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SQLextract.py
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import pyodbc
import pandas as pd
import time
from datetime import datetime
def sql_connection(start_date, end_date):
start_date = str(datetime.strptime(start_date, '%Y-%m-%d'))
end_date = str(datetime.strptime(end_date, '%Y-%m-%d'))
server = '103.234.81.137'
database = 'jtrac'
username = 'vivian.ou'
password = 'Origo777'
#和SQL資料庫連接，輸出Utrac問題單
cnxn = pyodbc.connect('DRIVER={SQL Server}; SERVER=' + server + ';DATABASE=' + database + ';UID=' + username + ';PWD=' + password)
cursor = cnxn.cursor()
#SQL query->找到某段日期的回覆資料
sql_query = """SELECT items.id,
items.sequence_num,
items.time_stamp,
spaces.name,
spaces.description,
history.comment,
history.time_stamp,
history.summary,
history.detail,
history.status,
users.name,
history.cus_int_06,
history.cus_int_03
FROM history
LEFT JOIN items ON items.id=history.item_id
LEFT JOIN spaces ON spaces.id =items.space_id
LEFT JOIN users ON users.id=history.logged_by
WHERE (items.time_stamp > CONVERT(DATETIME, ?, 102)) AND (items.time_stamp <= CONVERT(DATETIME, ?, 102));
"""
params=(start_date, end_date)
#將資料存成DataFrame
result = cursor.execute(sql_query, params)
lst_result = list(result.fetchall())
df_columns = ['items_id','items_sequence_num', '時間戳記','spaces_name', '機關',
'回覆', 'history_time_stamp', '摘要', '細節', '狀態','紀錄者', '功能類別', '問題單分類']
df = pd.DataFrame((tuple(t) for t in lst_result), columns=df_columns)
df = df.reset_index(drop=True)
df['items_sequence_num'] = df['items_sequence_num'].astype('float').astype(int).astype(str)
df['單號'] = df['spaces_name'].str.cat(df['items_sequence_num'],sep="-")
df['temp']=df['單號'].str.replace('\-\d+', ' ')
df['temp'] =df['temp'].str.replace(' ', '')
df['主機關代碼'] = df['temp'].str.replace('\_\d+\_\w+', '')
df['主機關代碼'] = df['temp'].str.replace('\_\w+', '')
df = df.reset_index(drop=True)
df=df[['單號','主機關代碼','機關','時間戳記', '摘要','細節','回覆', '狀態','紀錄者','功能類別','spaces_name', '問題單分類']]
#將所有summary和detail全部填滿(因只有最一開始的問題有此兩個值
df['摘要']=df.sort_values(['單號','摘要'])['摘要'].ffill()
df['細節']=df.sort_values(['單號','細節'])['細節'].ffill()
#針對回覆進行處理
df = df.replace(r'\n','', regex=True)
df['回覆'] =df['回覆'].str.replace('\n', ' ')
df['回覆'] =df['回覆'].str.replace('\t', ' ')
df['回覆1'] =df['回覆'].str.replace(r'^.*原因', '')
mask =df['回覆1'].str.len()==df['回覆'].str.len()
df.loc[mask, '回覆1'] = df.loc[mask, '回覆1'].str.replace(r'^.+?(?=[處理])', '')
#去除非針對系統問題的問題單
df = df[df['紀錄者']!='自動填單']
#區分已結案的問題單
df_closed=df[df['狀態']==99.0]
#區分未結案的問題單
df_open=df[df['狀態']==1.0]
#新增功能類別欄位
sql_query_function = """SELECT value, text, column_type
FROM cus_int_options
"""
result_2 = cursor.execute(sql_query_function)
lst_result_2 = list(result_2.fetchall())
df2_columns=['value', '功能類別', '欄位']
# df1 = pd.DataFrame((tuple(t) for t in lst_result_2), columns=df2_columns)
# df2 = df2.reset_index(drop=True)
# df2 = df2[df2['欄位']=='cus_int_06']
# df2 = df2.reset_index(drop=True)
# df2 = df2.drop_duplicates('value')
df1 = pd.DataFrame((tuple(t) for t in lst_result_2), columns=df2_columns)
df1 = df1.reset_index(drop=True)
df2 = df1[df1['欄位'].isin(['cus_int_06'])]
df3 = df1[df1['欄位'].isin(['cus_int_03'])]
df2 = df2.reset_index(drop=True)
df2 = df2.drop_duplicates('value')
df3 = df3.reset_index(drop=True)
df3 = df3.drop_duplicates('value')
df3.columns = ['value', '問題單分類', '欄位']
# df = df.reset_index(drop=True)
df_open['功能類別'] = df_open['功能類別'].map(df2.set_index('value')['功能類別'])
df_closed['功能類別'] = df_closed['功能類別'].map(df2.set_index('value')['功能類別'])
df_open['問題單分類'] = df_open['問題單分類'].map(df3.set_index('value')['問題單分類'])
df_closed['問題單分類'] = df_closed['問題單分類'].map(df3.set_index('value')['問題單分類'])
#去除非針對系統問題的問題單
drop_facility=['000_AutoTest', '000_ENG_M', '000_UG', '000_UGAls', '000_UGAtt', '000_UGCar',
'000_UGCas', '000_UGCsw', '000_UGExp', '000_UGIsm', '000_UGMon', '000_UGOes', '000_UGProposal',
'000_UGRas', '000_UGRFP', 'WebITR', 'jTracDlp']
assignee_lst =['黃羿禎']
df_closed = df_closed[~df_closed['spaces_name'].isin(drop_facility)]
df_closed = df_closed[~df_closed['紀錄者'].isin(assignee_lst)]
df_open = df_open[~df_open['spaces_name'].isin(drop_facility)]
df_open = df_open[~df_open['紀錄者'].isin(assignee_lst)]
closed_lst= df_closed['單號'].tolist()
df_open = df_open[~df_open['單號'].isin(closed_lst)]
df_closed = df_closed.drop_duplicates(subset='單號')
df_open = df_open.drop_duplicates(subset='單號')
df_all = pd.concat([df_open, df_closed], axis=0)
df_all=df_all.drop_duplicates(subset='單號')
return df_all
def save_label_to_sql(label, id, correct_check):
#因為此張表之後會存在各個機關端，因此先不在utrac開此表，從json取
#不過，目前在local端測試，可以先使用sql server的資料
server = 'VIVIAN-HP\SQLEXPRESS'
database = 'QAlabel'
# username = 'username'
# password = 'yourpassword'
cnxn = pyodbc.connect('DRIVER={SQL Server};SERVER='+server+';DATABASE='+database)
cursor = cnxn.cursor()
sql_query="""
INSERT INTO QAlabel.dbo.jtrac_label
VALUES
(?,?,(SELECT Service_label from QAlabel.dbo.jtrac_pred_label_code WHERE jtrac_pred_label_code.Predict_label=?),?);
"""
param=(int(id), label, label, correct_check)
cursor.execute(sql_query, param)
cnxn.commit()
cursor.close()
cnxn.close()
print('in save_label_to_sql')
def extrac_QA_label(label, para):
server = 'VIVIAN-HP\SQLEXPRESS'
database = 'QAlabel'
# username = 'username'
# password = 'yourpassword'
cnxn = pyodbc.connect('DRIVER={SQL Server};SERVER='+server+';DATABASE='+database)
cursor = cnxn.cursor()
sql_query="""
SELECT para
from label_parameter
WHERE labels=? AND para_type=?
"""
param=(label, para)
cursor.execute(sql_query, param)
row = [item[0] for item in cursor.fetchall()]
cursor.close()
cnxn.close()
return row
def update_sql(utrac_id, correct_check):
server = 'VIVIAN-HP\SQLEXPRESS'
database = 'QAlabel'
# username = 'username'
# password = 'yourpassword'
cnxn = pyodbc.connect('DRIVER={SQL Server};SERVER='+server+';DATABASE='+database)
cursor = cnxn.cursor()
sql_query="""
Update QAlabel.dbo.jtrac_label
SET correct_check=?
WHERE id=?;
"""
param=(correct_check, utrac_id)
cursor.execute(sql_query, param)
cnxn.commit()
cursor.close()
cnxn.close()
print('in update_sql')
if __name__ == '__main__':
start_date = input('輸入起始日期:')
end_date=input('輸入結束日期:')
sqlquery = sql_connection(start_date, end_date)
sqlquery.info()