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Ask HN: What is the best tool to infer data type of tabular data?
Hi HN, I am looking for the most accurate tool which I can use to infer Data Types from Tabular data (csv,tsv,excel)
I need to be able to perform some small customization, if possible, to the detection algorithm. For example if I have a 9 digit number, starting with 0, then treat it as a String.
So far - I have found Frictionless Framework [0] which seems good, but I can't see any way of specifying customizations to the profiling algorithm, and Data Profiler [1] which uses ML for type detection, and it seems I should be able to train some new rules but I need a CUDA capable machine, which at the moment I do not have.
Hoping the collective HN brain can point me to something better if it exists.
[0] - https://framework.frictionlessdata.io/
[1] - https://github.com/capitalone/DataProfiler
- quickthrower2 5y agoI’d probably knock one up in nodejs ad follows: Import a csv reader library that can stream. Read each line and apply a series of regex, each one classifying on match. Eg ^0\d{8}$ Means string Then have a reduction rule e.g. If so far we think it’s a numeric column and we get a string then treat as string. If so far we think it’s a numeric column and we get a number it is still a numeric column. Then doing the regex and reduce in a loop will give you the final answers. Happy to knock up some example code if you wish.
- mahalel 5y agoHey that's pretty awesome, I'm ok at the regex stuff, but not that familiar with NodeJS. If you don't mind throwing me a snippet to develop from, that would be much appreciated.
- quickthrower2 5y agoIt doesn’t have to be nodejs I think this would be just as easy in python or Java etc. I’ll put something together though when I get some time
- mahalel 5y agoHey, I got it working in Python with pandas, thanks so much for the suggestions
- switch007 5y agoWhat kind of types? https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html https://pandas.pydata.org/pandas-docs/stable/reference/api/p... is pretty powerful (see also "parse_dates" and "converters" parameters). See also parse_excel() You can also use procedural code to look at the column data and change the type: # if all values in col c2 when converted to string begin with "0" and and values are of length 9, convert to int64 if df["col"].str.match("^0").all() and set(df["col"].str.len()) == {9}: df["col"] = df["col"].astype("int64")