pandas.read_sql

pandas.read_sql(sql, con, index_col=None, coerce_float=True, params=None, parse_dates=None, columns=None, chunksize=None)[source]

Read SQL query or database table into a DataFrame.

Parameters:

sql : string SQL query or SQLAlchemy Selectable (select or text object)

to be executed, or database table name.

con : SQLAlchemy connectable(engine/connection) or database string URI

or DBAPI2 connection (fallback mode) Using SQLAlchemy makes it possible to use any DB supported by that library. If a DBAPI2 object, only sqlite3 is supported.

index_col : string or list of strings, optional, default: None

Column(s) to set as index(MultiIndex)

coerce_float : boolean, default True

Attempt to convert values to non-string, non-numeric objects (like decimal.Decimal) to floating point, useful for SQL result sets

params : list, tuple or dict, optional, default: None

List of parameters to pass to execute method. The syntax used to pass parameters is database driver dependent. Check your database driver documentation for which of the five syntax styles, described in PEP 249’s paramstyle, is supported. Eg. for psycopg2, uses %(name)s so use params={‘name’ : ‘value’}

parse_dates : list or dict, default: None

List of column names to parse as dates

Dict of {column_name: format string} where format string is strftime compatible in case of parsing string times or is one of (D, s, ns, ms, us) in case of parsing integer timestamps

Dict of {column_name: arg dict}, where the arg dict corresponds to the keyword arguments of pandas.to_datetime() Especially useful with databases without native Datetime support, such as SQLite

columns : list, default: None

List of column names to select from sql table (only used when reading a table).

chunksize : int, default None

If specified, return an iterator where chunksize is the number of rows to include in each chunk.

Returns:

DataFrame