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Data Literacy With Python

You're reading from   Data Literacy With Python A Comprehensive Guide to Understanding and Analyzing Data with Python

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Product type Paperback
Published in Jul 2024
Publisher Mercury_Learning
ISBN-13 9781836640097
Length 271 pages
Edition 1st Edition
Languages
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Authors (2):
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Mercury Learning and Information Mercury Learning and Information
Author Profile Icon Mercury Learning and Information
Mercury Learning and Information
Oswald Campesato Oswald Campesato
Author Profile Icon Oswald Campesato
Oswald Campesato
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Table of Contents (9) Chapters Close

Preface
1. Chapter 1: Working With Data 2. Chapter 2: Outlier and Anomaly Detection FREE CHAPTER 3. Chapter 3: Cleaning Datasets 4. Chapter 4: Introduction to Statistics 5. Chapter 5: Matplotlib and Seaborn 6. Index
Appendix A: Introduction to Python 1. Appendix B: Introduction to Pandas

SAVING YOUR CODE IN A MODULE

Earlier you saw how to launch the Python interpreter from the command line and then enter Python commands. However, that everything that you type in the Python interpreter is only valid for the current session: if you exit the interpreter and then launch the interpreter again, your previous definitions are no longer valid. Fortunately, Python enables you to store code in a text file, as discussed in the next section.

A module in Python is a text file that contains Python statements. In the previous section, you saw how the Python interpreter enables you to test code snippets whose definitions are valid for the current session. If you want to retain the code snippets and other definitions, place them in a text file so that you can execute that code outside of the Python interpreter.

The outermost statements in a Python are executed from top to bottom when the module is imported for the first time, which will then set up its variables and functions.

A Python...

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