expr 0.0.1g

Draw little expression graphs; made to be hacked on.

Create simple visualisations of mathematical operations on small datasets by rendering an expression graph, show your friends or serialise it for later.

Contents

• Usage

  • Starting out

  • Less verbosity

  • Getting pandas involved

  • Serialising

• Known issues

• Also

Usage

Examples follow using the Python interactive shell

Starting out

Import some things from the module

from expr import Expr, NumExpr

Construct an expression

expr = Expr(
    operation_name='+',
    arguments=[
        NumExpr(number=1),
        Expr(
            operation_name='/',
            arguments=[
                NumExpr(number=2),
                NumExpr(number=3),
            ]
        )
    ]
)

Get an answer

>> expr.resolve()
1.6666666666666665

Draw a graph

>> graph = expr.graph()
>> graph.write_png('example.png')
True

example.png

Less verbosity

Import things using as to save your typing fingers by aliasing those characters away

>>> from expr import Expr as E, NumExpr as N
>>> expr = E('/', [N(22), N(7)])
>>> expr.resolve()
3.142857142857143
>>> expr.graph().write_png('pi.png')
True

pi.png

Getting pandas involved

We can create expressions that involve more than just numbers …

>>> import pandas
>>> from expr import (
...     Expr as E,
...     NumExpr as N,
...     DataFrameExpr as D,
... )

Create some stupid datasets

>>> def two_by_four():
...     data = [(n + 1, n + 1) for n in range(4)]
...     return pandas.DataFrame.from_records(data=data, columns=['a', 'b'])

>>> df_A = two_by_four()
>>> df_B = two_by_four()
>>> df_A
   a  b
0  1  1
1  2  2
2  3  3
3  4  4

Create the expression object, the DataFrameExpr object (aliased here as D) takes an optional argument name which will be used as a label if present, otherwise an automatically generated label will applied.

>>> expr = E('*', [N(3), E('+', [D(df_A, 'A'), D(df_B, 'B')])])
>>> expr.graph().write_png('dataframe.png')
True
>>> expr.resolve()
    0   1
0   6   6
1  12  12
2  18  18
3  24  24

dataframe.png

Serialising

Let’s serialise the above example using JSON, any arguments passed to the serialise method are used when the serialiser function is applied.

>>> import json
>>> print(expr.serialise(json.dumps, indent=4))
{
    "__type__": "Expr",
    "operation_name": "*",
    "arguments": [
        {
            "__type__": "NumExpr",
            "number": 3.0
        },
        {
            "__type__": "Expr",
            "operation_name": "+",
            "arguments": [
                {
                    "__type__": "DataFrameExpr",
                    "name": "A",
                    "dataframe": {
                        "a": {
                            "0": 1,
                            "1": 2,
                            "2": 3,
                            "3": 4
                        },
                        "b": {
                            "0": 1,
                            "1": 2,
                            "2": 3,
                            "3": 4
                        }
                    }
                },
                {
                    "__type__": "DataFrameExpr",
                    "name": "B",
                    "dataframe": {
                        "a": {
                            "0": 1,
                            "1": 2,
                            "2": 3,
                            "3": 4
                        },
                        "b": {
                            "0": 1,
                            "1": 2,
                            "2": 3,
                            "3": 4
                        }
                    }
                }
            ]
        }
    ]
}

Known Issues

If you like YAML, you may encounter some issues serialising pandas objects, but JSON should be fine.

Also

Colours courtesy of clrs.cc