- Time required: 5 minutes
You must have already:
- Signed up for a Matatika account
- Created a workspace through the Matatika app or API
- Published a dataset or access to an existing dataset
Refer to the previous Getting Started guides if you are unsure of these requirements.
Each dataset has a
data endpoint, which returns live data from the database workspace schema based on the dataset
query. The Matatika library
fetch method is used to tap into this endpoint and return a snapshot of the dataset data. Using a Jupyter Notebook, we can create an interactive environment to fetch some data and perform transform and visualisation operations.
You can follow along with this guide using our simple_jupyter_fetch example notebook.
Dataset data can be retrieved by invoking
fetch as follows:
from matatika.library import MatatikaClient # create the client and call 'fetch' client = MatatikaClient(auth_token, endpoint_url, None) data = client.fetch(dataset_id)
By default, the method will return a Python dictionary object constructed from the raw API response. From here, with the use of data-centric libraries such as pandas, NumPy or SciPy, it becomes easy to begin analysing, transforming and visualising the data in useful ways.
import pandas as pd # create the dataframe from the dataset data dictionary df = pd.DataFrame.from_dict(data) df.head()
The resulting dataframe can be visualised using the
plot method, which functions as a wrapper for the plotting backend (by default this is Matplotlib).
After some data clean-up, processing, and visualisation adjustments, it is possible to create plots that offer tailored insights.