1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211 | def getMongoDB(**context):
token = context.get("ti").xcom_pull(key="token")
response_s01 = requests.get(
url=f"{dRoW_api_end_url}/api/module/document-export/airflow/workflow/6597889461a8f490bf96667f?export_type=0",
headers={
"x-access-token": f"Bearer {token}",
"ICWPxAccessKey": "nd@201907ICWP_[1AG:4UdI){n=b~"
}
)
response_s02 = requests.get(
url=f"{dRoW_api_end_url}/api/module/document-export/airflow/workflow/65ae1f219aad62a7971a07bb?export_type=0",
headers={
"x-access-token": f"Bearer {token}",
"ICWPxAccessKey": "nd@201907ICWP_[1AG:4UdI){n=b~"
}
)
RISC_Data_01 = json.loads(response_s01.text)
RISC_Data_02 = json.loads(response_s02.text)
host = 'drowdatewarehouse.crlwwhgepgi7.ap-east-1.rds.amazonaws.com'
# User name of the database server
dbUserName = 'dRowAdmin'
# Password for the database user
dbUserPassword = 'drowsuper'
# Name of the database
database = 'drowDateWareHouse'
# Character set
charSet = "utf8mb4"
port = "5432"
# #cursor Type
# cusrsorType = pymysql.cursors.DictCursor
conn_string = ('postgres://' +
dbUserName + ':' +
dbUserPassword +
'@' + host + ':' + port +
'/' + database)
db = create_engine(conn_string)
conn = db.connect()
full_df = pd.DataFrame()
monthly_summary = {}
with conn:
for entry in RISC_Data_01:
df_nested_list = json_normalize(entry['data'])
# List to hold object for each table
df_list = []
# Get total number of tables
total_tables = len([key for key, val in df_nested_list.items() if 'Table' in key])
# Inspection date
date_of_inspection = df_nested_list['Date of Inspection'][0]
if (date_of_inspection == None):
continue
# Contract title
contract_title = df_nested_list['Contract Title'][0]
# Process each table dynamically
for i in range(1, total_tables):
table_key = f"Table {i}"
if table_key not in df_nested_list:
continue
df_table = df_nested_list[table_key]
for record in df_table[0]:
item_no = list(record.values())[0].split(" ")[0]
group_key = list(record.keys())[0]
dict_record = {
'Date of Inspection': date_of_inspection,
'Month': date_of_inspection[:7],
'Contract Title': contract_title,
'Group No.': str(i),
'Group': group_key,
'Item No.': item_no,
'Description': record[group_key].replace(f"{item_no} ", ""),
'Template': 'S01_Daily Site Safety Inspection Checklist',
}
record.pop(list(record.keys())[0])
for k, v in record.items():
dict_record[k.replace(f'{i}. ', "")] = v
if 'Date completed' not in dict_record or 'Agreed date for completion' not in dict_record:
dict_record['On Time'] = None
elif not dict_record['Date completed'] or not dict_record['Agreed date for completion']:
dict_record['On Time'] = None
elif dict_record['Date completed'] <= dict_record['Agreed date for completion']:
dict_record['On Time'] = "On-Time"
else:
dict_record['On Time'] = "Late"
df_list.append(dict_record)
if date_of_inspection[:7] in monthly_summary:
monthly_summary[date_of_inspection[:7]]['items'] += 1
if dict_record['Safety Compliance'] == 'No':
monthly_summary[date_of_inspection[:7]]['concern'] += 1
else:
monthly_summary[date_of_inspection[:7]] = {
'items': 1,
'concern': 1 if dict_record['Safety Compliance'] == 'No' else 0
}
df_combined = pd.DataFrame(data=df_list)
# Append non-compliant records
if not full_df.empty and not df_combined.empty:
full_df = pd.concat([full_df, df_combined], ignore_index=True)
elif not df_combined.empty:
full_df = df_combined
for entry in RISC_Data_02:
df_nested_list = json_normalize(entry['data'])
# List to hold object for each table
df_list = []
# Get total number of tables
total_tables = len([key for key, val in df_nested_list.items() if 'Table' in key])
# Inspection date
date_of_inspection = df_nested_list['Date of Inspection'][0]
if (date_of_inspection == None):
continue
# Contract title
contract_title = df_nested_list['Contract Title'][0]
# Process each table dynamically
for i in range(1, total_tables):
table_key = f"Table {i}"
if table_key not in df_nested_list:
continue
df_table = df_nested_list[table_key]
for record in df_table[0]:
item_no = list(record.values())[0].split(" ")[0]
group_key = list(record.keys())[0]
dict_record = {
'Date of Inspection': date_of_inspection,
'Month': date_of_inspection[:7],
'Contract Title': contract_title,
'Group No.': str(i),
'Group': group_key,
'Item No.': item_no,
'Description': record[group_key].replace(f"{item_no} ", ""),
'Template': 'S02_Weekly Site Safety Inspection Checklist',
}
record.pop(list(record.keys())[0])
for k, v in record.items():
dict_record[k.replace(f'{i}. ', "")] = v
if 'Date completed' not in dict_record or 'Agreed date for completion' not in dict_record:
dict_record['On Time'] = None
elif not dict_record['Date completed'] or not dict_record['Agreed date for completion']:
dict_record['On Time'] = None
elif dict_record['Date completed'] <= dict_record['Agreed date for completion']:
dict_record['On Time'] = "On-Time"
else:
dict_record['On Time'] = "Late"
df_list.append(dict_record)
if date_of_inspection[:7] in monthly_summary:
monthly_summary[date_of_inspection[:7]]['items'] += 1
if dict_record['Safety Compliance'] == 'No':
monthly_summary[date_of_inspection[:7]]['concern'] += 1
else:
monthly_summary[date_of_inspection[:7]] = {
'items': 1,
'concern': 1 if dict_record['Safety Compliance'] == 'No' else 0
}
df_combined = pd.DataFrame(data=df_list)
# Append non-compliant records
if not full_df.empty and not df_combined.empty:
full_df = pd.concat([full_df, df_combined], ignore_index=True)
elif not df_combined.empty:
full_df = df_combined
# Sort by date of inspection
non_compliant_df = full_df[full_df['Safety Compliance'] == 'No']
non_compliant_df.sort_values(by=['Date of Inspection', 'Contract Title', 'Item No.'], inplace=True)
# Clean up column names for SQL
non_compliant_df.columns = non_compliant_df.columns.str.replace(' ', '_').str.replace(r'[().%]', '', regex=True).str.replace('/', '_')
# Retrieve only relevant columns
final_df = non_compliant_df[['Date_of_Inspection', 'Month', 'Contract_Title', 'Template', 'Group_No', 'Group', 'Item_No', 'Description', 'Location', 'Safety_Compliance', 'Date_completed', 'Agreed_date_for_completion', 'On_Time']]
# Write to SQL database
final_df.to_sql('safety_inspection_dc202312', con=conn, if_exists='replace', index=False)
# Create a summary df
summary_dict = []
for k, v in monthly_summary.items():
summary_dict.append({
'Month': k,
'Items': v['items'],
'Concerns': v['concern']
})
summary_df = pd.DataFrame(data=summary_dict)
summary_df.to_sql('safety_inspection_summary_dc202312', con=conn, if_exists='replace', index=False)
|