Python基于pandas实现json格式转换成dataframe的方法

本文实例讲述了Python基于pandas实现json格式转换成dataframe的方法。分享给大家供大家参考，具体如下：

# -*- coding:utf-8 -*-
#!python3
import re
import json
from bs4 import BeautifulSoup
import pandas as pd
import requests
import os
from pandas.io.json import json_normalize
class image_structs():
  def __init__(self):
    self.picture_url = {
      "image_id": '',
      "picture_url": ''
    }
class data_structs():
  def __init__(self):
    # columns=['title', 'item_url', 'id','picture_url','std_desc','description','information','fitment'])
    self.info={
      "title":'',
      "item_url":'',
      "id":0,
      "picture_url":[],
      "std_desc":'',
      "description":'',
      "information":'',
      "fitment":''
    }
# "https://waldoch.com/store/catalogsearch/result/index/?cat=0&limit=200&p=1&q=nerf+bar"
# https://waldoch.com/store/new-oem-ford-f-150-f150-5-running-boards-nerf-bar-crew-cab-2015-w-brackets-fl34-16451-ge5fm6.html
def get_item_list(outfile):
  result = []
  for i in range(6):
    print(i)
    i = str(i+1)
    url = "https://waldoch.com/store/catalogsearch/result/index/?cat=0&limit=200&p="+i+"&q=nerf+bar"
    web = requests.get(url)
    soup = BeautifulSoup(web.text,"html.parser")
    alink = soup.find_all("a",class_="product-image")
    for a in alink:
      title = a["title"]
      item_url = a["href"]
      result.append([title,item_url])
  df = pd.DataFrame(result,columns=["title","item_url"])
  df = df.drop_duplicates()
  df["id"] =df.index
  df.to_excel(outfile,index=False)
def get_item_info(file,outfile):
  DEFAULT_FALSE = ""
  df = pd.read_excel(file)
  for i in df.index:
    id = df.loc[i,"id"]
    if os.path.exists(str(int(id))+".xlsx"):
      continue
    item_url = df.loc[i,"item_url"]
    url = item_url
    web = requests.get(url)
    soup = BeautifulSoup(web.text, "html.parser")
    # 图片
    imglink = soup.find_all("img", class_=re.compile("^gallery-image"))
    data = data_structs()
    data.info["title"] = df.loc[i,"title"]
    data.info["id"] = id
    data.info["item_url"] = item_url
    for a in imglink:
      image = image_structs()
      image.picture_url["image_id"] = a["id"]
      image.picture_url["picture_url"]=a["src"]
      print(image.picture_url)
      data.info["picture_url"].append(image.picture_url)
    print(data.info)
    # std_desc
    std_desc = soup.find("div", itemprop="description")
    try:
      strings_desc = []
      for ii in std_desc.stripped_strings:
        strings_desc.append(ii)
      strings_desc = "\n".join(strings_desc)
    except:
      strings_desc=DEFAULT_FALSE
    # description
    try:
      desc = soup.find('h2', text="Description")
      desc = desc.find_next()
    except:
      desc=DEFAULT_FALSE
    description=desc
    # information
    try:
      information = soup.find("h2", text='Information')
      desc = information
      desc = desc.find_next()
    except:
      desc=DEFAULT_FALSE
    information = desc
    # fitment
    try:
      fitment = soup.find('h2', text='Fitment')
      desc = fitment
      desc = desc.find_next()
    except:
      desc=DEFAULT_FALSE
    fitment=desc
    data.info["std_desc"] = strings_desc
    data.info["description"] = str(description)
    data.info["information"] = str(information)
    data.info["fitment"] = str(fitment)
    print(data.info.keys())
    singledf = json_normalize(data.info,"picture_url",['title', 'item_url', 'id', 'std_desc', 'description', 'information', 'fitment'])
    singledf.to_excel("test.xlsx",index=False)
    exit()
    # print(df.ix[i])
  df.to_excel(outfile,index=False)
# get_item_list("item_urls.xlsx")
get_item_info("item_urls.xlsx","item_urls_info.xlsx")

这里涉及到的几个Python模块都可以使用pip install命令进行安装，如：

pip install BeautifulSoup4

pip install xlrd

pip install openpyxl

PS：这里再为大家推荐几款比较实用的json在线工具供大家参考使用：

在线JSON代码检验、检验、美化、格式化工具：
http://tools.jb51.net/code/json

JSON在线格式化工具：
http://tools.jb51.net/code/jsonformat

在线XML/JSON互相转换工具：
http://tools.jb51.net/code/xmljson

json代码在线格式化/美化/压缩/编辑/转换工具：
http://tools.jb51.net/code/jsoncodeformat

在线json压缩/转义工具：
http://tools.jb51.net/code/json_yasuo_trans

更多Python相关内容感兴趣的读者可查看本站专题：《Python操作json技巧总结》、《Python编码操作技巧总结》、《Python数据结构与算法教程》、《Python函数使用技巧总结》、《Python字符串操作技巧汇总》、《Python入门与进阶经典教程》及《Python文件与目录操作技巧汇总》

希望本文所述对大家Python程序设计有所帮助。