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Polars Cookbook

You're reading from   Polars Cookbook Over 60 practical recipes to transform, manipulate, and analyze your data using Python Polars 1.x

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781805121152
Length 394 pages
Edition 1st Edition
Languages
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Author (1):
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Yuki Kakegawa Yuki Kakegawa
Author Profile Icon Yuki Kakegawa
Yuki Kakegawa
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Table of Contents (15) Chapters Close

Preface 1. Chapter 1: Getting Started with Python Polars FREE CHAPTER 2. Chapter 2: Reading and Writing Files 3. Chapter 3: An Introduction to Data Analysis in Python Polars 4. Chapter 4: Data Transformation Techniques 5. Chapter 5: Handling Missing Data 6. Chapter 6: Performing String Manipulations 7. Chapter 7: Working with Nested Data Structures 8. Chapter 8: Reshaping and Tidying Data 9. Chapter 9: Time Series Analysis 10. Chapter 10: Interoperability with Other Python Libraries 11. Chapter 11: Working with Common Cloud Data Sources 12. Chapter 12: Testing and Debugging in Polars 13. Index 14. Other Books You May Enjoy

Identifying missing data

The first step in handling missing data is to identify whether there is missing data and how many instances of it you have in your data. Polars provides several ways to accomplish that.

Getting ready

We’ll be using the NumPy library to generate NaN values. Note that you can still generate NaN values in native Python with code such as float('nan').

Install numpy with the following command if you haven’t already as Polars’ dependency:

>>> pip install numpy

We’ll be using a dataset that we manually create. Make sure to run the following code before proceeding to the next steps:

from datetime import date
import numpy as np
date_col = pl.date_range(date(2023, 1, 1), date(2023, 1, 15), '1d', eager=True)
avg_temp_c_list = [-3,None,6,-1,np.nan,6,4,None,1,2,np.nan,7,9,-2,None]
df = pl.DataFrame({
    'date': date_col,
    'avg_temp_celsius...
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