Building generic data queries

using Python AST

Paris.py meetup #7 - Paris
2015-09-16

Adrien Chauve @adrienchauve

Contents

1. Building generic data queries: why?
2. Python AST to the rescue
3. Walking the AST to build data queries

1. Building generic data queries: Why?

1. Building generic data queries: Why?

Context:

• You love data
• You want to watch a movie
• You know the MovieLens database (20M ratings on 27k movies by 138k users)

Disclaimer:

• could also be bigger data (sales) but less sexy!
• data could be stored on a SQL server instead of a CSV file

1. Context: Select the best movie (1/3)

Naïve sort: by Average Rating then by NbRatings

1. Context: Select the best movie (2/3)

Naïve sort: by NbRatings

1. Context: Select the best movie (3/3)

Better sort: by custom rating (k=1000)

1. Need computed columns to best analyze your data

New computed column: $$CustomRating = AverageRating * {NbRatings \over NbRatings + 1000}$$

Using pandas (python):

# df is a pandas.DataFrame instance
df['CustomRating'] = df['AverageRating'] * df['NbRatings'] / (df['NbRatings'] + 1000)
            

In SQL:

SELECT AverageRating * NbRatings / (NbRatings + 1000) AS CustomRating FROM ...;
            

How to generate both pandas and SQL from a single string?

2. Python AST to the rescue

2. AST: What is it?

• Abstract Syntax Tree
• represents your code as a tree object

                x + 42
            

2. AST: What is it?

• represents your code as a tree object

>>> import ast
>>> ast.dump(ast.parse("x + 42", mode="eval")

Expression(body=BinOp(left=Name(id='x', ctx=Load()),
                      op=Add(),
                      right=Num(n=42))))
            

2. AST: What is it?

$$CustomRating = AverageRating * NbRatings / (NbRatings + 1000)$$

>>> ast.dump(ast.parse("AverageRating * NbRatings / (NbRatings + 1000)",
                       mode="eval"))


Expression(body=BinOp(left=BinOp(left=Name(id='AverageRating', ctx=Load()),
                                 op=Mult(),
                                 right=Name(id='NbRatings', ctx=Load())),
                      op=Div(),
                      right=BinOp(left=Name(id='NbRatings', ctx=Load()),
                                  op=Add(),
                                  right=Num(n=1000))))
            

3. Walking the AST to build data queries

3. AST: Great, but what can we do with it?

Expression(body=BinOp(left=Name(id='x', ctx=Load()),
                      op=Add(),
                      right=Num(n=42)))
            

OPERATORS = {
    ast.Add: operator.add,
    ast.Mult: operator.mul,
    ast.Div: operator.truediv,
}

def eval_expr(expr):
    return _eval(ast.parse(expr, mode='eval').body)

def _eval(node):  # recursively evaluate tree nodes
    if isinstance(node, ast.Num):
        return node.n
    elif isinstance(node, ast.BinOp):
        return OPERATORS[type(node.op)](_eval(node.left), _eval(node.right))
    elif isinstance(node, ast.UnaryOp):
        return OPERATORS[type(node.op)](_eval(node.operand))
    elif isinstance(node, ast.Name):
        return ???
    raise TypeError(node)

3. AST: Building a pandas query

class PandasEvaluator(object):
    def __init__(self, dataframe):
        self._dataframe = dataframe

    def eval_expr(self, expr):
        return self._eval(ast.parse(expr, mode='eval').body)

    def _eval(self, node):  # recursively evaluate tree nodes
        if isinstance(node, ast.Num):
            return node.n
        elif isinstance(node, ast.BinOp):
            return OPERATORS[type(node.op)](self._eval(node.left),
                                            self._eval(node.right))
        elif isinstance(node, ast.UnaryOp):
            return OPERATORS[type(node.op)](self._eval(node.operand))
        elif isinstance(node, ast.Name):
            return self.dataframe[node.id]
        raise TypeError(node)

df = pandas.read_csv('ratings.csv')
formula = "AverageRating * NbRatings / (NbRatings + 1000)"
df['CustomRating'] = PandasEvaluator(df).eval_expr(formula)
            

3. AST: Building a SQL query using SQLAlchemy

class SQLEvaluator(object):
    def __init__(self, sql_table):
        self._sql_table = sql_table  # instance of SQLAlchemy Table class

    def eval_expr(self, expr):
        return self._eval(ast.parse(expr, mode='eval').body)

    def _eval(self, node):  # recursively evaluate tree nodes
        if isinstance(node, ast.Num):
            return node.n
        elif isinstance(node, ast.BinOp):
            return OPERATORS[type(node.op)](self._eval(node.left),
                                            self._eval(node.right))
        elif isinstance(node, ast.UnaryOp):
            return OPERATORS[type(node.op)](self._eval(node.operand))
        elif isinstance(node, ast.Name):
            return self._sql_table[node.id]
        raise TypeError(node)

session = sessionmaker(...)
sql_table = Table(...)
formula = "AverageRating * NbRatings / (NbRatings + 1000)"
custom_ratings_column = SQLEvaluator(sql_table).eval_expr(formula)
data = [row for row in session.query(custom_ratings_column)]
            

Building generic data queries using Python AST

What we did so far:

• Enter a formula as a string
• Parse it and generate the AST using python ast.parse
• Use AST evaluators to build pandas and SQL new columns
• In just ~20 lines of code!

Wait... there is more!

• Add support for python "> < = and or not" operators
• Use SqlAlchemy DSL to generate conditional queries:

  SELECT... CASE WHEN... ELSE ... END ... ;

• Use numpy masks to do the same with pandas dataframe

Great links

• The Hacker's Guide to Python
  Great Python book by Julien Danjou; has a part on AST
• MovieLens database by grouplens
• A detailed HowTo to add new computed columns in your dataset using Serenytics (including conditional formulas)

Module(body=[
    Print(dest=None,
          values=[Str(s='Thank you! Questions?')],
          nl=True)
])