pandas.read_sql_query¶
- pandas.read_sql_query(sql, con, index_col=None, coerce_float=True, params=None, parse_dates=None, chunksize=None)¶
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 : string
SQL query to be executed
con : SQLAlchemy engine or sqlite3 DBAPI2 connection
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
chunksize : int, default None
If specified, return an iterator where chunksize is the number of rows to include in each chunk.
Returns: DataFrame