下面是一段代码，它使用pandas库将您共享的csv转换为您共享的第二个屏幕截图的格式：

import pandas as pd

# Read in the csv
df = pd.read_csv('testalan.csv', header=None, names=['Machine', 'TempHeader'])

# Rename the temporary header to the metadata entry in the first row
df.rename(
    columns={
        'TempHeader': df.iloc[0].Machine
    },
    inplace=True
)

# Drop the first row that is now part of the header
df = df[1:]

# Drop all rows missing at least one element
df = df.dropna(axis='rows')

# Transpose the matrix to flip the rows and columns. Could replace df.transpose() with df.T
df = df.transpose()

print(df)

如果你还想按最下面的一行排序，你可以添加这行：

# Get the name of the 2nd row and sort the columns by the value of that row
df = df.sort_values(by=df.iloc[1].name, axis='columns')

下载csv：

df.to_csv('testalan_sorted.csv', header=False)

编辑：
我假设csv的格式是一行MachineName，后面跟一个条目，表示该机器的每个属性。下面是一段python代码，它将根据这个假设和您在注解中提供的描述解析csv文件：

import pandas as pd

# Read the original CSV file
df = pd.read_csv('testalan.csv', header=None)

# Initialize an empty dictionary to store the data
data = {}

# Loop through the rows of the DataFrame
for _, row in df.iterrows():
    if '(' in row[0]:
        machine_name = row[0]
        data[machine_name] = {}
    else:
        column_name, column_value = row[0], row[1]
        data[machine_name][column_name] = column_value

# Convert the dictionary to a DataFrame
result = pd.DataFrame.from_dict(data, orient='index')

# Save the result to a new CSV file
result.to_csv('output.csv', index_label='Machine')

编辑二：
此代码将允许重复的计算机。它将为每台计算机附加一个新行，而不管为该计算机定义了多少属性：

import pandas as pd

# Read the original CSV file
df = pd.read_csv('original file.csv - Hoja 1.csv', header=None)

# Initialize an empty dataframe to store the resulting data
result = pd.DataFrame()
machine_name = None

# Loop through the rows of the DataFrame
for _, row in df.iterrows():
    if '(' in row[0]:
        # Every time you switch to a new machine, add the current machine to the result set
        if machine_name is not None:
            machine_entries = pd.DataFrame.from_dict({ machine_name: machine_attributes }, orient='index')
            result = pd.concat([result, machine_entries], ignore_index=False)

        # Update the current machine name and clear the machine attributes
        machine_name = row[0]
        machine_attributes = {}
    else:
        column_name, column_value = row[0], row[1]
        machine_attributes[column_name] = column_value

# Save the result to a new CSV file
result.to_csv('output.csv', index_label='Machine')