Trouble with Gate.io API call - python

I'm working on python code to update and append token price and volume data using gate.io's API to a .csv file. Basically trying to check to see if it's up to date, and update with the most recently hour's data if not. The below code isn't throwing any errors, but it's not working. My columns are all in the same order as they are in the code. Any assistance would be greatly appreciated, thank you
import requests
import pandas as pd
from datetime import datetime
# Define API endpoint and parameters
host = "https://api.gateio.ws"
prefix = "/api/v4"
url = '/spot/candlesticks'
currency_pair = "BTC_USDT"
interval = "1h"
# Read the existing data from the csv file
df = pd.read_csv("price_calcs.csv")
# Extract the last timestamp from the csv file
last_timestamp = df["time1"].iloc[-1]
# Convert the timestamp to datetime and add an hour to get the new "from" parameter
from_time = datetime.utcfromtimestamp(last_timestamp).strftime('%Y-%m-%d %H:%M:%S')
to_time = datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S')
# Use the last timestamp to make a 'GET' request to the API to get the latest hourly data for the token
query_params = {"currency_pair": currency_pair, "from": from_time, "to": to_time, "interval": interval}
r = requests.get(host + prefix + url, params=query_params)
# Append the new data to the existing data from the csv file
new_data = pd.DataFrame(r.json(), columns=["time1", "volume1", "close1", "high1", "low1", "open1", "volume2"])
df = pd.concat([df, new_data])
# Write the updated data to the csv file
df.to_csv("price_calcs.csv", index=False)


Nevermind figured it out myself

Related

Convert Log file to Dataframe Pandas

I have log files, which have many lines in the form of :
<log uri="Brand" t="2017-01-24T11:33:54" u="Rohan" a="U" ref="00000000-2017-01" desc="This has been updated."></log>
I am trying to convert each line in the log file into a Data frame and store it in csv or excel format. I want only values of uri, t is nothing but time u for username and desc for description
Something like this
Columns :- uri Date Time User Description
Brand 2017-01-24 11:33:54 Rohan This has been updated.
and so on.

As mentionned by #Corralien in the comments, you can use some of beautifulsoup functions (Beautifulsoup and find_all) to parse each line in your logfile separately, then use pandas.DataFrame constructor with a listcomp to make a DataFrame for each line :
import pandas as pd
import bs4 #pip install beautifulsoup4
​
with open("/tmp/logfile.txt", "r") as f:
logFile = f.read()
​
soupObj = bs4.BeautifulSoup(logFile, "html5lib")
​
dfList = [pd.DataFrame([(x["uri"], *x["t"].split("T"), x["u"], x["desc"])],
columns=["uri", "Date", "Time", "User", "Description"])
for x in soupObj.find_all("log")]
#this bloc creates an Excel file for each df​
for lineNumber, df in enumerate(dfList, start=1):
df.to_excel(f"logfile_{lineNumber}.xlsx", index=False)
Output :
print(dfList[0])
uri Date Time User Description
0 Brand 2017-01-24 11:33:54 Rohan This has been updated.
Update :
If you need a single dataframe/spreadsheet for the all the lines, use this :
with open("/tmp/logfile.txt", "r") as f:
soupObj = bs4.BeautifulSoup(f, "html5lib")
df = pd.DataFrame([(x["uri"], *x["t"].split("T"), x["u"], x["desc"])
for x in soupObj.find_all("log")],
columns=["uri", "Date", "Time", "User", "Description"])
df.to_excel("logfile.xlsx", index=False)

Python API Call: JSON to Pandas DF

I'm working on pulling data from a public API and converting the response JSON file to a Pandas Dataframe. I've written the code to pull the data and gotten a successful JSON response. The issue I'm having is parsing through the file and converting the data to a dataframe. Whenever I run through my for loop, I get a dataframe that retruns 1 row when it should be returning approximately 2500 rows & 6 columns. I've copied and pasted my code below:
Things to note:
I've commented out my api key with "api_key".
I'm new(ish) to python so I understand that my code formatting might not be best practices. I'm open to changes.
Here is the link to the API that I am requesting from: https://developer.va.gov/explore/facilities/docs/facilities?version=current
facilities_data = pd.DataFrame(columns=['geometry_type', 'geometry_coordinates', 'id', 'facility_name', 'facility_type','facility_classification'])
# function that will make the api call and sort through the json data
def get_facilities_data(facilities_data):
# Make API Call
res = requests.get('https://sandboxapi.va.gov/services/va_facilities/v0/facilities/all',headers={'apikey': 'api_key'})
data = json.loads(res.content.decode('utf-8'))
time.sleep(1)
for facility in data['features']:
geometry_type = data['features'][0]['geometry']['type']
geometry_coordinates = data['features'][0]['geometry']['coordinates']
facility_id = data['features'][0]['properties']['id']
facility_name = data['features'][0]['properties']['name']
facility_type = data['features'][0]['properties']['facility_type']
facility_classification = data['features'][0]['properties']['classification']
# Save data into pandas dataframe
facilities_data = facilities_data.append(
{'geometry_type': geometry_type, 'geometry_coordinates': geometry_coordinates,
'facility_id': facility_id, 'facility_name': facility_name, 'facility_type': facility_type,
'facility_classification': facility_classification}, ignore_index=True)
return facilities_data
facilities_data = get_facilities_data(facilities_data)
print(facilities_data)```

As mentioned, you should
loop over facility instead of data['features'][0]
append within the loop
This will get you the result you are after.
facilities_data = pd.DataFrame(columns=['geometry_type', 'geometry_coordinates', 'id', 'facility_name', 'facility_type','facility_classification'])
def get_facilities_data(facilities_data):
# Make API Call
res = requests.get("https://sandbox-api.va.gov/services/va_facilities/v0/facilities/all",
headers={"apikey": "REDACTED"})
data = json.loads(res.content.decode('utf-8'))
time.sleep(1)
for facility in data['features']:
geometry_type = facility['geometry']['type']
geometry_coordinates = facility['geometry']['coordinates']
facility_id = facility['properties']['id']
facility_name = facility['properties']['name']
facility_type = facility['properties']['facility_type']
facility_classification = facility['properties']['classification']
# Save data into pandas dataframe
facilities_data = facilities_data.append(
{'geometry_type': geometry_type, 'geometry_coordinates': geometry_coordinates,
'facility_id': facility_id, 'facility_name': facility_name, 'facility_type': facility_type,
'facility_classification': facility_classification}, ignore_index=True)
return facilities_data
facilities_data = get_facilities_data(facilities_data)
print(facilities_data.head())
There are some more things we can improve upon;
json() can be called directly on requests output
time.sleep() is not needed
appending to a DataFrame on each iteration is discouraged; we can collect the data in another way and create the DataFrame afterwards.
Implementing these improvements results in;
def get_facilities_data():
data = requests.get("https://sandbox-api.va.gov/services/va_facilities/v0/facilities/all",
headers={"apikey": "REDACTED"}).json()
facilities_data = []
for facility in data["features"]:
facility_data = (facility["geometry"]["type"],
facility["geometry"]["coordinates"],
facility["properties"]["id"],
facility["properties"]["name"],
facility["properties"]["facility_type"],
facility["properties"]["classification"])
facilities_data.append(facility_data)
facilities_df = pd.DataFrame(data=facilities_data,
columns=["geometry_type", "geometry_coords", "id", "name", "type", "classification"])
return facilities_df

How to iterate over dataframe rows for individual API calls

I'm trying to set up a loop to pull in weather data for about 500 weather stations for an entire year which I have in my dataframe. The base URL stays the same, and the only part that changes is the weather station ID.
I'd like to create a dataframe with the results. I believe i'd use requests.get to pull in data for all the weather stations in my list, which the IDs to use in the URL are in a column called "API ID" in my dataframe. I am a python beginner - so any help would be appreciated! My code is below but doesn't work and returns an error:
"InvalidSchema: No connection adapters were found for '0 " http://www.ncei.noaa.gov/access/services/data/...\nName: API ID, Length: 497, dtype: object'
.
def callAPI(API_id):
for IDs in range(len(API_id)):
url = ('http://www.ncei.noaa.gov/access/services/data/v1?dataset=daily-summaries&dataTypes=PRCP,SNOW,TMAX,TMIN&stations=' + distances['API ID'] + '&startDate=2020-01-01&endDate=2020-12-31&includeAttributes=0&includeStationName=true&units=standard&format=json')
r = requests.request('GET', url)
d = r.json()
ll = []
for index1,rows1 in distances.iterrows():
station = rows1['Closest Station']
API_id = rows1['API ID']
data = callAPI(API_id)
ll.append([(data)])

I am not sure about your whole code base, but this is the function that will return the data from the API, If you have multiple station id on a single df column then you can use a for loop otherwise no need to do that.
Also, you are not returning the result from the function. Check the return keyword at the end of the function.
Working code:
import requests
def callAPI(API_id):
url = ('http://www.ncei.noaa.gov/access/services/data/v1?dataset=daily-summaries&dataTypes=PRCP,SNOW,TMAX,TMIN&stations=' + API_id + '&startDate=2020-01-01&endDate=2020-12-31&includeAttributes=0&includeStationName=true&units=standard&format=json')
r = requests.request('GET', url)
d = r.json()
return d
print(callAPI('USC00457180'))
So your full code will be something like this,
def callAPI(API_id):
url = ('http://www.ncei.noaa.gov/access/services/data/v1?dataset=daily-summaries&dataTypes=PRCP,SNOW,TMAX,TMIN&stations=' + API_id + '&startDate=2020-01-01&endDate=2020-12-31&includeAttributes=0&includeStationName=true&units=standard&format=json')
r = requests.request('GET', url)
d = r.json()
return d
ll = []
for index1,rows1 in distances.iterrows():
station = rows1['Closest Station']
API_id = rows1['API ID']
data = callAPI(API_id)
ll.append([(data)])
Note: Even better use asynchronous calls to the API to make the process faster. Something like this: https://stackoverflow.com/a/56926297/1138192

Creating a pandas dataframe in a while loop with exception handling not reading the dataframe after

This is my first post here. So I had python script to do some algorithmic trading. It worked fine on coinbase but when I tried switching some of the code to work with binance it's not working. Below is the error I get. I made sure I copy and paste the df name to make sure it's the same name. I'm not sure why the print statement is unable to print the df. I will also paste the part of the code that isn't working to see if someone can make it work. In my actual code instead of print is where I start calculating the Moving Averages but I get the same error with just print so some for some reason the dataframe isn't being created. Any help will be appreciated.
Error Encountered
Traceback (most recent call last):
File "test2.py", line 44, in
print(historic_df)
NameError: name 'historic_df' is not defined
The code:
import requests
import json
import time
import pandas as pd
currency = 'BTCUSD'
base = 'https://api.binance.com'
endpoint = '/api/v1/klines'
params = '?&symbol='+currency+'&interval=1h'
url = base + endpoint + params
#---------------------------------------------------------------------------------------------------
### Begin Loop and get Historic Data ###
while True:
try:
#Pulls the historical data from binance. The interval is defined in params variable
data = requests.get(url)
dictionary = json.loads(data.text)
# The line below just puts the historic data we pulled into a data frame
historic_df = pd.DataFrame.from_dict(dictionary)
historic_df = historic_df.drop(range(6,12), axis=1)
# This gives the columns meaning full names based on what we pull
historic_df.columns = ['time', 'open', 'high', 'low', 'close', 'volumne']
# Changing the columns to floats
historic_df['open'] = historic_df['open'].astype(float)
historic_df['high'] = historic_df['high'].astype(float)
historic_df['low'] = historic_df['low'].astype(float)
historic_df['close'] = historic_df['close'].astype(float)
historic_df['volume'] = historic_df['volume'].astype(float)
# Get latest data and show to the user for reference
btc_price = bi_client.get_symbol_ticker(symbol=currency)
currentPrice = float(btc_price['price'])
except:`enter code here`
print("Error Encountered")
print(historic_df)

The matter comes from how you manage your errors. It come from the line json.loads(data.text). data does not contains correct json. As you except every Exception, you print "Error Encountered" and your code does not create the variable historic_df as it fails before.
You should write something more like this:
import requests
import json
import time
import pandas as pd
from requests import HTTPError
currency = 'BTCUSD'
base = 'https://api.binance.com'
endpoint = '/api/v1/klines'
params = '?&symbol=' + currency + '&interval=1h'
url = base + endpoint + params
# ---------------------------------------------------------------------------------------------------
### Begin Loop and get Historic Data ###
while True:
try:
# Pulls the historical data from binance. The interval is defined in params variable
data = requests.get(url)
data.raise_for_status()
dictionary = data.json()
# The line below just puts the historic data we pulled into a data frame
historic_df = pd.DataFrame.from_dict(dictionary)
historic_df = historic_df.drop(range(6, 12), axis=1)
# I delete other code
except HTTPError as err:
print(err)
except as err:
print(err)
You should read for a more detailed the tutorial about exception.

Manipulating CSV data stored as a string Python

I have an API string which responds with an XML page, and has my data stored as CSV in the "data" tag (I can request it in JSON format but I haven't been able to handle the data correctly in my Python script in that format).
<reports.getAccountsStatsResponse xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="urn:com:gigya:api" xsi:schemaLocation="urn:com:gigya:api http://socialize-api.gigya.com/schema">
<statusCode>200</statusCode>
<errorCode>0</errorCode>
<statusReason>OK</statusReason>
<callId>ae1b3f13ba1c4e62ad3120afb1269c76</callId>
<time>2015-09-01T09:01:46.511Z</time>
<headers>
<header>date</header>
<header>initRegistrations</header>
<header>registrations</header>
<header>siteLogins</header>
<header>newUsers</header>
</headers>
<data xmlns:q1="http://www.w3.org/2001/XMLSchema" xsi:type="q1:string">
"date","initRegistrations","registrations","siteLogins","newUsers" "01/01/2015","0","0","0","0" "01/02/2015","0","0","0","0" "01/03/2015","0","0","0","0" "01/04/2015","0","0","0","0" "01/05/2015","0","0","0","0" "01/06/2015","0","0","0","0" "01/07/2015","0","0","0","0" "01/08/2015","0","0","0","0" "01/09/2015","0","0","0","0" "01/10/2015","0","0","0","0" "01/11/2015","0","0","0","0" "01/12/2015","0","0","0","0" "01/13/2015","0","0","0","0" "01/14/2015","0","0","0","0" "01/15/2015","0","0","0","0" "01/16/2015","0","0","0","0" "01/17/2015","0","0","0","0" "01/18/2015","0","0","0","0" "01/19/2015","0","0","0","0" "01/20/2015","34","34","72","34" "01/21/2015","33","23","58","23" "01/22/2015","19","19","49","19" "01/23/2015","21","21","50","21" "01/24/2015","1","1","2","1" "01/25/2015","0","0","0","0" "01/26/2015","8","4","49","4" "01/27/2015","8","8","35","8" "01/28/2015","4","2","16","2" "01/29/2015","7","7","27","7" "01/30/2015","69","58","516","58" "01/31/2015","9","6","76","6" "02/01/2015","0","0","2","0" "02/02/2015","304","203","2317","203" "02/03/2015","122","93","786","93" "02/04/2015","69","47","435","47" "02/05/2015","93","64","677","64" "02/06/2015","294","255","1327","255" "02/07/2015","0","0","0","0" "02/08/2015","0","0","0","0" "02/09/2015","0","0","3","0" "02/10/2015","1","0","1","0" "02/11/2015","3","3","7","3" "02/12/2015","0","0","0","0" "02/13/2015","2","2","4","2" "02/14/2015","0","0","1","0" "02/15/2015","0","0","0","0" "02/16/2015","0","0","0","0" "02/17/2015","3","3","7","3" "02/18/2015","0","0","0","0" "02/19/2015","1","1","3","1" "02/20/2015","3","3","10","3" "02/21/2015","0","0","0","0" "02/22/2015","0","0","1","0" "02/23/2015","1","1","4","1" "02/24/2015","0","0","1","0" "02/25/2015","0","0","0","0" "02/26/2015","0","0","0","0" "02/27/2015","0","0","1","0" "02/28/2015","1","1","2","1" "03/01/2015","1","1","3","1" "03/02/2015","19","9","348","9" "03/03/2015","14","9","132","9" "03/04/2015","4","4","41","4" "03/05/2015","8","5","101","5" "03/06/2015","6","5","71","5" "03/07/2015","8","4","42","4" "03/08/2015","7","4","45","4" "03/09/2015","5","4","30","4" "03/10/2015","7","7","39","7" "03/11/2015","9","9","41","9" "03/12/2015","1","1","20","1" "03/13/2015","3","3","26","3" "03/14/2015","2","0","21","0" "03/15/2015","3","3","28","3" "03/16/2015","3","3","38","3" "03/17/2015","4","4","43","4" "03/18/2015","5","3","45","3" "03/19/2015","19","16","108","16" "03/20/2015","11","8","96","8" "03/21/2015","276","261","807","261" "03/22/2015","197","192","604","192" "03/23/2015","0","0","3","0" "03/24/2015","1","1","4","1" "03/25/2015","181","166","401","166" "03/26/2015","124","109","265","109" "03/27/2015","53","47","124","47" "03/28/2015","41","39","99","39" "03/29/2015","75","65","173","65" "03/30/2015","249","239","536","239" "03/31/2015","222","212","487","212" "04/01/2015","40","29","394","29" "04/02/2015","16","10","132","10" "04/03/2015","13","10","125","10" "04/04/2015","6","4","49","4" "04/05/2015","2","1","46","1" "04/06/2015","4","3","38","3" "04/07/2015","1","0","32","0" "04/08/2015","4","2","16","2" "04/09/2015","9","8","30","8" "04/10/2015","31","29","96","29" "04/11/2015","17","14","90","14" "04/12/2015","10","7","46","7" "04/13/2015","19","13","69","13" "04/14/2015","63","58","199","58" "04/15/2015","17","16","58","16" "04/16/2015","13","12","41","12" "04/17/2015","7","5","51","5" "04/18/2015","51","46","165","46" "04/19/2015","51","45","179","45" "04/20/2015","28","21","110","21" "04/21/2015","32","24","290","24" "04/22/2015","47","31","329","31" "04/23/2015","30","27","183","27" "04/24/2015","71","65","284","65" "04/25/2015","25","17","268","17" "04/26/2015","26","24","268","24" "04/27/2015","72","67","172","67" "04/28/2015","28","25","96","25" "04/29/2015","72","48","159","48" "04/30/2015","50","22","136","22" "05/01/2015","33","23","126","23" "05/02/2015","22","17","112","17" "05/03/2015","31","21","169","21" "05/04/2015","29","21","182","21" "05/05/2015","12","10","24","10" "05/06/2015","369","354","790","354" "05/07/2015","409","401","839","401" "05/08/2015","258","253","539","253" "05/09/2015","227","221","469","221" "05/10/2015","138","134","297","134" "05/11/2015","14","13","32","13" "05/12/2015","57","24","452","24" "05/13/2015","23","12","300","12" "05/14/2015","7","5","70","5" "05/15/2015","7","6","15","6" "05/16/2015","3","3","7","3" "05/17/2015","3","3","8","3" "05/18/2015","2","4","4","2" "05/19/2015","10","16","24","8" "05/20/2015","4","8","10","4" "05/21/2015","7","12","14","6" "05/22/2015","9","14","33","7" "05/23/2015","9","14","19","7" "05/24/2015","16","32","39","16" "05/25/2015","11","9","21","7" "05/26/2015","23","16","87","16" "05/27/2015","30","24","87","24" "05/28/2015","12","12","39","12" "05/29/2015","14","12","37","12" "05/30/2015","8","7","19","7" "05/31/2015","5","4","17","4" "06/01/2015","10","10","31","10" "06/02/2015","23","20","95","20" "06/03/2015","11","9","31","9" "06/04/2015","14","13","36","13" "06/05/2015","12","11","27","11" "06/06/2015","8","6","20","6" "06/07/2015","9","9","21","9" "06/08/2015","16","16","37","16" "06/09/2015","24","17","40","17" "06/10/2015","8","8","34","8" "06/11/2015","46","27","464","27" "06/12/2015","45","23","383","23" "06/13/2015","12","9","143","9" "06/14/2015","22","15","112","15" "06/15/2015","14","13","74","13" "06/16/2015","63","56","197","56" "06/17/2015","28","25","114","25" "06/18/2015","17","15","85","15" "06/19/2015","143","135","460","135" "06/20/2015","54","46","217","46" "06/21/2015","60","55","211","55" "06/22/2015","91","78","249","78" "06/23/2015","99","87","295","87" "06/24/2015","115","103","315","103" "06/25/2015","455","380","964","380" "06/26/2015","585","489","1144","489" "06/27/2015","345","300","695","300" "06/28/2015","349","320","783","320" "06/29/2015","113","98","362","98" "06/30/2015","128","113","424","113" "07/01/2015","115","99","277","99" "07/02/2015","73","65","323","65" "07/03/2015","22","16","184","16" "07/04/2015","13","12","69","12" "07/05/2015","15","12","71","12" "07/06/2015","31","25","107","25" "07/07/2015","15","10","63","10" "07/08/2015","16","12","60","12" "07/09/2015","35","32","103","32" "07/10/2015","22","19","72","19" "07/11/2015","7","7","25","7" "07/12/2015","4","4","27","4" "07/13/2015","81","73","195","73" "07/14/2015","60","53","157","53" "07/15/2015","44","40","115","40" "07/16/2015","40","40","112","40" "07/17/2015","27","23","64","23" "07/18/2015","15","11","56","11" "07/19/2015","19","14","63","14" "07/20/2015","21","17","48","17" "07/21/2015","11","10","30","10" "07/22/2015","13","12","40","12" "07/23/2015","9","6","43","6" "07/24/2015","9","8","32","8" "07/25/2015","8","5","20","5" "07/26/2015","20","18","64","18" "07/27/2015","15","14","80","14" "07/28/2015","9","8","48","8" "07/29/2015","21","13","88","13" "07/30/2015","9","5","92","5" "07/31/2015","4","3","81","3" "08/01/2015","4","3","23","3" "08/02/2015","11","5","29","5" "08/03/2015","19","17","50","17" 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</data>
</reports.getAccountsStatsResponse>
And in JSON format:
{
"statusCode": 200,
"errorCode": 0,
"statusReason": "OK",
"callId": "99949da72d034b04ba910c91704ba4c0",
"time": "2015-09-01T09:19:30.569Z",
"headers": [
"date",
"initRegistrations",
"registrations",
"siteLogins",
"newUsers"
],
"data": "\"date\",\"initRegistrations\",\"registrations\",\"siteLogins\",\"newUsers\"\r\n\"01/01/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"01/02/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"01/03/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"01/04/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"01/05/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"01/06/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"01/07/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"01/08/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"01/09/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"01/10/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"01/11/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"01/12/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"01/13/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"01/14/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"01/15/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"01/16/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"01/17/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"01/18/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"01/19/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"01/20/2015\",\"34\",\"34\",\"72\",\"34\"\r\n\"01/21/2015\",\"33\",\"23\",\"58\",\"23\"\r\n\"01/22/2015\",\"19\",\"19\",\"49\",\"19\"\r\n\"01/23/2015\",\"21\",\"21\",\"50\",\"21\"\r\n\"01/24/2015\",\"1\",\"1\",\"2\",\"1\"\r\n\"01/25/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"01/26/2015\",\"8\",\"4\",\"49\",\"4\"\r\n\"01/27/2015\",\"8\",\"8\",\"35\",\"8\"\r\n\"01/28/2015\",\"4\",\"2\",\"16\",\"2\"\r\n\"01/29/2015\",\"7\",\"7\",\"27\",\"7\"\r\n\"01/30/2015\",\"69\",\"58\",\"516\",\"58\"\r\n\"01/31/2015\",\"9\",\"6\",\"76\",\"6\"\r\n\"02/01/2015\",\"0\",\"0\",\"2\",\"0\"\r\n\"02/02/2015\",\"304\",\"203\",\"2317\",\"203\"\r\n\"02/03/2015\",\"122\",\"93\",\"786\",\"93\"\r\n\"02/04/2015\",\"69\",\"47\",\"435\",\"47\"\r\n\"02/05/2015\",\"93\",\"64\",\"677\",\"64\"\r\n\"02/06/2015\",\"294\",\"255\",\"1327\",\"255\"\r\n\"02/07/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"02/08/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"02/09/2015\",\"0\",\"0\",\"3\",\"0\"\r\n\"02/10/2015\",\"1\",\"0\",\"1\",\"0\"\r\n\"02/11/2015\",\"3\",\"3\",\"7\",\"3\"\r\n\"02/12/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"02/13/2015\",\"2\",\"2\",\"4\",\"2\"\r\n\"02/14/2015\",\"0\",\"0\",\"1\",\"0\"\r\n\"02/15/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"02/16/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"02/17/2015\",\"3\",\"3\",\"7\",\"3\"\r\n\"02/18/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"02/19/2015\",\"1\",\"1\",\"3\",\"1\"\r\n\"02/20/2015\",\"3\",\"3\",\"10\",\"3\"\r\n\"02/21/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"02/22/2015\",\"0\",\"0\",\"1\",\"0\"\r\n\"02/23/2015\",\"1\",\"1\",\"4\",\"1\"\r\n\"02/24/2015\",\"0\",\"0\",\"1\",\"0\"\r\n\"02/25/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"02/26/2015\",\"0\",\"0\",\"0\",\"0\"\r\n\"02/27/2015\",\"0\",\"0\",\"1\",\"0\"\r\n\"02/28/2015\",\"1\",\"1\",\"2\",\"1\"\r\n\"03/01/2015\",\"1\",\"1\",\"3\",\"1\"\r\n\"03/02/2015\",\"19\",\"9\",\"348\",\"9\"\r\n\"03/03/2015\",\"14\",\"9\",\"132\",\"9\"\r\n\"03/04/2015\",\"4\",\"4\",\"41\",\"4\"\r\n\"03/05/2015\",\"8\",\"5\",\"101\",\"5\"\r\n\"03/06/2015\",\"6\",\"5\",\"71\",\"5\"\r\n\"03/07/2015\",\"8\",\"4\",\"42\",\"4\"\r\n\"03/08/2015\",\"7\",\"4\",\"45\",\"4\"\r\n\"03/09/2015\",\"5\",\"4\",\"30\",\"4\"\r\n\"03/10/2015\",\"7\",\"7\",\"39\",\"7\"\r\n\"03/11/2015\",\"9\",\"9\",\"41\",\"9\"\r\n\"03/12/2015\",\"1\",\"1\",\"20\",\"1\"\r\n\"03/13/2015\",\"3\",\"3\",\"26\",\"3\"\r\n\"03/14/2015\",\"2\",\"0\",\"21\",\"0\"\r\n\"03/15/2015\",\"3\",\"3\",\"28\",\"3\"\r\n\"03/16/2015\",\"3\",\"3\",\"38\",\"3\"\r\n\"03/17/2015\",\"4\",\"4\",\"43\",\"4\"\r\n\"03/18/2015\",\"5\",\"3\",\"45\",\"3\"\r\n\"03/19/2015\",\"19\",\"16\",\"108\",\"16\"\r\n\"03/20/2015\",\"11\",\"8\",\"96\",\"8\"\r\n\"03/21/2015\",\"276\",\"261\",\"807\",\"261\"\r\n\"03/22/2015\",\"197\",\"192\",\"604\",\"192\"\r\n\"03/23/2015\",\"0\",\"0\",\"3\",\"0\"\r\n\"03/24/2015\",\"1\",\"1\",\"4\",\"1\"\r\n\"03/25/2015\",\"181\",\"166\",\"401\",\"166\"\r\n\"03/26/2015\",\"124\",\"109\",\"265\",\"109\"\r\n\"03/27/2015\",\"53\",\"47\",\"124\",\"47\"\r\n\"03/28/2015\",\"41\",\"39\",\"99\",\"39\"\r\n\"03/29/2015\",\"75\",\"65\",\"173\",\"65\"\r\n\"03/30/2015\",\"249\",\"239\",\"536\",\"239\"\r\n\"03/31/2015\",\"222\",\"212\",\"487\",\"212\"\r\n\"04/01/2015\",\"40\",\"29\",\"394\",\"29\"\r\n\"04/02/2015\",\"16\",\"10\",\"132\",\"10\"\r\n\"04/03/2015\",\"13\",\"10\",\"125\",\"10\"\r\n\"04/04/2015\",\"6\",\"4\",\"49\",\"4\"\r\n\"04/05/2015\",\"2\",\"1\",\"46\",\"1\"\r\n\"04/06/2015\",\"4\",\"3\",\"38\",\"3\"\r\n\"04/07/2015\",\"1\",\"0\",\"32\",\"0\"\r\n\"04/08/2015\",\"4\",\"2\",\"16\",\"2\"\r\n\"04/09/2015\",\"9\",\"8\",\"30\",\"8\"\r\n\"04/10/2015\",\"31\",\"29\",\"96\",\"29\"\r\n\"04/11/2015\",\"17\",\"14\",\"90\",\"14\"\r\n\"04/12/2015\",\"10\",\"7\",\"46\",\"7\"\r\n\"04/13/2015\",\"19\",\"13\",\"69\",\"13\"\r\n\"04/14/2015\",\"63\",\"58\",\"199\",\"58\"\r\n\"04/15/2015\",\"17\",\"16\",\"58\",\"16\"\r\n\"04/16/2015\",\"13\",\"12\",\"41\",\"12\"\r\n\"04/17/2015\",\"7\",\"5\",\"51\",\"5\"\r\n\"04/18/2015\",\"51\",\"46\",\"165\",\"46\"\r\n\"04/19/2015\",\"51\",\"45\",\"179\",\"45\"\r\n\"04/20/2015\",\"28\",\"21\",\"110\",\"21\"\r\n\"04/21/2015\",\"32\",\"24\",\"290\",\"24\"\r\n\"04/22/2015\",\"47\",\"31\",\"329\"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}
Firstly, I would like to store the text from the "data" tag by referencing the name of it, but I've currently only had success by using this following:
response = requests.get(url)
root = ElementTree.fromstring(response.content)
dataString = root[6].text
Is there a separate command to be able to specify the name of the tag?
Next, my goal is to loop through different URL's (which correspond to different accounts), and append the name of those accounts to the end of the data. Is this possible, given that the data is stored as a string and I would need to add it to the end of each row? As a follow up, what's the best convention for saving multiple values in a variable to be able to loop through i.e. the list of accounts?
Apologies if this is unclear, I'm happy to provide any more information if it means anybody can help.

As far as I understood, you have a specific URL for each user and you want to collect data for all users given.
However, since you are not able to get the username out of the response you have to combine the response with the username corresponding to the URL the request was sent to. If so, you could use a dictionary to store the data of your response since the JSON-format is equivalent to Python's dictionary.
The code below simply iterates through a set of tuples containing the different user names and the corresponding URL. For each URL a request is sent, the data is extracted from the JSON-formatted response and stored in a dictionary with the username as a key. This dictionary is then stored (.update()) in a kind of main dictionary containing all your collected datasets.
# replace names 'url_xyz' with corresponding names and url
users = {('Albert', 'url_albert'), ('Steven', 'url_steven'), ('Mike', 'url_mike')}
all_data = dict()
for name, url in users:
response = requests.get(url)
data = response['data'].replace('\"', '')
all_data.update({name: data})

Thank you Albert.
Your JSON suggestion let me control the data in a much better way. The code below is what I ended up with to get to my desired output. Now just to work out how to convert the date from MM/DD/YYYY into DD/MM/YYYY.
startDate = '2015-01-01' # Must be in format YYYY-MM-DD
endDate = '2015-12-31' # Must be in format YYYY-MM-DD
dimensions = 'date' # Available dimensions are 'date' and 'cid'
format = 'json'
dataFormat = 'json'
measures = 'initRegistrations,registrations,siteLogins,newUsers'
allData = []
# Construct API URL
for i in range(0,len(apiKey)):
url = ('https://reports.eu1.gigya.com/reports.getAccountsStats?secret=' + secret + '&apiKey=' + apiKey[i] + \
'&uid=' + uid + '&startDate=' + startDate + '&endDate=' + endDate + '&dimensions='+ dimensions +\
'&measures=' + measures + '&format=' + format + '&dataFormat=' + dataFormat)
response = requests.get(url)
json = response.json()
data = json['data']
if i == 0:
headers = json['headers']
headers.append('brand')
for x in range(0,len(data)):
data[x].append(brand[i])
brandData = [headers] + data
else:
for x in range(0,len(data)):
data[x].append(brand[i])
brandData = data
allData += brandData
with open("testDataJSON.csv", "wb") as f:
writer = csv.writer(f)
writer.writerows(allData)
I don't know how well this follows best practice for Python but as I said, I am very new to it.

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