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expr-0.0.1h
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مشاهده بیشترتوضیحات
Draw little expression graphs; made to be hacked on.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | expr-0.0.1h |
| نام | expr |
| نسخه کتابخانه | 0.0.1h |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | bmcorser |
| ایمیل نویسنده | bmcorser@gmail.com |
| آدرس صفحه اصلی | https://github.com/bmcorser/expr |
| آدرس اینترنتی | https://pypi.org/project/expr/ |
| مجوز | UNKNOWN |
expr
==========
.. figure:: https://raw.githubusercontent.com/bmcorser/expr/master/expr.png
:alt: expr
Create simple visualisations of mathematical operations on `small datasets`_
by rendering an `expression graph`_, show your friends or serialise it for later.
.. _`expression graph`: https://code.google.com/p/pydot/
.. _`small datasets`: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html
Contents
--------
- `Usage`_
* `Starting out`_
* `Less verbosity`_
* `Involving pandas`_
* `Serialising`_
- `Known issues`_
- `Also`_
Usage
~~~~~
Examples follow using the Python interactive shell
Starting out
^^^^^^^^^^^^
Import some things from the module
.. code:: python
from expr import Expr, NumExpr
Construct an expression
.. code:: python
expr = Expr(
operation_name='+',
arguments=[
NumExpr(number=1),
Expr(
operation_name='/',
arguments=[
NumExpr(number=2),
NumExpr(number=3),
]
)
]
)
Get an answer
.. code:: python
>> expr.resolve()
1.6666666666666665
Draw a graph
.. code:: python
>> graph = expr.graph()
>> graph.write_png('example.png')
True
``example.png``
.. figure:: https://raw.githubusercontent.com/bmcorser/expr/master/example.png
:alt: example
Less verbosity
^^^^^^^^^^^^^^
Import things using ``as`` to save your typing fingers by aliasing those
characters away. In Python we don't really need to care if functions specify
keyword or positional signatures, so let's not bother with those wordy
keywords, hey.
.. code:: python
>>> 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``
.. figure:: https://raw.githubusercontent.com/bmcorser/expr/master/pi.png
:alt: pi
Involving pandas
^^^^^^^^^^^^^^^^
We can create expressions that involve more than just numbers ...
.. code:: python
>>> import pandas
>>> from expr import (
... Expr as E,
... NumExpr as N,
... DataFrameExpr as D,
... )
Create some stupid datasets
.. code:: python
>>> 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.
.. code:: python
>>> 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``
.. figure:: https://raw.githubusercontent.com/bmcorser/expr/master/dataframe.png
:alt: dataframe
Serialising
~~~~~~~~~~~
Let's serialise the above example using JSON, any arguments passed to the
``serialise`` method are used when the serialiser function is applied.
.. code:: python
>>> 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
}
}
}
]
}
]
}
Getting funccy
^^^^^^^^^^^^^^
The final expression provided in this package is ``FuncExpr``, which applies
arbitrary functions to the arguments supplied (which should be objects
implementing a ``resolve`` method). Keywords are also applied, probably for
specifying options, which do not have to be expression objects. A contrived
example of ``FuncExpr`` use follows.
Do some smart imports
.. code:: python
import pandas
from expr import (
Expr as E,
NumExpr as N,
DataFrameExpr as D,
FuncExpr as F,
)
Define a factory function for creating ``DataFrame`` objects
.. code:: python
def x_by_y(x, y, columns=False):
data = [[a + b for b in range(x)] for a in range(y)]
if columns is False:
columns = [chr(97 + c) for c in range(x)]
return pandas.DataFrame.from_records(data=data, columns=columns)
Crank out some df's for later use
.. code:: python
df_A = x_by_y(3, 4)
df_B = x_by_y(3, 4)
df_C = x_by_y(3, 4)
Now set up out expression and look at its graph
.. code:: python
expr = E('+',
[
E('*',
[
N(3),
F('pandas.concat',
[
D(x_by_y(1, 4, ['a'])),
D(x_by_y(1, 4, ['b'])),
D(x_by_y(1, 4, ['c'])),
], axis=1
)
]
),
D(x_by_y(3, 4))
]
)
G = expr.graph()
G.write_png('func.png')
``func.png``
.. figure:: https://raw.githubusercontent.com/bmcorser/expr/master/func.png
:alt: func
Crazy ish
^^^^^^^^^
Why not very clumsily visualise a call stack for your audience's pleasure?
Known Issues
~~~~~~~~~~~~
If you like YAML, serialising ``pandas`` objects, may_ be problematic_, but
JSON should be fine.
.. _may: http://pyyaml.org/ticket/254
.. _problematic: http://pyyaml.org/ticket/192
Also
~~~~
Colours courtesy of clrs.cc_
.. _clrs.cc: http://clrs.cc
نحوه نصب
نصب پکیج whl expr-0.0.1h:
pip install expr-0.0.1h.whl
نصب پکیج tar.gz expr-0.0.1h:
pip install expr-0.0.1h.tar.gz