plot different color for different categorical levels using matplotlib

Question

I have this data frame diamonds which is composed of variables like  carat  price  color   and I want to draw a scatter plot of price to carat for each color  which means different color has different color in the plot   This is easy in R with ggplot   ggplot aes x carat  y price  color color     by setting color color  ggplot automatically draw in different colors        data diamonds    geom point stat  summary   fun y median      I wonder how could this be done in Python using matplotlib    PS   I know about auxiliary plotting packages  such as seaborn and ggplot for python  and I donot prefer them  just want to find out if it is possible to do the job using matplotlib alone   P

User · Answer

Here s a succinct and generic solution to use a seaborn color palette   First find a color palette you like and optionally visualize it   sns palplot sns color palette  Set2   8     Then you can use it with matplotlib doing this     Unique category labels   D    F    G       color labels   df  color   unique      List of RGB triplets rgb values   sns color palette  Set2   8     Map label to RGB color map   dict zip color labels  rgb values      Finally use the mapped values plt scatter df  carat    df  price    c df  color   map color map

User · Answer

I had the same question  and have spent all day trying out different packages     I had originally used matlibplot  and was not happy with either mapping categories to predefined colors  or grouping aggregating then iterating through the groups  and still having to map colors    I just felt it was poor package implementation   Seaborn wouldn t work on my case  and Altair ONLY works inside of a Jupyter Notebook   The best solution for me was PlotNine  which  is an implementation of a grammar of graphics in Python  and based on ggplot2    Below is the plotnine code to replicate your R example in Python   from plotnine import   from plotnine data import diamonds  g   ggplot diamonds  aes x  carat   y  price   color  color      geom point stat  summary   print g      So clean and simple

User · Answer

Using Altair   from altair import   import pandas as pd  df   datasets load dataset  iris   Chart df  mark point   encode x  petalLength  y  sepalLength   color  species

User · Answer

You can pass plt scatter a c argument which will allow you to select the colors  The code below defines a colors dictionary to map your diamond colors to the plotting colors   import matplotlib pyplot as plt import pandas as pd  carat    5  10  20  30  5  10  20  30  5  10  20  30  price    100  100  200  200  300  300  400  400  500  500  600  600  color    D    D    D    E    E    E    F    F    F    G    G    G     df   pd DataFrame dict carat carat  price price  color color    fig  ax   plt subplots    colors     D   red    E   blue    F   green    G   black    ax scatter df  carat    df  price    c df  color   apply lambda x  colors x     plt show     df  color   apply lambda x  colors x   effectively maps the colours from  diamond  to  plotting     Forgive me for not putting another example image up  I think 2 is enough  P   With seaborn  You can use seaborn which is a wrapper around matplotlib that makes it look prettier by default  rather opinion-based  I know  P  but also adds some plotting functions   For this you could use seaborn lmplot with fit reg False  which prevents it from automatically doing some regression    The code below uses an example dataset  By selecting hue  color  you tell seaborn to split your dataframe up based on your colours and then plot each one   import matplotlib pyplot as plt import seaborn as sns  import pandas as pd  carat    5  10  20  30  5  10  20  30  5  10  20  30  price    100  100  200  200  300  300  400  400  500  500  600  600  color    D    D    D    E    E    E    F    F    F    G    G    G     df   pd DataFrame dict carat carat  price price  color color    sns lmplot  carat    price   data df  hue  color   fit reg False   plt show       Without seaborn using pandas groupby  If you don t want to use seaborn then you can use pandas groupby to get the colors alone and then plot them using just matplotlib  but you ll have to manually assign colors as you go  I ve added an example below   fig  ax   plt subplots    colors     D   red    E   blue    F   green    G   black    grouped   df groupby  color   for key  group in grouped      group plot ax ax  kind  scatter   x  carat   y  price   label key  color colors key    plt show     This code assumes the same DataFrame as above and then groups it based on color  It then iterates over these groups  plotting for each one  To select a color I ve created a colors dictionary which can map the diamond color  for instance D  to a real color  for instance red

User · Answer

You can convert the categorical column into a numerical one by using the commands   we converting it into categorical data cat col   df  column name   astype  categorical      we are getting codes for it  cat col   cat col cat codes     we are using c parameter to change the color  plt scatter df  column1   df  column2    c cat col

User · Answer

I usually do it using Seaborn which is built on top of matplotlib  import seaborn as sns iris   sns load dataset  iris   sns scatterplot x  sepal length   y  sepal width                 hue  species   data iris

User · Answer

With df plot     Normally when quickly plotting a DataFrame  I use pd DataFrame plot    This takes the index as the x value  the value as the y value and plots each column separately with a different color  A DataFrame in this form can be achieved by using set index and unstack   import matplotlib pyplot as plt import pandas as pd  carat    5  10  20  30  5  10  20  30  5  10  20  30  price    100  100  200  200  300  300  400  400  500  500  600  600  color    D    D    D    E    E    E    F    F    F    G    G    G     df   pd DataFrame dict carat carat  price price  color color    df set index   color    carat    unstack  color    price   plot style  o   plt ylabel  price       With this method you do not have to manually specify the colors   This procedure may make more sense for other data series  In my case I have timeseries data  so the MultiIndex consists of datetime and categories  It is also possible to use this approach for more than one column to color by  but the legend is getting a mess

User · Answer

Here a combination of markers and colors from a qualitative colormap in matplotlib   import itertools import numpy as np from matplotlib import markers import matplotlib pyplot as plt  m styles   markers MarkerStyle markers N   60 colormap   plt cm Dark2 colors    Qualitative colormap for i   marker  color  in zip range N   itertools product m styles  colormap        plt scatter  np random random 2   color color  marker marker  label i  plt legend bbox to anchor  1 05  1   loc 2  borderaxespad 0   ncol 4

[matplotlib] plot different color for different categorical levels using matplotlib

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