pancham 0.8.4

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Pancham Data Pipelines

Pancham simplifies the process of reading and processing data with Pandas.

What is it for

A common scenario in data migration projects is to need to take exports from one system, apply some transformation rules and write the data somewhere else. This could be files, a database or an API. All of this can be done with Pandas but we would need to write a lot of code, Pancham is here to make that process easier.

flowchart LR;
    s[Source Files]-->t[Transformation];
    t-->o[Output Files];

Key features

• Load files
• Rename fields
• Validate data types and not-null columns
• Apply python functions to transform values
• Return data as a Pandas DataFrame

Supported Source Files

• Excel
• SQL
• YAML
• CSV

Supported Output

• SQL
• CSV

Additional sources and output formats will be added with time.

Example

loader = DataFrameLoader({FileType.EXCEL_XLSX: ExcelFileLoader()}, PrintReporter())
configuration = DataFrameConfiguration(self.filename, FileType.EXCEL_XLSX, sheet='Sheet1')
configuration.add_field('Order', 'Order Id', int)
configuration.add_field('Date', 'Rec Date', datetime.datetime)
configuration.add_dynamic_field('Sent', field_type=bool, func=lambda row: row['Disp.'] == 'X')

data = loader.load(configuration)