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int, dtype, dataframe, object, toframe, dtypes, resetindex, values, column, names, bool, consistency, level, false, true, printfdfndtypesndfdtypes, sign, api, multiindextoframe, allowduplicates, conversions, pandas, projects, issue, johnzangwill, nan, raise, navigation, open, pull, requests, actions, security, cases, missing, number, leveln, duplicated, suggest, printnresetindex, pddataframeindexindexresetindex, valueerror, kwargs, return, cusersjohnonedrivedocumentsgithubpandasjohnzangwillpandascoreframepy, insert, exists, mentioned, labels, added,

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users\john\onedrive\documents\github\pandas_johnzangwill\pandas\util\_decorators users\john\onedrive\documents\github\pandas_johnzangwill\pandas\core\frame ~\appdata\local\temp/ipykernel_21224/810260565 personal information api bool missing column comment metadata assignees import pandas silently skips reset_index duplicated column names additional parameter allow_duplicates type projects github bool dtype projects milestone int64 dtype object dtype 'b'] dataframe consistency column number nan to_frame 'a'] dataframe bool to_frame default false index=false insert {column} as_index=false object to_frame bool values object reset_index object reset_index create dataframes behave differently current situation index=index recent call index = new_index milestone relationships index = pd add allow_duplicates object values {df}\ndtypes df = index df = pd dataframe ] dataframe level columns=names names=names column exists

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• 4434 # Should this be a different kind of error?
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DiscussionForumPosting:
      context:https://schema.org
      headline:API: Consistency between DataFrame, MultiIndex.to_frame and reset_index
      articleBody:DataFrame, MultiIndex.to_frame, Series.reset_index and DataFrame.reset_index all create DataFrames. In most cases they are the same. But in the following cases they behave differently: - bool values: - DataFrame: bool - to_frame: object - reset_index: bool - Missing column name: - DataFrame: NaN - to_frame: n (where n is the column number) - reset_index: level_n (where n is the column number) - Duplicated column names: - DataFrame: creates them - to_frame: silently skips - reset_index: raises I suggest the following changes: - bool values: to_frame should restore bool dtype - Missing level name: to_frame should use level_n, as do many other Pandas methods - Duplicated level name: to_frame should raise I would also suggest that both to_frame and reset_index have an additional parameter `allow_duplicates`, default False, but this seems controversial (#44755)... This illustrates the current situation: ``` import pandas as pd for (values, names) in [ ([(1, 2),(3, 4)],["a", "b"]), ([(1, True),(3, False)],["a", "b"]), ([(1, 2),(3, 4)],["a", None]), ([(1, 2),(3, 4)],["a", "a"]), ]: print(f"\nvalues: {values}, names: {names}") print("\nDataFrame") df = pd.DataFrame(values, columns=names) print(f"{df}\nDtypes:\n{df.dtypes}") print("\nto_frame") index = pd.MultiIndex.from_tuples(values, names=names) df = index.to_frame(index=False) print(f"{df}\nDtypes:\n{df.dtypes}") print("\nreset_index") df = pd.DataFrame(index=index).reset_index() print(f"{df}\nDtypes:\n{df.dtypes}") values: [(1, 2), (3, 4)], names: ['a', 'b'] DataFrame a b 0 1 2 1 3 4 Dtypes: a int64 b int64 dtype: object to_frame a b 0 1 2 1 3 4 Dtypes: a int64 b int64 dtype: object reset_index a b 0 1 2 1 3 4 Dtypes: a int64 b int64 dtype: object values: [(1, True), (3, False)], names: ['a', 'b'] DataFrame a b 0 1 True 1 3 False Dtypes: a int64 b bool dtype: object to_frame a b 0 1 True 1 3 False Dtypes: a int64 b object dtype: object reset_index a b 0 1 True 1 3 False Dtypes: a int64 b bool dtype: object values: [(1, 2), (3, 4)], names: ['a', None] DataFrame a NaN 0 1 2 1 3 4 Dtypes: a int64 NaN int64 dtype: object to_frame a 1 0 1 2 1 3 4 Dtypes: a int64 1 int64 dtype: object reset_index a level_1 0 1 2 1 3 4 Dtypes: a int64 level_1 int64 dtype: object values: [(1, 2), (3, 4)], names: ['a', 'a'] DataFrame a a 0 1 2 1 3 4 Dtypes: a int64 a int64 dtype: object to_frame a 0 2 1 4 Dtypes: a int64 dtype: object reset_index --------------------------------------------------------------------------- ValueError Traceback (most recent call last) ~\AppData\Local\Temp/ipykernel_21224/810260565.py in <module> 14 print(f"{df}\nDtypes:\n{df.dtypes}") 15 print("\nreset_index") ---> 16 df = pd.DataFrame(index=index).reset_index() 17 print(f"{df}\nDtypes:\n{df.dtypes}") c:\users\john\onedrive\documents\github\pandas_johnzangwill\pandas\util\_decorators.py in wrapper(*args, **kwargs) 309 stacklevel=stacklevel, 310 ) --> 311 return func(*args, **kwargs) 312 313 return wrapper c:\users\john\onedrive\documents\github\pandas_johnzangwill\pandas\core\frame.py in reset_index(self, level, drop, inplace, col_level, col_fill) 5832 ) 5833 -> 5834 new_obj.insert(0, name, level_values) 5835 5836 new_obj.index = new_index c:\users\john\onedrive\documents\github\pandas_johnzangwill\pandas\core\frame.py in insert(self, loc, column, value, allow_duplicates) 4433 if not allow_duplicates and column in self.columns: 4434 # Should this be a different kind of error?? -> 4435 raise ValueError(f"cannot insert {column}, already exists") 4436 if not isinstance(loc, int): 4437 raise TypeError("loc must be int") ValueError: cannot insert a, already exists ```
      author:
         url:https://github.com/johnzangwill
         type:Person
         name:johnzangwill
      datePublished:2022-01-07T12:23:23.000Z
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      url:https://github.com/45245/pandas/issues/45245
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      headline:API: Consistency between DataFrame, MultiIndex.to_frame and reset_index
      articleBody:DataFrame, MultiIndex.to_frame, Series.reset_index and DataFrame.reset_index all create DataFrames. In most cases they are the same. But in the following cases they behave differently: - bool values: - DataFrame: bool - to_frame: object - reset_index: bool - Missing column name: - DataFrame: NaN - to_frame: n (where n is the column number) - reset_index: level_n (where n is the column number) - Duplicated column names: - DataFrame: creates them - to_frame: silently skips - reset_index: raises I suggest the following changes: - bool values: to_frame should restore bool dtype - Missing level name: to_frame should use level_n, as do many other Pandas methods - Duplicated level name: to_frame should raise I would also suggest that both to_frame and reset_index have an additional parameter `allow_duplicates`, default False, but this seems controversial (#44755)... This illustrates the current situation: ``` import pandas as pd for (values, names) in [ ([(1, 2),(3, 4)],["a", "b"]), ([(1, True),(3, False)],["a", "b"]), ([(1, 2),(3, 4)],["a", None]), ([(1, 2),(3, 4)],["a", "a"]), ]: print(f"\nvalues: {values}, names: {names}") print("\nDataFrame") df = pd.DataFrame(values, columns=names) print(f"{df}\nDtypes:\n{df.dtypes}") print("\nto_frame") index = pd.MultiIndex.from_tuples(values, names=names) df = index.to_frame(index=False) print(f"{df}\nDtypes:\n{df.dtypes}") print("\nreset_index") df = pd.DataFrame(index=index).reset_index() print(f"{df}\nDtypes:\n{df.dtypes}") values: [(1, 2), (3, 4)], names: ['a', 'b'] DataFrame a b 0 1 2 1 3 4 Dtypes: a int64 b int64 dtype: object to_frame a b 0 1 2 1 3 4 Dtypes: a int64 b int64 dtype: object reset_index a b 0 1 2 1 3 4 Dtypes: a int64 b int64 dtype: object values: [(1, True), (3, False)], names: ['a', 'b'] DataFrame a b 0 1 True 1 3 False Dtypes: a int64 b bool dtype: object to_frame a b 0 1 True 1 3 False Dtypes: a int64 b object dtype: object reset_index a b 0 1 True 1 3 False Dtypes: a int64 b bool dtype: object values: [(1, 2), (3, 4)], names: ['a', None] DataFrame a NaN 0 1 2 1 3 4 Dtypes: a int64 NaN int64 dtype: object to_frame a 1 0 1 2 1 3 4 Dtypes: a int64 1 int64 dtype: object reset_index a level_1 0 1 2 1 3 4 Dtypes: a int64 level_1 int64 dtype: object values: [(1, 2), (3, 4)], names: ['a', 'a'] DataFrame a a 0 1 2 1 3 4 Dtypes: a int64 a int64 dtype: object to_frame a 0 2 1 4 Dtypes: a int64 dtype: object reset_index --------------------------------------------------------------------------- ValueError Traceback (most recent call last) ~\AppData\Local\Temp/ipykernel_21224/810260565.py in <module> 14 print(f"{df}\nDtypes:\n{df.dtypes}") 15 print("\nreset_index") ---> 16 df = pd.DataFrame(index=index).reset_index() 17 print(f"{df}\nDtypes:\n{df.dtypes}") c:\users\john\onedrive\documents\github\pandas_johnzangwill\pandas\util\_decorators.py in wrapper(*args, **kwargs) 309 stacklevel=stacklevel, 310 ) --> 311 return func(*args, **kwargs) 312 313 return wrapper c:\users\john\onedrive\documents\github\pandas_johnzangwill\pandas\core\frame.py in reset_index(self, level, drop, inplace, col_level, col_fill) 5832 ) 5833 -> 5834 new_obj.insert(0, name, level_values) 5835 5836 new_obj.index = new_index c:\users\john\onedrive\documents\github\pandas_johnzangwill\pandas\core\frame.py in insert(self, loc, column, value, allow_duplicates) 4433 if not allow_duplicates and column in self.columns: 4434 # Should this be a different kind of error?? -> 4435 raise ValueError(f"cannot insert {column}, already exists") 4436 if not isinstance(loc, int): 4437 raise TypeError("loc must be int") ValueError: cannot insert a, already exists ```
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      url:https://github.com/45245/pandas/issues/45245
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