I'm trying to generate 50 random samples of 30 continuous day periods from a list of corn prices (which is index by date).
So far I've got 'select 50 random days' on line one. For the second line, what I really want is an array of dataframes, each one containing 30 days from sample date. Currently it just returns the price on that day.
samples=np.random.choice(corn[:'1981'].index,50)
corn['Open'][samples] #line I need to fix
What's the cleanest way of doing that?
You could use
corn.loc[date:date+pd.Timedelta(days=29)]
to select 30 days worth of rows starting from date date. Note that .loc[start:end] includes both start and end (unlike Python slices, which use half-open intervals). Thus adding 29 days to date results in a DataFrame of length 30.
To get an list of DataFrames, use a list comprehension:
dfs = [corn.loc[date:date+pd.Timedelta(days=29)] for date in samples]
import numpy as np
import pandas as pd
N = 365
corn = pd.DataFrame({'Open': np.random.random(N)},
index=pd.date_range('1980-1-1', periods=N))
samples = np.random.choice(corn[:'1981'].index,50)
dfs = [corn.loc[date:date+pd.Timedelta(days=29)] for date in samples]
cornbefore hand?zip(samples, corn["Open"][samples])be what you are looking for?samplemethod to get random samples, I find it more elegant than the numpy method.