I've seen a bunch of issues related to this, but doesn't seem there is a strong fix. Basically, I'm trying to scrape all of the accounts an Instagram account follows, along with how many followers they have.
I have it now pushing to a spreadsheet, so that I can start to sort and analyze.
My only problem is the script keeps timing out. I've tried using time.sleep (off someone's recommendation) but it's only making the information load in slower & not fixing the issue.
Any suggestions? I could just be doing something entirely wrong - learning as I go.
import gspread
import instaloader
loader = instaloader.Instaloader()
gc = gspread.service_account(filename='creds.json')
sh = gc.open_by_key('1cD8mX8tR2iSQgSmpk6QxBVVHYVV8VxGs2uhtA8iBSpQ')
worksheet = sh.sheet1
loader.login("vcf1948", "VCF1948!")
profile = instaloader.Profile.from_username(loader.context, "michelleobama")
followees = profile.get_followees()
for followee in profile.get_followees():
print('{} has {} followees'.format(followee.username, followee.followers))
AddValue = [followee.username, int(followee.followees)]
worksheet.append_row(AddValue)
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 :)
So my question is can you use Python and/or Microsoft Graph to look into your outlook email and pull data out and put it into a excel document.
Here's what I'm trying to do, if there's anything way of doing it please feel free to let me know:
I want to create a folder in my outlook Inbox that get redirected emails. I'd like to make a script that looks at all those emails in that folder and extracts certain data within each email and puts it into a excel document.
For instance, you could set up a python script that is connected to the Outlook REST APIs. Get the access token by following the instruction in the above website and use the access token to login. You could set time intervals to re-check your message/mail box and process those data. There could be functions/parameters in the api which allows you to receive update automatically every n seconds (I have not looked into the details yet). Write your own process function to process data to mine the data for your own usage.
import time
def main():
data = get_my_messages(<your_access_token>)
time.sleep(5)
process(data)
main()
Such examples python code could be found in the website above.
def get_my_messages(access_token):
get_messages_url = graph_endpoint.format('/me/mailfolders/inbox/messages')
# Use OData query parameters to control the results
# - Only first 10 results returned
# - Only return the ReceivedDateTime, Subject, and From fields
# - Sort the results by the ReceivedDateTime field in descending order
query_parameters = {'$top': '10',
'$select': 'receivedDateTime,subject,from',
'$orderby': 'receivedDateTime DESC'}
r = make_api_call('GET', get_messages_url, access_token, parameters = query_parameters)
if (r.status_code == requests.codes.ok):
return r.json()
else:
return "{0}: {1}".format(r.status_code, r.text)
I have been trying to figure this out for a while now and just dont seem to be able to break through so hopefully someone out there has done this before.
My issue is that I am trying to do a batch update of a google spreadsheet using the gdata python client libraries and authenticating via oauth2. I have found an example of how to do the batch update using the gdata.spreadsheet.service module here: https://code.google.com/p/gdata-python-client/wiki/UsingBatchOperations
However that does not seem to work when authenticating via oauth2 and so I am having to use the gdata.spreadsheets.client module instead as discussed in this post: https://code.google.com/p/gdata-python-client/issues/detail?id=549
Using the gdata.spreadsheets.client module works for authentication and for updating the sheet however batch commands does not seem to work. Below is my latest variation of the code which is about the closest I have got. It seems to work but the sheet is not updated and the batch_status returned is: 'Insert not supported on batch.' (Note: I did try modifying the batch_operation and batch_id parameters of the CellEntries in the commented out code but this did not work either.)
Thanks for any help you can provide.
import gdata
import gdata.gauth
import gdata.service
import gdata.spreadsheets
import gdata.spreadsheets.client
import gdata.spreadsheets.data
token = gdata.gauth.OAuth2Token(client_id=Client_id,client_secret=Client_secret,scope=Scope,
access_token=ACCESS_TOKEN, refresh_token=REFRESH_TOKEN,
user_agent=User_agent)
client = gdata.spreadsheets.client.SpreadsheetsClient()
token.authorize(client)
range = "D6:D13"
cellq = gdata.spreadsheets.client.CellQuery(range=range, return_empty='true')
cells = client.GetCells(file_id, 'od6', q=cellq)
objData = gdata.spreadsheets.data
batch = objData.BuildBatchCellsUpdate(file_id, 'od6')
n = 1
for cell in cells.entry:
cell.cell.input_value = str(n)
batch.add_batch_entry(cell, cell.id.text, batch_id_string=cell.title.text, operation_string='update')
n = n + 1
client.batch(batch, force=True)
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/