Pandas 数据帧中的多索引分组依据
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MultiIndex Group By in Pandas Data Frame
提问by metersk
I have a data set that contains countries and statistics on economic indicators by year, organized like so:
我有一个数据集，其中包含按年份划分的国家和经济指标统计数据，其组织方式如下：
Country Metric 2011 2012 2013 2014
USA GDP 7 4 0 2
USA Pop. 2 3 0 3
GB GDP 8 7 0 7
GB Pop. 2 6 0 0
FR GDP 5 0 0 1
FR Pop. 1 1 0 5
How can I use MultiIndex in pandas to create a data frame that only shows GDP by Year for each country?
如何在 Pandas 中使用 MultiIndex 创建一个仅按年份显示每个国家/地区的 GDP 的数据框？
I tried:
我试过：
df = data.groupby(['Country', 'Metric'])
but it didn't work properly.
但它没有正常工作。
回答by Paul H
In this case, you don't actually need a groupby. You also don't have a MultiIndex. You can make one like this:
在这种情况下，您实际上并不需要groupby. 你也没有MultiIndex. 你可以做一个这样的：
import pandas
from io import StringIO
datastring = StringIO("""\
Country Metric 2011 2012 2013 2014
USA GDP 7 4 0 2
USA Pop. 2 3 0 3
GB GDP 8 7 0 7
GB Pop. 2 6 0 0
FR GDP 5 0 0 1
FR Pop. 1 1 0 5
""")
data = pandas.read_table(datastring, sep='\s\s+')
data.set_index(['Country', 'Metric'], inplace=True)
Then datalooks like this:
然后data看起来像这样：
2011 2012 2013 2014
Country Metric
USA GDP 7 4 0 2
Pop. 2 3 0 3
GB GDP 8 7 0 7
Pop. 2 6 0 0
FR GDP 5 0 0 1
Pop. 1 1 0 5
Now to get the GDPs, you can take a cross-section of the dataframe via the xsmethod:
现在要获得 GDP，您可以通过以下xs方法获取数据框的横截面：
data.xs('GDP', level='Metric')
2011 2012 2013 2014
Country
USA 7 4 0 2
GB 8 7 0 7
FR 5 0 0 1
It's so easy because your data are already pivoted/unstacked. IF they weren't and looked like this:
这非常简单，因为您的数据已经被旋转/未堆叠。如果它们不是并且看起来像这样：
data.columns.names = ['Year']
data = data.stack()
data
Country Metric Year
USA GDP 2011 7
2012 4
2013 0
2014 2
Pop. 2011 2
2012 3
2013 0
2014 3
GB GDP 2011 8
2012 7
2013 0
2014 7
Pop. 2011 2
2012 6
2013 0
2014 0
FR GDP 2011 5
2012 0
2013 0
2014 1
Pop. 2011 1
2012 1
2013 0
2014 5
You could then use groupbyto tell you something about the world as a whole:
然后你可以groupby用来告诉你关于整个世界的一些事情：
data.groupby(level=['Metric', 'Year']).sum()
Metric Year
GDP 2011 20
2012 11
2013 0
2014 10
Pop. 2011 5
2012 10
2013 0
2014 8
Or get real fancy:
或者得到真正的幻想：
data.groupby(level=['Metric', 'Year']).sum().unstack(level='Metric')
Metric GDP Pop.
Year
2011 20 5
2012 11 10
2013 0 0
2014 10 8
回答by Amit Verma
Is this what you are looking for:
这是你想要的：
df = df.groupby(['Metric'])
df.get_group('GDP')
Country Metric 2011 2012 2013 2014
0 USA GDP 7 4 0 2
2 GB GDP 8 7 0 7
4 FR GDP 5 0 0 1
