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        "author": "Diogo Munaro Vieira",
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        "description": "# Dataset Manager\n\nManage and automatize your datasets for your project with YAML files.\n\n\n[![Build Status](https://travis-ci.com/dmvieira/dataset-manager.svg?branch=master)](https://travis-ci.com/dmvieira/dataset-manager)\n\nCurrent Support: [![Python 3.5](https://img.shields.io/badge/python-3.5-blue.svg)](https://www.python.org/downloads/release/python-350/)[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)[![Python 3.7](https://img.shields.io/badge/python-3.7-blue.svg)](https://www.python.org/downloads/release/python-370/)[![Python 3.8](https://img.shields.io/badge/python-3.8-blue.svg)](https://www.python.org/downloads/release/python-380/)\n\n## How it Works\n\nThis project create a file called *identifier.yaml* in your dataset directory with these fields:\n\n```\nsource: https://raw.githubusercontent.com/pcsanwald/kaggle-titanic/master/train.csv\n\ndescription: this dataset is a test dataset\n\n```\n\n*identifier*: is the identifier for dataset reference is the file name with *yaml* extension.\n\n*source*: is location from dataset.\n\n*description*: describe your dataset to remember later.\n\nEach dataset is a YAML file inside dataset directory.\n\n## Installing\n\nWith pip just:\n\n```\npip install dataset_manager\n```\n\nWith conda:\n\n```\nconda install dataset_manager\n```\n\n## Using\n\nYou can manage your datasets with a list of commands and integrate with [Pandas](https://pandas.pydata.org/) or other data analysis tool.\n\n### Manager functions\n\n#### Show all Datasets\n\nReturn a table with all Datasets from dataset path\n\n```\nfrom dataset_manager import DatasetManager\n\nmanager = DatasetManager(dataset_path, local_path_to_download)\n\nmanager.show_datasets()\n```\n\n#### Create a Dataset\n\nCreate a Dataset with every information you want inside dataset_path defined.\n\n```\nfrom dataset_manager import DatasetManager\n\nmanager = DatasetManager(dataset_path, local_path_to_download)\n\nmanager.create_dataset(identifier, source, description, **kwargs)\n```\n\n#### Remove a Dataset\n\nRemove Dataset from dataset_path\n\n```\nfrom dataset_manager import DatasetManager\n\nmanager = DatasetManager(dataset_path, local_path_to_download)\n\nmanager.remove_dataset(identifier)\n```\n\n#### Prepare Datasets\n\nDownload and Unzip all Datasets\n\n```\nfrom dataset_manager import DatasetManager\n\nmanager = DatasetManager(dataset_path, local_path_to_download)\n\nmanager.prepare_datasets()\n```\n\n#### Using Multiple Filesystems\n\nThis manager is integrated with [Pyfilesystem2](https://github.com/PyFilesystem/pyfilesystem2) and you can use all [builtin](https://docs.pyfilesystem.org/en/latest/builtin.html) filesystems or with [third-party](https://www.pyfilesystem.org/page/index-of-filesystems/) extensions or [creating your own](https://docs.pyfilesystem.org/en/latest/extension.html) extension.\n\nWith Pyfilesystem2, you can download, extract and manage datasets in any place.\n\n```\nfrom fs.tempfs import TempFS\nfrom dataset_manager import DatasetManager\n\nmanager = DatasetManager(dataset_path, local_path_to_download, TempFS())\n\nmanager.prepare_datasets() # all datasets will be downloaded and extracted on temporary files respecting your local_path_to_download hierarchy\n```\n\n#### Get one Dataset\n\nGet Dataset line as dict\n\n```\nimport pandas as pd\nfrom dataset_manager import DatasetManager\n\nmanager = DatasetManager(dataset_path, local_path_to_download)\n\ndataset = manager.get_dataset(identifier)\n\ndf = pd.read_csv(dataset.uri)\n```\n\n### Dataset functions\n\n#### Download Dataset\n\nDownload Dataset based on source. This only download once because validates cache.\nIt works both with HTTP, HTTPS and FTP protocols.\n\n```\ndataset = manager.get_dataset(identifier)\n\ndataset.download()\n```\n\n#### Unzip Dataset\n\nUnzip Dataset based on dataset uri. It works with zip files and others from supported library: [fs.archive](https://pypi.org/project/fs.archive/)\n\n```\ndataset = manager.get_dataset(identifier)\n\ndataset.unzip()\n```\n\n#### Prepare Dataset\n\nPrepare Dataset combine these two before.\n\n```\ndataset = manager.get_dataset(identifier)\n\ndataset.prepare()\n```\n\n## Contributing\n\nJust make pull request and be happy!\n\nLet's grow together ;)\n\n\n",
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