Introduction to Data Science in Python problem

Question

Can any one tell my what that part (town = thisLine[:thisLine.index('(')-1])exactly do?

def get_list_of_university_towns():
'''Returns a DataFrame of towns and the states they are in from the 
university_towns.txt list. The format of the DataFrame should be:
DataFrame( [ ["Michigan", "Ann Arbor"], ["Michigan", "Yipsilanti"] ], 
columns=["State", "RegionName"]  )

The following cleaning needs to be done:
1. For "State", removing characters from "[" to the end.
2. For "RegionName", when applicable, removing every character from " (" to the end.
3. Depending on how you read the data, you may need to remove newline character '\n'. '''

data = []
state = None
state_towns = []
with open('university_towns.txt') as file:
    for line in file:
        thisLine = line[:-1]
        if thisLine[-6:] == '[edit]':
            state = thisLine[:-6]
            continue
        if '(' in line:
            town = thisLine[:thisLine.index('(')-1]
            state_towns.append([state,town])
        else:
            town = thisLine
            state_towns.append([state,town])
        data.append(thisLine)
df = pd.DataFrame(state_towns,columns = ['State','RegionName'])
return df

get_list_of_university_towns()

Answer 1

It performs this step:

2. For "RegionName", when applicable, removing every character from " (" to the end.

An index of -1 means the end of an array or list.

Answer 2

This line does the part of requirement 2 of the list of things cleaned up:

For example: if Line is:

line = "Michigan, (Ann Arbor"

Then your code will output Michigan,

Answer 3

import re
raw_data=open('university_towns.txt','r')
data=raw_data.readlines()
raw_data.close()
subs='[edit]'
state=''
region=''
df=pd.DataFrame(columns=('State','RegionName'))

for line in data:
    line.rstrip()
    if subs in line:
        state=line.replace(subs,'')
    else:
        region=re.sub(r" \(.*",'',line)
        df=df.append({'State':state,'RegionName':region},ignore_index=True)

df=df.replace('\n','',regex=True)
df