Section outline

◄Definition
►Regplot et Lmplot

    • Seaborn also allows you to create heatmaps using heatmap().

      Parameter name Description Format Example
      data

      The dataframe you are working on

      		 DataFrame, Series, dict, array, or list of arrays data=table
      cmap
       

      Heatmap colors. Either a Matplotlib palette or a custom one.

      		 String corresponding to a palette or a Seaborn color_palette cmap=”viridis” or cmap = sns.color_palette("light:blue", as_cmap=True)
      annot
       Parameter that determines whether to display the values inside the cells.
      		 Boolean annot=True, default is False
      vmin Minimum value that will be taken into account for the colormap. Float vmin=30.6
      vmax Maximum value that will be taken into account for the colormap. Float vmax=42
      linecolor
      Parameter used to choose the color of the lines between the cells.
      		 String corresponding to a color linecolor=”blue”
      linewidths
       

      Parameter controlling the thickness of the lines between the cells

      		 Float linewidths=0.2 or linewidths=10
      mask Parameter used to control the range of values taken into account in the heatmap. Boolean list, same format as data mask=table_mask

      Here is an example of code:

      glue=sns.load_dataset("glue").pivot(index="Model",columns="Task",values="Score")
sns.heatmap(glue)

      We use pivot to format the data in the order we want:

      • index defines the variable for the y-axis (ordinates).
      • columns defines the variable for the x-axis (abscissas).
      • values must be a numerical variable, and it is what the heatmap will use for coloring.

      heatmap simple

      sns.heatmap(glue,cmap="viridis",annot=True,vmin=20,vmax=80,linecolor="red",linewidths=0.1)

      With the vmin and vmax parameters, we can choose the range of values over which the heatmap will apply, and we also have visual options such as linecolor and linewidths for the lines between the cells.

      heatmap modifiée

      If we need clustering in the heatmap, we can use Seaborn’s clustermap(). One thing to know is that this function requires SciPy, so it must be installed in the environment you are working in. If you are using Colab, this will not be necessary, as you can import it directly.

      signature clustermap

      Parameter name Description Format Example
      data

      The dataframe you are working on

      		 DataFrame, Series, dict, array, or list of arrays data=table
      method SciPy method used to perform clustering. String corresponding to a SciPy method. method=’centroid’
      metric SciPy metric used for clustering. String corresponding to a SciPy metric. metric=’jaccard’
      z_score Parameter used to center and standardize the data.
      0 to standardize rows, 1 to standardize columns.
      		 z_score=0
      standard_scale Parameter used to normalize the data, without deviation.
       

      0 to standardize rows, 1 to standardize columns.

      		 standard_scale=1

      row_cluster,

      col_cluster
       

      Parameters used to choose the clustering axes.

      		 Boolean row_cluster=False
      figsize Parameter controlling the size of the figure. tuple (width, height) figsize=(4,4)
      dendrogram_ratio
       

      Parameter controlling the size ratio of the dendrograms.

      		 tuple (row ratio, column ratio) dendrogram_ratio=(0.2,0.1)
      cbar_pos
       

      Parameter controlling the position of the color bar.

      		 tuple (left, bottom, width, height) cbar_pos=(0,0.1,0.05,0.6)

       

      Here is an example of code:

      iris = sns.load_dataset("iris")
species = iris.pop("species")
sns.clustermap(iris)

      clustermap basique

      Now let’s explore different parameters:

      lut = dict(zip(species.unique(), "rbg"))
row_colors = species.map(lut)
sns.clustermap(iris,row_cluster=True,dendrogram_ratio=(0.2,0.1),row_colors=row_colors,method="weighted",metric="correlation",z_score=1,annot=True,figsize=(3,9),cbar_pos=(0,0.1,0.02,0.8))

      row_cluster allows grouping rows based on their similarity in order to reveal clusters. dendrogram_ratio controls the size of the dendrograms: the first value corresponds to the one on the left, and the second to the one at the top. row_colors allows adding a color indicator next to the rows. In this case, using the previous settings, the species of each row is shown. metric defines the similarity (distance) measure used, and method specifies the algorithm used for clustering. Setting z_score to 1 normalizes the data across rows. cbar_pos allows setting the position of the color bar.

      clustermap plus complexe

       
◄Definition
►Regplot et Lmplot