Series.rfloordiv(other, level=None, fill_value=None, axis=0) [source] Integer division of series and other, element-wise (binary operator rfloordiv). Equivalent to other // series, but with support to substitute a fill_value for missing data in one of the inputs. Parameters: other: Series or scalar value fill_value : None or float value, default None (NaN) Fill missing (NaN) values with this value. If both Series are missing, the result will be missing level : int or name Broadcast acro

MultiIndex.shift(periods=1, freq=None) [source] Shift Index containing datetime objects by input number of periods and DateOffset Returns: shifted : Index

Series.mad(axis=None, skipna=None, level=None) [source] Return the mean absolute deviation of the values for the requested axis Parameters: axis : {index (0)} skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar numeric_only : boolean, default None Include only float, int, boolean columns. If

Panel.all(axis=None, bool_only=None, skipna=None, level=None, **kwargs) [source] Return whether all elements are True over requested axis Parameters: axis : {items (0), major_axis (1), minor_axis (2)} skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a DataFrame bool_only : boolean, default None In

Series.sem(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs) [source] Return unbiased standard error of the mean over requested axis. Normalized by N-1 by default. This can be changed using the ddof argument Parameters: axis : {index (0)} skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, colla

Panel4D.kurt(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) [source] Return unbiased kurtosis over requested axis using Fisher?s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1 Parameters: axis : {labels (0), items (1), major_axis (2), minor_axis (3)} skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA level : int or level name, default None If the axis is a MultiIndex (hierarchical), count a

DataFrame.quantile(q=0.5, axis=0, numeric_only=True, interpolation='linear') [source] Return values at the given quantile over requested axis, a la numpy.percentile. Parameters: q : float or array-like, default 0.5 (50% quantile) 0 <= q <= 1, the quantile(s) to compute axis : {0, 1, ?index?, ?columns?} (default 0) 0 or ?index? for row-wise, 1 or ?columns? for column-wise interpolation : {?linear?, ?lower?, ?higher?, ?midpoint?, ?nearest?} New in version 0.18.0. This optional p

Series.str.extractall(pat, flags=0) [source] For each subject string in the Series, extract groups from all matches of regular expression pat. When each subject string in the Series has exactly one match, extractall(pat).xs(0, level=?match?) is the same as extract(pat). New in version 0.18.0. Parameters: pat : string Regular expression pattern with capturing groups flags : int, default 0 (no flags) re module flags, e.g. re.IGNORECASE Returns: A DataFrame with one row for each match

DataFrame.info(verbose=None, buf=None, max_cols=None, memory_usage=None, null_counts=None) [source] Concise summary of a DataFrame. Parameters: verbose : {None, True, False}, optional Whether to print the full summary. None follows the display.max_info_columns setting. True or False overrides the display.max_info_columns setting. buf : writable buffer, defaults to sys.stdout max_cols : int, default None Determines whether full summary or short summary is printed. None follows the displa

TimedeltaIndex.is_monotonic_increasing return if the index is monotonic increasing (only equal or increasing) values.