問(wèn)題描述

我有兩個(gè)不同長(zhǎng)度的數(shù)據(jù)框:

I have two dataframes of different length like those:

數(shù)據(jù)幀 A:

FirstName    LastName
Adam         Smith
John         Johnson

數(shù)據(jù)幀 B:

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

想象一下，我想做的是在DataFrame A"中創(chuàng)建一個(gè)新列，將所有具有匹配姓氏的值相加，因此A"中的輸出將是:

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，我會(huì)使用

If I were in Excel, I'd use

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

在 Python 中，我一直在嘗試多種解決方案，但同時(shí)使用 np.where、df.sum()、刪除索引等，但我迷路了.下面的代碼返回ValueError:只能比較標(biāo)記相同的系列對(duì)象"，但我認(rèn)為它無(wú)論如何都寫(xiě)不正確.

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']

非常感謝您的任何幫助.

Huge thanks in advance for any help.

推薦答案

使用 布爾索引 與 Series.isin 進(jìn)行過(guò)濾然后聚合sum:

df = (df_b[df_b['Last'].isin(df_a['LastName'])]
           .groupby(['First','Last'], as_index=False)['Value']
           .sum())

如果想同時(shí)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())

這篇關(guān)于Python:如果DataFrames之間的其他值匹配，則對(duì)DataFrame中的值求和的文章就介紹到這了，希望我們推薦的答案對(duì)大家有所幫助，也希望大家多多支持html5模板網(wǎng)！