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saibala29
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Pokedex_Data

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The dataset generation failed because of a cast error

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 2 new columns ({'query', 'question'}) and 13 missing columns ({'Speed', 'Type 2', 'Sp. Atk', 'Generation', 'Defense', 'Total', 'Type 1', 'Legendary', 'Sp. Def', 'Name', 'Attack', '#', 'HP'}).

This happened while the csv dataset builder was generating data using

hf://datasets/saibala29/Pokedex_Data/Pokemon_Final_Fixed_Questions_Queries.csv (at revision bc02d53487c4075a47b36dd54459117891cf3049)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              question: string
              query: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 487
              to
              {'#': Value(dtype='int64', id=None), 'Name': Value(dtype='string', id=None), 'Type 1': Value(dtype='string', id=None), 'Type 2': Value(dtype='string', id=None), 'Total': Value(dtype='int64', id=None), 'HP': Value(dtype='int64', id=None), 'Attack': Value(dtype='int64', id=None), 'Defense': Value(dtype='int64', id=None), 'Sp. Atk': Value(dtype='int64', id=None), 'Sp. Def': Value(dtype='int64', id=None), 'Speed': Value(dtype='int64', id=None), 'Generation': Value(dtype='int64', id=None), 'Legendary': Value(dtype='bool', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1321, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 935, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 2 new columns ({'query', 'question'}) and 13 missing columns ({'Speed', 'Type 2', 'Sp. Atk', 'Generation', 'Defense', 'Total', 'Type 1', 'Legendary', 'Sp. Def', 'Name', 'Attack', '#', 'HP'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/saibala29/Pokedex_Data/Pokemon_Final_Fixed_Questions_Queries.csv (at revision bc02d53487c4075a47b36dd54459117891cf3049)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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# int64	Name string	Type 1 string	Type 2 string	Total int64	HP int64	Attack int64	Defense int64	Sp. Atk int64	Sp. Def int64	Speed int64	Generation int64	Legendary bool
1	Bulbasaur	Grass	Poison	318	45	49	49	65	65	45	1	false
2	Ivysaur	Grass	Poison	405	60	62	63	80	80	60	1	false
3	Venusaur	Grass	Poison	525	80	82	83	100	100	80	1	false
3	VenusaurMega Venusaur	Grass	Poison	625	80	100	123	122	120	80	1	false
4	Charmander	Fire	null	309	39	52	43	60	50	65	1	false
5	Charmeleon	Fire	null	405	58	64	58	80	65	80	1	false
6	Charizard	Fire	Flying	534	78	84	78	109	85	100	1	false
6	CharizardMega Charizard X	Fire	Dragon	634	78	130	111	130	85	100	1	false
6	CharizardMega Charizard Y	Fire	Flying	634	78	104	78	159	115	100	1	false
7	Squirtle	Water	null	314	44	48	65	50	64	43	1	false
8	Wartortle	Water	null	405	59	63	80	65	80	58	1	false
9	Blastoise	Water	null	530	79	83	100	85	105	78	1	false
9	BlastoiseMega Blastoise	Water	null	630	79	103	120	135	115	78	1	false
10	Caterpie	Bug	null	195	45	30	35	20	20	45	1	false
11	Metapod	Bug	null	205	50	20	55	25	25	30	1	false
12	Butterfree	Bug	Flying	395	60	45	50	90	80	70	1	false
13	Weedle	Bug	Poison	195	40	35	30	20	20	50	1	false
14	Kakuna	Bug	Poison	205	45	25	50	25	25	35	1	false
15	Beedrill	Bug	Poison	395	65	90	40	45	80	75	1	false
15	BeedrillMega Beedrill	Bug	Poison	495	65	150	40	15	80	145	1	false
16	Pidgey	Normal	Flying	251	40	45	40	35	35	56	1	false
17	Pidgeotto	Normal	Flying	349	63	60	55	50	50	71	1	false
18	Pidgeot	Normal	Flying	479	83	80	75	70	70	101	1	false
18	PidgeotMega Pidgeot	Normal	Flying	579	83	80	80	135	80	121	1	false
19	Rattata	Normal	null	253	30	56	35	25	35	72	1	false
20	Raticate	Normal	null	413	55	81	60	50	70	97	1	false
21	Spearow	Normal	Flying	262	40	60	30	31	31	70	1	false
22	Fearow	Normal	Flying	442	65	90	65	61	61	100	1	false
23	Ekans	Poison	null	288	35	60	44	40	54	55	1	false
24	Arbok	Poison	null	438	60	85	69	65	79	80	1	false
25	Pikachu	Electric	null	320	35	55	40	50	50	90	1	false
26	Raichu	Electric	null	485	60	90	55	90	80	110	1	false
27	Sandshrew	Ground	null	300	50	75	85	20	30	40	1	false
28	Sandslash	Ground	null	450	75	100	110	45	55	65	1	false
29	Nidoran♀	Poison	null	275	55	47	52	40	40	41	1	false
30	Nidorina	Poison	null	365	70	62	67	55	55	56	1	false
31	Nidoqueen	Poison	Ground	505	90	92	87	75	85	76	1	false
32	Nidoran♂	Poison	null	273	46	57	40	40	40	50	1	false
33	Nidorino	Poison	null	365	61	72	57	55	55	65	1	false
34	Nidoking	Poison	Ground	505	81	102	77	85	75	85	1	false
35	Clefairy	Fairy	null	323	70	45	48	60	65	35	1	false
36	Clefable	Fairy	null	483	95	70	73	95	90	60	1	false
37	Vulpix	Fire	null	299	38	41	40	50	65	65	1	false
38	Ninetales	Fire	null	505	73	76	75	81	100	100	1	false
39	Jigglypuff	Normal	Fairy	270	115	45	20	45	25	20	1	false
40	Wigglytuff	Normal	Fairy	435	140	70	45	85	50	45	1	false
41	Zubat	Poison	Flying	245	40	45	35	30	40	55	1	false
42	Golbat	Poison	Flying	455	75	80	70	65	75	90	1	false
43	Oddish	Grass	Poison	320	45	50	55	75	65	30	1	false
44	Gloom	Grass	Poison	395	60	65	70	85	75	40	1	false
45	Vileplume	Grass	Poison	490	75	80	85	110	90	50	1	false
46	Paras	Bug	Grass	285	35	70	55	45	55	25	1	false
47	Parasect	Bug	Grass	405	60	95	80	60	80	30	1	false
48	Venonat	Bug	Poison	305	60	55	50	40	55	45	1	false
49	Venomoth	Bug	Poison	450	70	65	60	90	75	90	1	false
50	Diglett	Ground	null	265	10	55	25	35	45	95	1	false
51	Dugtrio	Ground	null	405	35	80	50	50	70	120	1	false
52	Meowth	Normal	null	290	40	45	35	40	40	90	1	false
53	Persian	Normal	null	440	65	70	60	65	65	115	1	false
54	Psyduck	Water	null	320	50	52	48	65	50	55	1	false
55	Golduck	Water	null	500	80	82	78	95	80	85	1	false
56	Mankey	Fighting	null	305	40	80	35	35	45	70	1	false
57	Primeape	Fighting	null	455	65	105	60	60	70	95	1	false
58	Growlithe	Fire	null	350	55	70	45	70	50	60	1	false
59	Arcanine	Fire	null	555	90	110	80	100	80	95	1	false
60	Poliwag	Water	null	300	40	50	40	40	40	90	1	false
61	Poliwhirl	Water	null	385	65	65	65	50	50	90	1	false
62	Poliwrath	Water	Fighting	510	90	95	95	70	90	70	1	false
63	Abra	Psychic	null	310	25	20	15	105	55	90	1	false
64	Kadabra	Psychic	null	400	40	35	30	120	70	105	1	false
65	Alakazam	Psychic	null	500	55	50	45	135	95	120	1	false
65	AlakazamMega Alakazam	Psychic	null	590	55	50	65	175	95	150	1	false
66	Machop	Fighting	null	305	70	80	50	35	35	35	1	false
67	Machoke	Fighting	null	405	80	100	70	50	60	45	1	false
68	Machamp	Fighting	null	505	90	130	80	65	85	55	1	false
69	Bellsprout	Grass	Poison	300	50	75	35	70	30	40	1	false
70	Weepinbell	Grass	Poison	390	65	90	50	85	45	55	1	false
71	Victreebel	Grass	Poison	490	80	105	65	100	70	70	1	false
72	Tentacool	Water	Poison	335	40	40	35	50	100	70	1	false
73	Tentacruel	Water	Poison	515	80	70	65	80	120	100	1	false
74	Geodude	Rock	Ground	300	40	80	100	30	30	20	1	false
75	Graveler	Rock	Ground	390	55	95	115	45	45	35	1	false
76	Golem	Rock	Ground	495	80	120	130	55	65	45	1	false
77	Ponyta	Fire	null	410	50	85	55	65	65	90	1	false
78	Rapidash	Fire	null	500	65	100	70	80	80	105	1	false
79	Slowpoke	Water	Psychic	315	90	65	65	40	40	15	1	false
80	Slowbro	Water	Psychic	490	95	75	110	100	80	30	1	false
80	SlowbroMega Slowbro	Water	Psychic	590	95	75	180	130	80	30	1	false
81	Magnemite	Electric	Steel	325	25	35	70	95	55	45	1	false
82	Magneton	Electric	Steel	465	50	60	95	120	70	70	1	false
83	Farfetch'd	Normal	Flying	352	52	65	55	58	62	60	1	false
84	Doduo	Normal	Flying	310	35	85	45	35	35	75	1	false
85	Dodrio	Normal	Flying	460	60	110	70	60	60	100	1	false
86	Seel	Water	null	325	65	45	55	45	70	45	1	false
87	Dewgong	Water	Ice	475	90	70	80	70	95	70	1	false
88	Grimer	Poison	null	325	80	80	50	40	50	25	1	false
89	Muk	Poison	null	500	105	105	75	65	100	50	1	false
90	Shellder	Water	null	305	30	65	100	45	25	40	1	false
91	Cloyster	Water	Ice	525	50	95	180	85	45	70	1	false
92	Gastly	Ghost	Poison	310	30	35	30	100	35	80	1	false

End of preview.

Pokémon Dataset Overview 📊

This dataset provides a comprehensive compilation of Pokémon data 🎮, covering various aspects such as stats, types, generations, and legendary status. It's designed for enthusiasts, researchers, and developers interested in exploring Pokémon data for analysis, machine learning models, and application development 🚀.

Dataset Description 📝

The Pokémon dataset includes the following key features:

Name: The name of the Pokémon. 🧚
Type 1: The primary type of the Pokémon. 🔥/💧/🌿
Type 2: The secondary type of the Pokémon (if any). ⚡/🪨/🧊
Total: Sum of all stats, providing an overall strength rating. 💪
HP: Hit Points or health. ❤️
Attack: The base modifier for normal attacks. 🗡️
Defense: The base damage resistance against normal attacks. 🛡️
Sp. Atk: Special Attack, the base modifier for special attacks. ✨
Sp. Def: Special Defense, the base damage resistance against special attacks. 🌟
Speed: Determines how quickly a Pokémon can act in battle. 💨
Generation: Indicates the generation a Pokémon belongs to. 🔄
Legendary: Indicates whether a Pokémon is legendary. 🌈

Dataset Structure 🏗️

Files and Folders 📁

Pokemon.csv: Main dataset file containing all Pokémon data. 📄
Pokemon_Final_Fixed_Questions_Queries.csv: Contains questions and MongoDB queries related to the Pokémon dataset, useful for database exercises and training AI models. 🤔💡

Data Fields 🛠️

A brief description of the dataset fields is as follows:

Name: String 📛
Type 1: String 🔥/💧/🌿
Type 2: String (nullable) ⚡/🪨/🧊
Total, HP, Attack, Defense, Sp. Atk, Sp. Def, Speed: Integer 📊
Generation: Integer 🔄
Legendary: Boolean ✨

Usage 📚

This dataset can be utilized for various purposes, including but not limited to:

Data analysis and visualization of Pokémon characteristics. 📈
Training machine learning models to predict outcomes of Pokémon battles. 🤖
Developing applications or games that leverage Pokémon data. 🎮

Acknowledgements 🙏

This dataset is made available for educational and research purposes. Please respect the Pokémon trademark and use this dataset responsibly.

License 📜

This dataset is provided for non-commercial, research, or educational purposes. Please review the specific license terms if applicable.

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