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I use python, matplotlib and pandas to filter my log file.

My log file contains two columns like this:

column_name = ["TIME", "IDPKT"]

The second column "IDPKT" contains id of packets in my simulation which have the following form: 1.1 ; 1.2 ; 1.10 ; etc. So, ths Ids 1.1 and 1.10 are two different packets (1.1 is the first packet generated by the node 1 and 1.10 is the tenth packet generated also by the node 1).

So, python reads automatically this column as float numbers. The problem that it considers the different ids 1.10 ; 1.100 ; 1.1000 as 1.1.

This issue causes a big problem in my work since they are different packets generated and it calculates only 1.1.

Can someone help me to solve this problem please?

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  • Someone may be able to help you if you show how you load the data. See minimal, complete, and verifyable example. Commented Mar 23, 2017 at 14:01
  • I think it's better to use strings rather than floats if you want to keep all their decimal places Commented Mar 23, 2017 at 14:02
  • Thank you for your response, @kazemakase I load my data like this: df = pd.read_csv(filename, sep=";", header=None, names=column_name) Commented Mar 23, 2017 at 14:03
  • @Mr.Xcoder Thank you for your answer, in my file there are defined like this: 12.25 ; 1.1 (12.25 is the time and 1.1 is the id packet), how can I force python to read 1.1 as string? Commented Mar 23, 2017 at 14:05
  • @Myriam Great! In that case you can find a solution in the q/a linked above (first comment) - have a look at the dtype argument. Commented Mar 23, 2017 at 14:06

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The problem is that you are reading the data in as a float (pandas does this automatically because it LOOKS like a float), but you want to treat it like a string. You can specify the data type when you read it in, e.g.:

df = pd.read_csv(filename, sep=";", header=None, names=column_name, dtype={'IDPKT':'str'})
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Thank you very much for your reply, it works.

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