Big data Analysis based on python-pandas data Storage (Code practice)

In the last part, we learned how to read data from pandas. This time, let's take a look at how to store the data, and the code will be rolled up.

Csv file

Format: to_csv (file path, sep='', index=TRUE, header=TRUE)

Index defaults to true with a line serial number

Header defaults to true with column name

From pandas import DataFrame

From pandas import Series

# create data

Df=DataFrame ({'age':Series ([265.85]),' name':Series (['xiaoqiang1','xiaoqiang2'])})

Df

# deposit in

Df.to_csv ('d:\ 1.csv')

Excel file

Format: to_excel (file path, index=TRUE, header=TRUE)

Explain the same as above, not nonsense

From pandas import DataFrame

From pandas import Series

# create data

Df=DataFrame ({'age':Series ([265.85]),' name':Series (['xiaoqiang1','xiaoqiang2'])})

Df

# deposit in

Df.to_excel ('d:\ 1.xlsx')

Mysql

Format: to_sql (name= table name, con= database link object)

From pandas import DataFrame

From pandas import Series

From sqlalchemy import create_engine

Engine=create_engine ('mysql+pymysql:// fill in user name: fill in password @ fill in ip address: 3306 / fill in database name? charset=utf8')

# create data

Df=DataFrame ({'age':Series ([265.85]),' name':Series (['xiaoqiang1','xiaoqiang2'])})

Df.to_sql (name= table name, con=engine, if_exists='append', index=False, index_label=False)