在 pandas 中将多行连接到一行 [英] concatenate multiple rows to one single row in pandas

问题描述

我有以下数据(这只是一小部分，实际数据有21行):

 wt_tmin wt_tmax wt_prec wt_sol_rad wt_ET0 33.142857 52.714286 0.031429 114.000000 0.1028571 40.142857 66.857143 0.280000 172.714286 0.1928572 41.714286 67.142857 0.001429 179.714286 0.191429

我想将所有行连接成一行，如下所示:

0 1 2 3 4 5 6 7 8 9 11 12 13 14 1533.142857 52.714286 0.031429 114.000000 0.102857 40.142857 66.857143 0.280000 172.714286 .7000000 0.102857 40.142857 66.857143 0.280000 172.714286 0.714286 .7000000 0.102859 20.7 19167879 7000000 0.19167879

这是我尝试做的:

 # K 是包含数据的 Pandas 数据框KE = pd.concat([K.icol(0), K.icol(1), K.icol(2), K.icol(3), K.icol(4)],axis=1).T

但这并没有给我想要的结果.请帮忙

解决方案

I'd drop down to numpy via values, reshape it到一行，然后从中创建一个新框架:

<预><代码>>>>pd.DataFrame(df.values.reshape(1, -1))0 1 2 3 4 5 6 7 \0 33.142857 52.714286 0.031429 114 0.102857 40.142857 66.857143 0.288 9 10 11 12 13 140 172.714286 0.192857 41.714286 67.142857 0.001429 179.714286 0.191429

.reshape(1, -1) 基本上意味着根据需要将形状调整为 1 行和尽可能多的列 (-1)".

当您想创建具有多行的一列时，同样适用:

<预><代码>>>>pd.DataFrame(df.values.reshape(-1, 1))

I have the following data (this is just a small part, there are 21 rows in actual data):

    wt_tmin    wt_tmax   wt_prec  wt_sol_rad     wt_ET   
0  33.142857  52.714286  0.031429  114.000000  0.102857    
1  40.142857  66.857143  0.280000  172.714286  0.192857
2  41.714286  67.142857  0.001429  179.714286  0.191429         

I want to concatenate all the rows to one row like as follows:

0            1          2          3           4       5         6             7       8        9          11        12          13       14         15        
33.142857  52.714286  0.031429  114.000000  0.102857 40.142857  66.857143  0.280000  172.714286  0.192857 41.714286  67.142857  0.001429  179.714286  0.191429

Here's what I tried to do:

 # K is a pandas dataframe with the  data
 KE = pd.concat([K.icol(0), K.icol(1), K.icol(2), K.icol(3), K.icol(4)], axis=1).T

but this does not give me the desired result. Please help

解决方案

I'd drop down to numpy via values, reshape it to one row, and then make a new frame from that:

>>> pd.DataFrame(df.values.reshape(1, -1))
          0          1         2    3         4          5          6     7   \
0  33.142857  52.714286  0.031429  114  0.102857  40.142857  66.857143  0.28   

           8         9          10         11        12          13        14  
0  172.714286  0.192857  41.714286  67.142857  0.001429  179.714286  0.191429  

.reshape(1, -1) basically means "reshape to 1 row and as many columns as necessary (-1)".

The same applies when you want to create one column with many rows:

>>> pd.DataFrame(df.values.reshape(-1, 1))

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