Python 分组数据框并获得总和和计数？
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Group dataframe and get sum AND count?
提问by Richard
I have a dataframe that looks like this:
我有一个看起来像这样的数据框：
Company Name Organisation Name Amount
10118 Vifor Pharma UK Ltd Welsh Assoc for Gastro & Endo 2700.00
10119 Vifor Pharma UK Ltd Welsh IBD Specialist Group, 169.00
10120 Vifor Pharma UK Ltd West Midlands AHSN 1200.00
10121 Vifor Pharma UK Ltd Whittington Hospital 63.00
10122 Vifor Pharma UK Ltd Ysbyty Gwynedd 75.93
How do I sum the Amountand count the Organisation Name, to get a new dataframe that looks like this?
我如何求和Amount并计算Organisation Name, 以获得看起来像这样的新数据框？
Company Name Organisation Count Amount
10118 Vifor Pharma UK Ltd 5 11000.00
I know how to sum orcount:
我知道如何求和或计数：
df.groupby('Company Name').sum()
df.groupby('Company Name').count()
But not how to do both!
但不是如何做到这两点！
回答by MaxU
try this:
尝试这个：
In [110]: (df.groupby('Company Name')
.....: .agg({'Organisation Name':'count', 'Amount': 'sum'})
.....: .reset_index()
.....: .rename(columns={'Organisation Name':'Organisation Count'})
.....: )
Out[110]:
Company Name Amount Organisation Count
0 Vifor Pharma UK Ltd 4207.93 5
or if you don't want to reset index:
或者如果您不想重置索引：
df.groupby('Company Name')['Amount'].agg(['sum','count'])
or
或者
df.groupby('Company Name').agg({'Amount': ['sum','count']})
Demo:
演示：
In [98]: df.groupby('Company Name')['Amount'].agg(['sum','count'])
Out[98]:
sum count
Company Name
Vifor Pharma UK Ltd 4207.93 5
In [99]: df.groupby('Company Name').agg({'Amount': ['sum','count']})
Out[99]:
Amount
sum count
Company Name
Vifor Pharma UK Ltd 4207.93 5
回答by cs95
Just in case you were wondering how to rename columns during aggregation, here's how for
以防万一您想知道如何在聚合期间重命名列，这里是如何
pandas >= 0.25: Named Aggregation
pandas >= 0.25：命名聚合
df.groupby('Company Name')['Amount'].agg(MySum='sum', MyCount='count')
Or,
或者，
df.groupby('Company Name').agg(MySum=('Amount', 'sum'), MyCount=('Amount', 'count'))
MySum MyCount
Company Name
Vifor Pharma UK Ltd 4207.93 5
回答by JSharm
If you have lots of columns and only one is different you could do:
如果您有很多列并且只有一个不同，您可以执行以下操作：
In[1]: grouper = df.groupby('Company Name')
In[2]: res = grouper.count()
In[3]: res['Amount'] = grouper.Amount.sum()
In[4]: res
Out[4]:
Organisation Name Amount
Company Name
Vifor Pharma UK Ltd 5 4207.93
Note you can then rename the Organisation Name column as you wish.
请注意，您可以根据需要重命名组织名称列。
回答by cvsnow
df.groupby('Company Name').agg({'Organisation name':'count','Amount':'sum'})\
.apply(lambda x: x.sort_values(['count','sum'], ascending=False))
