Time Series in Python Part 1: Read and Transform Your Data

In part 1 of this video series, learn how to read and index your data for time series using Python’s pandas package. We check if the data meets the requirements or assumptions for time series modeling by plotting to see if it follows a stationary pattern. We also transform our data by taking differences in the values to make them more stationary. This series is considered for intermediate and advanced users. Please watch our video on getting python setup for data science, link below.

Code, R & Python Script Repository

Watch Part 2 Here:
ARIMA modeling and Forcasting: Time Series in Python

Watch Part 3 Here:
Mean Absolute Error for Forecast Evaluation: Time Series in Python

Packages Used:
pandas
matplotlib
StatsModels
statistics

More Data Science Material:
[Video] Getting started with Python and R for Data Science
[Video] Web scraping in R

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Rebecca Merrett
About The Author
- Rebecca holds a bachelor’s degree of information and media from the University of Technology Sydney, and is undertaking her post graduate diploma in mathematics and statistics from the University of Southern Queensland. She has a background in writing for tech publications.

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