#82 Marginal plot with Seaborn

A marginal plot allows to study the relationship between 2 numeric variables. The central chart display their correlation. It is usually a scatterplot, a hexbin plot, a 2D histogram or a 2D density plot. The marginal charts, usually at the top and at the right, show the distribution of the 2 variables using histogram or density plot.

The seaborn library provides a joint plot function that is really handy to make this type of graphic. The top graph shows it default behaviour, and here are a few possible customizations. Seaborn has a nice documentation and some of these examples come from there.

Central plot
Marginal
Ratio

#82 Default Marginal plot

#82 Hexbin with marginal plot

#82 2D contour with marginal plots

Three main options are available for the central part: scatterplot (with possible variations), hexbin or 2D density plot. Here is the way to make them:
```
# library & dataset
import seaborn as sns
df = sns.load_dataset('iris')

# Custom the inside plot: options are: “scatter” | “reg” | “resid” | “kde” | “hex”
sns.jointplot(x=df["sepal_length"], y=df["sepal_width"], kind='scatter')
sns.jointplot(x=df["sepal_length"], y=df["sepal_width"], kind='hex')
sns.jointplot(x=df["sepal_length"], y=df["sepal_width"], kind='kde')
```
#82 Custom marginal plot

#82 Custom color of marginal plot

Note that you can custom these central graphics:
```
# Then you can pass arguments to each type:
sns.jointplot(x=df["sepal_length"], y=df["sepal_width"], kind='scatter', s=200, color='m', edgecolor="skyblue", linewidth=2)

# Custom the color
sns.set(style="white", color_codes=True)
sns.jointplot(x=df["sepal_length"], y=df["sepal_width"], kind='kde', color="skyblue")
```

You can easily custom the marginal plots using the marginal_kws argument. Do not hesitate to visit the histogram and density section of the gallery to see which customisation are available.


# library & dataset
import seaborn as sns
df = sns.load_dataset('iris')

# Custom the histogram and add rug:
sns.jointplot(x=df["sepal_length"], y=df["sepal_width"], kind='hex', marginal_kws=dict(bins=30, rug=True))

#82 Custom space in marginal plot

#82 Custom space in marginal plot

#82 Custom ratio in marginal plot

You can control the space between main and marginal plots, and the size ratio between these 2 parts:
```
# library & dataset
import seaborn as sns
df = sns.load_dataset('iris')

# No space
sns.jointplot(x=df["sepal_length"], y=df["sepal_width"], kind='kde', color="grey", space=0)

# Huge space
sns.jointplot(x=df["sepal_length"], y=df["sepal_width"], kind='kde', color="grey", space=3)

# Make marginal bigger:
sns.jointplot(x=df["sepal_length"], y=df["sepal_width"], kind='kde',ratio=1)
```