Pandas数据导入和导出：CSV、Excel、MySQL、JSON

MySQL查询导入

import pandas

con = "mysql+pymysql://user:pass@127.0.0.1/test"
sql = "SELECT * FROM `student` WHERE id = 2"

# sql查询
df1 = pandas.read_sql(sql=sql, con=con)
print(df1)

导入mysql整张表

# 整张表
df2 = pandas.read_sql_table(table_name="student", con=con)
print(df2)

遍历数据

# 按列遍历
for key, value in df2.items():
    print("Key:", key)  # 列名
    print("Value:", value)  # 列数据

# 按行遍历
for row in df2.itertuples():
    print(row)  # 命名元组类型
    print(row[0], row[1], row[2])
    print(row.age)

导出到CSV

# 导出到csv
df2.to_csv(path_or_buf="sql_table.csv", columns=['id', 'name'])

导出到Excel

# 导出到Excel
df2.to_excel(excel_writer="1.xlsx", sheet_name='Sheet_name_1')

导出到Mysql另一张表

# 导入Mysql另一张表
from sqlalchemy import create_engine

engine = create_engine(con, echo=False)
df2.to_sql(name="student1", con=engine, if_exists="append", index=False)

CSV导入：read_csv

import pandas  as pd
df = pd.read_csv('myc1.csv')
df

import pandas  as pd
df = pd.read_csv('myc1.csv',header=1)
df


import pandas  as pd
df = pd.read_csv('myc1.csv',header=3,names=['a','b','c','d','e'])
df

import pandas  as pd
df = pd.read_csv('myc1.csv',index_col='单价')
df

import pandas  as pd
df = pd.read_csv('myc1.csv',usecols=['水果名','单价'])
df

导入Excel

import pandas

df = pandas.read_excel("1.xlsx", index_col=0, header=0)
print(df)

参考

https://pandas.pydata.org/docs/reference/io.html