Making heatmap from pandas DataFrame

Question

I have a dataframe generated from Python s Pandas package  How can I generate  heatmap using DataFrame from pandas package    import numpy as np  from pandas import    Index    aaa   bbb   ccc   ddd   eee   Cols     A    B    C   D   df   DataFrame abs np random randn 5  4    index  Index  columns Cols    gt  gt  gt  df           A         B         C         D aaa  2 431645  1 248688  0 267648  0 613826 bbb  0 809296  1 671020  1 564420  0 347662 ccc  1 501939  1 126518  0 702019  1 596048 ddd  0 137160  0 147368  1 504663  0 202822 eee  0 134540  3 708104  0 309097  1 641090  gt  gt  gt

User · Answer

If you don t need a plot per say  and you re simply interested in adding color to represent the values in a table format  you can use the style background gradient   method of the pandas data frame  This method colorizes the HTML table that is displayed when viewing pandas data frames in e g  the JupyterLab Notebook and the result is similar to using  conditional formatting  in spreadsheet software   import numpy as np  import pandas as pd   index    aaa    bbb    ccc    ddd    eee   cols     A    B    C    D   df   pd DataFrame abs np random randn 5  4    index index  columns cols  df style background gradient cmap  Blues       For detailed usage  please see the more elaborate answer I provided on the same topic previously and the styling section of the pandas documentation

User · Answer

Surprised to see no one mentioned more capable  interactive and easier to use alternatives  A  You can use plotly   Just two lines and you get   interactivity   smooth scale   colors based on whole dataframe instead of individual columns   column names  amp  row indices on axes   zooming in   panning   built-in one-click ability to save it as a PNG format   auto-scaling   comparison on hovering   bubbles showing values so heatmap still looks good and you can see values wherever you want    import plotly express as px fig   px imshow df corr    fig show     B  You can also use Bokeh  All the same functionality with a tad much hassle  But still worth it if you do not want to opt-in for plotly and still want all these things  from bokeh plotting import figure  show  output notebook from bokeh models import ColumnDataSource  LinearColorMapper from bokeh transform import transform output notebook   colors      d7191c     fdae61     ffffbf     a6d96a     1a9641   TOOLS    quot hover save pan box zoom reset wheel zoom quot  data   df corr   stack   rename  quot value quot   reset index   p   figure x range list df columns   y range list df index   tools TOOLS  toolbar location  below              tooltips    Row  Column     level 0 x  level 1      value     value     height   500  width   500   p rect x  quot level 1 quot   y  quot level 0 quot   width 1  height 1         source data         fill color   field    value    transform   LinearColorMapper palette colors  low data value min    high data value max             line color None  color bar   ColorBar color mapper LinearColorMapper palette colors  low data value min    high data value max     major label text font size  quot 7px quot                        ticker BasicTicker desired num ticks len colors                         formatter PrintfTickFormatter format  quot  f quot                         label standoff 6  border line color None  location  0  0   p add layout color bar   right    show p

User · Answer

Please note that the authors of seaborn only want seaborn heatmap to work with categorical dataframes  It s not general   If your index and columns are numeric and or datetime values  this code will serve you well   Matplotlib heat-mapping function pcolormesh requires bins instead of indices  so there is some fancy code to build bins from your dataframe indices  even if your index isn t evenly spaced      The rest is simply np meshgrid and plt pcolormesh   import pandas as pd import numpy as np import matplotlib pyplot as plt  def conv index to bins index          Calculate bins to contain the index values      The start and end bin boundaries are linearly extrapolated from      the two first and last values  The middle bin boundaries are      midpoints       Example 1   0  1  - gt   -0 5  0 5  1 5      Example 2   0  1  4  - gt   -0 5  0 5  2 5  5 5      Example 3   4  1  0  - gt   5 5  2 5  0 5  -0 5         assert index is monotonic increasing or index is monotonic decreasing        the beginning and end values are guessed from first and last two     start   index 0  -  index 1 -index 0   2     end   index -1     index -1 -index -2   2        the middle values are the midpoints     middle   pd DataFrame   m1   index  -1    p1   index 1         middle   middle  m1      middle  p1  -middle  m1    2      if isinstance index  pd DatetimeIndex           idx   pd DatetimeIndex middle  union  start end       elif isinstance index   pd Float64Index pd RangeIndex pd Int64Index            idx   pd Float64Index middle  union  start end       else          print  Warning  guessing what to do with index type  s                   type index           idx   pd Float64Index middle  union  start end        return idx sort values ascending index is monotonic increasing   def calc df mesh df          Calculate the two-dimensional bins to hold the index and      column values         return np meshgrid conv index to bins df index                          conv index to bins df columns    def heatmap df          Plot a heatmap of the dataframe values using the index and      columns        X Y   calc df mesh df      c   plt pcolormesh X  Y  df values T      plt colorbar c    Call it using heatmap df   and see it using plt show

User · Answer

For people looking at this today  I would recommend the Seaborn heatmap   as documented here   The example above would be done as follows    import numpy as np  from pandas import DataFrame import seaborn as sns  matplotlib inline  Index    aaa    bbb    ccc    ddd    eee   Cols     A    B    C    D   df   DataFrame abs np random randn 5  4    index Index  columns Cols   sns heatmap df  annot True      Where  matplotlib is an IPython magic function for those unfamiliar

User · Answer

Useful sns heatmap api is here   Check out the parameters  there are a good number of them   Example   import seaborn as sns  matplotlib inline  idx    aaa   bbb   ccc   ddd   eee   cols   list  ABCD   df   DataFrame abs np random randn 5 4    index idx  columns cols      r reverses the normal order of the color map  RdYlGn  sns heatmap df  cmap  RdYlGn r   linewidths 0 5  annot True

User · Answer

You want matplotlib pcolor  import numpy as np  from pandas import DataFrame import matplotlib pyplot as plt  index     aaa    bbb    ccc    ddd    eee   columns     A    B    C    D   df   DataFrame abs np random randn 5  4    index index  columns columns   plt pcolor df  plt yticks np arange 0 5  len df index   1   df index  plt xticks np arange 0 5  len df columns   1   df columns  plt show    This gives

User · Answer

If you want an interactive heatmap from a Pandas DataFrame and you are running a Jupyter notebook  you can try the interactive Widget Clustergrammer-Widget  see interactive notebook on NBViewer here  documentation here    And for larger datasets you can try the in-development Clustergrammer2 WebGL widget  example notebook here

[python] Making heatmap from pandas DataFrame

Examples related to python

Examples related to pandas

Examples related to dataframe

Examples related to heatmap