6. 函数填充，计算列

书的编号、书的名字、标价、折扣、最终价钱

最终价钱Price=ListPrice * Discount = 标价 * 折扣

6.1 excel操作

6.2 pandas操作1

import pandas as pd

books = pd.read_excel('C:/Temp/Books.xlsx', index_col='ID')
print(books)

填充Price列，在excel中我们操作的是单元格，而pandas中我们操作的是列

• 操作符的重载：比如下面的 *（乘号操作符），当它左右两边是两列时，它就把两列前后对其，一个单元格乘以一个单元格的乘起来。

import pandas as pd

books = pd.read_excel('C:/Temp/Books.xlsx', index_col='ID')
books['Price'] = books['ListPrice'] * books['Discount']
print(books)

乘以 一个数也是可以的：

import pandas as pd

books = pd.read_excel('C:/Temp/Books.xlsx', index_col='ID')
books['Price'] = books['ListPrice'] * 0.8
print(books)

用循环来迭代DataFrame：(有点类似excel的单元格对单元格操作）

import pandas as pd

books = pd.read_excel('C:/Temp/Books.xlsx', index_col='ID')
for i in books.index:
	books['Price'].at[i] = books['ListPrice'].at[i] * books['Discount'].at[i]
print(books)

运算的时候，不想从头到尾运算，而是从其中的某一段开始运算

import pandas as pd

books = pd.read_excel('C:/Temp/Books.xlsx', index_col='ID')
for i in range(5, 16):
	books['Price'].at[i] = books['ListPrice'].at[i] * books['Discount'].at[i]
print(books)

6.3 pandas操作2

现在，每本书要涨价2元，

import pandas as pd

books = pd.read_excel('C:/Temp/Books.xlsx', index_col='ID')
books['ListPrice'] = books['ListPrice'] + 2
print(books)

调用series的apply()函数来实现上面的功能：

import pandas as pd

def add_2(x):
	return x + 2
books = pd.read_excel('C:/Temp/Books.xlsx', index_col='ID')
books['ListPrice'] = books['ListPrice'].apply(add_2)
print(books)

将得到上图同样的结果。
进一步简化代码：

import pandas as pd

books = pd.read_excel('C:/Temp/Books.xlsx', index_col='ID')
books['ListPrice'] = books['ListPrice'].apply(lambda x: x + 2)
print(books)

8. 数据筛选、过滤，[绘图前的必备功课]

8.1 excel操作

筛选，18<=年龄<=30的学生的分数状况，且分数>80的学生

全部选中，然后筛选即可。

8.2 Python操作

读取的时候，将 ‘ID’ 作为 index ，

import pandas as pd

students = pd.read_excel('C:/Temp/Students.xlsx', index_col='ID')

筛选数据：用函数的形式来表达条件
pd.series有apply()方法，

import pandas as pd

students = pd.read_excel('C:/Temp/Students.xlsx', index_col='ID')
students = students.loc[students['Age'].apply[age_18_to_30]]
print(students)

import pandas as pd

def age_18_to_30(a):
	return 18 <= a <30

def level_a(s):
	return 85 <=s <=100

students = pd.read_excel('C:/Temp/Students.xlsx', index_col='ID')
students = students.loc[students['Age'].apply[age_18_to_30]].loc[students['Score'].apply(level_a)]
print(students)

另一种写法：

students = students.loc[students.Age.apply[age_18_to_30]].loc[students.Score.apply(level_a)]

进一步优化代码：

students = students.loc[students.Age.apply[lambda a: 18<=a<30]].loc[students.Score.apply(lambda s: 85<=s<=100)]

代码太长，可以打一个 空格+ ’ \ '，然后回车即可

students = students.loc[students.Age.apply[lambda a: 18<=a<30]] \
.loc[students.Score.apply(lambda s: 85<=s<=100)]