A monorepo containing jupyter-blocks and jupyter-tidyblocks. Blockly extension for JupyterLab.
Add new datasets
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docs/blocks-reference.md
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docs/blocks-reference.md
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| Block label | Block type | dplyr equivalent | What it does | Python generated |
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|---|---|---|---|---|
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| penguins dataset | `tidyblocks_data_penguins` | — | Palmer Penguins dataset loaded via seaborn | `_df = sns.load_dataset('penguins')` |
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| iris dataset | `tidyblocks_data_iris` | — | Fisher iris dataset: sepal/petal measurements for 3 species (via seaborn) | `_df = sns.load_dataset('iris')` |
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| titanic dataset | `tidyblocks_data_titanic` | — | Titanic passenger survival dataset (via seaborn) | `_df = sns.load_dataset('titanic')` |
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| gapminder dataset | `tidyblocks_data_gapminder` | — | Gapminder life expectancy / GDP dataset across countries and years (via plotly.express) | `_df = px.data.gapminder()` |
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| colors dataset | `tidyblocks_data_colors` | — | Built-in table of 11 colors with RGB values | `_df = pd.DataFrame({...})` |
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| earthquakes dataset | `tidyblocks_data_earthquakes` | — | 2016 global earthquake data from gvwilson/tidyblocks | `_df = pd.read_csv('<url>')` |
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| sequence 1 to N as col | `tidyblocks_data_sequence` | — | Integer sequence 1..N in a named column | `_df = pd.DataFrame({'col': range(1, N+1)})` |
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packages/tidyblocks/src/blocks/data.ts
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packages/tidyblocks/src/blocks/data.ts
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import seaborn as sns
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`;
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// gapminder comes from plotly.express; needs its own preamble so that
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// px.data is available even when no plot block is on the canvas.
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const TOPLEVEL_INIT_PX = `import pandas as pd
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import numpy as np
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import plotly.express as px
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`;
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Blockly.defineBlocksWithJsonArray([
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{
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type: 'tidyblocks_data_colors',
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tooltip: 'Reference a previously saved DataFrame by variable name.'
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},
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{
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type: 'tidyblocks_data_iris',
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message0: 'iris dataset',
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nextStatement: null,
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colour: '#FEBE4C',
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tooltip: 'Classic Fisher iris dataset: sepal/petal measurements for 3 species (via seaborn).'
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},
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{
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type: 'tidyblocks_data_titanic',
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message0: 'titanic dataset',
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nextStatement: null,
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colour: '#FEBE4C',
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tooltip: 'Titanic passenger survival dataset (via seaborn).'
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},
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{
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type: 'tidyblocks_data_gapminder',
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message0: 'gapminder dataset',
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nextStatement: null,
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colour: '#FEBE4C',
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tooltip: 'Gapminder life expectancy / GDP dataset across countries and years (via plotly.express).'
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},
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{
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type: 'tidyblocks_data_csv',
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message0: 'read CSV %1',
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args0: [{ type: 'field_input', name: 'PATH', text: 'data.csv' }],
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Blockly.Blocks['tidyblocks_data_penguins'].toplevel_init = TOPLEVEL_INIT;
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Blockly.Blocks['tidyblocks_data_sequence'].toplevel_init = TOPLEVEL_INIT;
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Blockly.Blocks['tidyblocks_data_user'].toplevel_init = TOPLEVEL_INIT;
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Blockly.Blocks['tidyblocks_data_iris'].toplevel_init = TOPLEVEL_INIT;
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Blockly.Blocks['tidyblocks_data_titanic'].toplevel_init = TOPLEVEL_INIT;
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Blockly.Blocks['tidyblocks_data_gapminder'].toplevel_init = TOPLEVEL_INIT_PX;
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Blockly.Blocks['tidyblocks_data_csv'].toplevel_init = TOPLEVEL_INIT;
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packages/tidyblocks/src/generators/python/data.ts
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packages/tidyblocks/src/generators/python/data.ts
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return `_df = ${name}.copy()\n`;
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};
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pythonGenerator.forBlock['tidyblocks_data_iris'] = () => {
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return "_df = sns.load_dataset('iris')\n";
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};
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pythonGenerator.forBlock['tidyblocks_data_titanic'] = () => {
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return "_df = sns.load_dataset('titanic')\n";
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};
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pythonGenerator.forBlock['tidyblocks_data_gapminder'] = () => {
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return "_df = px.data.gapminder()\n";
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};
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pythonGenerator.forBlock['tidyblocks_data_csv'] = block => {
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const path = block.getFieldValue('PATH');
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return `_df = pd.read_csv('${path}')\n`;
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packages/tidyblocks/src/toolbox.ts
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packages/tidyblocks/src/toolbox.ts
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colour: '#FEBE4C',
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contents: [
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{ kind: 'block', type: 'tidyblocks_data_penguins' },
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{ kind: 'block', type: 'tidyblocks_data_iris' },
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{ kind: 'block', type: 'tidyblocks_data_titanic' },
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{ kind: 'block', type: 'tidyblocks_data_gapminder' },
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{ kind: 'block', type: 'tidyblocks_data_colors' },
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{ kind: 'block', type: 'tidyblocks_data_earthquakes' },
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{