I have a Python script that leverages lists in a Google Sheet and sends bulk SMS text messages using Twilio.
I'm fairly new to this and have struggled to get this far - any Python script I've created in the past, I've been able to just run off my local computer in VS Code.
I am trying to share this with a family member - I've read into tkinter and gui's a bit, but because the rest of this workflow is already in a Google Sheet, it would be perfect if any user could just run the Python script right from the spreadsheet itself.
I found this, but I don't really understand how to create a webservice in GAE. I've googled it all over but struggling to put into action -- Trigger python code from Google spreadsheets?
Is there a simple way to just tie my Python script into this spreadsheet so that anyone can run it? Or another way to go about this?
ChatGPT response says this, but I feel it is inaccurate (or I just can't get it working):
My code is here, if it helps:
from twilio.rest import Client
import gspread
# Your Account SID and Auth Token from twilio.com/console
account_sid = 'AC868ea4e1a779ff0816b466a13f201b02'
auth_token = '85469ae1eb492ffc814c095b5c6e0889'
client = Client(account_sid, auth_token)
gc = gspread.service_account(filename='creds.json')
# Open a spreadsheet by ID
sh = gc.open_by_key('1KRYITQ_O_-7exPZp8zj1VvAUPPutqtO4SrTgloCx8x4')
# Get the sheets
wk = sh.worksheet("Numbers to Send")
# E.G. the URLs are listed on Sheet 1 on Column A
numbers = wk.batch_get(('f3:f',))[0]
names = wk.batch_get(('g3:g',))[0]
# names = ['John', 'Jane', 'Jim']
# numbers = ['+16099725052', '+16099725052', '+16099725052']
# Loop through the names and numbers and send a text message to each phone number
for i in range(len(names)):
message = client.messages.create(
to=numbers[i],
from_='+18442251378',
body=f"Hello {names[i][0]}, this is a test message from Twilio.")
print(f"Message sent to {names[i]} at {numbers[i]}")
I'm connected to my APIs client, sent the credentials, I made the request, I asked the API for data and put it to a DF.
Then, I have to upload this data to a sheet, so then this sheet is gonna be connected to PowerBI as a datasource in order to develop a dashboard and monitor some KPIs and so on..
Simple and common ETL process. BUT: to be honest, I'm a rookie and I'm doing my best.
Above here is just code to connect to the API, here is where the "extraction" begins
if response_page.status_code == 200:
if page == 1 :
df = pd.DataFrame(json.loads(response_page.content)["list"])
else :
df2 = pd.DataFrame(json.loads(response_page.content)["list"])
df = df.append(df2)
Then I just pick up but I need:
columnas = ['orderId','totalValue','paymentNames']
df2 = df[columnas]
df2
This is what the DF looks like:
example: this df is which I need to append the new data
Then I've just connected to Sheets here, send the credentials, open the sheet("carrefourMetodosDePago") and the page("transacciones")
sa = gspread.service_account(filename="service_account.json")
sh = sa.open("carrefourMetodosDePago")
wks = sh.worksheet("transacciones")
The magic begins:
wks.update([df2.columns.values.tolist()] + df2.values.tolist())
With this sentence I upload what the picture shows, to the sheet!
I need the new data that generates the API to be appended/merged/concatenated to the current data so the code upload the current data PLUS the new everytime I run it and so forth.
How can I do that? Should I use a for loop and iterate over every new data en append it to the sheet?
This is the best I could have done, I think I reached my turning point here...
If I explained myself wrong just let me know.
If you reach up to here just let me thank you to give me some time :)
I'm experimenting with the exchangelib.
What I'm trying to do is, sync the mails from the inbox folder, between to mailboxes.
I'm not sure if my way is the right one, but
my idea is to use sync_items() on the source account and then safe it to the target.
source_items = source_account.inbox.sync_items()
for change_type, item in source_items:
print(change_type, item)
I can get all the mails I want with the code above. But I can't figure out, how to safe them to the target account. send_and_save() seems to send the mail again. I also tried export and upload, without success.
Is it even possible to do this? Any hints would be helpful.
Your solution depends on your needs. Some examples:
Forward the messages to the other account:
for m in this_account.inbox.all():
m.forward(...)
Save the messages to the other account:
for m in this_account:
m.account = other_account
m.folder = other_account.inbox
m.id = None
m.changekey = None
m.save()
Export and upload messages:
data = this_account.export(this_account.inbox.all())
other_account.upload(((other_account.inbox, d) for d in data))
I recently started using Python so I could interact with the Bloomberg API, and I'm having some trouble storing the data into a Pandas dataframe (or a panel). I can get the output in the command prompt just fine, so that's not an issue.
A very similar question was asked here:
Pandas wrapper for Bloomberg api?
The referenced code in the accepted answer for that question is for the old API, however, and it doesn't work for the new open API. Apparently the user who asked the question was able to easily modify that code to work with the new API, but I'm used to having my hand held in R, and this is my first endeavor with Python.
Could some benevolent user show me how to get this data into Pandas? There is an example in the Python API (available here: http://www.openbloomberg.com/open-api/) called SimpleHistoryExample.py that I've been working with that I've included below. I believe I'll need to modify mostly around the 'while(True)' loop toward the end of the 'main()' function, but everything I've tried so far has had issues.
Thanks in advance, and I hope this can be of help to anyone using Pandas for finance.
# SimpleHistoryExample.py
import blpapi
from optparse import OptionParser
def parseCmdLine():
parser = OptionParser(description="Retrieve reference data.")
parser.add_option("-a",
"--ip",
dest="host",
help="server name or IP (default: %default)",
metavar="ipAddress",
default="localhost")
parser.add_option("-p",
dest="port",
type="int",
help="server port (default: %default)",
metavar="tcpPort",
default=8194)
(options, args) = parser.parse_args()
return options
def main():
options = parseCmdLine()
# Fill SessionOptions
sessionOptions = blpapi.SessionOptions()
sessionOptions.setServerHost(options.host)
sessionOptions.setServerPort(options.port)
print "Connecting to %s:%s" % (options.host, options.port)
# Create a Session
session = blpapi.Session(sessionOptions)
# Start a Session
if not session.start():
print "Failed to start session."
return
try:
# Open service to get historical data from
if not session.openService("//blp/refdata"):
print "Failed to open //blp/refdata"
return
# Obtain previously opened service
refDataService = session.getService("//blp/refdata")
# Create and fill the request for the historical data
request = refDataService.createRequest("HistoricalDataRequest")
request.getElement("securities").appendValue("IBM US Equity")
request.getElement("securities").appendValue("MSFT US Equity")
request.getElement("fields").appendValue("PX_LAST")
request.getElement("fields").appendValue("OPEN")
request.set("periodicityAdjustment", "ACTUAL")
request.set("periodicitySelection", "DAILY")
request.set("startDate", "20061227")
request.set("endDate", "20061231")
request.set("maxDataPoints", 100)
print "Sending Request:", request
# Send the request
session.sendRequest(request)
# Process received events
while(True):
# We provide timeout to give the chance for Ctrl+C handling:
ev = session.nextEvent(500)
for msg in ev:
print msg
if ev.eventType() == blpapi.Event.RESPONSE:
# Response completly received, so we could exit
break
finally:
# Stop the session
session.stop()
if __name__ == "__main__":
print "SimpleHistoryExample"
try:
main()
except KeyboardInterrupt:
print "Ctrl+C pressed. Stopping..."
I use tia (https://github.com/bpsmith/tia/blob/master/examples/datamgr.ipynb)
It already downloads data as a panda dataframe from bloomberg.
You can download history for multiple tickers in one single call and even download some bloombergs reference data (Central Bank date meetings, holidays for a certain country, etc)
And you just install it with pip.
This link is full of examples but to download historical data is as easy as:
import pandas as pd
import tia.bbg.datamgr as dm
mgr = dm.BbgDataManager()
sids = mgr['MSFT US EQUITY', 'IBM US EQUITY', 'CSCO US EQUITY']
df = sids.get_historical('PX_LAST', '1/1/2014', '11/12/2014')
and df is a pandas dataframe.
Hope it helps
You can also use pdblp for this (Disclaimer: I'm the author). There is a tutorial showing similar functionality available here https://matthewgilbert.github.io/pdblp/tutorial.html, the functionality could be achieved using something like
import pdblp
con = pdblp.BCon()
con.start()
con.bdh(['IBM US Equity', 'MSFT US Equity'], ['PX_LAST', 'OPEN'],
'20061227', '20061231', elms=[("periodicityAdjustment", "ACTUAL")])
I've just published this which might help
http://github.com/alex314159/blpapiwrapper
It's basically not very intuitive to unpack the message, but this is what works for me, where strData is a list of bloomberg fields, for instance ['PX_LAST','PX_OPEN']:
fieldDataArray = msg.getElement('securityData').getElement('fieldData')
size = fieldDataArray.numValues()
fieldDataList = [fieldDataArray.getValueAsElement(i) for i in range(0,size)]
outDates = [x.getElementAsDatetime('date') for x in fieldDataList]
output = pandas.DataFrame(index=outDates,columns=strData)
for strD in strData:
outData = [x.getElementAsFloat(strD) for x in fieldDataList]
output[strD] = outData
output.replace('#N/A History',pandas.np.nan,inplace=True)
output.index = output.index.to_datetime()
return output
I've been using pybbg to do this sort of stuff. You can get it here:
https://github.com/bpsmith/pybbg
Import the package and you can then do (this is in the source code, bbg.py file):
banner('ReferenceDataRequest: single security, single field, frame response')
req = ReferenceDataRequest('msft us equity', 'px_last', response_type='frame')
print req.execute().response
The advantages:
Easy to use; minimal boilerplate, and parses indices and dates for you.
It's blocking. Since you mention R, I assume you are using this in some type of an interactive environment, like IPython. So this is what you want , rather than having to mess around with callbacks.
It can also do historical (i.e. price series), intraday and bulk data request (no tick data yet).
Disadvantages:
Only works in Windows, as far as I know (you must have BB workstationg installed and running).
Following on the above, it depends on the 32 bit OLE api for Python. It only works with the 32 bit version - so you will need 32 bit python and 32 bit OLE bindings
There are some bugs. In my experience, when retrieving data for a number of instruments, it tends to hang IPython. Not sure what causes this.
Based on the last point, I would suggest that if you are getting large amounts of data, you retrieve and store these in an excel sheet (one instrument per sheet), and then import these. read_excel isn't efficient for doing this; you need to use the ExcelReader (?) object, and then iterate over the sheets. Otherwise, using read_excel will reopen the file each time you read a sheet; this can take ages.
Tia https://github.com/bpsmith/tia is the best I've found, and I've tried them all... It allows you to do:
import pandas as pd
import datetime
import tia.bbg.datamgr as dm
mgr = dm.BbgDataManager()
sids = mgr['BAC US EQUITY', 'JPM US EQUITY']
df = sids.get_historical(['BEST_PX_BPS_RATIO','BEST_ROE'],
datetime.date(2013,1,1),
datetime.date(2013,2,1),
BEST_FPERIOD_OVERRIDE="1GY",
non_trading_day_fill_option="ALL_CALENDAR_DAYS",
non_trading_day_fill_method="PREVIOUS_VALUE")
print df
#and you'll probably want to carry on with something like this
df1=df.unstack(level=0).reset_index()
df1.columns = ('ticker','field','date','value')
df1.pivot_table(index=['date','ticker'],values='value',columns='field')
df1.pivot_table(index=['date','field'],values='value',columns='ticker')
The caching is nice too.
Both https://github.com/alex314159/blpapiwrapper and https://github.com/kyuni22/pybbg do the basic job (thanks guys!) but have trouble with multiple securities/fields as well as overrides which you will inevitably need.
The one thing this https://github.com/kyuni22/pybbg has that tia doesn't have is bds(security, field).
A proper Bloomberg API for python now exists which does not use COM. It has all of the hooks to allow you to replicate the functionality of the Excel addin, with the obvious advantage of a proper programming language endpoint. The request and response objects are fairly poorly documented, and are quite obtuse. Still, the examples in the API are good, and some playing around using the inspect module and printing of response messages should get you up to speed. Sadly, the standard terminal licence only works on Windows. For *nix you will need a server licence (even more expensive). I have used it quite extensively.
https://www.bloomberg.com/professional/support/api-library/
how can we hit a URL/service when a Google spreadsheet document is saved or modified. For example lets say I have a example spreadsheet on Google docs. I want to hit a URL each time when a change is made in that spreadsheet. How can we do this in Python? Any help with this will be appreciated.
Thanks
I just wrote a script which reports when documents are created/edited. You should be able to able to adapt this to hit a URL (or do whatever) when changes are seen.
https://gist.github.com/1646532 -- code below
# Stuart Powers
# report when any google docs are created or changed
import os
import sys
import simplejson
import gdata.docs.service
"""
This script will report which google docs have been modified or created since
it was last run.
It compares the timestamps retrieved from google with the timestamps from the
JSON file which is updated each time the script is called. It compares each
document's last-updated timestamp against what they were the previous time the
script was ran, it does this by using a 'docs.json' to save state.
Inspired by the stackoverflow question:
"How to hit a URL when Google docs spreadsheet is changed"
http://stackoverflow.com/questions/8927164/
"""
docs = gdata.docs.service.DocsService()
docs.ClientLogin('stuart.powers#gmail.com','xxxxxxxx')
# create a dictionary of doc_id/timestamp key/values
mydict = {}
for e in docs.GetDocumentListFeed().entry:
mydict[e.id.text] = e.updated.text
# if docs.json doesn't exist, create it with our dict's data and then exit
# because there's nothing to compare against
if not os.path.exists('docs.json'):
with open('docs.json','w') as o:
o.write(simplejson.JSONEncoder().encode(mydict))
sys.exit(0)
# otherwise, load the previous data from docs.json
last_data = simplejson.load(open('docs.json'))
# and compare the timestamps
for id in mydict.keys():
if id not in last_data:
print 'new: %s' % id
if mydict[id] != last_data[id]:
print 'changed: %s' % id
# update docs.json for next time and then quit
with open('docs.json','w') as o:
o.write(simplejson.JSONEncoder().encode(mydict))