kurasaiteja

*/Head.js*/
import React from 'react';

const head = (props) => {
	return(
		
		<p>I'm {props.name}.Welcome! </p>
		
		)
}

kurasaiteja

/*Register.js*/

import React from 'react';
import PropTypes from 'prop-types';
import classNames from 'classnames';
import { withStyles } from '@material-ui/core/styles';
import MenuItem from '@material-ui/core/MenuItem';
import Head from './Head'
import TextField from '@material-ui/core/TextField';

kurasaiteja

def perform_bernoulli_trials(n, p):
    #Perform n Bernoulli trials with success probability p and return number of successes.
    # Initialize number of successes: n_success
    n_success = 0
# Perform trials
    for i in range(n):
        # Choose random number between zero and one: random_number
         random_number = np.random.random()
# If less than p, it's a success so add one to n_success
         if random_number<p:

kurasaiteja

from tweepy import OAuthHandler
from tweepy import API

# Consumer key authentication(consumer_key,consumer_secret can be collected from our twitter developer profile)
auth = OAuthHandler(consumer_key, consumer_secret)

# Access key authentication(access_token,access_token_secret can be collected from our twitter developer profile)
auth.set_access_token(access_token, access_token_secret)

# Set up the API with the authentication handler

kurasaiteja

from tweepy import Stream

# Set up words to track  
keywords_to_track = ['#javascript','#python']

# Instantiate the SListener object 
listen = SListener(api)

# Instantiate the Stream object
stream = Stream(auth, listen)

kurasaiteja

tweets = []
files  = list(glob.iglob('/content/tweets.json'))
for f in files:
    fh = open(f, 'r', encoding = 'utf-8')
    tweets_json = fh.read().split("\n")

    ## remove empty lines
    tweets_json = list(filter(len, tweets_json))

    ## parse each tweet

kurasaiteja

def check_word_in_tweet(word, data):
    """Checks if a word is in a Twitter dataset's text. 
    Checks text and extended tweet (140+ character tweets) for tweets,
    retweets and quoted tweets.
    Returns a logical pandas Series.
    """
    contains_column = data['text'].str.contains(word, case = False)
    contains_column |= data['extended_tweet-full_text'].str.contains(word, case = False)
    contains_column |= data['quoted_status-text'].str.contains(word, case = False) 
    contains_column |= data['retweeted_status-text'].str.contains(word, case = False) 

kurasaiteja

# Print created_at to see the original format of datetime in Twitter data
print(df_tweet['created_at'].head())

# Convert the created_at column to np.datetime object
df_tweet['created_at'] = pd.to_datetime(df_tweet['created_at'])

# Print created_at to see new format
print(df_tweet['created_at'].head())

# Set the index of df_tweet to created_at

kurasaiteja

import matplotlib.pyplot as plt
# Average of python column by day
mean_python = df_tweet['python'].resample('1 min').mean()

# Average of js column by day
mean_js = df_tweet['js'].resample('1 min').mean()

# Plot mean python/js by day
plt.plot(mean_python.index.minute, mean_python, color = 'green')
plt.plot(mean_js.index.minute, mean_js, color = 'blue')

kurasaiteja

def calculateCentroid(place):
    """ Calculates the centroid from a bounding box."""
    # Obtain the coordinates from the bounding box.
    coordinates = place['bounding_box']['coordinates'][0]
    
    longs = np.unique( [x[0] for x in coordinates] )
    lats  = np.unique( [x[1] for x in coordinates] )
    
    if len(longs) == 1 and len(lats) == 1:
        # return a single coordinate