Python CSV file import and object manipulation
Style

Use snake_case instead of mixedCase when in Python. In general, follow PEP 8. 

Implementation

It seems you have a Player class defined somewhere. Then, you use global functions as getters and setters. This has two problems:


In OOP, getters and setters should be instance methods, e.g. of the Player class.
Python is not Java. Pythonic code wouldn't normally use getters and setters.


Design

Judging by loadCSV, each row of a CSV file will become a Player object. However, all Players do is store attributes for getting and setting. Save the hassle by using a dictionary for a player. Then just store all your dictionaries in a list. 

To understand this code, you will need to understand comprehensions.

import csv

# Map row to dictionary (dictionary comprehension)
def player(column_names, row):
    return {column_names[column]: data for column, data in enumerate(row) if column &lt; len(column_names)}

# Map CSV file to list of dictionaries (list comprehension)
players = [player(['name', 'position', 'game_info', 'price'], row) for row in csv.reader(open('dk.csv', 'r'))]


Running this code on the following CSV file


Louis Fernando,Batter,Washington,400
Hose Ricardo,Pitcher,San Fernando,900


sets players to a list of dictionaries as below.


[{'game_info': 'Washington',
  'name': 'Louis Fernando',
  'position': 'Batter',
  'price': '400'},
 {'game_info': 'San Fernando',
  'name': 'Hose Ricardo',
  'position': 'Pitcher',
  'price': '900'}]


Then player objects can be accessed like so

print(players[0]['game_info']) # prints Washington
players[1]['name'] = "James Jones"


Notes

Types

This doesn't take care of types, e.g. the price field maps to a string, not a number. I'm not exactly sure what you're doing overall, so I'll leave that to you.

CSV Format

Columns in your CSV file beyond those named in column_names will be ignored.

e.g. passing four names (['name', 'position', 'game_info', 'price']) to player when a row in the CSV file contains five columns.


Louis Fernando,Batter,Washington,400,lol
Hose Ricardo,Pitcher,San Fernando,900


will produce the same result as the first example.