![\"\"](\"https://github.com/modin-project/modin/blob/3d6368edf311995ad231ec5342a51cd9e4e3dc20/docs/img/MODIN_ver2_hrz.png?raw=true\")
\n![\"\"](\"https://img.shields.io/badge/discourse-forum-purple.svg?logo=discourse&logoColor=white\"){kind=icon}
\n![](\"https://codecov.io/gh/modin-project/modin/branch/master/graph/badge.svg\"){kind=icon}
\n![\"\"](\"https://travis-ci.com/modin-project/modin.svg?branch=master\")
\n\n
![](\"https://img.shields.io/badge/pandas%20api%20coverage-71.77%25-orange.svg\"){kind=icon}
\n![\"\"](\"https://img.shields.io/badge/pypi-0.6.2-blue.svg\"){kind=icon}
\n
To use Modin, replace the pandas import:
\n\n```python\n# import pandas as pd\nimport modin.pandas as pd\n```\n\n### Installation\n\nModin can be installed from PyPI:\n\n```bash\npip install modin\n```\n\nIf you don't have [Ray](https://github.com/ray-project/ray) or \n[Dask](https://github.com/dask/dask) installed, you will need to install Modin with one\nof the targets:\n\n```bash\npip install modin[ray] # Install Modin dependencies and Ray to run on Ray\npip install modin[dask] # Install Modin dependencies and Dask to run on Dask\npip install modin[all] # Install all of the above\n```\nModin will automatically detect which engine you have installed and use that for\nscheduling computation!\n\n##### Choosing a Compute Engine\n\nIf you want to choose a specific compute engine to run on, you can set the environment\nvariable `MODIN_ENGINE` and Modin will do computation with that engine:\n\n```bash\nexport MODIN_ENGINE=ray # Modin will use Ray\nexport MODIN_ENGINE=dask # Modin will use Dask\n```\n\nThis can also be done within a notebook/interpreter before you import Modin:\n\n```python\nimport os\n\nos.environ[\"MODIN_ENGINE\"] = \"ray\" # Modin will use Ray\nos.environ[\"MODIN_ENGINE\"] = \"dask\" # Modin will use Dask\n\nimport modin.pandas as pd\n```\n\n**Note: You should not change the engine after you have imported Modin as it will result in undefined behavior**\n\n##### Which engine should I use?\n\nIf you are on Windows, you must use Dask. Ray does not support Windows. If you are on\nLinux or Mac OS, you can install and use either engine. \n\n### Full Documentation\n\nVisit the complete documentation on readthedocs: https://modin.readthedocs.io\n\n### Scale your pandas workflow by changing a single line of code.\n\n\n```python\nimport modin.pandas as pd\nimport numpy as np\n\nframe_data = np.random.randint(0, 100, size=(2**10, 2**8))\ndf = pd.DataFrame(frame_data)\n```\n\nTo use Modin, you do not need to know how many cores your system has and you do not need\nto specify how to distribute the data. In fact, you can continue using your previous\npandas notebooks while experiencing a considerable speedup from Modin, even on a single\nmachine. Once you\u2019ve changed your import statement, you\u2019re ready to use Modin just like\nyou would pandas.\n\n\n#### Faster pandas, even on your laptop\n\n![](\"https://github.com/modin-project/modin/blob/master/docs/img/read_csv_benchmark.png?raw=true\")
\n\nThe `modin.pandas` DataFrame is an extremely light-weight parallel DataFrame. Modin \ntransparently distributes the data and computation so that all you need to do is\ncontinue using the pandas API as you were before installing Modin. Unlike other parallel\nDataFrame systems, Modin is an extremely light-weight, robust DataFrame. Because it is\nso light-weight, Modin provides speed-ups of up to 4x on a laptop with 4 physical cores.\n\nIn pandas, you are only able to use one core at a time when you are doing computation of\nany kind. With Modin, you are able to use all of the CPU cores on your machine. Even in\n`read_csv`, we see large gains by efficiently distributing the work across your entire\nmachine.\n\n```python\nimport modin.pandas as pd\n\ndf = pd.read_csv(\"my_dataset.csv\")\n```\n\n#### Modin is a DataFrame designed for datasets from 1MB to 1TB+ \n\nWe have focused heavily on bridging the solutions between DataFrames for small data \n(e.g. pandas) and large data. Often data scientists require different tools for doing\nthe same thing on different sizes of data. The DataFrame solutions that exist for 1KB do\nnot scale to 1TB+, and the overheads of the solutions for 1TB+ are too costly for \ndatasets in the 1KB range. With Modin, because of its light-weight, robust, and scalable\nnature, you get a fast DataFrame at small and large data. With preliminary [cluster](https://modin.readthedocs.io/en/latest/using_modin.html#using-modin-on-a-cluster)\nand [out of core](https://modin.readthedocs.io/en/latest/out_of_core.html)\nsupport, Modin is a DataFrame library with great single-node performance and high\nscalability in a cluster.\n\n#### Modin Architecture\n\nWe designed Modin to be modular so we can plug in different components as they develop\nand improve:\n\n\n\nVisit the [Documentation](https://modin.readthedocs.io/en/latest/architecture.html) for\nmore information!\n\n**`modin.pandas` is currently under active development. Requests and contributions are welcome!**\n\n\n### More information and Getting Involved\n\n- [Documentation](https://modin.readthedocs.io/en/latest/)\n- Ask questions or participate in discussions on our [Discourse](https://discuss.modin.org)\n- Join our mailing list [modin-dev@googlegroups.com](https://groups.google.com/forum/#!forum/modin-dev)\n- Submit bug reports to our [GitHub Issues Page](https://github.com/modin-project/modin/issues)\n- Contributions are welcome! Open a [pull request](https://github.com/modin-project/modin/pulls)\n\n\n",
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