Python 3 解法, 执行用时: 787ms, 内存消耗: 524288KB, 提交时间: 2022-07-12

import pandas as pd
signup = pd.read_csv('signup.csv')
items = pd.read_csv('items.csv')
df = pd.merge(signup,items,on="item_id")
df1 = df[(df["department"] == "functional") & (df["item_name"] == "javelin")]
df2 = df1[["employee_id","name","sex"]]
# df2.index = range(df2.shape[0])
print(df2)

Python 3 解法, 执行用时: 790ms, 内存消耗: 524288KB, 提交时间: 2022-06-28

import pandas as pd
signup = pd.read_csv('signup.csv')
items = pd.read_csv('items.csv')
new = pd.merge(items,signup,on = 'item_id')
new1 = new[(new['department'] == 'functional')&(new['item_name'] == 'javelin' )]
print(new1[['employee_id','name','sex']].reset_index(drop = True))

Python 3 解法, 执行用时: 792ms, 内存消耗: 524288KB, 提交时间: 2022-07-03

#请你统计职能部门（functional）中报名标枪(javenlin)的所有员工的员工编号（employee_id）、
#姓名（name）及性别（sex）。
import pandas as pd
signup = pd.read_csv('signup.csv')
items = pd.read_csv('items.csv')
new=pd.merge(items,signup,on='item_id')
new1 = new[(new['department']=='functional')&(new['item_name']=='javelin')]
new2=new1[['employee_id','name','sex']].reset_index(drop = True)
print(new2)

Python 3 解法, 执行用时: 796ms, 内存消耗: 524288KB, 提交时间: 2022-07-24

import pandas as pd
signup = pd.read_csv('signup.csv')
items = pd.read_csv('items.csv')
dt=pd.merge(signup,items,on='item_id',how='left')
a=dt[(dt['department']=='functional')&(dt['item_name']=='javelin')].reset_index()
print(a[['employee_id','name','sex']]) ## ！！ 要用双括号

Python 3 解法, 执行用时: 798ms, 内存消耗: 524288KB, 提交时间: 2022-07-28

import pandas as pd
signup = pd.read_csv('signup.csv')
items = pd.read_csv('items.csv')
data = pd.merge(signup,items,how='inner',on='item_id')
data=data[(data['department'] == 'functional') & (data['item_name'] == 'javelin')].reset_index(drop=True)
print(data[['employee_id','name','sex']])