pshapiro

hamletbatista

input_features:
    -
        name: Questions
        type: text
        level: word
        encoder: parallel_cnn

output_features:
    -
        name: Category0

hamletbatista

input_features:
    -
        name: text
        type: text
        level: word
        encoder: parallel_cnn

output_features:
    -
        name: category

quadrismegistus

def draw_graph3(networkx_graph,notebook=True,output_filename='graph.html',show_buttons=True,only_physics_buttons=False):
    """
    This function accepts a networkx graph object,
    converts it to a pyvis network object preserving its node and edge attributes,
    and both returns and saves a dynamic network visualization.
    
    Valid node attributes include:
        "size", "value", "title", "x", "y", "label", "color".
        
        (For more info: https://pyvis.readthedocs.io/en/latest/documentation.html#pyvis.network.Network.add_node)

korakot

# install chromium, its driver, and selenium
!apt update
!apt install libu2f-udev libvulkan1
!wget https://dl.google.com/linux/direct/google-chrome-stable_current_amd64.deb
!dpkg -i google-chrome-stable_current_amd64.deb
!wget https://edgedl.me.gvt1.com/edgedl/chrome/chrome-for-testing/118.0.5993.70/linux64/chromedriver-linux64.zip
!unzip -j chromedriver-linux64.zip chromedriver-linux64/chromedriver -d /usr/local/bin/
!pip install selenium chromedriver_autoinstaller

# set options to be headless, ..

korakot

import re
import pandas as pd
from google.cloud import bigquery
from google.colab import auth

PROJECT_ID = 'sql-hunt'  # change to your own project

class BqTable:
  
  def __init__(self, table, dataset='samples', active_project='bigquery-public-data', client=None):

onurmatik

from xml.dom import minidom
from cStringIO import StringIO


class Graph():
    """
    Create a Graph object and get its gexf representation.
    Add nodes and edges using add_node() and add_edge() methods.
    Use gexf() to get the file-like object containing the graph. 
    """

PurpleBooth

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

yong27

import multiprocessing
import pandas as pd
import numpy as np

def _apply_df(args):
    df, func, kwargs = args
    return df.apply(func, **kwargs)

def apply_by_multiprocessing(df, func, **kwargs):
    workers = kwargs.pop('workers')