The problem you're experiencing comes from the fact that array.append, as implemented in NumPy, actually creates a new copy of the array every time called, which must allocated in the memory completely new, especially if called often and on large arrays this should definitely be avoided. There are two solutions to solve this issue:

1. If you don't know how big the arrays are going to be, you can use python lists during the processing and then convert them to NumPy arrays afterwards. Python lists alter in memory and are therefore much more performant for using append. The downside of this is that you have larger memory usage, as you can't use NumPys more efficient data types.

2. If you know how big the arrays are going to be you can preallocate the whole array, by using the zeros method of NumPy and create sort of an empty array which you then add the values to as you progress. This may requires changing of your processing algorithm and therefore could be slower, but it can be way more memory efficient.


NumPy array append slow

For a university project I need to implement an inverted index in python. This consists of a dictionary containing NumPy arrays, to which I append new values as I process more text. We've been told to use NumPy instead of regular Python lists, to decrease memory usage as it is possible to use smaller defined datatypes. However using list append and then swapping the value in the dictionary proves to be terribly slow. Is there a better way to implement this?

Docker uses way more memory than python program on its own

I have created a program in python that simulates business processes. For extremely large processes, the simulation can run for hours and the memory requirements are in GB. However, if I create a docker container, the docker image consumes cca 3x more memory than the original python program and if I limit the memory of the docker to even 2x the original python memory, the python program inside the docker will fail with out of memory error.


Streaming API, extend the StreamListener() to customise the way we process the incoming data, based on a certain #hashtag in Twitter.



How to Aggregate the Tweets by User

Hot to Keep the connection open and gather the upcoming tweets, when using the Twitter API for mining Twitter Data with Python.

Honestly I am not quite sure what the actual solution was to you problem as you still had problems when reading your last paragraph


How to run MobileWorks with Python at Windows?

I need to get MobileWorks running with Python at Windows. Therefore I have a couple of questions: <ul> <li>What are the necessary steps to do?</li> <li>What do I have to install?</li> <li>In which order do I have to install which tool?</li> </ul>

Python lxml

There is a simple library lxml in python. With this a sample script for looking up information in a website could look like this:

from urllib import urlopen, urlencode
from httplib import HTTPConnection
from lxml import etree

sock= urlopen(url)

f= open(fn,"w")

htmlParser= etree.HTMLParser()
tree= etree.parse(fn, htmlParser)
info= tree.xpath(" path ")


Speed up parsing HTML pages in Python

Using a command-line Python utility a website is queried and parsed, consecutively other websites are queried and parsed. Loading and parsing each website takes a considerable amount of time. For loading the websites <code>urllib2</code> is used and for parsing them <code>BeautifulSoup</code> is used.

Capturing network traffic with Python

<p> It is necessary to capture the traffic on a specific network-interface between a server and a client. Most of the messages can be ignored, but some of them should be filtered and evaluated.<br /> This depends on the message-body. </p> <p> Other requirements: <li>The network-interface should be choosable from a list of all available devices.<li>The body should be readable by a human-beings. <li>only SYN-Packages, no ACKs<li>specific IP-Adresses </p> It should run on Linux.
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