dataquest birth years

main.py

# convert 

['2000,1,1,6,9083',
 '2000,1,2,7,8006',
 '2000,1,3,1,11363',
 '2000,1,4,2,13032',
 '2000,1,5,3,12558',
 '2000,1,6,4,12466',
 '2000,1,7,5,12516',
 '2000,1,8,6,8934',
 '2000,1,9,7,7949',
 '2000,1,10,1,11668']

# to

[[2000, 1, 1, 6, 9083],
 [2000, 1, 2, 7, 8006],
 [2000, 1, 3, 1, 11363],
 [2000, 1, 4, 2, 13032],
 [2000, 1, 5, 3, 12558],
 [2000, 1, 6, 4, 12466],
 [2000, 1, 7, 5, 12516],
 [2000, 1, 8, 6, 8934],
 [2000, 1, 9, 7, 7949],
 [2000, 1, 10, 1, 11668],
 [2000, 1, 11, 2, 12611]]

def read_csv(filename):
    data = open(filename).read().split('\n')
    string_list = data[1:]
    final_list = []
    
    for each in string_list:
        int_fields = []
        string_fields = each.split(',')
        int_fields = [int(x) for x in string_fields]
        final_list.append(int_fields)
    
    return final_list
   
def month_births(list_of_lists):
    births_per_month = {}
    for l in list_of_lists:
        month = l[1]
        births = l[4]
        if month in births_per_month.keys():
            births_per_month[month] += births
        births_per_month[month] = births
    return births_per_month
   
cdc_month_births = month_births(cdc_list)
# {1: 11843,
#  2: 11671,
#  3: 11511,
#  4: 11591,
#  5: 8462,
#  6: 12243,
#  7: 12673,
#  8: 7884,
#  9: 12959,
#  10: 10837,
#  11: 7228,
#  12: 11990}
   
def dow_births(list_of_lists):
    births_per_dow = {}
    for l in list_of_lists:
        dow = l[3]
        births = l[4]
        if dow in births_per_dow.keys():
            births_per_dow[dow] += births
        births_per_dow[dow] = births
    return births_per_dow
   
cdc_day_births = dow_births(cdc_list)
# {1: 12811, 2: 13634, 3: 11990, 4: 6749, 5: 10386, 6: 8656, 7: 7724}

 
def calc_counts(data, column):
    results = {}
    for d in data:
        unit = d[column]
        if unit in results.keys():
            results[unit] += d[4]
        results[unit] = d[4]
    return results
   
cdc_year_births = calc_counts(cdc_list, 0)
# {2000: 7892,
#  2001: 10272,
#  2002: 12582,
#  2003: 12540,
#  2004: 10130,
#  2005: 8635,
#  2006: 7569,
#  2007: 11102,
#  2008: 12906,
#  2009: 11667,
#  2010: 9751,
#  2011: 8035,
#  2012: 10634,
#  2013: 12525,
#  2014: 11990}