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"# Plotly Express Gallery\n",
"\n",
"Plotly Express is a terse, consistent, high-level wrapper around [Plotly.py](https://plot.ly/python) for rapid data exploration and figure generation.\n",
"\n",
"This notebook demonstrates various `plotly_express` features. [Reference documentation](https://plotly.github.io/plotly_express/plotly_express/) and a [step by step walkthrough notebook](https://nbviewer.jupyter.org/github/plotly/plotly_express/blob/master/walkthrough.ipynb) are also available.\n",
"\n",
"You can also read our [Medium announcement article](https://medium.com/@plotlygraphs/introducing-plotly-express-808df010143d) for more information on this library."
]
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"## A single import"
]
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"metadata": {},
"outputs": [],
"source": [
"import plotly_express as px"
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"source": [
"## Built-in sample datasets"
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"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Each row represents a flower.\n",
"\n",
" https://en.wikipedia.org/wiki/Iris_flower_data_set\n",
"\n",
" Returns:\n",
" A `pandas.DataFrame` with 150 rows and the following columns: `['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species',\n",
" 'species_id']`.\n",
" \n"
]
}
],
"source": [
"print(px.data.iris.__doc__)\n",
"iris = px.data.iris()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"tips = px.data.tips()\n",
"gapminder = px.data.gapminder()\n",
"election = px.data.election()\n",
"wind = px.data.wind()\n",
"carshare = px.data.carshare()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scatter and Line plots"
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"source": [
"px.set_mapbox_access_token(open(\".mapbox_token\").read())\n",
"px.scatter_mapbox(carshare, lat=\"centroid_lat\", lon=\"centroid_lon\", color=\"peak_hour\", size=\"car_hours\", \n",
" color_continuous_scale=px.colors.cyclical.IceFire, size_max=15, zoom=10)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
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