python循环某一特定列的所有行数据(方法示例)

pip install pandas

id,name,age  
1,Alice,30  
2,Bob,25  
3,Charlie,35

import pandas as pd  
# 读取CSV文件  
df = pd.read_csv('data.csv')  
# 打印整个DataFrame以供参考  
print("整个DataFrame:")  
print(df)  
# 循环遍历'name'列的所有行数据  
for index, row in df.iterrows():  
    # row是一个Series对象，代表当前行的数据  
    # 可以通过列名访问特定列的值  
    name = row['name']  
    print(f"行号: {index}, 姓名: {name}")  
# 或者，更直接地只遍历'name'列  
for name in df['name']:  
    print(f"姓名: {name}")

id,name,department,salary  
1,Alice,HR,50000  
2,Bob,Engineering,60000  
3,Charlie,Marketing,55000

import pandas as pd  
# 读取CSV文件  
df = pd.read_csv('employees.csv')  
# 遍历'name'列  
for index, row in df.iterrows():  
    name = row['name']  
    print(f"员工ID: {row['id']}, 姓名: {name}")  
# 或者更简洁地遍历'name'列  
for name in df['name']:  
    print(f"姓名: {name}")

# 基于'department'列的值进行筛选  
engineering_dept = df[df['department'] == 'Engineering']  
# 打印筛选结果  
print("Engineering部门的员工:")  
for index, row in engineering_dept.iterrows():  
    print(f"员工ID: {row['id']}, 姓名: {row['name']}, 部门: {row['department']}, 薪水: {row['salary']}")

# 对'salary'列的数据进行转换（增加10%）  
df['salary'] = df['salary'] * 1.1  
# 打印转换后的DataFrame  
print("增加薪水后的DataFrame:")  
print(df)

# 计算'department'列的唯一值  
unique_departments = df['department'].unique()  
# 打印唯一值  
print("不同的部门:")  
for dept in unique_departments:  
    print(dept)

# 根据'salary'列的值进行排序（默认升序）  
sorted_df = df.sort_values(by='salary')  
# 打印排序后的DataFrame  
print("按薪水升序排序的DataFrame:")  
print(sorted_df)  
# 如果需要降序排序，可以添加ascending=False参数  
sorted_df_desc = df.sort_values(by='salary', ascending=False)  
# 打印降序排序后的DataFrame  
print("按薪水降序排序的DataFrame:")  
print(sorted_df_desc)