翻译Read the planetary data in the text file S12_planet.txt into a Pandas DataFrame. The file will be given in wechat group. Task1, calculate the density of each planet relative to that of the Earth (i.e., the Earth’s density is 1) and add the results as a “density” column in the DataFrame. Print out the DataFrame sorted from the largest to smallest based on the planet diameter. The density formula is � � � � � � � = � / � = 3 � / ( 4 � ∗ � 3 ) density=M/V=3M/(4π∗r 3 ) Task2, read the 2 items in input as "A" and "Standard". A is a "String" and Standard is a "float". Create a Pandas Series that have "A" greater than "Standard", sorted from most to least based on the planet's "A". For example, if input is "year 500", then collect the data of planets whose years are greater than 500.

理解了您的问题，我开始回答：

首先，您需要使用Pandas从文本文件"S12_planet.txt"中读取行星数据，并将其转换为DataFrame。然后，您需要添加一个"density"列，该列将每个行星的密度相对于地球的密度进行计算。密度公式为：density=M/V=3M/(4π∗r^3)，其中M是质量，V是体积，r是半径。

以下是实现此任务的代码：

import pandas as pd
import numpy as np

# 读取数据文件
data = pd.read_csv('S12_planet.txt', sep='\s+')

# 计算密度并添加"density"列
earth_density = 1.0
data['density'] = 3 * data['mass'] / (4 * np.pi * (data['diameter']/2)**3) / earth_density

# 按行星直径排序并打印DataFrame
data = data.sort_values(by='diameter', ascending=False)
print(data)

接下来，您需要读取两个输入项"A"和"Standard"，并创建一个Pandas Series，该Series包含"A"大于"Standard"的行星数据，按"A"值从大到小排序。例如，如果输入是"year 500"，则收集年份大于500的行星数据。

以下是实现此任务的代码：

# 读取输入
A = input("请输入A：")
Standard = float(input("请输入Standard："))

# 创建Series
series = data[data[A] > Standard].sort_values(by=A, ascending=False)[A]

# 打印Series
print(series)