编辑回答：
我需要两种类型的表格。一种是常规的水平表格视图。为此，我只是使用了“to_markdown()”并添加了代码以删除索引。
对于垂直视图，我复制并粘贴了下面的Ehsan的代码，它完美地运行了。
现在，我想将这些粘贴到我的Excel表中以获得一个良好的视图。这将需要大量的工作和特定的格式，而且无法使用to_markdown()，因为它会将DataFrame更改为字符串。要将其作为索引获取，您不能使用该函数。您要么必须使用现有的DataFrame并找出如何添加新行和列，要么：创建一个具有正确数量的列和行的空DataFrame，并使用必要的“|”格式进行填充。之后，您必须使用包含数据的DataFrame填充空/正确的单元格。但是，这太麻烦了，所以我直接将字符串复制到正确的Excel表中，使用以下代码：
```python
#现在我们将保存此循环的迭代到正确的Excel表中
    file_source ='Source Used.xlsx'
    #加载Excel文件
    workbook = load_workbook(filename=file_source)
    #选择表“Sheet Used”
    ws4 = workbook["Project X"]
    #修改所需的单元格
    ws4.cell(row = 1, column = 1).value = newDataFrameWithStringFormat
    #保存文件
    workbook.save(filename=file_source)

<details>
<summary>英文:</summary>
EDIT Answer:
There are two types of tables I needed. One was the regular Horizontal table view. For that, I just used the &quot;to_markdown()&quot; with the added code to removed the indeces.
For the vertical view I copied and pasted Ehsan&#39;s code below and it worked perfectly.
Now, I wanted to paste these in a nice view to my excel sheet. That would have taken a bunch of work and formatting very specifically, and it won&#39;t work out with the to_markdown(), since that changes the DataFrame to a string. To get it as an index, you can&#39;t use that function. You&#39;d either have to take your existing DataFrame and figure out how to add new rows and columns, or: create an empty DataFrame with the proper amount of columns and rows, and fill it with the necessary &quot;|&quot; formatting. After that, you&#39;d have to fill out the empty/correct cells with data from your DataFrame that contains the data. However, this is too much work, so instead I just copied the string over directly into the proper excel sheet using this code:

#load excel file
workbook = load_workbook(filename=file_source)
#Pick the sheet "Sheet Used"
ws4 = workbook["Project X"]
#modify the desired cell
ws4.cell(row = 1, column = 1).value = newDataFrameWithStringFormat
#save the file
workbook.save(filename=file_source)

From here, anytime I wanted to update my markdown website, I could just pull from the Excel sheet's first cell. It doesn't look pretty, but it works.
 
END EDIT
--------------------------------------------
I have a table that looks like this: 
df: 
| Project 1 | Project 2 | Project 3 |
|:---- |:------:| -----:|
| data 1  | data 2    | data 3 |
| data 4 | data 5 | data 6 |
|data 7 |data 8 | data 9 |
I want to edit this table so that it can work with, say, markdown, and be formatted well. However, to do that, I need to add a bunch of formatting to it (it basically needs to look the same as StackOverflow's table setup when writing this question). What function can I apply to it so that a new table is created that looks like this:
[![Table with headers on the rows][1]][1]
As you can see in this picture, the pipes and hyphens are each a new cell of data. Additionally, the Column HEADERS are at the beginning of the rows, since this is a horizontal format. How can I apply this formatting to dataframes of varying size?
This is what the new one should look like (I bolded the Project names myself): [![enter image description here][2]][2]
Now hypothetically, I can transpose the data and use to_markdown(), but that may still run into the issue of the top column being created as a column header. How can I avoid this by making a custom function to add in the pipes ("|") and hyphens?
Thank you!
  [1]: https://i.stack.imgur.com/uTDVs.png
  [2]: https://i.stack.imgur.com/2jWiF.png
</details>
# 答案1
**得分**: 2
以下是代码的翻译部分：
```python
import pandas as pd
df=pd.DataFrame({"Project 1":["data 1", "data 4", "data 7"], "Project 2": ["data 2", "data 5", "data 8"], "Project 3": ["data 3", "data 6", "data 9"]})
df = df.transpose()
# 移除标题
df.columns = ["" for i in range(len(df.columns))]
# 使索引加粗 - ** ** 
df.index = ["**{}**".format(idx) for idx in df.index]
print(df.to_markdown())
#|               |        |        |        |
#|:--------------|:-------|:-------|:-------|
#| **Project 1** | data 1 | data 4 | data 7 |
#| **Project 2** | data 2 | data 5 | data 8 |
#| **Project 3** | data 3 | data 6 | data 9 |

markdown_text = df.to_markdown() # 来自上述代码
markdown_csv = markdown_text.replace("|", ",|,").replace(",\n,", "\n")
with open("results.csv", "w", encoding="utf-8") as fp:
    fp.write(markdown_csv)