Question

我试图在Postgresql中运行此查询。我得到了我想要的结果，但我试图将结果限制为列中的值。但是，它不允许我访问该列：

select count(event), event, u.userid, u.campaign_id, 
rank() over (partition by u.campaign_id order by count(event) desc) as THISHERE 
from events e join users u on u.userid = e.userid 
where THISHERE=1 
group by event, 3 , 4 
order by 1 desc 
limit 20;

错误：列＆＃34;其中＆＃34;不存在

Answer 1

您需要使用子查询：

import numpy
import pandas as pd
import pyodbc
import time


class MyDfInsert:
    def __init__(self, cnxn, sql_stub, data_frame, rows_per_batch=1000):
        # NB: hard limit is 1000 for SQL Server table value constructor
        self._rows_per_batch = 1000 if rows_per_batch > 1000 else rows_per_batch

        self._cnxn = cnxn
        self._sql_stub = sql_stub
        self._num_columns = None
        self._row_placeholders = None
        self._num_rows_previous = None
        self._all_placeholders = None
        self._sql = None

        row_count = 0
        param_list = list()
        for df_row in data_frame.itertuples():
            param_list.append(tuple(df_row[1:]))  # omit zero-based row index
            row_count += 1
            if row_count >= self._rows_per_batch:
                self._send_insert(param_list)  # send a full batch
                row_count = 0
                param_list = list()
        self._send_insert(param_list)  # send any remaining rows

    def _send_insert(self, param_list):
        if len(param_list) > 0:
            if self._num_columns is None:
                # print('[DEBUG] (building items that depend on the number of columns ...)')
                # this only happens once
                self._num_columns = len(param_list[0])
                self._row_placeholders = ','.join(['?' for x in range(self._num_columns)])
                # e.g. '?,?'
            num_rows = len(param_list)
            if num_rows != self._num_rows_previous:
                # print('[DEBUG] (building items that depend on the number of rows ...)')
                self._all_placeholders = '({})'.format('),('.join([self._row_placeholders for x in range(num_rows)]))
                # e.g. '(?,?),(?,?),(?,?)'
                self._sql = f'{self._sql_stub} VALUES {self._all_placeholders}'
                self._num_rows_previous = num_rows
            params = [int(element) if isinstance(element, numpy.int64) else element
                      for row_tup in param_list for element in row_tup]
            # print('[DEBUG]    sql: ' + repr(self._sql))
            # print('[DEBUG] params: ' + repr(params))
            crsr = self._cnxn.cursor()
            crsr.execute(self._sql, params)


if __name__ == '__main__':
    conn_str = (
        'DRIVER=ODBC Driver 11 for SQL Server;'
        'SERVER=192.168.1.134,49242;'
        'Trusted_Connection=yes;'
    )
    cnxn = pyodbc.connect(conn_str, autocommit=True)
    crsr = cnxn.cursor()
    crsr.execute("CREATE TABLE #tmp (id INT PRIMARY KEY, txt NVARCHAR(50))")

    df = pd.read_csv(r'C:\Users\Gord\Desktop\Query1.txt', sep=';', header=0)

    t0 = time.time()

    MyDfInsert(cnxn, "INSERT INTO #tmp (id, txt)", df, rows_per_batch=1000)

    print()
    print(f'Inserts completed in {time.time() - t0:.2f} seconds.')

    cnxn.close()

我认为您也可以使用select eu.* from (select count(event), event, u.userid, u.campaign_id, rank() over (partition by u.campaign_id order by count(event) desc) as THISHERE from events e join users u on u.userid = e.userid group by event, 3 , 4 ) eu where THISHERE = 1 order by 1 desc limit 20;执行您想要的操作：

distinct on

我如何访问排名

1 个答案: