pandas的dataframe条件过滤性能优化？

[codenamea]

目前我有一段代码，整个代码主要的时间消耗都在dataframe的以上两句数据筛选上了。
temp_df = df[df["data_date"].isin(date_list)]
temp = temp_df[rule[2]][temp_df["data_date"] == d]

目前的仅仅是四个条件的筛选rule下，执行大概需要30-50秒左右，请问有什么好的优化方法吗？
因为后续可能会添加到10多个甚至更多的rule去综合筛选。

Function: digg_data_byrule at line 56

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
    56                                           def digg_data_byrule(df,rules):
    57      1546       1398.0      0.9      0.0          info=""
    58      7730       6465.0      0.8      0.0          for rule in rules:
    59      6184    2076181.0    335.7      9.4              date_list=utils.datelist(rule[0],rule[1])
    60      6184       9205.0      1.5      0.0              days = len(date_list)
    61      6184    4966183.0    803.1     22.6              temp_df = df[df["data_date"].isin(date_list)]
    62      6184       7782.0      1.3      0.0              flag = False
    63      6184      20854.0      3.4      0.1              if len(temp_df) == days:
    64      3100       2613.0      0.8      0.0                  temp_array = []
    65     16960      13525.0      0.8      0.1                  for d in date_list:
    66     13860   14703156.0   1060.8     66.9                      temp = temp_df[rule[2]][temp_df["data_date"] == d]
    67     13860      72778.0      5.3      0.3                      if len(temp) > 0:
    68     13860      62733.0      4.5      0.3                          temp_array.append(temp.values[0])
    69      7102       8265.0      1.2      0.0                  for i in range(len(temp_array)):
    70      7048       4693.0      0.7      0.0                      if i > 0:
    71      3948       9257.0      2.3      0.0                          if (temp_array[i] - temp_array[i - 1]) / temp_array[i - 1] > rule[4]:
    72       902        608.0      0.7      0.0                              flag = True
    73                                                                   else:
    74      3046       1805.0      0.6      0.0                              flag = False
    75      3046       2206.0      0.7      0.0                              break
    76      6184       4376.0      0.7      0.0              if flag==True:
    77        54        181.0      3.4      0.0                  info += "%s:%s:%d:%.2f;" % (rule[3], rule[2], len(date_list), rule[4])
    78      1546        874.0      0.6      0.0          return info

Total time: 40.1706 s
File: test.py
Function: main_test at line 16
----------------------------------------



def digg_data2(df,rules):
    temp_df= df.groupby("product_id").apply(digg_data_byrule,rules)
    temp_df=temp_df[temp_df!=""].reset_index()
    temp_df.rename(columns={0:"flag"},inplace=True)
    temp_df=temp_df.set_index("product_id")
    return temp_df.to_dict("index")
    

def digg_data_byrule(df,rules):
    info=""
    for rule in rules:
        date_list=utils.datelist(rule[0],rule[1])
        days = len(date_list)
        temp_df = df[df["data_date"].isin(date_list)]
        flag = False
        if len(temp_df) == days:
            temp_array = []
            for d in date_list:
                temp = temp_df[rule[2]][temp_df["data_date"] == d]
                if len(temp) > 0:
                    temp_array.append(temp.values[0])
            for i in range(len(temp_array)):
                if i > 0:
                    if (temp_array[i] - temp_array[i - 1]) / temp_array[i - 1] > rule[4]:
                        flag = True
                    else:
                        flag = False
                        break
        if flag==True:
            info += "%s:%s:%d:%.2f;" % (rule[3], rule[2], len(date_list), rule[4])
    return info


rules = [
        (max_date - timedelta(days=5), max_date, "product_add_cart", "increase_percent", 0.25),
        (max_date - timedelta(days=5), max_date, "amount", "increase_percent", 0.25),
        (max_date - timedelta(days=2), max_date, "product_add_cart", "increase_percent", 0.15),
        (max_date - timedelta(days=2), max_date, "amount", "increase_percent", 0.15),
    ]
    
    data = pd.read_sql(sql, db.engine)
    data = data.groupby(['data_date', 'product_id']).sum().reset_index()
    temp1 = digg_data2(data, rules)