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Polars-Cookbook

(★ 361)

Polars Cookbook, Published by Packt

파일 탐색기

최종 버전 다운로드 (.zip)

- ch01.ipynb
- ch02.ipynb
- config.py
- create_iceberg_catalog_and_table.py
- ch03.ipynb
- us_state_mappings.py
- ch04.ipynb
- ch05.ipynb
- ch06.ipynb
- ch07.ipynb
- ch08.ipynb
- ch09.ipynb
- ch10.ipynb
- ch11.ipynb
- - demo.py
- ch12.ipynb
- - 0-64177f1a-67b3-4f18-b78c-354bd74700dc-0.parquet
- academic.csv
- academic_detail.csv
- contoso_sales.csv
- covid_19_deaths.csv
- customer_shopping_data.csv
- customer_shopping_data_no_header.csv
- ga_20170801.json
- google_store_reviews.csv
- historical_temperatures.csv
- pokemon.csv
- pytest_expected_output.csv
- README.md
- status.csv
- student_performance.csv
- temperatures.csv
- titanic_dataset.csv
- toronto_weather.csv
- us_videos.csv
- venture_funding_deals.parquet
- world_population.avro
- world_population.json
- world_population.jsonl
.gitignore
LICENSE
README.md
requirements.txt

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/PacktPublishing/Polars-Cookbook

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd Polars-Cookbook

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

jupyter notebook

브라우저에서 노트북(.ipynb) 파일들을 열람하고 실행할 수 있는 Jupyter 화면을 켭니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

// repository documentation

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# Polars-Cookbook
<a href="https://www.packtpub.com/en-us/product/polars-cookbook-9781805121152"><img src="https://content.packt.com/_/image/xxlarge/B21621/cover_image_large.jpg" height="256px" align="right"></a>

B21621 - Polars Cookbook - [Available as an ebook and a physical copy on Amazon](https://www.amazon.com/Polars-Cookbook-practical-transform-manipulate/dp/1805121154)

## [Chapter 1: Getting Started with Python Polars](https://github.com/PacktPublishing/Polars-Cookbook/blob/main/Chapter01/ch01.ipynb)
- Introducing Key Features in Polars 
- The Polars DataFrame 
- The Polars Series 
- The Polars LazyFrame 
- Selecting columns and filtering data 
- Creating, modifying, and deleting columns 
- Understanding method chaining 
- Processing datasets larger than RAM

## [Chapter 2: Reading and Writing Files](https://github.com/PacktPublishing/Polars-Cookbook/blob/main/Chapter02/ch02.ipynb)
- Reading and writing CSV files 
- Reading and writing parquet files 
- Reading and writing delta tables 
- Reading and writing JSON files 
- Reading and writing excel files 
- Reading and writing other data file formats 
- Reading and writing multiple files 
- Working with databases

## [Chapter 3: An Introduction to Data Analysis in Python Polars](https://github.com/PacktPublishing/Polars-Cookbook/blob/main/Chapter03/ch03.ipynb)
- Inspecting the DataFrame 
- Casting data types 
- Handling duplicate values 
- Masking sensitive data 
- Visualizing data using Plotly 
- Detecting and handling outliers

## [Chapter 4: Data Transformation Techniques](https://github.com/PacktPublishing/Polars-Cookbook/blob/main/Chapter04/ch04.ipynb)
- Exploring basic aggregations
- Using group by aggregations
- Aggregating values across multiple columns
- Computing with window functions
- Applying UDFs
- Using SQL for data transformations

## [Chapter 5: Handling Missing Data](https://github.com/PacktPublishing/Polars-Cookbook/blob/main/Chapter05/ch05.ipynb)
- Identifying missing data 
- Deleting rows and columns containing missing data 
- Filling missing data

## [Chapter 6: Performing String Manipulations](https://github.com/PacktPublishing/Polars-Cookbook/blob/main/Chapter06/ch06.ipynb)
- Filtering strings 
- Converting strings into a Date/Datetime/Time 
- Extracting substrings 
- Cleaning strings 
- Splitting strings into lists and structs 
- Concatenating and combining strings

## [Chapter 7: Working with Nested Data Structures](https://github.com/PacktPublishing/Polars-Cookbook/blob/main/Chapter07/ch07.ipynb)
- Creating lists 
- Aggregating elements in lists 
- Accessing and selecting elements in lists 
- Applying logic to each element in lists 
- Working with structs and JSON data

## [Chapter 8: Reshaping and Tidying Data](https://github.com/PacktPublishing/Polars-Cookbook/blob/main/Chapter08/ch08.ipynb)
- Turning columns into rows 
- Turning rows into columns  
- Joining DataFrames 
- Concatenating DataFrames 
- Other reshaping techniques

## [Chapter 9: Time Series Analysis](https://github.com/PacktPublishing/Polars-Cookbook/blob/main/Chapter09/ch09.ipynb)
- Working with date and time 
- Applying rolling windows calculations 
- Resampling techniques 
- Time series forecasting with the functime library

## [Chapter 10: Interoperability With Other Python Libraries](https://github.com/PacktPublishing/Polars-Cookbook/blob/main/Chapter10/ch10.ipynb)
- Converting to and from a pandas DataFrame 
- Converting to and from NumPy arrays
- Interoperating with PyArrow 
- Integration with DuckDB

## [Chapter 11: Working With Common Cloud Sources](https://github.com/PacktPublishing/Polars-Cookbook/blob/main/Chapter11/ch11.ipynb)
- Amazon S3 
- Azure Blog Storage 
- Google Cloud Storage 
- BigQuery 
- Snowflake

## [Chapter 12: Testing and Debugging in Polars](https://github.com/PacktPublishing/Polars-Cookbook/blob/main/Chapter12/ch12.ipynb)
- Debugging chained operations 
- Inspecting and optimizing the query plan 
- Testing data quality with cuallee 
- Running unit tests with Pytest

## Errata
* Page 06 (code block 1): **Import polars as pl** _should be_ **import polars as pl**
* Page 23 (code block 2, line 2): **df.select(cs.numeric()).head()** _should be_ **df.select(cs.string()).head()**
  
## New Outstanding Features and Breaking Changes NOT Captured in the Book
- Version 1.6.0
  - Use Altair in DataFrame.plot ([#17995](https://github.com/pola-rs/polars/pull/17995)).