Section outline

◄Displot
►Definition

    • The relplot() function allows you to create scatter plots and line plots.

      Here is the function signature:

      Signature relplot

      There is of course documentation available online, so we will only go over the most essential elements in order to display what we need as quickly as possible, namely:

      Parameter name Description Format Example
      data

      The dataframe you are working on

      		 DataFrame, Series, dict, array, or list of arrays data=table
      x Variable for the x-axis String corresponding to a variable x="weight"
      y Variable for the y-axis String corresponding to a variable y=”height”
      hue Allows to add a variable as different colors String corresponding to a variable hue=”age”
      size Allows to add a variable as the size of points String corresponding to a variable size=”money”
      style Allows to add a variable as the type of points String corresponding to a variable style=”sex”
      row Allows to create a table of plots, controling the number of rows String corresponding to a variable row=”category”
      col Allows to create a table of plots, controling the number of columns String corresponding to a variable col=”job”
      kind Type of plot you want String corresponding to a type kind=”scatter” or kind=”line”

      Here is an example with the following code:

      data = sns.load_dataset("penguins")
sns.relplot(data=data,x="bill_length_mm",y="bill_depth_mm",hue="species",style="sex",size="body_mass_g",col="island")
plt.show()

      Which produces the following result:

      Graphique relplot scatter.

      We can see that col allows us to create different plots within the same figure.

      We can also add ellipses to relplot() scatter plots; to draw on them, we need to retrieve the axis (ax):

      df = sns.load_dataset("penguins")
df = df[["species", "bill_length_mm", "body_mass_g"]].dropna()

g = sns.relplot(
    data=df,
    x="bill_length_mm",
    y="body_mass_g",
    hue="species",
    kind="scatter",
    height=5
)
ax = g.ax

def add_confidence_ellipse(x, y, ax, n_std=2.0, **kwargs):
    cov = np.cov(x, y)
    mean = np.mean(x), np.mean(y)
    eigvals, eigvecs = np.linalg.eigh(cov)
    order = eigvals.argsort()[::-1]
    eigvals, eigvecs = eigvals[order], eigvecs[:, order]
    angle = np.degrees(np.arctan2(*eigvecs[:, 0][::-1]))
    width, height = 2 * n_std * np.sqrt(eigvals)
    ellipse = Ellipse(
        xy=mean,
        width=width,
        height=height,
        angle=angle,
        fill=False,
        **kwargs
    )
    ax.add_patch(ellipse)

palette = sns.color_palette()

for i, species in enumerate(df["species"].unique()):
    subset = df[df["species"] == species]
    add_confidence_ellipse(
        subset["bill_length_mm"],
        subset["body_mass_g"],
        ax,
        edgecolor=palette[i],
        linewidth=2
    )

ax.set_xlabel("Bill length (mm)")
ax.set_ylabel("Body mass (g)")
ax.set_title("95% Confidence Ellipse by Species — Penguins")

plt.show()

      Ellipse comes from matplotlib.patches.

      And here is the result of this code:

      graphe ellipse

      We can also display lines by changing the kind:

      df = sns.load_dataset("penguins")
sns.relplot(data=df,x="bill_length_mm",y="bill_depth_mm",hue="species",style="sex",col="island",kind="line")
plt.show()
       

      size cannot be used with line plots; here is the result of the code:


      graphique lines

       

       
       
◄Displot
►Definition