Funnel.io
Supermetrics
DataTap
Looker
Google Analytics
Workato
Improvado.io
Stitch
Matplotlib
Pandas
NumPy
Seaborn
D3.js
Plotly
GnuPlot
Jupyter
Funnel.io
MatplotlibBased on our record, Matplotlib seems to be a lot more popular than Funnel.io. While we know about 114 links to Matplotlib, we've tracked only 10 mentions of Funnel.io. 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.
There are like a 100 services that will do that for you. Something like this. Source: over 3 years ago
Any digital marketers have experience working with single connectors for Google Data Studio? I really like the idea of plugging all the data sources into the ETL platform and having one connector for GDS. It appears funnel.io does this but it's far too expensive for us. Windsor.ai also looks ok but their pricing structure isn't ideal. Played around with Adverity as well but looking for something that's more plug... Source: almost 4 years ago
From experience writing & maintaining custom ETLs in BigQuery, to paying/trying multiple data pipeline partners, to a sort-of middle ground like AirByte - this is not a plug - funnel.io has been the easiest and most cost effective by far. Source: over 4 years ago
You have to be careful with fb figures. Its well known in the industry that they arent accurate. With regards to funnel.io, if they are picking figures from FB then its also suspect. Source: over 4 years ago
The other platform (funnel.io) may use a different attribution window and/or it might not track across different devices (not sure here, never used funnel.io before). Source: over 4 years ago
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 / 4 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 / 7 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 / 8 months ago
NetworkX and Matplotlib were used to visualize the graph structure of the agent. - Source: dev.to / 9 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 / 10 months ago
Supermetrics - Supermetrics simplifies marketing analytics by connecting, consolidating, and centralizing data from 150+ platforms into your favorite tools. Trusted by 200K+ organizations, we empower marketers to focus on insights, not manual work.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
DataTap - Adverity is the best data intelligence software for data-driven decision making. Connect to all your sources and harmonize the data across all channels.
NumPy - NumPy is the fundamental package for scientific computing with 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.
Seaborn - Seaborn is a Python data visualization library that uses Matplotlib to make statistical graphics.