Transformer is not a robot. Transformer wrangles messy CSV-like files so you don't have to. The idea is simple: You write an ORM-like schema for your data files, and transformer does the rest.
Say you pulled a 30-column CSV file from data.gov, like the absentee voting dataset:
https://explore.data.gov/dataset/2008-Uniformed-and-Overseas-Citizens-Absentee-Voti/2tan-w4es
Say also that you just want a few columns. Instead of counting column numbers one by one and writing ugly code to extract them in certain ways, just write a schema:
from transformer import Document, Schema, Column, transforms
import string
class AbsenteeSchema(Schema):
Name = Column("JurisName", transform=string.capwords)
A1 = Column("A1", transform=lambda x: x/1000, title="A1 count in thousands")
City = Column("Location 1")
State = Column("Location 2")
_ordering = [Name, A1, City, State]
with open("exported.csv", "r") as f:
doc = Document(f)
AbsenteeSchema.transform(doc)As above, you can also describe different transformations, like, parsing and fiddling with date formats or applying regex replacements. Transformer comes with a small set of these already, but writing your own is as easy as writing a python callable. Combining and aggregating data from different columns is also possible.
Different CSV dialects are supported as in the python csv module.
Transformer is alpha for all purposes except the ones I use it for, which is why documentation is scarce. The code is fairly well annotated and clear, and your starting point should be the example in the examples/ directory.
Questions, comments, pull requests welcome. Good luck!