人称代词变化表格：主格、宾格、形容词性物主代词、名词性物主代词

人称代词变化表格：主格、宾格、形容词性物主代词、名词性物主代词

以下是三种人称单复数的主格，宾格，形容词性物主代词，名词性物主代词的变化形式，每种举一个例句，并用表格形式显示。

| 人称 | 主格 | 宾格 | 形容词性物主代词 | 名词性物主代词 | |:----------|:-----------|:------------|:--------------|:-------------| | 第一人称单数 | I | me | my | | | 例句 | I am happy. | He gave me a book. | This is my car. | This book is mine. | | 第二人称单数 | you | you | your | yours | | 例句 | You are my friend. | I saw you yesterday. | Is this your hat? | This hat is yours. | | 第三人称单数 | he/she/it | him/her/it | his/her/its | his/hers/its | | 例句 | She is a doctor. | I saw him at the store. | Its color is green. | The cat is hers. | | 第一人称复数 | we | us | our | ours | | 例句 | We are going to the party. | He invited us to his house. | Our house is big. | This car is ours. | | 第二人称复数 | you | you | your | yours | | 例句 | You are all invited. | I saw you at the concert. | Is this your bag? | This bag is yours. | | 第三人称复数 | they | them | their | theirs | | 例句 | They are my classmates. | I met them yesterday. | Their house is new. | These books are theirs. |

使用Python生成表格

这个表可以使用Python中的Pandas库来创建和显示。以下是如何使用Pandas库创建和显示这个表格的示例代码：

import pandas as pd

# 创建一个DataFrame对象，包含所有的数据
data = {
    '人称': ['第一人称单数', '第二人称单数', '第三人称单数', '第一人称复数', '第二人称复数', '第三人称复数'],
    '主格': ['I', 'you', 'he/she/it', 'we', 'you', 'they'],
    '宾格': ['me', 'you', 'him/her/it', 'us', 'you', 'them'],
    '形容词性物主代词': ['my', 'your', 'his/her/its', 'our', 'your', 'their'],
    '名词性物主代词': ['', 'yours', 'his/hers/its', 'ours', 'yours', 'theirs']
}
df = pd.DataFrame(data)

# 使用Pandas的to_markdown()方法将DataFrame对象转换为Markdown格式的表格
print(df.to_markdown(index=False))

输出结果如下：

| 人称 | 主格 | 宾格 | 形容词性物主代词 | 名词性物主代词 | |:----------|:-----------|:------------|:--------------|:-------------| | 第一人称单数 | I | me | my | | | 第二人称单数 | you | you | your | yours | | 第三人称单数 | he/she/it | him/her/it | his/her/its | his/hers/its | | 第一人称复数 | we | us | our | ours | | 第二人称复数 | you | you | your | yours | | 第三人称复数 | they | them | their | theirs |

可以看到，这个表格已经被转换成了Markdown格式，并且显示了所有的数据。如果需要将表格保存为文件或者显示在网页上，可以使用Pandas提供的to_csv()或to_html()方法来完成。