DatetimeIndex.delete(loc) [source] Make a new DatetimeIndex with passed location(s) deleted. Parameters: loc: int, slice or array of ints Indicate which sub-arrays to remove. Returns: new_index : DatetimeIndex

Series.notnull() [source] Return a boolean same-sized object indicating if the values are not null. See also isnull boolean inverse of notnull

Series.rolling(window, min_periods=None, freq=None, center=False, win_type=None, on=None, axis=0) [source] Provides rolling window calculcations. New in version 0.18.0. Parameters: window : int, or offset Size of the moving window. This is the number of observations used for calculating the statistic. Each window will be a fixed size. If its an offset then this will be the time period of each window. Each window will be a variable sized based on the observations included in the time-per

Series.dt.dayofyear The ordinal day of the year

Series.round(decimals=0, *args, **kwargs) [source] Round each value in a Series to the given number of decimals. Parameters: decimals : int Number of decimal places to round to (default: 0). If decimals is negative, it specifies the number of positions to the left of the decimal point. Returns: Series object See also numpy.around, DataFrame.round

TimedeltaIndex.is_monotonic_increasing return if the index is monotonic increasing (only equal or increasing) values.

DataFrame.mode(axis=0, numeric_only=False) [source] Gets the mode(s) of each element along the axis selected. Empty if nothing has 2+ occurrences. Adds a row for each mode per label, fills in gaps with nan. Note that there could be multiple values returned for the selected axis (when more than one item share the maximum frequency), which is the reason why a dataframe is returned. If you want to impute missing values with the mode in a dataframe df, you can just do this: df.fillna(df.mode().

DataFrame.rank(axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False) [source] Compute numerical data ranks (1 through n) along axis. Equal values are assigned a rank that is the average of the ranks of those values Parameters: axis: {0 or ?index?, 1 or ?columns?}, default 0 index to direct ranking method : {?average?, ?min?, ?max?, ?first?, ?dense?} average: average rank of group min: lowest rank in group max: highest rank in group first: ranks assign

MultiIndex.append(other) [source] Append a collection of Index options together Parameters: other : Index or list/tuple of indices Returns: appended : Index

DataFrame.at Fast label-based scalar accessor Similarly to loc, at provides label based scalar lookups. You can also set using these indexers.