02_file_reading.py

'''
Lesson on file reading using Airline Safety Data
https://github.com/fivethirtyeight/data/tree/master/airline-safety
'''

airlines = 'Aer Lingus,Aeroflot*,Aerolineas Argentinas,Aeromexico*,Air Canada,Air France,\
Air India*,Air New Zealand*,Alaska Airlines*,Alitalia,All Nippon Airways,American*,\
Austrian Airlines,Avianca,British Airways*,Cathay Pacific*,China Airlines,Condor,\
COPA,Delta / Northwest*,Egyptair,El Al,Ethiopian Airlines,Finnair,Garuda Indonesia,\
Gulf Air,Hawaiian Airlines,Iberia,Japan Airlines,Kenya Airways,KLM*,Korean Air,LAN Airlines,\
Lufthansa*,Malaysia Airlines,Pakistan International,Philippine Airlines,Qantas*,\
Royal Air Maroc,SAS*,Saudi Arabian,Singapore Airlines,South African,Southwest Airlines,\
Sri Lankan / AirLanka,SWISS*,TACA,TAM,TAP - Air Portugal,Thai Airways,Turkish Airlines,\
United / Continental*,US Airways / America West*,Vietnam Airlines,Virgin Atlantic,\
Xiamen Airlines'

""" PART 1 """
# How many airlines are there
airline_count = 0

 
# How many airlines include the letter 'A' (only capital letters)


# How many airlines include the letter 'A' (include 'A' & 'a')


# create a list of airlines


""" PART 2 """
# count every airline that starts with the letter 'A'
        
        
# count every airline that includes '*'


""" PART3 """
# read the whole file at once, return a single string (including newlines)
# 'rU' mode (read universal) converts different line endings into '\n'


# use a context manager to automatically close your file


# read the file into a list (each list element is one row)


# do the same thing using a list comprehension


# side note: splitting strings


# split each string (at the commas) into a list


# do the same thing using the csv module


# separate the header and data


"""number of available seat kilometers (ASKs), which is defined as the number
   of seats multiplied by the number of kilometers the airline flies"""
   
ask = '320906734,1197672318,385803648,596871813,1865253802,3004002661,869253552,\
710174817,965346773,698012498,1841234177,5228357340,358239823,396922563,\
3179760952,2582459303,813216487,417982610,550491507,6525658894,557699891,\
335448023,488560643,506464950,613356665,301379762,493877795,1173203126,\
1574217531,277414794,1874561773,1734522605,1001965891,3426529504,1039171244,\
348563137,413007158,1917428984,295705339,682971852,859673901,2376857805,\
651502442,3276525770,325582976,792601299,259373346,1509195646,619130754,\
1702802250,1946098294,7139291291,2455687887,625084918,1005248585,430462962'


'''
EXERCISES:

1. Create a list containing the average number of incidents per year for each airline.
Example for Aer Lingus: (2 + 0)/30 = 0.07
Expected output: [0.07, 2.73, 0.23, ...]
'''

# Part 1    
    
    
    
"""
EXERCISES:
2. Create a list of airline names (without the star).
Expected output: ['Aer Lingus', 'Aeroflot', 'Aerolineas Argentinas', ...]

3. Create a list (of the same length) that contains 1 if there's a star and 0 if not.
Expected output: [0, 1, 0, ...]

4. BONUS: Create a dictionary in which the key is the airline name (without the star)
   and the value is the average number of incidents.
Expected output: {'Aer Lingus': 0.07, 'Aeroflot': 2.73, ...}
"""

# Part 2


# Part 3


# Part 4


'''
A few extra things that will help you with the homework
'''

# 'set' data structure is useful for gathering unique elements
my_list = [1, 2, 1]
set(my_list)            # returns a set of 1, 2
len(set(my_list))       # count of unique elements

# 'in' statement is useful for lists
1 in my_list            # True
3 in my_list            # False

# 'in' is useful for strings (checks for substrings)
my_string = 'hello there'
'the' in my_string      # True
'then' in my_string     # False

# 'in' is useful for dictionaries (checks keys but not values)
my_dict = {'name':'Alex', 'title':'Hey You!'}
'name' in my_dict       # True
'Alex' in my_dict      # False

# 'count' method for strings counts how many times a character appears
my_string.count('e')    # 3