Monthly Archives: May 2017

Creating a time series from existing data in pandas

9 May 2017

pandas is a high-performance library for data analysis in Python. It’s generally excellent, but if you’re a beginner or you use it rarely, it can be tricky to find out how to do quite simple things — the code to do what you want is likely to be very clear once you work it out, but working it out can be relatively hard.

A case in point, which I’m posting here largely so that I can find it again next time I need to do the same thing… I had a list start_times of dictionaries, each of which had (amongst other properties) a timestamp and a value. I wanted to create a pandas time series object to represent those values.

The code to do that is this:

import pandas as pd
series = pd.Series([cs["value"] for cs in start_times], index=pd.DatetimeIndex([cs["timestamp"] for cs in start_times]))

Perfectly clear once you see it, but it did take upwards of 40 Google searches and help from two colleagues with a reasonable amount of pandas experience to work out what it should be.