kmcelwee

import wget
from os.path import join as pjoin

OUTPUT_DIR = 'tweet-imgs'
media_tweets = [tweet for tweet in tweets if 'media' in tweet['entities']]
for tweet in media_tweets:
    for i, media in enumerate(tweet['entities']['media']):
        url = media['media_url']
        extension = url.split('.')[-1]
        assert extension in ['jpg', 'png']

kmcelwee

Web Scraping & Data Analysis Cheat Sheet

Case Study

• Fortune 100 & BLM
• Coding notebook example (If it has trouble loading, try refreshing)

Definitions

kmcelwee

def get_verb(s):
    m = [x.root.head.text for x in nlp(s).noun_chunks if x.root.head.pos_ == 'VERB']
    standardized = [WordNetLemmatizer().lemmatize(x, 'v') for x in m]
    remove = set(['d', "’re", "’m", "’s"])
    filtered = [x for x in standardized if x not in remove]
    return None if len(filtered) == 0 else list(set(filtered))

kmcelwee

reddit = praw.Reddit(client_id=CLIENT_ID, client_secret=CLIENT_SECRET, user_agent=USER_AGENT)

j = []
latest_id = None
for page in range(10):
    sub = reddit.subreddit('FloridaMan')
    for s in s.top(params={'after': latest_id, 't': 'all'}):
        j.append({
            # all the data you want
        })

kmcelwee

def data_transform(data, timesteps, var='x'):
  m = []
  s = data.to_numpy()
  for i in range(s.shape[0]-timesteps):
      m.append(s[i:i+timesteps].tolist())

  if var == 'x':
      t = np.zeros((len(m), len(m[0]), len(m[0][0])))
      for i, x in enumerate(m):
          for j, y in enumerate(x):

kmcelwee

HOURS_AHEAD = 24
s = all_X.shape[1]

model = tf.keras.Sequential()
model.add(layers.Dense(s, activation=tf.nn.relu, input_shape=(HOURS_AHEAD, all_X.shape[1])))
model.add(layers.Dense(s, activation=tf.nn.relu))
model.add(layers.Dense(s, activation=tf.nn.relu))
model.add(layers.Dense(s, activation=tf.nn.relu))
model.add(layers.Dense(s, activation=tf.nn.relu))
model.add(layers.Flatten())

kmcelwee

def heat(l, alpha, time_steps):
    '''apply the heat equation to list l, given constants alpha and time_steps'''
    return_l = []
    for t in range(time_steps):
        if len(return_l) != 0:
            l = return_l
            return_l = []
        for i, x in enumerate(l):
            if i == 0:
                diff = (0 - l[i]) - (l[i] - l[i+1])

kmcelwee

from scipy.stats import norm

def peak_likelihood(hist=None, 
        tomorrow=None, tomorrow_std=None, 
        two_day=None, two_day_std=None, 
        three_day=None, three_day_std=None):
    '''
    Given the predictions and standard deviation of the three-day forecast, in
    addition to the highest load so far this month, what is the likelihood that
    a sample from tomorrow's distribution will be higher than the other three.