Khouli00

#serialize datetime to json
#https://stackoverflow.com/questions/35869985/datetime-datetime-is-not-json-serializable
import datetime
import json

def datetime_handler(x):
    if isinstance(x, datetime.datetime):
        return x.isoformat()
    raise TypeError("Unknown type")

Khouli00

#UTF-8 proof
with open(os.path.join('test','graph.json'),'w',encoding='utf-8') as fd:
  json.dump(json_graph.adjacency_data(Tree.graph),fd,indent=1,ensure_ascii=False)

Khouli00

import itertools
#collapse list
list2d = [[1,2,3],[4,5,6], [7], [8,9]]
merged = list(itertools.chain.from_iterable(list2d))

#efficient merge list
#courtesy http://stackoverflow.com/questions/952914/making-a-flat-list-out-of-list-of-lists-in-python#952946
l = [[1,2,3],[4,5,6], [7], [8,9]]
C = [item for sublist in l for item in sublist]

Khouli00

#add leading zero ...
print str(1).zfill(2) 
print str(10).zfill(2) 
>>01
>>10

#format https://pyformat.info/
'{:.2f}'.format(3.141592653589793)
>>'3.14'

Khouli00

-- Order report
--[u'OrderId', u'FirstName', u'LastName', u'Email', u'Address', u'Postcode', u'ProductList', u'Status', u'PaidDate', u'CompletedDate', u'PaymentMethod', u'Shipping', u'TotalOrder']

SELECT post_id                                                                   AS OrderId,
       Group_concat( IF( meta_key = '_billing_first_name', meta_value, NULL ) )  AS FirstName,
       Group_concat( IF( meta_key = '_billing_last_name', meta_value, NULL ) )   AS LastName,
       Group_concat( IF(meta_key = '_billing_email', meta_value, NULL))          AS Email,
       Group_concat( IF( meta_key = '_billing_address_1', meta_value, NULL ) )   AS Address,
       Group_concat( IF( meta_key = '_billing_postcode', meta_value, NULL ) )    AS Postcode,
       Group_concat( IF( meta_key = '_desc_content', meta_value, NULL ) )        AS ProductList,

Khouli00

Khouli00

def pandas_df_to_markdown_table(df,name):
        fmt = ['---' for i in range(len(df.columns))]
        df_fmt = pd.DataFrame([fmt], columns=df.columns)
        df_formatted = pd.concat([df_fmt, df])
        df_formatted.to_csv(name,sep="|", index=False)

Khouli00

<table>
{% for key, value in result.iteritems() %}
   <tr>
        <th> {{ key }} </th>
        <td> {{ value }} </td>
   </tr>
{% endfor %}
</table>

Khouli00

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        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

Khouli00

PSQL

Magic words:

psql -U postgres

If run with -E flag, it will describe the underlaying queries of the \ commands (cool for learning!).

Most \d commands support additional param of __schema__.name__ and accept wildcards like *.*