"Zero. The primes are countable. Any countable subset of ℝ has Hausdorff dimension 0. Done." *fires up pyplot* jkjkjk

pyplotくんでスケール指定の2d color contour図をノーヒントで描ける人っているんですか？

試験問題を作らないかんのに、TLに触発されて3dグラフをグリグリしだしてしまった。 チャッピーに頼んだら、pyplotで書いてた3dグラフを、htmlに出力できるplotlyに変換してくれたので、今期はこれで行こう。

You guyss I worked with pyplot today on python and genuinely I can say I am proud!🙂‍↕️ I actually did more projects too, today was sure productive. What did you work on today techies?

Somehow I didnt see it as rainbow, but PyPlot instead..

The year is 100,000 AD I survived until longevity escape velocity, and traveled the galaxy until I found an unused star, whereby my autonomous robots set up a Dyson sphere to power my own personal computations. I use it to adjust the axes on a pyplot.

Você tem que usar o pyplot ou matplotlib pô Pandas e numpy tbm pra ver os dataframes

Revisited Matplotlib today and strengthened the basics of data visual. • Pyplot API • Object-Oriented API • line plots • bar charts • scatter plots • histograms • subplots • labels, legends & custom Now able to visualize data with much more clarity. Onto Seaborn next.

I thought I was alone. I could never remember the details of git, pyplot, Latex formatting etc. even I have been using them for years. Coding agent saved my life

If you are only using codex to write code, you are missing out a lot. Codex takes away a lot of my mental burden: - git, uv, ssh keys - excel functions - Python plots (yes looking at you pyplot, manim and streamlit you are next!)

DATA VISUALIZATION WITH PYTHON: MASTERING MATPLOTLIB (PART 2) ✍️📊 After wrapping up the Model Development phase, I have spent the last few days diving deep into the Data Visualization with Python module of the IBM Data Analyst Professional Certificate. I have learned that effective visualization is not just about calling a function; it is about the coder's ability to manipulate the underlying libraries to tell a specific story. I will be honest, Python visualization depends entirely on the coder's deep understanding of specific libraries. It is not just about making things look pretty; it is about knowing how to paint with code. To ensure I truly own the syntax, I have continued my practice of highlighting key points and handwriting chunks of code. From transposing dataframes to setting up axes, writing it by hand has been my secret weapon for retention. I have compiled these notes into a PDF to document my progress from raw data to visual insights (shared on my LinkedIn account). THE TWO FACES OF MATPLOTLIB A major takeaway from my notes is the distinction between the two ways we interact with Matplotlib: a) The Scripting Layer (Pyplot): This is the procedural interface using import matplotlib.pyplot as plt. It is highly efficient for quick exploration and generating standard plots with minimal code. It is great for when you need to see a trend quickly. b) The Artist Layer (Object-Oriented): This is where the real control happens. By using fig, ax = plt.subplots(), I can treat every element of the chart as an individual object. This allows for granular, surgical control over line styles, markers, and labels—essentially moving from generating a chart to professional data storytelling. BUILDING THE PLOTTING MUSCLE Using the United Nations dataset on Canadian Immigration (1980-2013), I have moved past the basics to master several key visualization types: a) Line Plots: Perfect for tracking the 33-year immigration trends of specific countries. I practiced using markers, markersize, and custom line styles to make data points distinct and readable. b) Area Plots: I explored both stacked and unstacked versions. Using the alpha parameter for transparency was key to ensuring that overlapping data remained visible and professional. c) Histograms: These allowed me to visualize the frequency distribution of immigrants, helping to identify common volume ranges across different years. d) Bar Charts: I implemented both vertical and horizontal charts. Horizontal bars (kind=barh) proved especially useful for maintaining readability when dealing with longer country names. This post is dedicated to showcasing the charts and graphs I have created so far using Matplotlib. Seeing the transition from pen and paper to a fully rendered chart in Jupyter is incredibly rewarding. If you can understand the logic on the page, the implementation in the notebook becomes second nature. I am curious to hear from other data analysts in the community about their experience. When you first started with Matplotlib, did you find the Scripting layer or the Artist layer more intuitive for your specific workflows? Also, what are your favorite tricks for making complex charts more readable? Connect with me on LinkedIn: linkedin.com/in/emycodesanal… Live Portfolio: github.com/emycodesanalytics… © EmyCodes Analytics | March 4, 2026 #EmyCodesAnalytics #DataVisualization #Python #Matplotlib #HandwrittenCode #LearningInPublic #IBMDataAnalyst #DataScience #DataAnalytics #Pandas #MatplotlibArtist #CertificationJourney #2026Goals

import tensorflow as plt import pandas as tf import numpy as pd import matplotlib. pyplot as np data scientists after seeing this

「SAR衛星データ解析入門」が届いたので早速見ているけど、Python Pyplotでインパルス応答の可視化から始まるの楽しい。ちなみに書籍掲載のサンプルコードは要点のみで、ソースコード全文は別途githubから入手する必要があるのね

i dont even trust llms to write a correct pyplot function for a dashboard or whatever and these "people" give them full access to their bank account

Obrigado amigo! Todo o tratamento de dados é feito em python, bem como as visualizações onde utilizo pyplot

This is also literally a Pyplot graph, which can be made by anybody with some coding know-how within 10 minutes.

plotly..express or matplotlib..pyplot?

will finish read doc and test tmrw, did too much pyplot today

ChatGPT is so stupid that sometimes when you ask for a thumbnail it breaks out pyplot instead of using dalle.

Day 9 of #100DaysOfCode 🚀 – Matplotlib & Data Visualization -covered concepts like Figures , Axes , Subplots & Plot customization -Explored Pyplot functions to plot graphs -Created Line Graphs & Bar Graphs to represent data visually #LearnInPublic #Python