Iteratively writing to DataFrame crashing shell at random points
Background: the following code snippet iterates through individual text messages of a thread of an XML iOS backup file returning the content, time sent/received, and direction of each text message from a specified contact.
else: # if no need to chunk
msg_count = len(top["dict"][i]["array"][2].getchildren()) # enumerate messages
for n in range(msg_count): # iterate through individual messages
try:
time = str(top["dict"][i]["array"][2]["dict"][n]["date"][0]) # fetch time
msg = str(top["dict"][i]["array"][2]["dict"][n]["string"][2]) # fetch content
if str(top["dict"][i]["array"][2]["dict"][n]["string"][4]) == 'Sent': # sent/received?
sent = True
else:
sent = False
container.loc[n] = [time, msg, sent]
print('Processed thread ' + str(i) + ' message ' + str(n) + '/' + str(msg_count))
except:
print('Error at thread ' + str(i) + ' message ' + str(n) + '/' + str(msg_count))
Also, for reference, here are the variables initialized at the top of the program:
path = r'C:UsersxxxDocumentsPythonMessages.xml'
tree = objectify.parse(path)
root = tree.getroot()
top = root.dict.array
target = ''
chunk_max = 500
container = pd.DataFrame(data=None, columns=['time', 'content', 'sent'])
What happens is, no matter how many messages I have with the given contact, the program crashes at a random point with no error code. For a contact with whom I have around 140 messages, it crashes at around message 90. When handling a contact with nearly 150,000 messages, it hangs around 1,000. The exact amount of texts it is able to process varies. There is no error code; my shell simply crashes and restarts.
I have tried running this on both Windows and Mac OS X, with both the native Python shell and Spyder's iPython shell on each platform. Same thing in each instance, although oddly it consistently makes it through more messages in the native shell. Also, notably, my resource usage never spikes when running the program.
python xml pandas lxml
asked Nov 21 '18 at 20:52
RyanRyan
12
add a comment |
Background: the following code snippet iterates through individual text messages of a thread of an XML iOS backup file returning the content, time sent/received, and direction of each text message from a specified contact.
else: # if no need to chunk
msg_count = len(top["dict"][i]["array"][2].getchildren()) # enumerate messages
for n in range(msg_count): # iterate through individual messages
try:
time = str(top["dict"][i]["array"][2]["dict"][n]["date"][0]) # fetch time
msg = str(top["dict"][i]["array"][2]["dict"][n]["string"][2]) # fetch content
if str(top["dict"][i]["array"][2]["dict"][n]["string"][4]) == 'Sent': # sent/received?
sent = True
else:
sent = False
container.loc[n] = [time, msg, sent]
print('Processed thread ' + str(i) + ' message ' + str(n) + '/' + str(msg_count))
except:
print('Error at thread ' + str(i) + ' message ' + str(n) + '/' + str(msg_count))
Also, for reference, here are the variables initialized at the top of the program:
path = r'C:UsersxxxDocumentsPythonMessages.xml'
tree = objectify.parse(path)
root = tree.getroot()
top = root.dict.array
target = ''
chunk_max = 500
container = pd.DataFrame(data=None, columns=['time', 'content', 'sent'])
What happens is, no matter how many messages I have with the given contact, the program crashes at a random point with no error code. For a contact with whom I have around 140 messages, it crashes at around message 90. When handling a contact with nearly 150,000 messages, it hangs around 1,000. The exact amount of texts it is able to process varies. There is no error code; my shell simply crashes and restarts.
I have tried running this on both Windows and Mac OS X, with both the native Python shell and Spyder's iPython shell on each platform. Same thing in each instance, although oddly it consistently makes it through more messages in the native shell. Also, notably, my resource usage never spikes when running the program.
python xml pandas lxml
asked Nov 21 '18 at 20:52
RyanRyan
12
add a comment |
Background: the following code snippet iterates through individual text messages of a thread of an XML iOS backup file returning the content, time sent/received, and direction of each text message from a specified contact.
else: # if no need to chunk
msg_count = len(top["dict"][i]["array"][2].getchildren()) # enumerate messages
for n in range(msg_count): # iterate through individual messages
try:
time = str(top["dict"][i]["array"][2]["dict"][n]["date"][0]) # fetch time
msg = str(top["dict"][i]["array"][2]["dict"][n]["string"][2]) # fetch content
if str(top["dict"][i]["array"][2]["dict"][n]["string"][4]) == 'Sent': # sent/received?
sent = True
else:
sent = False
container.loc[n] = [time, msg, sent]
print('Processed thread ' + str(i) + ' message ' + str(n) + '/' + str(msg_count))
except:
print('Error at thread ' + str(i) + ' message ' + str(n) + '/' + str(msg_count))
Also, for reference, here are the variables initialized at the top of the program:
path = r'C:UsersxxxDocumentsPythonMessages.xml'
tree = objectify.parse(path)
root = tree.getroot()
top = root.dict.array
target = ''
chunk_max = 500
container = pd.DataFrame(data=None, columns=['time', 'content', 'sent'])
What happens is, no matter how many messages I have with the given contact, the program crashes at a random point with no error code. For a contact with whom I have around 140 messages, it crashes at around message 90. When handling a contact with nearly 150,000 messages, it hangs around 1,000. The exact amount of texts it is able to process varies. There is no error code; my shell simply crashes and restarts.
I have tried running this on both Windows and Mac OS X, with both the native Python shell and Spyder's iPython shell on each platform. Same thing in each instance, although oddly it consistently makes it through more messages in the native shell. Also, notably, my resource usage never spikes when running the program.
python xml pandas lxml
asked Nov 21 '18 at 20:52
RyanRyan
12
Background: the following code snippet iterates through individual text messages of a thread of an XML iOS backup file returning the content, time sent/received, and direction of each text message from a specified contact.
else: # if no need to chunk
msg_count = len(top["dict"][i]["array"][2].getchildren()) # enumerate messages
for n in range(msg_count): # iterate through individual messages
try:
time = str(top["dict"][i]["array"][2]["dict"][n]["date"][0]) # fetch time
msg = str(top["dict"][i]["array"][2]["dict"][n]["string"][2]) # fetch content
if str(top["dict"][i]["array"][2]["dict"][n]["string"][4]) == 'Sent': # sent/received?
sent = True
else:
sent = False
container.loc[n] = [time, msg, sent]
print('Processed thread ' + str(i) + ' message ' + str(n) + '/' + str(msg_count))
except:
print('Error at thread ' + str(i) + ' message ' + str(n) + '/' + str(msg_count))
Also, for reference, here are the variables initialized at the top of the program:
path = r'C:UsersxxxDocumentsPythonMessages.xml'
tree = objectify.parse(path)
root = tree.getroot()
top = root.dict.array
target = ''
chunk_max = 500
container = pd.DataFrame(data=None, columns=['time', 'content', 'sent'])
What happens is, no matter how many messages I have with the given contact, the program crashes at a random point with no error code. For a contact with whom I have around 140 messages, it crashes at around message 90. When handling a contact with nearly 150,000 messages, it hangs around 1,000. The exact amount of texts it is able to process varies. There is no error code; my shell simply crashes and restarts.
I have tried running this on both Windows and Mac OS X, with both the native Python shell and Spyder's iPython shell on each platform. Same thing in each instance, although oddly it consistently makes it through more messages in the native shell. Also, notably, my resource usage never spikes when running the program.
python xml pandas lxml
python xml pandas lxml
asked Nov 21 '18 at 20:52
RyanRyan
12
asked Nov 21 '18 at 20:52
RyanRyan
12
asked Nov 21 '18 at 20:52
RyanRyan
12
asked Nov 21 '18 at 20:52
RyanRyan
12
asked Nov 21 '18 at 20:52
RyanRyan
12
12
add a comment |
add a comment |
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