Ziflow's enterprise online proofing software helps deliver marketing projects faster by streamlining the review and approval of creative content. Developed by the founders of ProofHQ, Ziflow is the industry's leading enterprise-ready standalone online proofing solution, designed for agencies and brands.
Pandas is particularly recommended for data scientists, analysts, and engineers who need to perform data cleaning, transformation, and analysis as part of their work. It is also suitable for academics and researchers dealing with data in various formats and needing powerful tools for their data-driven research.
We were in the 'stone age' before Ziflow - we were using email, dropbox and a project management platform to collect feedback on creative files/projects - it was a nightmare to manage. Ziflow helps streamline this process, and much more. Some features we love include... Ziflow accepts so many different creative file types (we haven't found any they don't accept). Workflows with automated stages make sure we have the right people reviewing at the right time. Security was important to our company, and Ziflow goes well beyond what the competitors offer. Customer support is great - if we ever have questions or problems they quickly respond and address.
I've tried other online proofing software but Ziflow is head and shoulders above the others. It's a bit more money than some of the other cheaper proofing solutions but you certainly get you money's worth with features and customer support.
Based on our record, Pandas seems to be more popular. It has been mentiond 219 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.
Libraries for data science and deep learning that are always changing. - Source: dev.to / about 1 month 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 2 months 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 / 2 months 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 / 4 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 / 10 months ago
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