#197 Available color palettes with Matplotlib

The post #196 describes how to pick up a single color when working with python and matplotlib. This post aims to describe a few color palette that are provided, and thus make your life easier when plotting several color. There are 3 types of color palettes: Sequential, Discrete and Diverging. Here are a few explanations for each:

• Sequential
• Diverging
• Discrete

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  #197 Reverse a palette

  

  #197 Sequential palette

  

  #197 Sequential palette

  
  

  A sequential color palette allows to describe a graduation. It goes from one bright colot to its dark form, from white to purple for example. In this case, the higher the value of X is, the darker is the colour. Find below a list of sequential palette. Note that you can easily reverse the palette just adding ‘_r‘ at the end of its name!

  

  
  # library & dataset
  from matplotlib import pyplot as plt
  import numpy as np
  
  # create data
  x = np.random.rand(15)
  y = x+np.random.rand(15)
  z = x+np.random.rand(15)
  z=z*z
  
  # Use it with a call in cmap
  plt.scatter(x, y, s=z*2000, c=x, cmap="BuPu", alpha=0.4, edgecolors="grey", linewidth=2)
  
  # You can reverse it:
  plt.scatter(x, y, s=z*2000, c=x, cmap="BuPu_r", alpha=0.4, edgecolors="grey", linewidth=2)
  
  # OTHER: viridis / inferno / plasma / magma
  plt.scatter(x, y, s=z*2000, c=x, cmap="plasma", alpha=0.4, edgecolors="grey", linewidth=2)
  
  

• 

  #197 Diverging palette

  

  #197 Diverging palette

  
  

  A diverging color palette is slightly different from a sequential color palette, even if it is used to show a graduation as well. It uses a first color graduation from the minimum to a critical midpoint (orange until 0 in our example), and then use another color to go to the maximum (purple in our example). Well a picture speaks better than thousand of words:

  

  
  # library & dataset
  from matplotlib import pyplot as plt
  import numpy as np
  
  # create data
  x = np.random.rand(80) - 0.5
  y = x+np.random.rand(80)
  z = x+np.random.rand(80)
  
  # plot
  plt.scatter(x, y, s=z*2000, c=x, cmap="PuOr", alpha=0.4, edgecolors="grey", linewidth=2)
  
  # reverse. Just load the seaborn library for a nice looking appearance.
  import seaborn as sns
  plt.scatter(x, y, s=z*2000, c=x, cmap="PuOr_r", alpha=0.4, edgecolors="grey", linewidth=2)
  
  

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  A discrete color palette is used to represent, well, a discrete or categorical variable! For example, if you have 3 groups in the same scatterplot, you probably want to represent them with different colors. Using a palette helps you with your choice: it provides colors that go well together, that are distincts, and color blind friendly!

   

   

   

   

   

   

   

  

   

  
  # library & dataset
  from matplotlib import pyplot as plt
  import numpy as np
  
  # Data
  import seaborn as sns
  df = sns.load_dataset('iris')
  
  # We use the specie column to choose the color. We need to make a numerical vector from it:
  df['species']=pd.Categorical(df['species'])
  df['species'].cat.codes
  
  # Scatter
  plt.scatter(df['sepal_length'], df['sepal_width'], s=62, c=df['species'].cat.codes, cmap="Set1", alpha=0.9, linewidth=0)