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Documentation Index

Fetch the complete documentation index at: https://docs.textql.com/llms.txt

Use this file to discover all available pages before exploring further.

After Ana queries your data with Text to SQL or Ontology, Python transforms and visualizes it. Just describe what charts or analysis you want—you never need to write code yourself.
Works with SQL Tools: Python is typically used alongside Text to SQL or Ontology. Ana queries your data first, then uses Python to create visualizations and perform additional analysis.
Python execution cell

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Primary Use: Data Visualization

Python is mainly used to create visualizations from data retrieved by Text to SQL or Ontology queries:

Charts & Graphs

“Create a bar chart of this data”“Show monthly trends as a line chart”“Make a pie chart of sales by category”

Advanced Visualizations

“Build a scatter plot with trend line”“Create a heat map of correlations”“Show this as a stacked area chart”

Interactive Maps

“Plot customer locations on a map”“Show sales by region on a map”“Create an interactive map with markers”

Statistical Plots

“Show the distribution as a histogram”“Create a box plot by segment”“Make a correlation matrix”

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When to Use

Python works best after you’ve retrieved data with Text to SQL or Ontology:
1

Query Your Data First

Start by getting the data you need using Text to SQL or Ontology.Example: “Show me monthly sales by region for 2024”
2

Then Visualize or Analyze

Once you have the data, ask Ana to create visualizations or perform calculations.Example: “Create a line chart of those trends” or “Calculate the growth rate”

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Common Workflows

The most common pattern:
  1. “Get total sales by product category”
  2. “Create a bar chart of that data”
Ana queries your database, then visualizes the results.

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Tips for Better Visualizations

Be Specific About Chart Type

Tell Ana exactly what kind of chart you want.Good: “Create a stacked bar chart showing monthly revenue by product line”Vague: “Make a chart”

Request Changes Easily

If a visualization isn’t quite right, just ask for adjustments.Examples:
  • “Make that chart larger”
  • “Use different colors”
  • “Sort by highest to lowest”
  • “Add a trend line”

Combine Visualizations

You can ask for multiple charts to compare different views.Example: “Show me a bar chart and a trend line of the same data”

Ask for Insights

Ana can calculate additional metrics to visualize.Example: “Show the data with moving averages” or “Add percentage changes”

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Example Visualization Requests

Real examples of how SQL + Python work together:
Request:
  • “Get sales by month, then show it as a line chart”
  • “Query top 10 products and create a bar chart”
  • “Show customer distribution by state on a map”

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What Else Python Can Do

While visualization is the primary use case, Python can also:
  • Calculate growth rates and percentages
  • Compute moving averages
  • Find totals and subtotals
  • Determine min/max values
  • Export processed data as CSV
  • Create Excel files with multiple sheets
  • Generate formatted reports
  • Calculate correlations
  • Perform basic regression
  • Show distributions
  • Identify outliers
Best Practice: Always start with a SQL query (Text to SQL or Ontology) to get your data, then use Python to visualize or analyze it. This workflow gives you the best results.