Building a Tableau-Like BI Application with Atoti in Jupyter Notebook

Are you tired of relying on others to explore your model's data or struggling with complex code to visualize your results? With Atoti, a powerful Python module, you can quickly create your interactive BI application in your Jupyter Notebook!

Let's explore how Atoti empowers you to analyze and visualize your data effortlessly.

Step 1: Install Atoti

Start by installing Atoti using pip:

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pip install atoti         

Step 2: Import Libraries and Load Data

Import the required libraries and load your model's data into a Pandas DataFrame.

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import pandas as pd import atoti as tt # Load your data into a Pandas DataFrame data = pd.read_csv('your_data.csv')         

Step 3: Create Atoti Cube and Dashboard

Now, create an Atoti Cube using the loaded data and define the measures and dimensions for analysis.

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# Create an Atoti session session = tt.create_session() # Create an Atoti Cube cube = session.create_cube(data, name='MyCube') # Define dimensions and hierarchies cube.create_dimension('Dimension1', hierarchies=['Attribute1', 'Attribute2']) cube.create_dimension('Dimension2', hierarchies=['Attribute3', 'Attribute4']) # Define measures for analysis (e.g., sum, count, average) cube.create_measures('Measure1', tt.agg.sum('Column1')) cube.create_measures('Measure2', tt.agg.average('Column2'))         

Step 4: Build Interactive Dashboards

Atoti allows you to build interactive dashboards easily using simple Python code.

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# Create a dashboard dashboard = session.create_dashboard() # Add charts and visualizations to the dashboard chart1 = dashboard.add_chart(tt.visualization.BarChart, title="Bar Chart") chart1.data = cube.query('SELECT [Dimension1].[Attribute1], [Measures].[Measure1]') chart2 = dashboard.add_chart(tt.visualization.LineChart, title="Line Chart") chart2.data = cube.query('SELECT [Dimension2].[Attribute3], [Measures].[Measure2]')         

Step 5: Analyze and Play Around

Congratulations! Your Atoti-based BI application is ready. Run the notebook, and you'll see the interactive dashboard within your Jupyter environment.

Atoti's intuitive UI allows you to explore and play around with the data effortlessly. Use filters, slicers, and drill-downs to analyze your model's results from different perspectives, all without the need to write complex code or depend on a standalone BI solution.

Step 6: Share and Collaborate

The best part is that you can easily share your Jupyter Notebook with colleagues, allowing them to interact with the BI application and explore the data produced by your model collaboratively.

So, #unlockthepower of #Atoti and take control of your data analysis journey with interactive visualizations, all within your Jupyter Notebook!

(Note: Replace 'your_data.csv' with your dataset's path.)