Matplotlib
Pandas
NumPy
Seaborn
D3.js
Plotly
GnuPlot
Jupyter
Bloom Analytics
Triple Whale
BeProfit
Conversific
RetentionX
OWOX BI
Polar Analytics
Rockerbox
Bloom tracks your true ecommerce profit after ads, shipping, COGS, transaction fees, refunds, and operating expenses, so you stop relying on vanity metrics and see which products, campaigns, and channels actually drive profit. Track financial performance across products, orders, countries, ad campaigns, and email campaigns, drilling into Product Intelligence, Country Profits, and Email Profits to understand exactly where profit comes from and where it leaks. Build custom dashboards tailored to your business to surface the metrics that matter most, all in one clean view. View a detailed Profit & Loss table with a toggle to switch between Shopify and Amazon revenue, or see them separately, and measure ROAS, POAS, Contribution Margin, and Net Profit from a single dashboard. Connect Google Ads, Meta Ads, TikTok, Pinterest, and Snapchat to see which channels bring profitable customers, not just clicks, and connect Klaviyo, Mailchimp, and Omnisend to see email profits per campaign. Use multi-touch attribution to understand the complete customer journey and accurately track campaign performance across channels, then apply custom cost rules based on product, quantity, country, shipping zone, and operational expenses to calculate true profit with precision. Get profit insights in Email and Slack, including scheduled Slack summaries, to quickly spot wasted ad spend, declining margins, underperforming products, scaling opportunities, and hidden profit leaks. Connect Bloom to Claude or ChatGPT via MCP to query your profit data in natural language. Whether you run one Shopify store or many, stop guessing and start scaling what actually makes money.
Matplotlib Bloom AnalyticsBloom Analytics's answer:
The primary audience includes Shopify e-commerce businesses focused on improving profitability, tracking marketing performance, and making data-driven growth decisions.
Bloom Analytics's answer:
Bloom Analytics is primarily built using Ruby on Rails to create a fast, reliable, and scalable analytics platform for Shopify businesses.
Bloom Analytics's answer:
-CAPS -Curio Blvd -OMOYE -thecupcakequeens
Bloom Analytics's answer:
It is budget friendly, It focuses on Profit calculation and attribution, also helps in customer journey and company performances Profitability.
Bloom Analytics's answer:
Bloom Analytics helps you clearly understand your business profit across products, marketing channels, countries, and order fulfillment. It shows how each part of your store contributes to profit — all from one simple dashboard.
Bloom Analytics's answer:
While working with Shopify brands, we kept hearing the same feedback that we’re making sales, but we still don’t know our actual profit. It made sense. With ad spending, shipping costs, product costs, discounts, and fees, tracking real profit can get messy quickly. Most store owners find themselves hopping between different dashboards just to understand what’s working. So, we built Bloom Analytics. It’s a simple profit analytics platform that helps Shopify businesses understand- What products are profitable, which countries and campaigns provide the best returns, which ad platforms truly generate profit through multi-touch attribution, and all from one clear dashboard. No confusing spreadsheets. No endless tabs. Just clear profit insights that help brands make better decisions.
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 — 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 / 6 months ago
Numbers are useful, but sometimes it’s 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 / 9 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 / 10 months ago
NetworkX and Matplotlib were used to visualize the graph structure of the agent. - Source: dev.to / 11 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, A…. - Source: dev.to / 12 months ago
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Triple Whale - Triple Whale helps ecommerce brands make better decisions with better data.
NumPy - NumPy is the fundamental package for scientific computing with Python
BeProfit - Track and understand your Shopify data. Optimize profits!
Seaborn - Seaborn is a Python data visualization library that uses Matplotlib to make statistical graphics.
Conversific - Conversific is a Business Intelligence platform designed to capture and analyze the data from your Shopify store. It includes built-in tips from ecommerce gurus and provides instant guidance.
