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"source": [
"# Explore the Gapminder Dataset with Plotly Express\n",
"In this notebook I'll try to explore gapminder dataset using interactive data visualization library called Plotly. About the Dataset:\n",
"[Data Source](https://www.gapminder.org/tools/#$state$time$value=2007;;&chart-type=bubbles)\n",
"\n",
"- Task-1: Import Libraries \n",
"- Task-2: Loading Dataset \n",
"- Task-3: Creating Table \n",
"- Task-4: Quick Visualizations with Bar Charts \n",
"- Task-5: Plot Life Expectency `vs` GDP Per Capita\n",
"- Task-6: Create Interactive Bubble Charts \n",
"- Task-7: Create Interactive Animations and Facet Plots \n",
"- Task-8: Interactive Line Plots and Area Plots \n",
"- Task-9: Represent Geographic Data as Animated Maps\n",
"- Task-10: Using Plotly Template in Any Graphs "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task-1: Import Libraries "
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
},
"outputs": [],
"source": [
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import plotly.express as px # for visualization\n",
"import plotly.offline as py\n",
"import plotly.graph_objs as go\n",
"from plotly.figure_factory import create_table # for creating nice table"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task-2: Loading the Dataset "
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
"_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
},
"outputs": [],
"source": [
"# load built-in gapminder dataset from plotly\n",
"gapminder = px.data.gapminder()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
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\n",
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\n",
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" | \n",
" country | \n",
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" year | \n",
" lifeExp | \n",
" pop | \n",
" gdpPercap | \n",
" iso_alpha | \n",
" iso_num | \n",
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\n",
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" Afghanistan | \n",
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" AFG | \n",
" 4 | \n",
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\n",
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" | 1 | \n",
" Afghanistan | \n",
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" 9240934 | \n",
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" AFG | \n",
" 4 | \n",
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\n",
" \n",
" | 2 | \n",
" Afghanistan | \n",
" Asia | \n",
" 1962 | \n",
" 31.997 | \n",
" 10267083 | \n",
" 853.100710 | \n",
" AFG | \n",
" 4 | \n",
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\n",
" \n",
" | 3 | \n",
" Afghanistan | \n",
" Asia | \n",
" 1967 | \n",
" 34.020 | \n",
" 11537966 | \n",
" 836.197138 | \n",
" AFG | \n",
" 4 | \n",
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\n",
" \n",
" | 4 | \n",
" Afghanistan | \n",
" Asia | \n",
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" country continent year lifeExp pop gdpPercap iso_alpha \\\n",
"0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG \n",
"1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG \n",
"2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG \n",
"3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG \n",
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" iso_num \n",
"0 4 \n",
"1 4 \n",
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},
"execution_count": 27,
"metadata": {},
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}
],
"source": [
"# examine first few rows\n",
"gapminder.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task-3: Creating a Table "
]
},
{
"cell_type": "code",
"execution_count": 28,
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"metadata": {},
"source": [
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},
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"execution_count": 29,
"metadata": {},
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},
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"execution_count": 30,
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"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
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"bgcolor": "#E5ECF6",
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},
{
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"execution_count": 31,
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"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergl"
}
],
"scattermapbox": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattermapbox"
}
],
"scatterpolar": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolar"
}
],
"scatterpolargl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolargl"
}
],
"scatterternary": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterternary"
}
],
"surface": [
{
"colorbar": {
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"ticks": ""
},
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[
0,
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],
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0.1111111111111111,
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],
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0.2222222222222222,
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],
[
0.3333333333333333,
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],
[
0.4444444444444444,
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],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
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],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "surface"
}
],
"table": [
{
"cells": {
"fill": {
"color": "#EBF0F8"
},
"line": {
"color": "white"
}
},
"header": {
"fill": {
"color": "#C8D4E3"
},
"line": {
"color": "white"
}
},
"type": "table"
}
]
},
"layout": {
"annotationdefaults": {
"arrowcolor": "#2a3f5f",
"arrowhead": 0,
"arrowwidth": 1
},
"autotypenumbers": "strict",
"coloraxis": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"colorscale": {
"diverging": [
[
0,
"#8e0152"
],
[
0.1,
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],
[
0.2,
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],
[
0.3,
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],
[
0.4,
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],
[
0.5,
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],
[
0.6,
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],
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0.7,
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[
0.8,
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],
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0.9,
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1,
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1,
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0,
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0.8888888888888888,
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1,
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"colorway": [
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"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"xaxis": {
"anchor": "y",
"domain": [
0,
1
],
"title": {
"text": "gdpPercap"
}
},
"yaxis": {
"anchor": "x",
"domain": [
0,
1
],
"title": {
"text": "lifeExp"
}
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# color by continent\n",
"fig = px.scatter(gapminder2007, x='gdpPercap', y='lifeExp', color='continent')\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task-6: Create Interactive Bubble Charts"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
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pop=%{marker.size}",
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pop=%{marker.size}",
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"sizemode": "area",
"sizeref": 366300.86,
"symbol": "circle"
},
"mode": "markers",
"name": "Americas",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
12779.37964,
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"xaxis": "x",
"y": [
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],
"yaxis": "y"
},
{
"hovertemplate": "%{hovertext}
continent=Oceania
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}",
"hovertext": [
"Australia",
"New Zealand"
],
"legendgroup": "Oceania",
"marker": {
"color": "#FFA15A",
"size": [
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],
"sizemode": "area",
"sizeref": 366300.86,
"symbol": "circle"
},
"mode": "markers",
"name": "Oceania",
"orientation": "v",
"showlegend": true,
"type": "scatter",
"x": [
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],
"xaxis": "x",
"y": [
81.235,
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],
"yaxis": "y"
}
],
"layout": {
"legend": {
"itemsizing": "constant",
"title": {
"text": "continent"
},
"tracegroupgap": 0
},
"margin": {
"t": 60
},
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
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"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
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"marker": {
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"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
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"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"type": "carpet"
}
],
"choropleth": [
{
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},
"type": "choropleth"
}
],
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},
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],
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],
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],
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],
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],
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],
[
1,
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]
],
"type": "contour"
}
],
"contourcarpet": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "contourcarpet"
}
],
"heatmap": [
{
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},
"colorscale": [
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0,
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],
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],
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],
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],
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0.5555555555555556,
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],
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0.6666666666666666,
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],
[
0.7777777777777778,
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],
[
0.8888888888888888,
"#fdca26"
],
[
1,
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]
],
"type": "heatmap"
}
],
"heatmapgl": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
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0.2222222222222222,
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0.6666666666666666,
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],
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],
[
1,
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]
],
"type": "heatmapgl"
}
],
"histogram": [
{
"marker": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "histogram"
}
],
"histogram2d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
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0.6666666666666666,
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[
0.7777777777777778,
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],
[
0.8888888888888888,
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[
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],
"type": "histogram2d"
}
],
"histogram2dcontour": [
{
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"ticks": ""
},
"colorscale": [
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"type": "histogram2dcontour"
}
],
"mesh3d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "mesh3d"
}
],
"parcoords": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "parcoords"
}
],
"pie": [
{
"automargin": true,
"type": "pie"
}
],
"scatter": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"type": "scatter"
}
],
"scatter3d": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatter3d"
}
],
"scattercarpet": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattercarpet"
}
],
"scattergeo": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergeo"
}
],
"scattergl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergl"
}
],
"scattermapbox": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattermapbox"
}
],
"scatterpolar": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolar"
}
],
"scatterpolargl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolargl"
}
],
"scatterternary": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterternary"
}
],
"surface": [
{
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"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
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],
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],
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],
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],
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],
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1,
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],
"type": "surface"
}
],
"table": [
{
"cells": {
"fill": {
"color": "#EBF0F8"
},
"line": {
"color": "white"
}
},
"header": {
"fill": {
"color": "#C8D4E3"
},
"line": {
"color": "white"
}
},
"type": "table"
}
]
},
"layout": {
"annotationdefaults": {
"arrowcolor": "#2a3f5f",
"arrowhead": 0,
"arrowwidth": 1
},
"autotypenumbers": "strict",
"coloraxis": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"colorscale": {
"diverging": [
[
0,
"#8e0152"
],
[
0.1,
"#c51b7d"
],
[
0.2,
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],
[
0.3,
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],
[
0.4,
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],
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0.5,
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],
[
0.6,
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],
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0.7,
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[
0.8,
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[
0.9,
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],
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1,
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],
"sequential": [
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],
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1,
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],
"sequentialminus": [
[
0,
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],
[
0.1111111111111111,
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],
[
0.2222222222222222,
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0.3333333333333333,
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0.4444444444444444,
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0.5555555555555556,
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0.6666666666666666,
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],
[
0.7777777777777778,
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],
[
0.8888888888888888,
"#fdca26"
],
[
1,
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]
]
},
"colorway": [
"#636efa",
"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"xaxis": {
"anchor": "y",
"domain": [
0,
1
],
"title": {
"text": "gdpPercap"
}
},
"yaxis": {
"anchor": "x",
"domain": [
0,
1
],
"title": {
"text": "lifeExp"
}
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# hover name\n",
"fig = px.scatter(gapminder2007, x='gdpPercap', y='lifeExp', color='continent', size='pop', size_max=60,\n",
" hover_name='country')\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task-7: Create Interactive Animations and Facet Plots"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"hovertemplate": "%{hovertext}
continent=Asia
gdpPercap=%{x}
lifeExp=%{y}
pop=%{marker.size}",
"hovertext": [
"Afghanistan",
"Afghanistan",
"Afghanistan",
"Afghanistan",
"Afghanistan",
"Afghanistan",
"Afghanistan",
"Afghanistan",
"Afghanistan",
"Afghanistan",
"Afghanistan",
"Afghanistan",
"Bahrain",
"Bahrain",
"Bahrain",
"Bahrain",
"Bahrain",
"Bahrain",
"Bahrain",
"Bahrain",
"Bahrain",
"Bahrain",
"Bahrain",
"Bahrain",
"Bangladesh",
"Bangladesh",
"Bangladesh",
"Bangladesh",
"Bangladesh",
"Bangladesh",
"Bangladesh",
"Bangladesh",
"Bangladesh",
"Bangladesh",
"Bangladesh",
"Bangladesh",
"Cambodia",
"Cambodia",
"Cambodia",
"Cambodia",
"Cambodia",
"Cambodia",
"Cambodia",
"Cambodia",
"Cambodia",
"Cambodia",
"Cambodia",
"Cambodia",
"China",
"China",
"China",
"China",
"China",
"China",
"China",
"China",
"China",
"China",
"China",
"China",
"Hong Kong, China",
"Hong Kong, China",
"Hong Kong, China",
"Hong Kong, China",
"Hong Kong, China",
"Hong Kong, China",
"Hong Kong, China",
"Hong Kong, China",
"Hong Kong, China",
"Hong Kong, China",
"Hong Kong, China",
"Hong Kong, China",
"India",
"India",
"India",
"India",
"India",
"India",
"India",
"India",
"India",
"India",
"India",
"India",
"Indonesia",
"Indonesia",
"Indonesia",
"Indonesia",
"Indonesia",
"Indonesia",
"Indonesia",
"Indonesia",
"Indonesia",
"Indonesia",
"Indonesia",
"Indonesia",
"Iran",
"Iran",
"Iran",
"Iran",
"Iran",
"Iran",
"Iran",
"Iran",
"Iran",
"Iran",
"Iran",
"Iran",
"Iraq",
"Iraq",
"Iraq",
"Iraq",
"Iraq",
"Iraq",
"Iraq",
"Iraq",
"Iraq",
"Iraq",
"Iraq",
"Iraq",
"Israel",
"Israel",
"Israel",
"Israel",
"Israel",
"Israel",
"Israel",
"Israel",
"Israel",
"Israel",
"Israel",
"Israel",
"Japan",
"Japan",
"Japan",
"Japan",
"Japan",
"Japan",
"Japan",
"Japan",
"Japan",
"Japan",
"Japan",
"Japan",
"Jordan",
"Jordan",
"Jordan",
"Jordan",
"Jordan",
"Jordan",
"Jordan",
"Jordan",
"Jordan",
"Jordan",
"Jordan",
"Jordan",
"Korea, Dem. Rep.",
"Korea, Dem. Rep.",
"Korea, Dem. Rep.",
"Korea, Dem. Rep.",
"Korea, Dem. Rep.",
"Korea, Dem. Rep.",
"Korea, Dem. Rep.",
"Korea, Dem. Rep.",
"Korea, Dem. Rep.",
"Korea, Dem. Rep.",
"Korea, Dem. Rep.",
"Korea, Dem. Rep.",
"Korea, Rep.",
"Korea, Rep.",
"Korea, Rep.",
"Korea, Rep.",
"Korea, Rep.",
"Korea, Rep.",
"Korea, Rep.",
"Korea, Rep.",
"Korea, Rep.",
"Korea, Rep.",
"Korea, Rep.",
"Korea, Rep.",
"Kuwait",
"Kuwait",
"Kuwait",
"Kuwait",
"Kuwait",
"Kuwait",
"Kuwait",
"Kuwait",
"Kuwait",
"Kuwait",
"Kuwait",
"Kuwait",
"Lebanon",
"Lebanon",
"Lebanon",
"Lebanon",
"Lebanon",
"Lebanon",
"Lebanon",
"Lebanon",
"Lebanon",
"Lebanon",
"Lebanon",
"Lebanon",
"Malaysia",
"Malaysia",
"Malaysia",
"Malaysia",
"Malaysia",
"Malaysia",
"Malaysia",
"Malaysia",
"Malaysia",
"Malaysia",
"Malaysia",
"Malaysia",
"Mongolia",
"Mongolia",
"Mongolia",
"Mongolia",
"Mongolia",
"Mongolia",
"Mongolia",
"Mongolia",
"Mongolia",
"Mongolia",
"Mongolia",
"Mongolia",
"Myanmar",
"Myanmar",
"Myanmar",
"Myanmar",
"Myanmar",
"Myanmar",
"Myanmar",
"Myanmar",
"Myanmar",
"Myanmar",
"Myanmar",
"Myanmar",
"Nepal",
"Nepal",
"Nepal",
"Nepal",
"Nepal",
"Nepal",
"Nepal",
"Nepal",
"Nepal",
"Nepal",
"Nepal",
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"Oman",
"Oman",
"Oman",
"Oman",
"Oman",
"Oman",
"Oman",
"Oman",
"Oman",
"Oman",
"Oman",
"Oman",
"Pakistan",
"Pakistan",
"Pakistan",
"Pakistan",
"Pakistan",
"Pakistan",
"Pakistan",
"Pakistan",
"Pakistan",
"Pakistan",
"Pakistan",
"Pakistan",
"Philippines",
"Philippines",
"Philippines",
"Philippines",
"Philippines",
"Philippines",
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"source": [
"# create a facet plot\n",
"fig = px.scatter(gapminder, x='gdpPercap', y='lifeExp', color='continent', size='pop', size_max=60,\n",
" hover_name='country', facet_col='continent')\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
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},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"xaxis": {
"anchor": "y",
"domain": [
0,
0.18400000000000002
],
"title": {
"text": "gdpPercap"
},
"type": "log"
},
"xaxis2": {
"anchor": "y2",
"domain": [
0.20400000000000001,
0.388
],
"matches": "x",
"title": {
"text": "gdpPercap"
},
"type": "log"
},
"xaxis3": {
"anchor": "y3",
"domain": [
0.40800000000000003,
0.5920000000000001
],
"matches": "x",
"title": {
"text": "gdpPercap"
},
"type": "log"
},
"xaxis4": {
"anchor": "y4",
"domain": [
0.6120000000000001,
0.7960000000000002
],
"matches": "x",
"title": {
"text": "gdpPercap"
},
"type": "log"
},
"xaxis5": {
"anchor": "y5",
"domain": [
0.8160000000000001,
1
],
"matches": "x",
"title": {
"text": "gdpPercap"
},
"type": "log"
},
"yaxis": {
"anchor": "x",
"domain": [
0,
1
],
"title": {
"text": "lifeExp"
}
},
"yaxis2": {
"anchor": "x2",
"domain": [
0,
1
],
"matches": "y",
"showticklabels": false
},
"yaxis3": {
"anchor": "x3",
"domain": [
0,
1
],
"matches": "y",
"showticklabels": false
},
"yaxis4": {
"anchor": "x4",
"domain": [
0,
1
],
"matches": "y",
"showticklabels": false
},
"yaxis5": {
"anchor": "x5",
"domain": [
0,
1
],
"matches": "y",
"showticklabels": false
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# log scale on x-axis\n",
"fig = px.scatter(gapminder, x='gdpPercap', y='lifeExp', color='continent', size='pop', size_max=60,\n",
" hover_name='country', facet_col='continent', log_x=True)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" country | \n",
" continent | \n",
" year | \n",
" lifeExp | \n",
" pop | \n",
" gdpPercap | \n",
" iso_alpha | \n",
" iso_num | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" Afghanistan | \n",
" Asia | \n",
" 1952 | \n",
" 28.801 | \n",
" 8425333 | \n",
" 779.445314 | \n",
" AFG | \n",
" 4 | \n",
"
\n",
" \n",
" | 1 | \n",
" Afghanistan | \n",
" Asia | \n",
" 1957 | \n",
" 30.332 | \n",
" 9240934 | \n",
" 820.853030 | \n",
" AFG | \n",
" 4 | \n",
"
\n",
" \n",
" | 2 | \n",
" Afghanistan | \n",
" Asia | \n",
" 1962 | \n",
" 31.997 | \n",
" 10267083 | \n",
" 853.100710 | \n",
" AFG | \n",
" 4 | \n",
"
\n",
" \n",
" | 3 | \n",
" Afghanistan | \n",
" Asia | \n",
" 1967 | \n",
" 34.020 | \n",
" 11537966 | \n",
" 836.197138 | \n",
" AFG | \n",
" 4 | \n",
"
\n",
" \n",
" | 4 | \n",
" Afghanistan | \n",
" Asia | \n",
" 1972 | \n",
" 36.088 | \n",
" 13079460 | \n",
" 739.981106 | \n",
" AFG | \n",
" 4 | \n",
"
\n",
" \n",
" | ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
"
\n",
" \n",
" | 1699 | \n",
" Zimbabwe | \n",
" Africa | \n",
" 1987 | \n",
" 62.351 | \n",
" 9216418 | \n",
" 706.157306 | \n",
" ZWE | \n",
" 716 | \n",
"
\n",
" \n",
" | 1700 | \n",
" Zimbabwe | \n",
" Africa | \n",
" 1992 | \n",
" 60.377 | \n",
" 10704340 | \n",
" 693.420786 | \n",
" ZWE | \n",
" 716 | \n",
"
\n",
" \n",
" | 1701 | \n",
" Zimbabwe | \n",
" Africa | \n",
" 1997 | \n",
" 46.809 | \n",
" 11404948 | \n",
" 792.449960 | \n",
" ZWE | \n",
" 716 | \n",
"
\n",
" \n",
" | 1702 | \n",
" Zimbabwe | \n",
" Africa | \n",
" 2002 | \n",
" 39.989 | \n",
" 11926563 | \n",
" 672.038623 | \n",
" ZWE | \n",
" 716 | \n",
"
\n",
" \n",
" | 1703 | \n",
" Zimbabwe | \n",
" Africa | \n",
" 2007 | \n",
" 43.487 | \n",
" 12311143 | \n",
" 469.709298 | \n",
" ZWE | \n",
" 716 | \n",
"
\n",
" \n",
"
\n",
"
1704 rows × 8 columns
\n",
"
"
],
"text/plain": [
" country continent year lifeExp pop gdpPercap iso_alpha \\\n",
"0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG \n",
"1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG \n",
"2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG \n",
"3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG \n",
"4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG \n",
"... ... ... ... ... ... ... ... \n",
"1699 Zimbabwe Africa 1987 62.351 9216418 706.157306 ZWE \n",
"1700 Zimbabwe Africa 1992 60.377 10704340 693.420786 ZWE \n",
"1701 Zimbabwe Africa 1997 46.809 11404948 792.449960 ZWE \n",
"1702 Zimbabwe Africa 2002 39.989 11926563 672.038623 ZWE \n",
"1703 Zimbabwe Africa 2007 43.487 12311143 469.709298 ZWE \n",
"\n",
" iso_num \n",
"0 4 \n",
"1 4 \n",
"2 4 \n",
"3 4 \n",
"4 4 \n",
"... ... \n",
"1699 716 \n",
"1700 716 \n",
"1701 716 \n",
"1702 716 \n",
"1703 716 \n",
"\n",
"[1704 rows x 8 columns]"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gapminder"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" country | \n",
" continent | \n",
" year | \n",
" lifeExp | \n",
" pop | \n",
" gdpPercap | \n",
" iso_alpha | \n",
" iso_num | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" Afghanistan | \n",
" Asia | \n",
" 1952 | \n",
" 28.801 | \n",
" 8425333 | \n",
" 779.445314 | \n",
" AFG | \n",
" 4 | \n",
"
\n",
" \n",
" | 1 | \n",
" Afghanistan | \n",
" Asia | \n",
" 1957 | \n",
" 30.332 | \n",
" 9240934 | \n",
" 820.853030 | \n",
" AFG | \n",
" 4 | \n",
"
\n",
" \n",
" | 2 | \n",
" Afghanistan | \n",
" Asia | \n",
" 1962 | \n",
" 31.997 | \n",
" 10267083 | \n",
" 853.100710 | \n",
" AFG | \n",
" 4 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" country continent year lifeExp pop gdpPercap iso_alpha \\\n",
"0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG \n",
"1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG \n",
"2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG \n",
"\n",
" iso_num \n",
"0 4 \n",
"1 4 \n",
"2 4 "
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gapminder.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[0;31mSignature:\u001b[0m\n",
"\u001b[0mpx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter_mapbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mdata_frame\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mlat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mlon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mhover_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mhover_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mcustom_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0manimation_frame\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0manimation_group\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mcategory_orders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mcolor_discrete_sequence\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mcolor_discrete_map\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mcolor_continuous_scale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mrange_color\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mcolor_continuous_midpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mopacity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0msize_max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mzoom\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mcenter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mmapbox_style\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mtemplate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_objs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_figure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mDocstring:\u001b[0m\n",
" In a Mapbox scatter plot, each row of `data_frame` is represented by a\n",
" symbol mark on a Mapbox map.\n",
" \n",
"Parameters\n",
"----------\n",
"data_frame: DataFrame or array-like or dict\n",
" This argument needs to be passed for column names (and not keyword\n",
" names) to be used. Array-like and dict are transformed internally to a\n",
" pandas DataFrame. Optional: if missing, a DataFrame gets constructed\n",
" under the hood using the other arguments.\n",
"lat: str or int or Series or array-like\n",
" Either a name of a column in `data_frame`, or a pandas Series or\n",
" array_like object. Values from this column or array_like are used to\n",
" position marks according to latitude on a map.\n",
"lon: str or int or Series or array-like\n",
" Either a name of a column in `data_frame`, or a pandas Series or\n",
" array_like object. Values from this column or array_like are used to\n",
" position marks according to longitude on a map.\n",
"color: str or int or Series or array-like\n",
" Either a name of a column in `data_frame`, or a pandas Series or\n",
" array_like object. Values from this column or array_like are used to\n",
" assign color to marks.\n",
"text: str or int or Series or array-like\n",
" Either a name of a column in `data_frame`, or a pandas Series or\n",
" array_like object. Values from this column or array_like appear in the\n",
" figure as text labels.\n",
"hover_name: str or int or Series or array-like\n",
" Either a name of a column in `data_frame`, or a pandas Series or\n",
" array_like object. Values from this column or array_like appear in bold\n",
" in the hover tooltip.\n",
"hover_data: str, or list of str or int, or Series or array-like, or dict\n",
" Either a name or list of names of columns in `data_frame`, or pandas\n",
" Series, or array_like objects or a dict with column names as keys, with\n",
" values True (for default formatting) False (in order to remove this\n",
" column from hover information), or a formatting string, for example\n",
" ':.3f' or '|%a' or list-like data to appear in the hover tooltip or\n",
" tuples with a bool or formatting string as first element, and list-like\n",
" data to appear in hover as second element Values from these columns\n",
" appear as extra data in the hover tooltip.\n",
"custom_data: str, or list of str or int, or Series or array-like\n",
" Either name or list of names of columns in `data_frame`, or pandas\n",
" Series, or array_like objects Values from these columns are extra data,\n",
" to be used in widgets or Dash callbacks for example. This data is not\n",
" user-visible but is included in events emitted by the figure (lasso\n",
" selection etc.)\n",
"size: str or int or Series or array-like\n",
" Either a name of a column in `data_frame`, or a pandas Series or\n",
" array_like object. Values from this column or array_like are used to\n",
" assign mark sizes.\n",
"animation_frame: str or int or Series or array-like\n",
" Either a name of a column in `data_frame`, or a pandas Series or\n",
" array_like object. Values from this column or array_like are used to\n",
" assign marks to animation frames.\n",
"animation_group: str or int or Series or array-like\n",
" Either a name of a column in `data_frame`, or a pandas Series or\n",
" array_like object. Values from this column or array_like are used to\n",
" provide object-constancy across animation frames: rows with matching\n",
" `animation_group`s will be treated as if they describe the same object\n",
" in each frame.\n",
"category_orders: dict with str keys and list of str values (default `{}`)\n",
" By default, in Python 3.6+, the order of categorical values in axes,\n",
" legends and facets depends on the order in which these values are first\n",
" encountered in `data_frame` (and no order is guaranteed by default in\n",
" Python below 3.6). This parameter is used to force a specific ordering\n",
" of values per column. The keys of this dict should correspond to column\n",
" names, and the values should be lists of strings corresponding to the\n",
" specific display order desired.\n",
"labels: dict with str keys and str values (default `{}`)\n",
" By default, column names are used in the figure for axis titles, legend\n",
" entries and hovers. This parameter allows this to be overridden. The\n",
" keys of this dict should correspond to column names, and the values\n",
" should correspond to the desired label to be displayed.\n",
"color_discrete_sequence: list of str\n",
" Strings should define valid CSS-colors. When `color` is set and the\n",
" values in the corresponding column are not numeric, values in that\n",
" column are assigned colors by cycling through `color_discrete_sequence`\n",
" in the order described in `category_orders`, unless the value of\n",
" `color` is a key in `color_discrete_map`. Various useful color\n",
" sequences are available in the `plotly.express.colors` submodules,\n",
" specifically `plotly.express.colors.qualitative`.\n",
"color_discrete_map: dict with str keys and str values (default `{}`)\n",
" String values should define valid CSS-colors Used to override\n",
" `color_discrete_sequence` to assign a specific colors to marks\n",
" corresponding with specific values. Keys in `color_discrete_map` should\n",
" be values in the column denoted by `color`. Alternatively, if the\n",
" values of `color` are valid colors, the string `'identity'` may be\n",
" passed to cause them to be used directly.\n",
"color_continuous_scale: list of str\n",
" Strings should define valid CSS-colors This list is used to build a\n",
" continuous color scale when the column denoted by `color` contains\n",
" numeric data. Various useful color scales are available in the\n",
" `plotly.express.colors` submodules, specifically\n",
" `plotly.express.colors.sequential`, `plotly.express.colors.diverging`\n",
" and `plotly.express.colors.cyclical`.\n",
"range_color: list of two numbers\n",
" If provided, overrides auto-scaling on the continuous color scale.\n",
"color_continuous_midpoint: number (default `None`)\n",
" If set, computes the bounds of the continuous color scale to have the\n",
" desired midpoint. Setting this value is recommended when using\n",
" `plotly.express.colors.diverging` color scales as the inputs to\n",
" `color_continuous_scale`.\n",
"opacity: float\n",
" Value between 0 and 1. Sets the opacity for markers.\n",
"size_max: int (default `20`)\n",
" Set the maximum mark size when using `size`.\n",
"zoom: int (default `8`)\n",
" Between 0 and 20. Sets map zoom level.\n",
"center: dict\n",
" Dict keys are `'lat'` and `'lon'` Sets the center point of the map.\n",
"mapbox_style: str (default `'basic'`, needs Mapbox API token)\n",
" Identifier of base map style, some of which require a Mapbox API token\n",
" to be set using `plotly.express.set_mapbox_access_token()`. Allowed\n",
" values which do not require a Mapbox API token are `'open-street-map'`,\n",
" `'white-bg'`, `'carto-positron'`, `'carto-darkmatter'`, `'stamen-\n",
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" which do require a Mapbox API token are `'basic'`, `'streets'`,\n",
" `'outdoors'`, `'light'`, `'dark'`, `'satellite'`, `'satellite-\n",
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"title: str\n",
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"template: str or dict or plotly.graph_objects.layout.Template instance\n",
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"width: int (default `None`)\n",
" The figure width in pixels.\n",
"height: int (default `None`)\n",
" The figure height in pixels.\n",
"\n",
"Returns\n",
"-------\n",
" plotly.graph_objects.Figure\n",
"\u001b[0;31mFile:\u001b[0m ~/.conda/envs/alpaca/lib/python3.11/site-packages/plotly/express/_chart_types.py\n",
"\u001b[0;31mType:\u001b[0m function"
]
}
],
"source": [
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]
},
{
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"redraw": false
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"fromcurrent": true,
"mode": "immediate",
"transition": {
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"source": [
"# let's add animation\n",
"fig = px.scatter(gapminder, x='gdpPercap', y='lifeExp', color='continent', size='pop',\n",
" size_max=40,hover_name='country', log_x=True, animation_frame='year',\n",
" animation_group='country', range_x=[25, 10000], range_y=[25,90])\n",
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{
"args": [
[
null
],
{
"frame": {
"duration": 0,
"redraw": false
},
"fromcurrent": true,
"mode": "immediate",
"transition": {
"duration": 0,
"easing": "linear"
}
}
],
"label": "◼",
"method": "animate"
}
],
"direction": "left",
"pad": {
"r": 10,
"t": 70
},
"showactive": false,
"type": "buttons",
"x": 0.1,
"xanchor": "right",
"y": 0,
"yanchor": "top"
}
],
"xaxis": {
"anchor": "y",
"domain": [
0,
1
],
"range": [
1.3979400086720375,
4
],
"title": {
"text": "GDP Per Capita"
},
"type": "log"
},
"yaxis": {
"anchor": "x",
"domain": [
0,
1
],
"range": [
25,
90
],
"title": {
"text": "Life Expectency"
}
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# customize the labels\n",
"fig = px.scatter(gapminder, x='gdpPercap', y='lifeExp', color='continent', size='pop', size_max=40,\n",
" hover_name='country',log_x=True, animation_frame='year',\n",
" animation_group='country', range_x=[25, 10000], range_y=[25,90],\n",
" labels=dict(pop=\"Population\", gdpPercap=\"GDP Per Capita\", lifeExp=\"Life Expectency\"))\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task-9: Represent Geographic Data as Animated Maps"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"geo": "geo",
"hovertemplate": "continent=Asia
iso_alpha=%{location}",
"legendgroup": "Asia",
"line": {
"color": "#636efa",
"dash": "solid"
},
"locations": [
"AFG",
"BHR",
"BGD",
"KHM",
"CHN",
"HKG",
"IND",
"IDN",
"IRN",
"IRQ",
"ISR",
"JPN",
"JOR",
"KOR",
"KOR",
"KWT",
"LBN",
"MYS",
"MNG",
"MMR",
"NPL",
"OMN",
"PAK",
"PHL",
"SAU",
"SGP",
"LKA",
"SYR",
"TWN",
"THA",
"VNM",
"PSE",
"YEM"
],
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "Asia",
"showlegend": true,
"type": "scattergeo"
},
{
"geo": "geo",
"hovertemplate": "continent=Europe
iso_alpha=%{location}",
"legendgroup": "Europe",
"line": {
"color": "#EF553B",
"dash": "solid"
},
"locations": [
"ALB",
"AUT",
"BEL",
"BIH",
"BGR",
"HRV",
"CZE",
"DNK",
"FIN",
"FRA",
"DEU",
"GRC",
"HUN",
"ISL",
"IRL",
"ITA",
"MNE",
"NLD",
"NOR",
"POL",
"PRT",
"ROU",
"SRB",
"SVK",
"SVN",
"ESP",
"SWE",
"CHE",
"TUR",
"GBR"
],
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "Europe",
"showlegend": true,
"type": "scattergeo"
},
{
"geo": "geo",
"hovertemplate": "continent=Africa
iso_alpha=%{location}",
"legendgroup": "Africa",
"line": {
"color": "#00cc96",
"dash": "solid"
},
"locations": [
"DZA",
"AGO",
"BEN",
"BWA",
"BFA",
"BDI",
"CMR",
"CAF",
"TCD",
"COM",
"COD",
"COG",
"CIV",
"DJI",
"EGY",
"GNQ",
"ERI",
"ETH",
"GAB",
"GMB",
"GHA",
"GIN",
"GNB",
"KEN",
"LSO",
"LBR",
"LBY",
"MDG",
"MWI",
"MLI",
"MRT",
"MUS",
"MAR",
"MOZ",
"NAM",
"NER",
"NGA",
"REU",
"RWA",
"STP",
"SEN",
"SLE",
"SOM",
"ZAF",
"SDN",
"SWZ",
"TZA",
"TGO",
"TUN",
"UGA",
"ZMB",
"ZWE"
],
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "Africa",
"showlegend": true,
"type": "scattergeo"
},
{
"geo": "geo",
"hovertemplate": "continent=Americas
iso_alpha=%{location}",
"legendgroup": "Americas",
"line": {
"color": "#ab63fa",
"dash": "solid"
},
"locations": [
"ARG",
"BOL",
"BRA",
"CAN",
"CHL",
"COL",
"CRI",
"CUB",
"DOM",
"ECU",
"SLV",
"GTM",
"HTI",
"HND",
"JAM",
"MEX",
"NIC",
"PAN",
"PRY",
"PER",
"PRI",
"TTO",
"USA",
"URY",
"VEN"
],
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "Americas",
"showlegend": true,
"type": "scattergeo"
},
{
"geo": "geo",
"hovertemplate": "continent=Oceania
iso_alpha=%{location}",
"legendgroup": "Oceania",
"line": {
"color": "#FFA15A",
"dash": "solid"
},
"locations": [
"AUS",
"NZL"
],
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "Oceania",
"showlegend": true,
"type": "scattergeo"
}
],
"layout": {
"geo": {
"center": {},
"domain": {
"x": [
0,
1
],
"y": [
0,
1
]
},
"projection": {
"type": "orthographic"
}
},
"legend": {
"title": {
"text": "continent"
},
"tracegroupgap": 0
},
"margin": {
"t": 60
},
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"type": "carpet"
}
],
"choropleth": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "choropleth"
}
],
"contour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
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],
[
0.6666666666666666,
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],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "contour"
}
],
"contourcarpet": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "contourcarpet"
}
],
"heatmap": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
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0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
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],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmap"
}
],
"heatmapgl": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
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"#46039f"
],
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0.2222222222222222,
"#7201a8"
],
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0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmapgl"
}
],
"histogram": [
{
"marker": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "histogram"
}
],
"histogram2d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
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],
[
0.2222222222222222,
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],
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0.3333333333333333,
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],
[
0.4444444444444444,
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],
[
0.5555555555555556,
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],
[
0.6666666666666666,
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[
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],
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],
"type": "histogram2d"
}
],
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{
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},
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0.7777777777777778,
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[
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],
[
1,
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],
"type": "histogram2dcontour"
}
],
"mesh3d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "mesh3d"
}
],
"parcoords": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "parcoords"
}
],
"pie": [
{
"automargin": true,
"type": "pie"
}
],
"scatter": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"type": "scatter"
}
],
"scatter3d": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatter3d"
}
],
"scattercarpet": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattercarpet"
}
],
"scattergeo": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergeo"
}
],
"scattergl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergl"
}
],
"scattermapbox": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattermapbox"
}
],
"scatterpolar": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolar"
}
],
"scatterpolargl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolargl"
}
],
"scatterternary": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterternary"
}
],
"surface": [
{
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"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
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],
[
0.6666666666666666,
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],
[
0.7777777777777778,
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],
[
0.8888888888888888,
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],
[
1,
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]
],
"type": "surface"
}
],
"table": [
{
"cells": {
"fill": {
"color": "#EBF0F8"
},
"line": {
"color": "white"
}
},
"header": {
"fill": {
"color": "#C8D4E3"
},
"line": {
"color": "white"
}
},
"type": "table"
}
]
},
"layout": {
"annotationdefaults": {
"arrowcolor": "#2a3f5f",
"arrowhead": 0,
"arrowwidth": 1
},
"autotypenumbers": "strict",
"coloraxis": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"colorscale": {
"diverging": [
[
0,
"#8e0152"
],
[
0.1,
"#c51b7d"
],
[
0.2,
"#de77ae"
],
[
0.3,
"#f1b6da"
],
[
0.4,
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],
[
0.5,
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],
[
0.6,
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],
[
0.7,
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[
0.8,
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],
[
0.9,
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],
[
1,
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]
],
"sequential": [
[
0,
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],
[
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],
[
0.2222222222222222,
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0.3333333333333333,
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],
[
0.4444444444444444,
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],
[
0.5555555555555556,
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],
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0.6666666666666666,
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],
[
0.7777777777777778,
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],
[
0.8888888888888888,
"#fdca26"
],
[
1,
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]
],
"sequentialminus": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
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],
[
0.4444444444444444,
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],
[
0.5555555555555556,
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[
0.6666666666666666,
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],
[
0.7777777777777778,
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],
[
0.8888888888888888,
"#fdca26"
],
[
1,
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]
]
},
"colorway": [
"#636efa",
"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# create a map using line_geo()\n",
"fig = px.line_geo(gapminder.query('year == 2007'), locations='iso_alpha', color='continent', projection='orthographic')\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"coloraxis": "coloraxis",
"geo": "geo",
"hovertemplate": "%{hovertext}
year=1952
iso_alpha=%{location}
lifeExp=%{z}",
"hovertext": [
"Afghanistan",
"Albania",
"Algeria",
"Angola",
"Argentina",
"Australia",
"Austria",
"Bahrain",
"Bangladesh",
"Belgium",
"Benin",
"Bolivia",
"Bosnia and Herzegovina",
"Botswana",
"Brazil",
"Bulgaria",
"Burkina Faso",
"Burundi",
"Cambodia",
"Cameroon",
"Canada",
"Central African Republic",
"Chad",
"Chile",
"China",
"Colombia",
"Comoros",
"Congo, Dem. Rep.",
"Congo, Rep.",
"Costa Rica",
"Cote d'Ivoire",
"Croatia",
"Cuba",
"Czech Republic",
"Denmark",
"Djibouti",
"Dominican Republic",
"Ecuador",
"Egypt",
"El Salvador",
"Equatorial Guinea",
"Eritrea",
"Ethiopia",
"Finland",
"France",
"Gabon",
"Gambia",
"Germany",
"Ghana",
"Greece",
"Guatemala",
"Guinea",
"Guinea-Bissau",
"Haiti",
"Honduras",
"Hong Kong, China",
"Hungary",
"Iceland",
"India",
"Indonesia",
"Iran",
"Iraq",
"Ireland",
"Israel",
"Italy",
"Jamaica",
"Japan",
"Jordan",
"Kenya",
"Korea, Dem. Rep.",
"Korea, Rep.",
"Kuwait",
"Lebanon",
"Lesotho",
"Liberia",
"Libya",
"Madagascar",
"Malawi",
"Malaysia",
"Mali",
"Mauritania",
"Mauritius",
"Mexico",
"Mongolia",
"Montenegro",
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"line": {
"color": "white"
}
},
"type": "table"
}
]
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"arrowhead": 0,
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"#FECB52"
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"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
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"linecolor": "white",
"ticks": ""
},
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"radialaxis": {
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"linecolor": "white",
"ticks": ""
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},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
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"gridcolor": "white",
"gridwidth": 2,
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"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
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}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"updatemenus": [
{
"buttons": [
{
"args": [
null,
{
"frame": {
"duration": 500,
"redraw": true
},
"fromcurrent": true,
"mode": "immediate",
"transition": {
"duration": 500,
"easing": "linear"
}
}
],
"label": "▶",
"method": "animate"
},
{
"args": [
[
null
],
{
"frame": {
"duration": 0,
"redraw": true
},
"fromcurrent": true,
"mode": "immediate",
"transition": {
"duration": 0,
"easing": "linear"
}
}
],
"label": "◼",
"method": "animate"
}
],
"direction": "left",
"pad": {
"r": 10,
"t": 70
},
"showactive": false,
"type": "buttons",
"x": 0.1,
"xanchor": "right",
"y": 0,
"yanchor": "top"
}
]
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# create a map using choropleth\n",
"fig = px.choropleth(gapminder, locations='iso_alpha', color='lifeExp', hover_name='country',\n",
" animation_frame='year', color_continuous_scale=px.colors.sequential.Plasma, projection='natural earth')\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 10: Using Plotly Template in Any Graphs"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Templates configuration\n",
"-----------------------\n",
" Default template: 'plotly'\n",
" Available templates:\n",
" ['ggplot2', 'seaborn', 'simple_white', 'plotly',\n",
" 'plotly_white', 'plotly_dark', 'presentation', 'xgridoff',\n",
" 'ygridoff', 'gridon', 'none']"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# print available themes or template\n",
"import plotly.io as pio\n",
"pio.templates"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"alignmentgroup": "True",
"hovertemplate": "year=%{x}
Population of Canada=%{y}
lifeExp=%{marker.color}",
"legendgroup": "",
"marker": {
"color": [
68.75,
69.96,
71.3,
72.13,
72.88,
74.21,
75.76,
76.86,
77.95,
78.61,
79.77,
80.653
],
"coloraxis": "coloraxis",
"pattern": {
"shape": ""
}
},
"name": "",
"offsetgroup": "",
"orientation": "v",
"showlegend": false,
"textposition": "auto",
"type": "bar",
"x": [
1952,
1957,
1962,
1967,
1972,
1977,
1982,
1987,
1992,
1997,
2002,
2007
],
"xaxis": "x",
"y": [
14785584,
17010154,
18985849,
20819767,
22284500,
23796400,
25201900,
26549700,
28523502,
30305843,
31902268,
33390141
],
"yaxis": "y"
}
],
"layout": {
"barmode": "relative",
"coloraxis": {
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"title": {
"text": "lifeExp"
}
},
"colorscale": [
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},
"height": 400,
"legend": {
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},
"margin": {
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},
"template": {
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},
"error_y": {
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"size": 10,
"solidity": 0.2
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},
"type": "barpolar"
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"carpet": [
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"gridcolor": "#506784",
"linecolor": "#506784",
"minorgridcolor": "#506784",
"startlinecolor": "#A2B1C6"
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"baxis": {
"endlinecolor": "#A2B1C6",
"gridcolor": "#506784",
"linecolor": "#506784",
"minorgridcolor": "#506784",
"startlinecolor": "#A2B1C6"
},
"type": "carpet"
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],
"choropleth": [
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},
"type": "choropleth"
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],
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"source": [
"# let's use plotly_dark in our previous bar chart\n",
"fig = px.bar(canada_data, x='year', y='pop', color='lifeExp', labels={'pop': 'Population of Canada'},\n",
" height=400, template='plotly_dark')\n",
"fig.show()"
]
},
{
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"showgrid": true,
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"bgcolor": "rgb(234,234,242)",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
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"linecolor": "white",
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"showgrid": true,
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},
"bgcolor": "rgb(234,234,242)",
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"linecolor": "white",
"showgrid": true,
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},
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"gridcolor": "white",
"linecolor": "white",
"showgrid": true,
"ticks": "",
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}
},
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0,
1
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}
},
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"domain": [
0,
1
],
"title": {
"text": "Population of Canada"
}
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# seaborn\n",
"fig = px.bar(canada_data, x='year', y='pop', color='lifeExp', labels={'pop': 'Population of Canada'},\n",
" height=400, template='seaborn')\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 48,
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1957,
1962,
1967,
1972,
1977,
1982,
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},
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"linecolor": "white",
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"tickcolor": "rgb(51,51,51)",
"ticks": "outside"
},
"baxis": {
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"linecolor": "white",
"showgrid": true,
"tickcolor": "rgb(51,51,51)",
"ticks": "outside"
},
"bgcolor": "rgb(237,237,237)",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"showgrid": true,
"tickcolor": "rgb(51,51,51)",
"ticks": "outside"
}
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"showgrid": true,
"tickcolor": "rgb(51,51,51)",
"ticks": "outside",
"title": {
"standoff": 15
},
"zerolinecolor": "white"
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"showgrid": true,
"tickcolor": "rgb(51,51,51)",
"ticks": "outside",
"title": {
"standoff": 15
},
"zerolinecolor": "white"
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}
},
"xaxis": {
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"domain": [
0,
1
],
"title": {
"text": "year"
}
},
"yaxis": {
"anchor": "x",
"domain": [
0,
1
],
"title": {
"text": "Population of Canada"
}
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# ggplot2\n",
"fig = px.bar(canada_data, x='year', y='pop', color='lifeExp', labels={'pop': 'Population of Canada'},\n",
" height=400, template='ggplot2')\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Further Reading \n",
"- https://plotly.com/python/templates/\n",
"- https://plotly.com/python/plotly-fundamentals/\n",
"- https://plotly.com/python/plotly-express/"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}