pshapiro

# library imports

import pandas as pd
from bokeh.io import output_notebook, show
from bokeh.plotting import figure, output_file, ColumnDataSource
from bokeh.models import HoverTool, BoxAnnotation, BoxSelectTool, BoxZoomTool, WheelZoomTool, ResetTool
from bokeh.resources import CDN
from bokeh.embed import file_html

# Import csv into pandas dataframe, direct to KNIME version to follow

jroakes

Browser Built
Navigating to: https://locomotive.agency/ 

{
  "title": [
    "LOCOMOTIVE\u00ae - Enterprise Technical SEO Agency"
  ],
  "description": [
    "LOCOMOTIVE\u00ae - 2019 U.S. Search Awards \"Best SEO Agency\". We are an agency team of enterprise technical, and on-page SEO specialists: Moving you forward."
  ],

a-know

Description

Get and Show cell's value from Google Spreadsheet Widget for Dashing.

Dependency

• gem 'google-api-client'
• gem 'google_drive'

Installation

dashing install e0ad37c2e137d2da0916

jroakes

Machine Learning Repository for SEO

SEO is a field that is rich with data, yet many young SEOs may not be equipped to learn tools that will prepare them for the future. We want to support our community by using our expertise to provide access to more advanced tools that will allow SEOs of all levels to play with the technologies that will shape the future of our work.

Objectives

• Provide a repositiory that makes it possible to learn about ML specifically targeted to those interested in SEO
• Provide a repository that allows a novice user to run a simple model on something meaningful for SEO.
• Provide a repository that allows advanced users to save time on data getting, cleaning, preprocessing, and model selection.
• Allow users to showcase work and models developed.
• Have users get involved with the future development of the repo.

pshapiro

import csv
import os
from sumy.parsers.html import HtmlParser
from sumy.parsers.plaintext import PlaintextParser
from sumy.nlp.tokenizers import Tokenizer
from sumy.summarizers.lsa import LsaSummarizer as Lsa
from sumy.summarizers.luhn import LuhnSummarizer as Luhn
from sumy.summarizers.text_rank import TextRankSummarizer as TxtRank
from sumy.summarizers.lex_rank import LexRankSummarizer as LexRank
from sumy.summarizers.sum_basic import SumBasicSummarizer as SumBasic

pshapiro

dsottimano

//serpApiKey from serpapi.com

var GLOBAL_VARIABLES = {
  
  serpApiKey : "add your api key"  
}

/*
/***************************
/BEGIN PRESENTATION SCRIPTS

ericmjl

UPDATE: I have baked the ideas in this file inside a Python CLI tool called pyds-cli. Please find it here: https://github.com/ericmjl/pyds-cli

How to organize your Python data science project

Having done a number of data projects over the years, and having seen a number of them up on GitHub, I've come to see that there's a wide range in terms of how "readable" a project is. I'd like to share some practices that I have come to adopt in my projects, which I hope will bring some organization to your projects.

Disclaimer: I'm hoping nobody takes this to be "the definitive guide" to organizing a data project; rather, I hope you, the reader, find useful tips that you can adapt to your own projects.

Disclaimer 2: What I’m writing below is primarily geared towards Python language users. Some ideas may be transferable to other languages; others may not be so. Please feel free to remix whatever you see here!