import pandas as pd
df = pd.read_csv('./data/data_cts_prisons_and_prisoners.csv')
print(df.head()) Iso3_code Country Region \
0 BRB Barbados Americas
1 CRI Costa Rica Americas
2 DMA Dominica Americas
3 GBR_NI United Kingdom (Northern Ireland) Europe
4 ITA Italy Europe
Subregion Indicator \
0 Latin America and the Caribbean Persons entering prison
1 Latin America and the Caribbean Persons entering prison
2 Latin America and the Caribbean Persons entering prison
3 Northern Europe Persons entering prison
4 Southern Europe Persons entering prison
Dimension Category Sex Age Year \
0 by selected crime Intentional Homicide Total Total 2016
1 by selected crime Intentional Homicide Total Total 2016
2 by selected crime Intentional Homicide Total Total 2016
3 by selected crime Intentional Homicide Total Total 2016
4 by selected crime Intentional Homicide Total Total 2016
Unit of measurement VALUE Source
0 Counts 0.0 CTS
1 Counts 173.0 CTS
2 Counts 65.0 CTS
3 Counts 61.0 CTS
4 Counts 1443.0 CTS item = df.pop('Country')
print(df) Iso3_code Region Subregion \
0 BRB Americas Latin America and the Caribbean
1 CRI Americas Latin America and the Caribbean
2 DMA Americas Latin America and the Caribbean
3 GBR_NI Europe Northern Europe
4 ITA Europe Southern Europe
... ... ... ...
84020 ZWE Africa Sub-Saharan Africa
84021 ZWE Africa Sub-Saharan Africa
84022 ZWE Africa Sub-Saharan Africa
84023 ZWE Africa Sub-Saharan Africa
84024 ZWE Africa Sub-Saharan Africa
Indicator Dimension Category \
0 Persons entering prison by selected crime Intentional Homicide
1 Persons entering prison by selected crime Intentional Homicide
2 Persons entering prison by selected crime Intentional Homicide
3 Persons entering prison by selected crime Intentional Homicide
4 Persons entering prison by selected crime Intentional Homicide
... ... ... ...
84020 Persons held Total Total
84021 Persons held Total Total
84022 Persons held Total Total
84023 Persons held Total Total
84024 Persons held Total Total
Sex Age Year Unit of measurement VALUE Source
0 Total Total 2016 Counts 0.000000 CTS
1 Total Total 2016 Counts 173.000000 CTS
2 Total Total 2016 Counts 65.000000 CTS
3 Total Total 2016 Counts 61.000000 CTS
4 Total Total 2016 Counts 1443.000000 CTS
... ... ... ... ... ... ...
84020 Female Total 2021 Rate per 100,000 population 5.197952 CTS
84021 Male Total 2022 Rate per 100,000 population 296.818546 CTS
84022 Female Total 2022 Rate per 100,000 population 7.049325 CTS
84023 Male Total 2023 Rate per 100,000 population 281.238749 CTS
84024 Female Total 2023 Rate per 100,000 population 7.663652 CTS
[84025 rows x 12 columns]item = df.pop('Indicator')print(df) Iso3_code Region Subregion Dimension \
0 BRB Americas Latin America and the Caribbean by selected crime
1 CRI Americas Latin America and the Caribbean by selected crime
2 DMA Americas Latin America and the Caribbean by selected crime
3 GBR_NI Europe Northern Europe by selected crime
4 ITA Europe Southern Europe by selected crime
... ... ... ... ...
84020 ZWE Africa Sub-Saharan Africa Total
84021 ZWE Africa Sub-Saharan Africa Total
84022 ZWE Africa Sub-Saharan Africa Total
84023 ZWE Africa Sub-Saharan Africa Total
84024 ZWE Africa Sub-Saharan Africa Total
Category Sex Age Year Unit of measurement \
0 Intentional Homicide Total Total 2016 Counts
1 Intentional Homicide Total Total 2016 Counts
2 Intentional Homicide Total Total 2016 Counts
3 Intentional Homicide Total Total 2016 Counts
4 Intentional Homicide Total Total 2016 Counts
... ... ... ... ... ...
84020 Total Female Total 2021 Rate per 100,000 population
84021 Total Male Total 2022 Rate per 100,000 population
84022 Total Female Total 2022 Rate per 100,000 population
84023 Total Male Total 2023 Rate per 100,000 population
84024 Total Female Total 2023 Rate per 100,000 population
VALUE Source
0 0.000000 CTS
1 173.000000 CTS
2 65.000000 CTS
3 61.000000 CTS
4 1443.000000 CTS
... ... ...
84020 5.197952 CTS
84021 296.818546 CTS
84022 7.049325 CTS
84023 281.238749 CTS
84024 7.663652 CTS
[84025 rows x 11 columns]deep = df.copy(deep=True)shallow = df.copy(deep=False)df['Region'] = 'origin changed'
shallow['Subregion'] = 'changed'
print(shallow)
print(f'-------------------------------------------------\n{df}') Iso3_code Region Subregion Dimension \
0 BRB origin changed changed by selected crime
1 CRI origin changed changed by selected crime
2 DMA origin changed changed by selected crime
3 GBR_NI origin changed changed by selected crime
4 ITA origin changed changed by selected crime
... ... ... ... ...
84020 ZWE origin changed changed Total
84021 ZWE origin changed changed Total
84022 ZWE origin changed changed Total
84023 ZWE origin changed changed Total
84024 ZWE origin changed changed Total
Category Sex Age Year Unit of measurement \
0 Intentional Homicide Total Total 2016 Counts
1 Intentional Homicide Total Total 2016 Counts
2 Intentional Homicide Total Total 2016 Counts
3 Intentional Homicide Total Total 2016 Counts
4 Intentional Homicide Total Total 2016 Counts
... ... ... ... ... ...
84020 Total Female Total 2021 Rate per 100,000 population
84021 Total Male Total 2022 Rate per 100,000 population
84022 Total Female Total 2022 Rate per 100,000 population
84023 Total Male Total 2023 Rate per 100,000 population
84024 Total Female Total 2023 Rate per 100,000 population
VALUE Source
0 0.000000 CTS
1 173.000000 CTS
2 65.000000 CTS
3 61.000000 CTS
4 1443.000000 CTS
... ... ...
84020 5.197952 CTS
84021 296.818546 CTS
84022 7.049325 CTS
84023 281.238749 CTS
84024 7.663652 CTS
[84025 rows x 11 columns]
-------------------------------------------------
Iso3_code Region Subregion \
0 BRB origin changed Latin America and the Caribbean
1 CRI origin changed Latin America and the Caribbean
2 DMA origin changed Latin America and the Caribbean
3 GBR_NI origin changed Northern Europe
4 ITA origin changed Southern Europe
... ... ... ...
84020 ZWE origin changed Sub-Saharan Africa
84021 ZWE origin changed Sub-Saharan Africa
84022 ZWE origin changed Sub-Saharan Africa
84023 ZWE origin changed Sub-Saharan Africa
84024 ZWE origin changed Sub-Saharan Africa
Dimension Category Sex Age Year \
0 by selected crime Intentional Homicide Total Total 2016
1 by selected crime Intentional Homicide Total Total 2016
2 by selected crime Intentional Homicide Total Total 2016
3 by selected crime Intentional Homicide Total Total 2016
4 by selected crime Intentional Homicide Total Total 2016
... ... ... ... ... ...
84020 Total Total Female Total 2021
84021 Total Total Male Total 2022
84022 Total Total Female Total 2022
84023 Total Total Male Total 2023
84024 Total Total Female Total 2023
Unit of measurement VALUE Source
0 Counts 0.000000 CTS
1 Counts 173.000000 CTS
2 Counts 65.000000 CTS
3 Counts 61.000000 CTS
4 Counts 1443.000000 CTS
... ... ... ...
84020 Rate per 100,000 population 5.197952 CTS
84021 Rate per 100,000 population 296.818546 CTS
84022 Rate per 100,000 population 7.049325 CTS
84023 Rate per 100,000 population 281.238749 CTS
84024 Rate per 100,000 population 7.663652 CTS
[84025 rows x 11 columns]print(df.drop(labels=2, axis=0)) Iso3_code Region Subregion \
0 BRB origin changed Latin America and the Caribbean
1 CRI origin changed Latin America and the Caribbean
3 GBR_NI origin changed Northern Europe
4 ITA origin changed Southern Europe
5 LTU origin changed Northern Europe
... ... ... ...
84020 ZWE origin changed Sub-Saharan Africa
84021 ZWE origin changed Sub-Saharan Africa
84022 ZWE origin changed Sub-Saharan Africa
84023 ZWE origin changed Sub-Saharan Africa
84024 ZWE origin changed Sub-Saharan Africa
Dimension Category Sex Age Year \
0 by selected crime Intentional Homicide Total Total 2016
1 by selected crime Intentional Homicide Total Total 2016
3 by selected crime Intentional Homicide Total Total 2016
4 by selected crime Intentional Homicide Total Total 2016
5 by selected crime Intentional Homicide Total Total 2016
... ... ... ... ... ...
84020 Total Total Female Total 2021
84021 Total Total Male Total 2022
84022 Total Total Female Total 2022
84023 Total Total Male Total 2023
84024 Total Total Female Total 2023
Unit of measurement VALUE Source
0 Counts 0.000000 CTS
1 Counts 173.000000 CTS
3 Counts 61.000000 CTS
4 Counts 1443.000000 CTS
5 Counts 417.000000 CTS
... ... ... ...
84020 Rate per 100,000 population 5.197952 CTS
84021 Rate per 100,000 population 296.818546 CTS
84022 Rate per 100,000 population 7.049325 CTS
84023 Rate per 100,000 population 281.238749 CTS
84024 Rate per 100,000 population 7.663652 CTS
[84024 rows x 11 columns]print(df.drop(labels='Region', axis=1)) Iso3_code Subregion Dimension \
0 BRB Latin America and the Caribbean by selected crime
1 CRI Latin America and the Caribbean by selected crime
2 DMA Latin America and the Caribbean by selected crime
3 GBR_NI Northern Europe by selected crime
4 ITA Southern Europe by selected crime
... ... ... ...
84020 ZWE Sub-Saharan Africa Total
84021 ZWE Sub-Saharan Africa Total
84022 ZWE Sub-Saharan Africa Total
84023 ZWE Sub-Saharan Africa Total
84024 ZWE Sub-Saharan Africa Total
Category Sex Age Year Unit of measurement \
0 Intentional Homicide Total Total 2016 Counts
1 Intentional Homicide Total Total 2016 Counts
2 Intentional Homicide Total Total 2016 Counts
3 Intentional Homicide Total Total 2016 Counts
4 Intentional Homicide Total Total 2016 Counts
... ... ... ... ... ...
84020 Total Female Total 2021 Rate per 100,000 population
84021 Total Male Total 2022 Rate per 100,000 population
84022 Total Female Total 2022 Rate per 100,000 population
84023 Total Male Total 2023 Rate per 100,000 population
84024 Total Female Total 2023 Rate per 100,000 population
VALUE Source
0 0.000000 CTS
1 173.000000 CTS
2 65.000000 CTS
3 61.000000 CTS
4 1443.000000 CTS
... ... ...
84020 5.197952 CTS
84021 296.818546 CTS
84022 7.049325 CTS
84023 281.238749 CTS
84024 7.663652 CTS
[84025 rows x 10 columns]print(df.drop(index=3)) Iso3_code Region Subregion \
0 BRB origin changed Latin America and the Caribbean
1 CRI origin changed Latin America and the Caribbean
2 DMA origin changed Latin America and the Caribbean
4 ITA origin changed Southern Europe
5 LTU origin changed Northern Europe
... ... ... ...
84020 ZWE origin changed Sub-Saharan Africa
84021 ZWE origin changed Sub-Saharan Africa
84022 ZWE origin changed Sub-Saharan Africa
84023 ZWE origin changed Sub-Saharan Africa
84024 ZWE origin changed Sub-Saharan Africa
Dimension Category Sex Age Year \
0 by selected crime Intentional Homicide Total Total 2016
1 by selected crime Intentional Homicide Total Total 2016
2 by selected crime Intentional Homicide Total Total 2016
4 by selected crime Intentional Homicide Total Total 2016
5 by selected crime Intentional Homicide Total Total 2016
... ... ... ... ... ...
84020 Total Total Female Total 2021
84021 Total Total Male Total 2022
84022 Total Total Female Total 2022
84023 Total Total Male Total 2023
84024 Total Total Female Total 2023
Unit of measurement VALUE Source
0 Counts 0.000000 CTS
1 Counts 173.000000 CTS
2 Counts 65.000000 CTS
4 Counts 1443.000000 CTS
5 Counts 417.000000 CTS
... ... ... ...
84020 Rate per 100,000 population 5.197952 CTS
84021 Rate per 100,000 population 296.818546 CTS
84022 Rate per 100,000 population 7.049325 CTS
84023 Rate per 100,000 population 281.238749 CTS
84024 Rate per 100,000 population 7.663652 CTS
[84024 rows x 11 columns]print(df.drop(columns='VALUE')) Iso3_code Region Subregion \
0 BRB origin changed Latin America and the Caribbean
1 CRI origin changed Latin America and the Caribbean
2 DMA origin changed Latin America and the Caribbean
3 GBR_NI origin changed Northern Europe
4 ITA origin changed Southern Europe
... ... ... ...
84020 ZWE origin changed Sub-Saharan Africa
84021 ZWE origin changed Sub-Saharan Africa
84022 ZWE origin changed Sub-Saharan Africa
84023 ZWE origin changed Sub-Saharan Africa
84024 ZWE origin changed Sub-Saharan Africa
Dimension Category Sex Age Year \
0 by selected crime Intentional Homicide Total Total 2016
1 by selected crime Intentional Homicide Total Total 2016
2 by selected crime Intentional Homicide Total Total 2016
3 by selected crime Intentional Homicide Total Total 2016
4 by selected crime Intentional Homicide Total Total 2016
... ... ... ... ... ...
84020 Total Total Female Total 2021
84021 Total Total Male Total 2022
84022 Total Total Female Total 2022
84023 Total Total Male Total 2023
84024 Total Total Female Total 2023
Unit of measurement Source
0 Counts CTS
1 Counts CTS
2 Counts CTS
3 Counts CTS
4 Counts CTS
... ... ...
84020 Rate per 100,000 population CTS
84021 Rate per 100,000 population CTS
84022 Rate per 100,000 population CTS
84023 Rate per 100,000 population CTS
84024 Rate per 100,000 population CTS
[84025 rows x 10 columns]print(df.drop(labels=['Category', 'Not exist'],axis=1, errors='raise'))---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[107], line 1
----> 1 print(df.drop(labels=['Category', 'Not exist'],axis=1, errors='raise'))
File ~\AppData\Local\Programs\Python\Python314\Lib\site-packages\pandas\core\frame.py:5603, in DataFrame.drop(self, labels, axis, index, columns, level, inplace, errors)
5455 def drop(
5456 self,
5457 labels: IndexLabel | None = None,
(...) 5464 errors: IgnoreRaise = "raise",
5465 ) -> DataFrame | None:
5466 """
5467 Drop specified labels from rows or columns.
5468
(...) 5601 weight 1.0 0.8
5602 """
-> 5603 return super().drop(
5604 labels=labels,
5605 axis=axis,
5606 index=index,
5607 columns=columns,
5608 level=level,
5609 inplace=inplace,
5610 errors=errors,
5611 )
File ~\AppData\Local\Programs\Python\Python314\Lib\site-packages\pandas\core\generic.py:4810, in NDFrame.drop(self, labels, axis, index, columns, level, inplace, errors)
4808 for axis, labels in axes.items():
4809 if labels is not None:
-> 4810 obj = obj._drop_axis(labels, axis, level=level, errors=errors)
4812 if inplace:
4813 self._update_inplace(obj)
File ~\AppData\Local\Programs\Python\Python314\Lib\site-packages\pandas\core\generic.py:4852, in NDFrame._drop_axis(self, labels, axis, level, errors, only_slice)
4850 new_axis = axis.drop(labels, level=level, errors=errors)
4851 else:
-> 4852 new_axis = axis.drop(labels, errors=errors)
4853 indexer = axis.get_indexer(new_axis)
4855 # Case for non-unique axis
4856 else:
File ~\AppData\Local\Programs\Python\Python314\Lib\site-packages\pandas\core\indexes\base.py:7136, in Index.drop(self, labels, errors)
7134 if mask.any():
7135 if errors != "ignore":
-> 7136 raise KeyError(f"{labels[mask].tolist()} not found in axis")
7137 indexer = indexer[~mask]
7138 return self.delete(indexer)
KeyError: "['Category', 'Not exist'] not found in axis"print(df.drop(labels=['Category', 'Not exist'],axis=1, errors='ignore')) Iso3_code Region Subregion \
0 BRB origin changed Latin America and the Caribbean
1 CRI origin changed Latin America and the Caribbean
2 DMA origin changed Latin America and the Caribbean
3 GBR_NI origin changed Northern Europe
4 ITA origin changed Southern Europe
... ... ... ...
84020 ZWE origin changed Sub-Saharan Africa
84021 ZWE origin changed Sub-Saharan Africa
84022 ZWE origin changed Sub-Saharan Africa
84023 ZWE origin changed Sub-Saharan Africa
84024 ZWE origin changed Sub-Saharan Africa
Dimension Sex Age Year Unit of measurement \
0 by selected crime Total Total 2016 Counts
1 by selected crime Total Total 2016 Counts
2 by selected crime Total Total 2016 Counts
3 by selected crime Total Total 2016 Counts
4 by selected crime Total Total 2016 Counts
... ... ... ... ... ...
84020 Total Female Total 2021 Rate per 100,000 population
84021 Total Male Total 2022 Rate per 100,000 population
84022 Total Female Total 2022 Rate per 100,000 population
84023 Total Male Total 2023 Rate per 100,000 population
84024 Total Female Total 2023 Rate per 100,000 population
VALUE Source
0 0.000000 CTS
1 173.000000 CTS
2 65.000000 CTS
3 61.000000 CTS
4 1443.000000 CTS
... ... ...
84020 5.197952 CTS
84021 296.818546 CTS
84022 7.049325 CTS
84023 281.238749 CTS
84024 7.663652 CTS
[84025 rows x 10 columns]print(df.drop(labels=['Category', 'Year'],axis=1, inplace=True))Noneprint(df) Iso3_code Region Subregion \
0 BRB origin changed Latin America and the Caribbean
1 CRI origin changed Latin America and the Caribbean
2 DMA origin changed Latin America and the Caribbean
3 GBR_NI origin changed Northern Europe
4 ITA origin changed Southern Europe
... ... ... ...
84020 ZWE origin changed Sub-Saharan Africa
84021 ZWE origin changed Sub-Saharan Africa
84022 ZWE origin changed Sub-Saharan Africa
84023 ZWE origin changed Sub-Saharan Africa
84024 ZWE origin changed Sub-Saharan Africa
Dimension Sex Age Unit of measurement \
0 by selected crime Total Total Counts
1 by selected crime Total Total Counts
2 by selected crime Total Total Counts
3 by selected crime Total Total Counts
4 by selected crime Total Total Counts
... ... ... ... ...
84020 Total Female Total Rate per 100,000 population
84021 Total Male Total Rate per 100,000 population
84022 Total Female Total Rate per 100,000 population
84023 Total Male Total Rate per 100,000 population
84024 Total Female Total Rate per 100,000 population
VALUE Source
0 0.000000 CTS
1 173.000000 CTS
2 65.000000 CTS
3 61.000000 CTS
4 1443.000000 CTS
... ... ...
84020 5.197952 CTS
84021 296.818546 CTS
84022 7.049325 CTS
84023 281.238749 CTS
84024 7.663652 CTS
[84025 rows x 9 columns]print(df.truncate(before=4, after=84021, axis=0)) Iso3_code Region Subregion \
4 ITA origin changed Southern Europe
5 LTU origin changed Northern Europe
6 MAC origin changed Eastern Asia
7 MEX origin changed Latin America and the Caribbean
8 NOR origin changed Northern Europe
... ... ... ...
84017 ZWE origin changed Sub-Saharan Africa
84018 ZWE origin changed Sub-Saharan Africa
84019 ZWE origin changed Sub-Saharan Africa
84020 ZWE origin changed Sub-Saharan Africa
84021 ZWE origin changed Sub-Saharan Africa
Dimension Sex Age Unit of measurement \
4 by selected crime Total Total Counts
5 by selected crime Total Total Counts
6 by selected crime Total Total Counts
7 by selected crime Total Total Counts
8 by selected crime Total Total Counts
... ... ... ... ...
84017 Total Male Total Rate per 100,000 population
84018 Total Female Total Rate per 100,000 population
84019 Total Male Total Rate per 100,000 population
84020 Total Female Total Rate per 100,000 population
84021 Total Male Total Rate per 100,000 population
VALUE Source
4 1443.000000 CTS
5 417.000000 CTS
6 2.000000 CTS
7 9869.000000 CTS
8 39.000000 CTS
... ... ...
84017 255.474868 CTS
84018 4.524110 CTS
84019 260.183157 CTS
84020 5.197952 CTS
84021 296.818546 CTS
[84018 rows x 9 columns]df_sorted = df.sort_index(axis=1)
print(df_sorted.truncate(before='Region', after='Sex', axis=1)) Region Sex
0 origin changed Total
1 origin changed Total
2 origin changed Total
3 origin changed Total
4 origin changed Total
... ... ...
84020 origin changed Female
84021 origin changed Male
84022 origin changed Female
84023 origin changed Male
84024 origin changed Female
[84025 rows x 2 columns]short_df = df_sorted.truncate(after=4, axis=0)short_df = short_df.truncate(before='Region', after='Sex', axis=1)print(short_df) Region Sex
0 origin changed Total
1 origin changed Total
2 origin changed Total
3 origin changed Total
4 origin changed Totalshort_df.insert(1, 'New', ['hello', 'hello', 'hello', 'dd', 'dd'],allow_duplicates=True)print(short_df.drop_duplicates()) Region New Sex
0 origin changed hello Total
3 origin changed dd Totaldf2 = pd.DataFrame({'Region': ['Hawai', 'Costarica'],
'Country': ['Country1', 'Country2']
})
result = pd.concat([short_df, df2])
print(result) Region New Sex Country
0 origin changed hello Total NaN
1 origin changed hello Total NaN
2 origin changed hello Total NaN
3 origin changed dd Total NaN
4 origin changed dd Total NaN
0 Hawai NaN NaN Country1
1 Costarica NaN NaN Country2print(pd.concat([short_df, df2], ignore_index=True)) Region New Sex Country
0 origin changed hello Total NaN
1 origin changed hello Total NaN
2 origin changed hello Total NaN
3 origin changed dd Total NaN
4 origin changed dd Total NaN
5 Hawai NaN NaN Country1
6 Costarica NaN NaN Country2print(pd.concat([short_df, df2], keys=['k1', 'k2'])) Region New Sex Country
k1 0 origin changed hello Total NaN
1 origin changed hello Total NaN
2 origin changed hello Total NaN
3 origin changed dd Total NaN
4 origin changed dd Total NaN
k2 0 Hawai NaN NaN Country1
1 Costarica NaN NaN Country2print(pd.concat([short_df, df2], keys=['k1', 'k2'], names=['name1', 'name2'])) Region New Sex Country
name1 name2
k1 0 origin changed hello Total NaN
1 origin changed hello Total NaN
2 origin changed hello Total NaN
3 origin changed dd Total NaN
4 origin changed dd Total NaN
k2 0 Hawai NaN NaN Country1
1 Costarica NaN NaN Country2print(pd.concat([short_df, df2], join='inner')) Region
0 origin changed
1 origin changed
2 origin changed
3 origin changed
4 origin changed
0 Hawai
1 Costaricaprint(pd.concat([short_df, df2], axis=1)) Region New Sex Region Country
0 origin changed hello Total Hawai Country1
1 origin changed hello Total Costarica Country2
2 origin changed hello Total NaN NaN
3 origin changed dd Total NaN NaN
4 origin changed dd Total NaN NaNprint(pd.concat([short_df, df2], verify_integrity=True))---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[138], line 1
----> 1 print(pd.concat([short_df, df2], verify_integrity=True))
File ~\AppData\Local\Programs\Python\Python314\Lib\site-packages\pandas\core\reshape\concat.py:395, in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
380 copy = False
382 op = _Concatenator(
383 objs,
384 axis=axis,
(...) 392 sort=sort,
393 )
--> 395 return op.get_result()
File ~\AppData\Local\Programs\Python\Python314\Lib\site-packages\pandas\core\reshape\concat.py:671, in _Concatenator.get_result(self)
669 for obj in self.objs:
670 indexers = {}
--> 671 for ax, new_labels in enumerate(self.new_axes):
672 # ::-1 to convert BlockManager ax to DataFrame ax
673 if ax == self.bm_axis:
674 # Suppress reindexing on concat axis
675 continue
File pandas/_libs/properties.pyx:36, in pandas._libs.properties.CachedProperty.__get__()
File ~\AppData\Local\Programs\Python\Python314\Lib\site-packages\pandas\core\reshape\concat.py:703, in _Concatenator.new_axes(self)
699 @cache_readonly
700 def new_axes(self) -> list[Index]:
701 ndim = self._get_result_dim()
702 return [
--> 703 self._get_concat_axis if i == self.bm_axis else self._get_comb_axis(i)
704 for i in range(ndim)
705 ]
File pandas/_libs/properties.pyx:36, in pandas._libs.properties.CachedProperty.__get__()
File ~\AppData\Local\Programs\Python\Python314\Lib\site-packages\pandas\core\reshape\concat.py:766, in _Concatenator._get_concat_axis(self)
761 else:
762 concat_axis = _make_concat_multiindex(
763 indexes, self.keys, self.levels, self.names
764 )
--> 766 self._maybe_check_integrity(concat_axis)
768 return concat_axis
File ~\AppData\Local\Programs\Python\Python314\Lib\site-packages\pandas\core\reshape\concat.py:774, in _Concatenator._maybe_check_integrity(self, concat_index)
772 if not concat_index.is_unique:
773 overlap = concat_index[concat_index.duplicated()].unique()
--> 774 raise ValueError(f"Indexes have overlapping values: {overlap}")
ValueError: Indexes have overlapping values: Index([0, 1], dtype='int64')new_row = pd.Series({"ding": "dong", "dang": "dong"})print(pd.concat([result, new_row.to_frame().T], ignore_index=True)) Region New Sex Country ding dang
0 origin changed hello Total NaN NaN NaN
1 origin changed hello Total NaN NaN NaN
2 origin changed hello Total NaN NaN NaN
3 origin changed dd Total NaN NaN NaN
4 origin changed dd Total NaN NaN NaN
5 Hawai NaN NaN Country1 NaN NaN
6 Costarica NaN NaN Country2 NaN NaN
7 NaN NaN NaN NaN dong dong
오랜시간 망설였던 코딩을 다시 해보려고 노력하고 있는 사람