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Transforming Code into Beautiful, Idiomatic Python

Notes from Raymond Hettinger's talk at pycon US 2013 video, slides.

The code examples and direct quotes are all from Raymond's talk. I've reproduced them here for my own edification and the hopes that others will find them as handy as I have!

Looping over a range of numbers

for i in [0, 1, 2, 3, 4, 5]:

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class JsonSerializableMixin(object):

    def __json__(self, request):
        """
        Converts all the properties of the object into a dict for use in json.
        You can define the following in your class

        _json_eager_load :
            list of which child classes need to be eagerly loaded. This applies
            to one-to-many relationships defined in SQLAlchemy classes.

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from datetime import datetime
from sqlalchemy import Column, Integer, DateTime, ForeignKey
from sqlalchemy.orm import relationship
from sqlalchemy.ext.declarative import declared_attr
from flask_security import current_user


class AuditMixin(object):
    created_at = Column(DateTime, default=datetime.now)
    updated_at = Column(DateTime, default=datetime.now, onupdate=datetime.now)

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{% macro form_field(field) -%}
    {% set with_label = kwargs.pop('with_label', False) %}
    {% set placeholder = '' %}
    {% if not with_label %}
        {% set placeholder = field.label.text %}
    {% endif %}
    <div class="control-group {% if field.errors %}error{% endif %}">
        {% if with_label %}
            <label for="{{ field.id }}" class="control-label">
                {{ field.label.text }}{% if field.flags.required %} *{% endif %}:

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#include <Python.h>
#include <numpy/arrayobject.h>
#include "chi2.h"

/* Docstrings */
static char module_docstring[] =
    "This module provides an interface for calculating chi-squared using C.";
static char chi2_docstring[] =
    "Calculate the chi-squared of some data given a model.";

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Latency Comparison Numbers
--------------------------
L1 cache reference                            0.5 ns
Branch mispredict                             5   ns
L2 cache reference                            7   ns             14x L1 cache
Mutex lock/unlock                            25   ns
Main memory reference                       100   ns             20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy              3,000   ns
Send 1K bytes over 1 Gbps network        10,000   ns    0.01 ms
Read 4K randomly from SSD*              150,000   ns    0.15 ms

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import sys

from pyspark.context import SparkContext
from numpy import array, random as np_random
from sklearn import linear_model as lm
from sklearn.base import copy

N = 10000   # Number of data points
D = 10      # Numer of dimensions
ITERATIONS = 5

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data = sqlContext.load("/home/rxin/ints.parquet")
data.groupBy("a").agg(col("a"), avg("num")).collect()

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#List unique values in a DataFrame column
pd.unique(df.column_name.ravel())

#Convert Series datatype to numeric, getting rid of any non-numeric values
df['col'] = df['col'].astype(str).convert_objects(convert_numeric=True)

#Grab DataFrame rows where column has certain values
valuelist = ['value1', 'value2', 'value3']
df = df[df.column.isin(value_list)]

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/*
SQL script to grab the worst performing indexes
in the whole server
*/
SELECT
  t.TABLE_SCHEMA AS `db`
 , t.TABLE_NAME AS `table`
 , s.INDEX_NAME AS `inde name`
 , s.COLUMN_NAME AS `field name`
 , s.SEQ_IN_INDEX `seq in index`