插入熊猫数据框的上方和第一行的URL链接

Question

如何在pandas dataframe的顶部单元格上插入一个URL链接？

myurl ='www.xyz.com'

假设这是我的代码：

import pandas as pd
import numpy as np
dates = pd.date_range('20130101', periods=4)
df = pd.DataFrame(np.random.randn(6,4), columns=dates)

代码的输出：

   2013-01-01  2013-01-02  2013-01-03  2013-01-04
0   -1.359604    2.236055   -1.247478   -0.466911
1    0.632732    1.177155    0.572847    0.024058
2    0.301902   -0.118306   -1.162931    0.180230
3   -0.851283   -0.427693    0.070223   -1.469248
4   -0.309400    0.935575    1.938843    1.898458
5   -0.598406    0.519100   -0.700112    0.539412

所需的输出1：

www.xyz.com
   2013-01-01  2013-01-02  2013-01-03  2013-01-04
0   -1.359604    2.236055   -1.247478   -0.466911
1    0.632732    1.177155    0.572847    0.024058
2    0.301902   -0.118306   -1.162931    0.180230
3   -0.851283   -0.427693    0.070223   -1.469248
4   -0.309400    0.935575    1.938843    1.898458
5   -0.598406    0.519100   -0.700112    0.539412

所需的输出2：

   2013-01-01  2013-01-02  2013-01-03  2013-01-04
0   www.xyz.com
1   -1.359604    2.236055   -1.247478   -0.466911
2    0.632732    1.177155    0.572847    0.024058
3    0.301902   -0.118306   -1.162931    0.180230
4   -0.851283   -0.427693    0.070223   -1.469248
5   -0.309400    0.935575    1.938843    1.898458
6   -0.598406    0.519100   -0.700112    0.539412

Answer 1

这是您想要的吗？（重命名列和索引名称）

df.rename_axis('www.xyz.com',axis=1)
www.xyz.com  2013-01-01  2013-01-02  2013-01-03  2013-01-04
0             -0.168198   -1.129815    0.427983   -0.788428
1              0.382714    0.539336   -0.568658    0.783393
2             -0.738407    0.276275    0.347246    0.956509
3              1.620627    0.373145   -0.308667   -1.366621
4              0.778496   -0.432625   -0.863804    1.362475
5              0.629325    0.435807   -0.681608   -0.077567
df.rename_axis('www.xyz.com',axis=0)
Out[138]: 
             2013-01-01  2013-01-02  2013-01-03  2013-01-04
www.xyz.com                                                
0             -0.168198   -1.129815    0.427983   -0.788428
1              0.382714    0.539336   -0.568658    0.783393
2             -0.738407    0.276275    0.347246    0.956509
3              1.620627    0.373145   -0.308667   -1.366621
4              0.778496   -0.432625   -0.863804    1.362475
5              0.629325    0.435807   -0.681608   -0.077567

基于您的要求。

所需的输出2：

pd.concat([pd.DataFrame({df.columns[0]:['www.xyz.com']}),df],axis=0).fillna('').reset_index(drop=True)
Out[146]: 
    2013-01-01 2013-01-02 2013-01-03 2013-01-04
0  www.xyz.com                                 
1    -0.168198   -1.12982   0.427983  -0.788428
2     0.382714   0.539336  -0.568658   0.783393
3    -0.738407   0.276275   0.347246   0.956509
4      1.62063   0.373145  -0.308667   -1.36662
5     0.778496  -0.432625  -0.863804    1.36248
6     0.629325   0.435807  -0.681608 -0.0775674

和

所需的输出1：

print('www.xyz.com','n',df)
www.xyz.com 
    2013-01-01  2013-01-02  2013-01-03  2013-01-04
0   -0.168198   -1.129815    0.427983   -0.788428
1    0.382714    0.539336   -0.568658    0.783393
2   -0.738407    0.276275    0.347246    0.956509
3    1.620627    0.373145   -0.308667   -1.366621
4    0.778496   -0.432625   -0.863804    1.362475
5    0.629325    0.435807   -0.681608   -0.077567