pandas练习（四）--- 应用Apply函数

探索学生对酒的消费情况

数据见github

步骤1 - 导入必要的库

import pandas as pd
import numpy as np

步骤2 - 数据集

path4 = "./data/student-mat.csv"   

步骤3 将数据命名为student

student = pd.read_csv(path4)
student.head()

输出：

步骤4 从'school'到'guardian'将数据切片

stud_alcoh = student.loc[: , "school":"guardian"]
stud_alcoh.head()

输出：

步骤5 创建一个捕获字符串的lambda函数

captalizer = lambda x: x.upper()

步骤6 使'Fjob'列都大写

stud_alcoh['Fjob'].apply(captalizer)

输出：

0       TEACHER
1         OTHER
2         OTHER
3      SERVICES
4         OTHER
5         OTHER
6         OTHER
7       TEACHER
8         OTHER
9         OTHER
10       HEALTH
11        OTHER
12     SERVICES
13        OTHER
14        OTHER
15        OTHER
16     SERVICES
17        OTHER
18     SERVICES
19        OTHER
20        OTHER
21       HEALTH
22        OTHER
23        OTHER
24       HEALTH
25     SERVICES
26        OTHER
27     SERVICES
28        OTHER
29      TEACHER
         ...
365       OTHER
366    SERVICES
367    SERVICES
368    SERVICES
369     TEACHER
370    SERVICES
371    SERVICES
372     AT_HOME
373       OTHER
374       OTHER
375       OTHER
376       OTHER
377    SERVICES
378       OTHER
379       OTHER
380     TEACHER
381       OTHER
382    SERVICES
383    SERVICES
384       OTHER
385       OTHER
386     AT_HOME
387       OTHER
388    SERVICES
389       OTHER
390    SERVICES
391    SERVICES
392       OTHER
393       OTHER
394     AT_HOME
Name: Fjob, dtype: object

步骤7 打印数据集的最后几行元素

stud_alcoh.tail()

输出：

步骤8 注意到原始数据框仍然是小写字母，接下来改进一下

stud_alcoh['Mjob'] = stud_alcoh['Mjob'].apply(captalizer)
stud_alcoh['Fjob'] = stud_alcoh['Fjob'].apply(captalizer)
stud_alcoh.tail()

输出：

步骤9 创建一个名为majority的函数，它返回一个布尔值到一个名为legal_drinker的新列（多数年龄大于17岁）

def majority(x):
    if x > 17:
        return True
    else:
        return False

stud_alcoh['legal_drinker'] = stud_alcoh['age'].apply(majority)
stud_alcoh.head()

输出：

步骤10 将数据集的每个数字乘以10

def times10(x):
    if type(x) is int:
        return 10 * x
    return x

stud_alcoh.applymap(times10).head(10)

输出：

参考链接：

1、http://pandas.pydata.org/pandas-docs/stable/cookbook.html#cookbook

2、https://www.analyticsvidhya.com/blog/2016/01/12-pandas-techniques-python-data-manipulation/

3、https://github.com/guipsamora/pandas_exercises