👀 Views: 82 💬 Answers: 1 📅 Created: 2025-06-06

pandas dataframe time-series Python

I've been struggling with this for a few days now and could really use some help. I'm having trouble with resampling a time series DataFrame in Pandas where I'm trying to aggregate multiple columns but getting unexpected results. I'm using Pandas version 1.3.5. My DataFrame has a DateTime index with hourly frequency data and I want to resample it to daily frequency while calculating the mean of one column and the sum of another. Here's a snippet of my DataFrame: ```python import pandas as pd import numpy as np dates = pd.date_range(start='2021-01-01', periods=24, freq='H') data = { 'temperature': np.random.rand(24) * 30, 'humidity': np.random.rand(24) * 100 } df = pd.DataFrame(data, index=dates) print(df) ``` I expected to get a daily DataFrame with the average temperature and total humidity for each day. However, when I use the following resampling code: ```python result = df.resample('D').agg({'temperature': 'mean', 'humidity': 'sum'}) print(result) ``` The output seems inconsistent; for instance, on some days, the mean temperature appears unusually high or low, and the sum of humidity seems to exceed what I would expect based on the hourly data. I've double-checked the data types and ensured there are no missing values in the DateTime index. I'm also aware that resampling operations can result in some nuanced behavior, especially with mixed aggregation functions. What could be causing these discrepancies? Are there any best practices or configurations I should be aware of when performing such operations with Pandas? Any insights would be greatly appreciated! My development environment is Linux. Any ideas what could be causing this?