{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Installation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To install Chart Studio's python package, use the package manager **pip** inside your terminal.
\n", "If you don't have **pip** installed on your machine, [click here](https://pip.pypa.io/en/latest/installing.html) for pip's installation instructions.\n", "
\n", "
\n", "`$ pip install chart_studio`\n", "
or\n", "
`$ sudo pip install chart_studio`\n", "
\n", "
\n", "Plotly's Python package is installed alongside the Chart Studio package and it is [updated frequently](https://github.com/plotly/plotly.py/blob/master/CHANGELOG.md)! To upgrade, run:\n", "
\n", "
\n", "`$ pip install plotly --upgrade`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initialization for Online Plotting\n", "Chart Studio provides a web-service for hosting graphs! Create a [free account](https://plotly.com/api_signup) to get started. Graphs are saved inside your online Chart Studio account and you control the privacy. Public hosting is free, for private hosting, check out our [paid plans](https://plotly.com/products/cloud/).\n", "
\n", "
\n", "After installing the Chart Studio package, you're ready to fire up python:\n", "
\n", "
\n", "`$ python`\n", "
\n", "
\n", "and set your credentials:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import chart_studio\n", "chart_studio.tools.set_credentials_file(username='DemoAccount', api_key='lr1c37zw81')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You'll need to replace **'DemoAccount'** and **'lr1c37zw81'** with *your* Plotly username and [API key](https://plotly.com/settings/api).
\n", "Find your API key [here](https://plotly.com/settings/api).\n", "
\n", "
\n", "The initialization step places a special **.plotly/.credentials** file in your home directory. Your **~/.plotly/.credentials** file should look something like this:\n", "
\n", "```\n", "{\n", " \"username\": \"DemoAccount\",\n", " \"stream_ids\": [\"ylosqsyet5\", \"h2ct8btk1s\", \"oxz4fm883b\"],\n", " \"api_key\": \"lr1c37zw81\"\n", "}\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Online Plot Privacy\n", "\n", "Plot can be set to three different type of privacies: public, private or secret.\n", "- **public**: Anyone can view this graph. It will appear in your profile and can appear in search engines. You do not need to be logged in to Chart Studio to view this chart.\n", "- **private**: Only you can view this plot. It will not appear in the Plotly feed, your profile, or search engines. You must be logged in to Plotly to view this graph. You can privately share this graph with other Chart Studio users in your online Chart Studio account and they will need to be logged in to view this plot.\n", "- **secret**: Anyone with this secret link can view this chart. It will not appear in the Chart Studio feed, your profile, or search engines. If it is embedded inside a webpage or an IPython notebook, anybody who is viewing that page will be able to view the graph. You do not need to be logged in to view this plot.\n", "\n", "By default all plots are set to **public**. Users with free account have the permission to keep one private plot. If you need to save private plots, [upgrade to a pro account](https://plotly.com/plans). If you're a [Personal or Professional user](https://plotly.com/settings/subscription/?modal=true&utm_source=api-docs&utm_medium=support-oss) and would like the default setting for your plots to be private, you can edit your Chart Studio configuration:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import chart_studio\n", "chart_studio.tools.set_config_file(world_readable=False,\n", " sharing='private')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For more examples on privacy settings please visit [Python privacy documentation](https://plotly.com/python/privacy/)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Special Instructions for [Chart Studio Enterprise](https://plotly.com/product/enterprise/) Users" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Your API key for account on the public cloud will be different than the API key in Chart Studio Enterprise. Visit https://plotly.your-company.com/settings/api/ to find your Chart Studio Enterprise API key. Remember to replace \"your-company.com\" with the URL of your Chart Studio Enterprise server.\n", "If your company has a Chart Studio Enterprise server, change the Python API endpoint so that it points to your company's Plotly server instead of Plotly's cloud.\n", "
\n", "
\n", "In python, enter:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import chart_studio\n", "chart_studio.tools.set_config_file(\n", " plotly_domain='https://plotly.your-company.com',\n", " plotly_api_domain='https://plotly.your-company.com',\n", " plotly_streaming_domain='https://stream-plotly.your-company.com'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make sure to replace **\"your-company.com\"** with the URL of *your* Chart Studio Enterprise server." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Additionally, you can set your configuration so that you generate **private plots by default**. For more information on privacy settings see: https://plotly.com/python/privacy/
\n", "
\n", "In python, enter:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import chart_studio\n", "chart_studio.tools.set_config_file(\n", " plotly_domain='https://plotly.your-company.com',\n", " plotly_api_domain='https://plotly.your-company.com',\n", " plotly_streaming_domain='https://stream-plotly.your-company.com',\n", " world_readable=False,\n", " sharing='private'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotly Using virtualenv\n", "Python's `virtualenv` allows us create multiple working Python environments which can each use different versions of packages. We can use `virtualenv` from the command line to create an environment using plotly.py version 3.3.0 and a separate one using plotly.py version 2.7.0. See [the virtualenv documentation](https://virtualenv.pypa.io/en/stable) for more info.\n", "\n", "**Install virtualenv globally**\n", "
`$ sudo pip install virtualenv`\n", "\n", "**Create your virtualenvs**\n", "
`$ mkdir ~/.virtualenvs`\n", "
`$ cd ~/.virtualenvs`\n", "
`$ python -m venv plotly2.7`\n", "
`$ python -m venv plotly3.3`\n", "\n", "**Activate the virtualenv.**\n", "You will see the name of your virtualenv in parenthesis next to the input promt.\n", "
`$ source ~/.virtualenvs/plotly2.7/bin/activate`\n", "
`(plotly2.7) $`\n", "\n", "**Install plotly locally to virtualenv** (note that we don't use sudo).\n", "
`(plotly2.7) $ pip install plotly==2.7`\n", "\n", "**Deactivate to exit**\n", "
\n", "`(plotly2.7) $ deactivate`\n", "
`$`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Jupyter Setup\n", "**Install Jupyter into a virtualenv**\n", "
`$ source ~/.virtualenvs/plotly3.3/bin/activate`\n", "
`(plotly3.3) $ pip install notebook`\n", "\n", "**Start the Jupyter kernel from a virtualenv**\n", "
`(plotly3.3) $ jupyter notebook`\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Start Plotting Online\n", "When plotting online, the plot and data will be saved to your cloud account. There are two methods for plotting online: `py.plot()` and `py.iplot()`. Both options create a unique url for the plot and save it in your Plotly account.\n", "- Use `py.plot()` to return the unique url and optionally open the url.\n", "- Use `py.iplot()` when working in a Jupyter Notebook to display the plot in the notebook.\n", "\n", "Copy and paste one of the following examples to create your first hosted Plotly graph using the Plotly Python library:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'https://plotly.com/~PythonPlotBot/27/'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import chart_studio.plotly as py\n", "import plotly.graph_objects as go\n", "\n", "trace0 = go.Scatter(\n", " x=[1, 2, 3, 4],\n", " y=[10, 15, 13, 17]\n", ")\n", "trace1 = go.Scatter(\n", " x=[1, 2, 3, 4],\n", " y=[16, 5, 11, 9]\n", ")\n", "data = [trace0, trace1]\n", "\n", "py.plot(data, filename = 'basic-line', auto_open=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Checkout the docstrings for more information:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function plot in module chart_studio.plotly.plotly:\n", "\n", "plot(figure_or_data, validate=True, **plot_options)\n", " Create a unique url for this plot in Plotly and optionally open url.\n", " \n", " plot_options keyword arguments:\n", " filename (string) -- the name that will be associated with this figure\n", " auto_open (default=True) -- Toggle browser options\n", " True: open this plot in a new browser tab\n", " False: do not open plot in the browser, but do return the unique url\n", " sharing ('public' | 'private' | 'secret') -- Toggle who can view this\n", " graph\n", " - 'public': Anyone can view this graph. It will appear in your profile\n", " and can appear in search engines. You do not need to be\n", " logged in to Plotly to view this chart.\n", " - 'private': Only you can view this plot. It will not appear in the\n", " Plotly feed, your profile, or search engines. You must be\n", " logged in to Plotly to view this graph. You can privately\n", " share this graph with other Plotly users in your online\n", " Plotly account and they will need to be logged in to\n", " view this plot.\n", " - 'secret': Anyone with this secret link can view this chart. It will\n", " not appear in the Plotly feed, your profile, or search\n", " engines. If it is embedded inside a webpage or an IPython\n", " notebook, anybody who is viewing that page will be able to\n", " view the graph. You do not need to be logged in to view\n", " this plot.\n", " world_readable (default=True) -- Deprecated: use \"sharing\".\n", " Make this figure private/public\n", "\n" ] } ], "source": [ "import chart_studio.plotly as py\n", "help(py.plot)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import chart_studio.plotly as py\n", "import plotly.graph_objects as go\n", "\n", "trace0 = go.Scatter(\n", " x=[1, 2, 3, 4],\n", " y=[10, 15, 13, 17]\n", ")\n", "trace1 = go.Scatter(\n", " x=[1, 2, 3, 4],\n", " y=[16, 5, 11, 9]\n", ")\n", "data = [trace0, trace1]\n", "\n", "py.iplot(data, filename = 'basic-line')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See more examples in our [IPython notebook documentation](https://plotly.com/ipython-notebooks/) or check out the `py.iplot()` docstring for more information." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function iplot in module chart_studio.plotly.plotly:\n", "\n", "iplot(figure_or_data, **plot_options)\n", " Create a unique url for this plot in Plotly and open in IPython.\n", " \n", " plot_options keyword arguments:\n", " filename (string) -- the name that will be associated with this figure\n", " sharing ('public' | 'private' | 'secret') -- Toggle who can view this graph\n", " - 'public': Anyone can view this graph. It will appear in your profile\n", " and can appear in search engines. You do not need to be\n", " logged in to Plotly to view this chart.\n", " - 'private': Only you can view this plot. It will not appear in the\n", " Plotly feed, your profile, or search engines. You must be\n", " logged in to Plotly to view this graph. You can privately\n", " share this graph with other Plotly users in your online\n", " Plotly account and they will need to be logged in to\n", " view this plot.\n", " - 'secret': Anyone with this secret link can view this chart. It will\n", " not appear in the Plotly feed, your profile, or search\n", " engines. If it is embedded inside a webpage or an IPython\n", " notebook, anybody who is viewing that page will be able to\n", " view the graph. You do not need to be logged in to view\n", " this plot.\n", " world_readable (default=True) -- Deprecated: use \"sharing\".\n", " Make this figure private/public\n", "\n" ] } ], "source": [ "import chart_studio.plotly as py\n", "help(py.iplot)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also create plotly graphs with **matplotlib** syntax. Learn more in our [matplotlib documentation](https://plotly.com/matplotlib/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initialization for Offline Plotting\n", "Plotly allows you to create graphs offline and save them locally. There are also two methods for interactive plotting offline: `plotly.io.write_html()` and `plotly.io.show()`.\n", "- Use `plotly.io.write_html()` to create and standalone HTML that is saved locally and opened inside your web browser.\n", "- Use `plotly.io.show()` when working offline in a Jupyter Notebook to display the plot in the notebook.\n", "\n", "For information on all of the ways that plotly figures can be displayed, see [*Displaying plotly figures with plotly for Python*](https://plotly.com/python/renderers/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copy and paste one of the following examples to create your first offline Plotly graph using the Plotly Python library:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import plotly.graph_objects as go\n", "import plotly.io as pio\n", "\n", "fig = go.Figure(go.Scatter(x=[1, 2, 3, 4], y=[4, 3, 2, 1]))\n", "fig.update_layout(title_text='hello world')\n", "pio.write_html(fig, file='hello_world.html', auto_open=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Learn more by calling `help()`:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function write_html in module plotly.io._html:\n", "\n", "write_html(fig, file, config=None, auto_play=True, include_plotlyjs=True, include_mathjax=False, post_script=None, full_html=True, animation_opts=None, validate=True, default_width='100%', default_height='100%', auto_open=False)\n", " Write a figure to an HTML file representation\n", " \n", " Parameters\n", " ----------\n", " fig:\n", " Figure object or dict representing a figure\n", " file: str or writeable\n", " A string representing a local file path or a writeable object\n", " (e.g. an open file descriptor)\n", " config: dict or None (default None)\n", " Plotly.js figure config options\n", " auto_play: bool (default=True)\n", " Whether to automatically start the animation sequence on page load\n", " if the figure contains frames. Has no effect if the figure does not\n", " contain frames.\n", " include_plotlyjs: bool or string (default True)\n", " Specifies how the plotly.js library is included/loaded in the output\n", " div string.\n", " \n", " If True, a script tag containing the plotly.js source code (~3MB)\n", " is included in the output. HTML files generated with this option are\n", " fully self-contained and can be used offline.\n", " \n", " If 'cdn', a script tag that references the plotly.js CDN is included\n", " in the output. HTML files generated with this option are about 3MB\n", " smaller than those generated with include_plotlyjs=True, but they\n", " require an active internet connection in order to load the plotly.js\n", " library.\n", " \n", " If 'directory', a script tag is included that references an external\n", " plotly.min.js bundle that is assumed to reside in the same\n", " directory as the HTML file. If `file` is a string to a local file path\n", " and `full_html` is True then\n", " \n", " If 'directory', a script tag is included that references an external\n", " plotly.min.js bundle that is assumed to reside in the same\n", " directory as the HTML file. If `file` is a string to a local file\n", " path and `full_html` is True, then the plotly.min.js bundle is copied\n", " into the directory of the resulting HTML file. If a file named\n", " plotly.min.js already exists in the output directory then this file\n", " is left unmodified and no copy is performed. HTML files generated\n", " with this option can be used offline, but they require a copy of\n", " the plotly.min.js bundle in the same directory. This option is\n", " useful when many figures will be saved as HTML files in the same\n", " directory because the plotly.js source code will be included only\n", " once per output directory, rather than once per output file.\n", " \n", " If 'require', Plotly.js is loaded using require.js. This option\n", " assumes that require.js is globally available and that it has been\n", " globally configured to know how to find Plotly.js as 'plotly'.\n", " This option is not advised when full_html=True as it will result\n", " in a non-functional html file.\n", " \n", " If a string that ends in '.js', a script tag is included that\n", " references the specified path. This approach can be used to point\n", " the resulting HTML file to an alternative CDN or local bundle.\n", " \n", " If False, no script tag referencing plotly.js is included. This is\n", " useful when the resulting div string will be placed inside an HTML\n", " document that already loads plotly.js. This option is not advised\n", " when full_html=True as it will result in a non-functional html file.\n", " \n", " include_mathjax: bool or string (default False)\n", " Specifies how the MathJax.js library is included in the output html\n", " div string. MathJax is required in order to display labels\n", " with LaTeX typesetting.\n", " \n", " If False, no script tag referencing MathJax.js will be included in the\n", " output.\n", " \n", " If 'cdn', a script tag that references a MathJax CDN location will be\n", " included in the output. HTML div strings generated with this option\n", " will be able to display LaTeX typesetting as long as internet access\n", " is available.\n", " \n", " If a string that ends in '.js', a script tag is included that\n", " references the specified path. This approach can be used to point the\n", " resulting HTML div string to an alternative CDN.\n", " post_script: str or list or None (default None)\n", " JavaScript snippet(s) to be included in the resulting div just after\n", " plot creation. The string(s) may include '{plot_id}' placeholders\n", " that will then be replaced by the `id` of the div element that the\n", " plotly.js figure is associated with. One application for this script\n", " is to install custom plotly.js event handlers.\n", " full_html: bool (default True)\n", " If True, produce a string containing a complete HTML document\n", " starting with an tag. If False, produce a string containing\n", " a single
element.\n", " animation_opts: dict or None (default None)\n", " dict of custom animation parameters to be passed to the function\n", " Plotly.animate in Plotly.js. See\n", " https://github.com/plotly/plotly.js/blob/master/src/plots/animation_attributes.js\n", " for available options. Has no effect if the figure does not contain\n", " frames, or auto_play is False.\n", " default_width, default_height: number or str (default '100%')\n", " The default figure width/height to use if the provided figure does not\n", " specify its own layout.width/layout.height property. May be\n", " specified in pixels as an integer (e.g. 500), or as a css width style\n", " string (e.g. '500px', '100%').\n", " validate: bool (default True)\n", " True if the figure should be validated before being converted to\n", " JSON, False otherwise.\n", " auto_open: bool (default True\n", " If True, open the saved file in a web browser after saving.\n", " This argument only applies if `full_html` is True.\n", " Returns\n", " -------\n", " str\n", " Representation of figure as an HTML div string\n", "\n" ] } ], "source": [ "import plotly\n", "help(plotly.io.write_html)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plotly.com" }, "data": [ { "type": "scatter", "x": [ 1, 2, 3, 4 ], "y": [ 4, 3, 2, 1 ] } ], "layout": { "autosize": true, "template": { "data": { "bar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "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, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 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" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "heatmapgl" } ], "histogram": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" 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", 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\n", " \n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import plotly.graph_objects as go\n", "import plotly.io as pio\n", "\n", "fig = go.Figure(go.Scatter(x=[1, 2, 3, 4], y=[4, 3, 2, 1]))\n", "fig.update_layout(title_text='hello world')\n", "pio.show(fig)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also call plotly.io.show directly from the go.Figure object." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plotly.com" }, "data": [ { "type": "scatter", "x": [ 1, 2, 3, 4 ], "y": [ 4, 3, 2, 1 ] } ], "layout": { "autosize": true, "template": { "data": { "bar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "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, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 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" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "heatmapgl" } ], "histogram": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 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"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": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#506784", "arrowhead": 0, "arrowwidth": 1 }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, 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"#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": { "fillcolor": "#506784", "line": { "width": 0 }, "opacity": 0.4 }, "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": "", "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "hello world" }, "xaxis": { "autorange": true, "range": [ 0.8171959721146398, 4.182804027885361 ], "type": "linear" }, "yaxis": { "autorange": true, "range": [ 0.7802547770700636, 4.219745222929936 ], "type": "linear" } } }, "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function show in module plotly.io._renderers:\n", "\n", "show(fig, renderer=None, validate=True, **kwargs)\n", " Show a figure using either the default renderer(s) or the renderer(s)\n", " specified by the renderer argument\n", " \n", " Parameters\n", " ----------\n", " fig: dict of Figure\n", " The Figure object or figure dict to display\n", " \n", " renderer: str or None (default None)\n", " A string containing the names of one or more registered renderers\n", " (separated by '+' characters) or None. If None, then the default\n", " renderers specified in plotly.io.renderers.default are used.\n", " \n", " validate: bool (default True)\n", " True if the figure should be validated before being shown,\n", " False otherwise.\n", " \n", " Returns\n", " -------\n", " None\n", "\n" ] } ], "source": [ "import plotly\n", "help(plotly.io.show)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For more examples on plotting offline with Plotly in python please visit our [offline documentation](https://plotly.com/python/offline/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using Plotly with Pandas\n", "\n", "To use Plotly with Pandas first `$ pip install pandas` and then import pandas in your code like in the example below." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import chart_studio.plotly as py\n", "import plotly.graph_objects as go\n", "import pandas as pd\n", "\n", "df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminder2007.csv')\n", "\n", "fig = go.Figure(go.Scatter(x=df.gdpPercap, y=df.lifeExp, text=df.country, mode='markers', name='2007'))\n", "fig.update_xaxes(title_text='GDP per Capita', type='log')\n", "fig.update_yaxes(title_text='Life Expectancy')\n", "\n", "py.iplot(fig, filename='pandas-multiple-scatter')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [MORE EXAMPLES](https://plotly.com/python/)\n", "Check out more examples and tutorials for using Plotly in python [here](https://plotly.com/python)!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "jupytext": { "notebook_metadata_filter": "all", "text_representation": { "extension": ".md", "format_name": "markdown", "format_version": "1.1", "jupytext_version": "1.1.7" } }, "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" }, "plotly": { "description": "Installation and Initialization Steps for Using Chart Studio in Python.", "display_as": "chart_studio", "has_thumbnail": true, "ipynb": "~notebook_demo/123/installation", "language": "python", "layout": "base", "name": "Getting Started with Plotly for Python", "order": 0.1, "page_type": "example_index", "permalink": "python/getting-started-with-chart-studio/", "thumbnail": "thumbnail/bubble.jpg", "title": "Getting Started with Chart Studio for Python | plotly", "v4upgrade": true } }, "nbformat": 4, "nbformat_minor": 4 }