This guide details how to connect to a C3 cluster, search for data you want to use,
fetch that data, and convert it into a form that you can easily analyze.

Connecting to a C3 Cluster

Install Jupyter Notebook & Connect to the C3 Container

Using Browser to Connect to the C3 Suite

Fetching Instances of Types

Data Fetching Documentations if you are:

1. Using Browser Developing Tools: https://developer.c3.ai/docs/7.12.0/guide/guide-c3aisuite-basic/di-monitoring-and-troubleshooting
2.  using Python Jupyter Notebook: https://developer.c3.ai/docs/7.12.0/topic/ds-jupyter-notebooks

A Code Example in Jupyter Notebook:

from c3python import get_c3 
raw_df = c3.BlockInfo.fetch(spec={
    'limit': -1,
    'filter': 'exists(prp_bf_lr)',
    'order': 'descending(id)',
    'include': 'pct_i_l,pct_t_l,prp_res_lr,pop10_ha_lr,hu10_ha_lr,eroom_ha_lr,med10_age,prp_bf_lr'
})

Converting Fetch results to usable forms in Jupyter Notebook

A Code Example in Jupyter Notebook:

## continue from above ##
import pandas as pd
df = pd.DataFrame(raw_df.objs.toJson())
df.head()
df.drop('meta', axis=1, inplace=True)
df.drop('type', axis=1, inplace=True)
df.drop('version', axis=1, inplace=True)
df.drop('id', axis=1, inplace=True)
df.head()

Users can operations like after you convert the c3 Dataset into the pandas dataframe.

Executing Metrics on Time series data

TODO:

Constructing, Training, and Scoring a Machine Learning Model:

Code example using both Python and JS: https://developer.c3.ai/docs/7.12.0/topic/mlpipe-code-examples#construct-tensorflowpipe