数据分析——从入门到精通_python

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

加载文本数据

pd.read_csv()
pd.read_table() # deprecated在当前版本过时
pd.read_json()

pd.read_csv('data/adults.txt')

	age	workclass	final_weight	education	education_num	marital_status	occupation	relationship	race	sex	capital_gain	capital_loss	hours_per_week	native_country	salary
0	39	State-gov	77516	Bachelors	13	Never-married	Adm-clerical	Not-in-family	White	Male	2174	0	40	United-States	<=50K
1	50	Self-emp-not-inc	83311	Bachelors	13	Married-civ-spouse	Exec-managerial	Husband	White	Male	0	0	13	United-States	<=50K
2	38	Private	215646	HS-grad	9	Divorced	Handlers-cleaners	Not-in-family	White	Male	0	0	40	United-States	<=50K
3	53	Private	234721	11th	7	Married-civ-spouse	Handlers-cleaners	Husband	Black	Male	0	0	40	United-States	<=50K
4	28	Private	338409	Bachelors	13	Married-civ-spouse	Prof-specialty	Wife	Black	Female	0	0	40	Cuba	<=50K
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
32556	27	Private	257302	Assoc-acdm	12	Married-civ-spouse	Tech-support	Wife	White	Female	0	0	38	United-States	<=50K
32557	40	Private	154374	HS-grad	9	Married-civ-spouse	Machine-op-inspct	Husband	White	Male	0	0	40	United-States	>50K
32558	58	Private	151910	HS-grad	9	Widowed	Adm-clerical	Unmarried	White	Female	0	0	40	United-States	<=50K
32559	22	Private	201490	HS-grad	9	Never-married	Adm-clerical	Own-child	White	Male	0	0	20	United-States	<=50K
32560	52	Self-emp-inc	287927	HS-grad	9	Married-civ-spouse	Exec-managerial	Wife	White	Female	15024	0	40	United-States	>50K

32561 rows × 15 columns

pd.read_csv('data/seeds.tsv')

	15.26\t14.84\t0.871\t5.763\t3.312\t2.221\t5.22\tKama
0	14.88\t14.57\t0.8811\t5.554\t3.333\t1.018\t4.9...
1	14.29\t14.09\t0.905\t5.291\t3.337\t2.699\t4.82...
2	13.84\t13.94\t0.8955\t5.324\t3.379\t2.259\t4.8...
3	16.14\t14.99\t0.9034\t5.658\t3.562\t1.355\t5.1...
4	14.38\t14.21\t0.8951\t5.386\t3.312\t2.462\t4.9...
...	...
204	12.19\t13.2\t0.8783\t5.137\t2.981\t3.631\t4.87...
205	11.23\t12.88\t0.8511\t5.14\t2.795\t4.325\t5.00...
206	13.2\t13.66\t0.8883\t5.236\t3.232\t8.315\t5.05...
207	11.84\t13.21\t0.8521\t5.175\t2.836\t3.598\t5.0...
208	12.3\t13.34\t0.8684\t5.243\t2.974\t5.637\t5.06...

209 rows × 1 columns

pd.read_csv('data/seeds.tsv',sep='\t',header=None)

	0	1	2	3	4	5	6	7
0	15.26	14.84	0.8710	5.763	3.312	2.221	5.220	Kama
1	14.88	14.57	0.8811	5.554	3.333	1.018	4.956	Kama
2	14.29	14.09	0.9050	5.291	3.337	2.699	4.825	Kama
3	13.84	13.94	0.8955	5.324	3.379	2.259	4.805	Kama
4	16.14	14.99	0.9034	5.658	3.562	1.355	5.175	Kama
...	...	...	...	...	...	...	...	...
205	12.19	13.20	0.8783	5.137	2.981	3.631	4.870	Canadian
206	11.23	12.88	0.8511	5.140	2.795	4.325	5.003	Canadian
207	13.20	13.66	0.8883	5.236	3.232	8.315	5.056	Canadian
208	11.84	13.21	0.8521	5.175	2.836	3.598	5.044	Canadian
209	12.30	13.34	0.8684	5.243	2.974	5.637	5.063	Canadian

210 rows × 8 columns

pd.read_table('data/seeds.tsv',header=None)

	0	1	2	3	4	5	6	7
0	15.26	14.84	0.8710	5.763	3.312	2.221	5.220	Kama
1	14.88	14.57	0.8811	5.554	3.333	1.018	4.956	Kama
2	14.29	14.09	0.9050	5.291	3.337	2.699	4.825	Kama
3	13.84	13.94	0.8955	5.324	3.379	2.259	4.805	Kama
4	16.14	14.99	0.9034	5.658	3.562	1.355	5.175	Kama
...	...	...	...	...	...	...	...	...
205	12.19	13.20	0.8783	5.137	2.981	3.631	4.870	Canadian
206	11.23	12.88	0.8511	5.140	2.795	4.325	5.003	Canadian
207	13.20	13.66	0.8883	5.236	3.232	8.315	5.056	Canadian
208	11.84	13.21	0.8521	5.175	2.836	3.598	5.044	Canadian
209	12.30	13.34	0.8684	5.243	2.974	5.637	5.063	Canadian

210 rows × 8 columns

pd.read_csv('data/usa_election.txt',low_memory=False)

---------------------------------------------------------------------------

NameError                                 Traceback (most recent call last)

 in 
----> 1 pd.read_csv('data/usa_election.txt',low_memory=False)


NameError: name 'pd' is not defined

pd.read_csv('data/seeds.tsv',sep='\t',header=None).to_json('data/seeds.json')

# 此时，在data文件夹里就会多出来一个seeds.json文件
pd.read_json('data/seeds.json')

	0	1	2	3	4	5	6	7
0	15.26	14.84	0.8710	5.763	3.312	2.221	5.220	Kama
1	14.88	14.57	0.8811	5.554	3.333	1.018	4.956	Kama
2	14.29	14.09	0.9050	5.291	3.337	2.699	4.825	Kama
3	13.84	13.94	0.8955	5.324	3.379	2.259	4.805	Kama
4	16.14	14.99	0.9034	5.658	3.562	1.355	5.175	Kama
...	...	...	...	...	...	...	...	...
205	12.19	13.20	0.8783	5.137	2.981	3.631	4.870	Canadian
206	11.23	12.88	0.8511	5.140	2.795	4.325	5.003	Canadian
207	13.20	13.66	0.8883	5.236	3.232	8.315	5.056	Canadian
208	11.84	13.21	0.8521	5.175	2.836	3.598	5.044	Canadian
209	12.30	13.34	0.8684	5.243	2.974	5.637	5.063	Canadian

210 rows × 8 columns

# 重新调整索引编号
pd.read_json('data/seeds.json').reset_index()

	index	0	1	2	3	4	5	6	7
0	0	15.26	14.84	0.8710	5.763	3.312	2.221	5.220	Kama
1	1	14.88	14.57	0.8811	5.554	3.333	1.018	4.956	Kama
2	2	14.29	14.09	0.9050	5.291	3.337	2.699	4.825	Kama
3	3	13.84	13.94	0.8955	5.324	3.379	2.259	4.805	Kama
4	4	16.14	14.99	0.9034	5.658	3.562	1.355	5.175	Kama
...	...	...	...	...	...	...	...	...	...
205	205	12.19	13.20	0.8783	5.137	2.981	3.631	4.870	Canadian
206	206	11.23	12.88	0.8511	5.140	2.795	4.325	5.003	Canadian
207	207	13.20	13.66	0.8883	5.236	3.232	8.315	5.056	Canadian
208	208	11.84	13.21	0.8521	5.175	2.836	3.598	5.044	Canadian
209	209	12.30	13.34	0.8684	5.243	2.974	5.637	5.063	Canadian

210 rows × 9 columns

pd.read_json('data/seeds.json').reset_index().sort_values('index').set_index('index')

	0	1	2	3	4	5	6	7
index
0	15.26	14.84	0.8710	5.763	3.312	2.221	5.220	Kama
1	14.88	14.57	0.8811	5.554	3.333	1.018	4.956	Kama
2	14.29	14.09	0.9050	5.291	3.337	2.699	4.825	Kama
3	13.84	13.94	0.8955	5.324	3.379	2.259	4.805	Kama
4	16.14	14.99	0.9034	5.658	3.562	1.355	5.175	Kama
...	...	...	...	...	...	...	...	...
205	12.19	13.20	0.8783	5.137	2.981	3.631	4.870	Canadian
206	11.23	12.88	0.8511	5.140	2.795	4.325	5.003	Canadian
207	13.20	13.66	0.8883	5.236	3.232	8.315	5.056	Canadian
208	11.84	13.21	0.8521	5.175	2.836	3.598	5.044	Canadian
209	12.30	13.34	0.8684	5.243	2.974	5.637	5.063	Canadian

210 rows × 8 columns

加载excel的数据

pd.read_excel()

pd.read_excel('data/students.xlsx')

	id	name	age	sex	height(cm)	weight(kg)
0	1	吴果	20	男	165	50
1	2	李平	25	女	158	45
2	3	王和	21	男	179	75
3	4	刘言	27	男	180	80
4	5	王平	20	女	164	82
5	6	宁国柱	19	男	178	85
6	7	刘佳敏	25	女	183	90
7	8	张先	30	女	165	55
8	9	刘四七	27	女	170	61
9	10	关小弟	29	男	190	94

# 把id作为索引值
pd.read_excel('data/students.xlsx',index_col='id')

	name	age	sex	height(cm)	weight(kg)
id
1	吴果	20	男	165	50
2	李平	25	女	158	45
3	王和	21	男	179	75
4	刘言	27	男	180	80
5	王平	20	女	164	82
6	宁国柱	19	男	178	85
7	刘佳敏	25	女	183	90
8	张先	30	女	165	55
9	刘四七	27	女	170	61
10	关小弟	29	男	190	94

加载网络数据

pd.read_csv()/table()/json()都可以加载网络资源，主要看数据的结构

在data文件夹下，新建data_IO文件夹，添加数据

按windows+R,输入cmd,回车，执行下面代码：

启动后，打开页面，可以看到，数据可以加载：

然后在jupyter notebook里面继续 *** 作：

df = pd.read_csv('http://127.0.0.1:8000/testSet.txt')

df

	-0.017612\t14.053064\t0
0	-1.395634\t4.662541\t1
1	-0.752157\t6.538620\t0
2	-1.322371\t7.152853\t0
3	0.423363\t11.054677\t0
4	0.406704\t7.067335\t1
...	...
94	0.677983\t2.556666\t1
95	0.761349\t10.693862\t0
96	-2.168791\t0.143632\t1
97	1.388610\t9.341997\t0
98	0.317029\t14.739025\t0

99 rows × 1 columns

数据库加载

pd.read_sql()

加载sqlite3数据库

import sqlite3

# 新建数据文件  放置在data里，命名为persin.db
# 如果文件不存在，会自动创建
conn = sqlite3.connect('data/person.db')

# 如何给db文件写数据
cursor = conn.cursor()     # 打开一个游标，游标主要是与数据库进行交互的
# 创建一张表
# sqlite3 微型数据库，不需要去声明字段
cursor.execute('create table tb_person(id integer primary key,name,age,sex,phone)')

help(cursor.execute)

Help on built-in function execute:

execute(...) method of sqlite3.Cursor instance
    Executes a SQL statement.

# pymysql/mysqlclitent的包中，cursor的cursor.execute(sql,args=()|{})  argsd两种类型，一个是元组，一个是字典，args常用元组的方式实现
# 但是sqlite3里面就没有这个参数，所以执行语句的时候应该怎么做呢？

# sqlite3插入数据
cursor.execute("insert into tb_person(name,age,sex,phone) values ('Jack',18,'男','12123423434')")

# 此时数据库就有数据了，且只有一条数据
# 再插入一条
cursor.execute("insert into tb_person(name,age,sex,phone) values ('Blus',18,'女','12123423544')")

# 查询数据，并全部显示
cursor.execute('select * from tb_person')
list(cursor.fetchall())

[(1, 'Jack', 18, '男', '12123423434'), (2, 'Blus', 18, '女', '12123423544')]

# 关闭连接
conn.close()

# 再次连接
conn =sqlite3.connect('data/person.db')

# 读取
pd.read_sql('select * from tb_person',conn)
# 此时没有数据：原因：刚才关闭了连接，所以没有提交事务

	id	name	age	sex	phone

# 重新打开游标，执行sql
cursor = conn.cursor()
# 插入sql
cursor.execute("insert into tb_person(name,age,sex,phone) values ('Jack',18,'男','12123423434')")
cursor.execute("insert into tb_person(name,age,sex,phone) values ('Blus',18,'女','12123423544')")
cursor.execute("insert into tb_person(name,age,sex,phone) values ('Blua',18,'女','12123423544')")
cursor.execute("insert into tb_person(name,age,sex,phone) values ('Blux',18,'女','12123423544')")
# 提交事务
conn.commit()

# 读取
pd.read_sql('select * from tb_person',conn)

	id	name	age	sex	phone
0	1	Jack	18	男	12123423434
1	2	Blus	18	女	12123423544
2	3	Blua	18	女	12123423544
3	4	Blux	18	女	12123423544

# 再次插入sql
cursor.execute("insert into tb_person(name,age,sex,phone) values ('Blan',22,'女','2123423784')")
cursor.execute("insert into tb_person(name,age,sex,phone) values ('Blro',21,'女','2125453784')")
# 提交事务
conn.commit()

# 读取
pd.read_sql('select * from tb_person',conn)

	id	name	age	sex	phone
0	1	Jack	18	男	12123423434
1	2	Blus	18	女	12123423544
2	3	Blua	18	女	12123423544
3	4	Blux	18	女	12123423544
4	5	Blan	22	女	2123423784
5	6	Blro	21	女	2125453784

# SQLite的cursor.execute不能传参，无法写入参数

pd.read_sql("select * from tb_person where phone like '12%'",conn)

	id	name	age	sex	phone
0	1	Jack	18	男	12123423434
1	2	Blus	18	女	12123423544
2	3	Blua	18	女	12123423544
3	4	Blux	18	女	12123423544

df

	-0.017612\t14.053064\t0
0	-1.395634\t4.662541\t1
1	-0.752157\t6.538620\t0
2	-1.322371\t7.152853\t0
3	0.423363\t11.054677\t0
4	0.406704\t7.067335\t1
...	...
94	0.677983\t2.556666\t1
95	0.761349\t10.693862\t0
96	-2.168791\t0.143632\t1
97	1.388610\t9.341997\t0
98	0.317029\t14.739025\t0

99 rows × 1 columns

# 将df数据写入到数据库中，命名为test_set
df.to_sql('test_set',conn)

# 读取
pd.read_sql('select * from test_set',conn)

	index	-0.017612\t14.053064\t0
0	0	-1.395634\t4.662541\t1
1	1	-0.752157\t6.538620\t0
2	2	-1.322371\t7.152853\t0
3	3	0.423363\t11.054677\t0
4	4	0.406704\t7.067335\t1
...	...	...
94	94	0.677983\t2.556666\t1
95	95	0.761349\t10.693862\t0
96	96	-2.168791\t0.143632\t1
97	97	1.388610\t9.341997\t0
98	98	0.317029\t14.739025\t0

99 rows × 2 columns

# 读取时指定索引列
pd.read_sql('select * from test_set',conn,index_col='index')

	-0.017612\t14.053064\t0
index
0	-1.395634\t4.662541\t1
1	-0.752157\t6.538620\t0
2	-1.322371\t7.152853\t0
3	0.423363\t11.054677\t0
4	0.406704\t7.067335\t1
...	...
94	0.677983\t2.556666\t1
95	0.761349\t10.693862\t0
96	-2.168791\t0.143632\t1
97	1.388610\t9.341997\t0
98	0.317029\t14.739025\t0

99 rows × 1 columns

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原文地址: http://outofmemory.cn/langs/579744.html

数据分析——从入门到精通

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