Panel4D.as_blocks(copy=True) [source] Convert the frame to a dict of dtype -> Constructor Types that each has a homogeneous dtype. NOTE: the dtypes of the blocks WILL BE PRESERVED HERE (unlike in as_matrix) Parameters: copy : boolean, default True Returns: values : a dict of dtype -> Constructor Types

Panel.rename(items=None, major_axis=None, minor_axis=None, **kwargs) [source] Alter axes input function or functions. Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don?t throw an error. Alternatively, change Series.name with a scalar value (Series only). Parameters: items, major_axis, minor_axis : scalar, list-like, dict-like or function, optional Scalar or list-like will alter the Series.name attribute, and

Index.fillna(value=None, downcast=None) [source] Fill NA/NaN values with the specified value Parameters: value : scalar Scalar value to use to fill holes (e.g. 0). This value cannot be a list-likes. downcast : dict, default is None a dict of item->dtype of what to downcast if possible, or the string ?infer? which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible) Returns: filled : %(klass)s

Series.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 : type

DataFrameGroupBy.agg(arg, *args, **kwargs) [source] Aggregate using input function or dict of {column -> function} Parameters: arg : function or dict Function to use for aggregating groups. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply. If passed a dict, the keys must be DataFrame column names. Accepted Combinations are: string cythonized function name function list of functions dict of columns -> functions nested dict of names -> dict

TimedeltaIndex.shift(n, freq=None) [source] Specialized shift which produces a DatetimeIndex Parameters: n : int Periods to shift by freq : DateOffset or timedelta-like, optional Returns: shifted : DatetimeIndex

DataFrame.squeeze(**kwargs) [source] Squeeze length 1 dimensions.

StataReader.data(**kwargs) [source] DEPRECATED: Reads observations from Stata file, converting them into a dataframe This is a legacy method. Use read in new code. Parameters: convert_dates : boolean, defaults to True Convert date variables to DataFrame time values convert_categoricals : boolean, defaults to True Read value labels and convert columns to Categorical/Factor variables index : identifier of index column identifier of column that should be used as index of the DataFrame c

Panel.subtract(other, axis=0) [source] Subtraction of series and other, element-wise (binary operator sub). Equivalent to panel - other. Parameters: other : DataFrame or Panel axis : {items, major_axis, minor_axis} Axis to broadcast over Returns: Panel See also Panel.rsub

Series.str.find(sub, start=0, end=None) [source] Return lowest indexes in each strings in the Series/Index where the substring is fully contained between [start:end]. Return -1 on failure. Equivalent to standard str.find(). Parameters: sub : str Substring being searched start : int Left edge index end : int Right edge index Returns: found : Series/Index of integer values See also rfind Return highest indexes in each strings