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@ -205,6 +205,8 @@ class Components :
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reader = factory.instance(**args['store']['source'])
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if 'file' in args :
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df = pd.read_csv(args['file'])
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elif 'data' in _args :
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df = _args['data']
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else:
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if 'row_limit' in args and 'sql' in args:
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df = reader.read(sql=args['sql'],limit=args['row_limit'])
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@ -226,25 +228,45 @@ class Components :
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columns = args['columns'] if 'columns' in args else df.columns
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columns = list(set(columns) - set(_cols))
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for name in columns :
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i = np.arange(df.shape[0])
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np.random.shuffle(i)
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if name in x_cols :
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df[name] = self.approximate(df.iloc[i][name].values)
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df[name] = df.iloc[i][name]
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# for name in columns:
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# i = np.arange(df.shape[0])
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# np.random.shuffle(i)
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# if name in x_cols :
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# if df[name].unique().size > 0 :
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# df[name] = self.approximate(df.iloc[i][name].fillna(0).values)
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# df[name] = df[name].copy().astype(str)
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# pass
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df.index = np.arange(df.shape[0])
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self.post(data=df,schema=schema,store=args['store']['target'])
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def post(self,**_args) :
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_schema = _args['schema'] if 'schema' in _args else None
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writer = factory.instance(**_args['store'])
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_df = _args['data']
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if _schema :
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columns = []
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for _item in _schema :
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if _item['type'] in ['DATE','TIMESTAMP','DATETIME'] :
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_df[_item['name']] = _df[_item['name']].astype(str)
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name = _item['name']
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_type = str
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_value = 0
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if _item['type'] in ['DATE','TIMESTAMP','DATETIMESTAMP','DATETIME'] :
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if _item['type'] == 'DATE' :
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_df[name] = _df[name].dt.date
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else:
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if _item['type'] == 'INTEGER' :
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_type = np.int64
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elif _item['type'] in ['FLOAT','NUMERIC']:
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_type = np.float64
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else:
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_value = ''
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_df[name] = _df[name].fillna(_value).astype(_type)
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columns.append(name)
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writer.write(_df,schema=_schema,table=args['from'])
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else:
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writer.write(_df,table=args['from'])
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writer.write(_df[columns],table=args['from'])
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# @staticmethod
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def generate(self,args):
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