pandas.read_sql(sql, con, index_col=None, coerce_float=True, params=None, parse_dates=None, columns=None, chunksize=None) [source] Read SQL query or database table into a DataFrame. Parameters: sql : string SQL query or SQLAlchemy Selectable (select or text object) to be executed, or database table name. con : SQLAlchemy connectable(engine/connection) or database string URI or DBAPI2 connection (fallback mode) Using SQLAlchemy makes it possible to use any DB supported by that library. I

Panel.head(n=5) [source]

Series.flags

Panel4D.ndim Number of axes / array dimensions

pandas.merge_asof(left, right, on=None, left_on=None, right_on=None, left_index=False, right_index=False, by=None, left_by=None, right_by=None, suffixes=('_x', '_y'), tolerance=None, allow_exact_matches=True) [source] Perform an asof merge. This is similar to a left-join except that we match on nearest key rather than equal keys. For each row in the left DataFrame, we select the last row in the right DataFrame whose ?on? key is less than or equal to the left?s key. Both DataFrames must be s

Series.iat Fast integer location scalar accessor. Similarly to iloc, iat provides integer based lookups. You can also set using these indexers.

SeriesGroupBy.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True) [source]

TimedeltaIndex.ravel(order='C') [source] return an ndarray of the flattened values of the underlying data See also numpy.ndarray.ravel

StataReader.value_labels() [source] Returns a dict, associating each variable name a dict, associating each value its corresponding label

SeriesGroupBy.unique() Return np.ndarray of unique values in the object. Significantly faster than numpy.unique. Includes NA values. The order of the original is preserved. Returns: uniques : np.ndarray