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# pandas.read_sql

`pandas.``read_sql`(sql, con, index_col=None, coerce_float=True, params=None, parse_dates=None, columns=None)

  Read SQL query or database table into a DataFrame.

  Parameters : | sql : string SQL query to be executed or database table name. con : SQLAlchemy engine or DBAPI2 connection (legacy mode) Using SQLAlchemy makes it possible to use any DB supported by that library. If a DBAPI2 object, only sqlite3 is supported. index_col : string, optional column name to use as index for the returned DataFrame object. coerce_float : boolean, default True Attempt to convert values to non-string, non-numeric objects (like decimal.Decimal) to floating point, useful for SQL result sets params : list, tuple or dict, optional List of parameters to pass to execute method. parse_dates : list or dict 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 List of column names to select from sql table (only used when reading a table).
  Returns : | DataFrame

  See also

  `read_sql_table`
    Read SQL database table into a DataFrame
  `read_sql_query`
    Read SQL query into a DataFrame

  Notes

  This function is a convenience wrapper around `read_sql_table` and `read_sql_query` (and for backward compatibility) and will delegate to the specific function depending on the provided input (database table name or sql query).

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