python输出结果的一点迷惑
用什么方法可以将结果中的方括号去掉,结果截图如下:
data:image/png;base64,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
这是部分代码
for line in lines:
lng = float(line.split(','))
lat = float(line.split(','))
result = func(lng,lat)
i=i+1
with open('result.txt', mode='a', newline='', encoding='utf8') as file:
id=str(i)# 转为str类型
LNG=str(lng)
LAT=str(lat)
ll=str(result)
file.writelines(id+','+LNG+LAT+','+ll)
file.write('\r\n')
file.close()
图片插不了,权限不够{:5_104:}
现在的结果是这样的:
1,113.33108423.112223,
2,113.33108423.112223,
多了方括号 yangwy 发表于 2021-9-6 16:10
图片插不了,权限不够
现在的结果是这样的:
1,113.33108423.112223,
result = func(lng,lat)上这里去寻找估计这个函数返回的是一个列表 string = "1,113.33108423.112223,"
string = ''.join(')])
print(string)
1,113.33108423.112223,113.34307784898164, 23.11529977037563 wp231957 发表于 2021-9-6 16:40
result = func(lng,lat)上这里去寻找估计这个函数返回的是一个列表
多谢多谢,问题就出在了fun(lng,lat)这里 傻眼貓咪 发表于 2021-9-6 16:41
多谢,参照你的代码,解决了
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