pandas 占 groupby 总数的百分比

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这显然很简单，但作为一个麻木的新手，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、州、办公室 ID 和该办公室的销售额.

I have a CSV file that contains 3 columns, the State, the Office ID, and the Sales for that office.

我想计算给定州每个办公室的销售额百分比(每个州的所有百分比总和为 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'})

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"到 groupby 的 state 级别来总计 sales为整个 state 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 的回答 是正确的，您将拥有创建第二个 groupby 对象，但您可以用更简单的方式计算百分比——只需 groupby state_office 并划分 sales 列按其总和.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