@@ -3060,57 +3060,67 @@ class max_speed
30603060 ... ('mammal', 'monkey')],
30613061 ... names=['class', 'name'])
30623062 >>> columns = pd.MultiIndex.from_tuples([('speed', 'max'),
3063- ... ('speed ', 'type')])
3063+ ... ('species ', 'type')])
30643064 >>> df = pd.DataFrame([(389.0, 'fly'),
30653065 ... ( 24.0, 'fly'),
30663066 ... ( 80.5, 'run'),
30673067 ... (np.nan, 'jump')],
30683068 ... index=index,
30693069 ... columns=columns)
30703070 >>> df
3071- speed
3072- max type
3071+ speed species
3072+ max type
30733073 class name
3074- bird falcon 389.0 fly
3075- parrot 24.0 fly
3076- mammal lion 80.5 run
3077- monkey NaN jump
3074+ bird falcon 389.0 fly
3075+ parrot 24.0 fly
3076+ mammal lion 80.5 run
3077+ monkey NaN jump
30783078
30793079 If the index has multiple levels, we can reset a subset of them:
30803080
30813081 >>> df.reset_index(level='class')
3082- class speed
3083- max type
3082+ class speed species
3083+ max type
30843084 name
3085- falcon bird 389.0 fly
3086- parrot bird 24.0 fly
3087- lion mammal 80.5 run
3088- monkey mammal NaN jump
3085+ falcon bird 389.0 fly
3086+ parrot bird 24.0 fly
3087+ lion mammal 80.5 run
3088+ monkey mammal NaN jump
30893089
30903090 If we are not dropping the index, by default, it is placed in the top
30913091 level. We can place it in another level:
30923092
30933093 >>> df.reset_index(level='class', col_level=1)
3094- speed
3095- class max type
3094+ speed species
3095+ class max type
30963096 name
3097- falcon bird 389.0 fly
3098- parrot bird 24.0 fly
3099- lion mammal 80.5 run
3100- monkey mammal NaN jump
3097+ falcon bird 389.0 fly
3098+ parrot bird 24.0 fly
3099+ lion mammal 80.5 run
3100+ monkey mammal NaN jump
31013101
31023102 When the index is inserted under another level, we can specify under
3103- which one with the parameter `col_fill`. If we specify a nonexistent
3104- level, it is created:
3103+ which one with the parameter `col_fill`:
31053104
31063105 >>> df.reset_index(level='class', col_level=1, col_fill='species')
3107- species speed
3108- class max type
3106+ species speed species
3107+ class max type
31093108 name
3110- falcon bird 389.0 fly
3111- parrot bird 24.0 fly
3112- lion mammal 80.5 run
3113- monkey mammal NaN jump
3109+ falcon bird 389.0 fly
3110+ parrot bird 24.0 fly
3111+ lion mammal 80.5 run
3112+ monkey mammal NaN jump
3113+
3114+ If we specify a nonexistent level for `col_fill`, it is created:
3115+
3116+ >>> df.reset_index(level='class', col_level=1, col_fill='genus')
3117+ genus speed species
3118+ class max type
3119+ name
3120+ falcon bird 389.0 fly
3121+ parrot bird 24.0 fly
3122+ lion mammal 80.5 run
3123+ monkey mammal NaN jump
31143124 """
31153125 inplace = validate_bool_kwarg (inplace , 'inplace' )
31163126 if inplace :
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