diff --git a/all-block-types-no-outputs.deepnote b/all-block-types-no-outputs.deepnote
new file mode 100644
index 0000000..71ada7b
--- /dev/null
+++ b/all-block-types-no-outputs.deepnote
@@ -0,0 +1,738 @@
+metadata:
+ createdAt: 2025-09-16T10:11:17.611Z
+ modifiedAt: 2025-09-17T09:30:51.468Z
+project:
+ id: c4604eab-9672-4d68-ab40-4c27af41fbc7
+ name: all-block-types
+ notebooks:
+ - blocks:
+ - content: Code
+ id: f6359663a8984338a1b2c4cfd0d4c171
+ metadata:
+ formattedRanges: []
+ sortingKey: a0
+ type: text-cell-p
+ - content: |-
+ import pandas as pd
+ import numpy as np
+
+ # Create sample data
+ data = {
+ 'Name': ['John', 'Emma', 'Michael', 'Sarah', 'David'],
+ 'Age': [28, 34, 42, 25, 31],
+ 'Salary': [65000, 78000, 95000, 62000, 70000],
+ 'Department': ['IT', 'HR', 'Finance', 'Marketing', 'IT'],
+ 'Experience': [3, 7, 12, 2, 5]
+ }
+
+ # Create DataFrame
+ df = pd.DataFrame(data)
+
+ # Create salary_max variable
+ salary_max = df['Salary'].max()
+
+ # Create salary_min variable
+ salary_min = df['Salary'].min()
+
+ # Display the DataFrame
+ df
+ executionCount: 1
+ id: 347b6e16651c453881cf75a0c7365618
+ metadata:
+ execution_start: 1758017228976
+ execution_millis: 13
+ execution_context_id: 43d850b5-85c3-4bc8-b2d5-b50f2529e42c
+ outputs: []
+ sortingKey: m
+ type: code
+ - content: Markdown
+ id: f8e64035510d4918a22bbac0bc997982
+ metadata:
+ formattedRanges: []
+ sortingKey: s
+ type: text-cell-p
+ - content: >-
+ # Markdown heading
+
+
+ Markdown body text with a [link](https://www.google.com) and a
+ **bold** word.
+ id: 73f558db2f114d50b4eca9ebbff82870
+ metadata: {}
+ sortingKey: v
+ type: markdown
+ - content: Paragraph
+ id: 34aebdcb65d84cdba24cc6b0185beeba
+ metadata:
+ formattedRanges: []
+ sortingKey: x
+ type: text-cell-p
+ - content: A paragraph of text
+ id: f0a69c045b0b4a47ac3a19ca2e04cad7
+ metadata:
+ formattedRanges: []
+ sortingKey: y
+ type: text-cell-p
+ - content: Heading 1
+ id: 300cda58d0814c928dd3bfeb72965696
+ metadata:
+ formattedRanges: []
+ sortingKey: yU
+ type: text-cell-p
+ - content: Heading 1
+ id: 1cf63fe584e6403bb1c646f6481ed1e1
+ metadata:
+ formattedRanges: []
+ sortingKey: yj
+ type: text-cell-h1
+ - content: Heading 2
+ id: b3aa9896fc6e4b97ba3eed8fdfb8995a
+ metadata:
+ formattedRanges: []
+ sortingKey: yr
+ type: text-cell-p
+ - content: Heading 2
+ id: 9ede33b832b14c7781888a2f778f6785
+ metadata:
+ formattedRanges: []
+ sortingKey: yv
+ type: text-cell-h2
+ - content: Heading 3
+ id: 1fdd93c5ff004dac896214c52c2111e9
+ metadata:
+ formattedRanges: []
+ sortingKey: yx
+ type: text-cell-p
+ - content: Heading 3
+ id: 6514a011a46a4a149046d188ab0eb271
+ metadata:
+ formattedRanges: []
+ sortingKey: yy
+ type: text-cell-h3
+ - content: Bulleted list
+ id: faa890e3bdc647039b735657ad0aefbf
+ metadata:
+ formattedRanges: []
+ sortingKey: yyU
+ type: text-cell-p
+ - content: Bullet one
+ id: aec4ba3615954acaabdedf70f142c332
+ metadata:
+ formattedRanges: []
+ sortingKey: yyj
+ type: text-cell-bullet
+ - content: Bullet two
+ id: 9f9f6c22499645a0945b46ec2ab5998a
+ metadata:
+ formattedRanges: []
+ sortingKey: yyr
+ type: text-cell-bullet
+ - content: Bullet three
+ id: 6a1360e12cf648078226ee019c87f483
+ metadata:
+ formattedRanges: []
+ sortingKey: yyv
+ type: text-cell-bullet
+ - content: To-do list
+ id: 54fea078b60b40caa7ed54d98b74c2f7
+ metadata:
+ formattedRanges: []
+ sortingKey: yyx
+ type: text-cell-p
+ - content: To-do item one
+ id: b8213fd33984429da8ab939a6cafe53f
+ metadata:
+ formattedRanges: []
+ sortingKey: yyy
+ type: text-cell-todo
+ - content: To-do item two
+ id: 8757f7e24b824c6e8c1c2ee3e76e5e50
+ metadata:
+ checked: false
+ formattedRanges: []
+ sortingKey: yyyU
+ type: text-cell-todo
+ - content: To-do item three
+ id: 30cf5a1a68fc4579ae86a498ab916ab3
+ metadata:
+ checked: false
+ formattedRanges: []
+ sortingKey: yyyj
+ type: text-cell-todo
+ - content: Callout
+ id: b6729542fe4c47079a398657a66a75c3
+ metadata:
+ formattedRanges: []
+ sortingKey: yyyr
+ type: text-cell-p
+ - content: A callout with some text
+ id: b3dc04fdfc9c4571be3a960e9674e3a1
+ metadata:
+ formattedRanges: []
+ sortingKey: yyyv
+ type: text-cell-callout
+ - content: Separator
+ id: 3cb803815f694153bbf763d4f82772f2
+ metadata:
+ formattedRanges: []
+ sortingKey: yyyx
+ type: text-cell-p
+ - content: ""
+ id: 091223f162044c2bab926c0f36a688e8
+ metadata: {}
+ sortingKey: yyyy
+ type: separator
+ - content: SQL
+ id: de73a7dcfae44252acb9e92f46008bc1
+ metadata:
+ formattedRanges: []
+ sortingKey: yyyyU
+ type: text-cell-p
+ - content: SELECT * FROM df;
+ executionCount: 2
+ id: 3cbce657e51247f2b01b730661260cb3
+ metadata:
+ execution_start: 1758017229128
+ execution_millis: 371
+ sql_integration_id: deepnote-dataframe-sql
+ execution_context_id: 43d850b5-85c3-4bc8-b2d5-b50f2529e42c
+ deepnote_variable_name: df
+ outputs: []
+ sortingKey: yyyyj
+ type: sql
+ - content: Chat
+ id: b4d48a1639bc4a8b83a7b4000085a862
+ metadata:
+ formattedRanges: []
+ sortingKey: yyyyr
+ type: text-cell-p
+ - content: ""
+ executionCount: 3
+ id: 157db3d3f69845be87d3eee17eb677fe
+ metadata:
+ execution_start: 1758017230093
+ execution_millis: 125
+ execution_context_id: 43d850b5-85c3-4bc8-b2d5-b50f2529e42c
+ deepnote_variable_name: df
+ deepnote_visualization_spec:
+ layer:
+ - layer:
+ - layer:
+ - mark:
+ clip: true
+ type: bar
+ color: "#2266D3"
+ tooltip: true
+ encoding:
+ x:
+ axis:
+ title: Employee name
+ sort: null
+ type: nominal
+ field: Name
+ scale:
+ type: linear
+ y:
+ axis:
+ title: Salary
+ format:
+ type: number
+ decimals: null
+ formatType: numberFormatFromNumberType
+ type: quantitative
+ field: Salary
+ scale:
+ type: linear
+ format:
+ type: default
+ decimals: null
+ aggregate: sum
+ formatType: numberFormatFromNumberType
+ color:
+ type: nominal
+ datum: Salary
+ scale:
+ range:
+ - "#2266D3"
+ domain:
+ - Salary
+ xOffset:
+ datum: series_0
+ transform: []
+ resolve:
+ scale:
+ color: independent
+ title: Salary by employee
+ config:
+ legend:
+ disable: false
+ $schema: https://vega.github.io/schema/vega-lite/v5.json
+ encoding: {}
+ usermeta:
+ seriesNames:
+ - Salary
+ seriesOrder:
+ - 0
+ specSchemaVersion: 2
+ tooltipDefaultMode: true
+ outputs: []
+ sortingKey: yyyyv
+ type: visualization
+ - content: Big number
+ id: 4792b856d35b40c3ac4ecc880513ba67
+ metadata:
+ formattedRanges: []
+ sortingKey: yyyyx
+ type: text-cell-p
+ - content: >
+
+ def __deepnote_big_number__():
+ import json
+ import jinja2
+ from jinja2 import meta
+
+ def render_template(template):
+ parsed_content = jinja2.Environment().parse(template)
+
+ required_variables = meta.find_undeclared_variables(parsed_content)
+
+ context = {
+ variable_name: globals().get(variable_name)
+ for variable_name in required_variables
+ }
+
+ result = jinja2.Environment().from_string(template).render(context)
+
+ return result
+
+ rendered_title = render_template("Max salary")
+ rendered_comparison_title = render_template("Difference with min")
+
+ return json.dumps({
+ "comparisonTitle": rendered_comparison_title,
+ "comparisonValue": f"{salary_min}",
+ "title": rendered_title,
+ "value": f"{salary_max}"
+ })
+
+ __deepnote_big_number__()
+ executionCount: 4
+ id: 63bd93dd0c7b4940bb7e2f281ef527ea
+ metadata:
+ execution_start: 1758017230260
+ execution_millis: 1
+ execution_context_id: 43d850b5-85c3-4bc8-b2d5-b50f2529e42c
+ deepnote_big_number_title: Max salary
+ deepnote_big_number_value: salary_max
+ deepnote_big_number_format: currency
+ deepnote_big_number_comparison_type: ""
+ deepnote_big_number_comparison_title: Difference with min
+ deepnote_big_number_comparison_value: salary_min
+ deepnote_big_number_comparison_format: ""
+ deepnote_big_number_comparison_enabled: true
+ outputs: []
+ sortingKey: yyyyy
+ type: big-number
+ - content: Text input
+ id: b4ef3456ab3541d59c32127a4ec3623a
+ metadata:
+ formattedRanges: []
+ sortingKey: yyyyyU
+ type: text-cell-p
+ - content: text_input = ''
+ executionCount: 5
+ id: 9617772dd620494588d82ff362f8723b
+ metadata:
+ execution_start: 1758017230310
+ execution_millis: 0
+ deepnote_input_label: Text input display name
+ execution_context_id: 43d850b5-85c3-4bc8-b2d5-b50f2529e42c
+ deepnote_variable_name: text_input
+ deepnote_variable_value: ""
+ deepnote_variable_default_value: Some text input value
+ outputs: []
+ sortingKey: yyyyyj
+ type: input-text
+ - content: Text area input
+ id: d68f1407519648cf9352322a78706262
+ metadata:
+ formattedRanges: []
+ sortingKey: yyyyyr
+ type: text-cell-p
+ - content: text_area_input = ''
+ executionCount: 6
+ id: 78b702ad9e4249c6a734e6540fd6d594
+ metadata:
+ execution_start: 1758017230360
+ execution_millis: 0
+ deepnote_input_label: Text area input
+ execution_context_id: 43d850b5-85c3-4bc8-b2d5-b50f2529e42c
+ deepnote_variable_name: text_area_input
+ deepnote_variable_value: ""
+ deepnote_variable_default_value: Some text area value
+ outputs: []
+ sortingKey: yyyyyv
+ type: input-textarea
+ - content: Select input
+ id: 6b4cf64deb7e4889a12ff8224f403e6e
+ metadata:
+ formattedRanges: []
+ sortingKey: yyyyyx
+ type: text-cell-p
+ - content: select_input = 'Option 1'
+ executionCount: 7
+ id: ad0dc815587c414e8350b7b2c277f526
+ metadata:
+ execution_start: 1758017230410
+ execution_millis: 0
+ deepnote_input_label: Select input display name
+ execution_context_id: 43d850b5-85c3-4bc8-b2d5-b50f2529e42c
+ deepnote_variable_name: select_input
+ deepnote_variable_value: Option 1
+ deepnote_variable_options:
+ - Option 1
+ - Option 2
+ deepnote_variable_select_type: from-options
+ deepnote_variable_default_value: Option 1
+ deepnote_variable_custom_options:
+ - Option 1
+ - Option 2
+ deepnote_variable_selected_variable: ""
+ outputs: []
+ sortingKey: yyyyyy
+ type: input-select
+ - content: Slider input
+ id: d71623200b3a4139b78a3f64f11d1fee
+ metadata:
+ formattedRanges: []
+ sortingKey: yyyyyyU
+ type: text-cell-p
+ - content: slider_input = 5
+ executionCount: 8
+ id: f5bde14ac91b4c619d4721119f6dc097
+ metadata:
+ execution_start: 1758017230460
+ execution_millis: 0
+ deepnote_input_label: Slider input display name
+ deepnote_slider_step: 1
+ execution_context_id: 43d850b5-85c3-4bc8-b2d5-b50f2529e42c
+ deepnote_variable_name: slider_input
+ deepnote_variable_value: "5"
+ deepnote_slider_max_value: 10
+ deepnote_slider_min_value: 0
+ deepnote_variable_default_value: "7"
+ outputs: []
+ sortingKey: yyyyyyj
+ type: input-slider
+ - content: Checkbox input
+ id: 1025124c6e2d423db37f1a2801d919c8
+ metadata:
+ formattedRanges: []
+ sortingKey: yyyyyyr
+ type: text-cell-p
+ - content: checkbox_input = False
+ executionCount: 9
+ id: 2b8c96e18b9047e1abdbc1030e64e483
+ metadata:
+ execution_start: 1758017230510
+ execution_millis: 0
+ deepnote_input_label: Checkbox input display name
+ execution_context_id: 43d850b5-85c3-4bc8-b2d5-b50f2529e42c
+ deepnote_variable_name: checkbox_input
+ deepnote_variable_value: false
+ deepnote_input_checkbox_label: Checkbox label
+ deepnote_variable_default_value: true
+ outputs: []
+ sortingKey: yyyyyyv
+ type: input-checkbox
+ - content: Date input
+ id: 5574b42d69b84dd485a5af45eed8ed24
+ metadata:
+ formattedRanges: []
+ sortingKey: yyyyyyx
+ type: text-cell-p
+ - content: |
+
+ from dateutil.parser import parse as _deepnote_parse
+ date_input = _deepnote_parse('2025-09-16T00:00:00.000Z').date()
+ executionCount: 1
+ id: da864431beb24d7ea254caf07469b64f
+ metadata:
+ execution_start: 1758101400493
+ execution_millis: 1
+ deepnote_input_label: Date input display name
+ execution_context_id: c8ddf739-5e27-4b0f-a7a1-6f638a515fbc
+ deepnote_variable_name: date_input
+ deepnote_variable_value: 2025-09-16T00:00:00.000Z
+ deepnote_input_date_version: 2
+ deepnote_variable_default_value: 2025-08-27T00:00:00.000Z
+ outputs: []
+ sortingKey: yyyyyyy
+ type: input-date
+ - content: Date range input
+ id: 453586f33cff44b8817b90f8fe96bb7c
+ metadata:
+ formattedRanges: []
+ sortingKey: yyyyyyyU
+ type: text-cell-p
+ - content: date_range_input = [None, None]
+ executionCount: 11
+ id: c0d58153fea24d749787d145a7184a95
+ metadata:
+ execution_start: 1758017230610
+ execution_millis: 0
+ deepnote_input_label: Date range input display name
+ execution_context_id: 43d850b5-85c3-4bc8-b2d5-b50f2529e42c
+ deepnote_variable_name: date_range_input
+ deepnote_variable_value: ""
+ deepnote_variable_default_value:
+ - 2025-08-12
+ - 2025-08-14
+ outputs: []
+ sortingKey: yyyyyyyj
+ type: input-date-range
+ - content: File input
+ id: 067e15e2da45411996f2596f0b771bc2
+ metadata:
+ formattedRanges: []
+ sortingKey: yyyyyyyr
+ type: text-cell-p
+ - content: file_input = 'file_input_uploads/user_events_mini.csv'
+ executionCount: 12
+ id: 4aaf723a72644849aeac5850a31899a0
+ metadata:
+ execution_start: 1758017230660
+ execution_millis: 0
+ deepnote_input_label: File input display name
+ execution_context_id: 43d850b5-85c3-4bc8-b2d5-b50f2529e42c
+ deepnote_variable_name: file_input
+ deepnote_variable_value: file_input_uploads/user_events_mini.csv
+ deepnote_allowed_file_extensions: .csv
+ outputs: []
+ sortingKey: yyyyyyyv
+ type: input-file
+ - content: Button
+ id: 07b4d5b4d6974ee2bc1ba0cebc222756
+ metadata:
+ formattedRanges: []
+ sortingKey: yyyyyyyx
+ type: text-cell-p
+ - content: ""
+ id: a697f0202b9a4c67a4cb7120efef8867
+ metadata:
+ execution_start: 1758017230710
+ execution_millis: 0
+ execution_context_id: 43d850b5-85c3-4bc8-b2d5-b50f2529e42c
+ deepnote_button_title: Set variable
+ deepnote_variable_name: button_input_set_variable
+ deepnote_button_behavior: set_variable
+ deepnote_button_color_scheme: blue
+ outputs: []
+ sortingKey: yyyyyyyy
+ type: button
+ - content: ""
+ id: 8b4935f7cefe4d819cad51b66c93fd79
+ metadata:
+ execution_start: 1758017230760
+ execution_millis: 0
+ execution_context_id: 43d850b5-85c3-4bc8-b2d5-b50f2529e42c
+ deepnote_button_title: Run notebook
+ deepnote_variable_name: button_1
+ deepnote_button_behavior: run
+ deepnote_button_color_scheme: blue
+ outputs: []
+ sortingKey: yyyyyyyyU
+ type: button
+ - content: Module
+ id: 0a334a952e274c2e915002636375b331
+ metadata:
+ formattedRanges: []
+ sortingKey: yyyyyyyyj
+ type: text-cell-p
+ - content: ""
+ executionCount: 1
+ id: 8e613308b24f419eb550573eda2602ab
+ metadata:
+ execution_start: 1758017517949
+ execution_millis: 145407
+ execution_context_id: f5da58fe-0dad-4318-867a-0ab9925d7c28
+ function_notebook_id: e960b15d27074d38a6b07ff87eb42c89
+ function_notebook_inputs: {}
+ last_function_run_started_at: 1758017655789
+ function_notebook_export_mappings:
+ Monthly_signups:
+ enabled: true
+ variable_name: Monthly_signups
+ last_executed_function_notebook_id: e960b15d27074d38a6b07ff87eb42c89
+ outputs: []
+ sortingKey: yyyyyyyyr
+ type: notebook-function
+ executionMode: block
+ id: 686ad8158f6947f9b2796fb1add22277
+ isModule: false
+ name: All block types
+ workingDirectory: /
+ settings:
+ requirements:
+ - Please
+ - don't
+ - install
+ - "`jupyter`"
+ - or
+ - "`jedi`"
+ - packages,
+ - they
+ - would
+ - break
+ - your
+ - Deepnote
+ - environment.
+ - Also,
+ - no
+ - need
+ - to
+ - put
+ - "`!pip"
+ - install`s
+ - here,
+ - we
+ - already
+ - save
+ - those
+ - automatically!
+ - then
+ - ./requirements.txt
+ - fi
+ - unidecode
+ - cairosvg
+ - wordcloud
+ - pandas
+ - requests
+ - nltk
+ - vaderSentiment
+ - matplotlib
+ - seaborn
+ - transformers
+ - '"pandas>=2.0"'
+ - ipython
+ - requirements.txt
+ - python
+ - pip
+ - wheel
+ - plotly
+ - Finally,
+ - will
+ - sort
+ - the
+ - dataframe
+ - based
+ - on
+ - percentage
+ - change
+ - in
+ - descending
+ - order
+ - and
+ - select
+ - top
+ - "5."
+ - If
+ - "`yfinance`"
+ - is
+ - not
+ - installed
+ - Python
+ - environment,
+ - it
+ - using
+ - "`!yfinance`."
+ - pytrends
+ - pandas==1.1.0
+ - /work/testmc-0.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+ - else
+ - Plotly
+ - a
+ - powerful
+ - library
+ - for
+ - creating
+ - interactive
+ - visualizations.
+ - In
+ - this
+ - notebook,
+ - used
+ - generate
+ - bar
+ - charts,
+ - line
+ - histograms.
+ - The
+ - "`!plotly`"
+ - command
+ - ensures
+ - that
+ - available
+ - use.
+ - note
+ - symbols
+ - provided
+ - are
+ - just
+ - reference.
+ - You
+ - can
+ - replace
+ - them
+ - according
+ - interests.
+ - code
+ - snippet
+ - assumes
+ - you
+ - have
+ - (`pd`)
+ - installed.
+ - not,
+ - "`!pandas`."
+ - jinja2
+ - Jinja2==3.0.0
+ - altair
+ - vega_datasets
+ - sentry-sdk
+ - responses
+ - parameterized
+ - python-socketio
+ - eventlet
+ - vllm
+ - .
+ - running
+ - after
+ - executing
+ - "!fastapi"
+ - '"sideloading'
+ - packages"
+ - notebook
+ - ipywidgets
+ - jupyterlab
+ - psutil
+ - Absolutely,
+ - here
+ - few
+ - examples
+ - of
+ - how
+ - Notion
+ - API
+ - be
+ - Python.
+ - It
+ - uses
+ - "`notion`"
+ - package.
+ - installed,
+ - via
+ - "`!notion`."
+ - "%fastapi"
+ - "%-q"
+version: 1.0.0
diff --git a/all-block-types-no-outputs.ipynb b/all-block-types-no-outputs.ipynb
new file mode 100644
index 0000000..3283107
--- /dev/null
+++ b/all-block-types-no-outputs.ipynb
@@ -0,0 +1,795 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "f6359663a8984338a1b2c4cfd0d4c171",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "Code",
+ "block_group": "a2621902d0a7472595a71e28f01bb3da"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "source_hash": "8a1ac92c",
+ "execution_start": 1758017228976,
+ "execution_millis": 13,
+ "execution_context_id": "43d850b5-85c3-4bc8-b2d5-b50f2529e42c",
+ "cell_id": "347b6e16651c453881cf75a0c7365618",
+ "deepnote_cell_type": "code"
+ },
+ "source": "import pandas as pd\nimport numpy as np\n\n# Create sample data\ndata = {\n 'Name': ['John', 'Emma', 'Michael', 'Sarah', 'David'],\n 'Age': [28, 34, 42, 25, 31],\n 'Salary': [65000, 78000, 95000, 62000, 70000],\n 'Department': ['IT', 'HR', 'Finance', 'Marketing', 'IT'],\n 'Experience': [3, 7, 12, 2, 5]\n}\n\n# Create DataFrame\ndf = pd.DataFrame(data)\n\n# Create salary_max variable\nsalary_max = df['Salary'].max()\n\n# Create salary_min variable\nsalary_min = df['Salary'].min()\n\n# Display the DataFrame\ndf",
+ "block_group": "2a432c3f8c6a4602a4299b5094cd1b35",
+ "execution_count": 1,
+ "outputs": [],
+ "outputs_reference": null,
+ "content_dependencies": null
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "f8e64035510d4918a22bbac0bc997982",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "Markdown",
+ "block_group": "51672b4375be4e208ca0cb6d58ea17da"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "73f558db2f114d50b4eca9ebbff82870",
+ "deepnote_cell_type": "markdown"
+ },
+ "source": "# Markdown heading\n\nMarkdown body text with a [link](https://www.google.com) and a **bold** word.",
+ "block_group": "10a282b7984f4ad98f90ac490c9bed3c"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "34aebdcb65d84cdba24cc6b0185beeba",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "Paragraph",
+ "block_group": "7f88f5051e844b36a0cf97ddae05c9fc"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "f0a69c045b0b4a47ac3a19ca2e04cad7",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "A paragraph of text",
+ "block_group": "af1ca519408e49a3b766ebc98d77db48"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "300cda58d0814c928dd3bfeb72965696",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "Heading 1",
+ "block_group": "550d948208fb4ced92d8600653be15b4"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "1cf63fe584e6403bb1c646f6481ed1e1",
+ "deepnote_cell_type": "text-cell-h1"
+ },
+ "source": "# Heading 1",
+ "block_group": "9a7f7bf77b3b45f6becf7ae73e6eee62"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "b3aa9896fc6e4b97ba3eed8fdfb8995a",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "Heading 2",
+ "block_group": "ac657f5a4adf478ba1b76a03c9d9361a"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "9ede33b832b14c7781888a2f778f6785",
+ "deepnote_cell_type": "text-cell-h2"
+ },
+ "source": "## Heading 2",
+ "block_group": "371421bb44344a7d91a221f501ccc08c"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "1fdd93c5ff004dac896214c52c2111e9",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "Heading 3",
+ "block_group": "8111c8912fbd4211a90ad965b6b79ec1"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "6514a011a46a4a149046d188ab0eb271",
+ "deepnote_cell_type": "text-cell-h3"
+ },
+ "source": "### Heading 3",
+ "block_group": "259ddfd167f648e285a87d92577e2d41"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "faa890e3bdc647039b735657ad0aefbf",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "Bulleted list",
+ "block_group": "9766707de7bc4685948657bff622b9f6"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "aec4ba3615954acaabdedf70f142c332",
+ "deepnote_cell_type": "text-cell-bullet"
+ },
+ "source": "- Bullet one",
+ "block_group": "c9a02867dfa04173a4d2f51464fac194"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "9f9f6c22499645a0945b46ec2ab5998a",
+ "deepnote_cell_type": "text-cell-bullet"
+ },
+ "source": "- Bullet two",
+ "block_group": "8d21db08da194515a3932e066f77fb77"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "6a1360e12cf648078226ee019c87f483",
+ "deepnote_cell_type": "text-cell-bullet"
+ },
+ "source": "- Bullet three",
+ "block_group": "edc1ed0fb7614023bb7617fdc1c0abea"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "54fea078b60b40caa7ed54d98b74c2f7",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "To-do list",
+ "block_group": "f371ee9e94e1428ba8ec355e12dca38c"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "b8213fd33984429da8ab939a6cafe53f",
+ "deepnote_cell_type": "text-cell-todo"
+ },
+ "source": "- [ ] To-do item one",
+ "block_group": "b50b2211535a4f9782fce8051cacac66"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "checked": false,
+ "formattedRanges": [],
+ "cell_id": "8757f7e24b824c6e8c1c2ee3e76e5e50",
+ "deepnote_cell_type": "text-cell-todo"
+ },
+ "source": "- [ ] To-do item two",
+ "block_group": "0a4ff943c02e4fd4bfcf94a4ef386aa3"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "checked": false,
+ "formattedRanges": [],
+ "cell_id": "30cf5a1a68fc4579ae86a498ab916ab3",
+ "deepnote_cell_type": "text-cell-todo"
+ },
+ "source": "- [ ] To-do item three",
+ "block_group": "888a33aafca844b6ac1597313858ef21"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "b6729542fe4c47079a398657a66a75c3",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "Callout",
+ "block_group": "2d4e5049a24643c6a80a0a35559f8cc1"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "b3dc04fdfc9c4571be3a960e9674e3a1",
+ "deepnote_cell_type": "text-cell-callout"
+ },
+ "source": "> A callout with some text",
+ "block_group": "e792897153f44d20ae6ffaefc0457262"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "3cb803815f694153bbf763d4f82772f2",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "Separator",
+ "block_group": "db67578e7dd348a9a3b1eae1f677373d"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "091223f162044c2bab926c0f36a688e8",
+ "deepnote_cell_type": "separator"
+ },
+ "source": "
",
+ "block_group": "7f45e1873f044edaabbf98eb954bb2f4"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "de73a7dcfae44252acb9e92f46008bc1",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "SQL",
+ "block_group": "c78377f47a68485d89d50951e8e28300"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "source_hash": "a3523be3",
+ "execution_start": 1758017229128,
+ "execution_millis": 371,
+ "sql_integration_id": "deepnote-dataframe-sql",
+ "execution_context_id": "43d850b5-85c3-4bc8-b2d5-b50f2529e42c",
+ "deepnote_variable_name": "df",
+ "cell_id": "3cbce657e51247f2b01b730661260cb3",
+ "deepnote_cell_type": "sql",
+ "deepnote_sql_source": "SELECT * FROM df;"
+ },
+ "source": "df = _dntk.execute_sql(\n 'SELECT * FROM df;',\n 'SQL_DEEPNOTE_DATAFRAME_SQL',\n audit_sql_comment='',\n sql_cache_mode='cache_disabled',\n return_variable_type='dataframe'\n)\ndf",
+ "block_group": "19c9f74d74b741f2b76c2bbb19b9e072",
+ "execution_count": 2,
+ "outputs": [],
+ "outputs_reference": null,
+ "content_dependencies": null
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "b4d48a1639bc4a8b83a7b4000085a862",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "Chat",
+ "block_group": "90ed378eaa4d430d8f894680ca7a6b17"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "source_hash": "652dccd7",
+ "execution_start": 1758017230093,
+ "execution_millis": 125,
+ "execution_context_id": "43d850b5-85c3-4bc8-b2d5-b50f2529e42c",
+ "deepnote_variable_name": "df",
+ "deepnote_visualization_spec": {
+ "layer": [
+ {
+ "layer": [
+ {
+ "layer": [
+ {
+ "mark": {
+ "clip": true,
+ "type": "bar",
+ "color": "#2266D3",
+ "tooltip": true
+ },
+ "encoding": {
+ "x": {
+ "axis": {
+ "title": "Employee name"
+ },
+ "sort": null,
+ "type": "nominal",
+ "field": "Name",
+ "scale": {
+ "type": "linear"
+ }
+ },
+ "y": {
+ "axis": {
+ "title": "Salary",
+ "format": {
+ "type": "number",
+ "decimals": null
+ },
+ "formatType": "numberFormatFromNumberType"
+ },
+ "type": "quantitative",
+ "field": "Salary",
+ "scale": {
+ "type": "linear"
+ },
+ "format": {
+ "type": "default",
+ "decimals": null
+ },
+ "aggregate": "sum",
+ "formatType": "numberFormatFromNumberType"
+ },
+ "color": {
+ "type": "nominal",
+ "datum": "Salary",
+ "scale": {
+ "range": [
+ "#2266D3"
+ ],
+ "domain": [
+ "Salary"
+ ]
+ }
+ },
+ "xOffset": {
+ "datum": "series_0"
+ }
+ },
+ "transform": []
+ }
+ ]
+ }
+ ],
+ "resolve": {
+ "scale": {
+ "color": "independent"
+ }
+ }
+ }
+ ],
+ "title": "Salary by employee",
+ "config": {
+ "legend": {
+ "disable": false
+ }
+ },
+ "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
+ "encoding": {},
+ "usermeta": {
+ "seriesNames": [
+ "Salary"
+ ],
+ "seriesOrder": [
+ 0
+ ],
+ "specSchemaVersion": 2,
+ "tooltipDefaultMode": true
+ }
+ },
+ "cell_id": "157db3d3f69845be87d3eee17eb677fe",
+ "deepnote_cell_type": "visualization"
+ },
+ "source": "import inspect\n_dntk.DeepnoteChart(df, \"\"\"{\"layer\":[{\"layer\":[{\"layer\":[{\"mark\":{\"clip\":true,\"type\":\"bar\",\"color\":\"#2266D3\",\"tooltip\":true},\"encoding\":{\"x\":{\"axis\":{\"title\":\"Employee name\"},\"sort\":null,\"type\":\"nominal\",\"field\":\"Name\",\"scale\":{\"type\":\"linear\"}},\"y\":{\"axis\":{\"title\":\"Salary\",\"format\":{\"type\":\"number\",\"decimals\":null},\"formatType\":\"numberFormatFromNumberType\"},\"type\":\"quantitative\",\"field\":\"Salary\",\"scale\":{\"type\":\"linear\"},\"format\":{\"type\":\"default\",\"decimals\":null},\"aggregate\":\"sum\",\"formatType\":\"numberFormatFromNumberType\"},\"color\":{\"type\":\"nominal\",\"datum\":\"Salary\",\"scale\":{\"range\":[\"#2266D3\"],\"domain\":[\"Salary\"]}},\"xOffset\":{\"datum\":\"series_0\"}},\"transform\":[]}]}],\"resolve\":{\"scale\":{\"color\":\"independent\"}}}],\"title\":\"Salary by employee\",\"config\":{\"legend\":{\"disable\":false}},\"$schema\":\"https://vega.github.io/schema/vega-lite/v5.json\",\"encoding\":{},\"usermeta\":{\"seriesNames\":[\"Salary\"],\"seriesOrder\":[0],\"specSchemaVersion\":2,\"tooltipDefaultMode\":true}}\"\"\", **({'attach_selection': True} if 'attach_selection' in inspect.signature(_dntk.DeepnoteChart).parameters else {}), **({'filters': '[]'} if 'filters' in inspect.signature(_dntk.DeepnoteChart).parameters else {}))",
+ "block_group": "93602502de714543949d62c94101c476",
+ "execution_count": 3,
+ "outputs": [],
+ "outputs_reference": null
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "4792b856d35b40c3ac4ecc880513ba67",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "Big number",
+ "block_group": "d9c55e56e13745019ff43bad8b94ca6c"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "source_hash": "ea65eb57",
+ "execution_start": 1758017230260,
+ "execution_millis": 1,
+ "execution_context_id": "43d850b5-85c3-4bc8-b2d5-b50f2529e42c",
+ "deepnote_big_number_title": "Max salary",
+ "deepnote_big_number_value": "salary_max",
+ "deepnote_big_number_format": "currency",
+ "deepnote_big_number_comparison_type": "",
+ "deepnote_big_number_comparison_title": "Difference with min",
+ "deepnote_big_number_comparison_value": "salary_min",
+ "deepnote_big_number_comparison_format": "",
+ "deepnote_big_number_comparison_enabled": true,
+ "cell_id": "63bd93dd0c7b4940bb7e2f281ef527ea",
+ "deepnote_cell_type": "big-number"
+ },
+ "source": "\ndef __deepnote_big_number__():\n import json\n import jinja2\n from jinja2 import meta\n\n def render_template(template):\n parsed_content = jinja2.Environment().parse(template)\n\n required_variables = meta.find_undeclared_variables(parsed_content)\n\n context = {\n variable_name: globals().get(variable_name)\n for variable_name in required_variables\n }\n\n result = jinja2.Environment().from_string(template).render(context)\n\n return result\n\n rendered_title = render_template(\"Max salary\")\n rendered_comparison_title = render_template(\"Difference with min\")\n\n return json.dumps({\n \"comparisonTitle\": rendered_comparison_title,\n \"comparisonValue\": f\"{salary_min}\",\n \"title\": rendered_title,\n \"value\": f\"{salary_max}\"\n })\n\n__deepnote_big_number__()\n",
+ "block_group": "69a7615d8fec4e018ad29df4f7c9c8a6",
+ "execution_count": 4,
+ "outputs": [],
+ "outputs_reference": null
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "b4ef3456ab3541d59c32127a4ec3623a",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "Text input",
+ "block_group": "9300674080aa47178c3e80f9603bf522"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "source_hash": "73002490",
+ "execution_start": 1758017230310,
+ "execution_millis": 0,
+ "deepnote_input_label": "Text input display name",
+ "execution_context_id": "43d850b5-85c3-4bc8-b2d5-b50f2529e42c",
+ "deepnote_variable_name": "text_input",
+ "deepnote_variable_value": "",
+ "deepnote_variable_default_value": "Some text input value",
+ "cell_id": "9617772dd620494588d82ff362f8723b",
+ "deepnote_cell_type": "input-text"
+ },
+ "source": "text_input = ''",
+ "block_group": "cde392347c26426286cecd44eb7cc80e",
+ "execution_count": 5,
+ "outputs": [],
+ "outputs_reference": null
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "d68f1407519648cf9352322a78706262",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "Text area input",
+ "block_group": "843434fa29fb4926ac8e7d2c8708b683"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "source_hash": "dc8f2258",
+ "execution_start": 1758017230360,
+ "execution_millis": 0,
+ "deepnote_input_label": "Text area input",
+ "execution_context_id": "43d850b5-85c3-4bc8-b2d5-b50f2529e42c",
+ "deepnote_variable_name": "text_area_input",
+ "deepnote_variable_value": "",
+ "deepnote_variable_default_value": "Some text area value",
+ "cell_id": "78b702ad9e4249c6a734e6540fd6d594",
+ "deepnote_cell_type": "input-textarea"
+ },
+ "source": "text_area_input = ''",
+ "block_group": "0fbadd030dc44a55abdaba6c519a192c",
+ "execution_count": 6,
+ "outputs": [],
+ "outputs_reference": null
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "6b4cf64deb7e4889a12ff8224f403e6e",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "Select input",
+ "block_group": "4d3f0ded61ae433f943e667f6aa0acac"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "source_hash": "f30613eb",
+ "execution_start": 1758017230410,
+ "execution_millis": 0,
+ "deepnote_input_label": "Select input display name",
+ "execution_context_id": "43d850b5-85c3-4bc8-b2d5-b50f2529e42c",
+ "deepnote_variable_name": "select_input",
+ "deepnote_variable_value": "Option 1",
+ "deepnote_variable_options": [
+ "Option 1",
+ "Option 2"
+ ],
+ "deepnote_variable_select_type": "from-options",
+ "deepnote_variable_default_value": "Option 1",
+ "deepnote_variable_custom_options": [
+ "Option 1",
+ "Option 2"
+ ],
+ "deepnote_variable_selected_variable": "",
+ "cell_id": "ad0dc815587c414e8350b7b2c277f526",
+ "deepnote_cell_type": "input-select"
+ },
+ "source": "select_input = 'Option 1'",
+ "block_group": "8e1b14cb668a4a639812149ca1b076f0",
+ "execution_count": 7,
+ "outputs": [],
+ "outputs_reference": null
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "d71623200b3a4139b78a3f64f11d1fee",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "Slider input",
+ "block_group": "eeb937709b734d4ebfb10057b4f14427"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "source_hash": "903ebe60",
+ "execution_start": 1758017230460,
+ "execution_millis": 0,
+ "deepnote_input_label": "Slider input display name",
+ "deepnote_slider_step": 1,
+ "execution_context_id": "43d850b5-85c3-4bc8-b2d5-b50f2529e42c",
+ "deepnote_variable_name": "slider_input",
+ "deepnote_variable_value": "5",
+ "deepnote_slider_max_value": 10,
+ "deepnote_slider_min_value": 0,
+ "deepnote_variable_default_value": "7",
+ "cell_id": "f5bde14ac91b4c619d4721119f6dc097",
+ "deepnote_cell_type": "input-slider"
+ },
+ "source": "slider_input = 5",
+ "block_group": "ce9d87717518498a8e258cd7ca4c530c",
+ "execution_count": 8,
+ "outputs": [],
+ "outputs_reference": null
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "1025124c6e2d423db37f1a2801d919c8",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "Checkbox input",
+ "block_group": "0055729453894afa9c2a918ae9e4ad5d"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "source_hash": "43d96aab",
+ "execution_start": 1758017230510,
+ "execution_millis": 0,
+ "deepnote_input_label": "Checkbox input display name",
+ "execution_context_id": "43d850b5-85c3-4bc8-b2d5-b50f2529e42c",
+ "deepnote_variable_name": "checkbox_input",
+ "deepnote_variable_value": false,
+ "deepnote_input_checkbox_label": "Checkbox label",
+ "deepnote_variable_default_value": true,
+ "cell_id": "2b8c96e18b9047e1abdbc1030e64e483",
+ "deepnote_cell_type": "input-checkbox"
+ },
+ "source": "checkbox_input = False",
+ "block_group": "b33c667b572f4a8a981b26f3153fd113",
+ "execution_count": 9,
+ "outputs": [],
+ "outputs_reference": null
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "5574b42d69b84dd485a5af45eed8ed24",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "Date input",
+ "block_group": "997c3f11880641fcafd4e5d189f399c5"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "source_hash": "5506e6cf",
+ "execution_start": 1758101400493,
+ "execution_millis": 1,
+ "deepnote_input_label": "Date input display name",
+ "execution_context_id": "c8ddf739-5e27-4b0f-a7a1-6f638a515fbc",
+ "deepnote_variable_name": "date_input",
+ "deepnote_variable_value": "2025-09-16T00:00:00.000Z",
+ "deepnote_input_date_version": 2,
+ "deepnote_variable_default_value": "2025-08-27T00:00:00.000Z",
+ "cell_id": "da864431beb24d7ea254caf07469b64f",
+ "deepnote_cell_type": "input-date"
+ },
+ "source": "\nfrom dateutil.parser import parse as _deepnote_parse\ndate_input = _deepnote_parse('2025-09-16T00:00:00.000Z').date()\n",
+ "block_group": "76b2a29b9fbb43b88987d605df82c196",
+ "execution_count": 1,
+ "outputs": [],
+ "outputs_reference": null
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "453586f33cff44b8817b90f8fe96bb7c",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "Date range input",
+ "block_group": "7bb4afb3b9e64ad9ac641040fae0f39f"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "source_hash": "df64ed1d",
+ "execution_start": 1758017230610,
+ "execution_millis": 0,
+ "deepnote_input_label": "Date range input display name",
+ "execution_context_id": "43d850b5-85c3-4bc8-b2d5-b50f2529e42c",
+ "deepnote_variable_name": "date_range_input",
+ "deepnote_variable_value": "",
+ "deepnote_variable_default_value": [
+ "2025-08-12",
+ "2025-08-14"
+ ],
+ "cell_id": "c0d58153fea24d749787d145a7184a95",
+ "deepnote_cell_type": "input-date-range"
+ },
+ "source": "date_range_input = [None, None]",
+ "block_group": "adb47271070b4ec5bd7ae36b6374ed8c",
+ "execution_count": 11,
+ "outputs": [],
+ "outputs_reference": null
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "067e15e2da45411996f2596f0b771bc2",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "File input",
+ "block_group": "f0729b0fd20546cfa27eb6cec10cd15a"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "source_hash": "2a21d71e",
+ "execution_start": 1758017230660,
+ "execution_millis": 0,
+ "deepnote_input_label": "File input display name",
+ "execution_context_id": "43d850b5-85c3-4bc8-b2d5-b50f2529e42c",
+ "deepnote_variable_name": "file_input",
+ "deepnote_variable_value": "file_input_uploads/user_events_mini.csv",
+ "deepnote_allowed_file_extensions": ".csv",
+ "cell_id": "4aaf723a72644849aeac5850a31899a0",
+ "deepnote_cell_type": "input-file"
+ },
+ "source": "file_input = 'file_input_uploads/user_events_mini.csv'",
+ "block_group": "476d14d4916b408394563233abaf8b41",
+ "execution_count": 12,
+ "outputs": [],
+ "outputs_reference": null
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "07b4d5b4d6974ee2bc1ba0cebc222756",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "Button",
+ "block_group": "dca7628f959e4975abe29417fbb585e8"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "source_hash": "25dd0fc7",
+ "execution_start": 1758017230710,
+ "execution_millis": 0,
+ "execution_context_id": "43d850b5-85c3-4bc8-b2d5-b50f2529e42c",
+ "deepnote_button_title": "Set variable",
+ "deepnote_variable_name": "button_input_set_variable",
+ "deepnote_button_behavior": "set_variable",
+ "deepnote_button_color_scheme": "blue",
+ "cell_id": "a697f0202b9a4c67a4cb7120efef8867",
+ "deepnote_cell_type": "button"
+ },
+ "source": "button_input_set_variable = False",
+ "block_group": "82e1707478464399abb956e9fdddda46",
+ "execution_count": null,
+ "outputs": [],
+ "outputs_reference": null
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "source_hash": "b623e53d",
+ "execution_start": 1758017230760,
+ "execution_millis": 0,
+ "execution_context_id": "43d850b5-85c3-4bc8-b2d5-b50f2529e42c",
+ "deepnote_button_title": "Run notebook",
+ "deepnote_variable_name": "button_1",
+ "deepnote_button_behavior": "run",
+ "deepnote_button_color_scheme": "blue",
+ "cell_id": "8b4935f7cefe4d819cad51b66c93fd79",
+ "deepnote_cell_type": "button"
+ },
+ "source": "",
+ "block_group": "2c8494b7dec74944bacc78a7b584bb79",
+ "execution_count": null,
+ "outputs": [],
+ "outputs_reference": null
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "0a334a952e274c2e915002636375b331",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "Module",
+ "block_group": "44cdb835e3484a36b8e319e0a019b6da"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "source_hash": "b63b0b2e",
+ "execution_start": 1758017517949,
+ "execution_millis": 145407,
+ "execution_context_id": "f5da58fe-0dad-4318-867a-0ab9925d7c28",
+ "function_notebook_id": "e960b15d27074d38a6b07ff87eb42c89",
+ "function_notebook_inputs": {},
+ "last_function_run_started_at": 1758017655789,
+ "function_notebook_export_mappings": {
+ "Monthly_signups": {
+ "enabled": true,
+ "variable_name": "Monthly_signups"
+ }
+ },
+ "last_executed_function_notebook_id": "e960b15d27074d38a6b07ff87eb42c89",
+ "cell_id": "8e613308b24f419eb550573eda2602ab",
+ "deepnote_cell_type": "notebook-function"
+ },
+ "source": "def _deepnote_run_notebook_function(export_table_states_json):\n globals()['_deepnote_run_notebook_function'] = None\n\n return _dntk.run_notebook_function(\n scope=globals(),\n function_notebook_id='e960b15d27074d38a6b07ff87eb42c89',\n inputs={},\n export_mappings={'Monthly_signups': {'enabled': True,'variable_name': 'Monthly_signups'}},\n export_table_states_json=export_table_states_json,\n notebook_function_api_token='',\n parent_notebook_function_run_id=None,\n debug=False\n )",
+ "block_group": "15846815e8f743f582176f114aaf799f",
+ "execution_count": 1,
+ "outputs": [],
+ "outputs_reference": null
+ },
+ {
+ "cell_type": "markdown",
+ "source": "\n
\nCreated in Deepnote",
+ "metadata": {
+ "created_in_deepnote_cell": true,
+ "deepnote_cell_type": "markdown"
+ }
+ }
+ ],
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "deepnote_persisted_session": {
+ "createdAt": "2025-09-16T12:15:01.925Z"
+ },
+ "deepnote_notebook_id": "686ad8158f6947f9b2796fb1add22277"
+ }
+}
diff --git a/all-block-types-with-outputs.deepnote b/all-block-types-with-outputs.deepnote
new file mode 100644
index 0000000..cec4ea6
--- /dev/null
+++ b/all-block-types-with-outputs.deepnote
@@ -0,0 +1,1623 @@
+metadata:
+ createdAt: 2025-09-16T10:11:17.611Z
+ modifiedAt: 2025-09-16T10:13:03.273Z
+project:
+ id: c4604eab-9672-4d68-ab40-4c27af41fbc7
+ name: All block types
+ notebooks:
+ - blocks:
+ - content: Code
+ id: f6359663a8984338a1b2c4cfd0d4c171
+ metadata:
+ formattedRanges: []
+ sortingKey: a0
+ type: text-cell-p
+ - content: |-
+ import pandas as pd
+ import numpy as np
+
+ # Create sample data
+ data = {
+ 'Name': ['John', 'Emma', 'Michael', 'Sarah', 'David'],
+ 'Age': [28, 34, 42, 25, 31],
+ 'Salary': [65000, 78000, 95000, 62000, 70000],
+ 'Department': ['IT', 'HR', 'Finance', 'Marketing', 'IT'],
+ 'Experience': [3, 7, 12, 2, 5]
+ }
+
+ # Create DataFrame
+ df = pd.DataFrame(data)
+
+ # Create salary_max variable
+ salary_max = df['Salary'].max()
+
+ # Create salary_min variable
+ salary_min = df['Salary'].min()
+
+ # Display the DataFrame
+ df
+ executionCount: 1
+ id: 347b6e16651c453881cf75a0c7365618
+ metadata:
+ execution_start: 1758017228976
+ execution_millis: 13
+ execution_context_id: 43d850b5-85c3-4bc8-b2d5-b50f2529e42c
+ outputs:
+ - output_type: execute_result
+ execution_count: 1
+ data:
+ application/vnd.deepnote.dataframe.v3+json:
+ column_count: 5
+ columns:
+ - name: Name
+ dtype: object
+ stats:
+ unique_count: 5
+ nan_count: 0
+ min: null
+ max: null
+ histogram: null
+ categories:
+ - name: John
+ count: 1
+ - name: Emma
+ count: 1
+ - name: 3 others
+ count: 3
+ - name: Age
+ dtype: int64
+ stats:
+ unique_count: 5
+ nan_count: 0
+ min: "25"
+ max: "42"
+ histogram:
+ - bin_start: 25
+ bin_end: 26.7
+ count: 1
+ - bin_start: 26.7
+ bin_end: 28.4
+ count: 1
+ - bin_start: 28.4
+ bin_end: 30.1
+ count: 0
+ - bin_start: 30.1
+ bin_end: 31.8
+ count: 1
+ - bin_start: 31.8
+ bin_end: 33.5
+ count: 0
+ - bin_start: 33.5
+ bin_end: 35.2
+ count: 1
+ - bin_start: 35.2
+ bin_end: 36.9
+ count: 0
+ - bin_start: 36.9
+ bin_end: 38.6
+ count: 0
+ - bin_start: 38.6
+ bin_end: 40.3
+ count: 0
+ - bin_start: 40.3
+ bin_end: 42
+ count: 1
+ categories: null
+ - name: Salary
+ dtype: int64
+ stats:
+ unique_count: 5
+ nan_count: 0
+ min: "62000"
+ max: "95000"
+ histogram:
+ - bin_start: 62000
+ bin_end: 65300
+ count: 2
+ - bin_start: 65300
+ bin_end: 68600
+ count: 0
+ - bin_start: 68600
+ bin_end: 71900
+ count: 1
+ - bin_start: 71900
+ bin_end: 75200
+ count: 0
+ - bin_start: 75200
+ bin_end: 78500
+ count: 1
+ - bin_start: 78500
+ bin_end: 81800
+ count: 0
+ - bin_start: 81800
+ bin_end: 85100
+ count: 0
+ - bin_start: 85100
+ bin_end: 88400
+ count: 0
+ - bin_start: 88400
+ bin_end: 91700
+ count: 0
+ - bin_start: 91700
+ bin_end: 95000
+ count: 1
+ categories: null
+ - name: Department
+ dtype: object
+ stats:
+ unique_count: 4
+ nan_count: 0
+ min: null
+ max: null
+ histogram: null
+ categories:
+ - name: IT
+ count: 2
+ - name: HR
+ count: 1
+ - name: 2 others
+ count: 2
+ - name: Experience
+ dtype: int64
+ stats:
+ unique_count: 5
+ nan_count: 0
+ min: "2"
+ max: "12"
+ histogram:
+ - bin_start: 2
+ bin_end: 3
+ count: 1
+ - bin_start: 3
+ bin_end: 4
+ count: 1
+ - bin_start: 4
+ bin_end: 5
+ count: 0
+ - bin_start: 5
+ bin_end: 6
+ count: 1
+ - bin_start: 6
+ bin_end: 7
+ count: 0
+ - bin_start: 7
+ bin_end: 8
+ count: 1
+ - bin_start: 8
+ bin_end: 9
+ count: 0
+ - bin_start: 9
+ bin_end: 10
+ count: 0
+ - bin_start: 10
+ bin_end: 11
+ count: 0
+ - bin_start: 11
+ bin_end: 12
+ count: 1
+ categories: null
+ - name: _deepnote_index_column
+ dtype: int64
+ row_count: 5
+ preview_row_count: 5
+ rows:
+ - Name: John
+ Age: 28
+ Salary: 65000
+ Department: IT
+ Experience: 3
+ _deepnote_index_column: 0
+ - Name: Emma
+ Age: 34
+ Salary: 78000
+ Department: HR
+ Experience: 7
+ _deepnote_index_column: 1
+ - Name: Michael
+ Age: 42
+ Salary: 95000
+ Department: Finance
+ Experience: 12
+ _deepnote_index_column: 2
+ - Name: Sarah
+ Age: 25
+ Salary: 62000
+ Department: Marketing
+ Experience: 2
+ _deepnote_index_column: 3
+ - Name: David
+ Age: 31
+ Salary: 70000
+ Department: IT
+ Experience: 5
+ _deepnote_index_column: 4
+ type: dataframe
+ text/plain: |2-
+ Name Age Salary Department Experience
+ 0 John 28 65000 IT 3
+ 1 Emma 34 78000 HR 7
+ 2 Michael 42 95000 Finance 12
+ 3 Sarah 25 62000 Marketing 2
+ 4 David 31 70000 IT 5
+ text/html: |-
+
+
+
+
+
+ |
+ Name |
+ Age |
+ Salary |
+ Department |
+ Experience |
+
+
+
+
+ | 0 |
+ John |
+ 28 |
+ 65000 |
+ IT |
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+
+
+ | 1 |
+ Emma |
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+ HR |
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+
+
+ | 2 |
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+
+ | 3 |
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+ | 4 |
+ David |
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+ IT |
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+
+
+
+
+
+
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+
+ |
+ Name |
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+ | 0 |
+ John |
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+
+
+
+
\n\n
\n \n \n | \n Name | \n Age | \n Salary | \n Department | \n Experience | \n
\n \n \n \n | 0 | \n John | \n 28 | \n 65000 | \n IT | \n 3 | \n
\n \n | 1 | \n Emma | \n 34 | \n 78000 | \n HR | \n 7 | \n
\n \n | 2 | \n Michael | \n 42 | \n 95000 | \n Finance | \n 12 | \n
\n \n | 3 | \n Sarah | \n 25 | \n 62000 | \n Marketing | \n 2 | \n
\n \n | 4 | \n David | \n 31 | \n 70000 | \n IT | \n 5 | \n
\n \n
\n
",
+ "block_group": "7f45e1873f044edaabbf98eb954bb2f4"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "formattedRanges": [],
+ "cell_id": "d39d8bc9ab9e4c6caf7306ceddeceae7",
+ "deepnote_cell_type": "text-cell-p"
+ },
+ "source": "SQL",
+ "block_group": "c78377f47a68485d89d50951e8e28300"
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "source_hash": "a3523be3",
+ "execution_start": 1758017229128,
+ "execution_millis": 371,
+ "sql_integration_id": "deepnote-dataframe-sql",
+ "execution_context_id": "43d850b5-85c3-4bc8-b2d5-b50f2529e42c",
+ "deepnote_variable_name": "df",
+ "cell_id": "7c9da6307fea4f9f88bb4eedf53d9bdd",
+ "deepnote_cell_type": "sql",
+ "deepnote_sql_source": "SELECT * FROM df;"
+ },
+ "source": "df = _dntk.execute_sql(\n 'SELECT * FROM df;',\n 'SQL_DEEPNOTE_DATAFRAME_SQL',\n audit_sql_comment='',\n sql_cache_mode='cache_disabled',\n return_variable_type='dataframe'\n)\ndf",
+ "block_group": "19c9f74d74b741f2b76c2bbb19b9e072",
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "execution_count": 2,
+ "data": {
+ "application/vnd.deepnote.dataframe.v3+json": {
+ "column_count": 5,
+ "columns": [
+ {
+ "name": "Name",
+ "dtype": "object",
+ "stats": {
+ "unique_count": 5,
+ "nan_count": 0,
+ "min": null,
+ "max": null,
+ "histogram": null,
+ "categories": [
+ {
+ "name": "John",
+ "count": 1
+ },
+ {
+ "name": "Emma",
+ "count": 1
+ },
+ {
+ "name": "3 others",
+ "count": 3
+ }
+ ]
+ }
+ },
+ {
+ "name": "Age",
+ "dtype": "int64",
+ "stats": {
+ "unique_count": 5,
+ "nan_count": 0,
+ "min": "25",
+ "max": "42",
+ "histogram": [
+ {
+ "bin_start": 25,
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+ {
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+ },
+ {
+ "bin_start": 40.3,
+ "bin_end": 42,
+ "count": 1
+ }
+ ],
+ "categories": null
+ }
+ },
+ {
+ "name": "Salary",
+ "dtype": "int64",
+ "stats": {
+ "unique_count": 5,
+ "nan_count": 0,
+ "min": "62000",
+ "max": "95000",
+ "histogram": [
+ {
+ "bin_start": 62000,
+ "bin_end": 65300,
+ "count": 2
+ },
+ {
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+ {
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+ {
+ "bin_start": 85100,
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+ "count": 0
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+ {
+ "bin_start": 88400,
+ "bin_end": 91700,
+ "count": 0
+ },
+ {
+ "bin_start": 91700,
+ "bin_end": 95000,
+ "count": 1
+ }
+ ],
+ "categories": null
+ }
+ },
+ {
+ "name": "Department",
+ "dtype": "object",
+ "stats": {
+ "unique_count": 4,
+ "nan_count": 0,
+ "min": null,
+ "max": null,
+ "histogram": null,
+ "categories": [
+ {
+ "name": "IT",
+ "count": 2
+ },
+ {
+ "name": "HR",
+ "count": 1
+ },
+ {
+ "name": "2 others",
+ "count": 2
+ }
+ ]
+ }
+ },
+ {
+ "name": "Experience",
+ "dtype": "int64",
+ "stats": {
+ "unique_count": 5,
+ "nan_count": 0,
+ "min": "2",
+ "max": "12",
+ "histogram": [
+ {
+ "bin_start": 2,
+ "bin_end": 3,
+ "count": 1
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+ {
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+ {
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+ }
+ ],
+ "categories": null
+ }
+ },
+ {
+ "name": "_deepnote_index_column",
+ "dtype": "int64"
+ }
+ ],
+ "row_count": 5,
+ "preview_row_count": 5,
+ "rows": [
+ {
+ "Name": "John",
+ "Age": 28,
+ "Salary": 65000,
+ "Department": "IT",
+ "Experience": 3,
+ "_deepnote_index_column": 0
+ },
+ {
+ "Name": "Emma",
+ "Age": 34,
+ "Salary": 78000,
+ "Department": "HR",
+ "Experience": 7,
+ "_deepnote_index_column": 1
+ },
+ {
+ "Name": "Michael",
+ "Age": 42,
+ "Salary": 95000,
+ "Department": "Finance",
+ "Experience": 12,
+ "_deepnote_index_column": 2
+ },
+ {
+ "Name": "Sarah",
+ "Age": 25,
+ "Salary": 62000,
+ "Department": "Marketing",
+ "Experience": 2,
+ "_deepnote_index_column": 3
+ },
+ {
+ "Name": "David",
+ "Age": 31,
+ "Salary": 70000,
+ "Department": "IT",
+ "Experience": 5,
+ "_deepnote_index_column": 4
+ }
+ ],
+ "type": "dataframe"
+ },
+ "text/plain": " Name Age Salary Department Experience\n0 John 28 65000 IT 3\n1 Emma 34 78000 HR 7\n2 Michael 42 95000 Finance 12\n3 Sarah 25 62000 Marketing 2\n4 David 31 70000 IT 5",
+ "text/html": "\n\n
\n \n \n | \n Name | \n Age | \n Salary | \n Department | \n Experience | \n
\n \n \n \n | 0 | \n John | \n 28 | \n 65000 | \n IT | \n 3 | \n
\n \n | 1 | \n Emma | \n 34 | \n 78000 | \n HR | \n 7 | \n
\n \n | 2 | \n Michael | \n 42 | \n 95000 | \n Finance | \n 12 | \n
\n \n | 3 | \n Sarah | \n 25 | \n 62000 | \n Marketing | \n 2 | \n
\n \n | 4 | \n David | \n 31 | \n 70000 | \n IT | \n 5 | \n
\n \n
\n
\n\n
\n \n \n | \n week_number | \n plan | \n weekly_active_users | \n
\n \n \n \n | 0 | \n 1 | \n Team | \n 4 | \n
\n \n | 1 | \n 2 | \n Team | \n 4 | \n
\n \n | 2 | \n 3 | \n Team | \n 4 | \n
\n \n | 3 | \n 4 | \n Team | \n 3 | \n
\n \n | 4 | \n 5 | \n Team | \n 3 | \n
\n \n | 5 | \n 6 | \n Team | \n 5 | \n
\n \n | 6 | \n 7 | \n Team | \n 2 | \n
\n \n | 7 | \n 8 | \n Team | \n 2 | \n
\n \n | 8 | \n 9 | \n Team | \n 3 | \n
\n \n | 9 | \n 10 | \n Team | \n 4 | \n
\n \n | 10 | \n 11 | \n Team | \n 3 | \n
\n \n
\n
\n\n
\n \n \n | \n week_number | \n plan | \n weekly_active_users | \n
\n \n \n \n | 0 | \n 1 | \n Team | \n 4 | \n
\n \n | 1 | \n 2 | \n Team | \n 4 | \n
\n \n | 2 | \n 3 | \n Team | \n 4 | \n
\n \n | 3 | \n 4 | \n Team | \n 3 | \n
\n \n | 4 | \n 5 | \n Team | \n 3 | \n
\n \n | 5 | \n 6 | \n Team | \n 5 | \n
\n \n | 6 | \n 7 | \n Team | \n 2 | \n
\n \n | 7 | \n 8 | \n Team | \n 2 | \n
\n \n | 8 | \n 9 | \n Team | \n 3 | \n
\n \n | 9 | \n 10 | \n Team | \n 4 | \n
\n \n | 10 | \n 11 | \n Team | \n 3 | \n
\n \n
\n
"
+ elif btype == "input-text" or btype == "input-textarea" or btype == "input-slider":
+ # Generate Python assignment: {sanitized_variable_name} = {escaped_value}
+ var_name = sanitize_python_variable_name(
+ metadata.get("deepnote_variable_name", "")
+ )
+ value = escape_python_string(metadata.get("deepnote_variable_value", ""))
+ return f"{var_name} = {value}"
+ elif btype == "input-checkbox":
+ # Sanitize variable name and assign True/False based on value
+ var_name = sanitize_python_variable_name(
+ metadata.get("deepnote_variable_name", "")
+ )
+ value = metadata.get("deepnote_variable_value")
+ return f"{var_name} = {'True' if value else 'False'}"
+ elif btype == "input-select":
+ var_name = sanitize_python_variable_name(
+ metadata.get("deepnote_variable_name", "")
+ )
+ allow_multiple = metadata.get("deepnote_allow_multiple_values")
+ values = metadata.get("deepnote_variable_value", None)
+ # If allow_multiple is truthy or values is a list, treat as list assignment
+ if allow_multiple or isinstance(values, list):
+ # Ensure values is a list
+ if not isinstance(values, list):
+ values = [values] if values is not None else []
+ return (
+ f"{var_name} = [{', '.join(escape_python_string(v) for v in values)}]"
+ )
+ # If not allow_multiple and value is falsy (None or empty string), assign None
+ elif not allow_multiple and (values is None or values == ""):
+ return f"{var_name} = None"
+ else:
+ # Explicitly convert to string for type safety
+ return f"{var_name} = {escape_python_string(str(values or ''))}"
+ elif btype == "input-date":
+ var_name = sanitize_python_variable_name(
+ metadata.get("deepnote_variable_name", "")
+ )
+ escaped_value = escape_python_string(
+ metadata.get("deepnote_variable_value", "")
+ )
+ if not metadata.get("deepnote_variable_value"):
+ return f"{var_name} = None"
+ elif metadata.get("deepnote_input_date_version") == 2:
+ return (
+ f"from dateutil.parser import parse as _deepnote_parse\n"
+ f"{var_name} = _deepnote_parse({escaped_value}).date()"
+ )
+ else:
+ return (
+ f"from datetime import datetime as _deepnote_datetime\n"
+ f'{var_name} = _deepnote_datetime.strptime({escaped_value}, "%Y-%m-%dT%H:%M:%S.%fZ")'
+ )
+ elif btype == "input-date-range":
+ var_name = sanitize_python_variable_name(
+ metadata.get("deepnote_variable_name", "")
+ )
+ value = metadata.get("deepnote_variable_value", None)
+ # Absolute date range: [start, end]
+ if isinstance(value, (list, tuple)) and is_valid_absolute_date_range(value):
+ start, end = value
+ return date_range_absolute(var_name, start, end)
+ # Custom days: "customDaysN"
+ elif isinstance(value, str) and is_custom_date_range(value):
+ try:
+ days = int(str(value)[10:])
+ except Exception:
+ return f"{var_name} = None"
+ return date_range_custom_days(var_name, days)
+ # Relative interval: "past7days", etc.
+ elif isinstance(value, str) and is_valid_relative_date_interval(value):
+ fn = (
+ DATE_RANGE_INPUT_RELATIVE_RANGES.get(value)
+ if isinstance(value, str)
+ else None
+ )
+ if fn:
+ return fn(var_name)
+ else:
+ return f"{var_name} = [None, None]"
+ else:
+ return f"{var_name} = [None, None]"
+ elif btype == "big-number":
+ # Extract big-number fields from metadata and call helper
+ title = metadata.get("deepnote_big_number_title", "")
+ value_var = metadata.get("deepnote_big_number_value", "")
+ comparison_title = metadata.get("deepnote_big_number_comparison_title", "")
+ comparison_var = metadata.get("deepnote_big_number_comparison_value", "")
+ return execute_big_number(title, value_var, comparison_title, comparison_var)
+ elif btype == "sql":
+ integration_id = metadata.get("sql_integration_id")
+ if integration_id:
+ connection_env_var = f"SQL_{integration_id}"
+ else:
+ connection_env_var = "SQL_ALCHEMY_JSON_ENV_VAR"
+ var_name = metadata.get("deepnote_variable_name")
+ var_name = sanitize_python_variable_name(var_name) if var_name else None
+ return_variable_type = metadata.get(
+ "deepnote_return_variable_type", "dataframe"
+ )
+ return execute_sql_query(
+ content,
+ connection_env_var,
+ var_name or None,
+ "cache_disabled",
+ return_variable_type,
+ )
+ elif btype == "notebook-function":
+ return "Module blocks are a [Deepnote.com only feature](https://deepnote.com). Please import this .deepnote file into your workspace on [Deepnote.com](https://deepnote.com) to use this module block."
+ else:
+ return content
+
+
+# Helper: Convert a Deepnote block dict to a Jupyter cell
+def convert_deepnote_block_to_jupyter_cell(block: dict):
+ btype = block.get("type", "markdown")
+ content = block.get("content", "")
+ source = map_block_source(btype, content, block.get("metadata", {}))
+
+ jupyter_type = map_block_type_to_jupyter(btype)
+
+ if jupyter_type == "code":
+ return new_code_cell(source)
+ else:
+ return new_markdown_cell(source)
+
+
+# Helper: Convert a list of Deepnote blocks to sorted list of Jupyter cells
+def convert_blocks_to_cells(blocks: list[dict]):
+ return [
+ convert_deepnote_block_to_jupyter_cell(block)
+ for block in sorted(blocks, key=lambda b: b.get("sortingKey", ""))
+ ]