{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotly Express in Python\n", "\n", "来源:https://mp.weixin.qq.com/s/dg390bbxA_LEoggWg6dGlQ\n", "\n", "Plotly Express 是一个新的高级 Python 可视化库:它是 Plotly.py 的高级封装,它为复杂的图表提供了一个简单的语法。 \n", "\n", "受 Seaborn 和 ggplot2 的启发,它专门设计为具有简洁,一致且易于学习的 API :只需一次导入,您就可以在一个函数调用中创建丰富的交互式绘图,包括分面绘图(faceting)、地图、动画和趋势线。它带有数据集、颜色面板和主题,就像 Plotly.py 一样。Plotly是新一代的可视化神器,由TopQ量化团队开源。虽然Ploltly功能非常之强大,但是一直没有得到重视,主要原因还是其设置过于繁琐。因此,Plotly推出了其简化接口:Plotly_express,下文中统一简称为px。\n", "\n", "Plotly Express 完全免费:凭借其宽松的开源 MIT 许可证,您可以随意使用它(是的,甚至在商业产品中!)。 \n", "\n", "最重要的是,Plotly Express 与 Plotly 生态系统的其他部分完全兼容:在您的 Dash 应用程序中使用它,使用 Orca 将您的数据导出为几乎任何文件格式,或使用JupyterLab 图表编辑器在 GUI 中编辑它们!\n", "\n", "\n", "https://plotly.com/python/plotly-express/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 安装plotly_express" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2020-12-23T01:57:36.741709Z", "start_time": "2020-12-23T01:57:31.010418Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting plotly_express\n", " Downloading plotly_express-0.4.1-py2.py3-none-any.whl (2.9 kB)\n", "Requirement already satisfied: plotly>=4.1.0 in /opt/anaconda3/lib/python3.7/site-packages (from plotly_express) (4.11.0)\n", "Requirement already satisfied: patsy>=0.5 in /opt/anaconda3/lib/python3.7/site-packages (from plotly_express) (0.5.1)\n", "Requirement already satisfied: numpy>=1.11 in /opt/anaconda3/lib/python3.7/site-packages (from plotly_express) (1.18.1)\n", "Requirement already satisfied: pandas>=0.20.0 in /opt/anaconda3/lib/python3.7/site-packages (from plotly_express) (1.0.1)\n", "Requirement already satisfied: statsmodels>=0.9.0 in /opt/anaconda3/lib/python3.7/site-packages (from plotly_express) (0.11.0)\n", "Requirement already satisfied: scipy>=0.18 in /opt/anaconda3/lib/python3.7/site-packages (from plotly_express) (1.4.1)\n", "Requirement already satisfied: six in /opt/anaconda3/lib/python3.7/site-packages (from plotly>=4.1.0->plotly_express) (1.14.0)\n", "Requirement already satisfied: retrying>=1.3.3 in /opt/anaconda3/lib/python3.7/site-packages (from plotly>=4.1.0->plotly_express) (1.3.3)\n", "Requirement already satisfied: python-dateutil>=2.6.1 in /opt/anaconda3/lib/python3.7/site-packages (from pandas>=0.20.0->plotly_express) (2.8.1)\n", "Requirement already satisfied: pytz>=2017.2 in /opt/anaconda3/lib/python3.7/site-packages (from pandas>=0.20.0->plotly_express) (2019.3)\n", "Installing collected packages: plotly-express\n", "Successfully installed plotly-express-0.4.1\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "pip install plotly_express" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 导入数据" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2020-12-23T01:59:50.705607Z", "start_time": "2020-12-23T01:59:50.674233Z" } }, "outputs": [ { "data": { "text/html": [ "
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countrycontinentyearlifeExppopgdpPercapiso_alphaiso_num
0AfghanistanAsia195228.8018425333779.445314AFG4
1AfghanistanAsia195730.3329240934820.853030AFG4
2AfghanistanAsia196231.99710267083853.100710AFG4
3AfghanistanAsia196734.02011537966836.197138AFG4
4AfghanistanAsia197236.08813079460739.981106AFG4
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" ], "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", " iso_num \n", "0 4 \n", "1 4 \n", "2 4 \n", "3 4 \n", "4 4 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import plotly.express as px \n", "\n", "# 数据集\n", "gapminder = px.data.gapminder()\n", "gapminder.head() # 取出前5条数据" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2020-12-23T02:00:00.629244Z", "start_time": "2020-12-23T02:00:00.625226Z" } }, "outputs": [ { "data": { "text/plain": [ "(1704, 8)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gapminder.shape" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2020-12-23T03:03:02.359200Z", "start_time": "2020-12-23T03:03:02.357052Z" } }, "source": [ "## 可视化示例" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2020-12-23T02:00:21.318398Z", "start_time": "2020-12-23T02:00:20.296931Z" } }, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "%{hovertext}

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continent=Americas
year=1952
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year=1957
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year=1962
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year=1967
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year=1972
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year=1977
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year=1982
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year=1987
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year=1992
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continent=Asia
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iso_alpha=%{location}", "hovertext": [ "Afghanistan", "Bahrain", "Bangladesh", "Cambodia", "China", "Hong Kong, China", "India", "Indonesia", "Iran", "Iraq", "Israel", "Japan", "Jordan", "Korea, Dem. Rep.", "Korea, Rep.", "Kuwait", "Lebanon", "Malaysia", "Mongolia", "Myanmar", "Nepal", "Oman", "Pakistan", "Philippines", "Saudi Arabia", "Singapore", "Sri Lanka", "Syria", "Taiwan", "Thailand", "Vietnam", "West Bank and Gaza", "Yemen, Rep." ], "legendgroup": "Asia", "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": { "color": "#636efa", "size": [ 8425333, 120447, 46886859, 4693836, 556263527, 2125900, 372000000, 82052000, 17272000, 5441766, 1620914, 86459025, 607914, 8865488, 20947571, 160000, 1439529, 6748378, 800663, 20092996, 9182536, 507833, 41346560, 22438691, 4005677, 1127000, 7982342, 3661549, 8550362, 21289402, 26246839, 1030585, 4963829 ], "sizemode": "area", "sizeref": 3296707.74 }, "name": "Asia", "showlegend": true, "type": "scattergeo" }, { "geo": "geo", "hovertemplate": "%{hovertext}

continent=Europe
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iso_alpha=%{location}", "hovertext": [ "Albania", "Austria", "Belgium", "Bosnia and Herzegovina", "Bulgaria", "Croatia", "Czech Republic", "Denmark", "Finland", "France", "Germany", "Greece", "Hungary", "Iceland", "Ireland", "Italy", "Montenegro", "Netherlands", "Norway", "Poland", "Portugal", "Romania", "Serbia", "Slovak Republic", "Slovenia", "Spain", "Sweden", "Switzerland", "Turkey", "United Kingdom" ], "legendgroup": "Europe", "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": { "color": "#EF553B", "size": [ 1282697, 6927772, 8730405, 2791000, 7274900, 3882229, 9125183, 4334000, 4090500, 42459667, 69145952, 7733250, 9504000, 147962, 2952156, 47666000, 413834, 10381988, 3327728, 25730551, 8526050, 16630000, 6860147, 3558137, 1489518, 28549870, 7124673, 4815000, 22235677, 50430000 ], "sizemode": "area", "sizeref": 3296707.74 }, "name": "Europe", "showlegend": true, "type": "scattergeo" }, { "geo": "geo", "hovertemplate": "%{hovertext}

continent=Africa
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iso_alpha=%{location}", "hovertext": [ "Algeria", "Angola", "Benin", "Botswana", "Burkina Faso", "Burundi", "Cameroon", "Central African Republic", "Chad", "Comoros", "Congo, Dem. Rep.", "Congo, Rep.", "Cote d'Ivoire", "Djibouti", "Egypt", "Equatorial Guinea", "Eritrea", "Ethiopia", "Gabon", "Gambia", "Ghana", "Guinea", "Guinea-Bissau", "Kenya", "Lesotho", "Liberia", "Libya", "Madagascar", "Malawi", "Mali", "Mauritania", "Mauritius", "Morocco", "Mozambique", "Namibia", "Niger", "Nigeria", "Reunion", "Rwanda", "Sao Tome and Principe", "Senegal", "Sierra Leone", "Somalia", "South Africa", "Sudan", "Swaziland", "Tanzania", "Togo", "Tunisia", "Uganda", "Zambia", "Zimbabwe" ], "legendgroup": "Africa", "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": { "color": "#00cc96", "size": [ 9279525, 4232095, 1738315, 442308, 4469979, 2445618, 5009067, 1291695, 2682462, 153936, 14100005, 854885, 2977019, 63149, 22223309, 216964, 1438760, 20860941, 420702, 284320, 5581001, 2664249, 580653, 6464046, 748747, 863308, 1019729, 4762912, 2917802, 3838168, 1022556, 516556, 9939217, 6446316, 485831, 3379468, 33119096, 257700, 2534927, 60011, 2755589, 2143249, 2526994, 14264935, 8504667, 290243, 8322925, 1219113, 3647735, 5824797, 2672000, 3080907 ], "sizemode": "area", "sizeref": 3296707.74 }, "name": "Africa", "showlegend": true, "type": "scattergeo" }, { "geo": "geo", "hovertemplate": "%{hovertext}

continent=Americas
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iso_alpha=%{location}", "hovertext": [ "Argentina", "Bolivia", "Brazil", "Canada", "Chile", "Colombia", "Costa Rica", "Cuba", "Dominican Republic", "Ecuador", "El Salvador", "Guatemala", "Haiti", "Honduras", "Jamaica", "Mexico", "Nicaragua", "Panama", "Paraguay", "Peru", "Puerto Rico", "Trinidad and Tobago", "United States", "Uruguay", "Venezuela" ], "legendgroup": "Americas", "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": { "color": "#ab63fa", "size": [ 17876956, 2883315, 56602560, 14785584, 6377619, 12350771, 926317, 6007797, 2491346, 3548753, 2042865, 3146381, 3201488, 1517453, 1426095, 30144317, 1165790, 940080, 1555876, 8025700, 2227000, 662850, 157553000, 2252965, 5439568 ], "sizemode": "area", "sizeref": 3296707.74 }, "name": "Americas", "showlegend": true, "type": "scattergeo" }, { "geo": "geo", "hovertemplate": "%{hovertext}

continent=Oceania
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iso_alpha=%{location}", "hovertext": [ "Australia", "New Zealand" ], "legendgroup": "Oceania", "locations": [ "AUS", "NZL" ], "marker": { "color": "#FFA15A", "size": [ 8691212, 1994794 ], "sizemode": "area", "sizeref": 3296707.74 }, "name": "Oceania", "showlegend": true, "type": "scattergeo" } ], "frames": [ { "data": [ { "geo": "geo", "hovertemplate": "%{hovertext}

continent=Asia
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iso_alpha=%{location}", "hovertext": [ "Afghanistan", "Bahrain", "Bangladesh", "Cambodia", "China", "Hong Kong, China", "India", "Indonesia", "Iran", "Iraq", "Israel", "Japan", "Jordan", "Korea, Dem. Rep.", "Korea, Rep.", "Kuwait", "Lebanon", "Malaysia", "Mongolia", "Myanmar", "Nepal", "Oman", "Pakistan", "Philippines", "Saudi Arabia", "Singapore", "Sri Lanka", "Syria", "Taiwan", "Thailand", "Vietnam", "West Bank and Gaza", "Yemen, Rep." ], "legendgroup": "Asia", "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": { "color": "#636efa", "size": [ 8425333, 120447, 46886859, 4693836, 556263527, 2125900, 372000000, 82052000, 17272000, 5441766, 1620914, 86459025, 607914, 8865488, 20947571, 160000, 1439529, 6748378, 800663, 20092996, 9182536, 507833, 41346560, 22438691, 4005677, 1127000, 7982342, 3661549, 8550362, 21289402, 26246839, 1030585, 4963829 ], "sizemode": "area", "sizeref": 3296707.74 }, "name": "Asia", "showlegend": true, "type": "scattergeo" }, { "geo": "geo", "hovertemplate": "%{hovertext}

continent=Europe
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iso_alpha=%{location}", "hovertext": [ "Albania", "Austria", "Belgium", "Bosnia and Herzegovina", "Bulgaria", "Croatia", "Czech Republic", "Denmark", "Finland", "France", "Germany", "Greece", "Hungary", "Iceland", "Ireland", "Italy", "Montenegro", "Netherlands", "Norway", "Poland", "Portugal", "Romania", "Serbia", "Slovak Republic", "Slovenia", "Spain", "Sweden", "Switzerland", "Turkey", "United Kingdom" ], "legendgroup": "Europe", "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": { "color": "#EF553B", "size": [ 1282697, 6927772, 8730405, 2791000, 7274900, 3882229, 9125183, 4334000, 4090500, 42459667, 69145952, 7733250, 9504000, 147962, 2952156, 47666000, 413834, 10381988, 3327728, 25730551, 8526050, 16630000, 6860147, 3558137, 1489518, 28549870, 7124673, 4815000, 22235677, 50430000 ], "sizemode": "area", "sizeref": 3296707.74 }, "name": "Europe", "showlegend": true, "type": "scattergeo" }, { "geo": "geo", "hovertemplate": "%{hovertext}

continent=Africa
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continent=Americas
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iso_alpha=%{location}", "hovertext": [ "Argentina", "Bolivia", "Brazil", "Canada", "Chile", "Colombia", "Costa Rica", "Cuba", "Dominican Republic", "Ecuador", "El Salvador", "Guatemala", "Haiti", "Honduras", "Jamaica", "Mexico", "Nicaragua", "Panama", "Paraguay", "Peru", "Puerto Rico", "Trinidad and Tobago", "United States", "Uruguay", "Venezuela" ], "legendgroup": "Americas", "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": { "color": "#ab63fa", "size": [ 17876956, 2883315, 56602560, 14785584, 6377619, 12350771, 926317, 6007797, 2491346, 3548753, 2042865, 3146381, 3201488, 1517453, 1426095, 30144317, 1165790, 940080, 1555876, 8025700, 2227000, 662850, 157553000, 2252965, 5439568 ], "sizemode": "area", "sizeref": 3296707.74 }, "name": "Americas", "showlegend": true, "type": "scattergeo" }, { "geo": "geo", "hovertemplate": "%{hovertext}

continent=Oceania
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continent=Asia
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import plotly.graph_objects as go\n", "\n", "fig = go.Figure(data=go.Scattergeo(\n", " lat = [40.7127, 51.5072],\n", " lon = [-74.0059, 0.1275],\n", " mode = 'lines',\n", " line = dict(width = 2, color = 'blue'),\n", "))\n", "\n", "fig.update_layout(\n", " title_text = 'London to NYC Great Circle',\n", " showlegend = False,\n", " geo = dict(\n", " resolution = 50,\n", " showland = True,\n", " showlakes = True,\n", " landcolor = 'rgb(204, 204, 204)',\n", " countrycolor = 'rgb(204, 204, 204)',\n", " lakecolor = 'rgb(255, 255, 255)',\n", " projection_type = \"equirectangular\",\n", " coastlinewidth = 2,\n", " lataxis = dict(\n", " range = [20, 60],\n", " showgrid = True,\n", " dtick = 10\n", " ),\n", " lonaxis = dict(\n", " range = [-100, 20],\n", " showgrid = True,\n", " dtick = 20\n", " ),\n", " )\n", ")\n", "\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T12:25:22.080466Z", "start_time": "2021-05-14T12:25:22.057772Z" } }, "outputs": [ { "data": { "text/html": [ "
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iataairportcitystatecountrylatlongcnt
0ORDChicago O'Hare InternationalChicagoILUSA41.979595-87.90446425129
1ATLWilliam B Hartsfield-Atlanta IntlAtlantaGAUSA33.640444-84.42694421925
2DFWDallas-Fort Worth InternationalDallas-Fort WorthTXUSA32.895951-97.03720020662
3PHXPhoenix Sky Harbor InternationalPhoenixAZUSA33.434167-112.00805617290
4DENDenver IntlDenverCOUSA39.858408-104.66700213781
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" ], "text/plain": [ " iata airport city state country \\\n", "0 ORD Chicago O'Hare International Chicago IL USA \n", "1 ATL William B Hartsfield-Atlanta Intl Atlanta GA USA \n", "2 DFW Dallas-Fort Worth International Dallas-Fort Worth TX USA \n", "3 PHX Phoenix Sky Harbor International Phoenix AZ USA \n", "4 DEN Denver Intl Denver CO USA \n", "\n", " lat long cnt \n", "0 41.979595 -87.904464 25129 \n", "1 33.640444 -84.426944 21925 \n", "2 32.895951 -97.037200 20662 \n", "3 33.434167 -112.008056 17290 \n", "4 39.858408 -104.667002 13781 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.graph_objects as go\n", "import pandas as pd\n", "\n", "df_airports = pd.read_csv('./data/2011_february_us_airport_traffic.csv')\n", "df_airports.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T12:26:06.944719Z", "start_time": "2021-05-14T12:26:06.931298Z" } }, "outputs": [ { "data": { "text/html": [ "
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start_latstart_lonend_latend_lonairlineairport1airport2cnt
032.895951-97.03720035.040222-106.609194AADFWABQ444
141.979595-87.90446430.194533-97.669872AAORDAUS166
232.895951-97.03720041.938874-72.683228AADFWBDL162
318.439417-66.00183341.938874-72.683228AASJUBDL56
432.895951-97.03720033.562943-86.753550AADFWBHM168
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" ], "text/plain": [ " start_lat start_lon end_lat end_lon airline airport1 airport2 cnt\n", "0 32.895951 -97.037200 35.040222 -106.609194 AA DFW ABQ 444\n", "1 41.979595 -87.904464 30.194533 -97.669872 AA ORD AUS 166\n", "2 32.895951 -97.037200 41.938874 -72.683228 AA DFW BDL 162\n", "3 18.439417 -66.001833 41.938874 -72.683228 AA SJU BDL 56\n", "4 32.895951 -97.037200 33.562943 -86.753550 AA DFW BHM 168" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_flight_paths = pd.read_csv('./data/2011_february_aa_flight_paths.csv')\n", "df_flight_paths.head()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2021-05-14T13:25:41.844803Z", "start_time": "2021-05-14T13:25:41.674507Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hoverinfo": "text", "lat": [ 41.979595, 33.64044444, 32.89595056, 33.43416667, 39.85840806, 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"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": "sepal_width" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "sepal_length" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 对当前值加上下两个误差值\n", "iris[\"e\"] = iris[\"sepal_width\"] / 100\n", "px.scatter(\n", " iris, # 绘图数据集\n", " x=\"sepal_width\", # 横坐标\n", " y=\"sepal_length\", # 纵坐标\n", " color=\"species\", # 颜色值\n", " error_x=\"e\", # 横轴误差\n", " error_y=\"e\" # 纵轴误差\n", " )" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2020-12-23T02:05:56.044975Z", "start_time": "2020-12-23T02:05:55.983960Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "contours": { "coloring": "none" }, "hovertemplate": "species=setosa
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sepal_length=%{y}
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "px.colors.sequential.swatches()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 选择template" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "ExecuteTime": { "end_time": "2020-12-23T02:12:26.026129Z", "start_time": "2020-12-23T02:12:25.921512Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "species=setosa
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R2=0.209057

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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "px.scatter(\n", " iris, # 数据集\n", " x=\"sepal_width\", # 横坐标值\n", " y=\"sepal_length\", # 纵坐标取值\n", " color=\"species\", # 颜色\n", " marginal_x=\"box\", # 横坐标为箱型图\n", " marginal_y=\"histogram\", # 纵坐标为直方图\n", " height=600, # 高度\n", " trendline=\"ols\", # 显示趋势线\n", " template=\"plotly\") # 主题" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "ExecuteTime": { "end_time": "2020-12-23T02:12:37.823401Z", "start_time": "2020-12-23T02:12:37.717083Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "species=setosa
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R2=0.557681

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R2=0.276582

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"caxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } }, "xaxis": { "anchor": "y", "domain": [ 0, 0.7363 ], "title": { "text": "sepal_width" } }, "xaxis2": { "anchor": "y2", "domain": [ 0.7413, 1 ], "matches": "x2", "showgrid": true, "showline": false, "showticklabels": false, "ticks": "" }, "xaxis3": { "anchor": "y3", "domain": [ 0, 0.7363 ], "matches": "x", "showgrid": true, "showticklabels": false }, "xaxis4": { "anchor": "y4", "domain": [ 0.7413, 1 ], "matches": "x2", "showgrid": true, "showline": false, "showticklabels": false, "ticks": "" }, "yaxis": { "anchor": "x", "domain": [ 0, 0.7326 ], "title": { "text": "sepal_length" } }, "yaxis2": { "anchor": "x2", "domain": [ 0, 0.7326 ], "matches": "y", "showgrid": true, "showticklabels": false }, "yaxis3": { "anchor": "x3", "domain": [ 0.7426, 1 ], "matches": "y3", "showgrid": false, "showline": false, "showticklabels": false, "ticks": "" }, "yaxis4": { "anchor": "x4", "domain": [ 0.7426, 1 ], "matches": "y3", "showgrid": true, "showline": false, "showticklabels": false, "ticks": "" } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "px.scatter(\n", " iris, # 数据集\n", " x=\"sepal_width\", # 横坐标值\n", " y=\"sepal_length\", # 纵坐标取值\n", " color=\"species\", # 颜色\n", " marginal_x=\"box\", # 横坐标为箱型图\n", " marginal_y=\"histogram\", # 纵坐标为直方图\n", " height=600, # 高度\n", " trendline=\"ols\", # 显示趋势线\n", " template=\"plotly_white\") # 主题 " ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "ExecuteTime": { "end_time": "2020-12-23T02:12:48.345479Z", "start_time": "2020-12-23T02:12:48.239634Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "species=setosa
sepal_width=%{x}
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sepal_length=%{y}
count=%{x}", "legendgroup": "setosa", "marker": { "color": "#636efa" }, "name": "setosa", "offsetgroup": "setosa", "opacity": 0.5, "showlegend": false, "type": "histogram", "xaxis": "x2", "y": [ 5.1, 4.9, 4.7, 4.6, 5, 5.4, 4.6, 5, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5, 5, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5, 5.5, 4.9, 4.4, 5.1, 5, 4.5, 4.4, 5, 5.1, 4.8, 5.1, 4.6, 5.3, 5 ], "yaxis": "y2" }, { "hovertemplate": "OLS trendline
sepal_length = 0.690854 * sepal_width + 2.64466
R2=0.557681

species=setosa
sepal_width=%{x}
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R2=0.276582

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R2=0.209057

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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "px.scatter(\n", " iris, # 数据集\n", " x=\"sepal_width\", # 横坐标值\n", " y=\"sepal_length\", # 纵坐标取值\n", " color=\"species\", # 颜色\n", " marginal_x=\"box\", # 横坐标为箱型图\n", " marginal_y=\"histogram\", # 纵坐标为直方图\n", " height=600, # 高度\n", " trendline=\"ols\", # 显示趋势线\n", " template=\"plotly_dark\") # 主题 " ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2020-12-23T02:13:18.104529Z", "start_time": "2020-12-23T02:13:18.101135Z" } }, "source": [ "本文中利用大量的篇幅讲解了如何通过plotly_express来绘制:柱状图、线型图、散点图、小提琴图、极坐标图等各种常见的图形。通过观察上面Plotly_express绘制图形过程,我们不难发现它有三个主要的优点:\n", "\n", "- 快速出图,少量的代码就能满足多数的制图要求。基本上都是几个参数的设置我们就能快速出图\n", "- 图形漂亮,绘制出来的可视化图形颜色亮丽,也有很多的颜色供选择。\n", "- 图形是动态可视化的。文章中图形都是截图,如果是在Jupyter notebook中都是动态图形" ] } ], "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": "3.7.6" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 4 }