basic `dstruct` usage

1. sift through a data set for relevant information:



In [1]:

    
from dstruct import DataStruct, DataField



In [2]:

    
raw_data = {
    "a": 1,
    "b": {
        "c": 2,
        "d": 3
    }
}



In [3]:

    
from dstruct import DataStruct, DataField

class A(DataStruct):
    
    a = DataField()
    c = DataField('b', 'c')
    d = DataField('b', 'd')



In [4]:

    
A(raw_data)









    Out[4]:





{"a": 1, "c": 2, "d": 3}

2. import data sets from json files



In [5]:

    
from dstruct import DataStructFromJSON, DataField



In [6]:

    
class AccountSummaryFromJSON(DataStructFromJSON):
    
    user = DataField()
    type = DataField('account', 'account-type')
    ballance = DataField('account', 'account-ballance')
    # you can pass functions under the keyword "parser" to parse raw data parsing
    account_number = DataField('account', 'account-number', parser=lambda s: 'X'*len(s[:-4])+s[-4:])



In [7]:

    
AccountSummaryFromJSON('data_files/bank_data.json')









    Out[7]:





{"ballance": 1234.56, "type": "checking", "user": "John F. Doe", "account_number": "XXXXX6789"}

3. import data sets from csv files:

The datafield decorator, wraps complex parsers



In [8]:

    
from dstruct import DataStructFromCSV, datafield



In [9]:

    
class AverageUserFromCSV(DataStructFromCSV):

    @datafield(path=None)
    def age(self, data):
        total = 0
        for name in data:
            total += int(data[name]['Age'])
        return round(float(total)/len(data), 1)
    
    @datafield(path=None)
    def weight(self, data):
        total = 0
        for name in data:
            total += int(data[name]['Weight'])
        return round(float(total)/len(data), 1)



In [10]:

    
AverageUserFromCSV('data_files/wide.csv')









    Out[10]:





{"age": 40.0, "weight": 174.3}

+ DataStructFromCSV understands wide and narrow form data representations



In [11]:

    
narrow = AverageUserFromCSV('data_files/narrow.csv')

Person	Variable	Value
Bob	Age	32
Bob	Weight	178
Alice	Age	24
Alice	Weight	150
Steve	Age	64
Steve	Weight	195



In [12]:

    
wide = AverageUserFromCSV('data_files/wide.csv')

Person	Age	Weigth
Bob	32	178
Alice	24	150
Steve	64	195



In [13]:

    
narrow == wide









    Out[13]:





True



In [ ]:

basic dstruct usage

1. sift through a data set for relevant information:

2. import data sets from json files

3. import data sets from csv files:

basic `dstruct` usage