pandas 按groupby求和，但排除某些列 [英] Pandas sum by groupby, but exclude certain columns

问题描述

在 Pandas 数据帧上进行分组但从该分组中排除某些列的最佳方法是什么?例如我有以下数据框:

What is the best way to do a groupby on a Pandas dataframe, but exclude some columns from that groupby? e.g. I have the following dataframe:

Code   Country      Item_Code   Item    Ele_Code    Unit    Y1961    Y1962   Y1963
2      Afghanistan  15          Wheat   5312        Ha      10       20      30
2      Afghanistan  25          Maize   5312        Ha      10       20      30
4      Angola       15          Wheat   7312        Ha      30       40      50
4      Angola       25          Maize   7312        Ha      30       40      50

我想对 Country 和 Item_Code 列进行分组，并且只计算 Y1961、Y1962 和 Y1963 列下的行的总和.生成的数据框应如下所示:

I want to groupby the column Country and Item_Code and only compute the sum of the rows falling under the columns Y1961, Y1962 and Y1963. The resulting dataframe should look like this:

Code   Country      Item_Code   Item    Ele_Code    Unit    Y1961    Y1962   Y1963
2      Afghanistan  15          C3      5312        Ha      20       40       60
4      Angola       25          C4      7312        Ha      60       80      100

现在我正在这样做:

df.groupby('Country').sum()

然而，这也会将 Item_Code 列中的值相加.有什么方法可以指定哪些列要包含在 sum() 操作中，哪些要排除?

However this adds up the values in the Item_Code column as well. Is there any way I can specify which columns to include in the sum() operation and which ones to exclude?

推荐答案

您可以选择 groupby 的列:

You can select the columns of a groupby:

In [11]: df.groupby(['Country', 'Item_Code'])[["Y1961", "Y1962", "Y1963"]].sum()
Out[11]:
                       Y1961  Y1962  Y1963
Country     Item_Code
Afghanistan 15            10     20     30
            25            10     20     30
Angola      15            30     40     50
            25            30     40     50

请注意，传递的列表必须是列的子集，否则您会看到 KeyError.

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