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 Jan 2026

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

2. Chapter 1: Getting Started with Time Series Analysis FREE CHAPTER

3. Chapter 2: Reading Time Series Data from Files

4. Chapter 3: Reading Time Series Data from Databases

5. Chapter 4: Persisting Time Series Data to Files

6. Chapter 5: Persisting Time Series Data to Databases

7. Chapter 6: Working with Date and Time in Python

8. Chapter 7: Handling Missing Data

9. Chapter 8: Outlier Detection Using Statistical Methods

10. Chapter 9: Exploratory Data Analysis and Diagnosis

11. Chapter 10: Building Univariate Time Series Models Using Statistical Methods

12. Chapter 11: Additional Statistical Modeling Techniques for Time Series

13. Chapter 12: Forecasting Using Supervised Machine Learning

Writing time series data to Snowflake

Snowflake has become a very popular cloud database option for building big data analytics, due to its scalability, performance, and being SQL-oriented (a columnar-stored relational database).Snowflake's connector for Python simplifies the interaction with the database, whether it's for reading or writing data, or, more specifically, the built-in support for pandas DataFrames.

Getting ready

Refer to the Reading data from a Snowflake recipe in Chapter 3, Reading Time Series Data from Databases, as a refresher on the different ways to connect to Snowflake.

The recommended approach for the snowflake-connector-python library is to install it using pip, allowing you to install extras such as pandas, as shown:

pip install snowflake-sqlalchemy # SQLAlchemy integration
pip install snowflake-snowpark-python # Snowpark DataFrame API
pip install "snowflake-connector-python[pandas]" #includes pandas support