chelseatroy

import pandas as pd
import numpy as np
import itertool
from nltk import word_tokenize
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB

df = pd.read_csv('my_data_with_text.csv')
df.columns #id, text, category

chelseatroy

import time
import threading

class Elevator:
    def __init__(self, control, timer=Timer(), eventlog=EventLog()):
        self.control = control
        self.timer = timer
        self.eventlog = eventlog

        #Python thinks it's a dict if you

chelseatroy

import time

class ElevatorButtonPanel:
    def __init__(self, elevator):
        self.elevator = elevator

    # Logic of the elevator
    def request_button_pressed(self, floor):
        self.elevator.go_to_floor(floor)

chelseatroy

# metal.py
# 
# METAL, by Dave Beazley
# dabeaz.com

# One of the main roles of a compiler is taking high-level programs
# such as what you might write in C or Python and reducing them to
# instructions that can execute on actual hardware.
#
# This file implements a very tiny CPU in the form of a Python

chelseatroy

# Top level function that interprets an entire program. It creates the
# initial environment that's used for storing variables.

def interpret_program(model):
    # Make the initial environment (a dict).  The environment is
    # where you will create and store variables.
    env = {}
    for structure in model.statements:
        interpret(structure, env)

chelseatroy

# Top level function that interprets an entire program. It creates the
# initial environment that's used for storing variables.

def interpret_program(model):
    # Make the initial environment (a dict).  The environment is
    # where you will create and store variables.
    env = {'constants':{}, 'variables':{}}
    for structure in model.statements:
        interpret(structure, env)

chelseatroy

class Negative:
    def __init__(self, value):
        self.value = value

    def __repr__(self):
        return f"Negative({self.value})"

class Integer:
    '''
    Example: 42

chelseatroy

import numpy as np
import pandas as pd
import sklearn
import nltk

from sklearn.feature_extraction.text import TfidfVectorizer

class Content():
    _cached_data = None
    

chelseatroy

from visualizations.models import Visualization

class InfectionRateVisualization(Visualization):

    class Meta:
        proxy = True

    def from_data(csvfile):
        department_instances = []
        infection_type = []

chelseatroy

from __future__ import unicode_literals

from django.shortcuts import render
from models import Visualization
from exceptions import IncompleteDataException
from .forms import VisualizationForm
from django.contrib.auth.decorators import login_required

def _inject_visualization(name):
    if name == "infection_rate_visualization":