pandas.
read_sql
Read SQL query or database table into a DataFrame.
This function is a convenience wrapper around read_sql_table and read_sql_query (for backward compatibility). It will delegate to the specific function depending on the provided input. A SQL query will be routed to read_sql_query, while a database table name will be routed to read_sql_table. Note that the delegated function might have more specific notes about their functionality not listed here.
read_sql_table
read_sql_query
SQL query to be executed or a table name.
Using SQLAlchemy makes it possible to use any DB supported by that library. If a DBAPI2 object, only sqlite3 is supported. The user is responsible for engine disposal and connection closure for the SQLAlchemy connectable; str connections are closed automatically. See here.
Column(s) to set as index(MultiIndex).
Attempts to convert values of non-string, non-numeric objects (like decimal.Decimal) to floating point, useful for SQL result sets.
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’}.
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.
{column_name: format string}
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.
{column_name: arg dict}
pandas.to_datetime()
List of column names to select from SQL table (only used when reading a table).
If specified, return an iterator where chunksize is the number of rows to include in each chunk.
See also
Read SQL database table into a DataFrame.
Read SQL query into a DataFrame.