dataframe按列/行遍历数据

import pandas as pd

dict=[[1,2,3,4,5,6],[0,0,0,0,0,0]]
data=pd.DataFrame(dict)
print(data)
for indexs in data.index:
    print(data.loc[indexs].values[0:-1])

( 按行遍历数据)

import pandas as pd

dict=[[1,2,3,4,5,6],[0,0,0,0,0,0]]
data=pd.DataFrame(dict)
print(data)
for indexs in data.columns:
    print(data[indexs])

(按列遍历数据)

对于DataFrame遍历,可以使用iterrows()方法来实现。iterrows()方法会返回一个迭代器对象,该对象包含每一的索引和数据。可以使用for循环来逐处理数据。下面是一个示例代码: ```python import pandas as pd # 示例数据 data = {'name': ['刘一', '陈二', '张三', '李四', '王五'], 'age': [18, 19, 20, 21, 22], 'height': [175, 176, 177, 178, 179]} index = ['0001', '0002', '0003', '0004', '0005'] df = pd.DataFrame(data=data, index=index) df.index.name = 'id' # 按遍历 for index, row in df.iterrows(): print("Index:", index) print("Row:", row) # 输出每的索引值和对应的数据 ``` 这段代码会按遍历DataFrame,并输出每的索引值和对应的数据。你可以根据实际需求修改输出的内容或进其他操作。<span class="em">1</span><span class="em">2</span><span class="em">3</span> #### 引用[.reference_title] - *1* *2* [Dataframe遍历的几种方式](https://blog.youkuaiyun.com/weixin_48419914/article/details/120328571)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"] - *3* [pandas.DataFrame遍历和按遍历](https://blog.youkuaiyun.com/lly1122334/article/details/121775416)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"] [ .reference_list ]
评论 2
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值