Time Series:

Pandas provide a robust tool for working time with Time series data, especially in the financial sector. While working with time series data, we frequently come across the following −

• Generating sequence of time
• Convert the time series to different frequencies

Pandas provides a relatively compact and self-contained set of tools for performing the above tasks.

Get Current Time:

datetime.now()  gives you the current date and time.

import pandas as pd

print pd. datetime. now()

Its output is as follows −

2017-05-11 06:10:13.393147

Create a TimeStamp:

Time-stamped data is the most basic type of timeseries data that associates values with points in time. For pandas objects, it means using the points in time. Let’s take an example –

import pandas as pd

print pd. Timestamp ( '2017-03-01' )

Its output is as follows −

2017-03-01 00:00:00

It is also possible to convert integer or float epoch times. The default unit for these is nanoseconds (since these are how Timestamps are stored). However, often epochs are stored in another unit which can be specified. Let’s take another example

import pandas as pd

print pd. Timestamp ( 1587687255 , unit= 's' )

Its output is as follows −

2020-04-24 00:14:15

Create a Range of Time:

import pandas as pd

print pd. date_range( "11:00" , "13:30" , freq= "30min" ). time

Its output is as follows −

[datetime.time(11, 0) datetime.time(11, 30) datetime.time(12, 0)

datetime.time(12, 30) datetime.time(13, 0) datetime.time(13, 30)]

Change the Frequency of Time:

import pandas as pd

print pd. date_range( "11:00" , "13:30" , freq= "H" ). time

Its output is as follows −

[datetime.time(11, 0) datetime.time(12, 0) datetime.time(13, 0)]

Converting to Timestamps:

To convert a Series or list-like object of date-like objects, for example strings, epochs, or a mixture, you can use the to_datetime  function. When passed, this returns a Series (with the same index), while a list-like  is converted to a DatetimeIndex . Take a look at the following example −

import pandas as pd

print pd. to_datetime( pd. Series ([ 'Jul 31, 2009' , '2010-01-10' , None ]))

Its output is as follows −

0  2009-07-31

1  2010-01-10

2         NaT

dtype: datetime64[ns]

NaT means Not a Time (equivalent to NaN)

Let’s take another example.

import pandas as pd

print pd. to_datetime([ '2005/11/23' , '2010.12.31' , None ])

Its output is as follows −

DatetimeIndex(['2005-11-23', '2010-12-31', 'NaT'], dtype='datetime64[ns]', freq=None)