pandas.DataFrame.reset_index
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 the given levels from the index. Removes all levels by default drop: boolean, default False Do not try to insert index into dataframe columns. This resets the index to the default integer index. inplace: boolean, default False Modify the DataFrame in place (do not create a new object) col_level: int or str, default 0 If the columns have multiple levels, determines which level the labels are inserted into. By default it is inserted into the first level. col_fill: object, default ‘’ If the columns have multiple levels, determines how the other levels are named. If None then the index name is repeated. Returns: resetted: DataFrame Examples >>> df = pd.DataFrame([('bird', 389.0), ... ('bird', 24.0), ... ('mammal', 80.5), ... ('mammal', np.nan)], ... index=['falcon', 'parrot', 'lion', 'monkey'], ... columns=('class', 'max_speed')) >>> df class max_speed falcon bird 389.0 parrot bird 24.0 lion mammal 80.5 monkey mammal NaN When we reset the index, the old index is added as a column, and a new sequential index is used: >>> df.reset_index() index class max_speed 0 falcon bird 389.0 1 parrot bird 24.0 2 lion mammal 80.5 3 monkey mammal NaN We can use thedropparameter to avoid the old index being added as a column: >>> df.reset_index(drop=True) class max_speed 0 bird 389.0 1 bird 24.0 2 mammal 80.5 3 mammal NaN You can also usereset_indexwithMultiIndex. >>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'), ... ('bird', 'parrot'), ... ('mammal', 'lion'), ... ('mammal', 'monkey')], ... names=['class', 'name']) >>> columns = pd.MultiIndex.from_tuples([('speed', 'max'), ... ('species', 'type')]) >>> df = pd.DataFrame([(389.0, 'fly'), ... ( 24.0, 'fly'), ... ( 80.5, 'run'), ... (np.nan, 'jump')], ... index=index, ... columns=columns) >>> df speed species max type class name bird falcon 389.0 fly parrot 24.0 fly mammal lion 80.5 run monkey NaN jump If the index has multiple levels, we can reset a subset of them: >>> df.reset_index(level='class') class speed species max type name falcon bird 389.0 fly parrot bird 24.0 fly lion mammal 80.5 run monkey mammal NaN jump If we are not dropping the index, by default, it is placed in the top level. We can place it in another level: >>> df.reset_index(level='class', col_level=1) speed species class max type name falcon bird 389.0 fly parrot bird 24.0 fly lion mammal 80.5 run monkey mammal NaN jump When the index is inserted under another level, we can specify under which one with the parametercol_fill: >>> df.reset_index(level='class', col_level=1, col_fill='species') species speed species class max type name falcon bird 389.0 fly parrot bird 24.0 fly lion mammal 80.5 run monkey mammal NaN jump If we specify a nonexistent level forcol_fill, it is created: >>> df.reset_index(level='class', col_level=1, col_fill='genus') genus speed species class max type name falcon bird 389.0 fly parrot bird 24.0 fly lion mammal 80.5 run monkey mammal NaN jump
