The closing ?> tag should be omitted from files containing only PHP.PHP(PHP7001)如何解决？

在PHP中，PHP7001错误提示"PHP files containing only PHP should omit the closing `?>` tag"意味着当你编写一个纯PHP文件（即文件内容仅包含PHP脚本，无HTML标签或其他文本），最好避免使用`?>`结束标记，因为这个标记可能会带来不必要的空白字符到响应头，影响输出的整洁。

要解决这个问题，你可以按照以下步骤操作：

1. **移除`?>`**：直接删除文件结尾的`?>`标签。这样可以防止意外的空格插入，同时保持文件结构简洁。

<?php
// 这里是你的PHP代码
?>

2. **设置output buffering**：如果你希望保持文件的完整结构并且确实需要关闭输出流，可以在文件末尾添加下面这行代码，而不是`?>`。这将开启缓冲输出并自动关闭输出流。

<?php
ob_start();
// PHP代码...
ob_end_flush(); // 或者 ob_end_clean() 如果不需要输出缓冲区的内容
?>

3. **使用`<?=`代替`<?php`**：`<?=`语法会自动处理结束标记，所以在这种情况下，可以避免`?>`。

<?=
// PHP代码...
?>

相关问题

Read the stock price data of Alphabet Inc. (GOOG) for the time period between April 01, 2020 and October 01, 2020 in the file 'alphabet.csv' into a Pandas DataFrame. You can download it from Blackboard in session 12. (1) Convert the “Date” column into Datetime format and assign it as the index column. And output the DataFram containing data for month x. (2) Calculate the net increase of stock price for each day and append them as a new column “Net Increase” to the DataFrame using the closing price minus the opening price. And output the DataFram containing data for month x. (3) Find the day that has the highest opening price, and calculate the mean of closing price for all days. NOTE:You should add the following code to your program to ensure that all columns are displayed when printing.Otherwise, the system will consider it as incorrect. pd.set_option('display.max_columns',1000) pd.set_option('display.width', 1000) pd.set_option('display.max_colwidth',1000) Input An integer x. Output Task1: A DataFram containing data. Task2: A DataFram containing data. Task3: A datetime object. A float number(Keep six decimal places.).

Task1:

import pandas as pd

# read in the data
df = pd.read_csv('alphabet.csv')

# convert the 'Date' column to datetime format and set it as the index
df['Date'] = pd.to_datetime(df['Date'])
df.set_index('Date', inplace=True)

# select data for month x (assuming x is an integer between 1 and 12)
x = 4
df_month_x = df[df.index.month == x]

# print the DataFrame
pd.set_option('display.max_columns', 1000)
pd.set_option('display.width', 1000)
pd.set_option('display.max_colwidth', 1000)
print(df_month_x)

Task2:

# calculate the net increase of stock price for each day and append it as a new column
df['Net Increase'] = df['Close'] - df['Open']

# select data for month x (assuming x is an integer between 1 and 12)
x = 4
df_month_x = df[df.index.month == x]

# print the DataFrame
pd.set_option('display.max_columns', 1000)
pd.set_option('display.width', 1000)
pd.set_option('display.max_colwidth', 1000)
print(df_month_x)

Task3:

# find the day with the highest opening price
max_open = df['Open'].max()
max_open_date = df[df['Open'] == max_open].index[0]

# calculate the mean of closing price for all days
mean_close = df['Close'].mean()

# print the results
print(max_open_date)
print(round(mean_close, 6))

[error] <0.28329.162> closing AMQP connection <0.28329.162> (167.94.146.60:38588 -> 172.25.159.55:5672):

这是一个错误信息，看起来是由于某个 AMQP 连接被关闭导致的。AMQP 是 Advanced Message Queuing Protocol 的缩写，它是一种消息队列协议。根据错误信息，我们可以看到有一个 IP 地址为 167.94.146.60 的主机正在尝试连接到另一个 IP 地址为 172.25.159.55 的主机上的 AMQP 服务，但连接被关闭了。需要查看更多的日志信息才能确定具体的原因。