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I recently failed to finish a code for a job interview. One of the problems is that I decided to use Pandas (it made sense) but I was unfamiliar with it (however I know Python Scipy and Numpy), so it took a lot of time to figure out everything. It's the first time I wrote such kind of code manipulating Pandas data frames, thus I was wondering if you could give me advice to do things better.

The purpose of the code was to read a CSV table of trading data (for a finance company) and then manipulating the data in order to find certain properties. I did not finish it, but do you think I could have done something that make it run faster? it took 43 minutes to do just what it does now. The .csv file is a 1.2GB file.

Moreover, if you have any observation about style it is more than welcome.

import numpy as np
import pandas as pd
import csv
import time
import matplotlib.pyplot as plt

chunk_size = 10**5 #safe on memory
auction_division = 40000.0 #empirical
path = 'scandi.csv'

col_names = ['id','empty0','bid_price','ask_price','trade_price',\
    'bid_volume','ask_volume','trade_volume','update',\
    'empty1','date','seconds','opening','empty2','con_codes']

col_names_load = ['id','bid_price','ask_price','trade_price',\
    'trade_volume','update','date','seconds','con_codes']

start = time.time()
total=0
stocks=set()
days=set()
sub_chunks = []

for i,chunk in enumerate(pd.read_csv(path, sep=',,|,',names=col_names, usecols=\
    ['id','bid_price','ask_price','date','seconds'],\
    engine='python', chunksize=chunk_size)):

    ids_unique = set(chunk.id.unique().tolist())
    day_unique = set(chunk.date.unique().tolist()) 
    stocks|=ids_unique
    days|=day_unique

    auction_data = chunk[chunk['bid_price']>chunk['ask_price']]
    new_el = auction_data[['date','seconds']]
    sub_chunks.append(new_el)


days_list = list(days)
auction = pd.concat(sub_chunks)
au_bound = []
stocky=list(stocks)

for day in days_list:
    g=auction[auction['date']==day]
    slot1=g[g['seconds']<auction_division].seconds
    slot2=g[g['seconds']>auction_division].seconds
    au_bound.append((slot1.min(),slot1.max(),slot2.min(),slot2.max()))


the_last_element = pd.DataFrame(np.zeros((7, len(stocks))), columns=stocky)
#rows: 0: bid price, 1: ask price, 2: trade price, 3: trade volumes, 4: date, 5: seconds, 6: flag

for i,chunk in enumerate(pd.read_csv(path, sep=',,|,', names=col_names, 
usecols = col_names_load, engine='python', chunksize=chunk_size)):
    print i+1, 'th chunk'
    #I select just trade updates because ticks and bid-ask spreads make sense at the trade (that's what I think to have learnt from investopedia)
    #Tick: https://www.investopedia.com/terms/t/tick.asp
    #Bid-Ask Spread: https://www.investopedia.com/terms/b/bid-askspread.asp
    chunk_clean = chunk[ chunk['update']==1 & ((chunk.con_codes=='@1') \
        | (chunk.con_codes=='XT') | (chunk.con_codes=='XT|C') | (chunk.con_codes=='XT|O') ) ]

    stocky_b = set(chunk.id.unique().tolist())

    for stock in stocky_b:
        for day, ab in zip( days, au_bound ):

            stock_chunk=chunk_clean[(chunk_clean['id']==stock) & (chunk_clean['date']==day)]

            #time between trades subtraction
            stock_chunk.loc[:,'t_b_trades']=stock_chunk['seconds']-stock_chunk['seconds'].shift(1)
            #eliminate trades that cross auctions 

            stock_chunk.loc[stock_chunk[
               ( (stock_chunk['seconds'] > ab[0]) & (stock_chunk['seconds'] < ab[1]                  ) )\
             | ( (stock_chunk['seconds'] > ab[2]) & (stock_chunk['seconds'] < ab[3]                  ) )\
             | ( (stock_chunk['seconds'].shift(1) > ab[0]) & (stock_chunk['seconds'].shift(1) < ab[1]) )\
             | ( (stock_chunk['seconds'].shift(1) > ab[2]) & (stock_chunk['seconds'].shift(1) < ab[3]) )\
             | ( (stock_chunk['seconds'].shift(1) < ab[0]) & (stock_chunk['seconds'] > ab[1]         ) )\
             | ( (stock_chunk['seconds'].shift(1) < ab[2]) & (stock_chunk['seconds'] > ab[3]         ) )\
              ].index.values,'t_b_trades']  = np.nan

            #the first row is always wrong
            if (the_last_element[stock][6]==1 and the_last_element[stock][4]==day):
                if stock_chunk.empty==False:
                    stock_chunk.loc[stock_chunk[stock_chunk['seconds']==stock_chunk.seconds.iloc[0]].index.values,'t_b_trades'] \
                            = stock_chunk.seconds.iloc[0]-the_last_element[stock][5]

                    #eliminate trades that cross auctions 
                    if  ( ( (stock_chunk.seconds.iloc[0] >ab[0] ) & (stock_chunk.seconds.iloc[0]< ab[1])) \
                        | ( (stock_chunk.seconds.iloc[0] >ab[2] ) & (stock_chunk.seconds.iloc[0]< ab[3])) \
                        | ( (the_last_element[stock][5] > ab[0] ) & (the_last_element[stock][5] < ab[1])) \
                        | ( (the_last_element[stock][5] > ab[2] ) & (the_last_element[stock][5] < ab[3])) \
                        | ( (the_last_element[stock][5] < ab[0] ) & (stock_chunk.seconds.iloc[0]> ab[1])) \
                        | ( (the_last_element[stock][5] < ab[2] ) & (stock_chunk.seconds.iloc[0]> ab[3]))):

                        stock_chunk.loc[stock_chunk[stock_chunk['seconds'] == stock_chunk.seconds.iloc[0]].index.values,'t_b_trades'] = np.nan        
            else:
                if stock_chunk.empty==False:
                    stock_chunk.loc[stock_chunk[stock_chunk['seconds']==stock_chunk.seconds.iloc[0]].index.values,'t_b_trades'] = np.nan

        #fill the last row for the next chunk
            if stock_chunk.empty==False:
                the_last_element[stock][0]=stock_chunk.bid_price.iloc[-1]
                the_last_element[stock][1]=stock_chunk.ask_price.iloc[-1]
                the_last_element[stock][2]=stock_chunk.trade_price.iloc[-1]
                the_last_element[stock][3]=stock_chunk.trade_volume.iloc[-1]
                the_last_element[stock][4]=stock_chunk.date.iloc[-1]
                the_last_element[stock][5]=stock_chunk.seconds.iloc[-1]
                the_last_element[stock][6]=1


end = time.time()
tot_time = (end-start)/60.0
print tot_time, 'minutes for data! for', total, 'chunks of size', chunk_size 

Here the first 3 lines of the input file

ID,Underlying Type,Underlying,Risk-Free Rate,Days To Expiry,Strike,Option Type,Model Type,Market Price         
0,Future,1.9119,-0.0009,19.3599,2.0264,Call,Bachelier,0.096576518   
1,Future,0.8731,-0.0025,278.2703,1.0610,Call,Bachelier,0.40362827 
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  • 1
    \$\begingroup\$ Does it work as intended? What does the input and output data look like? Can you post examples of both? \$\endgroup\$ – Mast Jul 16 '18 at 6:31
  • \$\begingroup\$ @Mast yes the code works as intended, as I said in the post. Here there is an example of the input ID,Underlying Type,Underlying,Risk-Free Rate,Days To Expiry,Strike,Option Type,Model Type,Market Price 0,Future,1.9119,-0.0009,19.3599,2.0264,Call,Bachelier,0.096576518 1,Future,0.8731,-0.0025,278.2703,1.0610,Call,Bachelier,0.40362827 \$\endgroup\$ – spec3 Jan 5 at 8:34
  • \$\begingroup\$ Please add it to the question itself, exactly how it's written down in your file. \$\endgroup\$ – Mast Jan 6 at 16:31
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A few tips that might help you:

  • Good job declaring your global variables at the top of the file. Convention is to capitalize them.
  • Posting some example data here (even just a row or two) would help greatly.
  • To get the unique lists of ids and days, I don't believe you need both the .unique() method and the set() functions. I believe just the .unique() method is sufficient and the more performant option.
  • I would be surprised if you needed to call pd.read_csv in chunks for a 1.2 GB file. Further, I suspect this is really slowing you down.
  • Many of your for-loops and deeply nested if statements you might consider refactoring into functions to improve readability and re-usability.
  • The code is sparse on useful comments.

Apologies for the brevity of this response. Hopefully this is helpful.

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  • \$\begingroup\$ ID,Underlying Type,Underlying,Risk-Free Rate,Days To Expiry,Strike,Option Type,Model Type,Market Price 0,Future,1.9119,-0.0009,19.3599,2.0264,Call,Bachelier,0.096576518 1,Future,0.8731,-0.0025,278.2703,1.0610,Call,Bachelier,0.40362827 many rows like these! \$\endgroup\$ – spec3 Jan 5 at 8:32

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