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Module « pandas »

Fonction read_sql - module pandas

Signature de la fonction read_sql

def read_sql(sql, con, index_col: 'str | list[str] | None' = None, coerce_float: 'bool' = True, params=None, parse_dates=None, columns: 'list[str] | None' = None, chunksize: 'int | None' = None, dtype_backend: 'DtypeBackend | lib.NoDefault' = <no_default>, dtype: 'DtypeArg | None' = None) -> 'DataFrame | Iterator[DataFrame]' 

Description

help(pandas.read_sql)

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
----------
sql : str or SQLAlchemy Selectable (select or text object)
    SQL query to be executed or a table name.
con : ADBC 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 <https://docs.sqlalchemy.org/en/20/core/connections.html>`_.
index_col : str or list of str, optional, default: None
    Column(s) to set as index(MultiIndex).
coerce_float : bool, 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 :func:`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.
dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
    Back-end data type applied to the resultant :class:`DataFrame`
    (still experimental). Behaviour is as follows:

    * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
      (default).
    * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
      DataFrame.

    .. versionadded:: 2.0
dtype : Type 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.

    .. versionadded:: 2.0.0

Returns
-------
DataFrame or Iterator[DataFrame]

See Also
--------
read_sql_table : Read SQL database table into a DataFrame.
read_sql_query : Read SQL query into a DataFrame.

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')  # doctest:+SKIP

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

.. versionadded:: 2.2.0

   pandas now supports reading via ADBC drivers

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

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