'Frequency' for a sequence of values is the measure of how sharp the values in that sequence change.
For example, the sequence $x_1(n)=[\begin{array}[cccccccc]&\cdots &1, &1, &1, &1, &\cdots\end{array}]$ has a frequency 0 but the $x_2(n)=[\begin{array}[cccccccc] &\cdots &1 &1 &7 &1 &\cdots\end{array}]$ is a higher frequency signal since there is a sharp change in sequence value (1 to 7 and 7 to 1).
A low pass filter is a system which attenuates the high frequency components. OR it reduces the sharpness (or introduces blur) in case of an image. OR a low pass filter does not allow sudden changes in values and smoothens these changes. Figure below shows low passing various sequences ($\style{color:blue}{blue:input}$ $\style{color:red}{red:output}$).

Consider a system whose output is a sequence $y(n)$ where n$^{th}$ value in $y(n)$ is the average of n$^{th}$, (n-1)$^{th}$ and (n+1)$^{th}$ value of input sequence is a simple low pass filter. If $x_2(n)$ is fed as input to this filter, then output, will be $[\begin{array}[cccccccc] &\cdots &1 &3 &3 &3 &1 &\cdots\end{array}]$ and it is clear that the magnitude of change between consecutive values are reduced. or the high frequency components are attenuated.
Since you have a tuple, you have to separate it into two sequences ([x1,x2,....] and [y1,y2,....]). The low pass filtering should be done on these sequences separately.
In character recognition algorithms, low pass filtering is used in pre-processing to remove noises or unwanted small dots, having a width of one or two pixels. see an example of low pass filtering used for noise removal in image processing.

x_new[i] = 1/3*(x_old[i-1] + x_old[i] + x_old[i+1])
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