我有一個數據框df，其中包含城市人口的工作和年齡信息df User City Job Age0 A x Unemployed 331 B x Student 182 C x Unemployed 273 D y Data Scientist 284 E y Unemployed 455 F y Student 18對于每個城市，我想計算失業率和年齡中位數。對于失業率，我做了以下工作## Count the people in each citycust = insDataRed.groupby(['City'])['User'].count() ## Number of people for each citycust = pd.DataFrame(cust)cust.columns=['nCust']cust['City']=cust.indexcust=cust.reset_index(drop=True)## Count the people unemployed in each cityunempl = df[df['Job'] == 'Unemployed']unempl = unempl.groupby(['City'])['Job'].count()unempl = pd.DataFrame(unempl)unempl.columns=['unempl']unempl['City']=unempl.indexunempl=unempl.reset_index(drop=True)# 1. Fraction of UnemploymentunRate = pd.merge(unempl, cust, on = 'City')unRate['rate'] =(unRate['unempl']/unRate['nCust'])*100有沒有更優雅的解決方案？如何計算每個城市的年齡中值？
1 回答
撒科打諢
TA貢獻1934條經驗 獲得超2個贊
如果您只想按城市執行此操作：
df.groupby(by='City').median()
如果您想同時按城市和工作分組：
df.groupby(by=['City', 'Job']).median()
獲取每個城市的失業率：
import pandas as pd
df = pd.DataFrame({
'User': ['A', 'B', 'C', 'D', 'E', 'F'], 'City': ['x', 'x', 'x', 'y', 'y', 'y'],
'Job': ['Unemployed', 'Student', 'Unemployed', 'Data Scientist', 'Unemployed', 'Student'],
'Age':[33, 18, 27, 28, 45, 18],
})
df['count'] = 1
unmpl = df.groupby(by=['City', 'Job'])['count'].sum().reset_index()
unmpl_by_city = unmpl[unmpl['Job'] == 'Unemployed'].reset_index(drop=True)
count_by_city = df.groupby(by=['City'])['count'].sum().reset_index(drop=True)
frac_by_city = (unmpl_by_city['count'] * 100.0 /
count_by_city)
unmpl_by_city['frac'] = frac_by_city
unmpl_by_city
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