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##I Tested to see if this stil works. The CSS had changed a bit. I updated the xpaths for the login steps, but I don't have any more | |
##videos to watch so I couldn't check the page continue or the timer find stuff. Pretty easy to help trouble shoot if it doesn't work | |
##when you try it. | |
##You'll need Chromedriver in the same path as the python script | |
##https://chromedriver.storage.googleapis.com/index.html?path=84.0.4147.30/ | |
from bs4 import BeautifulSoup as soup | |
from selenium import webdriver | |
import time |
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from gpiozero import Motor, InputDevice | |
from time import sleep, time | |
SAMPLETIME = 1 | |
r = Motor(forward=5, backward=6) | |
state = False | |
b = InputDevice(4) | |
r.forward() |
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import pandas as pd | |
from selenium import webdriver | |
from bs4 import BeautifulSoup as soup | |
import time | |
import os | |
url = 'http://finra-markets.morningstar.com/BondCenter/Results.jsp' | |
driver = webdriver.Chrome() | |
driver.get(url) | |
time.sleep(5) |
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import pandas as pd | |
import matplotlib.pylab as plt | |
import datetime | |
import seaborn as sns | |
all_data = pd.DataFrame(columns=['Rating', 'Status', 'all', 'Company']) | |
dates = pd.read_excel('nowaitdates.xlsx') | |
companies = dates['Company'].tolist() | |
data = pd.read_excel('new.xlsx') |
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import os | |
import pandas as pd | |
import datetime | |
def waiting(text): | |
if ' wait ' in text.lower() or ' waiting' in text.lower(): | |
return 1 | |
else: | |
return 0 |
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import requests | |
from bs4 import BeautifulSoup as soup | |
import pandas as pd | |
import time | |
import json | |
import time | |
import urllib.request | |
def GetReviews(URL): | |
start = time.time() |
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import pandas as pd | |
import plotly.plotly as py | |
from plotly.graph_objs import * | |
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot | |
import matplotlib.pyplot as plt | |
mapbox_access_token = ### | |
df = pd.read_csv('https://data.nashville.gov/api/views/j7b8-4fv6/rows.csv?accessType=DOWNLOAD') | |
df['total_students'] = df.iloc[:, 6:21].sum(axis=1) | |
df['Percent Disabled'] = df['Disability'] / df['total_students'] |