pandas 熊猫合并具有相同值和相同索引的行
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Pandas merging rows with the same value and same index
提问by MScar
I have a DataFrame with an index called SubjectIDand a column Visit. Subjects have multiple Visits and either an integer value or an N/A for Value1and Value2. I want to collapse the rows that have the same SubjectIDand the same Visitnumber.
我有一个 DataFrame 有一个索引叫做SubjectID和一个 column Visit。受试者具有多个访问和任一整数值或用于N / AValue1和Value2。我想折叠具有SubjectID相同Visit编号的行。
Here is my data frame:
这是我的数据框：
SubjectID Visit Value1 Value2
B1 1 1.57 N/A
B1 1 N/A 1.75
B1 2 N/A 1.56
I want to it to look like this:
我想让它看起来像这样：
Subject ID Visit Value1 Value2
B1 1 1.57 1.75
B1 2 N/A 1.56
I was trying to use groupby()to solve this problem but I'm not sure how to make it take into account both the index and the values in the Visitcolumn.
我试图用来groupby()解决这个问题，但我不知道如何使它同时考虑Visit列中的索引和值。
回答by root
You can use groupby.firstor groupby.lastto get the first/last non-null value for each column within the group. For the example data, the output would be the same for either method:
您可以使用groupby.first或groupby.last获取组中每列的第一个/最后一个非空值。对于示例数据，两种方法的输出都相同：
df = df.groupby(['SubjectID', 'Visit']).first().reset_index()
The resulting output:
结果输出：
SubjectID Visit Value1 Value2
0 B1 1 1.57 1.75
1 B1 2 NaN 1.56
