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I'm trying to write a Python Pandas Dataframe to a MySQL database. I realize that it's possible to use sqlalchemy for this, but I'm wondering if there is another way that may be easier, preferably already built into Pandas. I've spent quite some time trying to do it with a For loop, but it's not realiable.
If anyone knows of a better way, it would be greatly appreciated.
Thanks a lot!
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Pandasql is a python library that allows manipulation of a Pandas Dataframe using SQL. Under the hood, Pandasql creates an SQLite table from the Pandas Dataframe of interest and allow users to query from the SQLite table using SQL.
Python needs a MySQL driver to access the MySQL database. In this tutorial we will use the driver "MySQL Connector". We recommend that you use PIP to install "MySQL Connector". PIP is most likely already installed in your Python environment.
The other option to sqlalchemy can be used to_sql but in future released will be deprecated but now in version pandas 0.18.1 documentation is still active.
According to pandas documentation pandas.DataFrame.to_sql you can use following syntax:
DataFrame.to_sql(name, con, flavor='sqlite', schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None)
you specify the con type/mode and flavor ‘mysql’, here is some description:
con : SQLAlchemy engine or DBAPI2 connection (legacy mode) Using SQLAlchemy makes it possible to use any DB supported by that library. If a DBAPI2 object, only sqlite3 is supported.
flavor : {‘sqlite’, ‘mysql’}, default ‘sqlite’ The flavor of SQL to use. Ignored when using SQLAlchemy engine. ‘mysql’ is deprecated and will be removed in future versions, but it will be further supported through SQLAlchemy engines.
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