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A resource normalizer for dataflows

Project description

Dataflows Resource write to db normalized

This library provides some dataflows processing for normalizing a resource.

It has special support for storing normalized data into DB tables.

What is normalization?

In short, it is the process of reducing duplication in a dataset.

More can be read about this concept here.


Let's take, as an example, this world cities dataset (we shall call it the fact resource):

from dataflows import Flow, load, printer

    load('', name='cities'),


# name country subcountry geonameid
1 les Escaldes Andorra Escaldes-Engordany 3040051
2 Andorra la Vella Andorra Andorra la Vella 3041563
23018 Chitungwiza Zimbabwe Harare 1106542

It seems that the country and subcountry columns are quite repetitive - let's extract them into a separate, deduplicated resource (we will call that a dimension resource).

To do that we use the normalize processor.

This processor receives a single resource name, and a list of NormGroup instances. Each of these groups specifies one new dimension resource to be extracted and deduplicated.

Let's see it in action:

from dataflows_normalize import normalize, NormGroup

    load('', name='cities'),
       NormGroup(['country', 'subcountry'], 'country_id', 'id') 
    ], resource='cities'),


# name geonameid country_id
1 les Escaldes 3040051 0
2 Andorra la Vella 3041563 1
3 Umm al Qaywayn 290594 2
4 Ras al-Khaimah 291074 3
5 Khawr Fakkān 291696 4
23014 Bulawayo 894701 2677
23015 Bindura 895061 2678
23016 Beitbridge 895269 2679
23017 Epworth 1085510 2676
23018 Chitungwiza 1106542 2676


# id country subcountry
1 30 Afghanistan Badakhshan
2 27 Afghanistan Badghis
3 21 Afghanistan Balkh
4 33 Afghanistan Bāmīān
5 31 Afghanistan Farah
6 19 Afghanistan Faryab
7 28 Afghanistan Ghaznī
8 13 Afghanistan Ghowr
9 22 Afghanistan Helmand
10 11 Afghanistan Herat
2671 2677 Zimbabwe Bulawayo
2672 2676 Zimbabwe Harare
2673 2673 Zimbabwe Manicaland
2674 2678 Zimbabwe Mashonaland Central
2675 2675 Zimbabwe Mashonaland East
2676 2674 Zimbabwe Mashonaland West
2677 2670 Zimbabwe Masvingo
2678 2671 Zimbabwe Matabeleland North
2679 2679 Zimbabwe Matabeleland South
2680 2672 Zimbabwe Midlands

If we follow the last line in the dataset (Chitungwiza), we can see that an entry for its region (Zimbabwe/Harare) was created with id 2676, and that id was added to the original row instead of the original values.

How much did we gain?

The original CSV file has a size of 895,586 bytes.

If we save the two new resources as CSVs, we would get

542,299 bytes for the fact resource and 68,023 for the regions dimension resource - a total of 610,322 bytes (or a reduction of 31% in size).

Not only this helps with size, it also improves greatly DB performance to store data in normalized form.

DB Normalization

Running similar code to above, only using normalize_to_db will do the following:

  • Load existing values from database dimension tables (in case these tables exist)
  • Normalize the input data, and split into fact and dimension resources
  • Update the DB tables with new values, while reusing existing references

The main difference in usage from normalize is that the names of DB tables are provided.

from dataflows_normalize import normalize_to_db, NormGroup

    load('', name='cities'),
            NormGroup(['country', 'subcountry'], 'country_id', 'id', db_table='countries_db_table') 
        'cities_db_table', 'cities',

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