将不同列数的多个数据帧合并为一个大数据帧 [英] Merge of multiple data frames of different number of columns into one big data frame

问题描述

我有两个CSV文件，具有不同的列和行数.第一个CSV文件具有M列和N行，第二个CSV文件具有H列和G行.一些列具有相同的名称.

I have two CSV files with different number of columns and rows. The first CSV file has M columns and N rows, the second has H columns and G rows. Some of the columns have the same name.

我想将两者结合到具有以下属性的数据框中:

I'd like to combine the two into data frame with following properties:

• N + G行
• (M，H)列的联合
• 如果列A是第一个CSV文件的元素，而不是第二个CSV文件的元素，则数据框在A的前N个条目中应包含与第一个CSV相同的值，对于其余列(因为第二个CSV中没有A数据) )应为NA.

这里是一个例子:

CSV1
City, Population,
Zagreb, 700000,
Rijeka, 142000

CSV2
City, Area,
Split, 200.00
Osijek, 171.00
Dubrovnik, 143.35

我想构建一个看起来像这样的数据框:

I'd like build a data frame that looks like this:

City      Population   Area
Zagreb    700000       NA
Rijeka    142000       NA
Split     NA           200.00
Osijek    NA           171.00
Dubrovnik NA           143.35

如果两个CSV文件都具有两个数据框并希望执行相同操作，例如，如果我将第一个csv加载到df1，然后将第二个csv加载到df2中，然后又想合并到看起来就像上面的示例.

Also what if instead two CSV files I had two data frames and wanted to do the same, for example if I loaded first csv to df1 and second one in df2 and then wanted to make a merge to df3 that would look like example above.

推荐答案

为什么不尝试 concat 函数:

Why not try the concat function:

In [25]: df1
Out[25]: 
     City  Population
0  Zagreb      700000
1  Rijeka      142000

In [26]: df2
Out[26]: 
        City    Area
0      Split  200.00
1     Osijek  171.00
2  Dubrovnik  143.35

In [27]: pd.concat([df1,df2])
Out[27]: 
     Area       City  Population
0     NaN     Zagreb      700000
1     NaN     Rijeka      142000
0  200.00      Split         NaN
1  171.00     Osijek         NaN
2  143.35  Dubrovnik         NaN

In [28]: pd.concat([df1,df2], ignore_index=True)
Out[28]: 
     Area       City  Population
0     NaN     Zagreb      700000
1     NaN     Rijeka      142000
2  200.00      Split         NaN
3  171.00     Osijek         NaN
4  143.35  Dubrovnik         NaN

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