熊猫比较CSV

-2

我不得不使用熊猫熊猫比较CSV

有包含产品代码以不同的形式第一路两个CSV文件在Python 2个CSV文件进行比较： -

LYSB00LW3ZL3K-ELECTRNCS 
LYSB00LW3ZL3K-ELECTRNCS- Standard Packaging- W20 - Dual Driver 
LYSB01KH2MDPU-ELECTRNCS 
LYSB01KH2MDPU-ELECTRNCS- Small Bangle 
LYSB01KH2MDPU-ELECTRNCS- Large Bangle 
LYSB06XXD7NYY-ELECTRNCS- Large 
LYSB06XXD7NYY-ELECTRNCS- Small 
LYSB01KM4T0PO-ELECTRNCS

方式二： - （如果删除LYS用于上述后的产品代码和所有的东西 - ）第二种形式存在）

B00LW3ZL3K 
B01KH2MDPU

，所以我要比较这两个文件，并进行新的CSV网络勒与第一列作为产品代码和第二列作为状态

结果应该给输出在2个不同的情况下

1）如果B00LW3ZL3K（产品代码）在第二个文件存在则它应该返回从所有属于产品代码第一个文件及其状态为'Product in stock'

2）如果第二个文件中不存在B01KM4T0PO（产品代码），它应该返回第一个文件中的所有归属产品代码及其状态为'Product out of stock '

Output: 
In-Stock 
    LYSB00LW3ZL3K-ELECTRNCS 
    LYSB00LW3ZL3K-ELECTRNCS- Standard Packaging- W20 - Dual Driver 
    LYSB01KH2MDPU-ELECTRNCS 
    LYSB01KH2MDPU-ELECTRNCS- Small Bangle 
    LYSB01KH2MDPU-ELECTRNCS- Large Bangle 

Out-of-Stock 
    LYSB06XXD7NYY-ELECTRNCS- Large 
    LYSB06XXD7NYY-ELECTRNCS- Small 
    LYSB01KM4T0PO-ELECTRNCS

来源

2017-07-18 Anurag Misra

所以你知道熊猫，但你有任何代码吗？ –

@ cricket_007 **不多** –

顺便说一句，Sqlite可能比csv文件更有意义查询/过滤 –

- 这是我对这个问题的解决方案

import pandas as pd 
import datetime 
import os 


class Update(object): 
    def __init__(self, category): 
     """Path to file""" 
     masterfile = os.path.realpath('lys_masterfile.txt') 
     update_file = os.path.realpath('Outputs/liveyoursport/Update_Spider/{}_Update.csv'.format(category)) 
     self.comparision(masterfile, update_file, category) 

    def comparision(self, output_file, update_file, category): 
     ''' Function to extract correct data by category ''' 
     sku_dict = { 
      'Electronics': 'ELECTRNCS', 
      'Sports Equipment': 'SPRTSEQIP', 
      'Health and Beauty': 'HLTHBTY', 
      "Women's Fashion Accessories": 'WMNFSHACCSS', 
      'Toys and Games': 'TOYS', 
      "Men's Fashion Shoes": 'MNFSHSHOE', 
      "Other Sports Shoes": 'OTHSPRTSSHOE', 
      "Women's Sports Shoes": 'WMNSPORTSHOE', 
      "Men's Running Shoes": 'MNSRUNSHOE', 
      "Amazon Global-Toys": 'GLBTOYS', 
      "Women's Running Shoes": 'WMNRUNSHOE', 
      "Women's Fashion Shoes": 'WMNFSHSHOE', 
      "Computer & Accessories": 'CMPTRACCS', 
      "Office Supplies": "OFFSUPPLIES", 
      "Clothing Accessories": "CLTHACCSS", 
      "TigerDirect": "TDRCT" 
     } 
     sku = sku_dict.get(category) 

     def extraction(value): 
      if isinstance(value, str) and sku in value: 
       asin = value.split('-')[0].replace('LYS', '') 
       return asin 
      else: 
       return 'None' 

     """Extract only necessary field from file """ 
     masterfile_sku = pd.read_csv(output_file, usecols=['Product Code/SKU'], delimiter='\t', skip_blank_lines=True) 

     """ Trying to extract SKU """ 
     masterfile_asin = masterfile_sku['Product Code/SKU'].apply(extraction) 

     """ Making another dataFrame for comparision """ 
     products_df = pd.DataFrame(
      {'sku': masterfile_asin, 'Product Code/SKU': masterfile_sku['Product Code/SKU']}).query("sku != 'None'") 

     """Fetching Update file and separating in_stock and out_stock """ 
     update_df = pd.read_csv(update_file, usecols=[2, 3], names=['sku', 'price']) 
     update_in_stock_df = update_df.query("price != 'nan'") 
     update_out_stock_df = update_df.query("price == 'nan'") 

     """ Check for instock Product """ 
     in_stock = pd.merge(products_df, update_in_stock_df, on='sku', how='inner') 
     # print in_stock 

     """ Check for out-of-stock Product """ 
     out_of_stock = pd.merge(in_stock, products_df, on='sku', how='right', indicator=True).query(
      "_merge == 'right_only'") 
     out_of_stock = pd.merge(out_of_stock, update_out_stock_df, on='sku', how='outer') 
     out_of_stock = out_of_stock.drop_duplicates(subset='sku') 

     """Writing all dataFrames""" 
     in_stock.to_csv(os.path.realpath('Outputs/liveyoursport/in_stock/Lys_{}_in_stock.csv'.format(category))) 
     out_of_stock.to_csv(
      os.path.realpath('Outputs/liveyoursport/out_of_stock/Lys_{}_out_of_stock.csv'.format(category))) 


if __name__ == '__main__': 
    a = datetime.datetime.now() 
    Update("Women's Running Shoes") 
    print 'Done' 
    print 'Completed in {}'.format(datetime.datetime.now() - a)

来源

2017-07-18 13:12:57

熊猫比较CSV

回答

相关问题