DataFrame.apply(func, axis=0, broadcast=False, raw=False, reduce=None, args=(), **kwds) [source] Applies function along input axis of DataFrame. Objects passed to functions are Series objects having index either the DataFrame?s index (axis=0) or the columns (axis=1). Return type depends on whether passed function aggregates, or the reduce argument if the DataFrame is empty. Parameters: func : function Function to apply to each column/row axis : {0 or ?index?, 1 or ?columns?}, default 0

DataFrame.plot.density(**kwds) [source] Kernel Density Estimate plot New in version 0.17.0. Parameters: **kwds : optional Keyword arguments to pass on to pandas.DataFrame.plot(). Returns: axes : matplotlib.AxesSubplot or np.array of them

DataFrame.cummin(axis=None, skipna=True, *args, **kwargs) [source] Return cumulative minimum over 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 Returns: cummin : Series

Panel4D.asof(where, subset=None) [source] The last row without any NaN is taken (or the last row without NaN considering only the subset of columns in the case of a DataFrame) New in version 0.19.0: For DataFrame If there is no good value, NaN is returned. Parameters: where : date or array of dates subset : string or list of strings, default None if not None use these columns for NaN propagation Returns: where is scalar value or NaN if input is Series Series if input is DataFrame wh

pandas.read_fwf(filepath_or_buffer, colspecs='infer', widths=None, **kwds) [source] Read a table of fixed-width formatted lines into DataFrame Also supports optionally iterating or breaking of the file into chunks. Additional help can be found in the online docs for IO Tools. Parameters: filepath_or_buffer : str, pathlib.Path, py._path.local.LocalPath or any object with a read() method (such as a file handle or StringIO) The string could be a URL. Valid URL schemes include http, ftp, s3,

Index.symmetric_difference(other, result_name=None) [source] Compute the symmetric difference of two Index objects. It?s sorted if sorting is possible. Parameters: other : Index or array-like result_name : str Returns: symmetric_difference : Index Notes symmetric_difference contains elements that appear in either idx1 or idx2 but not both. Equivalent to the Index created by idx1.difference(idx2) | idx2.difference(idx1) with duplicates dropped. Examples >>> idx1 = Index([1, 2, 3

DataFrame.filter(items=None, like=None, regex=None, axis=None) [source] Subset rows or columns of dataframe according to labels in the specified index. Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index. Parameters: items : list-like List of info axis to restrict to (must not all be present) like : string Keep info axis where ?arg in col == True? regex : string (regular expression) Keep info axis with re.search(regex, c

Panel4D.tz_convert(tz, axis=0, level=None, copy=True) [source] Convert tz-aware axis to target time zone. Parameters: tz : string or pytz.timezone object axis : the axis to convert level : int, str, default None If axis ia a MultiIndex, convert a specific level. Otherwise must be None copy : boolean, default True Also make a copy of the underlying data Raises: TypeError If the axis is tz-naive.

DataFrame.subtract(other, axis='columns', level=None, fill_value=None) [source] Subtraction of dataframe and other, element-wise (binary operator sub). Equivalent to dataframe - other, but with support to substitute a fill_value for missing data in one of the inputs. Parameters: other : Series, DataFrame, or constant axis : {0, 1, ?index?, ?columns?} For Series input, axis to match Series index on fill_value : None or float value, default None Fill missing (NaN) values with this value.

Series.str.lower() [source] Convert strings in the Series/Index to lowercase. Equivalent to str.lower(). Returns: converted : Series/Index of objects