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Module « pandas »

Fonction read_sql_query - module pandas

Signature de la fonction read_sql_query

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

Description

help(pandas.read_sql_query)

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
----------
sql : str SQL query or SQLAlchemy Selectable (select or text object)
    SQL query to be executed.
con : SQLAlchemy 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_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 mapping, 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.
chunksize : int, default None
    If specified, return an iterator where `chunksize` is the number of
    rows to include in each chunk.
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'}.

    .. versionadded:: 1.3.0
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

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

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

Notes
-----
Any datetime values with time zone information parsed via the `parse_dates`
parameter will be converted to UTC.

Examples
--------
>>> from sqlalchemy import create_engine  # doctest: +SKIP
>>> engine = create_engine("sqlite:///database.db")  # doctest: +SKIP
>>> with engine.connect() as conn, conn.begin():  # doctest: +SKIP
...     data = pd.read_sql_table("data", conn)  # doctest: +SKIP

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