Skip to main content

Pandas - concatenating selected rows from several groups with additional condition

I have an example

+─────────────+────────+────────+────────+
| main_group  | COL_A  | COL_B  | COL_C  |
+─────────────+────────+────────+────────+
| 0           | TXT1   |        | None   |
| 0           | TXT2   |        | None   |
| 0           | 5      |        | None   |
| 0           | 1.93   | 1.93   | 0      |
| 0           | 7.60   | 7.60   | 1      |
| 0           | 2.46   | 2.46   | 1      |
| 1           | TXT11  |        | None   |
| 1           | TXT12  |        | None   |
| 1           | 0.50   |        | None   |
| 1           | 0.45   | 0.45   | 0      |
| 1           | 0.31   | 0.31   | 1      |
| 1           | 0.35   | 0.35   | 1      |
| 1           | 0.73   | 0.73   | 1      |
| 2           | 0.5    |        | None   |
| 2           | 4.15   | 4.15   | 0      |
| 2           | 2.98   | 2.98   | 0      |
| 2           | 1.53   | 1.53   | 0      |
| 3           | 4.46   |        | None   |
| 3           | 4.00   | 4.00   | 0      |
| 3           | 0.95   | 0.95   | 1      |
| 3           | 1.35   | 1.35   | 1      |
| 3           | 1.79   | 1.79   | 1      |
+─────────────+────────+────────+────────+

I would like to move the value from COL_A for the last occurrence of a row with None in column COL_C per main_group. This value should be moved to the first element in group for COL_B column and then the previous contents should be removed.

Here is how it should look like for main_group == 0:

+─────────────+────────+────────+────────+
| main_group  | COL_A  | COL_B  | COL_C  |
+─────────────+────────+────────+────────+
| 0           | TXT1   | 5      | None   | <--- value "5" from the last row with "None" in `COL_C` in `main_group` == 0 was moved to the first row in the same group
| 0           | TXT2   |        | None   |
| 0           | 5      |        | None   | <--- After that this row should be deleted
| 0           | 1.93   | 1.93   | 0      |
| 0           | 7.60   | 7.60   | 1      |
| 0           | 2.46   | 2.46   | 1      |
+─────────────+────────+────────+────────+

+─────────────+────────+────────+────────+
| main_group  | COL_A  | COL_B  | COL_C  |
+─────────────+────────+────────+────────+
| 2           | 0.5    | 0.5    | None   | <---  value in column `COL_B` should be same as in column `COL_A` because there are no other rows in the same `main_group` with "None" in column `COL_C`
| 2           | 4.15   | 4.15   | 0      |
| 2           | 2.98   | 2.98   | 0      |
| 2           | 1.53   | 1.53   | 0      |
| 3           | 4.46   | 4.46   | None   | <---  value in column `COL_B` should be same as in column `COL_A` because there are no other rows in the same `main_group` with "None" in column `COL_C`
| 3           | 4.00   | 4.00   | 0      |
| 3           | 0.95   | 0.95   | 1      |
| 3           | 1.35   | 1.35   | 1      |
| 3           | 1.79   | 1.79   | 1      |
+─────────────+────────+────────+────────+

After this operation, df should looks like:

+─────────────+────────+────────+────────+
| main_group  | COL_A  | COL_B  | COL_C  |
+─────────────+────────+────────+────────+
| 0           | TXT1   | 5      | None   |
| 0           | TXT2   |        | None   |
| 0           | 1.93   | 1.93   | 0      |
| 0           | 7.60   | 7.60   | 1      |
| 0           | 2.46   | 2.46   | 1      |
| 1           | TXT11  | 0.50   | None   |
| 1           | TXT12  |        | None   |
| 1           | 0.45   | 0.45   | 0      |
| 1           | 0.31   | 0.31   | 1      |
| 1           | 0.35   | 0.35   | 1      |
| 1           | 0.73   | 0.73   | 1      |
| 2           | 0.5    | 0.5    | None   |
| 2           | 4.15   | 4.15   | 0      |
| 2           | 2.98   | 2.98   | 0      |
| 2           | 1.53   | 1.53   | 0      |
| 3           | 4.46   | 4.46   | None   |
| 3           | 4.00   | 4.00   | 0      |
| 3           | 0.95   | 0.95   | 1      |
| 3           | 1.35   | 1.35   | 1      |
| 3           | 1.79   | 1.79   | 1      |
+─────────────+────────+────────+────────+

In the last step, I would like to concatenating selected COL_A column in each main_group where value in COL_C is None.

Example:

+─────────────+────────+────────+────────+
| main_group  | COL_A  | COL_B  | COL_C  |
+─────────────+────────+────────+────────+
| 0           | TXT1   | 5      | None   |
| 0           | TXT2   |        | None   |
| 0           | 1.93   | 1.93   | 0      |
| 0           | 7.60   | 7.60   | 1      |
| 0           | 2.46   | 2.46   | 1      |

↓↓↓↓↓↓↓↓↓↓

+─────────────+────────────+────────+────────+
| main_group  | COL_A      | COL_B  | COL_C  |
+─────────────+────────────+────────+────────+
| 0           | TXT1 TXT2  | 5      | None   | <--- If there are more than 1 row with "None" in column `COL_C` in each group, then values in column `COL_A` should be "merged" into one row, and all others should be deleted
| 0           | 1.93       | 1.93   | 0      |
| 0           | 7.60       | 7.60   | 1      |
| 0           | 2.46       | 2.46   | 1      |

The first solution I have is to .loc those rows in the group that have a value "None" in COL_C column and then assign to the first element (.iloc) value from the last row. However, this solution is not quite correct, and I'm additionally convinced that this can be done using .groupby instead of iterating and searching for elements after each group but I can't do it correctly.

The solution I was able to get this way:

+─────────────+────────────+────────+────────+
| main_group  | COL_A      | COL_B  | COL_C  |
+─────────────+────────────+────────+────────+
| 0           | TXT1 TXT2  | 5      | None   |
| NaN         | NaN        | NaN    | NaN    |
| NaN         | NaN        | NaN    | NaN    |
| 0           | 1.93       | 1.93   | 0      |
| 0           | 7.60       | 7.60   | 1      |
| 0           | 2.46       | 2.46   | 1      |

This is partially transferred correctly, but there are still NaN values in the rows that should not be there anymore. Of course I can delete those rows and re-index df but this solution relies on loops, which will certainly not be efficient for large df.

How can I do it with a workaround of these loops over individual groups and swapping values using .loc and .iloc?



source https://stackoverflow.com/questions/72368280/pandas-concatenating-selected-rows-from-several-groups-with-additional-conditi

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