LiveChat is highly recommended for businesses of all sizes that wish to improve their customer support operations. It's particularly beneficial for eCommerce platforms, SMEs, and large enterprises looking to increase sales through enhanced customer communication and support.
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.
Based on our record, Pandas seems to be a lot more popular than LiveChat. While we know about 219 links to Pandas, we've tracked only 2 mentions of LiveChat. 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.
Some of it is going to depend on your budget and needs. Many (most?) livechat providers offer WP functionality. For example, livechat.com has a free WP plugin to offer livechat on WP sites:. Source: about 3 years ago
I am free to use any existing library or whatnot, what I was wondering is how easy it is to implement and deploy. I'm not being asked to build a full live chat from scratch, just try and implement a solution that won't add monthly charges to the predicted monthly cost of the website (ie pre-made solutions such as livechat.com that would cost atleast $16/mo). Source: over 4 years ago
Libraries for data science and deep learning that are always changing. - Source: dev.to / about 2 months 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 / 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
Intercom - Intercom is a customer relationship management and messaging tool for web businesses. Build relationships with users to create loyal customers.
NumPy - NumPy is the fundamental package for scientific computing with Python
tawk.to - tawk.to is a free live chat app that lets you monitor and chat with visitors on your website or from a free customizable page
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
LiveAgent - LiveAgent is a fully-featured web-based live chat and help desk software. It harnesses the power of a universal inbox, real-time live chat, built-in call center, and a robust customer service portal. Start your free 1 month trial today!
OpenCV - OpenCV is the world's biggest computer vision library