Python:如果其他值在数据框之间匹配，则对数据框中的值求和 [英] Python: Sum values in DataFrame if other values match between DataFrames

问题描述

我有两个不同长度的数据框，像这样:

I have two dataframes of different length like those:

DataFrame A:

DataFrame A:

FirstName    LastName
Adam         Smith
John         Johnson

DataFrame B:

DataFrame B:

First        Last        Value
Adam         Smith       1.2
Adam         Smith       1.5
Adam         Smith       3.0
John         Johnson     2.5

想象一下，我要做的是在"DataFrame A"中创建一个新列，将所有值与匹配的姓氏相加，因此"A"中的输出将是:

Imagine that what I want to do is to create a new column in "DataFrame A" summing all the values with matching last names, so the output in "A" would be:

FirstName    LastName    Sums
Adam         Smith       5.7
John         Johnson     2.5

如果我在Excel中，我会使用

If I were in Excel, I'd use

=SUMIF(dfB!B:B, B2, dfB!C:C)

在Python中，我一直在尝试多种解决方案，但同时使用了np.where，df.sum()，删除索引等，但是我迷路了.下面的代码返回"ValueError:只能比较标记相同的Series对象"，但我认为它无论如何都无法正确编写.

In Python I've been trying multiple solutions but using both np.where, df.sum(), dropping indexes etc., but I'm lost. Below code is returning "ValueError: Can only compare identically-labeled Series objects", but I don't think it's written correctly anyways.

df_a['Sums'] = df_a[df_a['LastName'] == df_b['Last']].sum()['Value']

在此先感谢您的帮助.

推荐答案

使用 布尔索引 与如果要同时匹配名字和姓氏

If want match both, first and last name:

df = (df_b.merge(df_a, left_on=['First','Last'], right_on=['FirstName','LastName'])
           .groupby(['First','Last'], as_index=False)['Value']
           .sum())

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