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 an SQLAlchemy connectable, returns a DataFrame. This function does not support DBAPI connections.


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). If None, use default schema (default).

index_col : string or list of strings, optional, default: None

Column(s) to set as index(MultiIndex)

coerce_float : boolean, default True

Attempt to convert values to 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 of pandas.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, return an iterator where chunksize is the number of rows to include in each chunk.



See also

Read SQL query into a DataFrame.



Any datetime values with time zone information will be converted to UTC

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