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

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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 CSVs and other delimited files

In this recipe, you will use the pandas.read_csv() function, which offers a large set of parameters that you will explore to ensure the data is properly read into a time series DataFrame. In addition, you will learn how to specify an index column, parse the index to be of the type DatetimeIndex, and parse string columns that contain dates into datetime objects.

Generally, using Python, data read from a CSV file will be in string format (text). When using the read_csv method in pandas, it will try to infer the appropriate data types (dtype), and, in most cases, it does a great job at that. However, there are situations where you will need to explicitly indicate which columns to cast to a specific data type. For example, you will specify which column(s) to parse as dates using the parse_dates parameter in this recipe.

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You will read a CSV file containing hypothetical box office numbers for a movie. The file is provided in the...

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

Table of Contents (13) Chapters

Reading data from CSVs and other delimited files

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

Table of Contents (13) Chapters

Reading data from CSVs and other delimited files

Getting ready

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Authors (1)

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