Python 2.7.10 memory error - list

Question

I defined the function to import five (each has 16 variables and with size around 120MB) csv.files together in Python 2.7.10, it works and then I selected four time variables to be formatted as Date time, the first three variables were transformed successfully but the last one failed by Memory Error. The function I defined was shown:
def reddat(filename,year1,year2):
bigdata=defaultdict(list)
for i in range(year1,year2):
string=filename+str(i)+".csv"
with open(string,'rb') as f:
reader=csv.reader(f)
headers=reader.next()
data1 = {h:[] for h in headers}
for row in reader:
for h, v in zip(headers, row):
data1[h].append(v)
for h in headers:
bigdata[h].append(data1[h])
return bigdata
dataall=reddat("Calls_for_Service_",2011,2016)
##This function works to import five years data and combined as one dictionary as dataall##
Then I selected four variables from dataall,
TimeCreate=[]
TimeDispatch=[]
TimeArrive=[]
TimeClosed=[]
for i in range(0,len(dataall['TimeCreate'])):
TimeCreate+=dataall['TimeCreate'][i]
TimeDispatch+=dataall['TimeDispatch'][i]
TimeArrive+=dataall['TimeArrive'][i]
TimeClosed+=dataall['TimeClosed'][i]
Now, four variables were selected from dataall as lists,these four lists contained string,I wanted to change them into date time format.I defined another function as follow:
def func(x):
try:
return dt.datetime.strptime(x, "%m/%d/%Y %I:%M:%S %p")
except:
return pd.NaT
I changed four string lists as date time lists:
TimeCreatenew=[func(d) for d in TimeCreate]
TimeDispatchnew=[func(d) for d in TimeDispatch]
TimeArrivenew=[func(d) for d in TimeArrive]
TimeClosednew=[func(d) for d in TimeClosed]
However, "TimeCreatnew", "TimeDispatchnew", and "TimeArrivenew" worked well, but when "TimeClosednew" changed format, Python said
Traceback (most recent call last):
File "C:\Users\....\DataScience\scriptnew.py" line 65, in <module>
TimeClosednew=[func(d) for d in TimeClosed]
MemoryError
My python 2.7.10 is 32bit, how could I address this problem? Or if my function "reddat" is not effective? THANKS VERY MUCH
Found out one solution for using Python 3.5
I employed Python 3.5 by Anaconda3 (64-bit), which addressed the problem without memory error. I think Python 2.7.10 may not handle with such large size data. If someone has some idea about this problem which can be solved under Python 2.7.10. Please share idea. Thanks very much