--- jupyter: jupytext: notebook_metadata_filter: all text_representation: extension: .md format_name: markdown format_version: '1.3' jupytext_version: 1.16.4 kernelspec: display_name: Python 3 (ipykernel) 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: 3.11.10 plotly: description: Getting Started with Plotly for Python. has_thumbnail: false language: python layout: base name: Getting Started with Plotly page_type: u-guide permalink: python/getting-started/ redirect_from: - python/getting_started/ - /python/pytables/ --- ### Overview The [`plotly` Python library](/python/) is an interactive, [open-source](/python/is-plotly-free) plotting library that supports over 40 unique chart types covering a wide range of statistical, financial, geographic, scientific, and 3-dimensional use-cases. Built on top of the Plotly JavaScript library ([plotly.js](https://plotly.com/javascript/)), `plotly` enables Python users to create beautiful interactive web-based visualizations that can be displayed in Jupyter notebooks, saved to standalone HTML files, or served as part of pure Python-built web applications using Dash. The `plotly` Python library is sometimes referred to as "plotly.py" to differentiate it from the JavaScript library. Thanks to deep integration with our [Kaleido](https://github.com/plotly/Kaleido) image export utility, `plotly` also provides great support for non-web contexts including desktop editors (e.g. QtConsole, Spyder, PyCharm) and static document publishing (e.g. exporting notebooks to PDF with high-quality vector images). This Getting Started guide explains how to install `plotly` and related optional pages. Once you've installed, you can use our documentation in three main ways: 1. You jump right in to **examples** of how to make [basic charts](/python/basic-charts/), [statistical charts](/python/statistical-charts/), [scientific charts](/python/scientific-charts/), [financial charts](/python/financial-charts/), [maps](/python/maps/), and [3-dimensional charts](/python/3d-charts/). 2. If you prefer to learn about the **fundamentals** of the library first, you can read about [the structure of figures](/python/figure-structure/), [how to create and update figures](/python/creating-and-updating-figures/), [how to display figures](/python/renderers/), [how to theme figures with templates](/python/templates/), [how to export figures to various formats](/python/static-image-export/) and about [Plotly Express, the high-level API](/python/plotly-express/) for doing all of the above. 3. You can check out our exhaustive **reference** guides: the [Python API reference](/python-api-reference) or the [Figure Reference](/python/reference) For information on using Python to build web applications containing plotly figures, see the [_Dash User Guide_](https://dash.plotly.com/). We also encourage you to join the [Plotly Community Forum](http://community.plotly.com/) if you want help with anything related to `plotly`. ### Installation `plotly` may be installed using `pip`: ``` $ pip install plotly ``` or `conda`: ``` $ conda install -c conda-forge plotly ``` If you want to use Plotly Express, install its required dependencies with: ``` pip install plotly[express] ``` You'll also need to install a [supported dataframe library](/python/px-arguments#supported-dataFrame-types). ### Plotly charts in Dash [Dash](https://plotly.com/dash/) is the best way to build analytical apps in Python using Plotly figures. To run the app below, run `pip install dash`, click "Download" to get the code and run `python app.py`. Get started with [the official Dash docs](https://dash.plotly.com/installation) and **learn how to effortlessly [style](https://plotly.com/dash/design-kit/) & publish apps like this with Dash Enterprise or Plotly Cloud.** ```python hide_code=true from IPython.display import IFrame snippet_url = 'https://python-docs-dash-snippets.herokuapp.com/python-docs-dash-snippets/' IFrame(snippet_url + 'getting-started', width='100%', height=1200) ```

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#### JupyterLab Support To use `plotly` in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/), install the `jupyterlab` and `anywidget` packages in the same environment as you installed `plotly`, using `pip`: ``` $ pip install jupyterlab anywidget ``` or `conda`: ``` $ conda install jupyterlab anywidget ``` Launch JupyterLab with: ``` $ jupyter lab ``` and display plotly figures inline: ```python import plotly.express as px fig = px.bar(x=["a", "b", "c"], y=[1, 3, 2]) fig.show() ``` or using `FigureWidget` objects. ```python import plotly.express as px fig = px.bar(x=["a", "b", "c"], y=[1, 3, 2]) import plotly.graph_objects as go fig_widget = go.FigureWidget(fig) fig_widget ``` See [_Displaying Figures in Python_](/python/renderers/) for more information on the renderers framework, and see [_Plotly FigureWidget Overview_](/python/figurewidget/) for more information on using `FigureWidget`. See the [Troubleshooting guide](/python/troubleshooting/) if you run into any problems with JupyterLab, particularly if you are using multiple Python environments inside Jupyter. #### Jupyter Notebook Support For use in the classic [Jupyter Notebook](https://jupyter.org/), install the `notebook` and `ipywidgets` packages using `pip`: ``` pip install "notebook>=7.0" "anywidget>=0.9.13" ``` or `conda`: ``` conda install "notebook>=7.0" "anywidget>=0.9.13" ``` These packages contain everything you need to run a Jupyter notebook... ``` $ jupyter notebook ``` and display plotly figures inline using the notebook renderer... ```python import plotly.express as px fig = px.bar(x=["a", "b", "c"], y=[1, 3, 2]) fig.show() ``` or using `FigureWidget` objects. ```python import plotly.express as px fig = px.bar(x=["a", "b", "c"], y=[1, 3, 2]) import plotly.graph_objects as go fig_widget = go.FigureWidget(fig) fig_widget ``` See [_Displaying Figures in Python_](/python/renderers/) for more information on the renderers framework, and see [_Plotly FigureWidget Overview_](/python/figurewidget/) for more information on using `FigureWidget`. ### Static Image Export plotly.py supports [static image export](https://plotly.com/python/static-image-export/), using the [`kaleido`](https://github.com/plotly/Kaleido) package (version 1.0.0 or greater). Kaleido has minimal dependencies and can be installed using pip... ``` $ pip install --upgrade kaleido ``` or conda. ``` $ conda install -c plotly python-kaleido ``` Kaleido requires Chrome or Chromium to generate images. By default, Kaleido will use the Chrome or Chromium version already installed on your system. If you don't have it installed or Kaleido can't find it, you may need to install it by running the following command: `plotly_get_chrome` ### Where to next? Once you've installed, you can use our documentation in three main ways: 1. You jump right in to **examples** of how to make [basic charts](/python/basic-charts/), [statistical charts](/python/statistical-charts/), [scientific charts](/python/scientific-charts/), [financial charts](/python/financial-charts/), [maps](/python/maps/), and [3-dimensional charts](/python/3d-charts/). 2. If you prefer to learn about the **fundamentals** of the library first, you can read about [the structure of figures](/python/figure-structure/), [how to create and update figures](/python/creating-and-updating-figures/), [how to display figures](/python/renderers/), [how to theme figures with templates](/python/templates/), [how to export figures to various formats](/python/static-image-export/) and about [Plotly Express, the high-level API](/python/plotly-express/) for doing all of the above. 3. You can check out our exhaustive **reference** guides: the [Python API reference](/python-api-reference) or the [Figure Reference](/python/reference) For information on using Python to build web applications containing plotly figures, see the [_Dash User Guide_](https://dash.plotly.com/). We also encourage you to join the [Plotly Community Forum](http://community.plotly.com/) if you want help with anything related to `plotly`.