Pandas
NumPy
Scikit-learn
OpenCV
Dataiku
Exploratory
htm.java
WEKA
Whatagraph
Databox
Supermetrics
Owler
QlikSense
Pyramid Analytics
Foxmetrics
Looker
Pandas WhatagraphBased on our record, Pandas seems to be a lot more popular than Whatagraph. While we know about 219 links to Pandas, we've tracked only 4 mentions of Whatagraph. 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.
Libraries for data science and deep learning that are always changing. - Source: dev.to / 19 days ago
# Read the content of nda.txt Try: Import os, types Import pandas as pd From botocore.client import Config Import ibm_boto3 Def __iter__(self): return 0 # @hidden_cell # The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials. # You might want to remove those credentials before you share the notebook. Cos_client = ibm_boto3.client(service_name='s3', ... - Source: dev.to / about 1 month ago
As with any web scraping or data processing project, I had to write a fair amount of code to clean this up and shape it into a format I needed for further analysis. I used a combination of Pandas and regular expressions to clean it up (full code here). - Source: dev.to / about 1 month ago
Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 3 months ago
This tutorial provides a concise and foundational guide to exploring a dataset, specifically the Sample SuperStore dataset. This dataset, which appears to originate from a fictional e-commerce or online marketplace company's annual sales data, serves as an excellent example for learning and how to work with real-world data. The dataset includes a variety of data types, which demonstrate the full range of... - Source: dev.to / 9 months ago
I recommend pulling this easily into whatagraph.com through drag & drop functionality. Amazing integration depth, also! Source: almost 4 years ago
Try whatagraph.com. Should do the job for you. Source: almost 4 years ago
Hey everyone, Just like the title says that's what Whatagraph.com is - those of you who are looking to significantly improve your data aggregation, visualization, and reporting capabilities, I would love to invite you to our webinar next week on Tuesday at 3pm BST.https://www.linkedin.com/events/6793088092371763200/. Source: about 4 years ago
The space I am more aware of is the data integration part of the process, and my team uses hotglue (though hotglue is built for developers) to collate the data into one place, do any transformations necessary (the transformations are done in Python in hotglue), and then send it to the tool we use (we recently switched from Databox to Whatagraph). The nice thing about this for us is we can actually remain on the... Source: about 4 years ago
NumPy - NumPy is the fundamental package for scientific computing with Python
Databox - Databox is an easy-to-use analytics platform that helps growing businesses centralize their data, and use it to make better decisions and improve performance.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
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.
OpenCV - OpenCV is the world's biggest computer vision library
Owler - Owler is a crowdsourced data model allowing users to follow, track, and research companies.