{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#### New to Plotly?\n", "Plotly's Python library is free and open source! [Get started](https://plotly.com/python/getting-started/) by downloading the client and [reading the primer](https://plotly.com/python/getting-started/).\n", "
You can set up Plotly to work in [online](https://plotly.com/python/getting-started/#initialization-for-online-plotting) or [offline](https://plotly.com/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plotly.com/python/getting-started/#start-plotting-online).\n", "
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started!\n", "#### Version Check\n", "Plotly's python package is updated frequently. Run `pip install plotly --upgrade` to use the latest version. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'2.7.0'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly\n", "plotly.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Basic Bar Chart" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "\n", "data = [go.Bar(\n", " x=['giraffes', 'orangutans', 'monkeys'],\n", " y=[20, 14, 23]\n", " )]\n", "\n", "py.iplot(data, filename='basic-bar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Grouped Bar Chart" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "\n", "trace1 = go.Bar(\n", " x=['giraffes', 'orangutans', 'monkeys'],\n", " y=[20, 14, 23],\n", " name='SF Zoo'\n", ")\n", "trace2 = go.Bar(\n", " x=['giraffes', 'orangutans', 'monkeys'],\n", " y=[12, 18, 29],\n", " name='LA Zoo'\n", ")\n", "\n", "data = [trace1, trace2]\n", "layout = go.Layout(\n", " barmode='group'\n", ")\n", "\n", "fig = go.Figure(data=data, layout=layout)\n", "py.iplot(fig, filename='grouped-bar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Stacked Bar Chart" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "\n", "trace1 = go.Bar(\n", " x=['giraffes', 'orangutans', 'monkeys'],\n", " y=[20, 14, 23],\n", " name='SF Zoo'\n", ")\n", "trace2 = go.Bar(\n", " x=['giraffes', 'orangutans', 'monkeys'],\n", " y=[12, 18, 29],\n", " name='LA Zoo'\n", ")\n", "\n", "data = [trace1, trace2]\n", "layout = go.Layout(\n", " barmode='stack'\n", ")\n", "\n", "fig = go.Figure(data=data, layout=layout)\n", "py.iplot(fig, filename='stacked-bar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bar Chart with Hover Text" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "\n", "trace0 = go.Bar(\n", " x=['Product A', 'Product B', 'Product C'],\n", " y=[20, 14, 23],\n", " text=['27% market share', '24% market share', '19% market share'],\n", " marker=dict(\n", " color='rgb(158,202,225)',\n", " line=dict(\n", " color='rgb(8,48,107)',\n", " width=1.5,\n", " )\n", " ),\n", " opacity=0.6\n", ")\n", "\n", "data = [trace0]\n", "layout = go.Layout(\n", " title='January 2013 Sales Report',\n", ")\n", "\n", "fig = go.Figure(data=data, layout=layout)\n", "py.iplot(fig, filename='text-hover-bar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bar Chart with Direct Labels" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "\n", "x = ['Product A', 'Product B', 'Product C']\n", "y = [20, 14, 23]\n", "\n", "data = [go.Bar(\n", " x=x,\n", " y=y,\n", " text=y,\n", " textposition = 'auto',\n", " marker=dict(\n", " color='rgb(158,202,225)',\n", " line=dict(\n", " color='rgb(8,48,107)',\n", " width=1.5),\n", " ),\n", " opacity=0.6\n", " )]\n", "\n", "py.iplot(data, filename='bar-direct-labels')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Grouped Bar Chart with Direct Labels" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "\n", "x = ['Product A', 'Product B', 'Product C']\n", "y = [20, 14, 23]\n", "y2 = [16,12,27]\n", "\n", "trace1 = go.Bar(\n", " x=x,\n", " y=y,\n", " text=y,\n", " textposition = 'auto',\n", " marker=dict(\n", " color='rgb(158,202,225)',\n", " line=dict(\n", " color='rgb(8,48,107)',\n", " width=1.5),\n", " ),\n", " opacity=0.6\n", ")\n", "\n", "trace2 = go.Bar(\n", " x=x,\n", " y=y2,\n", " text=y2,\n", " textposition = 'auto',\n", " marker=dict(\n", " color='rgb(58,200,225)',\n", " line=dict(\n", " color='rgb(8,48,107)',\n", " width=1.5),\n", " ),\n", " opacity=0.6\n", ")\n", "\n", "data = [trace1,trace2]\n", "\n", "py.iplot(data, filename='grouped-bar-direct-labels')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Rotated Bar Chart Labels" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "\n", "trace0 = go.Bar(\n", " x=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',\n", " 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'],\n", " y=[20, 14, 25, 16, 18, 22, 19, 15, 12, 16, 14, 17],\n", " name='Primary Product',\n", " marker=dict(\n", " color='rgb(49,130,189)'\n", " )\n", ")\n", "trace1 = go.Bar(\n", " x=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',\n", " 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'],\n", " y=[19, 14, 22, 14, 16, 19, 15, 14, 10, 12, 12, 16],\n", " name='Secondary Product',\n", " marker=dict(\n", " color='rgb(204,204,204)',\n", " )\n", ")\n", "\n", "data = [trace0, trace1]\n", "layout = go.Layout(\n", " xaxis=dict(tickangle=-45),\n", " barmode='group',\n", ")\n", "\n", "fig = go.Figure(data=data, layout=layout)\n", "py.iplot(fig, filename='angled-text-bar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Customizing Individual Bar Colors" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "\n", "trace0 = go.Bar(\n", " x=['Feature A', 'Feature B', 'Feature C',\n", " 'Feature D', 'Feature E'],\n", " y=[20, 14, 23, 25, 22],\n", " marker=dict(\n", " color=['rgba(204,204,204,1)', 'rgba(222,45,38,0.8)',\n", " 'rgba(204,204,204,1)', 'rgba(204,204,204,1)',\n", " 'rgba(204,204,204,1)']),\n", ")\n", "\n", "data = [trace0]\n", "layout = go.Layout(\n", " title='Least Used Feature',\n", ")\n", "\n", "fig = go.Figure(data=data, layout=layout)\n", "py.iplot(fig, filename='color-bar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Customizing Individual Bar Widths" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "\n", "trace0 = go.Bar(\n", " x=[1, 2, 3, 5.5, 10],\n", " y=[10, 8, 6, 4, 2],\n", " width = [0.8, 0.8, 0.8, 3.5, 4]\n", ")\n", "\n", "data = [trace0]\n", "\n", "fig = go.Figure(data=data)\n", "py.iplot(fig, filename='width-bar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Customizing Individual Bar Base" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "\n", "data = [\n", " go.Bar(\n", " x = ['2016','2017','2018'],\n", " y = [500,600,700],\n", " base = [-500,-600,-700],\n", " marker = dict(\n", " color = 'red'\n", " ),\n", " name = 'expenses'\n", " ),\n", " go.Bar(\n", " x = ['2016','2017','2018'],\n", " y = [300,400,700],\n", " base = 0,\n", " marker = dict(\n", " color = 'blue'\n", " ),\n", " name = 'revenue'\n", " )\n", "]\n", "\n", "\n", "fig = go.Figure(data=data)\n", "py.iplot(fig, filename='base-bar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Colored and Styled Bar Chart" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "\n", "trace1 = go.Bar(\n", " x=[1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n", " 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012],\n", " y=[219, 146, 112, 127, 124, 180, 236, 207, 236, 263,\n", " 350, 430, 474, 526, 488, 537, 500, 439],\n", " name='Rest of world',\n", " marker=dict(\n", " color='rgb(55, 83, 109)'\n", " )\n", ")\n", "trace2 = go.Bar(\n", " x=[1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n", " 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012],\n", " y=[16, 13, 10, 11, 28, 37, 43, 55, 56, 88, 105, 156, 270,\n", " 299, 340, 403, 549, 499],\n", " name='China',\n", " marker=dict(\n", " color='rgb(26, 118, 255)'\n", " )\n", ")\n", "data = [trace1, trace2]\n", "layout = go.Layout(\n", " title='US Export of Plastic Scrap',\n", " xaxis=dict(\n", " tickfont=dict(\n", " size=14,\n", " color='rgb(107, 107, 107)'\n", " )\n", " ),\n", " yaxis=dict(\n", " title='USD (millions)',\n", " titlefont=dict(\n", " size=16,\n", " color='rgb(107, 107, 107)'\n", " ),\n", " tickfont=dict(\n", " size=14,\n", " color='rgb(107, 107, 107)'\n", " )\n", " ),\n", " legend=dict(\n", " x=0,\n", " y=1.0,\n", " bgcolor='rgba(255, 255, 255, 0)',\n", " bordercolor='rgba(255, 255, 255, 0)'\n", " ),\n", " barmode='group',\n", " bargap=0.15,\n", " bargroupgap=0.1\n", ")\n", "\n", "fig = go.Figure(data=data, layout=layout)\n", "py.iplot(fig, filename='style-bar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Waterfall Bar Chart" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "\n", "x_data = ['Product
Revenue', 'Services
Revenue',\n", " 'Total
Revenue', 'Fixed
Costs',\n", " 'Variable
Costs', 'Total
Costs', 'Total']\n", "y_data = [400, 660, 660, 590, 400, 400, 340]\n", "text = ['$430K', '$260K', '$690K', '$-120K', '$-200K', '$-320K', '$370K']\n", "\n", "# Base\n", "trace0 = go.Bar(\n", " x=x_data,\n", " y=[0, 430, 0, 570, 370, 370, 0],\n", " marker=dict(\n", " color='rgba(1,1,1, 0.0)',\n", " )\n", ")\n", "# Revenue\n", "trace1 = go.Bar(\n", " x=x_data,\n", " y=[430, 260, 690, 0, 0, 0, 0],\n", " marker=dict(\n", " color='rgba(55, 128, 191, 0.7)',\n", " line=dict(\n", " color='rgba(55, 128, 191, 1.0)',\n", " width=2,\n", " )\n", " )\n", ")\n", "# Costs\n", "trace2 = go.Bar(\n", " x=x_data,\n", " y=[0, 0, 0, 120, 200, 320, 0],\n", " marker=dict(\n", " color='rgba(219, 64, 82, 0.7)',\n", " line=dict(\n", " color='rgba(219, 64, 82, 1.0)',\n", " width=2,\n", " )\n", " )\n", ")\n", "# Profit\n", "trace3 = go.Bar(\n", " x=x_data,\n", " y=[0, 0, 0, 0, 0, 0, 370],\n", " marker=dict(\n", " color='rgba(50, 171, 96, 0.7)',\n", " line=dict(\n", " color='rgba(50, 171, 96, 1.0)',\n", " width=2,\n", " )\n", " )\n", ")\n", "data = [trace0, trace1, trace2, trace3]\n", "layout = go.Layout(\n", " title='Annual Profit- 2015',\n", " barmode='stack',\n", " paper_bgcolor='rgba(245, 246, 249, 1)',\n", " plot_bgcolor='rgba(245, 246, 249, 1)',\n", " showlegend=False\n", ")\n", "\n", "annotations = []\n", "\n", "for i in range(0, 7):\n", " annotations.append(dict(x=x_data[i], y=y_data[i], text=text[i],\n", " font=dict(family='Arial', size=14,\n", " color='rgba(245, 246, 249, 1)'),\n", " showarrow=False,))\n", " layout['annotations'] = annotations\n", "\n", "fig = go.Figure(data=data, layout=layout)\n", "py.iplot(fig, filename='waterfall-bar-profit')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bar Chart with Relative Barmode" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = [1, 2, 3, 4]\n", "\n", "trace1 = {\n", " 'x': x,\n", " 'y': [1, 4, 9, 16],\n", " 'name': 'Trace1',\n", " 'type': 'bar'\n", "};\n", "trace2 = {\n", " 'x': x,\n", " 'y': [6, -8, -4.5, 8],\n", " 'name': 'Trace2',\n", " 'type': 'bar'\n", "};\n", "trace3 = {\n", " 'x': x,\n", " 'y': [-15, -3, 4.5, -8],\n", " 'name': 'Trace3',\n", " 'type': 'bar'\n", " }\n", " \n", "trace4 = {\n", " 'x': x,\n", " 'y': [-1, 3, -3, -4],\n", " 'name': 'Trace4',\n", " 'type': 'bar'\n", " }\n", " \n", "data = [trace1, trace2, trace3, trace4];\n", "layout = {\n", " 'xaxis': {'title': 'X axis'},\n", " 'yaxis': {'title': 'Y axis'},\n", " 'barmode': 'relative',\n", " 'title': 'Relative Barmode'\n", "};\n", "py.iplot({'data': data, 'layout': layout}, filename='barmode-relative')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Horizontal Bar Charts\n", "See examples of horizontal bar charts [here](https://plotly.com/python/horizontal-bar-charts/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dash Example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Dash](https://plotly.com/products/dash/) is an Open Source Python library which can help you convert plotly figures into a reactive, web-based application. Below is a simple example of a dashboard created using Dash. Its [source code](https://github.com/plotly/simple-example-chart-apps/tree/master/dash-barplot) can easily be deployed to a PaaS." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import IFrame\n", "IFrame(src= \"https://dash-simple-apps.plotly.host/dash-barplot/\", width=\"100%\", height=\"650px\", frameBorder=\"0\")\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import IFrame\n", "IFrame(src= \"https://dash-simple-apps.plotly.host/dash-barplot/code\", width=\"80%\", height=500, frameBorder=\"0\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reference\n", "See https://plotly.com/python/reference/#bar for more information and chart attribute options!" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Collecting git+https://github.com/plotly/publisher.git\n", " Cloning https://github.com/plotly/publisher.git to /private/var/folders/s5/vjqn03zs7nn8zs_fwzcf14r40000gn/T/pip-req-build-uj6kwvwa\n", "Building wheels for collected packages: publisher\n", " Building wheel for publisher (setup.py) ... \u001b[?25ldone\n", "\u001b[?25h Stored in directory: /private/var/folders/s5/vjqn03zs7nn8zs_fwzcf14r40000gn/T/pip-ephem-wheel-cache-gf3f4eb5/wheels/99/3e/a0/fbd22ba24cca72bdbaba53dbc23c1768755fb17b3af0f33966\n", "Successfully built publisher\n", "Installing collected packages: publisher\n", " Found existing installation: publisher 0.13\n", " Uninstalling publisher-0.13:\n", " Successfully uninstalled publisher-0.13\n", "Successfully installed publisher-0.13\n", "\u001b[33mYou are using pip version 19.0.3, however version 19.1.1 is available.\n", "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n" ] } ], "source": [ "from IPython.display import display, HTML\n", "\n", "display(HTML(''))\n", "display(HTML(''))\n", "\n", "#! pip install git+https://github.com/plotly/publisher.git --upgrade\n", "import publisher\n", "publisher.publish(\n", " 'bars.ipynb', 'python/bar-charts/', 'Python Bar Charts | plotly',\n", " 'How to make Bar Charts in Python with Plotly.',\n", " title = 'Bar Charts | plotly',\n", " name = 'Bar Charts',\n", " thumbnail='thumbnail/bar.jpg', language='python',\n", " page_type='example_index', has_thumbnail='true', display_as='basic', order=4,\n", " ipynb= '~notebook_demo/186')" ] } ], "metadata": { "anaconda-cloud": {}, "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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 }