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)[source]¶ 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 ofpandas.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
Notes
Any datetime values with time zone information will be converted to UTC.