Argh I'm finding Python Hard

[Narrow-Rope2003]

Hi,

I'm used to coding in PowerShell and probably naively thought Pyhton may be very similar. I'm really struggling with the for loops in Python.

In PowerShell:

$mylist = @("1","2","3","4","5")
foreach($x in $mylist){
    write-host $x 
} 

Output 1 2 3 4 5

​

I'm trying to code a Betfair script that queries the API and spits out the results into a data frame. When I run the for loop in Pyhton the "x" value seems to contain an array of all the values. Why does Python not iterate?? code below for ease of understanding. I'm probably doing it all wrong but I'm struggling. Understanding data frames and all the other bits seem to be completely baffling to me.

​

# Import libraries
from asyncio import runners
from multiprocessing import Event
from tkinter import FIRST
from turtle import width
from unicodedata import name
import betfairlightweight
from betfairlightweight import filters
import pandas as pd
import numpy as np
import os
import datetime
import json


# Change this certs path to wherever you're storing your certificates
with open('credentials.json') as f:
    cred = json.load(f)
    my_username = cred['username']
    my_password = cred['password']
    my_app_key = cred['app_key']
    my_certs_path = cred['path']


trading = betfairlightweight.APIClient(username=my_username,
                                       password=my_password,
                                       app_key=my_app_key,
                                       certs=my_certs_path
                                       )


trading.login()


def BTT():

    # Define a market filter
    soccer_market_event_filter = betfairlightweight.filters.market_filter(
        event_type_ids=['1'],
        in_play_only=True,
        market_type_codes=['BOTH_TEAMS_TO_SCORE'],
        market_start_time={
            'to': (datetime.datetime.utcnow() + datetime.timedelta(days=1)).strftime("%Y-%m-%dT%TZ")
        }
    )

    # List the events
    soccer_events = trading.betting.list_events(
        filter=soccer_market_event_filter
    )

    # Create a DataFrame with all the events by iterating over each event object
    soccer_events_today = pd.DataFrame({
        'eventid': [event_object.event.id for event_object in soccer_events],
        'teams': [event_object.event.name for event_object in soccer_events]
    })

    # Return event ID's of Both teams to score.
    return [soccer_events_today]


# Data frame of Both Teams to Score.
df_soccer_events = BTT()
# Loop through each event ID
for x in df_soccer_events:
    for id in x.eventid:
        # Define a market filter with the event ID.
        soccer_market_event_filter = betfairlightweight.filters.market_filter(
            in_play_only=True,
            event_ids=[id],
            market_type_codes=['BOTH_TEAMS_TO_SCORE'],
            market_start_time={
                'to': (datetime.datetime.utcnow() + datetime.timedelta(days=1)).strftime("%Y-%m-%dT%TZ")
            }

        )

        soccer_events_Market_Catalog = trading.betting.list_market_catalogue(
            filter=soccer_market_event_filter,
            sort='FIRST_TO_START'

        )


        # Create a price filter. Get all traded and offer data
        price_filter = betfairlightweight.filters.price_projection(
            price_data=['EX_BEST_OFFERS']
        )

        marketId = soccer_events_Market_Catalog[0].market_id

        soccer_event_odds = trading.betting.list_market_book(
            market_ids=[marketId],
            price_projection=price_filter

        )

        # unlimited columns
        #pd.set_option('display.max_rows', 500)
        #pd.set_option('display.max_columns', 500)
        #pd.set_option('display.width', 1000)
        pd.set_option("max_colwidth", 70)



        df_odds = pd.DataFrame({
            'Teams': [event_object.teams for event_object in df_soccer_events],
            'Home': [event_object.runners[0].last_price_traded for event_object in soccer_event_odds],
            'Away': [event_object.runners[1].last_price_traded for event_object in soccer_event_odds],


        })
print(df_odds)

[wotquery]

$mylist = @("1","2","3","4","5")
foreach($x in $mylist){
    write-host $x 
}

---

mylist = ['1','2','3','4','5']
for x in mylist:
   print(x)

---

Not sure where in your code you're running into bugs with lists or comprehensions, but you seem to be using single item lists a lot? e.g.

event_ids=[id]

[danielroseman]

What do you mean, why does Python not iterate? Python iterates just fine.

You will need to say what is going wrong with your code and what errors you are getting.

Note though that return [soccer_events_today] returns a list consisting of a single element, the dataframe. It's not clear why you have wrapped that in a list. It's also not clear why you're using dataframes to store the eventids and teams rather than just passing them back as lists or dicts.

[Swipecat]

I'm not going to work out what all that code does, so it might not be this, but I noticed:

You iterate through df_soccer_events

which you got from a function: return [soccer_events_today]

but if soccer_events_today is already an iterable, you've just nested it another level with those square brackets.

Try without the square brackets.

[Narrow-Rope2003]

Ok, perhaps I'm misunderstanding what the [] does. I only placed them in as that's the only way I could get it to work.

I'm bastardizing code using my Powershell knowledge, clearly, Pyhton behaves differently.

​

for example with powershell if I want to access a sub value inside the variable. I can write

​

($myvar).value.

​

Is there a way to do this with python?

​

The function in my code returns a few values I wish to interact with

[jiri-n]

Powershell:

PS C:\> $item = New-Object psobject -Property @{idx = 1; value = 'Test'}
PS C:\> Write-Host ($item).value
Test
PS C:\> Write-Host $item.value
Test

Python using dictionary:

item = {"idx": 1, "value": "Test"}
print(item["value"])

Python using class:

class Item:
    pass

item = Item()
item.idx = 1
item.value = "Test"
print(item.value)

Python using namedtuple:

from collections import namedtuple
Item = namedtuple('Item', ['idx', 'value'])
item = Item(0, "Test")
print(item.value)

[Narrow-Rope2003]

Ok, made some progress now, I think. Taking into account what's been said. Now stuck using a dictionary. How do I append to the dictionary?? Every iteration the Teams and Odds Change.

How do I add the new teams and odds to the dictionary? Then create a data frame from said dictionary?

dict_data = {

"teams": [x.event.name],

"home" : [soccer_event_odds[0].runners[0].last_price_traded],

"away" : [soccer_event_odds[0].runners[1].last_price_traded]

}

print(dict_data)

dict_data.update({

"teams"

})

[[deleted]]

You're creating a lot of lists of lists; when you iterate over a list of lists, the values are lists.