Efficient Re-indexing of Pandas Timestamp Dataframe

I have ~36 pandas dataframes with the same column names, each representing a different experiment. The experiments were performed at different times, and the index of the pandas dataframes is the timestamp.

I want to plot each of the 36 experiments on one plot - in order to do so I need to make each of the 36 dataframes' indices start at the same point. I did this with the following:

import pandas

dfs_indexed = []

#dfs is a list of the 36 dataframes with timestamp indices
for i in range(len(dfs)):
     dfs_indexed[i] = dfs[i].copy()
     dfs_indexed[i].reset_index(inplace=True)

Doing this, I was able to get my intended output: enter image description here Is there a better way to do this without making a copy of each dataframe? Also, this works for my data because the sampling rate is the same between each experiment. If the sampling rate was different, would there be a method to maintain the true temporal relationships in the re-indexed data?

Open to any approaches for this problem, thank you!

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