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Welcome to Good Tables’s documentation! — Good Tables 0.1 documentation

Welcome to Good Tables’s documentation! Welcome to Good Tables’s documentation! Good Tables is a python library and command line tool for validating and transforming tabular data. In the form of CSV or Excel is passed through a pipeline of validators. These validators can check structure. For example are their blank rows or columns, do rows have the same length as the header etc, and they can also validate against a schema. Optionally, the data source is transformed. As it passes through the pipeline.

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Welcome to Good Tables’s documentation! Welcome to Good Tables’s documentation! Good Tables is a python library and command line tool for validating and transforming tabular data. In the form of CSV or Excel is passed through a pipeline of validators. These validators can check structure. For example are their blank rows or columns, do rows have the same length as the header etc, and they can also validate against a schema. Optionally, the data source is transformed. As it passes through the pipeline.

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Welcome to Good Tables’s documentation! — Good Tables 0.1 documentation | goodtables.readthedocs.io Reviews

https://goodtables.readthedocs.io

Welcome to Good Tables’s documentation! Welcome to Good Tables’s documentation! Good Tables is a python library and command line tool for validating and transforming tabular data. In the form of CSV or Excel is passed through a pipeline of validators. These validators can check structure. For example are their blank rows or columns, do rows have the same length as the header etc, and they can also validate against a schema. Optionally, the data source is transformed. As it passes through the pipeline.

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goodtables.readthedocs.io

1

Quickstart — Good Tables 0.1 documentation

http://goodtables.readthedocs.io/en/latest/quickstart.html

Let’s get started. Good Tables runs on Python 2.7, 3.3 and 3.4. Git clone https:/ github.com/okfn/goodtables.git. Provided by Read the Docs. On Read the Docs. Free document hosting provided by Read the Docs.

2

Batch — Good Tables 0.1 documentation

http://goodtables.readthedocs.io/en/latest/batch.html

Allows the configuration and running of pipelines on multiple sources. Data sources can be extracted from either a CSV file, or a Data Package. Filepath to the lust of data sources to run the batch against. 8216;csv’ (CSV file) or ‘dp’ (Data Package file). Is ‘csv’, then this is the name of the header that indicates the data URL. Is ‘csv’, then this is the name of the header that indicates the schema URL. Keyword argument for the. For an example of the batch processor at work, including use of.

3

Pipeline — Good Tables 0.1 documentation

http://goodtables.readthedocs.io/en/latest/pipeline.html

Register by instance method. Class implements the processing pipeline. Keyword argument, which is a list of validators to run in the pipeline. Each value in the. List is expected to be a string describing the path to a validator class, for import via. Optionally, for builtin validators, the. Property can be used as a shorthand convenience. Register by instance method. Once you have a. Instance, you can also register validators via the. You can define the position in the pipeline explicitly using the.

4

Welcome to Good Tables’s documentation! — Good Tables 0.1 documentation

http://goodtables.readthedocs.io/en/stable

Welcome to Good Tables’s documentation! Welcome to Good Tables’s documentation! Good Tables is a python library and command line tool for validating and transforming tabular data. In the form of CSV or Excel is passed through a pipeline of validators. These validators can check structure. For example are their blank rows or columns, do rows have the same length as the header etc, and they can also validate against a schema. Optionally, the data source is transformed. As it passes through the pipeline.

5

Reports — Good Tables 0.1 documentation

http://goodtables.readthedocs.io/en/latest/reports.html

The results of any run over data, either by a standalone processor or a pipeline, are written to a report. Each report is an instance of a. Which is a small library we also developed (See the TellMe. Library for more information on its API). Reports can then be generated in a variety of output formats supported by TellMe. In a pipeline, the. Each processor writes report results to the pipeline’s report instance. After processing of the data is complete, additional calculations are performed for a summary.

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Welcome to Good Tables’s documentation! — Good Tables 0.1 documentation

Welcome to Good Tables’s documentation! Welcome to Good Tables’s documentation! Good Tables is a python library and command line tool for validating and transforming tabular data. In the form of CSV or Excel is passed through a pipeline of validators. These validators can check structure. For example are their blank rows or columns, do rows have the same length as the header etc, and they can also validate against a schema. Optionally, the data source is transformed. As it passes through the pipeline.

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