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本篇内容介绍了“怎么使用Python中Pandas的索引对齐方法”的有关知识，在实际案例的操作过程中，不少人都会遇到这样的困境，接下来就让小编带领大家学习一下如何处理这些情况吧！希望大家仔细阅读，能够学有所成！
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一.索引对象支持集合运算：联合、交叉、求差、对称差
Demo1:
import pandas as pd
import numpy as np
college = pd.read_csv('data/college.csv')
columns = college.columns
c1 = columns[:4]
c2 = columns[2:5]
print(c1.union(c2))
print(c1 | c2)
Demo2:
import pandas as pd
import numpy as np
college = pd.read_csv('data/college.csv')
columns = college.columns
c1 = columns[:4]
c2 = columns[2:5]
print("c1 : ",c1)
print("c2 : ",c2)
print(c1.symmetric_difference(c2))
print(c1 ^ c2)
二.用copy()产生新的数据
A is B:表明二者指向的同一个对象。这意味着，如果修改一个，另一个也会去改变。
Demo1：
import pandas as pd
import numpy as np
employee = pd.read_csv('data/employee.csv', index_col='RACE')
salary1 = employee['BASE_SALARY']
salary2 = employee['BASE_SALARY']
print(salary1 is salary2)
salary1 = employee['BASE_SALARY'].copy()
salary2 = employee['BASE_SALARY'].copy()
print(salary1 is salary2)三.不等索引(索引的difference方法)
Demo1:
用difference，找到哪些索引标签在baseball_14中，却不在baseball_15、baseball_16中
import pandas as pd
import numpy as np
baseball_14 = pd.read_csv('data/baseball14.csv', index_col='playerID')
baseball_15 = pd.read_csv('data/baseball15.csv', index_col='playerID')
baseball_16 = pd.read_csv('data/baseball16.csv', index_col='playerID')
print(baseball_14.index.difference(baseball_15.index))
print(baseball_14.index.difference(baseball_16.index))
四.使用fill_value避免在算术运算时产生缺失值
Demo1:
import pandas as pd
import numpy as np
baseball_14 = pd.read_csv('data/baseball14.csv', index_col='playerID')
baseball_15 = pd.read_csv('data/baseball15.csv', index_col='playerID')
#H列:每名球员的击球数
hits_14 = baseball_14['H']
hits_15 = baseball_15['H']
print(hits_14.head())
print(hits_15.head())
print(hits_14.head() + hits_15.head())
下面四条数据是有记录的，但是因为不同时存在14，15两张表中，所以相加会产生NaN，需要用fill_value
Demo2:
import pandas as pd
import numpy as np
baseball_14 = pd.read_csv('data/baseball14.csv', index_col='playerID')
baseball_15 = pd.read_csv('data/baseball15.csv', index_col='playerID')
baseball_16 = pd.read_csv('data/baseball16.csv', index_col='playerID')
#H列:每名球员的击球数
hits_14 = baseball_14['H']
hits_15 = baseball_15['H']
hits_16 = baseball_16['H']
print(hits_14.head().add(hits_15.head(),fill_value=0))
*如果一个元素在两个Series都是缺失值，即便使用了fill_value，相加的结果也仍是缺失值
五.从不同的DataFrame追加列
Demo:
import pandas as pd
import numpy as np
employee = pd.read_csv('data/employee.csv')
d1 = employee[['DEPARTMENT', 'BASE_SALARY']]
print("排序前：")
print(d1.head())
# 在每个部门内，对BASE_SALARY进行排序
d2 = d1.sort_values(['DEPARTMENT', 'BASE_SALARY'],ascending = [True,False])
print("排序后：")
print(d2.head())
#用drop_duplicates方法保留每个部门的第一行
d3 = d2.drop_duplicates(subset = 'DEPARTMENT')
print('去重后：')
print(d3.head())
#使用DEPARTMENT作为行索引
d3 = d3.set_index('DEPARTMENT')
employee = employee.set_index('DEPARTMENT')
#向employee的DataFrame新增一列
#新增时，对应缺项的为缺失值
#存储每个Department的最高工资
employee['MAX_SALARY'] = d3['BASE_SALARY']
pd.options.display.max_columns = 3
print('合并后：')
print(employee.head())
#用query查看是否有BASE_SALARY大于MAX_DEPT_SALARY的
#输出应该为0
print('query结果：')
print(employee.query('BASE_SALARY > MAX_SALARY'))employee[‘MAX_SALARY’] = d3[‘BASE_SALARY’]
这行语句能执行成功的条件是：d3中不含有重复索引，即执行过drop_duplicates
运行结果：
“怎么使用Python中Pandas的索引对齐方法”的内容就介绍到这里了，感谢大家的阅读。如果想了解更多行业相关的知识可以关注创新互联网站，小编将为大家输出更多高质量的实用文章！
