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Try it Yourself. Apply the concepts taught in this tutorial to your data science workflows. The next time you create a new data structure or store the output of a calculation in a variable, serialize it for later use instead of running all your code again and again. What you will presumably want is some module that has your shared data type, and then the client and server can communicate with that type.

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you are given two pcaps, one gathered on a SPAN port on an access switch, and another on an application server a few L3 hops away. At some point the application server sporadically becomes slow (retransmits on both sides, TCP windows shrinking etc.). Prove that it is (or is not) because of the network. The solution in this case is to exclude the object from the serialization process and to reinitialize the connection after the object is deserialized. Use the argparse module to get the pcap file name from the command line. If your argparse knowledge needs a little brushing up, you can look at my argparse recipe book, or at any other of the dozens of tutorials on the web. import argparse import os import sys def process_pcap ( file_name ): print ( 'Opening {}...' . format ( file_name )) if __name__ == '__main__' : parser = argparse . ArgumentParser ( description = 'PCAP reader' ) parser . add_argument ( '--pcap' , metavar = '' , help = 'pcap file to parse' , required = True ) args = parser . parse_args () file_name = args . pcap if not os . path . isfile ( file_name ): print ( '"{}" does not exist' . format ( file_name ), file = sys . stderr ) sys . exit ( - 1 ) process_pcap ( file_name ) sys . exit ( 0 ) subsequently, use the extracted data from the “custom” file for analysis, display, gaining insight etc.In all these cases, it is immensely helpful to write a custom program to parse the pcaps and yield the data points you are looking for. Then, just like we did before, let’s call the dump() function to serialize this array to a file: with open('my_array.pkl','wb') as f: Are you tired of rerunning your Python code every time you need to access a previously created data frame, variable, or machine learning model?

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The goal in this iteration of the code is to generate a graphical plot of the TCP Receive window on the Client. The end result is a graph that looks like this: you are processing untrusted data. See Comparison with json. Relationship to other Python modules ¶ Comparison with marshal ¶ To do this, let’s first generate some fake data and build a linear regression model with the Scikit-Learn library: from sklearn.linear_model import LinearRegression

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if 'S' in str ( tcp_pkt . flags ): for ( opt_name , opt_value ,) in tcp_pkt . options : if opt_name == 'WScale' : if direction == PktDirection . client_to_server : client_recv_window_scale = opt_value else : server_recv_window_scale = opt_value break # Create a dictionary and populate it with data that we'll need in the There are several reasons to choose the JSON format: It’s human readable and language independent, and it’s lighter than XML. With the json module, you can serialize and deserialize several standard Python types:

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Notice that since we can only write string objects to text files, we have converted the dictionary to a string using the str() function. This means that the original state of our dictionary is lost. Now that we have confirmed that the student object is a dictionary type, let’s proceed to write it to a text file without serialization: with open('student_info.txt','w') as data:

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Finally, let’s serialize the dictionary that we wrote to a text file in the first section of the tutorial: students = { The code below was written and executed on Linux (Linux Mint 18.3 64-bit), but the code is OS-agnostic; it should work as well in other environments, with little or no modification. However, this process is slower than serialization and can become extremely time-consuming if the data frame is large.