implementing Conditional Column Creation in Pandas Based on Multiple Criteria
I'm stuck trying to I keep running into I'm refactoring my project and I've been struggling with this for a few days now and could really use some help. I'm trying to create a new column in my DataFrame based on multiple conditions from existing columns, but I'm running into trouble with the logic. I'm using Pandas 1.4.0 and I have a DataFrame that looks like this: ```python import pandas as pd data = { 'A': [1, 2, 3, 4, 5], 'B': [10, 20, 30, 40, 50], 'C': ['low', 'medium', 'high', 'medium', 'low'] } df = pd.DataFrame(data) ``` I want to create a new column `D` that assigns values based on the following logic: - If `A` is greater than 3 and `B` is less than 30, then `D` should be `'Type 1'` - If `C` is `'high'`, then `D` should be `'Type 2'` - Otherwise, `D` should be `'Type 3'` I attempted to use `np.where()` for this condition, but I ended up with unexpected results: ```python import numpy as np df['D'] = np.where((df['A'] > 3) & (df['B'] < 30), 'Type 1', np.where(df['C'] == 'high', 'Type 2', 'Type 3')) ``` While this code runs without errors, the logic to combine the conditions doesn’t seem to work as expected. The values in column `D` are not what I anticipated. I suspect the order of operations is affecting the output, but I'm not sure how to structure the conditions properly. Can anyone provide insight on how to achieve the desired logic correctly? Any help or suggestions for alternative approaches would be greatly appreciated! For context: I'm using Python on Linux. Any feedback is welcome! Has anyone dealt with something similar? This is my first time working with Python latest.