dictify | Dictify : Documents Schema / Validation made simple | Validation library

In [6]: from xml.etree import cElementTree as ET

In [7]: ET.XML(tif_tags["FEI_TITAN"])
Out[7]: 

In [8]: from copy import copy
   ...: 
   ...: def dictify(r,root=True):
   ...:     if root:
   ...:         return {r.tag : dictify(r, Fals

def dictify(g):
    return {k: list(v) if len(v) > 1 else v.item() 
            for k, v in g.groupby('Property')['Value']}

s = df.set_index('Item no.')
s.columns = s.columns.str.split(expand=True)

s.stack().groupby(level=[0, 1]).appl

def dictify(s):
    if s.index.nlevels == 1: return s.to_dict()
    return {k: dictify(g.droplevel(0)) for k, g in s.groupby(level=0)}

records = [{k: v} for k, v in dictify(df).items()]

>>> df

DE        

from collections import defaultdict

def dictify(df):
    dct = defaultdict(list)
    for k, g in df.groupby(['n1', df.groupby(['n1', 'v1']).cumcount()]):
        dct[k[0]].append(dict([*g[['v1', 'v2']].values]))
    return dict(dct)

def toVertex(self,g):
    label=type(self).__name__;
    t=g.addV(label)
    for name,value in vars(self).items():
        if debug:
            print("%s=%s" % (name,value))
        t=t.property(name,value)
    t.iterate()    

def dictify(data):
    result = dict()
    for key, value in data:
        if key in result:
            result[key].add(value)
        else:
            result[key] = set([value])
    return result

>>> da

import itertools

def flatten(data):
    flattened = []
    for key, value in itertools.groupby(data, type):
        if key == int:
            flattened.append(next(value))
        else:
            flattened.append(list(itertools.chain.f

from functools import partial

with open('mydata.db', 'rb') as f:
    for block in iter(partial(f.read, 64), b''):
        process_block(block)

with open('mydata.db', 'r') as f:
    for block in iter(partial(f.read

{k: g.to_dict(orient='records') for k, g in df.groupby(level=0)}
# {'bob': [{'age': 20, 'name': 'bob'}, {'age': 30, 'name': 'bob'}],
#  'jim': [{'age': 25, 'name': 'jim'}]}

for k, g in df.groupby(level=0):
    prin