👀 Views: 363 💬 Answers: 1 📅 Created: 2025-06-12

pandas datetime dataframe Python

I've been banging my head against this for hours... I'm relatively new to this, so bear with me. I'm trying to use `pd.cut` to categorize a series of timestamps from a DataFrame into specific bins, but it seems like the bins are not aligning correctly with the datetime index. I have a DataFrame with a datetime index and a corresponding value column, and I want to group these timestamps into hourly bins. My DataFrame looks like this: ```python import pandas as pd import numpy as np dates = pd.date_range('2023-01-01', periods=120, freq='15T') data = np.random.rand(len(dates)) df = pd.DataFrame(data, index=dates, columns=['value']) ``` Now, I am trying to use `pd.cut` to categorize the values into hourly bins like this: ```python bins = pd.date_range(start='2023-01-01', end='2023-01-01 23:59:59', freq='H') df['hour_bin'] = pd.cut(df.index, bins=bins, right=False) ``` However, I'm getting the following warning: ``` UserWarning: bin edges must be unique: 2023-01-01 00:00:00, 2023-01-01 00:00:00 ``` And when I check the resulting DataFrame, the `hour_bin` column contains `NaN` values for many of the entries: ```python print(df[['value', 'hour_bin']].head(10)) ``` It seems like the bins are not being recognized as unique, and it's causing issues with binning. I've tried adjusting the `right` parameter and using different boundaries for the bins, but the question continues. Is there something I'm missing in how `pd.cut` interacts with a datetime index, or is there a better approach to achieve this categorization? Any insights would be greatly appreciated! I'm working on a service that needs to handle this. Any help would be greatly appreciated! For context: I'm using Python on macOS.