#!/usr/bin/env python
# coding: utf-8

# # Joint plot

# ## Table of Contents
# 
# 1. [Default Presentation of Joint Plot](#default)
# 
# 2. [Change Geom Types](#change-geom)
# 
# 3. [Geometries Customization](#geom-customization)
# 
# 4. [Marginal Layers Customization](#marginal-customization)
# 
# 5. [Grouping](#grouping)
# 
# 6. [Additional Layer](#additional_layer)

# In[1]:


import pandas as pd

from lets_plot.bistro import *
from lets_plot import *


# In[2]:


LetsPlot.setup_html()


# In[3]:


df = pd.read_csv("https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/iris.csv")
print(df.shape)
df.head()


# <a id="default"></a>
# 
# ## 1. Default Presentation of Joint Plot
# 
# In the simplest case, assign `x` and `y` to create a scatterplot (using `geom_point()`) with marginal histograms (using `geom_histogram()`).

# In[4]:


joint_plot(df, "petal_length", "petal_width")


# <a id="change-geom"></a>
# 
# ## 2. Change Geom Types
# 
# Besides the points there are another two types of geoms: `tile` and `density2d(f)`.

# In[5]:


joint_plot(df, "petal_length", "petal_width", geom='tile')


# In[6]:


joint_plot(df.dropna(), "petal_length", "petal_width", color_by="species", geom='density2d')


# <a id="geom-customization"></a>
# 
# ## 3. Change Geom Parameters
# 
# Use additional parameters for better customization: `color`, `size`, `alpha`, etc.

# In[7]:


joint_plot(df, "petal_length", "petal_width", color="#756bb1", size=8, alpha=.5, se=False)


# <a id="marginal-customization"></a>
# 
# ## 4. Marginal Layers Customization
# 
# `marginal` parameter is a shortcut for the `ggmarginal()` layer.

# In[8]:


joint_plot(df, "petal_length", "petal_width", color="black", marginal="box:lb:.03,hist:t:.4,hist:r") + \
    ggmarginal("tr", layer=geom_area(stat='density', color="magenta", fill="magenta", alpha=.1)) + \
    theme(axis_line_x='blank', axis_line_y='blank')


# <a id="grouping"></a>
# 
# ## 5. Grouping
# 
# The `color_by` parameter sets the mapping to the fill and color aesthetics.

# In[9]:


joint_plot(df, "petal_length", "petal_width", color_by="species", marginal="hist:tr")


# <a id="additional_layer"></a>
# 
# ## 6. Additional Layer
# 
# Add any other layer that supports `x` and `y` aesthetics (e.g. points layer with the `geom_point()` function).

# In[10]:


joint_plot(df, "petal_length", "petal_width", geom='density2df', \
           color="#993404", alpha=1/3, reg_line=False) + \
    geom_point(size=5, shape=21, color="#993404", fill="#ffffd4") + \
    scale_fill_gradient(low="#d95f0e", high="#fff7bc")