将DataFrame索引转为Datetime并获取第二列最大值对应时间及相关问题

Hey there! Let's walk through your two questions with clear, actionable steps using your sample data.

1. How to Convert Index to Datetime While Keeping Rest of the DataFrame #

Your current code pq = pq.index.to_datetime() only creates a standalone DatetimeIndex object—it doesn’t update your original DataFrame’s index or preserve the other columns. To fix this, you just need to assign the converted DatetimeIndex back to your DataFrame’s index:

import pandas as pd

# Your sample DataFrame
pq = pd.DataFrame([203, 250, 318, 786, 321, 135], columns=['metric'])

# Convert index to DatetimeIndex and update the original DataFrame
pq.index = pd.to_datetime(pq.index, unit='ns')

Why this works: #

• pd.to_datetime(pq.index, unit='ns') converts your integer index (0,1,2...) to a DatetimeIndex using nanoseconds as the unit (which matches the 1970-01-01 timestamp output you saw earlier).
• Assigning this back to pq.index modifies the original DataFrame’s index while keeping all your existing columns completely intact.

2. Equivalent Method for pd.DataFrame.id... (Finding Max Value's Index) #

I’m guessing you’re referring to the method that finds the index corresponding to the maximum value in a column—that’s idxmax()! Since you want the datetime index for the maximum value in the second column, here’s how to do it:

# Get the datetime index where the second column (named 'metric' here) has its maximum value
max_value_datetime = pq['metric'].idxmax()

# If you prefer referencing columns by position instead of name:
max_value_datetime = pq.iloc[:, 1].idxmax()

Example Output: #

For your sample data, the maximum value is 786 at index 3, so max_value_datetime will return:
Timestamp('1970-01-01 00:00:00.000000003')

内容的提问来源于stack exchange，提问作者Artur Müller Romanov