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

# ### Installation

# To install Chart Studio's python package, use the package manager **pip** inside your terminal.<br>
# If you don't have **pip** installed on your machine, [click here](https://pip.pypa.io/en/latest/installing.html) for pip's installation instructions.
# <br>
# <br>
# `$ pip install chart_studio`
# <br>or
# <br>`$ sudo pip install chart_studio`
# <br>
# <br>
# Plotly's Python package is installed alongside the Chart Studio package and it is [updated frequently](https://github.com/plotly/plotly.py/blob/master/CHANGELOG.md)! To upgrade, run:
# <br>
# <br>
# `$ pip install plotly --upgrade`

# ### Initialization for Online Plotting
# Chart Studio provides a web-service for hosting graphs! Create a [free account](https://plotly.com/api_signup) to get started. Graphs are saved inside your online Chart Studio account and you control the privacy. Public hosting is free, for private hosting, check out our [paid plans](https://plotly.com/products/cloud/).
# <br>
# <br>
# After installing the Chart Studio package, you're ready to fire up python:
# <br>
# <br>
# `$ python`
# <br>
# <br>
# and set your credentials:

# In[1]:


import chart_studio
chart_studio.tools.set_credentials_file(username='DemoAccount', api_key='lr1c37zw81')


# You'll need to replace **'DemoAccount'** and **'lr1c37zw81'** with *your* Plotly username and [API key](https://plotly.com/settings/api).<br>
# Find your API key [here](https://plotly.com/settings/api).
# <br>
# <br>
# The initialization step places a special **.plotly/.credentials** file in your home directory. Your **~/.plotly/.credentials** file should look something like this:
# <br>
# ```
# {
#     "username": "DemoAccount",
#     "stream_ids": ["ylosqsyet5", "h2ct8btk1s", "oxz4fm883b"],
#     "api_key": "lr1c37zw81"
# }
# ```

# ### Online Plot Privacy
# 
# Plot can be set to three different type of privacies: public, private or secret.
# - **public**: Anyone can view this graph. It will appear in your profile and can appear in search engines. You do not need to be logged in to Chart Studio to view this chart.
# - **private**: Only you can view this plot. It will not appear in the Plotly feed, your profile, or search engines. You must be logged in to Plotly to view this graph. You can privately share this graph with other Chart Studio users in your online Chart Studio account and they will need to be logged in to view this plot.
# - **secret**: Anyone with this secret link can view this chart. It will not appear in the Chart Studio feed, your profile, or search engines. If it is embedded inside a webpage or an IPython notebook, anybody who is viewing that page will be able to view the graph. You do not need to be logged in to view this plot.
# 
# By default all plots are set to **public**.  Users with free account have the permission to keep one private plot. If you need to save private plots, [upgrade to a pro account](https://plotly.com/plans). If you're a [Personal or Professional user](https://plotly.com/settings/subscription/?modal=true&utm_source=api-docs&utm_medium=support-oss) and would like the default setting for your plots to be private, you can edit your Chart Studio configuration:

# In[2]:


import chart_studio
chart_studio.tools.set_config_file(world_readable=False,
                             sharing='private')


# For more examples on privacy settings please visit [Python privacy documentation](https://plotly.com/python/privacy/)

# ### Special Instructions for [Chart Studio Enterprise](https://plotly.com/product/enterprise/) Users

# Your API key for account on the public cloud will be different than the API key in Chart Studio Enterprise. Visit https://plotly.your-company.com/settings/api/ to find your Chart Studio Enterprise API key. Remember to replace "your-company.com" with the URL of your Chart Studio Enterprise server.
# If your company has a Chart Studio Enterprise server, change the Python API endpoint so that it points to your company's Plotly server instead of Plotly's cloud.
# <br>
# <br>
# In python, enter:

# In[3]:


import chart_studio
chart_studio.tools.set_config_file(
    plotly_domain='https://plotly.your-company.com',
    plotly_api_domain='https://plotly.your-company.com',
    plotly_streaming_domain='https://stream-plotly.your-company.com'
)


# Make sure to replace **"your-company.com"** with the URL of *your* Chart Studio Enterprise server.

# Additionally, you can set your configuration so that you generate **private plots by default**. For more information on privacy settings see: https://plotly.com/python/privacy/<br>
# <br>
# In python, enter:

# In[4]:


import chart_studio
chart_studio.tools.set_config_file(
    plotly_domain='https://plotly.your-company.com',
    plotly_api_domain='https://plotly.your-company.com',
    plotly_streaming_domain='https://stream-plotly.your-company.com',
    world_readable=False,
    sharing='private'
)


# ### Plotly Using virtualenv
# Python's `virtualenv` allows us create multiple working Python environments which can each use different versions of packages. We can use `virtualenv` from the command line to create an environment using plotly.py version 3.3.0 and a separate one using plotly.py version 2.7.0. See [the virtualenv documentation](https://virtualenv.pypa.io/en/stable) for more info.
# 
# **Install virtualenv globally**
# <br>`$ sudo pip install virtualenv`
# 
# **Create your virtualenvs**
# <br>`$ mkdir ~/.virtualenvs`
# <br>`$ cd ~/.virtualenvs`
# <br>`$ python -m venv plotly2.7`
# <br>`$ python -m venv plotly3.3`
# 
# **Activate the virtualenv.**
# You will see the name of your virtualenv in parenthesis next to the input promt.
# <br>`$ source ~/.virtualenvs/plotly2.7/bin/activate`
# <br>`(plotly2.7) $`
# 
# **Install plotly locally to virtualenv** (note that we don't use sudo).
# <br>`(plotly2.7) $ pip install plotly==2.7`
# 
# **Deactivate to exit**
# <br>
# `(plotly2.7) $ deactivate`
# <br>`$`

# ### Jupyter Setup
# **Install Jupyter into a virtualenv**
# <br>`$ source ~/.virtualenvs/plotly3.3/bin/activate`
# <br>`(plotly3.3) $ pip install notebook`
# 
# **Start the Jupyter kernel from a virtualenv**
# <br>`(plotly3.3) $ jupyter notebook`
# 
# 

# ### Start Plotting Online
# When plotting online, the plot and data will be saved to your cloud account. There are two methods for plotting online: `py.plot()` and `py.iplot()`. Both options create a unique url for the plot and save it in your Plotly account.
# - Use `py.plot()` to return the unique url and optionally open the url.
# - Use `py.iplot()` when working in a Jupyter Notebook to display the plot in the notebook.
# 
# Copy and paste one of the following examples to create your first hosted Plotly graph using the Plotly Python library:

# In[5]:


import chart_studio.plotly as py
import plotly.graph_objects as go

trace0 = go.Scatter(
    x=[1, 2, 3, 4],
    y=[10, 15, 13, 17]
)
trace1 = go.Scatter(
    x=[1, 2, 3, 4],
    y=[16, 5, 11, 9]
)
data = [trace0, trace1]

py.plot(data, filename = 'basic-line', auto_open=True)


# Checkout the docstrings for more information:

# In[6]:


import chart_studio.plotly as py
help(py.plot)


# In[7]:


import chart_studio.plotly as py
import plotly.graph_objects as go

trace0 = go.Scatter(
    x=[1, 2, 3, 4],
    y=[10, 15, 13, 17]
)
trace1 = go.Scatter(
    x=[1, 2, 3, 4],
    y=[16, 5, 11, 9]
)
data = [trace0, trace1]

py.iplot(data, filename = 'basic-line')


# See more examples in our [IPython notebook documentation](https://plotly.com/ipython-notebooks/) or check out the `py.iplot()` docstring for more information.

# In[8]:


import chart_studio.plotly as py
help(py.iplot)


# You can also create plotly graphs with **matplotlib** syntax. Learn more in our [matplotlib documentation](https://plotly.com/matplotlib/).

# ### Initialization for Offline Plotting
# Plotly allows you to create graphs offline and save them locally. There are also two methods for interactive plotting offline: `plotly.io.write_html()` and `plotly.io.show()`.
# - Use `plotly.io.write_html()` to create and standalone HTML that is saved locally and opened inside your web browser.
# - Use `plotly.io.show()` when working offline in a Jupyter Notebook to display the plot in the notebook.
# 
# For information on all of the ways that plotly figures can be displayed, see [*Displaying plotly figures with plotly for Python*](https://plotly.com/python/renderers/).

# Copy and paste one of the following examples to create your first offline Plotly graph using the Plotly Python library:

# In[9]:


import plotly.graph_objects as go
import plotly.io as pio

fig = go.Figure(go.Scatter(x=[1, 2, 3, 4], y=[4, 3, 2, 1]))
fig.update_layout(title_text='hello world')
pio.write_html(fig, file='hello_world.html', auto_open=True)


# Learn more by calling `help()`:

# In[10]:


import plotly
help(plotly.io.write_html)


# In[11]:


import plotly.graph_objects as go
import plotly.io as pio

fig = go.Figure(go.Scatter(x=[1, 2, 3, 4], y=[4, 3, 2, 1]))
fig.update_layout(title_text='hello world')
pio.show(fig)


# You can also call plotly.io.show directly from the go.Figure object.

# In[12]:


fig.show()


# In[13]:


import plotly
help(plotly.io.show)


# For more examples on plotting offline with Plotly in python please visit our [offline documentation](https://plotly.com/python/offline/).

# ### Using Plotly with Pandas
# 
# To use Plotly with Pandas first `$ pip install pandas` and then import pandas in your code like in the example below.

# In[14]:


import chart_studio.plotly as py
import plotly.graph_objects as go
import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminder2007.csv')

fig = go.Figure(go.Scatter(x=df.gdpPercap, y=df.lifeExp, text=df.country, mode='markers', name='2007'))
fig.update_xaxes(title_text='GDP per Capita', type='log')
fig.update_yaxes(title_text='Life Expectancy')

py.iplot(fig, filename='pandas-multiple-scatter')


# ### [MORE EXAMPLES](https://plotly.com/python/)
# Check out more examples and tutorials for using Plotly in python [here](https://plotly.com/python)!

# In[ ]: