DataFrame.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

DataFrame.astype(dtype, copy=True, raise_on_error=True, **kwargs) [source] Cast object to input numpy.dtype Return a copy when copy = True (be really careful with this!) Parameters: dtype : data type, or dict of column name -> data type Use a numpy.dtype or Python type to cast entire pandas object to the same type. Alternatively, use {col: dtype, ...}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame?s columns to column-specific

DataFrame.assign(**kwargs) [source] Assign new columns to a DataFrame, returning a new object (a copy) with all the original columns in addition to the new ones. New in version 0.16.0. Parameters: kwargs : keyword, value pairs keywords are the column names. If the values are callable, they are computed on the DataFrame and assigned to the new columns. The callable must not change input DataFrame (though pandas doesn?t check it). If the values are not callable, (e.g. a Series, scalar, or

DataFrame.asfreq(freq, method=None, how=None, normalize=False) [source] Convert TimeSeries to specified frequency. Optionally provide filling method to pad/backfill missing values. Parameters: freq : DateOffset object, or string method : {?backfill?/?bfill?, ?pad?/?ffill?}, default None Method to use for filling holes in reindexed Series (note this does not fill NaNs that already were present): ?pad? / ?ffill?: propagate last valid observation forward to next valid ?backfill? / ?bfill?: u

DataFrame.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

DataFrame.append(other, ignore_index=False, verify_integrity=False) [source] Append rows of other to the end of this frame, returning a new object. Columns not in this frame are added as new columns. Parameters: other : DataFrame or Series/dict-like object, or list of these The data to append. ignore_index : boolean, default False If True, do not use the index labels. verify_integrity : boolean, default False If True, raise ValueError on creating index with duplicates. Returns: app

DataFrame.applymap(func) [source] Apply a function to a DataFrame that is intended to operate elementwise, i.e. like doing map(func, series) for each series in the DataFrame Parameters: func : function Python function, returns a single value from a single value Returns: applied : DataFrame See also DataFrame.apply For operations on rows/columns Examples >>> df = pd.DataFrame(np.random.randn(3, 3)) >>> df 0 1 2 0 -0.029638 1.081563 1.2803

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.any(axis=None, bool_only=None, skipna=None, level=None, **kwargs) [source] Return whether any element is True 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 level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series bool_only : boolean, default None Include only boolean c

DataFrame.all(axis=None, bool_only=None, skipna=None, level=None, **kwargs) [source] Return whether all elements are True 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 level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series bool_only : boolean, default None Include only boolean