CODEWITHSUNDEEP
pandasmultithreadingmultiprocessing
M LUY
M LUY

Reputation: 33

Pandas Making multiple HTTP requests

I have below code that reads from a csv file a number of ticker symbols into a dataframe.
Each ticker calls the Web Api returning a dafaframe df which is then attached to the last one until complete.
The code works , but when a large number of tickers is used the code slows down tremendously.
I understand I can use multiprocessing and threads to speed up my code but dont know where to start and what would be the most suited in my particular case.

What code should I use to get my data into a combined daframe in the fastest possible manner?

import pandas as pd
import numpy as np
import json

tickers=pd.read_csv("D:/verhuizen/pensioen/MULTI.csv",names=['symbol','company'])
read_str='https://financialmodelingprep.com/api/v3/income-statement/AAPL?limit=120&apikey=demo'
df = pd.read_json (read_str)
df = pd.DataFrame(columns=df.columns)

for ind in range(len(tickers)):
    read_str='https://financialmodelingprep.com/api/v3/income-statement/'+ tickers['symbol'][ind] +'?limit=120&apikey=demo'
    df1 = pd.read_json (read_str)       
    df=pd.concat([df,df1], ignore_index=True)
  
df.set_index(['date','symbol'], inplace=True)
df.sort_index(inplace=True)

df.to_csv('D:/verhuizen/pensioen/MULTI_out.csv')

The code provided works fine for smaller data sets, but when I use a large number of tickers (>4,000) at some point I get the below error. Is this because the web api gets overloaded or is there another problem?

Traceback (most recent call last):
  File "D:/Verhuizen/Pensioen/Equity_Extractor_2021.py", line 43, in <module>
    data = pool.starmap(download_data, enumerate(TICKERS, start=1))
  File "C:\Users\MLUY\AppData\Local\Programs\Python\Python37-32\lib\multiprocessing\pool.py", line 276, in starmap
    return self._map_async(func, iterable, starmapstar, chunksize).get()
  File "C:\Users\MLUY\AppData\Local\Programs\Python\Python37-32\lib\multiprocessing\pool.py", line 657, in get
    raise self._value
multiprocessing.pool.MaybeEncodingError: Error sending result: '<multiprocessing.pool.ExceptionWithTraceback object at 0x00C33E30>'. Reason: 'TypeError("cannot serialize '_io.BufferedReader' object")'

Process finished with exit code 1

It keeps giving the same error (for a larger amount of tickers) code is exactly as provided:

def download_data(pool_id, symbols):
    df = []
    for symbol in symbols:
        print("[{:02}]: {}".format(pool_id, symbol))
        #do stuff here
        read_str = BASEURL.format(symbol)
        df.append(pd.read_json(read_str))
        #df.append(pd.read_json(fake_data(symbol)))
    return pd.concat(df, ignore_index=True)

It failed again with the pool.map, but one strange thing I noticed. Each time it fails it does so around 12,500 tickers (total is around 23,000 tickers) Similar error:

Traceback (most recent call last):
  File "C:/Users/MLUY/AppData/Roaming/JetBrains/PyCharmCE2020.1/scratches/Equity_naive.py", line 21, in <module>
    data = pool.map(download_data, TICKERS)
  File "C:\Users\MLUY\AppData\Local\Programs\Python\Python37-32\lib\multiprocessing\pool.py", line 268, in map
    return self._map_async(func, iterable, mapstar, chunksize).get()
  File "C:\Users\MLUY\AppData\Local\Programs\Python\Python37-32\lib\multiprocessing\pool.py", line 657, in get
    raise self._value
multiprocessing.pool.MaybeEncodingError: Error sending result: '<multiprocessing.pool.ExceptionWithTraceback object at 0x078D1BF0>'. Reason: 'TypeError("cannot serialize '_io.BufferedReader' object")'

Process finished with exit code 1

I get the tickers also from a API call https://financialmodelingprep.com/api/v3/financial-statement-symbol-lists?apikey=demo (I noticed it does not work without subscription), I wanted to attach the data it as a csv file but I dont have sufficient rights. I dont think its a good idea to paste the returned data here...

I tried adding time.sleep(0.2) before return as suggested, but again I ge the same error at ticker 12,510. Strange everytime its around the same location. As there are multiple processes going on I cannot see at what point its breaking

Traceback (most recent call last):
  File "C:/Users/MLUY/AppData/Roaming/JetBrains/PyCharmCE2020.1/scratches/Equity_naive.py", line 24, in <module>
    data = pool.map(download_data, TICKERS)
  File "C:\Users\MLUY\AppData\Local\Programs\Python\Python37-32\lib\multiprocessing\pool.py", line 268, in map
    return self._map_async(func, iterable, mapstar, chunksize).get()
  File "C:\Users\MLUY\AppData\Local\Programs\Python\Python37-32\lib\multiprocessing\pool.py", line 657, in get
    raise self._value
multiprocessing.pool.MaybeEncodingError: Error sending result: '<multiprocessing.pool.ExceptionWithTraceback object at 0x00F32C90>'. Reason: 'TypeError("cannot serialize '_io.BufferedReader' object")'

Process finished with exit code 1

Something very very strange is going on , I have split the data in chunks of 10,000 / 5,000 / 4,000 and 2,000 and each time the code breaks approx 100 tickers from the end. Clearly there is something going on that not right

import time
import pandas as pd
import multiprocessing

# get tickers from your csv
df=pd.read_csv('D:/Verhuizen/Pensioen/All_Symbols.csv',header=None)

# setting the Dataframe to a list (in total 23,000 tickers)
df=df[0]
TICKERS=df.tolist()

#Select how many tickers I want
TICKERS=TICKERS[0:2000]

BASEURL = "https://financialmodelingprep.com/api/v3/income-statement/{}?limit=120&apikey=demo"

def download_data(symbol):
    print(symbol)
    # do stuff here
    read_str = BASEURL.format(symbol)
    df = pd.read_json(read_str)
    #time.sleep(0.2)
    return df

if __name__ == "__main__":
    with multiprocessing.Pool(multiprocessing.cpu_count()) as pool:
        data = pool.map(download_data, TICKERS)
        df = pd.concat(data).set_index(["date", "symbol"]).sort_index()
    df.to_csv('D:/verhuizen/pensioen/Income_2000.csv')

In this particular example the code breaks at position 1,903


RPAI
Traceback (most recent call last):
  File "C:/Users/MLUY/AppData/Roaming/JetBrains/PyCharmCE2020.1/scratches/Equity_testing.py", line 27, in <module>
    data = pool.map(download_data, TICKERS)
  File "C:\Users\MLUY\AppData\Local\Programs\Python\Python37-32\lib\multiprocessing\pool.py", line 268, in map
    return self._map_async(func, iterable, mapstar, chunksize).get()
  File "C:\Users\MLUY\AppData\Local\Programs\Python\Python37-32\lib\multiprocessing\pool.py", line 657, in get
    raise self._value
multiprocessing.pool.MaybeEncodingError: Error sending result: '<multiprocessing.pool.ExceptionWithTraceback object at 0x0793EAF0>'. Reason: 'TypeError("cannot serialize '_io.BufferedReader' object")'

Upvotes: 0

Views: 1037

Answers (1)

Corralien
Corralien

Reputation: 120409

First optimization is to avoid concatenate your dataframe at each iteration.
You can try something like that:

url = "https://financialmodelingprep.com/api/v3/income-statement/{}?limit=120&apikey=demo"
df = []

for symbol in tickers["symbol"]:
    read_str = url.format(symbol)
    df.append(pd.read_json(read_str))

df = pd.concat(df, ignore_index=True)

If it's not sufficient, we will see to use async, threading or multiprocessing.

Edit:
The code below can do the job:

import pandas as pd
import numpy as np
import multiprocessing
import time
import random

PROCESSES = 4  # number of parallel process
CHUNKS = 6  # one process handle n symbols

# get tickers from your csv
TICKERS = ["BCDA", "WBAI", "NM", "ZKIN", "TNXP", "FLY", "MYSZ", "GASX", "SAVA", "GCE",
           "XNET", "SRAX", "SINO", "LPCN", "XYF", "SNSS", "DRAD", "WLFC", "OILD", "JFIN",
           "TAOP", "PIC", "DIVC", "MKGI", "CCNC", "AEI", "ZCMD", "YVR", "OCG", "IMTE",
           "AZRX", "LIZI", "ORSN", "ASPU", "SHLL", "INOD", "NEXI", "INR", "SLN", "RHE-PA",
           "MAX", "ARRY", "BDGE", "TOTA", "PFMT", "AMRH", "IDN", "OIS", "RMG", "IMV",
           "CHFS", "SUMR", "NRG", "ULBR", "SJI", "HOML", "AMJL", "RUBY", "KBLMU", "ELP"]

# create a list of n sublist
TICKERS = [TICKERS[i:i + CHUNKS] for i in range(0, len(TICKERS), CHUNKS)]

BASEURL = "https://financialmodelingprep.com/api/v3/income-statement/{}?limit=120&apikey=demo"


def fake_data(symbol):
    dti = pd.date_range("1985", "2020", freq="Y")
    df =  pd.DataFrame({"date": dti, "symbol": symbol,
                        "A": np.random.randint(0, 100, size=len(dti)),
                        "B": np.random.randint(0, 100, size=len(dti))})
    time.sleep(random.random())  # to simulate network delay
    return df.to_json()


def download_data(pool_id, symbols):
    df = []
    for symbol in symbols:
        print("[{:02}]: {}".format(pool_id, symbol))
        # do stuff here
        # read_str = BASEURL.format(symbol)
        # df.append(pd.read_json(read_str))
        df.append(pd.read_json(fake_data(symbol)))
    return pd.concat(df, ignore_index=True)


if __name__ == "__main__":
    with multiprocessing.Pool(PROCESSES) as pool:
        data = pool.starmap(download_data, enumerate(TICKERS, start=1))
        df = pd.concat(data).set_index(["date", "symbol"]).sort_index()

In this example, I split the list of tickers into sublists for each process retrieves data for multiple symbols and limits overhead due to create and destroy processes.

The delay is to simulate the response time from the network connection and highlight the multiprocess behaviour.

Edit 2: simpler but naive version for your needs

import pandas as pd
import multiprocessing

# get tickers from your csv
TICKERS = ["BCDA", "WBAI", "NM", "ZKIN", "TNXP", "FLY", "MYSZ", "GASX", "SAVA", "GCE",
           "XNET", "SRAX", "SINO", "LPCN", "XYF", "SNSS", "DRAD", "WLFC", "OILD", "JFIN",
           "TAOP", "PIC", "DIVC", "MKGI", "CCNC", "AEI", "ZCMD", "YVR", "OCG", "IMTE",
           "AZRX", "LIZI", "ORSN", "ASPU", "SHLL", "INOD", "NEXI", "INR", "SLN", "RHE-PA",
           "MAX", "ARRY", "BDGE", "TOTA", "PFMT", "AMRH", "IDN", "OIS", "RMG", "IMV",
           "CHFS", "SUMR", "NRG", "ULBR", "SJI", "HOML", "AMJL", "RUBY", "KBLMU", "ELP"]

BASEURL = "https://financialmodelingprep.com/api/v3/income-statement/{}?limit=120&apikey=demo"


def download_data(symbol):
    print(symbol)
    # do stuff here
    read_str = BASEURL.format(symbol)
    df = pd.read_json(read_str)
    return df


if __name__ == "__main__":
    with multiprocessing.Pool(multiprocessing.cpu_count()) as pool:
        data = pool.map(download_data, TICKERS)
        df = pd.concat(data).set_index(["date", "symbol"]).sort_index()

Note about pool.map: for each symbol in TICKERS, create a process and call function download_data.

Upvotes: 2

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