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

# # Module 4
# 
# ## Video 19: Querying the SDK
# **Python for the Energy Industry**
# 
# To access data from a given endpoint, we do a 'search', which returns all data matching a given criteria. 
# 
# Note the Vessels endpoint documentation can be [found here](https://vortechsa.github.io/python-sdk/endpoints/vessels/).

# In[1]:


import vortexasdk as v
query = v.Vessels().search(vessel_classes='vlcc', term='ocean')

print(query)


# The data returned by the query is not interperatable in this format. We can think of it as a list of items, which each correspond to an item (in this case a vessel) matching the search query. We can see how many matching items were found:

# In[2]:


len(query)


# So there are 8 matching vessels. The data makes a little more sense if we look at just one of these:

# In[3]:


query[0]


# There's a lot of information here! Looking at the keys in this dictionary structure, we can see basic information about the vessel, along with the cargo that it is currently carrying. 
# 
# We can use a list comprehension to get the values of a particular key for each vessel in the returned data:

# In[4]:


[vessel['name'] for vessel in query]


# The query has a function `.to_df` which will convert the data into a pandas DataFrame structure. By default, it will only include some of the more important bits of information. We can also specifically list which colulmns we would like:

# In[5]:


query.to_df(columns=['name', 'imo', 'mmsi', 'related_names'])


# ### Exercise
# 
# 

# Using the Geographies endpoint, do a search for the term 'portsmouth'. How many matching queries are there? Where are they located?
# 
# Note the Geographies endpoint documentation can be [found here](https://vortechsa.github.io/python-sdk/endpoints/geographies/).

# In[ ]: