DanielSnipes

def show_coefs(data, classifier_name):
    cols = data.columns
    coefficients = pd.concat([pd.DataFrame(cols), pd.DataFrame(np.transpose(classifier_name.coef_))], axis = 1)
    coefficients.columns = ['name', 'coef']
    coefficients['OR'] = np.e**coefficients['coef']
    return coefficients.dropna()

DanielSnipes

import io
import requests
import pandas as pd
from bokeh.models import ColumnDataSource, HoverTool, ResizeTool, SaveTool
from bokeh.models.widgets import TextInput, Button
from bokeh.plotting import figure, curdoc
from bokeh.layouts import row, widgetbox


TICKER = ""

DanielSnipes

Title

<Additional information about your API call. Try to use verbs that match both request type (fetching vs modifying) and plurality (one vs multiple).>

• URL

  <The URL Structure (path only, no root url)>

• Method:

DanielSnipes

A curated list of AWS resources to prepare for the AWS Certifications

A curated list of awesome AWS resources you need to prepare for the all 5 AWS Certifications. This gist will include: open source repos, blogs & blogposts, ebooks, PDF, whitepapers, video courses, free lecture, slides, sample test and many other resources.

For more about AWS and AWS Certifications and updates to this Gist you should follow me @leonardofed

---

DanielSnipes

AWS region code	AWS region name	Number of AZs	AZ names
us-east-1	Virginia	4	us-east-1a, us-east-1b, us-east-1c, us-east-1e
us-west-1	N. California	2	us-west-1a, us-west-1b
us-west-2	Oregon	3	us-west-2a, us-west-2b, us-west-2c
eu-west-1	Ireland	3	eu-west-1a, eu-west-1b, eu-west-1c
eu-central-1	Frankfurt	2	eu-central-1a, eu-central-1b
ap-southeast-1	Singapore	2	ap-southeast-1a, ap-southeast-1b
ap-southeast-2	Sydney	2	ap-southeast-2a, ap-southeast-2b, ap-southeast-2c
ap-northeast-1	Tokyo	2	ap-northeast-1a, ap-nort

DanielSnipes

import json
import urllib.parse
import boto3

#need these to return random string
import random
import string

print('Loading function')

DanielSnipes

DanielSnipes

import pandas as pd
from pyspark.sql.types import *

#Create Pandas DataFrame
pd_person = pd.DataFrame({'PERSONID':'0','LASTNAME':'Doe','FIRSTNAME':'John','ADDRESS':'Museumplein','CITY':'Amsterdam'}, index=[0])

#Create PySpark DataFrame Schema
p_schema = StructType([StructField('ADDRESS',StringType(),True),StructField('CITY',StringType(),True),StructField('FIRSTNAME',StringType(),True),StructField('LASTNAME',StringType(),True),StructField('PERSONID',DecimalType(),True)])

#Create Spark DataFrame from Pandas