Matplotlib VS Grapple
Compare Matplotlib VS Grapple and see what are their differences
Generate professional standard operating procedures in minutes. AI-powered SOP creation built on 10,000+ industry procedures.
featured
Matplotlib
Pandas
NumPy
Seaborn
D3.js
Plotly
GnuPlot
Jupyter
Grapple
Looker
Tableau
Microsoft Power BI
DataPalo
Datakit.page
Google Data Studio
Coefficient.io
Matplotlib GrappleGrapple's answer:
Grapple is built for small to medium-sized companies who haven't successfully implemented traditional BI software like Looker, Power BI, Tableau, or other solutions like DataRails and Domo. Traditional BI software requires a lot of technical knowledge to setup, maintain, and they often make it really difficult for less technical users to customize. This means your dashboards either 1) don't work, or 2) aren't flexible and easy to use enough to let operators adjust them as they need during the course of businesses.
Grapple is designed from the beginning for non-technical operators across marketing, sales, and finance.
Grapple's answer:
Grapple is great for data savvy operators who love working with data. We're particularly helpful for companies with 25 to 500 employees who serve other businesses (B2B) and are focused on improving their CRM analytics and SaaS metrics. If you're using apps like Salesforce, Hubspot, Zendesk, Stripe, or Asana, Grapple is for you!
If you want to write data notebooks in python or SQL and want under-the-hood control of your data stack, Grapple is not for you. We recommend you try Omni or Hex.
Grapple's answer:
Jack, co-founder/CEO, had the idea for Grapple a couple years ago after spending almost a decade building in Tableau, Looker, Google Data Studio, Trevor.io, and the list goes on! Jack spent a lot of time collaborating with non-technical users in sales, marketing, bizops, finance on dashboards and after one particularly simple report that was still difficult to generate, he thought there must be a better way! Turns out, most data platforms require a ton of other tools and a ton of other people all of which are slow and expensiveโdelaying your time-to-insight. Jack had the idea to compress the data stack into a single tool, that maybe couldn't do everything, but would be the fastest, easiest way to pull the types of reports he pulled all the time over the last 10 years. Fast-forward to today, Andrew joined as co-founder/CTO and Grapple is now generally available and includes a suite of AI features to take Grapple's speed and ease of use even further. Let us know what you think!
Grapple's answer:
Grapple is a fully vertically integrated data platform and does not require any additional tooling. Unlike competitors Looker and PowerBI, Grapple includes everything you need to get started. Frustrated by your slow data team? Get started with Grapple right away.
In nerd speak, Grapple seamlessly bundles the following tools, you won't even need to manage them: - An ETL and data warehouse for centralizing your data - Automatic data modeling so your data is queryable right away - Visualization and analytics UI
And on top of all that, Grapple provides modern functionality too: - Unlimited Viewers: share your dashboards with as many users as you want, just like a Google Docs - AI/Natural Language: customize your dashboard with natural language instead of SQL or spreadsheet formulas - Straightforward pricing: pay as you go with monthly per user pricing
Grapple's answer:
Grapple's application layer is written in React + Laravel and under the hood uses a mixture of PostgreSQL and No-SQL to deliver data warehousing and analytics capabilities.
Based on our record, Matplotlib seems to be more popular. It has been mentiond 114 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
In February, an AI agent named MJ Rathbun submitted a pull request to matplotlib โ the Python plotting library used by half the scientific computing world. Scott Shambaugh, a volunteer maintainer, rejected it. Standard code review. Nothing unusual. - Source: dev.to / 5 months ago
Numbers are useful, but sometimes itโs easier to spot patterns when you can actually see your data. Pandas works seamlessly with Matplotlib, a popular Python library for creating visualizations. Together, they make it easy to turn raw numbers into clear charts. - Source: dev.to / 8 months ago
We are storing the results in JSON files, which we combine, analyze and visualize using matplotlib in Python. Here's the structure of a benchmark result file:. - Source: dev.to / 9 months ago
NetworkX and Matplotlib were used to visualize the graph structure of the agent. - Source: dev.to / 10 months ago
The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, Aโฆ. - Source: dev.to / 11 months ago
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Looker - Looker makes it easy for analysts to create and curate custom data experiencesโso everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.
NumPy - NumPy is the fundamental package for scientific computing with Python
Tableau - Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.
Seaborn - Seaborn is a Python data visualization library that uses Matplotlib to make statistical graphics.
Microsoft Power BI - BI visualization and reporting for desktop, web or mobile