pandas.timedelta_range(start=None, end=None, periods=None, freq='D', name=None, closed=None) [source] Return a fixed frequency timedelta index, with day as the default frequency Parameters: start : string or timedelta-like, default None Left bound for generating dates end : string or datetime-like, default None Right bound for generating dates periods : integer or None, default None If None, must specify start and end freq : string or DateOffset, default ?D? (calendar daily) Frequen

pandas.test = > [source] Run tests for module using nose. Parameters: label : {?fast?, ?full?, ??, attribute identifier}, optional Identifies the tests to run. This can be a string to pass to the nosetests executable with the ?-A? option, or one of several special values. Special values are: ?fast? - the default - which corresponds to the nosetests -A option of ?not slow?. ?full? - fast (as above) and slow tests as in the ?no -A? option to nosetests - this is the same as ??. None or ??

pandas.set_option(pat, value) = Sets the value of the specified option. Available options: display.[chop_threshold, colheader_justify, column_space, date_dayfirst, date_yearfirst, encoding, expand_frame_repr, float_format, height, large_repr] display.latex.[escape, longtable, repr] display.[line_width, max_categories, max_columns, max_colwidth, max_info_columns, max_info_rows, max_rows, max_seq_items, memory_usage, mpl_style, multi_sparse, notebook_repr_html, pprint_nest_depth, precision,

pandas.read_table(filepath_or_buffer, sep='\t', delimiter=None, header='infer', names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False,

pandas.reset_option(pat) = Reset one or more options to their default value. Pass ?all? as argument to reset all options. Available options: display.[chop_threshold, colheader_justify, column_space, date_dayfirst, date_yearfirst, encoding, expand_frame_repr, float_format, height, large_repr] display.latex.[escape, longtable, repr] display.[line_width, max_categories, max_columns, max_colwidth, max_info_columns, max_info_rows, max_rows, max_seq_items, memory_usage, mpl_style, multi_sparse,

pandas.read_stata(filepath_or_buffer, convert_dates=True, convert_categoricals=True, encoding=None, index=None, convert_missing=False, preserve_dtypes=True, columns=None, order_categoricals=True, chunksize=None, iterator=False) [source] Read Stata file into DataFrame Parameters: filepath_or_buffer : string or file-like object Path to .dta file or object implementing a binary read() functions convert_dates : boolean, defaults to True Convert date variables to DataFrame time values conve

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. 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

pandas.read_sql_query(sql, con, index_col=None, coerce_float=True, params=None, parse_dates=None, chunksize=None) [source] 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 or SQLAlchemy Selectable (select or text object) to be executed. con : SQLAlchemy connectable(engine

pandas.read_sas(filepath_or_buffer, format=None, index=None, encoding=None, chunksize=None, iterator=False) [source] Read SAS files stored as either XPORT or SAS7BDAT format files. Parameters: filepath_or_buffer : string or file-like object Path to the SAS file. format : string {?xport?, ?sas7bdat?} or None If None, file format is inferred. If ?xport? or ?sas7bdat?, uses the corresponding format. index : identifier of index column, defaults to None Identifier of column that should be

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