Passing multiple variables of different types from dataframe to function

Question

I have the following dataframe:

import pandas as pd

data = {'selectionId': [8567238,7450487,12787737,9541421,10162696,7208966,8826166,7256678],

 'Price': [4.1,4.6,5.5,7.2,7.8,17.0,32.0,34.0],

 'Win_Percentage': [0.245870,0.212396,0.178922,0.145448,0.111974,0.078501,0.045027,0.011553],

  'Fit':[0.245870,0.212396,0.178922,0.145448,0.111974,0.078501,0.045027,0.011553],

   'size':[2.708701,2.373962,2.039223,1.704484,1.369744,1.035005,0.700266,0.365527]}

df = pd.DataFrame(data, columns=['selectionId', 'Price', 'Win_Percentage','Fit','size'])

I also have the following function:

def test(marketId, selectionId):

    global place_order_Req

    place_order_Req = '{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"' + marketId + '","instructions":'\


'[{"selectionId":"' + str(
        selectionId) + '","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":"1.9","price":"1.1","persistenceType":"LAPSE"}}],"customerRef":"test12121212121"}, "id": 1}'"""
print(place_order_Req)
"""

The marketId variable always has value marketId = "1.156196315"

I would like to pass the selectionId value in df to the function.

I would also like to pass the values of the size column to the function to change "size":"1.9" part of the function.

To summarise I would like the following to be returned from the function:

'{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"8567238","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":"2.708701","price":"1.1","persistenceType":"LAPSE"}}],"customerRef":"test12121212121"}, "id": 1}\n    print(place_order_Req)\n    '

and for this to be done for each row of the dataframe.

To do this I have tried the following:

selectionId = df['selectionId']

size = df['size'].astype(str)

def test(marketId, selectionId, size):

    global place_order_Req, place_order_Req_list, place_order_Req_size_list

    place_order_Req_list = []

    place_order_Req_size_list = []

    for i in selectionId:
        place_order_Req = '{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"' + marketId + '","instructions":'\
                                                                                                                              '[{"selectionId":"' + str(
            i) + '","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":"1","price":"1.1","persistenceType":"LAPSE"}}],"customerRef":"test12121212121"}, "id": 1}'
        """
    print(place_order_Req)
    """
        place_order_Req_list.append(place_order_Req)

    for j in place_order_Req_list:

        place_order_Req = place_order_Req[:208] + j + place_order_Req[:209]

        place_order_Req_size_list.append(place_order_Req)

    print(place_order_Req_size_list)

This changes the selectionId variable fine but when I try to change the "1.9" it does not work. It also returns itself twice for each input to the place_order_Req_size_list list.

I also think there must be a smarter way than using two loops.

This is the list that it returns:

['{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"7256678","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"8567238","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":"1","price":"1.1","persistenceType":"LAPSE"}}],"customerRef":"test12121212121"}, "id": 1}{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"7256678","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":"', '{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"7256678","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"7450487","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":"1","price":"1.1","persistenceType":"LAPSE"}}],"customerRef":"test12121212121"}, "id": 1}{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"7256678","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":{', '{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"7256678","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"12787737","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":"1","price":"1.1","persistenceType":"LAPSE"}}],"customerRef":"test12121212121"}, "id": 1}{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"7256678","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":{', '{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"7256678","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"9541421","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":"1","price":"1.1","persistenceType":"LAPSE"}}],"customerRef":"test12121212121"}, "id": 1}{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"7256678","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":{', '{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"7256678","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"10162696","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":"1","price":"1.1","persistenceType":"LAPSE"}}],"customerRef":"test12121212121"}, "id": 1}{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"7256678","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":{', '{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"7256678","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"7208966","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":"1","price":"1.1","persistenceType":"LAPSE"}}],"customerRef":"test12121212121"}, "id": 1}{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"7256678","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":{', '{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"7256678","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"8826166","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":"1","price":"1.1","persistenceType":"LAPSE"}}],"customerRef":"test12121212121"}, "id": 1}{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"7256678","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":{', '{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"7256678","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"7256678","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":"1","price":"1.1","persistenceType":"LAPSE"}}],"customerRef":"test12121212121"}, "id": 1}{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"1.156196315","instructions":[{"selectionId":"7256678","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":{']

Any help would be great, cheers. Sandy

dvitsios · Accepted Answer · 2019-03-16 13:23:57Z

2

You can just apply a function to each row of your data frame. You can read the fields 'selectionId' and 'size' for each row and pass them on to your place_order_Req variable. Also, I'm not sure if you actually need to define/use place_order_Req as a global variable.

def test(x):    
    marketId = "1.156196315" #static value
    selectionId = x['selectionId']
    size = x['size']

#     global place_order_Req
    place_order_Req = '{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"' + marketId + '","instructions":' '[{"selectionId":"' + str(selectionId) + '","handicap":"0","side":"BACK","orderType":"LIMIT", "limitOrder":{"size": "'+ str(size) + '","price":"1.1","persistenceType":"LAPSE"}}],"customerRef":"test12121212121"}, "id": 1}'
    print(place_order_Req)

df.apply(test, axis=1)

answered Mar 16, 2019 at 13:23

dvitsios

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Comments

Alexander Caskie · Accepted Answer · 2019-03-16 13:18:42Z

I've now reviewed the code and it works with the following but I still think there must be a better method than using two loops:

size = df['size'].astype(str)
size = '"' + size + '"'

def test(marketId, selectionId, size):

    global place_order_Req, place_order_Req_list, place_order_Req_size_list

    place_order_Req_list = []

    place_order_Req_size_list = []

    for i in selectionId:
        place_order_Req = '{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"' + marketId + '","instructions":'\
                                                                                                                              '[{"selectionId":"' + str(
            i) + '","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":"1","price":"1.1","persistenceType":"LAPSE"}}],"customerRef":"test12121212121"}, "id": 1}'
        """
    print(place_order_Req)
    """
        place_order_Req_list.append(place_order_Req)

    for j in size:

        place_order_Req = place_order_Req[:208] + j + place_order_Req[:209]

        place_order_Req_size_list.append(place_order_Req)

    print(place_order_Req_size_list)

dzang · Accepted Answer · 2019-03-16 13:30:40Z

0

You can use apply:

def query(marketId, selectionId, size):

    global place_order_Req

    place_order_Req = '{"jsonrpc": "2.0", "method": "SportsAPING/v1.0/placeOrders", "params": {"marketId":"' + marketId + '","instructions":'\
                      '[{"selectionId":"' + str(selectionId) + '","handicap":"0","side":"BACK","orderType":"LIMIT","limitOrder":{"size":"' \
                      + str(size) + '","price":"1.1","persistenceType":"LAPSE"}}],"customerRef":"test12121212121"}, "id": 1}'

    return place_order_Req

marketId = "1.156196315"
queries = df.apply(lambda x: query(marketId, x.selectionId, x.size), axis=1)

queries_list = queries.values.tolist() # list of your queries strings

answered Mar 16, 2019 at 13:30

dzang

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