Python - how to normalize time-series data

The solutions given are good for a series that aren’t incremental nor decremental(stationary). In financial time series( or any other series with a a bias) the formula given is not right. It should, first be detrended or perform a scaling based in the latest 100-200 samples.
And if the time series doesn't come from a normal distribution ( as is the case in finance) there is advisable to apply a non linear function ( a standard CDF funtion for example) to compress the outliers.
Aronson and Masters book (Statistically sound Machine Learning for algorithmic trading) uses the following formula ( on 200 day chunks ):

V = 100 * N ( 0.5( X -F50)/(F75-F25)) -50

Where:
X : data point
F50 : mean of the latest 200 points
F75 : percentile 75
F25 : Percentile 25
N : normal CDF

Assuming that your timeseries is an array, try something like this:

(timeseries-timeseries.min())/(timeseries.max()-timeseries.min())

This will confine your values between 0 and 1