249 lines
6.8 KiB
Plaintext
249 lines
6.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# So easy, *voilà*!\n",
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"\n",
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"In this example notebook, we demonstrate how Voilà can render Jupyter notebooks with interactions requiring a roundtrip to the kernel."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Jupyter Widgets"
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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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"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": "9d234bc95ca5460ea86e664a957764e3",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"VBox(children=(FloatSlider(value=4.0, description='$x$'), FloatText(value=16.0, description='$x^2$', disabled=…"
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]
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},
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"execution_count": 1,
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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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"import ipywidgets as widgets\n",
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"\n",
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"slider = widgets.FloatSlider(description='$x$')\n",
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"text = widgets.FloatText(disabled=True, description='$x^2$')\n",
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"\n",
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"def compute(*ignore):\n",
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" text.value = str(slider.value ** 2)\n",
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"\n",
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"slider.observe(compute, 'value')\n",
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"\n",
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"slider.value = 4\n",
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"\n",
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"widgets.VBox([slider, text])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Basic outputs of code cells"
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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": 2,
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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>sepal_length</th>\n",
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" <th>sepal_width</th>\n",
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" <th>petal_length</th>\n",
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" <th>petal_width</th>\n",
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" <th>species</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>0</th>\n",
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" <td>5.1</td>\n",
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" <td>3.5</td>\n",
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" <td>1.4</td>\n",
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" <td>0.2</td>\n",
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" <td>setosa</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>4.9</td>\n",
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" <td>3.0</td>\n",
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" <td>1.4</td>\n",
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" <td>0.2</td>\n",
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" <td>setosa</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>4.7</td>\n",
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" <td>3.2</td>\n",
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" <td>1.3</td>\n",
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" <td>0.2</td>\n",
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" <td>setosa</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>4.6</td>\n",
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" <td>3.1</td>\n",
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" <td>1.5</td>\n",
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" <td>0.2</td>\n",
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" <td>setosa</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>5.0</td>\n",
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" <td>3.6</td>\n",
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" <td>1.4</td>\n",
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" <td>0.2</td>\n",
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" <td>setosa</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>145</th>\n",
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" <td>6.7</td>\n",
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" <td>3.0</td>\n",
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" <td>5.2</td>\n",
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" <td>2.3</td>\n",
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" <td>virginica</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>146</th>\n",
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" <td>6.3</td>\n",
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" <td>2.5</td>\n",
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" <td>5.0</td>\n",
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" <td>1.9</td>\n",
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" <td>virginica</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>147</th>\n",
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" <td>6.5</td>\n",
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" <td>3.0</td>\n",
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" <td>5.2</td>\n",
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" <td>2.0</td>\n",
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" <td>virginica</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>148</th>\n",
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" <td>6.2</td>\n",
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" <td>3.4</td>\n",
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" <td>5.4</td>\n",
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" <td>2.3</td>\n",
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" <td>virginica</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>149</th>\n",
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" <td>5.9</td>\n",
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" <td>3.0</td>\n",
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" <td>5.1</td>\n",
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" <td>1.8</td>\n",
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" <td>virginica</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>150 rows × 5 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" sepal_length sepal_width petal_length petal_width species\n",
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"0 5.1 3.5 1.4 0.2 setosa\n",
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"1 4.9 3.0 1.4 0.2 setosa\n",
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"2 4.7 3.2 1.3 0.2 setosa\n",
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"3 4.6 3.1 1.5 0.2 setosa\n",
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"4 5.0 3.6 1.4 0.2 setosa\n",
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".. ... ... ... ... ...\n",
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"145 6.7 3.0 5.2 2.3 virginica\n",
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"146 6.3 2.5 5.0 1.9 virginica\n",
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"147 6.5 3.0 5.2 2.0 virginica\n",
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"148 6.2 3.4 5.4 2.3 virginica\n",
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"149 5.9 3.0 5.1 1.8 virginica\n",
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"\n",
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"[150 rows x 5 columns]"
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]
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},
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"execution_count": 2,
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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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"import pandas as pd\n",
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"\n",
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"iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')\n",
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"iris"
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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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"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": "3.10.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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