Python

Tablix includes a full Python code editor and execution engine that runs entirely in your browser — no server round-trip needed to execute your code (only the AI code-generation step calls out to the AI service).

Opening the Python Editor

Open the code editor sidebar and choose Python as the language (you can switch between Python and SQL). It’s a Monaco-based editor (the same engine behind VS Code), with:

Syntax highlighting and autocomplete (keywords, built-ins, pandas/numpy/scikit-learn/scipy import snippets, and your sheet’s actual column headers as suggestions)
Inline error markers pointing at the failing line
A resizable console panel showing print() output, tables, and errors
Dark mode toggle
An AI prompt box inside the editor so you can ask the AI to write the code for you without leaving the editor
A prompt “optimizer” that rewrites a rough request into a more precise technical instruction before generating code

Execution Engine

Python code runs via Pyodide (CPython compiled to WebAssembly) in a background Web Worker, so it doesn’t freeze the UI.

Available libraries: pandas (as pd), numpy (as np), scikit-learn (sklearn), scipy.Other libraries (e.g. matplotlib, seaborn) are not available — charts are created via the create_chart() bridge function instead. See Charts.

Reading Your Sheet: `q.cells()`

import pandas as pd

# Load a range as a DataFrame (headers included by default)
df = q.cells("A:D")        # whole columns A through D
df = q.cells("A1:D50")     # a specific range

q.cells() automatically loads the requested range from the active sheet into a pandas DataFrame, using the first row of the range as column headers.

Writing Back to the Sheet

Depending on what you’re doing, use the matching bridge function rather than one big generic “set everything” call:

Function	Purpose
`set_data(data)`	Write a new table (DataFrame, Series, list-of-lists, or scalar) to the sheet — used for aggregations/pivots/new tables
`apply_format(rules)`	Apply cell formatting (colors, number formats, bold, borders, etc.) without changing values
`update_cells(updates)`	Update specific cell values in place, or delete rows, without rebuilding the whole table
`insert_col(index, header, values)`	Insert a new column in place
`insert_row(index, values)`	Insert a new row in place
`sort_table(col_name_or_index, ascending=True)`	Sort the existing table in place
`apply_filter(col_name_or_index, condition_type, value=None)`	Apply a filter to the existing table in place
`clear_filter(col_name_or_index=None)`	Clear one filter or all filters
`create_chart(chart_type, ...)`	Create a chart directly on the sheet — see full reference below

import pandas as pd

df = q.cells("A:C")
pivot = df.groupby('Region')['Sales'].mean().reset_index()
set_data(pivot)

import pandas as pd

df = q.cells("A:E")
df = df.dropna(how='all').reset_index(drop=True)

seen = set()
updates = []
for i, row in df.iterrows():
    key = tuple(row)
    if key in seen:
        updates.append({'action': 'delete_row', 'row': i + 1})
    else:
        seen.add(key)
update_cells(updates)

apply_filter("Status", "is_equal_to", "Active")
apply_filter("Price", "greater_than", 100)
sort_table("Sales", ascending=False)

Valid apply_filter condition types

is_equal_to, is_not_equal_to, text_contains, greater_than, less_than, greater_than_or_equal, less_than_or_equal, is_empty, is_not_empty, text_starts_with, text_ends_with, values (for matching a list of values).

Text Output

Use print() for explanations, summaries, or “Key Insights” style analysis instead of set_data() — this shows up in the console/chat as text rather than writing a new table to the sheet.

Creating Charts with `create_chart()`

Use create_chart() to build a chart directly from your Python code and place it on the sheet — no matplotlib/seaborn needed (those libraries aren’t available).

create_chart(
    chart_type,
    label_col=None,
    value_cols=None,
    agg_func='sum',
    title=None,
    width=600,
    height=370,
    filter_labels=None,
    top_n=None,
    bottom_n=None,
    labels=None,
    values=None,
    series_names=None
)

chart_type accepts any of Tablix’s chart types — 'line', 'area', 'column', 'bar', 'pie', 'doughnut', 'scatter', 'radar', 'waterfall', 'funnel', 'combo', 'histogram', and more. See the full list at Charts → Chart Types.

Option 1 — Chart Existing Columns

Point it at columns already on your sheet and let it aggregate for you:

create_chart(
    chart_type='column',
    label_col='Region',
    value_cols='Sales',
    agg_func='sum',
    title='Total Sales by Region'
)

This groups every row by Region, sums Sales per group, and charts the result — no need to pre-aggregate with pandas first. Multiple value columns become multiple series:

create_chart(
    chart_type='line',
    label_col='Month',
    value_cols=['Revenue', 'Profit'],
    title='Revenue vs Profit by Month'
)

Only the top or bottom N, or specific categories:

# Top 5 products by units sold
create_chart(chart_type='bar', label_col='Product', value_cols='Units Sold', top_n=5)

# Only these specific countries
create_chart(chart_type='pie', label_col='Country', value_cols='Revenue',
              filter_labels=['USA', 'Canada', 'Mexico'])

Option 2 — Chart Data You’ve Already Computed

If you’ve already calculated the numbers yourself (e.g. with pandas.groupby, or values that don’t map to raw columns), pass them directly with labels and values:

import pandas as pd

df = q.cells("A:C")
grouped = df.groupby('Region')['Sales'].sum().sort_values(ascending=False)

create_chart(
    chart_type='column',
    labels=grouped.index.tolist(),
    values=grouped.values.tolist(),
    title='Sales by Region'
)

Multiple series — pass a list of lists for values, with matching series_names:

create_chart(
    chart_type='line',
    labels=['Jan', 'Feb', 'Mar'],
    values=[[1000, 1200, 900], [700, 650, 800]],
    series_names=['Revenue', 'Cost'],
    title='Revenue vs Cost'
)

Use either label_col/value_cols or labels/values — don’t mix the two in one call. Placement on the sheet and chart colors are both handled automatically; you don’t need to set a position or pick colors yourself.

You don’t have to write any of this by hand — describing the chart you want to the AI Assistant generates the right create_chart() call for you. See Charts → Creating a Chart with AI for prompt examples.

AI-Generated Python

You don’t have to write Python yourself — describe what you want in the AI Assistant sidebar or the in-editor AI prompt box, and the AI generates the appropriate code using the bridge functions above, then runs it automatically.

See Error Handling for a full list of Python error messages and what causes them.

Overview

Core Features

Reference

Opening the Python Editor

Execution Engine

Reading Your Sheet: `q.cells()`

Writing Back to the Sheet

Text Output

Creating Charts with `create_chart()`

Option 1 — Chart Existing Columns

Option 2 — Chart Data You’ve Already Computed

AI-Generated Python

​Opening the Python Editor

​Execution Engine

​Reading Your Sheet: q.cells()

​Writing Back to the Sheet

​Text Output

​Creating Charts with create_chart()

​Option 1 — Chart Existing Columns

​Option 2 — Chart Data You’ve Already Computed

​AI-Generated Python

Opening the Python Editor

Execution Engine

Reading Your Sheet: `q.cells()`

Writing Back to the Sheet

Text Output

Creating Charts with `create_chart()`

Option 1 — Chart Existing Columns

Option 2 — Chart Data You’ve Already Computed

AI-Generated Python