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 MongoDB

MongoDB is a document database system that stores data in BSON format. When you query data from MongoDB, the data will be represented in JSON format. BSON is similar to JSON; it is the binary encoding of JSON. Unlike JSON, though, it is not in a human-readable format. JSON is great for transmitting data and is system-agnostic. BSON is designed to store data and is associated with MongoDB.

In this recipe, you will explore writing a pandas DataFrame to MongoDB.

Getting ready

You should refer to the recipe “Reading data from a document database” in Chapter 3, Reading Time Series Data from Databases as a refresher on the different ways to connect to MongoDB.

In the Reading data from a document database recipe in Chapter 3, Reading Time Series Data from Databases, we installed pymongo. For this recipe, you will be using that same library again.

To install using conda, run the following: