#Day34 - #100DaysOfCode. Today, I learnt about creating s dictionary in python, and converting it to a Dataframe using pandas, then using the Dataframe to plot charts for visualization. 📊. Learnt also that visualization tells a story, so the audience must understand your charts

TIL the parameter .before in mutate() from {dplyr}. It adds the new column(s) created to the *beginning* of the dataframe, instead of to the end. Quite useful!. Source: #RforDataScience (2e) .(sometimes you should read stuff you think you already know). #rstats

Is your pandas DataFrame using 10x more memory than it should? Here's a simple optimization that changes everything! . Uses 10x less memory and is 10x faster

1歳でDataFrameに興味を持つとは、大したやつだ。

It should be illegal for a python dataframe library to have a website that looks like this

❄️ Want to automatically replace your #Pandas code with #Polars code?. Mito's Data Copilot can do this for any notebook in a single command. Polars is a DataFrame alternative to Pandas DataFrames. It is growing in popularity because it can handle larger datasets and process

Daft was one of the first to natively integrate with @unitycatalog_io. You can use Daft’s catalog API to query Unity tables using both SQL and Python DataFrame, such as filtering with a WHERE clause in a .sql() expression. Try it yourself and get started: pip install daft[unity]

Is there a Pythonic spell to make dataframe values vanish and reappear?. Source: #DevCommunity #PythonProgramming #AI #Coding

Creating a racing bar chart is simple in Python. You can use the bar_chart_race library. The input must be a Pandas DataFrame:.- Each row represents a time stamp. - Each column holds the value for a particular category. This produces the racing bar chart shown below.

昨日のPython in Excelの会は拝聴できなかったのだけど、おいしみさん情報によればこのようにPowerQueryの出力をそのままPython(のDataframe)で受けられるので、

Building High-Performance Financial Analytics Pipelines with Polars: Lazy Evaluation, Advanced Expressions, and SQL Integration. This tutorial demonstrates how to build a scalable financial analytics pipeline using Polars, a high-performance DataFrame library for Python. By

1/. Assigning unique IDs to combinations of multiple columns in an R dataframe?.Here’s a fast, elegant trick using dplyr you don’t want to miss. 👇

TabularBuilder makes converting any swift object to a tabulardata dataframe has never been easier!. - declarative syntax in the style of swiftui.- type-safe data mapping.- efficient dataframe creation.- supports conditional mapping and transformation.

Modern #data teams often juggle multiple #DataFrame libraries. #Narwhals lets your functions run natively across pandas, Polars, DuckDB, and PySpark—without rewriting code or adding heavy dependencies. 📕 View the full article:

#100DaysOfCode .Day 26.Tackled list & dictionary comprehensions.Learned how to iterate over a Pandas DataFrame.Built a NATO Phonetic Converter: "bola" - ['Bravo', 'Oscar', 'Lima', 'Alfa'].Python makes it fun!.#Python

help I'm doing a Spark tutorial and the tutorial is like,. you can pass a spark dataframe to/from pyspark pandas api dataframe and I'm like. Programming languages are so unserious. this is like guys posting two nail polishes and going you're telling me they're not the same?!

We've got Kimi K2 set up so I can go from a python dataframe to a HTML chart in 1 prompt and 3 clicks. And then update it and refresh, updates are deployed . Takes a few second

🚀 New Tutorial and Notebook: Build a Gemini-Powered DataFrame Agent for Natural Language Data Analysis with Pandas and LangChain. This hands-on tutorial walks you through how to build an interactive DataFrame agent using Google Gemini, LangChain, and Pandas—all applied to the

📊 Working with data in Kotlin Notebook?. Run `%use dataframe` to integrate interactive tables with sorting, pagination, and nested views. Say goodbye to messy console dumps!. Try it in @intellijidea