Thanks for the responses. 

I have it working ok with pandas, using 'skiprows' and only reading out the 
number of rows I need via 'nrows'

This seems to help as the file gets bigger, but I need to test it over the 
course of a few hours and see what effect this has on performance.



On Friday, February 8, 2019 at 10:21:11 AM UTC, Alex Strickland wrote:
>
> Hi there,
>
> I'm pretty new to python (am an embedded C programmer by trade), but I 
> have a need to be able to plot data being written to a CSV file and show 
> the plot in pseudo-real time in pyqtgraph.
>
> I have built the GUI with the plots and am happy with it plotting static 
> data and rolling round (it reads data from a CSV file once using pandas) 
> but I'm having trouble getting pandas to work for real time data that is 
> being written to the CSV file by another process once a second.
>
> I am wondering if pandas are the right way to go for this? Once a second 
> my CSV file will be written to, it has a variable data rate of either 
> 1,16,32 or 128Hz and each sample (on a new line) contains a timestamp and 
> three vectors (it's telemetry data from a magnetometer). 
>  
> So depending on the data rate I need to read each CSV file once a second 
> and pull out either 1, 16,32 or 128 lines and then put the vectors into a 
> list and then update the plot moving the data along at the data rate speed.
>
> I wonder if there are better solutions than pandas for this? As in my 
> update function I am forced to re-read the CSV file and pull the whole file 
> out each time and tail the necessary lines into a copy then using the 
> tolist() method
>
> But as the file grows this is probably fighting a losing battle to be 
> efficient and could even slow down the drawing of the plots?
>
> Any advice appreciated! Thanks, Alex
>
>
>
>
>

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