[英]Groupby sum and count on multiple columns in python
I have a pandas dataframe that looks like this我有一个看起来像这样的熊猫数据框
ID country month revenue profit ebit
234 USA 201409 10 5 3
344 USA 201409 9 7 2
532 UK 201410 20 10 5
129 Canada 201411 15 10 5
I want to group by ID, country, month and count the IDs per month and country and sum the revenue, profit, ebit.我想按 ID、国家/地区、月份分组并计算每个月和国家/地区的 ID,然后总结收入、利润、ebit。 The output for the above data would be:上述数据的输出将是:
country month revenue profit ebit count
USA 201409 19 12 5 2
UK 201409 20 10 5 1
Canada 201411 15 10 5 1
I have tried different variations of groupby, sum and count functions of pandas but I am unable to figure out how to apply groupby sum and count all together to give the result as shown.我已经尝试了熊猫的 groupby、sum 和 count 函数的不同变体,但我无法弄清楚如何应用 groupby sum 和 count 来给出如图所示的结果。 Please share any ideas that you might have.请分享您可能有的任何想法。 Thanks!谢谢!
It can be done using pivot_table
this way:可以通过以下方式使用pivot_table
来完成:
>>> df1=pd.pivot_table(df, index=['country','month'],values=['revenue','profit','ebit'],aggfunc=np.sum)
>>> df1
ebit profit revenue
country month
Canada 201411 5 10 15
UK 201410 5 10 20
USA 201409 5 12 19
>>> df2=pd.pivot_table(df, index=['country','month'], values='ID',aggfunc=len).rename('count')
>>> df2
country month
Canada 201411 1
UK 201410 1
USA 201409 2
>>> pd.concat([df1,df2],axis=1)
ebit profit revenue count
country month
Canada 201411 5 10 15 1
UK 201410 5 10 20 1
USA 201409 5 12 19 2
You can do the groupby, and then map the counts of each country to a new column.您可以进行分组,然后将每个国家/地区的计数映射到一个新列。
g = df.groupby(['country', 'month'])['revenue', 'profit', 'ebit'].sum().reset_index()
g['count'] = g['country'].map(df['country'].value_counts())
g
Out[3]:
country month revenue profit ebit count
0 Canada 201411 15 10 5 1
1 UK 201410 20 10 5 1
2 USA 201409 19 12 5 2
Edit编辑
To get the counts per country and month, you can do another groupby, and then join the two DataFrames together.要获得每个国家和每个月的计数,您可以执行另一个 groupby,然后将两个 DataFrame 连接在一起。
g = df.groupby(['country', 'month'])['revenue', 'profit', 'ebit'].sum()
j = df.groupby(['country', 'month']).size().to_frame('count')
pd.merge(g, j, left_index=True, right_index=True).reset_index()
Out[6]:
country month revenue profit ebit count
0 Canada 201411 15 10 5 1
1 UK 201410 20 10 5 1
2 UK 201411 10 5 2 1
3 USA 201409 19 12 5 2
I added another record for the UK with a different date - notice how there are now two UK entries in the merged DataFrame, with the appropriate counts.我为英国添加了另一条日期不同的记录 - 请注意现在合并的 DataFrame 中有两个英国条目,并具有适当的计数。
The following solution seems the simplest.以下解决方案似乎最简单。
Group by country and month:按国家和月份分组:
grouped_df = df.groupby(['country', 'month'])
Apply sum to columns of interest (revenue, profit, ebit):将 sum 应用于感兴趣的列(收入、利润、ebit):
final = grouped_df[['revenue', 'profit', 'ebit']].agg('sum')
Assign the size of the grouped_df to a new column in 'final':将 grouped_df 的大小分配给“final”中的新列:
final['count'] = grouped_df.size()
print(final)
Out[256]:
revenue profit ebit count
country month
Canada 201411 15 10 5 1
UK 201410 20 10 5 1
USA 201409 19 12 5 2
All done!全部搞定!
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