DateTime Index¶

Often the time and date information won’t just be in a separate column, but instead be the actual datatime index, so let's learn how to deal with this sort of data with pandas!

Imports¶

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

Built-in Date and Time Library¶

Note that from datetime import datetime is differnt from import datetime

from datetime import datetime

Create a DateTime Object¶

To illustrate the order of arguments Note that hour in 24 hour format

# To illustrate the order of arguments
my_year = 2017
my_month = 1
my_day = 2
my_hour = 13
my_minute = 30
my_second = 15

Create a datetime object -Use datetime() method to create a datetime object, January 2nd, 2017

my_date = datetime(my_year,my_month,my_day)

Time Defaults to 0:00

my_date

datetime.datetime(2017, 1, 2, 0, 0)

You can specify hours, minutes, seconds in hh, mm, ss format January 2nd, 2017 at 13:30:15

my_date_time = datetime(my_year,my_month,my_day,my_hour,my_minute,my_second)

Alternatively,

my_date_time

datetime.datetime(2017, 1, 2, 13, 30, 15)

The data type is datetime.datetime

type(my_date_time)

datetime.datetime

Get DateTime Info¶

You can grab any part of the datetime object you want

my_date.day

2

my_date_time.hour

13

Using Pandas with Datetime Index¶

You'll usually deal with time series as an index when working with pandas dataframes obtained from some sort of financial API. Fortunately pandas has a lot of functions and methods to work with time series!

Create an Example Datetime List/Array¶

Use datetime() method to create a datetime object

first_two = [datetime(2016, 1, 1), datetime(2016, 1, 2)]
first_two

[datetime.datetime(2016, 1, 1, 0, 0), datetime.datetime(2016, 1, 2, 0, 0)]

Converted to an DateTime Index .DatetimeIndex()¶

To create an index, pass the list created to DatetimeIndex()

dt_ind = pd.DatetimeIndex(first_two)
dt_ind

DatetimeIndex(['2016-01-01', '2016-01-02'], dtype='datetime64[ns]', freq=None)

Create a Pandas DataFrame .DateFrame()¶

Attached Some Random Data

data = np.random.randn(2,2)
print(data)
cols = ['A','B']

[[-1.58270607  0.47766839]
 [ 0.34171008  0.5889566 ]]

Use DataFrame() to create a pandas DataFrame

df = pd.DataFrame(data,dt_ind,cols)
df

df.index

DatetimeIndex(['2016-01-01', '2016-01-02'], dtype='datetime64[ns]', freq=None)

Get Latest Date Location .argmax()¶

df.index.argmax()

1

Get Lastest Location Data .max()¶

df.index.max()

Timestamp('2016-01-02 00:00:00')

Get Earliest Date Index Location .argmin()¶

df.index.argmin()

0

Get Earliest Location Data .min()¶

df.index.min()

Timestamp('2016-01-01 00:00:00')

	A	B
2016-01-01	0.165224	-0.767629
2016-01-02	-0.482305	0.307934

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