如何在Pandas DataFrame中按日期计算uv_index最大值并新增uv_max列

Here's a straightforward, efficient way to create the uv_max column you need, using Pandas' built-in grouping and transformation tools:

Step 1: Ensure your datetime column is properly formatted #

First, make sure your date time column is parsed as a datetime type (this is critical for grouping by date later). If it's currently stored as a string, convert it:

import pandas as pd

# Convert the datetime column if it's not already a datetime type
df['date time'] = pd.to_datetime(df['date time'])

Step 2: Calculate and assign the daily maximum values #

Use groupby combined with transform to compute the daily UV index maximum and broadcast it to every row in your DataFrame:

# Group rows by their calendar date, compute max uv_index, and assign to uv_max
df['uv_max'] = df.groupby(df['date time'].dt.date)['uv_index'].transform('max')

What this does #

• df['date time'].dt.date extracts just the calendar date (e.g., 2016-01-01) from the full timestamp, letting us group all hourly entries for the same day together.
• transform('max') calculates the highest uv_index value for each daily group, then copies that maximum value to every row in the original group. This ensures every entry gets the correct daily maximum in the new uv_max column.

After running this code, print(df.head(24)) will produce exactly the output you expected—all rows for 2016-01-01 will have uv_max set to 2.0, matching the peak UV index from that day.

内容的提问来源于stack exchange，提问作者CG7