|
@@ -62,10 +62,11 @@ class FlattenData():
|
|
"Parameter 'incoming_key' be of String type"
|
|
"Parameter 'incoming_key' be of String type"
|
|
|
|
|
|
result_dict = {}
|
|
result_dict = {}
|
|
- if incoming_key not in labels_to_ignore:
|
|
|
|
- for index, row in dataframe.iterrows():
|
|
|
|
- temp_result_dict = {}
|
|
|
|
- for key, value in row.iteritems():
|
|
|
|
|
|
+
|
|
|
|
+ for index, row in dataframe.iterrows():
|
|
|
|
+ temp_result_dict = {}
|
|
|
|
+ for key, value in row.iteritems():
|
|
|
|
+ if key not in labels_to_ignore:
|
|
temp_result = {}
|
|
temp_result = {}
|
|
if incoming_key is not None:
|
|
if incoming_key is not None:
|
|
key = incoming_key + '_' + key
|
|
key = incoming_key + '_' + key
|
|
@@ -76,10 +77,13 @@ class FlattenData():
|
|
else:
|
|
else:
|
|
temp_result_dict[key] = value
|
|
temp_result_dict[key] = value
|
|
|
|
|
|
- if len(temp_result) > 0:
|
|
|
|
|
|
+ else:
|
|
|
|
+ temp_result_dict[key] = value
|
|
|
|
+
|
|
|
|
+ if len(temp_result) > 0:
|
|
temp_result_dict = self.append_to_dict(temp_result_dict, temp_result)
|
|
temp_result_dict = self.append_to_dict(temp_result_dict, temp_result)
|
|
|
|
|
|
- result_dict[index] = copy.deepcopy(temp_result_dict)
|
|
|
|
|
|
+ result_dict[index] = copy.deepcopy(temp_result_dict)
|
|
|
|
|
|
return result_dict
|
|
return result_dict
|
|
|
|
|
|
@@ -96,9 +100,8 @@ class FlattenData():
|
|
|
|
|
|
|
|
|
|
result_dict = {}
|
|
result_dict = {}
|
|
- if incoming_key not in labels_to_ignore:
|
|
|
|
- for key in dictionary:
|
|
|
|
-
|
|
|
|
|
|
+ for key in dictionary:
|
|
|
|
+ if key not in labels_to_ignore:
|
|
temp_dataframe = dictionary[key]
|
|
temp_dataframe = dictionary[key]
|
|
temp_result = {}
|
|
temp_result = {}
|
|
if incoming_key is not None:
|
|
if incoming_key is not None:
|
|
@@ -109,8 +112,10 @@ class FlattenData():
|
|
temp_result = self.flatten_dict(temp_dataframe, key, labels_to_ignore)
|
|
temp_result = self.flatten_dict(temp_dataframe, key, labels_to_ignore)
|
|
else:
|
|
else:
|
|
result_dict[key] = temp_dataframe
|
|
result_dict[key] = temp_dataframe
|
|
|
|
+ else:
|
|
|
|
+ result_dict[key] = temp_dataframe
|
|
|
|
|
|
- if len(temp_result) > 0:
|
|
|
|
|
|
+ if len(temp_result) > 0:
|
|
result_dict = self.append_to_dict(result_dict, temp_result)
|
|
result_dict = self.append_to_dict(result_dict, temp_result)
|
|
|
|
|
|
return result_dict
|
|
return result_dict
|
|
@@ -133,30 +138,30 @@ class FlattenData():
|
|
temp_dataframe = item
|
|
temp_dataframe = item
|
|
temp_result = {}
|
|
temp_result = {}
|
|
key = incoming_key
|
|
key = incoming_key
|
|
- if incoming_key not in labels_to_ignore:
|
|
|
|
- if incoming_key is not None:
|
|
|
|
- # OEBB SPECIFIC IF STATEMENT
|
|
|
|
- if type(data_list[iteration]) is dict and 'stationsnummer' in data_list[iteration].keys():
|
|
|
|
- key = incoming_key + '_' + str(data_list[iteration]['stationsnummer'])
|
|
|
|
-
|
|
|
|
- elif type(data_list[iteration]) is dict and 'stationsnummer' in data_list[iteration].keys() and 'stage' in data_list[iteration].keys() :
|
|
|
|
- key = incoming_key + '_' + str(data_list[iteration]['stationsnummer']) + '_' + str(data_list[iteration]['stage'])
|
|
|
|
-
|
|
|
|
- else:
|
|
|
|
- key = incoming_key + '_' + str(iteration)
|
|
|
|
|
|
+
|
|
|
|
+ if incoming_key is not None:
|
|
|
|
+ # OEBB SPECIFIC IF STATEMENT
|
|
|
|
+ if type(data_list[iteration]) is dict and 'stationsnummer' in data_list[iteration].keys():
|
|
|
|
+ key = incoming_key + '_' + str(data_list[iteration]['stationsnummer'])
|
|
|
|
+
|
|
|
|
+ elif type(data_list[iteration]) is dict and 'stationsnummer' in data_list[iteration].keys() and 'stage' in data_list[iteration].keys() :
|
|
|
|
+ key = incoming_key + '_' + str(data_list[iteration]['stationsnummer']) + '_' + str(data_list[iteration]['stage'])
|
|
|
|
+
|
|
else:
|
|
else:
|
|
- key = str(iteration)
|
|
|
|
- if type(temp_dataframe) == list:
|
|
|
|
- temp_result = self.flatten_list(temp_dataframe, key, labels_to_ignore)
|
|
|
|
|
|
+ key = incoming_key + '_' + str(iteration)
|
|
|
|
+ else:
|
|
|
|
+ key = str(iteration)
|
|
|
|
+ if type(temp_dataframe) == list:
|
|
|
|
+ temp_result = self.flatten_list(temp_dataframe, key, labels_to_ignore)
|
|
|
|
|
|
- elif type(temp_dataframe) == dict:
|
|
|
|
- temp_result = self.flatten_dict(temp_dataframe, key, labels_to_ignore)
|
|
|
|
|
|
+ elif type(temp_dataframe) == dict:
|
|
|
|
+ temp_result = self.flatten_dict(temp_dataframe, key, labels_to_ignore)
|
|
|
|
|
|
- else:
|
|
|
|
- result_dict[key] = temp_dataframe
|
|
|
|
|
|
+ else:
|
|
|
|
+ result_dict[key] = temp_dataframe
|
|
|
|
|
|
- if len(temp_result) > 0:
|
|
|
|
- result_dict = self.append_to_dict(result_dict, temp_result)
|
|
|
|
|
|
+ if len(temp_result) > 0:
|
|
|
|
+ result_dict = self.append_to_dict(result_dict, temp_result)
|
|
|
|
|
|
return result_dict
|
|
return result_dict
|
|
|
|
|