Subject
15 entries
Pandas
Bookmarks
pygwalker: Pandas DataFrame to Tableau-Style UI
pygwalker turns a pandas DataFrame into a drag-and-drop Tableau-style visual analysis interface inside Jupyter notebooks — one line of code to replace writing matplotlib/seaborn boilerplate with interactive visual exploration. Strong tool for exploratory data analysis.
redframes — Python Data Manipulation Library
redframes is a general-purpose Python data manipulation library that wraps pandas with a more consistent, readable API — aimed at making common data wrangling tasks less verbose without abandoning the pandas ecosystem.
Ponder: Pandas at Scale
Ponder is a startup that makes Pandas run at scale without rewriting your code — a drop-in compatibility layer that runs standard Pandas operations on distributed backends. Targets the massive installed base of data scientists who know Pandas but hit its single-machine limits.
Practical SQL for Data Analysis
Haki Benita's essay showing how SQL can replace Pandas for a surprising range of data analysis tasks — window functions, aggregations, pivoting, and data quality checks. Makes the case that analysts often reach for Python when SQL would be faster and simpler.
PandasGUI — A GUI for Pandas DataFrames
PandasGUI is a graphical interface for exploring and visualizing pandas DataFrames — drag-and-drop CSV import, interactive filtering, statistical summaries, and plot creation without writing code. Useful for rapid EDA.
Pandas Pivot Table Explained
A step-by-step tutorial on using Pandas pivot tables for business data analysis from the Practical Business Python blog. Pivot tables are the single most useful tool for quickly summarizing and reshaping tabular data, and this covers the full API clearly.
Up and Down PyData 2014 — Rob Story
Rob Story's PyData SV 2014 talk notebook on 'Up and Down' — covering the landscape of Python data visualization tools from low-level (Matplotlib) to high-level (Bokeh, Vincent, Folium). A snapshot of the visualization stack debate of that era.
Learn Pandas — IPython Notebook Tutorial Series
Bitbucket-hosted IPython notebook series for learning Pandas from scratch — one of the early hands-on Pandas tutorials when official documentation was sparse. Covers data loading, manipulation, groupby, and time series.
Converting Categorical Data into Numbers with Pandas and Scikit-Learn
FastML tutorial on converting categorical variables to numeric form using Pandas and scikit-learn's LabelEncoder and OneHotEncoder. A foundational data preprocessing step that trips up many beginners.
Data Science in Python — Yhat Tutorial
Yhat's end-to-end data science tutorial in Python using pandas for data manipulation and scikit-learn for modeling. One of the cleaner introductory pipelines from 2014, before this kind of content became ubiquitous.
A Pandas Cookbook — Julia Evans
Julia Evans's hands-on pandas cookbook — eight chapters of real-dataset exercises covering groupby, merging, text ops, and timestamp handling. The go-to resource that made pandas approachable before the official docs caught up.
Weather Forecasting with Twitter and Pandas
ŷhat blog post using Twitter emoticon sentiment as a proxy signal for weather prediction, analyzed with pandas. An early example of using social media signals for real-world forecasting — creative but ultimately a data exploration exercise.
Intro to pandas Data Structures
Greg Reda's introduction to pandas data structures — Series, DataFrame, and Index — written in 2013 when pandas was still new enough to need a clear on-ramp. A canonical early tutorial that helped many data scientists learn the library.
sklearn-pandas: Bridge Between pandas and scikit-learn
sklearn-pandas is a library bridging pandas DataFrames and scikit-learn's pipeline API — enabling column-level transformations with named features rather than anonymous numpy arrays. Fills a friction point that frustrated every data scientist using both libraries together.
Pandas and Python: Top 10
Manish Amde's top 10 Pandas techniques for data scientists — written in March 2013 when pandas was still a young library (0.10.x era). Captures the practical workflows that made pandas the dominant tool for tabular data manipulation in Python.