i in range(9): # 1st for loop over polynomial orders poly_order = i X_train = make_polynomial(x, poly_order) loss_at_order = [] # initiate a set to collect loss for CV for train_index, test_index in cv.split(X_train): print('TRAIN:', train_index, 'TEST:', test_index) X_train_cv, X_test_cv = X_train[train_index], X_test[test_index] t_train_cv, t_test_cv = t[train_index], t[test_index] reg.fit(X_train_cv, t_train_cv) loss_at_order.append(np.mean((t_test_cv - reg.predict(X_test_cv))**2)) # collect loss at fold all_loss.append(np.mean(loss_at_order)) # collect loss at order plt.plot(np.log(al...
Post a Comment
Read more

Sorting large arrays of big numeric stings

I was solving bigSorting() problem from hackerrank: Consider an array of numeric strings where each string is a positive number with anywhere from to digits. Sort the array's elements in non-decreasing, or ascending order of their integer values and return the sorted array. I know it works as follows: def bigSorting(unsorted): return sorted(unsorted, key=int) But I didnt guess this approach earlier. Initially I tried below: def bigSorting(unsorted): int_unsorted = [int(i) for i in unsorted] int_sorted = sorted(int_unsorted) return [str(i) for i in int_sorted] However, for some of the test cases, it was showing time limit exceeded. Why is it so? PS: I dont know exactly what those test cases were as hacker rank does not reveal all test cases. source https://stackoverflow.com/questions/73007397/sorting-large-arrays-of-big-numeric-stings
Post a Comment
Read more

How to load Javascript with imported modules?

I am trying to import modules from tensorflowjs, and below is my code. test.html <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <title>Document</title </head> <body> <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@2.0.0/dist/tf.min.js"></script> <script type="module" src="./test.js"></script> </body> </html> test.js import * as tf from "./node_modules/@tensorflow/tfjs"; import {loadGraphModel} from "./node_modules/@tensorflow/tfjs-converter"; const MODEL_URL = './model.json'; const model = await loadGraphModel(MODEL_URL); const cat = document.getElementById('cat'); model.execute(tf.browser.fromPixels(cat)); Besides, I run the server using python -m http.server in my command prompt(Windows 10), and this is the error prompt in the console log of my browser: Failed to loa...
Post a Comment
Read more