Scatter plots in Pandas Pyplot How to plot by category

Question

I am trying to make a simple scatter plot in pyplot using a Pandas DataFrame object  but want an efficient way of plotting two variables but have the symbols dictated by a third column  key   I have tried various ways using df groupby  but not successfully  A sample df script is below  This colours the markers according to  key1   but Id like to see a legend with  key1  categories  Am I close  Thanks   import numpy as np import pandas as pd import matplotlib pyplot as plt df   pd DataFrame np random normal 10 1 30  reshape 10 3   index   pd date range  2010-01-01   freq    M   periods   10   columns     one    two    three    df  key1      4 4 4 6 6 6 8 8 8 8  fig1   plt figure 1  ax1   fig1 add subplot 111  ax1 scatter df  one    df  two    marker    o   c   df  key1    alpha   0 8  plt show

User · Answer

This is simple to do with Seaborn  pip install seaborn  as a oneliner sns scatterplot x vars  quot one quot   y vars  quot two quot   data df  hue  quot key1 quot     import seaborn as sns import pandas as pd import numpy as np np random seed 1974   df   pd DataFrame      np random normal 10  1  30  reshape 10  3       index pd date range  2010-01-01   freq  M   periods 10       columns   one    two    three    df  key1      4  4  4  6  6  6  8  8  8  8   sns scatterplot x  quot one quot   y  quot two quot   data df  hue  quot key1 quot     Here is the dataframe for reference   Since you have three variable columns in your data  you may want to plot all pairwise dimensions with  sns pairplot vars   quot one quot   quot two quot   quot three quot    data df  hue  quot key1 quot     https   rasbt github io mlxtend user guide plotting category scatter  is another option

User · Answer

seaborn has a wrapper function scatterplot that does it more efficiently  sns scatterplot data   df  x    one   y    two   data     key1

User · Answer

You can use scatter for this  but that requires having numerical values for your key1  and you won t have a legend  as you noticed   It s better to just use plot for discrete categories like this   For example   import matplotlib pyplot as plt import numpy as np import pandas as pd np random seed 1974     Generate Data num   20 x  y   np random random  2  num   labels   np random choice   a    b    c    num  df   pd DataFrame dict x x  y y  label labels    groups   df groupby  label      Plot fig  ax   plt subplots   ax margins 0 05    Optional  just adds 5  padding to the autoscaling for name  group in groups      ax plot group x  group y  marker  o   linestyle     ms 12  label name  ax legend    plt show       If you d like things to look like the default pandas style  then just update the rcParams with the pandas stylesheet and use its color generator   I m also tweaking the legend slightly    import matplotlib pyplot as plt import numpy as np import pandas as pd np random seed 1974     Generate Data num   20 x  y   np random random  2  num   labels   np random choice   a    b    c    num  df   pd DataFrame dict x x  y y  label labels    groups   df groupby  label      Plot plt rcParams update pd tools plotting mpl stylesheet  colors   pd tools plotting  get standard colors len groups   color type  random    fig  ax   plt subplots   ax set color cycle colors  ax margins 0 05  for name  group in groups      ax plot group x  group y  marker  o   linestyle     ms 12  label name  ax legend numpoints 1  loc  upper left    plt show

User · Answer

It s rather hacky  but you could use one1 as a Float64Index to do everything in one go   df set index  one   sort index   groupby  key1    two   plot style  --o   legend True      Note that as of 0 20 3  sorting the index is necessary  and the legend is a bit wonky

User · Answer

From matplotlib 3 1 onwards you can use  legend elements    An example is shown in Automated legend creation  The advantage is that a single scatter call can be used   In this case   import numpy as np import pandas as pd import matplotlib pyplot as plt  df   pd DataFrame np random normal 10 1 30  reshape 10 3                      index   pd date range  2010-01-01   freq    M   periods   10                      columns     one    two    three    df  key1      4 4 4 6 6 6 8 8 8 8    fig  ax   plt subplots   sc   ax scatter df  one    df  two    marker    o   c   df  key1    alpha   0 8  ax legend  sc legend elements    plt show       In case the keys were not directly given as numbers  it would look as  import numpy as np import pandas as pd import matplotlib pyplot as plt  df   pd DataFrame np random normal 10 1 30  reshape 10 3                      index   pd date range  2010-01-01   freq    M   periods   10                      columns     one    two    three    df  key1     list  AAABBBCCCC    labels  index   np unique df  key1    return inverse True   fig  ax   plt subplots   sc   ax scatter df  one    df  two    marker    o   c   index  alpha   0 8  ax legend sc legend elements   0   labels  plt show

User · Answer

With plt scatter  I can only think of one  to use a proxy artist   df   pd DataFrame np random normal 10 1 30  reshape 10 3   index   pd date range  2010-01-01   freq    M   periods   10   columns     one    two    three    df  key1      4 4 4 6 6 6 8 8 8 8  fig1   plt figure 1  ax1   fig1 add subplot 111  x ax1 scatter df  one    df  two    marker    o   c   df  key1    alpha   0 8   ccm x get cmap   circles  Line2D range 1   range 1   color  w   marker  o   markersize 10  markerfacecolor item  for item in ccm  array  4 6 8  -4 0  4   leg   plt legend circles    4   6   8    loc    center left   bbox to anchor    1  0 5   numpoints   1    And the result is

User · Answer

You can also try Altair or ggpot which are focused on declarative visualisations   import numpy as np import pandas as pd np random seed 1974     Generate Data num   20 x  y   np random random  2  num   labels   np random choice   a    b    c    num  df   pd DataFrame dict x x  y y  label labels     Altair code  from altair import Chart c   Chart df  c mark circle   encode x  x   y  y   color  label       ggplot code  from ggplot import   ggplot aes x  x   y  y   color  label    data df     geom point size 50     theme bw

User · Answer

You can use df plot scatter  and pass an array to c  argument defining the color of each point    import numpy as np import pandas as pd import matplotlib pyplot as plt df   pd DataFrame np random normal 10 1 30  reshape 10 3   index   pd date range  2010-01-01   freq    M   periods   10   columns     one    two    three    df  key1      4 4 4 6 6 6 8 8 8 8  colors   np where df  key1    4  r   -   colors df  key1    6     g  colors df  key1    8     b  print colors  df plot scatter x  one  y  two  c colors  plt show

[python] Scatter plots in Pandas/Pyplot: How to plot by category

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