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

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. In this recipe, you will use the sensor IoT dataset prepared in the Writing time series data to InfluxDB recipe. The technique applies to any pandas DataFrame that you plan to write to Snowflake.

Getting ready

You should refer to the recipe Reading data from a Snowflake 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: