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# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in 

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory

import os
print(os.listdir("../input"))
train = pd.read_csv("../input/train_V2.csv")

train = train.dropna()

# Any results you write to the current directory are saved as output.
##Original code
#train.head()
## Let us remove any unnecessary columns
train = train.drop(['Id', 'groupId', 'matchId'], axis=1)
l,u = pd.factorize(train['matchType'])
train = train.drop(['matchType'], axis=1)
train['matchtype'] = l