01_time_series_solutions.ipynb

Kernel: Python 2

Working With Time Series Data

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Independent Practice

Instructor Note: These are optional and can be assigned as student practice questions outside of class.

1) Create a datetime object representing today's date.

from datetime import datetime
from datetime import timedelta

# Time this lesson plan was written:
lesson_date = datetime(2018, 2, 25, 23, 31, 1, 844089)

print(lesson_date)

2018-02-25 23:31:01.844089

2) Load the UFO data set from the internet.

import pandas as pd
from datetime import timedelta
%matplotlib inline

ufo = pd.read_csv('http://bit.ly/uforeports')

ufo.head()

ufo.dtypes

City               object
Colors Reported    object
Shape Reported     object
State              object
Time               object
dtype: object

3) Convert the Time column to a datetime object.

ufo['Time'] = pd.to_datetime(ufo.Time)

ufo.head()

ufo.dtypes

City                       object
Colors Reported            object
Shape Reported             object
State                      object
Time               datetime64[ns]
dtype: object

4) Set the Time column to the index of the DataFrame.

ufo.set_index('Time', inplace=True)

ufo.head()

5) Create a timestamp object for the date January 1, 1999.

ts = pd.to_datetime('1/1/1999')

6) Use the timestamp object to perform logical filtering on the DataFrame and create a subset of entries with a date above or equal to January 1, 1999.

recent_ufos = ufo.loc[ufo.index >= ts, :]

recent_ufos.head()