pandas dataframe window using date index

The following results in the requested output (pandas 0.22.0, python 3.6.7):

import pandas as pd
from datetime import timedelta

# Width of the time window: 5min
dt = timedelta(minutes=5)
# Step of the sliding window: 1min
step = timedelta(minutes=1)

start = df.index[0]
stop = df.index[-1]
while start <= (stop-dt+step):
    idx = (start <= df.index) & (df.index < start+dt)
    start += step
    print(df[idx])
    print()

One can specify two parameters: the width dt of the time window and the step by which to move forward the "sliding window".

An advantage of this approach is that one operates with indices only, avoiding unnecessary copies of overlapping data (though I bet that python/pandas make a good job in avoiding this as much as possible, in case someone finds an alternative way to accomplish the job).

I tested with the following dataframe:

df = pd.DataFrame([["2018-11-20 18:45:00",  176.73,  176.95,  176.54,  176.89,  176.582983],
                   ["2018-11-20 18:46:00",  176.89,  177.02,  176.81,  176.81,  176.603020],
                   ["2018-11-20 18:47:00",  176.80,  176.80,  176.43,  176.43,  176.612706],
                   ["2018-11-20 18:48:00",  176.45,  176.46,  176.21,  176.21,  176.599967],
                   ["2018-11-20 18:49:00",  176.22,  176.32,  176.14,  176.26,  176.586624],
                   ["2018-11-20 18:50:00",  176.26,  176.38,  176.23,  176.28,  176.577114],
                   ["2018-11-20 18:51:00",  176.31,  176.43,  176.20,  176.20,  176.562641],
                   ["2018-11-20 18:52:00",  176.22,  176.25,  176.15,  176.18,  176.544664],
                   ["2018-11-20 18:53:00",  176.19,  176.19,  175.97,  176.00,  176.506937],
                   ["2018-11-20 18:54:00",  176.00,  176.30,  175.97,  176.30,  176.493768],
                   ["2018-11-20 18:55:00",  176.29,  176.92,  176.11,  176.91,  176.518353],
                   ["2018-11-20 18:56:00",  176.92,  177.03,  176.67,  176.76,  176.554964],
                   ["2018-11-20 18:57:00",  176.78,  176.89,  176.74,  176.76,  176.566201],
                   ["2018-11-20 18:58:00",  176.77,  176.87,  176.56,  176.65,  176.571326],
                   ["2018-11-20 18:59:00",  176.65,  177.17,  176.59,  176.94,  176.681413],],
                  columns=["date", "open", "high", "low", "close", "vwap"])
df = df.set_index("date")
df.index = pd.to_datetime(df.index)