这显然很简单，但作为一个麻木的新手，我被卡住了。
我有一个CSV文件，其中包含3列，州，办公室ID，以及该办公室的销售。
我想计算给定州中每个办事处的销售额百分比(每个州所有百分比的总和为100%)。
df = pd.DataFrame({'state': ['CA', 'WA', 'CO', 'AZ'] * 3,
'office_id': list(range(1, 7)) * 2,
'sales': [np.random.randint(100000, 999999)
for _ in range(12)]})
df.groupby(['state', 'office_id']).agg({'sales': 'sum'})
这将返回:
sales
state office_id
AZ 2 839507
4 373917
6 347225
CA 1 798585
3 890850
5 454423
CO 1 819975
3 202969
5 614011
WA 2 163942
4 369858
6 959285
我似乎不知道如何“达到”集团的州级，通过合计整个州的销售来计算分数。
import numpy as np
import pandas as pd
np.random.seed(0)
df = pd.DataFrame({'state': ['CA', 'WA', 'CO', 'AZ'] * 3,
'office_id': list(range(1, 7)) * 2,
'sales': [np.random.randint(100000, 999999) for _ in range(12)]})
df.groupby(['state', 'office_id'])['sales'].sum().rename("weightage").groupby(level = 0).transform(lambda x: x/x.sum())
df.reset_index()
输出:
state office_id weightage
0 AZ 2 0.169814
1 AZ 4 0.192500
2 AZ 6 0.637686
3 CA 1 0.193319
4 CA 3 0.338587
5 CA 5 0.468094
6 CO 1 0.368519
7 CO 3 0.198743
8 CO 5 0.432739
9 WA 2 0.347072
10 WA 4 0.355113
11 WA 6 0.297815
更新2022 - 03
这个由caner使用变换的答案看起来比我原来的答案要好得多!
df['sales'] / df.groupby('state')['sales'].transform('sum')
感谢Paul Rougieux的评论。
原答案(2014)
Paul H的回答是正确的，您将必须创建第二个groupby对象，但是您可以用更简单的方法计算百分比——只需groupby state_office并将sales列除以它的和。复制Paul H回答的开头:
# From Paul H
import numpy as np
import pandas as pd
np.random.seed(0)
df = pd.DataFrame({'state': ['CA', 'WA', 'CO', 'AZ'] * 3,
'office_id': list(range(1, 7)) * 2,
'sales': [np.random.randint(100000, 999999)
for _ in range(12)]})
state_office = df.groupby(['state', 'office_id']).agg({'sales': 'sum'})
# Change: groupby state_office and divide by sum
state_pcts = state_office.groupby(level=0).apply(lambda x:
100 * x / float(x.sum()))
返回:
sales
state office_id
AZ 2 16.981365
4 19.250033
6 63.768601
CA 1 19.331879
3 33.858747
5 46.809373
CO 1 36.851857
3 19.874290
5 43.273852
WA 2 34.707233
4 35.511259
6 29.781508
df = pd.DataFrame({'state': ['CA', 'WA', 'CO', 'AZ'] * 3,
'office_id': list(range(1, 7)) * 2,
'sales': [np.random.randint(100000, 999999)
for _ in range(12)]})
grouped = df.groupby(['state', 'office_id'])
100*grouped.sum()/df[["state","sales"]].groupby('state').sum()
返回:
sales
state office_id
AZ 2 54.587910
4 33.009225
6 12.402865
CA 1 32.046582
3 44.937684
5 23.015735
CO 1 21.099989
3 31.848658
5 47.051353
WA 2 43.882790
4 10.265275
6 45.851935
即通过自动匹配列名和索引名来实现操作。这段代码应该相当于@exp1orer接受答案的一个逐步版本
使用df，我将用别名state_office_sales调用它:
sales
state office_id
AZ 2 839507
4 373917
6 347225
CA 1 798585
3 890850
5 454423
CO 1 819975
3 202969
5 614011
WA 2 163942
4 369858
6 959285
State_total_sales是state_office_sales，按索引级别0(最左边)中的总和分组。
In: state_total_sales = df.groupby(level=0).sum()
state_total_sales
Out:
sales
state
AZ 2448009
CA 2832270
CO 1495486
WA 595859
因为这两个数据框架共享一个索引名和一个列名，pandas将通过共享索引找到合适的位置，例如:
In: state_office_sales / state_total_sales
Out:
sales
state office_id
AZ 2 0.448640
4 0.125865
6 0.425496
CA 1 0.288022
3 0.322169
5 0.389809
CO 1 0.206684
3 0.357891
5 0.435425
WA 2 0.321689
4 0.346325
6 0.331986
为了更好地说明这一点，这里有一个没有等价物的XX的部分总数。Pandas将根据索引和列名匹配位置，如果没有重叠，Pandas将忽略它:
In: partial_total = pd.DataFrame(
data = {'sales' : [2448009, 595859, 99999]},
index = ['AZ', 'WA', 'XX' ]
)
partial_total.index.name = 'state'
Out:
sales
state
AZ 2448009
WA 595859
XX 99999
In: state_office_sales / partial_total
Out:
sales
state office_id
AZ 2 0.448640
4 0.125865
6 0.425496
CA 1 NaN
3 NaN
5 NaN
CO 1 NaN
3 NaN
5 NaN
WA 2 0.321689
4 0.346325
6 0.331986
当没有共享索引或列时，这一点变得非常明显。这里missing_index_totals等于state_total_sales，只是它没有索引名。
In: missing_index_totals = state_total_sales.rename_axis("")
missing_index_totals
Out:
sales
AZ 2448009
CA 2832270
CO 1495486
WA 595859
In: state_office_sales / missing_index_totals
Out: ValueError: cannot join with no overlapping index names
import numpy as np
import pandas as pd
np.random.seed(0)
df = pd.DataFrame({'state': ['CA', 'WA', 'CO', 'AZ'] * 3,
'office_id': list(range(1, 7)) * 2,
'sales': [np.random.randint(100000, 999999) for _ in range(12)]})
df.groupby(['state', 'office_id'])['sales'].sum().rename("weightage").groupby(level = 0).transform(lambda x: x/x.sum())
df.reset_index()
输出:
state office_id weightage
0 AZ 2 0.169814
1 AZ 4 0.192500
2 AZ 6 0.637686
3 CA 1 0.193319
4 CA 3 0.338587
5 CA 5 0.468094
6 CO 1 0.368519
7 CO 3 0.198743
8 CO 5 0.432739
9 WA 2 0.347072
10 WA 4 0.355113
11 WA 6 0.297815
我使用的简单方法是在2组之后合并，然后做简单的除法。
import numpy as np
import pandas as pd
np.random.seed(0)
df = pd.DataFrame({'state': ['CA', 'WA', 'CO', 'AZ'] * 3,
'office_id': list(range(1, 7)) * 2,
'sales': [np.random.randint(100000, 999999) for _ in range(12)]})
state_office = df.groupby(['state', 'office_id'])['sales'].sum().reset_index()
state = df.groupby(['state'])['sales'].sum().reset_index()
state_office = state_office.merge(state, left_on='state', right_on ='state', how = 'left')
state_office['sales_ratio'] = 100*(state_office['sales_x']/state_office['sales_y'])
state office_id sales_x sales_y sales_ratio
0 AZ 2 222579 1310725 16.981365
1 AZ 4 252315 1310725 19.250033
2 AZ 6 835831 1310725 63.768601
3 CA 1 405711 2098663 19.331879
4 CA 3 710581 2098663 33.858747
5 CA 5 982371 2098663 46.809373
6 CO 1 404137 1096653 36.851857
7 CO 3 217952 1096653 19.874290
8 CO 5 474564 1096653 43.273852
9 WA 2 535829 1543854 34.707233
10 WA 4 548242 1543854 35.511259
11 WA 6 459783 1543854 29.781508
