MemphisMeng

def scatterit(week):
    """
    Filters and plot the dataframe as a scatter plot of teams' defence performance
Args:
    -----
        * week (str): the period to filter on, or "Overall" to display the entire season
Returns:
    --------
        A matplotlib scatter plot
"""

MemphisMeng

def barit(week, measure):
    """
    Filters and plot the dataframe as a bar plot of teams' defence performance
    Args:
    -----
        * week (str): the period to filter on, or "Overall" to display the entire season
        * measure (str): the option of measurement
    Returns:
    --------
        A matplotlib bar plot

MemphisMeng

measure = widgets.Dropdown(
    options=['playResult', 'offensePlayResult', 'epa'],
    value='playResult',
    description='Measure',
)
week = widgets.Dropdown(
    options=['Overall', 'Week 1', 'Week 2', 'Week 3', 'Week 4', 'Week 5', 'Week 6', 'Week 7', 'Week 8',
             'Week 9', 'Week 10', 'Week 11', 'Week 12', 'Week 13', 'Week 14', 'Week 15', 'Week 16', 'Week 17'],
    value='Overall',
    description='Period',

MemphisMeng

# data preparation
plays = pd.read_csv('../input/nfl-big-data-bowl-2021/plays.csv')
games = pd.read_csv('../input/nfl-big-data-bowl-2021/games.csv')
players = pd.read_csv('../input/nfl-big-data-bowl-2021/players.csv')
dataDir = "/kaggle/input/nfl-big-data-bowl-2021/"
weeks = pd.DataFrame(columns=['time', 'x', 'y', 's', 'a', 'dis', 'o', 'dir', 'event', 'nflId',
       'displayName', 'jerseyNumber', 'position', 'frameId', 'team', 'gameId',
       'playId', 'playDirection', 'route'])
for i in range(1,18):
    weeks = pd.concat([weeks, pd.read_csv(dataDir+"week{}.csv".format(i))], ignore_index=True)

MemphisMeng

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import base64
import os
import re
import math
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.patches as patches

MemphisMeng

from ipywidgets import widgets, interactive, interact, interact_manual

MemphisMeng

import os
from pymongo import MongoClient

# define on heroku settings tab
MONGODB_URI = os.environ['MONGODB_URI']

cluster = MongoClient(MONGODB_URI)
db = cluster['QnA']
collection = db['QnA']

MemphisMeng

# if the request was not get, it must be POST and we can just proceed with sending a message back to user
    else:
        # get whatever message a user sent the bot
        output = request.get_json()
        for event in output['entry']:
            messaging = event['messaging']
            for message in messaging:
                if message.get('message'):
                    # Facebook Messenger ID for user so we know where to send response back to
                    recipient_id = message['sender']['id']

MemphisMeng

response, similarity = transformer.match_query(message['message'].get('text'))

                        # no acceptable answers found in the pool
                        if similarity < 0.5:
                            response = "Please wait! Our representative is on the way to help you!"
                            bot.send_text_message(recipient_id, response)
                        else:
                            responses = response.split('|')
                            for r in responses:
                                if r != '':

MemphisMeng

from collections import Counter
import en_core_web_sm

# preprocessing
    nlp = en_core_web_sm.load()
    tweet_article = nlp('|'.join(tweets.tweets))
    # make sure the entities we need are persons
    items = [x.text for x in tweet_article.ents if x.label_ == 'PERSON']
    # exclude the obvious misclassified entities
    items = [celebrity[0] for celebrity in Counter(items).most_common(20) if