如果我们有一个数据表格(DataFrame),我们可以使用pandas库将其保存为JSON文件。 importpandasaspd data={"Name":["Alice","Bob","Charlie"],"Age":[30,25,35],"City":["New York","San Francisco","Los Angeles"]}df=pd.DataFrame(data)df.to_json("data.json",orient="records") 1. 2. 3. 4. ...
dumps为'dump string'的缩写,用于将python对象转为json格式的字符串 import json # python对象 data_list = [1, 2, 3] data_dict = {"name": ["Alice", "Bob"], "age": [25, 30]} # 转为json对象 json_data_list = json.dumps(data_list) json_data_dict = json.dumps(data_dict) print(f...
#getURL = form.getvalue('netURL') #接受xmlhttp.open("GET","/cgi-bin/SqltoHtml.py?q="+str,true)传递的参数 getNum = form.getvalue('q') def convert_to_json_string2(contxt,str_ft): ret = []# 需要序列化的列表 tmp = {'contxt':contxt ,'footer':str_ft}# 通过data的每一个元...
data=t.selectdata() _re= ResultModel(data=data) self.write(_re.to_json())exceptException as e: _re= ResultModel(code=1,msg=str(e.args)) self.write(_re.to_json()) 5、_re.to_josn() 结果 { "code": 0, "data": [{"price": 22.0,"id": 1,"createtime":"2020-01-07 21:46...
import json import collections area_data=pd.read_csv('1_data.csv',encoding='gb18030',sep=',') print (area_data.head()) indicator=area_data.columns[3:].tolist() print (indicator) f = open("newAreaData.json", "w+") for i in range(len(area_data)): ...
1.data参数也就是这种格式:key1=value1&key2=value2...这种格式很明显没有大括号 点开Raw查看,跟上面的json区别还是很大的 2.因为这个是非json的,所以点开Json这个菜单是不会有解析的数据的,这种数据在WebForms里面查看 3.可以看到这种参数显示在Body部分,左边的Name这项就是key值,右边的Value就是对应的value值...
json_data = df.to_json(orient="records") print(json_data) 输出结果为: 复制 [{"name":"John","age":30,"city":"New York"},{"name":"Alice","age":25,"city":"London"},{"name":"Bob","age":35,"city":"Paris"}] 在上述示例中,首先使用pandas库创建了一个DataFrame对象df。然后,使用...
>>> sjo.to_json(orient='table') >>> '{"schema":{"fields":[{"name":"index","type":"string"},{"name":"D","type":"integer"}],"primaryKey":["index"],"pandas_version":"0.20.0"},"data":[{"index":"x","D":15},{"index":"y","D":16},{"index":"z","D":17}]}'...
df.to_json(orient='table') # {"schema":{"fields":[{"name":"index","type":"integer"},{"name":"name","type":"string"},{"name":"age","type":"integer"}],"primaryKey":["index"],"pandas_version":"0.20.0"},"data":[{"index":0,"name":"tian","age":19},{"index":1,"...