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New Introduction to the data_algebra

John Mount
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We’ve had really good progress in bringing the Python data_algebra to feature parity with R rquery. In fact we are able to reproduced the New Introduction to rquery article as a “New Introduction to the data_algebra” here.

The idea is: you may have good reasons to want to work in R or to want to work in Python. And Win-Vector LLC wants to leave the choice of R versus Python to you, by providing equivalent strong tools for each platform.

In the article below notice we can explore the same concepts in R or in Python (with a different syntax emphasizing quotation and method-chaining, to be more natively “Pythonic”).

Introduction to the data_algebra

The data_algebra is a data wrangling system designed to express complex data manipulation as a series of simple data transforms. This is in the spirit of R‘s base::transform(), dplyr‘s dplyr::mutate(), or rquery‘s rquery::extend() and uses a method chaining notation. The operators themselves follow the selections in Codd’s relational algebra, with the addition of the traditional SQL “window functions.” More on the background and context of data_algebra can be found here.

The Python/data_algebra version of this introduction is here, and theR/rquery version of this introduction is here.

In transform formulations data manipulation is written as transformations that produce new DataFrames, instead of as alterations of a primary data structure (as is the case with data.table). Transform system can use more space and time than in-place methods. However, in our opinion, transform systems have a number of pedagogical advantages.

In data_algebra‘s case the primary set of data operators is as follows:

  • drop_columns
  • select_columns
  • rename_columns
  • select_rows
  • order_rows
  • extend
  • project
  • natural_join
  • convert_records.

These operations break into a small number of themes:

  • Simple column operations (selecting and re-naming columns).
  • Simple row operations (selecting and re-ordering rows).
  • Creating new columns or replacing columns with new calculated values.
  • Aggregating or summarizing data.
  • Combining results between two DataFrames.
  • General conversion of record layouts.

The point is: Codd worked out that a great number of data transformations can be decomposed into a small number of the above steps. data_algebra supplies a high performance implementation of these methods that scales from in-memory scale up through big data scale (to just about anything that supplies a sufficiently powerful SQL interface, such as PostgreSQL, Apache Spark, or Google BigQuery).

We will work through simple examples/demonstrations of the data_algebra data manipulation operators.

data_algebra operators

Simple column operations (selecting and re-naming columns)

The simple column operations are as follows.

  • drop_columns
  • select_columns
  • rename_columns

These operations are easy to demonstrate.

We set up some simple data.

In [1]:
import pandas

d = pandas.DataFrame({
  'x': [1, 1, 2],
  'y': [5, 4, 3],
  'z': [6, 7, 8],
})

d
Out[1]:
x y z
0 1 5 6
1 1 4 7
2 2 3 8
For example: drop_columns works as follows. drop_columns creates a new DataFrame without certain columns. We can start by wrapping our DataFrame d for processing, then applying the drop_columns() operators, and finally ending and executing the chain with the ex() method.
In [2]:
from data_algebra.data_ops import *

wrap(d). \
    drop_columns(['y', 'z']). \
    ex()
Out[2]:
x
0 1
1 1
2 2
In all cases the first argument of a data_algebra operator is either the data to be processed, or an earlier data_algebra pipeline to be extended. We will take about composing data_algebra operations after we work through examples of all of the basic operations.
select_columns‘s action is also obvious from example.
In [3]:
wrap(d). \
  select_columns(['x', 'y']). \
  ex()
Out[3]:
x y
0 1 5
1 1 4
2 2 3
rename_columns is given as name-assignments of the form 'new_name': 'old_name':
In [4]:
wrap(d). \
  rename_columns({
     'x_new_name': 'x',
     'y_new_name': 'y'
    }). \
  ex()
Out[4]:
x_new_name y_new_name z
0 1 5 6
1 1 4 7
2 2 3 8

Simple row operations (selecting and re-ordering rows)

The simple row operations are:

  • select_rows
  • order_rows

select_rows keeps the set of rows that meet a given predicate expression.

In [5]:
wrap(d). \
  select_rows('x == 1'). \
  ex()
Out[5]:
x y z
0 1 5 6
1 1 4 7
order_rows re-orders rows by a selection of column names (and allows reverse ordering by naming which columns to reverse in the optional reverse argument). Multiple columns can be selected in the order, each column breaking ties in the earlier comparisons.
In [6]:
wrap(d). \
  order_rows(
             ['x', 'y'],
             reverse = ['x']). \
  ex()
Out[6]:
x y z
0 2 3 8
1 1 4 7
2 1 5 6
General data_algebra operations do not depend on row-order and are not guaranteed to preserve row-order, so if you do want to order rows you should make it the last step of your pipeline.

Creating new columns or replacing columns with new calculated values

The important create or replace column operation is:

  • extend

extend accepts arbitrary expressions to create new columns (or replace existing ones). For example:

In [7]:
wrap(d). \
  extend({'zzz': 'y / x'}). \
  ex()
Out[7]:
x y z zzz
0 1 5 6 5.0
1 1 4 7 4.0
2 2 3 8 1.5
We can use = or := for column assignment. In these examples we will use := to keep column assignment clearly distinguishable from argument binding.

extend allows for very powerful per-group operations akin to what SQL calls “window functions”. When the optional partitionby argument is set to a vector of column names then aggregate calculations can be performed per-group. For example.

In [8]:
wrap(d). \
  extend({
         'max_y': 'y.max()',
         'shift_z': 'z.shift()',
         'row_number': '_row_number()',
         'cumsum_z': 'z.cumsum()',},
         partition_by = 'x',
         order_by = ['y', 'z']). \
  ex()
Out[8]:
x y z max_y shift_z row_number cumsum_z
0 1 5 6 5 7.0 2 13
1 1 4 7 5 NaN 1 7
2 2 3 8 3 NaN 1 8
Notice the aggregates were performed per-partition (a set of rows with matching partition key values, specified by partitionby) and in the order determined by the orderby argument (without the orderby argument order is not guaranteed, so always set orderby for windowed operations that depend on row order!).

More on the window functions can be found here.

Aggregating or summarizing data

The main aggregation method for data_algebra is:

  • project

project performs per-group calculations, and returns only the grouping columns (specified by groupby) and derived aggregates. For example:

In [9]:
wrap(d). \
  project({
         'max_y': 'y.max()',
         'count': '_size()',},
         group_by = ['x']). \
  ex()
Out[9]:
x max_y count
0 1 5 2
1 2 3 1
Notice we only get one row for each unique combination of the grouping variables. We can also aggregate into a single row by not specifying any groupby columns.
In [10]:
wrap(d). \
  project({
         'max_y': 'y.max()',
         'count': '_size()',
          }). \
  ex()
Out[10]:
max_y count
0 5 3

Combining results between two DataFrames

To combine multiple tables in data_algebra one uses what we call the natural_join operator. In the data_algebra natural_join, rows are matched by column keys and any two columns with the same name are coalesced (meaning the first table with a non-missing values supplies the answer). This is easiest to demonstrate with an example.

Let’s set up new example tables.

In [11]:
d_left = pandas.DataFrame({
  'k': ['a', 'a', 'b'],
  'x': [1, None, 3],
  'y': [1, None, None],
})

d_left
Out[11]:
k x y
0 a 1.0 1.0
1 a NaN NaN
2 b 3.0 NaN
In [12]:
d_right = pandas.DataFrame({
  'k': ['a', 'b', 'q'],
  'y': [10, 20, 30],
})

d_right
Out[12]:
k y
0 a 10
1 b 20
2 q 30
To perform a join we specify which set of columns our our row-matching conditions (using the by argument) and what type of join we want (using the jointype argument). For example we can use jointype = 'LEFT' to augment our d_left table with additional values from d_right.
In [13]:
ops = describe_table(d_left, table_name = 'd_left'). \
  natural_join(b = describe_table(d_right, table_name = 'd_right'),
               by = 'k',
               jointype = 'LEFT')

ops.eval({'d_left': d_left, 'd_right': d_right})
Out[13]:
k x y
0 a 1.0 1.0
1 a NaN 10.0
2 b 3.0 20.0
In a left-join (as above) if the right-table has unique keys then we get a table with the same structure as the left-table- but with more information per row. This is a very useful type of join in data science projects. Notice columns with matching names are coalesced into each other, which we interpret as “take the value from the left table, unless it is missing.”

General conversion of record layouts

Record transformation is “simple once you get it”. However, we suggest reading up on that as a separate topic here.

Composing operations

We could, of course, perform complicated data manipulation by sequencing data_algebra operations, and saving intermediate values.
data_algebra operators can also act on data_algebra pipelines instead of acting on data. We can write our operations as follows.

We can use the wrap()/ex() pattern to capture both the operator pipeline and to apply it.

In [14]:
wrapped_ops = wrap(d). \
  extend({
         'row_number': '_row_number()',
         },
         partition_by = ['x'],
         order_by = ['y', 'z']). \
  select_rows(
              'row_number == 1') . \
  drop_columns(
               "row_number")

wrapped_ops.underlying
Out[14]:
TableDescription(
 table_name='data_frame',
 column_names=[
   'x', 'y', 'z']) .\
   extend({
    'row_number': '_row_number()'},
   partition_by=['x'],
   order_by=['y', 'z']) .\
   select_rows('row_number == 1') .\
   drop_columns(['row_number'])
In [15]:
wrapped_ops.ex()
Out[15]:
x y z
0 1 4 7
1 2 3 8
data_algebra operators can also act on data_algebra pipelines instead of acting on data. We can write our operations as follows:
In [16]:
ops = describe_table(d). \
  extend({
         'row_number': '_row_number()',
         },
         partition_by = ['x'],
         order_by = ['y', 'z']). \
  select_rows(
              'row_number == 1') . \
  drop_columns(
               "row_number")

ops
Out[16]:
TableDescription(
 table_name='data_frame',
 column_names=[
   'x', 'y', 'z']) .\
   extend({
    'row_number': '_row_number()'},
   partition_by=['x'],
   order_by=['y', 'z']) .\
   select_rows('row_number == 1') .\
   drop_columns(['row_number'])
And we can re-use this pipeline, both on local data and to generate SQL to be run in remote databases. Applying this operator pipeline to our DataFrame d is performed as follows.
In [17]:
ops.transform(d)
Out[17]:
x y z
0 1 4 7
1 2 3 8
What we are trying to illustrate above: there is a continuum of notations possible between:
  • Working over values with explicit intermediate variables.
  • Working over values with a pipeline.
  • Working over operators with a pipeline.

Being able to see these as all related gives some flexibility in decomposing problems into solutions. We have some more advanced notes on the differences in working modalities here and here.

Conclusion

data_algebra supplies a very teachable grammar of data manipulation based on Codd’s relational algebra and experience with pipelined data transforms (such as base::transform(), dplyr, data.table, Pandas, and rquery).

For in-memory situations data_algebra uses Pandas as the implementation provider.

For bigger than memory situations data_algebra can translate to any sufficiently powerful SQL dialect, allowing data_algebra pipelines to be executed on PostgreSQL, Apache Spark, or Google BigQuery.

In addition the rquery R package supplies a nearly identical system for working with data in R. The two systems can even share data manipulation code between each other (allowing very powerful R/Python inter-operation or helping port projects from one to the other).

In [ ]:
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