pandas.read_sql_query#

pandas.read_sql_query(sql, con, index_col=None, coerce_float=True, params=None, parse_dates=None, chunksize=None, dtype=None, dtype_backend=_NoDefault.no_default)[source]#

Read SQL query into a DataFrame.

Returns a DataFrame corresponding to the result set of the query string. Optionally provide an index_col parameter to use one of the columns as the index, otherwise default integer index will be used.

Parameters

sqlstr SQL query or SQLAlchemy Selectable (select or text object)

SQL query to be executed.

conSQLAlchemy connectable, str, or sqlite3 connection

Using SQLAlchemy makes it possible to use any DB supported by that library. If a DBAPI2 object, only sqlite3 is supported.

index_colstr or list of str, optional, default: None

Column(s) to set as index(MultiIndex).

coerce_floatbool, default True

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

paramslist, 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_dateslist 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.

chunksizeint, default None

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

dtypeType name or dict of columns

Data type for data or columns. E.g. np.float64 or {‘a’: np.float64, ‘b’: np.int32, ‘c’: ‘Int64’}.

New in version 1.3.0.

dtype_backend{“numpy_nullable”, “pyarrow”}, defaults to NumPy backed DataFrames

Which dtype_backend to use, e.g. whether a DataFrame should have NumPy arrays, nullable dtypes are used for all dtypes that have a nullable implementation when “numpy_nullable” is set, pyarrow is used for all dtypes if “pyarrow” is set.

The dtype_backends are still experimential.

New in version 2.0.

Returns

DataFrame or Iterator[DataFrame]