jcdevilleres

# This is a sample pandas script which you can integrate with your Tableau Prep flow
# 'import pandas as pd' can be skipped as it is already loaded on your server
# 'df is your input dataset' which you connected

def get_data(df):
    return df.head(100)

def drop_duplicates(df):
    return df.drop_duplicates()

jcdevilleres

import paho.mqtt.client as mqtt 
import time

def on_message(client, userdata, message):
    print("message received " ,str(message.payload.decode("utf-8")))
    print("message topic=",message.topic)

broker = "{Broker IP Address}" 
username = "{MQTT Client Username}"
password = "{MQTT Client Password}"

jcdevilleres

responsibilities	ability to control & maintain large scale data coming from different sources.	mining data from primary and secondary sources, then reorganizing said data in a format that can be easily read by either human or machine.	using statistical tools to interpret data sets, paying particular attention to trends and patterns that could be valuable for diagnostic and predictive analytics efforts.	preparing reports for executive leadership that effectively dashboard, communicate trends, patterns, and predictions using relevant data.	records macros & knowledge of basic programming of vb through excel sheets
responsibilities    collecting and interpreting data    analyzing results    reporting the results back to the relevant members of the business    identifying patterns and trends in data sets    working alongside teams within the business or the management team to establish business needs    defining new data collection and analysis processes    controlling existing database    processing weekly and mo

jcdevilleres

import os

# Initialize variables
directory = str(input("Enter directory for documents: ")) # Wait for input directory
document = ""
corpus = ""

# Utility functions
def doc_to_cor(document):
    fedit = open(directory.replace('/','') + ".cor", "a")

jcdevilleres

import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = [12, 10] # Set out plot size

# Import required libraries, including gensim, matplotlib, and pandas
import pprint
import sys
from collections import defaultdict

import gensim
from gensim import corpora

jcdevilleres

# Import libraries required for neutral network
import torch
# Vision-specific library which will contain our dataset and help us define how our images are loaded
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim

# Create image transform when we load the dataset of PIL images
transform = transforms.Compose(
    [transforms.ToTensor(), #Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.