References 관련

---

Jetbrains

PyCharm

• How to Move From pandas to Polars

---

Medium

• betterprogramming / Pandas Illustrated: The Definitive Visual Guide to Pandas

---

dev.to

• oscarleo / How to Create a Beautiful Polar Histogram With Python and Matplotlib

---

velog

• @euisuk-chung / [Pandas] 실무에서 쓰려고 정리해둔 Code Snippet

---

Brunch

• @yysttong / Polars #08
• @yysttong / Polars #09

---

tistory

• webnautes / 멈춤보단 천천히라도
  • Optuna를 사용하여 XGBoost 최적 하이퍼 파라미터 구하는 예제코드
  • RandomizedSearchCV를 사용하여 XGBoost 최적 하이퍼 파라미터 구하는 예제코드
  • Pandas 강좌 3 - 데이터 선택하는 방법, iloc
• aspdotnet / 재우니의 블로그
  • PYTHON : pandas 로 MS-SQL 연결하여 EXCEL, CSV 생성하기

---

Python⇒Speed

• Using Polars in a Pandas world

---

InfluxDB Time Series Data Platform | InfluxData

• Pandas Profiling: A Detailed Explanation

---

Data Wrangling Recipes — Data Wrangling and Automation

• Fast and Efficient Inequality Joins in Pandas

---

A Geospatial Data Science Blog

• Saint Michael Sword

---

Ponder | Pandas at Scale

• Professional Pandas: Handling Missing Data With Pandas Dropna

---

HackerNoon - read, write and learn about any technology

• How I Used Python and Folium to Visualize My Outdoor Activities

---

YA-Hwang 기술 블로그

• aws-sdk-pandas Lambda로 활용하기
• pandas보다 빠르게 데이터 저장하기

---

Erik Marsja

• Coefficient of Variation in Python with Pandas & NumPy

---

Confessions of a Data Guy

• REPLACING PANDAS WITH POLARS. A PRACTICAL GUIDE.
• POLARS VS PANDAS. INSIDE AN AWS LAMBDA.

---

Math ∩ Programming — CodeSolid.com

• Python Parquet and Arrow: Using PyArrow with Pandas

---

wrighters.io

• Options to run pandas DataFrame.apply in parallel

---

OlegWock · Personal blog

• Guide to data tools landscape for developers

---