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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.

This function is a convenience wrapper around read_sql_table and read_sql_query (for backward compatibility). It will delegate to the specific function depending on the provided input. A SQL query will be routed to read_sql_query, while a database table name will be routed to read_sql_table. Note that the delegated function might have more specific notes about their functionality not listed here.

Parameters:

Parameters:	`sql : string or SQLAlchemy Selectable (select or text object)` SQL query to be executed or a 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` Attempts to convert values of 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

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

SQL query to be executed or a 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

Attempts to convert values of 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