.. currentmodule:: pandas

.. ipython:: python
   :suppress:

   import numpy as np
   import pandas as pd
   import os
   np.random.seed(123456)
   np.set_printoptions(precision=4, suppress=True)
   import matplotlib
   matplotlib.style.use('ggplot')
   import matplotlib.pyplot as plt
   pd.options.display.max_rows = 8

Time Series
-----------

pandas has simple, powerful, and efficient functionality for performing
resampling operations during frequency conversion (e.g., converting secondly
data into 5-minutely data). This is extremely common in, but not limited to,
financial applications. See the :ref:`Time Series section <timeseries>`

.. ipython:: python

   rng = pd.date_range('1/1/2012', periods=100, freq='S')
   ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
   ts.resample('5Min').sum()

Time zone representation

.. ipython:: python

   rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D')
   ts = pd.Series(np.random.randn(len(rng)), rng)
   ts
   ts_utc = ts.tz_localize('UTC')
   ts_utc

Convert to another time zone

.. ipython:: python

   ts_utc.tz_convert('US/Eastern')

Converting between time span representations

.. ipython:: python

   rng = pd.date_range('1/1/2012', periods=5, freq='M')
   ts = pd.Series(np.random.randn(len(rng)), index=rng)
   ts
   ps = ts.to_period()
   ps
   ps.to_timestamp()

Converting between period and timestamp enables some convenient arithmetic
functions to be used. In the following example, we convert a quarterly
frequency with year ending in November to 9am of the end of the month following
the quarter end:

.. ipython:: python

   prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')
   ts = pd.Series(np.random.randn(len(prng)), prng)
   ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9
   ts.head()