# pandas.read_sql_table

`pandas.``read_sql_table`(table_name, con, schema=None, index_col=None, coerce_float=True, parse_dates=None, columns=None, chunksize=None)

  Read SQL database table into a DataFrame.

  Given a table name and a SQLAlchemy connectable, returns a DataFrame. This function does not support DBAPI connections.

  Parameters:

  table_name : string

  > Name of SQL table in database.

  con : SQLAlchemy connectable (or database string URI)

  > SQLite DBAPI connection mode not supported.

  schema : string, default None

  > Name of SQL schema in database to query (if database flavor supports this). Uses default schema if None (default).

  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. Can result in loss of Precision.

  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

  chunksize : int, default None

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

  Returns: | DataFrame

  See also

  `read_sql_query`
    Read SQL query into a DataFrame.

  `read_sql`

  Notes

  Any datetime values with time zone information will be converted to UTC.

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