根据不同的查询日期使用Cx_Oracle创建数据框

Question

下面是数据库表的示例

date          id    name
01.02.11      4     aaaa
21.05.19      5     aaaa
31.12.12      5     aaaa
01.05.15      6     aaaa

为了以正确的方式查询数据（避免重复），在查询时，我必须将“报告日期”设置为第一个月的日期。

下面的代码为我提供了所需的结果，但只提供了一个月。

sql = 'select * from db where date = '01.03.20''


def oracle(user, pwd, dsn, sql, columns):

    # Connection to databases
    con = cx_Oracle.connect(user=user, password=pwd, dsn=dsn, encoding="UTF-8")
    con.outputtypehandler = OutputHandler

    # Cursor allows Python code to execute PostgreSQL command in a database session
    cur = con.cursor()

    # Check Connection
    print('Connected')

    # Create DF
    df = pd.DataFrame(cur.execute(sql).fetchall(), columns= columns, dtype='object')[:]

    print('Shape:', df.shape)

    return df

问题：如何使用具有不同报告日期的CX_Oracle查询数据，而无需手动执行？

有多种方法可以直接使用SQL解决此问题。但是，预期的解决方案应使用“ a for循环”。

我正在考虑使用[更改报告日期

for i in [str(i).zfill(2) for i in range(1,13)]: 
    for j in [str(j).zfill(2) for j in range(0,21)]
           sql = f'select * from db where date = '01.{i}.{j}''

• 例如：日期= 01.01.19

想法是查询该日期的数据->将其存储在DF中

转到下个月01.02.19->将其存储在DF中

依此类推，直到达到范围21或到达上一个月（最新日期）

如果有人有想法使用cx_Oracle和Pandas使用循环来查询数据的不同日期，谢谢您的帮助！

Answer 1

这样的事情怎么样

from datetime import date, datetime, timedelta
import calendar

# Choose Start Month
start_month = date(2019, 1, 1)

# Get Current Month
current_month = date(datetime.today().year, datetime.today().month, 1)

# Create list to collect all successfully run queries
executed_sql_queries = []
# Create list for failed queries
failed_queries = []

# Create list to collect dfs
dfs = []

while start_month <= current_month:
    query_date = start_month.strftime('%d.%m.%y')

    sql = f"""select * from db where date = '{query_date}' """

    try:
        df = oracle(user, pwd, dsn, sql=sql, columns)

    except sql_error as e:
        print(e)
        failed_queries.append(sql)
        pass # move onto the next query or you can try re-running the query

    else:
        executed_sql_queries.append(sql)
        dfs.append(df)

    finally:
        # Add one Month to the date for each run
        days_in_month = calendar.monthrange(start_month.year, start_month.month)[1]
        start_month = start_month + timedelta(days=days_in_month)


all_dfs = pd.concat(dfs)

executed_sql_queries：
["select * from db where date = '01.01.19' ",
 "select * from db where date = '01.02.19' ",
 "select * from db where date = '01.03.19' ",
 "select * from db where date = '01.04.19' ",
 "select * from db where date = '01.05.19' ",
 "select * from db where date = '01.06.19' ",
 "select * from db where date = '01.07.19' ",
 "select * from db where date = '01.08.19' ",
 "select * from db where date = '01.09.19' ",
 "select * from db where date = '01.10.19' ",
 "select * from db where date = '01.11.19' ",
 "select * from db where date = '01.12.19' ",
 "select * from db where date = '01.01.20' ",
 "select * from db where date = '01.02.20' ",
 "select * from db where date = '01.03.20' ",
 "select * from db where date = '01.04.20' "]