pandas.read_sql(sql, con, index_col=None, coerce_float=True, params=None, parse_dates=None, columns=None, chunksize=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 (fallback 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. 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

  • 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).

chunksize : int, default None

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

Returns :


See also

Read SQL database table into a DataFrame
Read SQL query into a DataFrame


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).