Plotting a 2D heatmap with Matplotlib

Question

Using Matplotlib  I want to plot a 2D heat map  My data is an n-by-n Numpy array  each with a value between 0 and 1  So for the  i  j  element of this array  I want to plot a square at the  i  j  coordinate in my heat map  whose color is proportional to the element s value in the array   How can I do this

User · Accepted Answer

The imshow   function with parameters interpolation  nearest  and cmap  hot  should do what you want   import matplotlib pyplot as plt import numpy as np  a   np random random  16  16   plt imshow a  cmap  hot   interpolation  nearest   plt show

User · Answer

I would use matplotlib s pcolor pcolormesh function since it allows nonuniform spacing of the data   Example taken from matplotlib   import matplotlib pyplot as plt import numpy as np    generate 2 2d grids for the x  amp  y bounds y  x   np meshgrid np linspace -3  3  100   np linspace -3  3  100    z    1 - x   2    x    5   y    3    np exp -x    2 - y    2    x and y are bounds  so z should be the value  inside  those bounds    Therefore  remove the last value from the z array  z   z  -1   -1  z min  z max   -np abs z  max    np abs z  max    fig  ax   plt subplots    c   ax pcolormesh x  y  z  cmap  RdBu   vmin z min  vmax z max  ax set title  pcolormesh     set the limits of the plot to the limits of the data ax axis  x min    x max    y min    y max     fig colorbar c  ax ax   plt show

User · Answer

For a 2d numpy array  simply use imshow   may help you     import matplotlib pyplot as plt import numpy as np   def heatmap2d arr  np ndarray       plt imshow arr  cmap  viridis       plt colorbar       plt show     test array   np arange 100   100  reshape 100  100  heatmap2d test array      This code produces a continuous heatmap   You can choose another built-in colormap from here

User · Answer

Seaborn takes care of a lot of the manual work and automatically plots a gradient at the side of the chart etc   import numpy as np import seaborn as sns import matplotlib pylab as plt  uniform data   np random rand 10  12  ax   sns heatmap uniform data  linewidth 0 5  plt show       Or  you can even plot upper   lower left   right triangles of square matrices  for example a correlation matrix which is square and is symmetric  so plotting all values would be redundant anyway   corr   np corrcoef np random randn 10  200   mask   np zeros like corr  mask np triu indices from mask     True with sns axes style  white        ax   sns heatmap corr  mask mask  vmax  3  square True   cmap  YlGnBu       plt show

User · Answer

Here s how to do it from a csv  import numpy as np import matplotlib pyplot as plt from scipy interpolate import griddata    Load data from CSV dat   np genfromtxt  dat xyz   delimiter     skip header 0  X dat   dat   0  Y dat   dat   1  Z dat   dat   2     Convert from pandas dataframes to numpy arrays X  Y  Z    np array      np array      np array     for i in range len X dat            X   np append X  X dat i           Y   np append Y  Y dat i           Z   np append Z  Z dat i      create x-y points to be used in heatmap xi   np linspace X min    X max    1000  yi   np linspace Y min    Y max    1000     Interpolate for plotting zi   griddata  X  Y   Z   xi None     yi   None    method  cubic      I control the range of my colorbar by removing data    outside of my range of interest zmin   3 zmax   12 zi  zi lt zmin     zi gt zmax     None    Create the contour plot CS   plt contourf xi  yi  zi  15  cmap plt cm rainbow                    vmax zmax  vmin zmin  plt colorbar     plt show    where dat xyz is in the form x1 y1 z1 x2 y2 z2

[python] Plotting a 2D heatmap with Matplotlib

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