Python 组合熊猫中的行
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Combining rows in pandas
提问by lightlike
I have a DataFrame with an index called city_idof cities in the format [city],[state](e.g., new york,nycontaining integer counts in the columns. The problem is that I have multiple rows for the same city, and I want to collapse the rows sharing a city_idby adding their column values. I looked at groupby()but it wasn't immediately obvious how to apply it to this problem.
我有一个 DataFrame，其索引city_id格式为城市[city],[state]（例如，new york,ny列中包含整数计数。问题是我有同一个城市的多行，我想city_id通过添加它们的列值来折叠共享 a 的行. 我看了看，groupby()但不是很明显如何将它应用于这个问题。
Edit:
编辑：
An example: I'd like to change this:
一个例子：我想改变这个：
city_id val1 val2 val3
houston,tx 1 2 0
houston,tx 0 0 1
houston,tx 2 1 1
into this:
进入这个：
city_id val1 val2 val3
houston,tx 3 3 2
if there are ~10-20k rows.
如果有 ~10-20k 行。
采纳答案by DSM
Starting from
从...开始
>>> df
val1 val2 val3
city_id
houston,tx 1 2 0
houston,tx 0 0 1
houston,tx 2 1 1
somewhere,ew 4 3 7
I might do
我可能会
>>> df.groupby(df.index).sum()
val1 val2 val3
city_id
houston,tx 3 3 2
somewhere,ew 4 3 7
or
或者
>>> df.reset_index().groupby("city_id").sum()
val1 val2 val3
city_id
houston,tx 3 3 2
somewhere,ew 4 3 7
The first approach passes the index values (in this case, the city_idvalues) to groupbyand tells it to use those as the group keys, and the second resets the index and then selects the city_idcolumn. See this sectionof the docs for more examples. Note that there are lots of other methods in the DataFrameGroupByobjects, too:
第一种方法将索引值（在本例中为city_id值）传递给groupby并告诉它使用这些作为组键，第二种方法重置索引，然后选择city_id列。有关更多示例，请参阅文档的这一部分。请注意，对象中还有许多其他方法DataFrameGroupBy：
>>> df.groupby(df.index)
<pandas.core.groupby.DataFrameGroupBy object at 0x1045a1790>
>>> df.groupby(df.index).max()
val1 val2 val3
city_id
houston,tx 2 2 1
somewhere,ew 4 3 7
>>> df.groupby(df.index).mean()
val1 val2 val3
city_id
houston,tx 1 1 0.666667
somewhere,ew 4 3 7.000000
回答by LonelySoul
Something in the same line. Sorry not the exact replica.
在同一行的东西。抱歉不是确切的复制品。
mydata = [{'subid' : 'B14-111', 'age': 75, 'fdg':1.78},
{'subid' : 'B14-112', 'age': 22, 'fdg':1.56},{'subid' : 'B14-112', 'age': 40, 'fdg':2.00},]
df = pandas.DataFrame(mydata)
gg = df.groupby("subid",sort=True).sum()
