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grtlr
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emilk
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Example showcasing send_table from notebooks (#9522)
Also removes a forgotten `re_log::info!` statement from development.
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‎crates/viewer/re_viewer/src/web.rs

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@@ -909,7 +909,6 @@ pub fn set_email(email: String) {
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// This is required to send bytes around in the notebook.
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// If you ever change this, you also need to adapt `notebook.py` too.
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pub fn from_arrow_encoded(data: &RecordBatch) -> Result<TableMsg, Box<dyn std::error::Error>> {
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re_log::info!("{:?}", data);
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let mut metadata = data.schema().metadata().clone();
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let id = metadata
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.remove("__table_id")

‎examples/python/notebook/send_table.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "85c477c3-5f26-4294-a2ea-4042a0c38b7f",
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"metadata": {},
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"source": [
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"### How to run\n",
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"\n",
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"```sh\n",
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"# pixi run py-build-examples\n",
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"# pixi run -e examples py-build-notebook\n",
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"# pixi run -e examples jupyter notebook examples/python/notebook/send_table.ipynb\n",
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"```"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "e5789224-afa8-4250-9eae-2ca570d46088",
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"metadata": {},
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"outputs": [],
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"source": [
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"from __future__ import annotations\n",
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"\n",
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"import os\n",
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"\n",
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"os.environ[\"RERUN_NOTEBOOK_ASSET\"] = \"inline\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "c993d2ab-5bbb-4af0-89a5-1c370b7b9523",
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"metadata": {},
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"outputs": [],
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"source": [
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"import rerun as rr\n",
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"\n",
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"import pyarrow as pa\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e317e818-4a96-4c63-a9a0-cce25073b2e5",
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"metadata": {},
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"source": [
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"### Send a basic table"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "aea716a4-164c-436f-9b6a-ca6f171214b5",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "bd5a1609428f42d689dbde5a2b79d0db",
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"version_major": 2,
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"version_minor": 1
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},
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"text/plain": [
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"Viewer()"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"viewer = rr.notebook.Viewer(width=1024, height=700)\n",
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"viewer.display()\n",
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"viewer.send_table(\n",
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" \"Hello from Notebook\",\n",
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" pa.RecordBatch.from_pydict({\"Column A\": [1, 2, 3], \"Column B\": [\"https://www.rerun.io\", \"Hello\", \"World\"]}),\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "59ebc9cb-cb8f-47e9-9beb-a9ff96a7ca17",
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"metadata": {},
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"source": [
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"### Send a Pandas dataframe"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "e15fcb31-4e74-47f9-9eba-b6bcdc545c2b",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>A</th>\n",
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" <th>B</th>\n",
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" <th>C</th>\n",
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" <th>D</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>2013-01-01</th>\n",
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" <td>0.823557</td>\n",
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" <td>-0.290076</td>\n",
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" <td>0.238599</td>\n",
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" <td>-0.629761</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2013-01-02</th>\n",
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" <td>-0.520894</td>\n",
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" <td>-0.817653</td>\n",
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" <td>-0.169291</td>\n",
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" <td>-0.506261</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2013-01-03</th>\n",
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" <td>-0.002897</td>\n",
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" <td>0.752245</td>\n",
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" <td>-0.613818</td>\n",
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" <td>1.111361</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2013-01-04</th>\n",
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" <td>0.938248</td>\n",
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" <td>-1.109515</td>\n",
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" <td>0.536320</td>\n",
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" <td>-1.075220</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2013-01-05</th>\n",
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" <td>0.033130</td>\n",
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" <td>0.771321</td>\n",
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" <td>0.310634</td>\n",
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" <td>-0.595946</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2013-01-06</th>\n",
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" <td>1.205717</td>\n",
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" <td>-2.282729</td>\n",
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" <td>1.290203</td>\n",
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" <td>0.592006</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" A B C D\n",
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"2013-01-01 0.823557 -0.290076 0.238599 -0.629761\n",
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"2013-01-02 -0.520894 -0.817653 -0.169291 -0.506261\n",
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"2013-01-03 -0.002897 0.752245 -0.613818 1.111361\n",
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"2013-01-04 0.938248 -1.109515 0.536320 -1.075220\n",
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"2013-01-05 0.033130 0.771321 0.310634 -0.595946\n",
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"2013-01-06 1.205717 -2.282729 1.290203 0.592006"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dates = pd.date_range(\"20130101\", periods=6)\n",
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"df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list(\"ABCD\"))\n",
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"df_reset = df.reset_index().rename(columns={'index': 'date'})\n",
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"df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "837e06ba-ad72-4dff-a6f4-5e7e211a137d",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "aa0fb7b7b5884f3fa7f7653b2f81bb39",
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"version_major": 2,
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"version_minor": 1
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},
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"text/plain": [
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"Viewer()"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"viewer = rr.notebook.Viewer(width=1024, height=700)\n",
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"viewer.display()\n",
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"viewer.send_table(\"Hello from Pandas\", pa.RecordBatch.from_pandas(df))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7de877bf-f3ba-4f73-867a-1dd7930a2063",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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