Pandas——练习题一

作业一：(使用jupyter notebook 工具)

Step 1. 导入相应的模块

import pandas as pd
import numpy as np
from pandas import Series,DataFrame

Step 2. 给定的原始数据集

 # Create an example dataframe about a fictional army
raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'],
            'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'],
            'deaths': [523, 52, 25, 616, 43, 234, 523, 62, 62, 73, 37, 35],
            'battles': [5, 42, 2, 2, 4, 7, 8, 3, 4, 7, 8, 9],
            'size': [1045, 957, 1099, 1400, 1592, 1006, 987, 849, 973, 1005, 1099, 1523],
            'veterans': [1, 5, 62, 26, 73, 37, 949, 48, 48, 435, 63, 345],
            'readiness': [1, 2, 3, 3, 2, 1, 2, 3, 2, 1, 2, 3],
            'armored': [1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1],
            'deserters': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],
            'origin': ['Arizona', 'California', 'Texas', 'Florida', 'Maine', 'Iowa', 'Alaska', 'Washington', 'Oregon', 'Wyoming', 'Louisana', 'Georgia']}

Step 3. 根据原始数据集创建一个DataFrame，并赋值给变量army

army = DataFrame(raw_data)

army

$~~~$	armored	battles	company	deaths	deserters	origin	readiness	regiment	size	veterans
0	1	5	1st	523	4	Arizona	1	Nighthawks	1045	1
1	0	42	1st	52	24	California	2	Nighthawks	957	5
2	1	2	2nd	25	31	Texas	3	Nighthawks	1099	62
3	1	2	2nd	616	2	Florida	3	Nighthawks	1400	26
4	0	4	1st	43	3	Maine	2	Dragoons	1592	73
5	1	7	1st	234	4	Iowa	1	Dragoons	1006	37
6	0	8	2nd	523	24	Alaska	2	Dragoons	987	949
7	1	3	2nd	62	31	Washington	3	Dragoons	849	48
8	0	4	1st	62	2	Oregon	2	Scouts	973	48
9	0	7	1st	73	3	Wyoming	1	Scouts	1005	435
10	1	8	2nd	37	2	Louisana	2	Scouts	1099	63
11	1	9	2nd	35	3	Georgia	3	Scouts	1523	345

Step 4. 设定指定列为索引：设定数据中的origin字段为索引

army1 = army.set_index(["origin"])

army1

$~~~~$	armored	battles	company	deaths	deserters	readiness	regiment	size	veterans
origin
Arizona	1	5	1st	523	4	1	Nighthawks	1045	1
California	0	42	1st	52	24	2	Nighthawks	957	5
Texas	1	2	2nd	25	31	3	Nighthawks	1099	62
Florida	1	2	2nd	616	2	3	Nighthawks	1400	26
Maine	0	4	1st	43	3	2	Dragoons	1592	73
Iowa	1	7	1st	234	4	1	Dragoons	1006	37
Alaska	0	8	2nd	523	24	2	Dragoons	987	949
Washington	1	3	2nd	62	31	3	Dragoons	849	48
Oregon	0	4	1st	62	2	2	Scouts	973	48
Wyoming	0	7	1st	73	3	1	Scouts	1005	435
Louisana	1	8	2nd	37	2	2	Scouts	1099	63
Georgia	1	9	2nd	35	3	3	Scouts	1523	345

Step 5. 打印列名为veterans的所有值

army1["veterans"]

origin
Arizona         1
California      5
Texas          62
Florida        26
Maine          73
Iowa           37
Alaska        949
Washington     48
Oregon         48
Wyoming       435
Louisana       63
Georgia       345
Name: veterans, dtype: int64

Step 6. 打印列名为 ‘veterans’ 和 ‘deaths’ 的所有数据

$~$	veterans	deaths
origin
Arizona	1	523
California	5	52
Texas	62	25
Florida	26	616
Maine	73	43
Iowa	37	234
Alaska	949	523
Washington	48	62
Oregon	48	62
Wyoming	435	73
Louisana	63	37
Georgia	345	35

Step 7. 打印出所有的列索引的值

army1.columns

Index(['armored', 'battles', 'company', 'deaths', 'deserters', 'readiness',
       'regiment', 'size', 'veterans'],
      dtype='object')

Step 8. 筛选出列 regiments 的值不为”Dragoons”的所有数据

army1.loc[army1["regiment"] != "Dragoons"]

$~$	armored	battles	company	deaths	deserters	readiness	regiment	size	veterans
origin
Arizona	1	5	1st	523	4	1	Nighthawks	1045	1
California	0	42	1st	52	24	2	Nighthawks	957	5
Texas	1	2	2nd	25	31	3	Nighthawks	1099	62
Florida	1	2	2nd	616	2	3	Nighthawks	1400	26
Oregon	0	4	1st	62	2	2	Scouts	973	48
Wyoming	0	7	1st	73	3	1	Scouts	1005	435
Louisana	1	8	2nd	37	2	2	Scouts	1099	63
Georgia	1	9	2nd	35	3	3	Scouts	1523	345

---

Step 9.筛选出 第 3 到 7 行，第 3 到 6 列的所有数据

army1.iloc[2:6,[2,6]]

$~$	company	regiment
origin
Texas	2nd	Nighthawks
Florida	2nd	Nighthawks
Maine	1st	Dragoons
Iowa	1st	Dragoons

---

作业二：

在校生饮酒消费数据分析

Step 1. 导入相关的模块

import pandas as pd
import numpy as np
from pandas import Series,DataFrame

---

Step 2. 导入数据，并赋值给变量df

df = pd.read_csv("./datasets/Student_Alcohol.csv")

df

$~$	school	sex	age	address	famsize	Pstatus	Medu	Fedu	Mjob	Fjob	…	absences	G1	G2	G3
0	GP	F	18	U	GT3	A	4	4	at_home	teacher	…	6	5	6	6
1	GP	F	17	U	GT3	T	1	1	at_home	other	…	4	5	5	6
2	GP	F	15	U	LE3	T	1	1	at_home	other	…	10	7	8	10
3	GP	F	15	U	GT3	T	4	2	health	services	…	2	15	14	15
4	GP	F	16	U	GT3	T	3	3	other	other	…	4	6	10	10
5	GP	M	16	U	LE3	T	4	3	services	other	…	10	15	15	15
…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…
391	MS	M	17	U	LE3	T	3	1	services	services	…	3	14	16	16
392	MS	M	21	R	GT3	T	1	1	other	other	…	3	10	8	7
393	MS	M	18	R	LE3	T	3	2	services	other	…	0	11	12	10
394	MS	M	19	U	LE3	T	1	1	other	at_home	…	5	8	9	9

395 rows × 33 columns

---

Step 3. 连续切片（获取[school:guardian]两列以及中间的所有数据）

df.iloc[:,0:12]    

$~$	school	sex	age	address	famsize	Pstatus	Medu	Fedu	Mjob	Fjob	reason	guardian
0	GP	F	18	U	GT3	A	4	4	at_home	teacher	course	mother
1	GP	F	17	U	GT3	T	1	1	at_home	other	course	father
2	GP	F	15	U	LE3	T	1	1	at_home	other	other	mother
3	GP	F	15	U	GT3	T	4	2	health	services	home	mother
4	GP	F	16	U	GT3	T	3	3	other	other	home	father
…	…	…	…	…	…	…	…	…	…	…	…	…
391	MS	M	17	U	LE3	T	3	1	services	services	course	mother
392	MS	M	21	R	GT3	T	1	1	other	other	course	other
393	MS	M	18	R	LE3	T	3	2	services	other	course	mother
394	MS	M	19	U	LE3	T	1	1	other	at_home	course	father

395 rows × 12 columns

---

Step 5. 将数据列 Mjob 和 Fjob中所有数据实现首字母大写

data2 = df.iloc[:,[8,9]]                         #获取 "Mjob"，"Fjob" 两列
data21 = Series(data2["Mjob"])                   #将两列转成Series格式
data22 = Series(data2["Fjob"])
df["Mjob"] =data21.map(lambda x:x.capitalize())  #将"Mjob"列所有值 首字母大写
df["Fjob"] =data22.map(lambda x:x.capitalize())  #将"Fjob"列所有值 首字母大写

df         #查看效果

$~$	school	sex	age	address	famsize	Pstatus	Medu	Fedu	Mjob	Fjob	…	absences	G1	G2	G3
0	GP	F	18	U	GT3	A	4	4	At_home	Teacher	…	6	5	6	6
1	GP	F	17	U	GT3	T	1	1	At_home	Other	…	4	5	5	6
2	GP	F	15	U	LE3	T	1	1	At_home	Other	…	10	7	8	10
3	GP	F	15	U	GT3	T	4	2	Health	Services	…	2	15	14	15
4	GP	F	16	U	GT3	T	3	3	Other	Other	…	4	6	10	10
5	GP	M	16	U	LE3	T	4	3	Services	Other	…	10	15	15	15
…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…
390	MS	M	20	U	LE3	A	2	2	Services	Services	…	11	9	9	9
391	MS	M	17	U	LE3	T	3	1	Services	Services	…	3	14	16	16
392	MS	M	21	R	GT3	T	1	1	Other	Other	…	3	10	8	7
393	MS	M	18	R	LE3	T	3	2	Services	Other	…	0	11	12	10
394	MS	M	19	U	LE3	T	1	1	Other	At_home	…	5	8	9	9

395 rows × 12 columns

---

Step 6.创建一个名为majority函数，并根据age列数据返回一个布尔值添加到新的数据列，列名为 legal_drinker (根据年龄这一列数据，大于17岁为合法饮酒)

majority = lambda x:["合法" if x>17 else "不合法"] 
df["legal_drinker"] = df["age"].map(majority)

df

$~$	school	sex	age	address	famsize	Pstatus	Medu	Fedu	Mjob	Fjob	…	G1	G2	G3	legal_drinker
0	GP	F	18	U	GT3	A	4	4	At_home	Teacher	…	5	6	6	[合法]
1	GP	F	17	U	GT3	T	1	1	At_home	Other	…	5	5	6	[不合法]
2	GP	F	15	U	LE3	T	1	1	At_home	Other	…	7	8	10	[不合法]
3	GP	F	15	U	GT3	T	4	2	Health	Services	…	15	14	15	[不合法]
4	GP	F	16	U	GT3	T	3	3	Other	Other	…	6	10	10	[不合法]
…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…
391	MS	M	17	U	LE3	T	3	1	Services	Services	…	14	16	16	[不合法]
392	MS	M	21	R	GT3	T	1	1	Other	Other	…	10	8	7	[合法]
393	MS	M	18	R	LE3	T	3	2	Services	Other	…	11	12	10	[合法]
394	MS	M	19	U	LE3	T	1	1	Other	At_home	…	8	9	9	[合法]

395 rows × 12 columns