You're reading from Time Series Analysis with Python Cookbook Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation

Product type Paperback

Published in Apr 2025

Publisher

ISBN-13 9781805124283

Length 98 pages

Edition 2nd Edition

Languages

Python

Concepts

Data Analysis

Author (1):

Tarek A. Atwan

View More author details

Table of Contents (13) Chapters

1. Time Series Analysis with Python Cookbook, Second Edition: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation FREE CHAPTER

2. Getting Started with Time Series Analysis

3. Reading Time Series Data from Files

4. Reading Time Series Data from Databases

5. Persisting Time Series Data to Files

6. Persisting Time Series Data to Databases

7. Working with Date and Time in Python

8. Handling Missing Data

9. Outlier Detection Using Statistical Methods

10. Exploratory Data Analysis and Diagnosis

11. Building Univariate Time Series Models Using Statistical Methods

12. Additional Statistical Modeling Techniques for Time Series

13. Outlier Detection Using Unsupervised Machine Learning

Reading data from Snowflake

A very common place to extract data for analytics is usually a company's data warehouse. Data warehouses host a massive amount of data that, in most cases, contains integrated data to support various reporting and analytics needs, in addition to historical data from various source systems.

The evolution of the cloud brought us cloud data warehouses such as Amazon Redshift, Google BigQuery, Azure SQL Data Warehouse, and Snowflake.

In this recipe, you will work with Snowflake, a powerful Software as a Service (SaaS) cloud-based data warehousing platform that can be hosted on different cloud platforms, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. You will learn how to connect to Snowflake using Python to extract time series data and load it into a pandas DataFrame.