在现代数据处理中，JSON格式的数据因其轻量级和易读性而被广泛使用。许多网站和API都会返回JSON格式的数据，如何有效地解析这些数据并将其保存到Excel表格中，是数据分析师和开发者们经常面临的问题。本文将详细介绍如何使用Python解析网页上的JSON数据，并将其保存到Excel文件中，帮助您轻松处理和分析数据。
安装必要的库
import requests import pandas as pd import os import sys import io import urllib3 import json
测试数据
网页上的数据结构如下
{
"success": true,
"code": "CIFM_0000",
"encode": null,
"message": "ok",
"url": null,
"total": 3,
"items": [
{
"summaryDate": "20240611",
"summaryType": "naturalDay",
"workday": true,
"newCustNum": 1,
"haveCustNum": 1691627,
"newAccountNum": 2,
"haveAccountNum": 1692934,
"totalShare": 4947657341.69,
"netCash": -3523387.25,
"yield": 0.01386
},
{
"summaryDate": "20240612",
"summaryType": "naturalDay",
"workday": true,
"newCustNum": 5,
"haveCustNum": 1672766,
"newAccountNum": 5,
"haveAccountNum": 1674071,
"totalShare": 4927109080.29,
"netCash": -20735233.55,
"yield": 0.01387
},
{
"summaryDate": "20240613",
"summaryType": "naturalDay",
"workday": true,
"newCustNum": 4,
"haveCustNum": 1662839,
"newAccountNum": 5,
"haveAccountNum": 1664146,
"totalShare": 4927405885.59,
"netCash": 110659.8,
"yield": 0.01389
}
],
"data": null,
"info": null
}详细逻辑代码
import requests
import pandas as pd
import os
import sys
import io
import urllib3
import json
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
url = "https://ip/ma/web/trade/dailySummary?startDate={pi_startdate}&endDate={pi_enddate}"
headers = {
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7",
"Accept-Language": "zh-CN,zh;q=0.9",
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36 Edg/119.0.0.0",
}
def save_data(data, columns, excel_path, sheet_name):
df = pd.DataFrame(data, columns=columns)
if not os.path.exists(excel_path):
df.to_excel(excel_path, sheet_name=sheet_name, index=False)
else:
with pd.ExcelWriter(excel_path, engine='openpyxl', mode='a') as writer:
df.to_excel(writer, sheet_name=sheet_name, index=False)
def json2list(response_text):
# 把json数据转化为python用的类型
json_dict = json.loads(response_text)
src_total = json_dict["total"]
print("src_total: {}".format(src_total))
items = json_dict["items"]
excel_columns = ['summaryDate',
'summaryType',
'workday',
'newCustNum',
'haveCustNum',
'newAccountNum',
'haveAccountNum',
'totalShare',
'netCash',
'yield'
]
excel_data = []
# 使用XPath定位元素并打印内容
for item in items:
excel_row_data = []
for column_index in range(len(excel_columns)):
data = str(item[excel_columns[column_index]])
if excel_columns[column_index] == 'workday':
data = str(0 if data == "False" else 1)
excel_row_data.append(data)
excel_data.append(excel_row_data)
trg_total = len(excel_data)
# 稽核
print("trg_total: {}".format(trg_total))
vn_biasval = trg_total - src_total
if vn_biasval != 0:
print("This audit-rule is not passed,diff: {}".format(vn_biasval))
exit(-1)
else:
print("This audit-rule is passed,diff: {}".format(vn_biasval))
return excel_columns, excel_data
if __name__ == '__main__':
try:
excel_path = "C:/xxx/temp/ylb_dailySummary_{pi_startdate}_{pi_enddate}.xlsx"
sheet_name = 'result_data'
pi_startdate = 20240611
pi_enddate = 20240613
excel_path = excel_path.format(pi_startdate=pi_startdate, pi_enddate=pi_enddate)
url = url.format(pi_startdate=pi_startdate, pi_enddate=pi_enddate)
print("url:{}".format(url))
print("excel_path:{}".format(excel_path))
response_text = requests.get(url, headers=headers, timeout=(21, 300), verify=False).content.decode("utf8")
excel_columns, excel_data = json2list(response_text)
print("=================excel_columns=======================")
print(excel_columns)
print("=================excel_data==========================")
for x in excel_data:
print(x)
print("=====================================================")
# 文件存在，则删除
if os.path.exists(excel_path):
os.remove(excel_path)
# 保存文件
save_data(excel_data, excel_columns, excel_path, sheet_name)
print("save_data is end.")
except Exception as e:
print("[ERROR]：" + str(e))
exit(-1)代码解析
1.请求头
构造请求头
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
url = "https://ip/ma/web/trade/dailySummary?startDate={pi_startdate}&endDate={pi_enddate}"
headers = {
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7",
"Accept-Language": "zh-CN,zh;q=0.9",
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36 Edg/119.0.0.0",
}2.数据保存到excel
如果excel已经存在，那么则会将数据追加到excel中
def save_data(data, columns, excel_path, sheet_name): df = pd.DataFrame(data, columns=columns) if not os.path.exists(excel_path): df.to_excel(excel_path, sheet_name=sheet_name, index=False) else: with pd.ExcelWriter(excel_path, engine='openpyxl', mode='a') as writer: df.to_excel(writer, sheet_name=sheet_name, index=False)
解析json数据获取字段名称以及对应的数据list列表
def json2list(response_text):
# 把json数据转化为python用的类型
json_dict = json.loads(response_text)
src_total = json_dict["total"]
print("src_total: {}".format(src_total))
items = json_dict["items"]
excel_columns = ['summaryDate',
'summaryType',
'workday',
'newCustNum',
'haveCustNum',
'newAccountNum',
'haveAccountNum',
'totalShare',
'netCash',
'yield'
]
excel_data = []
# 使用XPath定位元素并打印内容
for item in items:
excel_row_data = []
for column_index in range(len(excel_columns)):
data = str(item[excel_columns[column_index]])
if excel_columns[column_index] == 'workday':
data = str(0 if data == "False" else 1)
excel_row_data.append(data)
excel_data.append(excel_row_data)
trg_total = len(excel_data)
# 稽核
print("trg_total: {}".format(trg_total))
vn_biasval = trg_total - src_total
if vn_biasval != 0:
print("This audit-rule is not passed,diff: {}".format(vn_biasval))
exit(-1)
else:
print("This audit-rule is passed,diff: {}".format(vn_biasval))
return excel_columns, excel_data3.测试方法入口
if __name__ == '__main__':
测试结果
会生成ylb_dailySummary_20240611_20240613.xlsx文件
总结
通过本文的介绍，我们了解了如何使用Python解析网页上的JSON数据，并将其保存到Excel文件中。这种方法不仅适用于简单的JSON数据结构，还可以扩展到更复杂的数据结构。通过掌握这些技能，您可以更高效地处理和分析网络上的数据，为您的工作和项目提供有力支持。希望本文的内容能够为您的数据处理之旅提供帮助，让您的数据处理工作更加高效和便捷。
本文来源于#脸ル粉嘟嘟，由@蜜芽 整理发布。如若内容造成侵权/违法违规/事实不符，请联系本站客服处理!
该文章观点仅代表作者本人，不代表本站立场。本站不承担相关法律责任。
如若转载，请注明出处：https://www.zhanid.com/biancheng/2306.html
THE END
