👀 Views: 3 💬 Answers: 1 📅 Created: 2025-06-10

numpy pandas dataframe Python

I'm getting frustrated with This might be a silly question, but After trying multiple solutions online, I still can't figure this out. I am trying to convert a 3D NumPy array to a Pandas DataFrame, but I'm running into issues with column names being generated unexpectedly... I have a NumPy array of shape (4, 3, 2), and when I attempt to reshape it and convert it into a DataFrame, the columns seem to be labeled as tuples instead of simple integers, which makes it harder to manipulate later. Here’s the code I'm using: ```python import numpy as np import pandas as pd # Sample 3D array array_3d = np.random.random((4, 3, 2)) # Reshaping the array to 2D array_reshaped = array_3d.reshape(-1, array_3d.shape[-1]) # Converting to DataFrame # This is where the issue arises df = pd.DataFrame(array_reshaped) print(df.columns) ``` The output of `print(df.columns)` is: ``` RangeIndex(start=0, stop=2, step=1) ``` I expected it to provide a clearer labeling for the columns. I have also tried assigning names manually like so: ```python df.columns = ['Feature1', 'Feature2'] ``` However, I am not sure if this is the best practice for handling this situation. Is there a more effective way to ensure my DataFrame columns are labeled in a way that corresponds to the multi-dimensional structure of the original NumPy array? Additionally, I’ve read that using the `pd.Panel` was recommended in the past for this kind of structure, but I see it's now deprecated. Is it better to stick with DataFrames and reshape accordingly, or am I missing a more suitable approach? I’m using NumPy version 1.21.2 and Pandas version 1.3.3. Any insights would be greatly appreciated! My development environment is Ubuntu. Any help would be greatly appreciated! For context: I'm using Python on macOS. What's the best practice here? This is part of a larger API I'm building. What's the best practice here? I'm working in a macOS environment. Has anyone else encountered this? For reference, this is a production web app. Is there a better approach?