Python 3 解法, 执行用时: 780ms, 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')

print(new.groupby('item_name').employee_id.count())

Python 3 解法, 执行用时: 782ms, 内存消耗: 524288KB, 提交时间: 2022-07-03

import pandas as pd
signup = pd.read_csv('signup.csv')
items = pd.read_csv('items.csv')
df = items.set_index('item_id').join(signup.set_index('item_id'),on='item_id',how="inner")
print(df.groupby('item_name').size())

Python 3 解法, 执行用时: 787ms, 内存消耗: 524288KB, 提交时间: 2022-07-03

import pandas as pd
signup = pd.read_csv('signup.csv')
items = pd.read_csv('items.csv')
df=items.set_index('item_id').join(signup.set_index('item_id'),on='item_id',how='inner')
print(df.groupby('item_name').size())

Python 3 解法, 执行用时: 788ms, 内存消耗: 524288KB, 提交时间: 2022-07-25

import pandas as pd

signup = pd.read_csv("signup.csv")
items = pd.read_csv("items.csv")
print(items.groupby("item_name").size() > 0)

Python 3 解法, 执行用时: 789ms, 内存消耗: 524288KB, 提交时间: 2022-07-17

import pandas as pd
signup = pd.read_csv('signup.csv')
items = pd.read_csv('items.csv')

he = pd.merge(signup, items, on='item_id', how='inner')

print(
    he.groupby(['item_name'])['item_name'].count()
)