Series.nonzero() [source] Return the indices of the elements that are non-zero This method is equivalent to calling numpy.nonzero on the series data. For compatability with NumPy, the return value is the same (a tuple with an array of indices for each dimension), but it will always be a one-item tuple because series only have one dimension. See also numpy.nonzero Examples >>> s = pd.Series([0, 3, 0, 4]) >>> s.nonzero() (array([1, 3]),) >>> s.iloc[s.nonzero()[0]]

Series.dt.floor(*args, **kwargs) [source] floor the index to the specified freq Parameters: freq : freq string/object Returns: index of same type Raises: ValueError if the freq cannot be converted

Index.sortlevel(level=None, ascending=True, sort_remaining=None) [source] For internal compatibility with with the Index API Sort the Index. This is for compat with MultiIndex Parameters: ascending : boolean, default True False to sort in descending order level, sort_remaining are compat parameters Returns: sorted_index : Index

TimedeltaIndex.nbytes return the number of bytes in the underlying data

DataFrame.reset_index(level=None, drop=False, inplace=False, col_level=0, col_fill='') [source] For DataFrame with multi-level index, return new DataFrame with labeling information in the columns under the index names, defaulting to ?level_0?, ?level_1?, etc. if any are None. For a standard index, the index name will be used (if set), otherwise a default ?index? or ?level_0? (if ?index? is already taken) will be used. Parameters: level : int, str, tuple, or list, default None Only remove

TimedeltaIndex.dtype_str = None

Series.dt.normalize(*args, **kwargs) [source] Return DatetimeIndex with times to midnight. Length is unaltered Returns: normalized : DatetimeIndex

CategoricalIndex.codes

DataFrame.set_index(keys, drop=True, append=False, inplace=False, verify_integrity=False) [source] Set the DataFrame index (row labels) using one or more existing columns. By default yields a new object. Parameters: keys : column label or list of column labels / arrays drop : boolean, default True Delete columns to be used as the new index append : boolean, default False Whether to append columns to existing index inplace : boolean, default False Modify the DataFrame in place (do not

DataFrame.insert(loc, column, value, allow_duplicates=False) [source] Insert column into DataFrame at specified location. If allow_duplicates is False, raises Exception if column is already contained in the DataFrame. Parameters: loc : int Must have 0 <= loc <= len(columns) column : object value : scalar, Series, or array-like