python编程中，经常会遇到两个或多个列表比较，或查找共有元素的问题，需要把两个或多个长度相等或长度不等的列表中的共有元素找出来，并保存到一个新的列表。解决这一问题，常见的方法有以下 6 种方法。

def intersection(lst1, lst2):
    lst3 = [value for value in lst1 if value in lst2]
    return lst3
 
# Driver Code
lst1 = [4, 9, 1, 17, 11, 26, 28, 54, 69]
lst2 = [9, 9, 74, 21, 45, 11, 63, 28, 26]
print(intersection(lst1, lst2))

结果如下：

[9, 11, 26, 28]

方法2，使用 set() 函数

def intersection(lst1, lst2):
    return list(set(lst1) & set(lst2))
 
# Driver Code
lst1 = [15, 9, 10, 56, 23, 78, 5, 4, 9]
lst2 = [9, 4, 5, 36, 47, 26, 10, 45, 87]
print(intersection(lst1, lst2))

结果如下：

[9, 10, 4, 5]

方法3：使用set()函数处理较长的列表，然后使用内置函数intersection() 计算出交集列表，其中 intersection() 是set的一级类方法。

def Intersection(lst1, lst2):
    return set(lst1).intersection(lst2)
     
# Driver Code
lst1 = [ 4, 9, 1, 17, 11, 26, 28, 28, 26, 66, 91]
lst2 = [9, 9, 74, 21, 45, 11, 63]
print(Intersection(lst1, lst2))

结果如下：

{9, 11}

方法4 ：这是一种混合方法，运行效率比较高。

def intersection(lst1, lst2):
 
    # Use of hybrid method
    temp = set(lst2)
    lst3 = [value for value in lst1 if value in temp]
    return lst3
 
# Driver Code
lst1 = [9, 9, 74, 21, 45, 11, 63]
lst2 = [4, 9, 1, 17, 11, 26, 28, 28, 26, 66, 91]
print(intersection(lst1, lst2))

结果如下：

[9, 9, 11]

方法 5 ：这种方法可以处理二维列表，查找其中一个一维列表与二维列表中的子列表的交集，使用filter() 函数

def intersection(lst1, lst2):
    lst3 = [list(filter(lambda x: x in lst1, sublist)) for sublist in lst2]
    return lst3
 
# Driver Code
lst1 = [1, 6, 7, 10, 13, 28, 32, 41, 58, 63]
lst2 = [[13, 17, 18, 21, 32], [7, 11, 13, 14, 28], [1, 5, 6, 8, 15, 16]]
print(intersection(lst1, lst2))

结果如下：

[[13, 32], [7, 13, 28], [1, 6]]

方法6：使用 pandas 中的 merge() 函数。这是一个较复杂的实例，从项目中提取。

问题：

从allstocks.csv、symbol_findata.csv 和
shenwan2_category_stocks.csv 文件中，找出申万二级分类所包含的股票。

文件 allstocks.csv 包括4292个股票代码，格式如下：

编码 1600000 的含义：

1 – 市场代码，1 表示沪市，0表示深市，600000 – 股票代码

文件 symbol_findata.csv 包括 4950 只股票及其总股本、流通股本和市净率，格式如下：

文件
shenwan2_category_stocks.csv，包括4416只股票的申万二级分类名称和编码、交易市场及股票代码、股票名称、总市值和流通市值，格式如下：

import pandas as pdimport csvimport functools as ftwith open('.\\work_data\\allstocks.csv','r',encoding='utf-8') as f:    reader = csv.reader(f)    allstocks_lst = []    for line in reader: #遍历        #print(line[0])        allstocks_lst.append(line[0][1:]) print(allstocks_lst[0:10])print('allstocks_lst len:',len(allstocks_lst))allstocks_df = pd.DataFrame(allstocks_lst, columns =['symbol']) print(allstocks_df[0:4])##2. symbol_findata.csv中的列： 'stock_code','total_shares','current_flowshares','netearningrate'symbol_financialdata = pd.read_csv('.\\work_data\\symbol_findata.csv',header = 0,                         dtype = {'stock_code':str,'total_shares':float,'current_flowshares':float,'netearningrate':float})print(symbol_financialdata[0:3])stockcode_lst1 = [x[3:] for x in symbol_financialdata.stock_code.iloc[:].values.tolist() if x[3:5] !='68']print('symbol_financialdata len：',len(stockcode_lst1))symbol_financialdata[['marketID','symbol']] = symbol_financialdata.stock_code.str.split("#",expand=True)print(symbol_financialdata[0:3])##3. shenwan2_category_stocks.csv中的列：shw2_code,category_name,category_mktcode,stock_code,stock_name,stock_mktcap,stock_nmcshw2_category_stocks = pd.read_csv('.\\work_data\\shenwan2_category_stocks.csv',header = 0,                         dtype = {'stock_code':str,'category_name':str,'category_mktcode':str,'stock_code':str,                        'stock_mktcap':float,'stock_nmc':float})print(shw2_category_stocks[0:3])stockcode_lst2 = shw2_category_stocks.stock_code.iloc[:].values.tolist()print('shw2_category_stocks len:',len(stockcode_lst2))shw2_category_stocks['symbol'] = shw2_category_stocks['stock_code']print(shw2_category_stocks[0:3])# filtered1_stockcode = [x for x in stockcode_lst2 if any(y in x for y in stockcode_lst1)]# print('filtered1_stockcode len:',len(filtered1_stockcode))df_merged = ft.reduce(lambda left, right: pd.merge(left, right, on='symbol'), [allstocks_df,symbol_financialdata,shw2_category_stocks])for i in range(0,5): # len(df_merged)    print(df_merged.symbol.iloc[i],df_merged.stock_code_x.iloc[i],        df_merged.total_shares.iloc[i],df_merged.current_flowshares.iloc[i],        df_merged.netearningrate.iloc[i],df_merged.marketID.iloc[i],        df_merged.shw2_code.iloc[i],df_merged.category_name.iloc[i],        df_merged.category_mktcode.iloc[i],df_merged.stock_code_y.iloc[i],        df_merged.stock_name.iloc[i],df_merged.stock_mktcap.iloc[i],        df_merged.stock_nmc.iloc[i])print(len(df_merged))    groups = df_merged.groupby(['category_name'])    positions_list = []everygroup_len = []for group_key, group_value in groups:    group = groups.get_group(group_key)    #quantity_tmp = sum(group['quantity']*group['buy_sell'])    print('------------------------------')    print(group)    everygroup_len.append([group_key,len(group)])print('groups len:',len(groups))for itm in everygroup_len:    print(itm)