pyplot scatter plot marker size

Question

In the pyplot document for scatter plot   matplotlib pyplot scatter x  y  s 20  c  b   marker  o   cmap None  norm None                            vmin None  vmax None  alpha None  linewidths None                            faceted True  verts None  hold None    kwargs    The marker size     s    size in points 2  It is a scalar or an array of the same length as x and y    What kind of unit is points 2  What does it mean  Does s 100 mean 10 pixel x 10 pixel   Basically I m trying to make scatter plots with different marker sizes  and I want to figure out what does the s number mean

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You can use markersize to specify the size of the circle in plot method  import numpy as np import matplotlib pyplot as plt  x1   np random randn 20  x2   np random randn 20  plt figure 1    you can specify the marker size two ways directly  plt plot x1   bo   markersize 20     blue circle with size 10  plt plot x2   ro   ms 10      ms is just an alias for markersize plt show     From here

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Because other answers here claim that s denotes the area of the marker  I m adding this answer to clearify that this is not necessarily the case    Size in points 2  The argument s in plt scatter denotes the markersize  2  As the documentation says     s   scalar or array like  shape  n     optional   size in points 2  Default is rcParams  lines markersize      2    This can be taken literally  In order to obtain a marker which is x points large  you need to square that number and give it to the s argument    So the relationship between the markersize of a line plot and the scatter size argument is the square  In order to produce a scatter marker of the same size as a plot marker of size 10 points you would hence call scatter      s 100      import matplotlib pyplot as plt  fig ax   plt subplots    ax plot  0   0   marker  o    markersize 10  ax plot  0 07 0 93   0 0      linewidth 10  ax scatter  1   0             s 100   ax plot  0   1   marker  o    markersize 22  ax plot  0 14 0 86   1 1      linewidth 22  ax scatter  1   1             s 22  2   plt show     Connection to  area   So why do other answers and even the documentation speak about  area  when it comes to the s parameter   Of course the units of points  2 are area units    For the special case of a square marker  marker  s   the area of the marker is indeed directly the value of the s parameter   For a circle  the area of the circle is area   pi 4 s   For other markers there may not even be any obvious relation to the area of the marker       In all cases however the area of the marker is proportional to the s parameter  This is the motivation to call it  area  even though in most cases it isn t really    Specifying the size of the scatter markers in terms of some quantity which is proportional to the area of the marker makes in thus far sense as it is the area of the marker that is perceived when comparing different patches rather than its side length or diameter  I e  doubling the underlying quantity should double the area of the marker      What are points   So far the answer to what the size of a scatter marker means is given in units of points  Points are often used in typography  where fonts are specified in points  Also linewidths is often specified in points  The standard size of points in matplotlib is 72 points per inch  ppi  - 1 point is hence 1 72 inches    It might be useful to be able to specify sizes in pixels instead of points  If the figure dpi is 72 as well  one point is one pixel  If the figure dpi is different  matplotlib default is fig dpi 100     1 point    fig dpi 72  pixels   While the scatter marker s size in points would hence look different for different figure dpi  one could produce a 10 by 10 pixels 2 marker  which would always have the same number of pixels covered       import matplotlib pyplot as plt  for dpi in  72 100 144        fig ax   plt subplots figsize  1 5 2   dpi dpi      ax set title  fig dpi     format dpi        ax set ylim -3 3      ax set xlim -2 2       ax scatter  0   1   s 10  2                  marker  s   linewidth 0  label  100 points 2       ax scatter  1   1   s  10 72  fig dpi   2                  marker  s   linewidth 0  label  100 pixels 2        ax legend loc 8 framealpha 1  fontsize 8       fig savefig  fig   png  format dpi   bbox inches  tight    plt show         If you are interested in a scatter in data units  check this answer

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It is the area of the marker  I mean if you have s1   1000 and then s2   4000  the relation between the radius of each circle is  r s2    2   r s1  See the following plot    plt scatter 2  1  s 4000  c  r   plt scatter 2  1  s 1000  c  b   plt scatter 2  1  s 10  c  g       I had the same doubt when I saw the post  so I did this example then I used a ruler on the screen to measure the radii

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I also attempted to use  scatter  initially for this purpose  After quite a bit of wasted time - I settled on the following solution   import matplotlib pyplot as plt input list      x  100  y  200  radius  50   color   0 1 0 2 0 3        output list         for point in input list      output list append plt Circle  point  x    point  y     point  radius    color point  color    fill False   ax   plt gca aspect  equal   ax cla   ax set xlim  0  1000   ax set ylim  0  1000   for circle in output list         ax add artist circle      This is based on an answer to  this question

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This can be a somewhat confusing way of defining the size but you are basically specifying the area of the marker  This means  to double the width  or height  of the marker you need to increase s by a factor of 4   because A   WH   gt   2W  2H  4A  There is a reason  however  that the size of markers is defined in this way  Because of the scaling of area as the square of width  doubling the width actually appears to increase the size by more than a factor 2  in fact it increases it by a factor of 4   To see this consider the following two examples and the output they produce    doubling the width of markers x    0 2 4 6 8 10  y    0  len x  s    20 4  n for n in range len x    plt scatter x y s s  plt show    gives  Notice how the size increases very quickly  If instead we have   doubling the area of markers x    0 2 4 6 8 10  y    0  len x  s    20 2  n for n in range len x    plt scatter x y s s  plt show    gives  Now the apparent size of the markers increases roughly linearly in an intuitive fashion  As for the exact meaning of what a  point  is  it is fairly arbitrary for plotting purposes  you can just scale all of your sizes by a constant until they look reasonable  Hope this helps  Edit   In response to comment from  Emma  It s probably confusing wording on my part  The question asked about doubling the width of a circle so in the first picture for each circle  as we move from left to right  it s width is double the previous one so for the area this is an exponential with base 4  Similarly the second example each circle has area double the last one which gives an exponential with base 2  However it is the second example  where we are scaling area  that doubling area appears to make the circle twice as big to the eye  Thus if we want a circle to appear a factor of n bigger we would increase the area by a factor n not the radius so the apparent size scales linearly with the area  Edit to visualize the comment by  TomaszGandor  This is what it looks like for different functions of the marker size   x    0 2 4 6 8 10 12 14 16 18  s exp    20 2  n for n in range len x    s square    20 n  2 for n in range len x    s linear    20 n for n in range len x    plt scatter x  1  len x  s s exp  label   s 2 n    lw 1  plt scatter x  0  len x  s s square  label   s n 2    plt scatter x  -1  len x  s s linear  label   s n    plt ylim -1 5 1 5  plt legend loc  center left   bbox to anchor  1 1  0 5   labelspacing 3  plt show

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If the size of the circles corresponds to the square of the parameter in s parameter  then assign a square root to each element you append to your size array  like this  s  1  1 414  1 73  2 0  2 24  such that when it takes these values and returns them  their relative size increase will be the square root of the squared progression  which returns a linear progression    If I were to square each one as it gets output to the plot  output  1  2  3  4  5   Try list interpretation  s  numpy sqrt i  for i in s

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