《七周数据分析师》第七周：Python学习笔记

快来打我* 2022-05-30 10:15 203阅读 0赞

Python作用：

1.利用Python写脚本

2.excel可视化有性能瓶颈，需要Python来实现。

第七周所有环节：

*  Python的数据科学环境
 *  Python基础
 *  Numpy和Pandas
 *  数据可视化
 *  数据分析案例
 *  数据分析平台（轻量级BI）

3.Numpy和pandas

1.Python groupby

mysql不支持分组排序

![201803090923_277.png][]

2.concat和merge

concat是强行耦合

merge，是有共同名，优先表进行耦合

3.多重索引![201803090957_123.png][]

4.文本函数

![201803091003_849.png][]

![201803091019_469.png][]

填充空值，None需要用np.nan，c语言形式的控制

pd.dropna()去除所有还有空值的行

![201803091029_596.png][]

5.Python pandas apply

![201803091029_424.png][]

![201803091030_467.png][]

![201803091031_529.png][]

![201803091035_132.png][]

6.聚合 apply

![201803091043_840.png][]

7。pandas数据透视

![201803091047_322.png][]

![201803091050_375.png][]

输出结果![201803091054_637.png][]

7.python连接数据库

import pymysql
    import pandas as pd
    from sqlalchemy import create_engine
    
    conn=pymysql.connect(
        host='localhost',
        user='root',
        password='123456',
        db='data_kejilie',
        port=3306,
        charset='utf8'
    )
    
    def reader(query,db):
        sql=query
        engine=create_engine('mysql+pymysql://root:123456@localhost/{0}?charset=utf8').format(db))
        df=pd.read_sql(sql,engine)
        return df
    
    cur.execute('select * from article_link ')
    
    cur.close()
    conn.commit()

[201803090923_277.png]: http://p3qgob3dc.bkt.clouddn.com/201803090923_277.png
[201803090957_123.png]: http://p3qgob3dc.bkt.clouddn.com/201803090957_123.png
[201803091003_849.png]: http://p3qgob3dc.bkt.clouddn.com/201803091003_849.png
[201803091019_469.png]: http://p3qgob3dc.bkt.clouddn.com/201803091019_469.png
[201803091029_596.png]: http://p3qgob3dc.bkt.clouddn.com/201803091029_596.png
[201803091029_424.png]: http://p3qgob3dc.bkt.clouddn.com/201803091029_424.png
[201803091030_467.png]: http://p3qgob3dc.bkt.clouddn.com/201803091030_467.png
[201803091031_529.png]: http://p3qgob3dc.bkt.clouddn.com/201803091031_529.png
[201803091035_132.png]: http://p3qgob3dc.bkt.clouddn.com/201803091035_132.png
[201803091043_840.png]: http://p3qgob3dc.bkt.clouddn.com/201803091043_840.png
[201803091047_322.png]: http://p3qgob3dc.bkt.clouddn.com/201803091047_322.png
[201803091050_375.png]: http://p3qgob3dc.bkt.clouddn.com/201803091050_375.png
[201803091054_637.png]: http://p3qgob3dc.bkt.clouddn.com/201803091054_637.png