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

# # Background
# this notebook is focused on documenting the use of [Matplotlib](https://matplotlib.org/) and [Seaborn](https://seaborn.pydata.org/) to plot various data in Python.
# 
# You can find the docs at:
# 

# # Setup

# for plotting in Jupyter, see https://ipython.readthedocs.io/en/stable/interactive/reference.html#plotting-with-matplotlib
# 
# That documentation states:
# 
#     If IPython is already running, you can run the `%matplotlib` magic. If no arguments are given, IPython will automatically detect your choice of matplotlib backend. You can also request a specific backend with `%matplotlib backend`, where `backend` must be one of: ‘tk’, ‘qt’, ‘wx’, ‘gtk’, ‘osx’. In the web notebook and Qt console, ‘inline’ is also a valid backend value, which produces static figures inlined inside the application window instead of matplotlib’s interactive figures that live in separate windows.
# 
# https://github.com/matplotlib/jupyter-matplotlib is the extension for interactive
# To install ipympl with pip:
# ```
# pip install ipympl
# # If using JupyterLab
# # Install nodejs: https://nodejs.org/en/download/
# jupyter labextension install @jupyter-widgets/jupyterlab-manager
# jupyter labextension install jupyter-matplotlib
# ```
# 
# To enable the jupyter-matplotlib backend, simply use the matplotlib Jupyter magic:
# 
# `%matplotlib widget`
# 

# In[1]:


# magic for plotting in notebook
get_ipython().run_line_magic('matplotlib', 'inline')


# In[2]:


import matplotlib.pyplot as plt


# In[4]:


import seaborn as sns


# # Get data

# In[7]:


# Seaborn has some datasets available
sns.get_dataset_names()


# In[8]:


iris = sns.load_dataset('iris')


# In[18]:


type(iris)


# In[9]:


# datasets are pandas dataframes
iris


# In[ ]:





# # Colors
# seaborns's color palettes is documented at https://seaborn.pydata.org/tutorial/color_palettes.html#palette-tutorial
# 
# `color_palette()` will accept the name of any seaborn palette or matplotlib colormap (except `jet`, which you should never use). It can also take a list of colors specified in any valid matplotlib format (RGB tuples, hex color codes, or HTML color names). The return value is always a list of RGB tuples.

# In[26]:


# get default color palette tuples
sns.color_palette()


# In[28]:


# or to look at them
sns.palplot(sns.color_palette())


# In[35]:


# predefined palettes `Blues`
sns.palplot(sns.color_palette("Blues"))


# In[41]:


# predefined palettes dark version
sns.palplot(sns.color_palette("Blues_d"))


# In[39]:


# set size of palette
sns.palplot(sns.color_palette("Blues", 10))


# In[40]:


# predefined palettes
sns.palplot(sns.color_palette("Greens"))


# In[ ]:





# # Scatter
# seaborns's `scatterplot()` is documented at https://seaborn.pydata.org/generated/seaborn.scatterplot.html#seaborn.scatterplot

# In[21]:


# start simple
sns.scatterplot(x="petal_length", y="petal_width", data=iris[iris.species == "setosa"])


# In[20]:


# can separate another column by color using `hue`
sns.scatterplot(x="petal_length", y="petal_width", data=iris, hue="species")


# In[30]:


# can choose colors redefining the `palette`
sns.scatterplot(x="petal_length", y="petal_width", data=iris, hue="species", palette=["r", "g", "b"])


# In[22]:


# can also separate another column by size using `size`
sns.scatterplot(x="petal_length", y="petal_width", data=iris, size="species")


# In[33]:


# can also separate another column by markers using `style`
sns.scatterplot(x="petal_length", y="petal_width", data=iris, style="species")


# In[34]:


# can mix combinations
sns.scatterplot(x="petal_length", y="petal_width", data=iris, hue="species", style="species")


# In[ ]: