Using Pandas groupby for Data Aggregation and Grouping

Objective: Learn to use Pandas for data aggregation and grouping.

Learning content: the groupby method and aggregation functions such as sum , mean , max , and min .

Code examples:

import pandas as pd
# create example dataset
data = {
    '姓名': ['张三', '李四', '王五', '张三', '赵六', '李四'],
    '部门': ['销售部', '市场部', '技术部', '销售部', '财务部', '市场部'],
    '销售额': [120, 150, 130, 160, 140, 170],
    '成本': [80, 90, 100, 110, 120, 130]
}
df = pd.DataFrame(data)
print(f"示例数据集: \n{df}")
# group by '部门'
grouped_by_department = df.groupby('部门')
print(f"按 '部门' 列分组的结果: \n{grouped_by_department}")
# iterate groups
for department, group in grouped_by_department:
    print(f"部门: {department}")
    print(f"数据: \n{group}\n")
# aggregation examples
mean_sales_by_department = grouped_by_department['销售额'].mean()
print(f"按 '部门' 列分组后，每组的销售额均值: \n{mean_sales_by_department}")
sum_sales_by_department = grouped_by_department['销售额'].sum()
print(f"按 '部门' 列分组后，每组的销售额总和: \n{sum_sales_by_department}")
max_sales_by_department = grouped_by_department['销售额'].max()
print(f"按 '部门' 列分组后，每组的销售额最大值: \n{max_sales_by_department}")
min_sales_by_department = grouped_by_department['销售额'].min()
print(f"按 '部门' 列分组后，每组的销售额最小值: \n{min_sales_by_department}")
# multi-column aggregation
mean_by_department = grouped_by_department[['销售额','成本']].mean()
print(f"按 '部门' 列分组后，每组的销售额和成本均值: \n{mean_by_department}")
sum_by_department = grouped_by_department[['销售额','成本']].sum()
print(f"按 '部门' 列分组后，每组的销售额和成本总和: \n{sum_by_department}")
# multi-level grouping
grouped_by_department_name = df.groupby(['部门','姓名'])
mean_sales_by_department_name = grouped_by_department_name['销售额'].mean()
print(f"按 '部门' 和 '姓名' 列分组后，每组的销售额均值: \n{mean_sales_by_department_name}")

Practice: Apply grouping on a dataset by a single column and compute the mean for each group.

Summary: After this exercise you should be able to perform data aggregation and grouping with Pandas using groupby and various aggregation functions, preparing you for further Python data processing topics.