这显然很简单,但是作为一个笨拙的新手,我被卡住了.
This is obviously simple, but as a numpy newbe I'm getting stuck.
我有一个CSV文件,其中包含3列,分别是该办公室的州,办公室ID和销售.
I have a CSV file that contains 3 columns, the State, the Office ID, and the Sales for that office.
我想计算给定状态下每个办公室的销售额百分比(每个州的所有百分比总计为100%).
I want to calculate the percentage of sales per office in a given state (total of all percentages in each state is 100%).
df = pd.DataFrame({'state': ['CA', 'WA', 'CO', 'AZ'] * 3, 'office_id': 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我似乎无法弄清楚如何达到" groupby的state级别以总计整个state的sales来计算分数.
I can't seem to figure out how to "reach up" to the state level of the groupby to total up the sales for the entire state to calculate the fraction.
推荐答案Paul H的答案是正确的,您将拥有来创建第二个groupby对象,但是您可以以更简单的方式计算百分比-只需groupby state_office并将sales列除以其和即可.复制Paul H答案的开头:
Paul H's answer is right that you will have to make a second groupby object, but you can calculate the percentage in a simpler way -- just groupby the state_office and divide the sales column by its sum. Copying the beginning of Paul H's answer:
# 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更多推荐
pandas 与groupby占总数的百分比
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