Precoro
Procurify
Ariba
BuyerQuest
Coupa
SAP Fieldglass
Bellwether
Jaggaer
Matplotlib
Pandas
NumPy
Seaborn
D3.js
Plotly
GnuPlot
Jupyter
Approve documents 2,5x faster from any device using email or Slack notifications. Streamline approval workflow by adding as many steps as you need and assigning specific roles for colleagues.
Save up to 19% of your purchasing budget. Track discounts and never spend more than planned. Increase cash flow transparency by monitoring corporate expenses (including reimbursements). Get clear analytics and insightful reports to plan your procurement strategy more thoughtfully.
Reduce manual data entry. Create, approve and track POs just in a few clicks or transfer your orders from Amazon Business via Punch-in. Manage suppliers, item catalogs, inventory, and more within one platform.
Connect Precoro with your ERP and other business tools using ready-made integrations (NetSuite, QuickBooks, Xero) or a free API. Forget about duplicated payments and manual document matching.
Keep all your data secure with SSO and reliable 2-factor authentication.
Available for both iOS and Android devices, the Precoro mobile app brings key aspects of your procurement workflow into the palm of your hand.
Precoro's user-friendly interface lets you forget about complex onboarding and long-lasting training. You'll get advisory and support from your CSM anytime you need it. Precoro grants you access to all features and updates regularly.
Sincerely yours, Precoro team
Precoro MatplotlibPrecoro is recommended for small to medium-sized businesses and organizations that need to automate their procurement processes, improve cost control, and enhance transparency in purchasing and spending. It is suitable for companies across various industries looking for a scalable solution to manage their procurement activities efficiently.
We can track each purchase order, who created it, and why. They are no longer lost, and we can always see the history. The dashboard allows us to monitor information about all essential documents and at what stage they are now and keep track of which invoices have not yet been paid or partially paid. By setting up budgets for each department, we can effectively plan the allocation and prevent overspending. The system is very flexible and convenient. I can't imagine my work without it.
I easily plan budgets for each project, promptly track spending, and create custom reports. Precoro allows doing this in 2 clicks. All suppliers and items are in one place, so purchase requests and orders are quickly created without errors. Precoro is a cloud-based tool, so I can use it from any device as long as I have internet. This greatly speeds up approval time and helps me keep everything under control no matter where I am.
Each employee has access to Precoro at least for purchase requisition creation. This is very convenient because the budgets of each department are visible to their employees. Attachments and comments can be added to the requisitions, and items are added directly from the loaded catalog. All subsequent docs (from purchase order to receipt) are created automatically, significantly saving time. All statuses can be tracked in real-time and are always correct. The approval process is customizable; creating 1 or more stages to comply with the company's rules is possible.
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 / 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
Procurify - Reinvent the way organizations spend to make the purchasing process more accessible, manageable and convenient.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Ariba - Ariba is a software and information technology services platform providing companies with collaborative business commerce solutions.
NumPy - NumPy is the fundamental package for scientific computing with Python
BuyerQuest - BuyerQuest is the cloud platform for buying at work. The only provider offering true one-stop shopping for goods and services for everyone in your organization. Join the procurement revolution.
Seaborn - Seaborn is a Python data visualization library that uses Matplotlib to make statistical graphics.