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MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![TypeScript](https://img.shields.io/badge/%3C%2F%3E-TypeScript-%230074c1.svg)](http://www.typescriptlang.org/)\n[![code style: prettier](https://img.shields.io/badge/code_style-prettier-f8bc45.svg)](https://github.com/prettier/prettier)\n\n![workflows](https://github.com/isair/tensorflow-load-csv/workflows/Release/badge.svg?branch=master)\n\nA library that aims to remove the overhead of creating tensors from CSV files completely; allowing you to dive right into the fun parts of your ML project.\n\n- [Lightweight](https://bundlephobia.com/result?p=tensorflow-load-csv).\n- Fast.\n- Flexible.\n- TypeScript compatible.\n- 100% test coverage.\n\n## Documentation\n\nYou can find the docs [here](https://barissencan.com/tensorflow-load-csv/modules/loadcsv.html#default).\n\n## Installation\n\nNPM:\n\n```sh\nnpm install tensorflow-load-csv\n```\n\nYarn:\n\n```sh\nyarn add tensorflow-load-csv\n```\n\n## Usage\n\nSimple usage:\n\n```js\nimport loadCsv from 'tensorflow-load-csv';\n\nconst { features, labels } = loadCsv('./data.csv', {\n  featureColumns: ['lat', 'lng', 'height'],\n  labelColumns: ['temperature'],\n});\n\nfeatures.print();\nlabels.print();\n```\n\nAdvanced usage:\n\n```js\nimport loadCsv from 'tensorflow-load-csv';\n\nconst { features, labels, testFeatures, testLabels } = loadCsv('./data.csv', {\n  featureColumns: ['lat', 'lng', 'height'],\n  labelColumns: ['temperature'],\n  mappings: {\n    height: (ft) =\u003e ft * 0.3048, // feet to meters\n    temperature: (f) =\u003e (f \u003c 50 ? [1, 0] : [0, 1]), // cold or hot classification\n  }, // Map values based on which column they are in before they are loaded into tensors.\n  flatten: ['temperature'], // Flattens the array result of a mapping so that each member is a new column.\n  shuffle: true, // Pass true to shuffle with a fixed seed, or a string to use as a seed for the shuffling.\n  splitTest: true, // Splits your data in half. You can also provide a certain row count for the test data, or a percentage string (e.g. '10%').\n  standardise: ['height'], // Calculates mean and variance for each feature column using data only in features, then standardises the values in features and testFeatures. Does not touch labels.\n  prependOnes: true, // Prepends a column of 1s to your features and testFeatures tensors, useful for regression problems.\n});\n\nfeatures.print();\nlabels.print();\n\ntestFeatures.print();\ntestLabels.print();\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fisair%2Ftensorflow-load-csv","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fisair%2Ftensorflow-load-csv","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fisair%2Ftensorflow-load-csv/lists"}