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This article introduces the data_algebra project: a data processing tool family available in R and Python. These tools are designed to transform data either in-memory or on remote databases.

In particular we will discuss the Python implementation (also called data_algebra) and its relation to the mature R implementations (rquery and rqdatatable).

## Introduction

Parts of the project are in early development (and not yet ready for production use), and other parts are mature and have been used in production.

The project intent is to realize a modern data processing language based on Codd’s relational operators that is easy to maintain, has helpful tooling, and has very similar realizations (or dialects) for:

• SQL databases accessed from Python (in development here, not yet ready for production use).
• Pandas Data.Frame objects in Python (in development here, not yet ready for production use).
• SQL databases access from R (implementation is here, and is mature and ready for production use).
• data.table objects in R (implementation is here, and is mature and ready for production use).

The idea is the notation should look idiomatic in each language. Working in Python should feel like working in Python, and working in R should feel like working in R. The data semantics, however, are designed to be close to the SQL realizations (given the close connection of SQL to the relational algebra; in particular row numbering starts at 1 and row and column order is not preserved except at row-order steps or select-columns steps respectively). The intent is: it should be very easy to use the system in either Python or R (a boon to multi-language data science projects) and it is easy to port either code or experience from one system to another (a boon for porting projects, or for data scientists working with more than one code base or computer language).

Related work includes:

The data_algebra principles include:

• Writing data transforms as a pipeline or method-chain of many simple transform steps.
• Treating data transform pipelines or directed acyclic graphs (DAGs) as themselves being sharable data.
• Being able to use the same transform specification many places (in memory, on databases, in R, in Python).

## Example

Let’s start with an example in Python.

For our example we will assume we have a data set of how many points different subjects score in a psychological survey. The goal is transform the data so that we see what fraction of the subjects answers are in each category (subject to an exponential transform, as often used in logistic regression). We then treat the per-subject renormalized data as a probabilty or diagnosis.

The exact meaning of such a scoring method are not the topic of this note. It is a notional example to show a non-trivial data transformation need. In particular: having to normalize per-subject (divide some set of scores per-subject by a per-subject total) is a classic pain point in data-processing. In classic SQL this can only be done by joining against a summary table, or in more modern SQL with a “window function.” We want to show by working in small enough steps this can be done simply.

### Set up

Let’s start our Python example. First we import the packages we are going to use, and set a few options.

In [1]:
import io
from pprint import pprint
import psycopg2    # http://initd.org/psycopg/
import pandas      # https://pandas.pydata.org
import yaml        # https://pyyaml.org
import db_helpers  # https://github.com/WinVector/data_algebra/blob/master/Examples/LogisticExample/db_helpers.py

pandas.set_option('display.max_columns', None)
pandas.set_option('display.expand_frame_repr', False)
pandas.set_option('max_colwidth', -1)

Now let’s type in our example data. Notice this is an in-memory Pandas Data.Frame.

In [2]:
d_local = pandas.DataFrame({
'subjectID':[1, 1, 2, 2],
'surveyCategory': [ "withdrawal behavior", "positive re-framing", "withdrawal behavior", "positive re-framing"],
'assessmentTotal': [5, 2, 3, 4],
'irrelevantCol1': ['irrel1']*4,
'irrelevantCol2': ['irrel2']*4,
})
d_local

Out[2]:
subjectID surveyCategory assessmentTotal irrelevantCol1 irrelevantCol2
0 1 withdrawal behavior 5 irrel1 irrel2
1 1 positive re-framing 2 irrel1 irrel2
2 2 withdrawal behavior 3 irrel1 irrel2
3 2 positive re-framing 4 irrel1 irrel2
Let’s also copy this data to a PostgreSQL database. Normally big data is already in the system one wants to work with, so the copying over is just to simulate the data already being there.

In [3]:
conn = psycopg2.connect(
database="johnmount",
user="johnmount",
host="localhost",
)
conn.autocommit=True

In [4]:
db_helpers.insert_table(conn, d_local, 'd')


Out[4]:
subjectid surveycategory assessmenttotal irrelevantcol1 irrelevantcol2
0 1.0 withdrawal behavior 5.0 irrel1 irrel2
1 1.0 positive re-framing 2.0 irrel1 irrel2
2 2.0 withdrawal behavior 3.0 irrel1 irrel2
3 2.0 positive re-framing 4.0 irrel1 irrel2
Normally one does not read data back from a database, but instead materializes results in the database with SQL commands such as CREATE TABLE tablename AS SELECT ....
Also note: case in columns is a bit of nightmare. It is often best to lower-case them all.

### Back to the data_algebra

Now we continue our example by importing the data_algebra components we need.

In [5]:
from data_algebra.data_ops import *  # https://github.com/WinVector/data_algebra
import data_algebra.env
import data_algebra.yaml
import data_algebra.PostgreSQL

# set some things in our environment
_, _1, _2, _get = [None, None, None, lambda x: x] # don't look unbound
data_algebra.env.push_onto_namespace_stack(locals())
# ask YAML to write simpler structures
data_algebra.yaml.fix_ordered_dict_yaml_rep()

db_model = data_algebra.PostgreSQL.PostgreSQLModel()

Now we use the data_algebra to define our processing pipeline: ops. We are writing this pipeline using a method chaining notation where we have placed Python method-dot at the end of lines using the .\ notation. This notation will look very much like a pipe) to R/magrittr users.

In [6]:
scale = 0.237

ops = TableDescription('d',
['subjectID',
'surveyCategory',
'assessmentTotal',
'irrelevantCol1',
'irrelevantCol2']) .\
extend({'probability': '(assessmentTotal * scale).exp()'}) .\
extend({'total': 'probability.sum()'},
partition_by='subjectID')  .\
extend({'probability': 'probability/total'})  .\
extend({'row_number':'_row_number()'},
partition_by=['subjectID'],
order_by=['probability', 'surveyCategory'],
reverse=['probability'])  .\
select_rows('row_number==1')   .\
select_columns(['subjectID', 'surveyCategory', 'probability']) .\
rename_columns({'diagnosis': 'surveyCategory'})  .\
order_rows(['subjectID'])

For a more pythonic way of writing the same pipeline we can show how the code would have been formatted by black.

In [7]:
py_source = ops.to_python(pretty=True)
print(py_source)

TableDescription(
table_name="d",
column_names=[
"subjectID",
"surveyCategory",
"assessmentTotal",
"irrelevantCol1",
"irrelevantCol2",
],
).extend({"probability": "(assessmentTotal * 0.237).exp()"}).extend(
{"total": "probability.sum()"}, partition_by=["subjectID"]
).extend(
{"probability": "probability / total"}
).extend(
{"row_number": "_row_number()"},
partition_by=["subjectID"],
order_by=["probability", "surveyCategory"],
reverse=["probability"],
).select_rows(
"row_number == 1"
).select_columns(
["subjectID", "surveyCategory", "probability"]
).rename_columns(
{"diagnosis": "surveyCategory"}
).order_rows(
["subjectID"]
)


In either case, the pipeline is read as a sequence of operations (top to bottom, and left to right). What it is saying is:

• We start with a table named “d” that is known to have columns “subjectID”, “surveyCategory”, “assessmentTotal”, “irrelevantCol1”, and “irrelevantCol2”.
• We produce a new table by transforming this table through a sequence of “extend” operations which add new columns.
• The first extend computes probability = exp(scale*assessmentTotal), this is similar to the inverse-link step of a logistic regression. We assume when writing this pipleline we were given this math as a requirement.
• The next few extend steps total the probabilty per-subject (this is controlled by the partition_by argument) and then rank the normalized probabilities per-subject (grouping again specified by the partition_by argument, and order contolled by the order_by clause).
• We then select the per-subject top-ranked rows by the select_rows step.
• And finally we clean up the results for presentation with the select_columns, rename_columns, and order_rows steps. The names of these methods are intedned to evoke what they do.

The point is: each step is deliberately so trivial one can reason about it. However the many steps in sequence do quite a lot.

### SQL

Once we have the ops object we can do quite a lot with it. We have already exhibited the pretty-printing of the pipeline. Next we demonstrate translating the operator pipeline into SQL.

In [8]:
sql = ops.to_sql(db_model, pretty=True)
print(sql)

SELECT "probability",
"subjectid",
"diagnosis"
FROM
(SELECT "probability",
"subjectid",
"surveycategory" AS "diagnosis"
FROM
(SELECT "probability",
"surveycategory",
"subjectid"
FROM
(SELECT "probability",
"surveycategory",
"subjectid"
FROM
(SELECT "probability",
"surveycategory",
"subjectid",
ROW_NUMBER() OVER (PARTITION BY "subjectid"
ORDER BY "probability" DESC, "surveycategory") AS "row_number"
FROM
(SELECT "surveycategory",
"subjectid",
"probability" / "total" AS "probability"
FROM
(SELECT "probability",
"surveycategory",
"subjectid",
SUM("probability") OVER (PARTITION BY "subjectid") AS "total"
FROM
(SELECT "surveycategory",
"subjectid",
EXP(("assessmenttotal" * 0.237)) AS "probability"
FROM
(SELECT "assessmenttotal",
"surveycategory",
"subjectid"
FROM "d") "sq_0") "sq_1") "sq_2") "sq_3") "sq_4"
WHERE "row_number" = 1 ) "sq_5") "sq_6") "sq_7"
ORDER BY "subjectid"

The SQL can be hard to read, as SQL expresses composition by inner-nesting (inside SELECT statements happen first). The operator pipeline expresses composition by sequencing or method-chaining, which can be a lot more legible. However the huge advantage of the SQL is: we can send it to the database for execution, as we do now.

Also notice the generate SQL has applied query narrowing: columns not used in the outer queries are removed from the inner queries. The “irrelevant” columns are not carried into the calculation as they would be with a SELECT *. This early optimization comes in quite handy.

In [9]:
db_helpers.read_query(conn, sql)

Out[9]:
probability subjectid diagnosis
0 0.670622 1.0 withdrawal behavior
1 0.558974 2.0 positive re-framing
What comes back is: one row per subject, with the highest per-subject diagnosis and the estimated probabilty. Again, the math of this is outside the scope of this note (think of that as something coming from a specification)- the ability to write such a pipeline is our actual topic.

The hope is that the data_algebra pipeline is easier to read, write, and maintain than the SQL query. If we wanted to change the calculation we would just add a stage to the data_algebra pipeline and then regenerate the SQL query.

### Pandas

An advantage of the pipeline is it can also be directly used on Pandas DataFrames. Let’s see how that is achieved.

In [10]:
ops.eval_pandas({'d': d_local})

Out[10]:
subjectID diagnosis probability
0 1 withdrawal behavior 0.670622
1 2 positive re-framing 0.558974
eval_pandas takes a dictionary of Pandas DataFrames (names matching names specified in the pipeline) and returns the result of applying the pipeline to the data using Pandas commands. Currently our Pandas implementation only allows very simple window functions. This is why did’t write probability = probability/sum(probability), but instead broken the calculation into multiple steps by introducing the total column (the SQL realizaition does in fact support more complex window functions). This is a small issue with the grammar: but our feeling encourange simple steps is in fact a good thing (improves debugabbility), and in SQL the query optimizers likely optimize the different query styles into very similar realizations anyway.

## Export/Import

Because our operator pipeline is a Python object with no references to external objects (such as the database connection), it can be saved through standard methods such as “pickling.”

However, data_algebra also supports exporting a pipeline to and from simple structures that are in turn optimized for conversion to YAML. The simple structure format is particularly useful for writing more data_algebra tools (such as pipeline analysis and presentation tools). And the YAML tooling makes moving a processing pipeline to another a language (such as R) quite easy.

We will demonstrate this next.

In [11]:
# convert pipeline to simple objects
objs_R = ops.collect_representation(dialect='R')
# print these objects
pprint(objs_R)

[OrderedDict([('op', 'TableDescription'),
('table_name', 'd'),
('qualifiers', {}),
('column_names',
['subjectID',
'surveyCategory',
'assessmentTotal',
'irrelevantCol1',
'irrelevantCol2']),
('key', 'd')]),
OrderedDict([('op', 'Extend'),
('ops', {'probability': 'exp(assessmentTotal * 0.237)'}),
('partition_by', []),
('order_by', []),
('reverse', [])]),
OrderedDict([('op', 'Extend'),
('ops', {'total': 'sum(probability)'}),
('partition_by', ['subjectID']),
('order_by', []),
('reverse', [])]),
OrderedDict([('op', 'Extend'),
('ops', {'probability': 'probability / total'}),
('partition_by', []),
('order_by', []),
('reverse', [])]),
OrderedDict([('op', 'Extend'),
('ops', {'row_number': 'row_number()'}),
('partition_by', ['subjectID']),
('order_by', ['probability', 'surveyCategory']),
('reverse', ['probability'])]),
OrderedDict([('op', 'SelectRows'), ('expr', 'row_number == 1')]),
OrderedDict([('op', 'SelectColumns'),
('columns', ['subjectID', 'surveyCategory', 'probability'])]),
OrderedDict([('op', 'Rename'),
('column_remapping', {'diagnosis': 'surveyCategory'})]),
OrderedDict([('op', 'Order'),
('order_columns', ['subjectID']),
('reverse', []),
('limit', None)])]

In the above data structure the recursive operator steps have been linearized into a list, and simplified to just ordered dictionaries of a few defining and derived fields. In particular, the key field of the TableDescription nodes is the unique identifier for the tables, two TableDescription with the same key are referring to the same table.

We can then write this representation to YAML format.

In [12]:
# convert objects to a YAML string
dmp_R = yaml.dump(objs_R)
# write to file
with open("pipeline_yaml.txt", "wt") as f:
print(dmp_R, file=f)


### R

This pipeline can be loaded into R and used as follows.

In [13]:
%load_ext rpy2.ipython

In [14]:
%%R

library(yaml)
library(wrapr)
library(rquery)
library(rqdatatable)
source('R_fns.R')

r_ops <- convert_yaml_to_pipleline(r_yaml)
cat(format(r_ops))

table(d;
subjectID,
surveyCategory,
assessmentTotal,
irrelevantCol1,
irrelevantCol2) %.>%
extend(.,
probability := exp(assessmentTotal * 0.237)) %.>%
extend(.,
total := sum(probability),
p= subjectID) %.>%
extend(.,
probability := probability / total) %.>%
extend(.,
row_number := row_number(),
p= subjectID,
o= "probability" DESC, "surveyCategory") %.>%
select_rows(.,
row_number == 1) %.>%
select_columns(.,
subjectID, surveyCategory, probability) %.>%
rename(.,
c('diagnosis' = 'surveyCategory')) %.>%
orderby(., subjectID)

The above representation is nearly “R code” (it is not actually executable, unlike the Python representation, but very similar to the actual rquery steps) written using wrapr dot pipe notation. However, it can be executed in R.

In [15]:
%%R

d_local <- build_frame(
"subjectID", "surveyCategory"     , "assessmentTotal", "irrelevantCol1", "irrelevantCol2" |
1L         , "withdrawal behavior", 5                , "irrel1"        , "irrel2"         |
1L         , "positive re-framing", 2                , "irrel1"        , "irrel2"         |
2L         , "withdrawal behavior", 3                , "irrel1"        , "irrel2"         |
2L         , "positive re-framing", 4                , "irrel1"        , "irrel2"         )

print(d_local)

  subjectID      surveyCategory assessmentTotal irrelevantCol1 irrelevantCol2
1         1 withdrawal behavior               5         irrel1         irrel2
2         1 positive re-framing               2         irrel1         irrel2
3         2 withdrawal behavior               3         irrel1         irrel2
4         2 positive re-framing               4         irrel1         irrel2

We can use the R pipeline by piping data into the r_ops object.

In [16]:
%%R

d_local %.>%
r_ops %.>%
print(.)

   subjectID           diagnosis probability
1:         1 withdrawal behavior   0.6706221
2:         2 positive re-framing   0.5589742

And the R rquery package can also perform its own SQL translation (and even execution management).

In [17]:
%%R

sql <- to_sql(r_ops, rquery_default_db_info())
cat(sql)

SELECT * FROM (
SELECT
"subjectID" AS "subjectID",
"surveyCategory" AS "diagnosis",
"probability" AS "probability"
FROM (
SELECT
"subjectID",
"surveyCategory",
"probability"
FROM (
SELECT * FROM (
SELECT
"subjectID",
"surveyCategory",
"probability",
row_number ( ) OVER (  PARTITION BY "subjectID" ORDER BY "probability" DESC, "surveyCategory" ) AS "row_number"
FROM (
SELECT
"subjectID",
"surveyCategory",
"probability" / "total"  AS "probability"
FROM (
SELECT
"subjectID",
"surveyCategory",
"probability",
sum ( "probability" ) OVER (  PARTITION BY "subjectID" ) AS "total"
FROM (
SELECT
"subjectID",
"surveyCategory",
exp ( "assessmentTotal" * 0.237 )  AS "probability"
FROM (
SELECT
"subjectID",
"surveyCategory",
"assessmentTotal"
FROM
"d"
) tsql_76525498125437036191_0000000000
) tsql_76525498125437036191_0000000001
) tsql_76525498125437036191_0000000002
) tsql_76525498125437036191_0000000003
) tsql_76525498125437036191_0000000004
WHERE "row_number" = 1
) tsql_76525498125437036191_0000000005
) tsql_76525498125437036191_0000000006
) tsql_76525498125437036191_0000000007 ORDER BY "subjectID"

The R implementation is mature, and appropriate to use in production. The rquery grammar is designed to have minimal state and minimal annotations (no grouping or ordering annotations!). This makes the grammar, in my opinion, a good design choice. rquery has very good performance, often much faster than dplyr or base-R due to its query generation ideas and use of data.table via rqdatatable. rquery is a mature pure R package; here is the same example being worked directly in R, with no translation from Python.

The R implementation supports additional features such as converting a pipeline into a diagram (though that would also be easy to implement in Python on top of the collect_representation() objects).

More of the R example (including how the diagram was produced) can be found here.

## Advantages of data_algebra

Multi-language data science is an important trend, so a cross-language query system that supports at least R and Python is going to be a useful tool or capability going forward. Obviously SQL itself is fairly cross-language, but data_algebra adds a few features we hope are real advantages.

In addition to the features shown above, a data_algebra operator pipeline carries around usable knowledge of the data transform. For example:

In [18]:
# report all tables used by the query, by name
ops.get_tables()

Out[18]:
{'d': TableDescription(table_name='d', column_names=['subjectID', 'surveyCategory', 'assessmentTotal', 'irrelevantCol1', 'irrelevantCol2'])}
In [19]:
# report all source table columns used by the query
ops.columns_used()

Out[19]:
{'d': {'assessmentTotal', 'subjectID', 'surveyCategory'}}
In [20]:
# what columns does this operation produce?
ops.column_names

Out[20]:
['subjectID', 'diagnosis', 'probability']

## Conclusion

The data_algebra is part of a powerful cross-language and mutli-implementaiton family data manipulation tools. These tools can greatly reduce the development and maintenance cost of data science projects, while improving the documentation of project intent.

Win Vector LLC is looking for sponsors and partners to further the package. In particular if your group is using both R and Python in big-data projects (where SQL is a need, including Apache Spark), or are porting a project from one of these languages to another- please get in touch.

## Appendix:

Demonstrate we can round-trip a data_algebra through YAML and recover the code.

In [21]:
# land the pipeline as a file
objs_Python = ops.collect_representation()
dmp_Python = yaml.dump(objs_Python)
with open("pipeline_Python.txt", "wt") as f:
print(dmp_Python, file=f)

In [22]:
# read back
with open("pipeline_Python.txt", "rt") as f:
print(ops_back.to_python(pretty=True))

TableDescription(
table_name="d",
column_names=[
"subjectID",
"surveyCategory",
"assessmentTotal",
"irrelevantCol1",
"irrelevantCol2",
],
).extend({"probability": "(assessmentTotal * 0.237).exp()"}).extend(
{"total": "probability.sum()"}, partition_by=["subjectID"]
).extend(
{"probability": "probability / total"}
).extend(
{"row_number": "_row_number()"},
partition_by=["subjectID"],
order_by=["probability", "surveyCategory"],
reverse=["probability"],
).select_rows(
"row_number == 1"
).select_columns(
["subjectID", "surveyCategory", "probability"]
).rename_columns(
{"diagnosis": "surveyCategory"}
).order_rows(
["subjectID"]
)


In [23]:
# confirm we have a data_algebra.data_ops.ViewRepresentation
# which is the class the data_algebra pipelines are derived from
isinstance(ops_back, data_algebra.data_ops.ViewRepresentation)

Out[23]:
True
In [24]:
# be neat
conn.close()