kperry2215

<View>
<Header value="Time Series classification" style="font-weight: normal"/>
<TimeSeriesLabels name="label" toName="ts"> 
<Label value="0″/>
<Label value="1″/>
</TimeSeriesLabels>
<TimeSeries name="ts" value="$csv" valueType="url">
<Channel column="first_column"/>
</TimeSeries> 
</View>

kperry2215

conda create -name label-studio
conda activate label-studio
pip install label-studio
label-studio start

kperry2215

df['timestamp'].dt.strftime("%Y-%m-%dT%H:%M:%S.000Z")

kperry2215

df = pd.DataFrame(columns = ["FC #", "Owner Name", "Street", "Zip", "Subdivision", "Balance Due", "Status"],
                             dtype=object)
                             
#Flip through the records and save them

for n in range(2, 15):
    for i in range(3):
        try:
            mytable = driver.find_element_by_css_selector("table[id='ctl00_ContentPlaceHolder1_gvSearchResults']")
            #Read in all of the data into the dataframe

kperry2215

#Select the 'Show All' option
accept_button = driver.find_element_by_id("ctl00_ContentPlaceHolder1_btnShowAll")
accept_button.click()

kperry2215

#Accept terms
accept_button = driver.find_element_by_id("ctl00_ContentPlaceHolder1_btnAcceptTerms") 
accept_button.click() #Wait 10 seconds for the load
driver.implicitly_wait(10)

kperry2215

from selenium import webdriver
import pandas as pd

#Website that we're going to scrape
url = "http://gts.co.jefferson.co.us/index.aspx"
# create a new Firefox session
driver = webdriver.Firefox(executable_path='C:/Users/kirst/Downloads/geckodriver-v0.32.0-win64/geckodriver.exe')
driver.implicitly_wait(30)
driver.get(url)

kperry2215

import quandl
import pandas as pd
import matplotlib.pyplot as plt

quandl_api_key = "YOUR API KEY HERE"

#Use the Quandl API to pull data
quandl.ApiConfig.api_key = quandl_api_key
data = quandl.get_table('WIKI/PRICES', ticker = ['MSFT'])
plot_data(df = data, 

kperry2215

import quandl
import pandas as pd
import matplotlib.pyplot as plt

quandl_api_key = "YOUR API KEY HERE"
#Use the Quandl API to pull data
quandl.ApiConfig.api_key = quandl_api_key
#Pull GDP Data
data = quandl.get('FRED/GDP')
data["date_time"] = data.index

kperry2215

from alpha_vantage.timeseries import TimeSeries
import pandas as pd
import matplotlib.pyplot as plt

alpha_vantage_api_key = "YOUR API KEY HERE"

def pull_daily_time_series_alpha_vantage(alpha_vantage_api_key, ticker_name, output_size = "compact"):
    """
    Pull daily time series by stock ticker name.
    Args: