ZoomInfo
Apollo.io
Lusha
Hunter.io
Datanyze
DiscoverOrg
Clearbit
UpLead
Matplotlib
Pandas
NumPy
Seaborn
D3.js
Plotly
GnuPlot
Jupyter
ZoomInfo MatplotlibZoomInfo makes it much easier to identify decision-makers, prioritize high-value accounts, and keep pipelies moving. With powerful AI automation and high-quality data, it's an all-in-one solution for sales, marketing, and RevOPs teams who want to save time and drive revenue growth.
As a B2B database, ZoomInfo certainly has a lot to offer. Its detailed business information on people and companies is impressive, and I've found it to be a useful resource for researching potential clients and partners.
However, I do have a few criticisms of the product. Firstly, its pricing is quite steep, especially compared to other B2B databases on the market. This makes it difficult for smaller businesses or startups to justify the cost. Additionally, while the information on ZoomInfo is generally accurate and up-to-date, I have come across a few instances where the information was incomplete or outdated.
Despite these drawbacks, ZoomInfo is still a good resource for business information. It just might not be the best option out there. If you're willing to pay for a premium service, then ZoomInfo could be worth considering. However, if you're looking for more cost-effective alternatives, there are several options to consider.
Based on our record, Matplotlib seems to be a lot more popular than ZoomInfo. While we know about 114 links to Matplotlib, we've tracked only 8 mentions of ZoomInfo. 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.
Skip ZoomInfo for this use case. It's enterprise-contract priced, built for outbound prospecting from company lists, and adds zero value for personal email resolution. Same story with Lusha โ excellent for Chrome extension-style lookups starting from a LinkedIn profile, wrong tool for an automated inbound pipeline. - Source: dev.to / about 2 months ago
Most SDR stacks start with a contact database โ ZoomInfo, Apollo, or Lusha โ and treat enrichment as a one-time step at the top of the funnel. The problem: these databases are 3โ18 months stale on average. Job titles change. Companies restructure. Decision-makers who were Director of Engineering in Q1 are VP by Q3. - Source: dev.to / 3 months ago
Kaspr is the most LinkedIn-native option on this list. The Chrome extension sits on LinkedIn profiles and exports contact data directly โ phone numbers, emails, and CRM sync. 120M+ European contacts is their differentiator; US coverage is noticeably thinner. Starting at $74/month, it's not cheap for light usage, but teams running 200+ LinkedIn outreach touches per month will find the unit economics work. Kaspr is... - Source: dev.to / 3 months ago
For the phone calls - there's a decent chance they got your number from zoominfo.com - you can go there and request to be removed. Source: about 3 years ago
I just found a few data collator sites, zoominfo.com and signalhire.com are just two - they seem to scrape sites like Linkedin etc and collate everything. Personal numbers can be found pretty easily, if you've ever signed up for a business identification number it could be there, could be in a data leak somewhere - these companies are pretty shady and will buy data from places to just get a lead. Source: over 3 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
Apollo.io - Apolloโs predictive prospecting, sales engagement, and actionable analytics help the teams to reach its full revenue potential.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Lusha - Search less. Sell more.
NumPy - NumPy is the fundamental package for scientific computing with Python
Hunter.io - Find all the email addresses related to a domain
Seaborn - Seaborn is a Python data visualization library that uses Matplotlib to make statistical graphics.
