DataFrame.copy(deep=True) [source] Make a copy of this objects data. Parameters: deep : boolean or string, default True Make a deep copy, including a copy of the data and the indices. With deep=False neither the indices or the data are copied. Note that when deep=True data is copied, actual python objects will not be copied recursively, only the reference to the object. This is in contrast to copy.deepcopy in the Standard Library, which recursively copies object data. Returns: copy : t

DataFrame.convert_objects(convert_dates=True, convert_numeric=False, convert_timedeltas=True, copy=True) [source] Deprecated. Attempt to infer better dtype for object columns Parameters: convert_dates : boolean, default True If True, convert to date where possible. If ?coerce?, force conversion, with unconvertible values becoming NaT. convert_numeric : boolean, default False If True, attempt to coerce to numbers (including strings), with unconvertible values becoming NaN. convert_timed

DataFrame.consolidate(inplace=False) [source] Compute NDFrame with ?consolidated? internals (data of each dtype grouped together in a single ndarray). Mainly an internal API function, but available here to the savvy user Parameters: inplace : boolean, default False If False return new object, otherwise modify existing object Returns: consolidated : type of caller

DataFrame.compound(axis=None, skipna=None, level=None) [source] Return the compound percentage of the values for the requested axis Parameters: axis : {index (0), columns (1)} 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 Series numeric_only : boolean, default None Include only float, int, bo

DataFrame.combineMult(other) [source] DEPRECATED. Use DataFrame.mul(other, fill_value=1.) instead. Multiply two DataFrame objects and do not propagate NaN values, so if for a (column, time) one frame is missing a value, it will default to the other frame?s value (which might be NaN as well) Parameters: other : DataFrame Returns: DataFrame See also DataFrame.mul

DataFrame.combine_first(other) [source] Combine two DataFrame objects and default to non-null values in frame calling the method. Result index columns will be the union of the respective indexes and columns Parameters: other : DataFrame Returns: combined : DataFrame Examples a?s values prioritized, use values from b to fill holes: >>> a.combine_first(b)

DataFrame.combine(other, func, fill_value=None, overwrite=True) [source] Add two DataFrame objects and do not propagate NaN values, so if for a (column, time) one frame is missing a value, it will default to the other frame?s value (which might be NaN as well) Parameters: other : DataFrame func : function fill_value : scalar value overwrite : boolean, default True If True then overwrite values for common keys in the calling frame Returns: result : DataFrame

DataFrame.clip_upper(threshold, axis=None) [source] Return copy of input with values above given value(s) truncated. Parameters: threshold : float or array_like axis : int or string axis name, optional Align object with threshold along the given axis. Returns: clipped : same type as input See also clip

DataFrame.combineAdd(other) [source] DEPRECATED. Use DataFrame.add(other, fill_value=0.) instead. Add two DataFrame objects and do not propagate NaN values, so if for a (column, time) one frame is missing a value, it will default to the other frame?s value (which might be NaN as well) Parameters: other : DataFrame Returns: DataFrame See also DataFrame.add

DataFrame.clip_lower(threshold, axis=None) [source] Return copy of the input with values below given value(s) truncated. Parameters: threshold : float or array_like axis : int or string axis name, optional Align object with threshold along the given axis. Returns: clipped : same type as input See also clip