pandas.read_sql#

pandas.read_sql(sql, con, index_col=None, coerce_float=True, params=None, parse_dates=None, columns=None, chunksize=None, dtype_backend=<no_default>, dtype=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:
sqlstr or SQLAlchemy Selectable (select or text object)

SQL query to be executed or a table name.

conADBC Connection, SQLAlchemy connectable, str, or sqlite3 connection

ADBC provides high performance I/O with native type support, where available. Using SQLAlchemy makes it possible to use any DB supported by that library. If a DBAPI2 object, only sqlite3 is supported. The user is responsible for engine disposal and connection closure for the ADBC connection and SQLAlchemy connectable; str connections are closed automatically. See here.

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.

columnslist, default: None

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

chunksizeint, default None

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

dtype_backend{‘numpy_nullable’, ‘pyarrow’}

Back-end data type applied to the resultant DataFrame (still experimental). If not specified, the default behavior is to not use nullable data types. If specified, the behavior is as follows:

Added in version 2.0.

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’}. The argument is ignored if a table is passed instead of a query.

Added in version 2.0.0.

Returns:
DataFrame or Iterator[DataFrame]

Returns a DataFrame object that contains the result set of the executed SQL query or an SQL Table based on the provided input, in relation to the specified database connection.

See also

read_sql_table

Read SQL database table into a DataFrame.

read_sql_query

Read SQL query into a DataFrame.

Notes

pandas does not attempt to sanitize SQL statements; instead it simply forwards the statement you are executing to the underlying driver, which may or may not sanitize from there. Please refer to the underlying driver documentation for any details. Generally, be wary when accepting statements from arbitrary sources.

Examples

Read data from SQL via either a SQL query or a SQL tablename. When using a SQLite database only SQL queries are accepted, providing only the SQL tablename will result in an error.

>>> from sqlite3 import connect
>>> conn = connect(":memory:")
>>> df = pd.DataFrame(
...     data=[[0, "10/11/12"], [1, "12/11/10"]],
...     columns=["int_column", "date_column"],
... )
>>> df.to_sql(name="test_data", con=conn)
2

>>> pd.read_sql("SELECT int_column, date_column FROM test_data", conn)
   int_column date_column
0           0    10/11/12
1           1    12/11/10

>>> pd.read_sql("test_data", "postgres:///db_name")

For parameterized query, using params is recommended over string interpolation.

>>> from sqlalchemy import text
>>> sql = text(
...     "SELECT int_column, date_column FROM test_data WHERE int_column=:int_val"
... )
>>> pd.read_sql(sql, conn, params={"int_val": 1})
   int_column date_column
0           1    12/11/10

Apply date parsing to columns through the parse_dates argument The parse_dates argument calls pd.to_datetime on the provided columns. Custom argument values for applying pd.to_datetime on a column are specified via a dictionary format:

>>> pd.read_sql(
...     "SELECT int_column, date_column FROM test_data",
...     conn,
...     parse_dates={"date_column": {"format": "%d/%m/%y"}},
... )
   int_column date_column
0           0  2012-11-10
1           1  2010-11-12

Added in version 2.2.0: pandas now supports reading via ADBC drivers

>>> from adbc_driver_postgresql import dbapi
>>> with dbapi.connect("postgres:///db_name") as conn:
...     pd.read_sql("SELECT int_column FROM test_data", conn)
   int_column
0           0
1           1
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