如何在Pandas DataFrame中获取最值及按姓名分组的班级数据

原始数据定义 #

import pandas as pd
data = [(1,"tom", 23),
        (1,"nick", 12),
        (1,"jim",13),
        (2,"tom", 44),
        (2,"nick", 56),
        (2,"jim",77),
        (3, "tom", 88),
        (3, "nick", 10),
        (3, "jim", 13),
        ]

df = pd.DataFrame(data, columns=['class', 'Name','number'])

对应的DataFrame输出：

class  Name  number
0      1   tom      23
1      1  nick      12
2      1   jim      13
3      2   tom      44
4      2  nick      56
5      2   jim      77
6      3   tom      88
7      3  nick      10
8      3   jim      13

问题1：获取DataFrame的最大值和最小值 #

• 全局列级最值：直接调用max()和min()方法，会返回每一列的最值

  # 各列最大值
  column_max = df.max()
  # 各列最小值
  column_min = df.min()
  

  输出示例：

  class       3
  Name      tom
  number     88
  dtype: object
  

• 特定列最值：指定列名后调用方法，仅返回目标列的最值

  # 获取number列的最大值
  num_max = df['number'].max()
  # 获取number列的最小值
  num_min = df['number'].min()
  

问题2：按要求提取并展示数据 #

需求：先获取class=1分组中各Name对应的number值，再按相同Name、不同class的顺序展示数据，输出指定格式。

实现代码：

# 按Name分组，遍历每个分组的数据
for name, group in df.groupby('Name'):
    # 按class升序排序，保证输出顺序正确
    sorted_group = group.sort_values('class')
    # 逐行输出指定格式内容
    for _, row in sorted_group.iterrows():
        print(f"[name ={row['Name']}, class={row['class']}, number ={row['number']}]")

执行后输出：

[name =jim, class=1, number =13]
[name =jim, class=2, number =77]
[name =jim, class=3, number =13]
[name =nick, class=1, number =12]
[name =nick, class=2, number =56]
[name =nick, class=3, number =10]
[name =tom, class=1, number =23]
[name =tom, class=2, number =44]
[name =tom, class=3, number =88]

如果需要仅展示class=1中存在的Name（当前示例中所有Name都满足），可以先筛选再处理：

# 提取class=1中的所有Name
target_names = df[df['class'] == 1]['Name'].unique()
# 过滤出目标Name的数据并分组展示
filtered_df = df[df['Name'].isin(target_names)]
for name, group in filtered_df.groupby('Name'):
    sorted_group = group.sort_values('class')
    for _, row in sorted_group.iterrows():
        print(f"[name ={row['Name']}, class={row['class']}, number ={row['number']}]")

内容的提问来源于stack exchange，提问作者Phan Steel