From ba88ef63f1157ee6f2b5111c3bd25329c2b1bd6e Mon Sep 17 00:00:00 2001 From: Mingu Kim Date: Tue, 26 May 2026 22:31:40 +0900 Subject: [PATCH] =?UTF-8?q?=EC=BD=94=EB=9E=A9=20=EA=B8=B0=EC=B4=88=20?= =?UTF-8?q?=EB=B0=8F=20=ED=8C=8C=EC=9D=B4=EC=8D=AC=20=EA=B8=B0=EC=B4=88=20?= =?UTF-8?q?=EC=88=98=EC=A0=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- colab/Behind the pipeline (PyTorch).ipynb | 1 + ... model with the Trainer API or Keras.ipynb | 1 + colab/Processing the data (PyTorch).ipynb | 1 + colab/Untitled | 1 + colab/Untitled0.ipynb | 1 + colab/Untitled1.ipynb | 1 + colab/Untitled2.ipynb | 1 + colab/cat.jfif | Bin 0 -> 5446 bytes colab/friends.csv | 4 + colab/obama.mp3 | Bin 0 -> 1370176 bytes colab/seoul.png | Bin 0 -> 509700 bytes colab/voice.mp3 | Bin 0 -> 14400 bytes colab/트랜스포머로 무엇을 할 수 있나요.ipynb | 1 + pythonSource/Basic/exam1.ipynb | 4217 ++++++++++++++++- pythonSource/Basic/hello.py | 15 +- pythonSource/Basic/info.txt | 500 ++ pythonSource/Basic/mod1.py | 12 + pythonSource/Basic/test.db | Bin 0 -> 12288 bytes pythonSource/Basic/test.json | 10 + pythonSource/Basic/test.txt | 1 + pythonSource/Basic/test1.json | 1 + pythonSource/Basic/test2.txt | 3 + pythonSource/gradio/app1.py | 13 + 23 files changed, 4707 insertions(+), 77 deletions(-) create mode 100755 colab/Behind the pipeline (PyTorch).ipynb create mode 100755 colab/Fine-tuning a model with the Trainer API or Keras.ipynb create mode 100644 colab/Processing the data (PyTorch).ipynb create mode 100644 colab/Untitled create mode 100644 colab/Untitled0.ipynb create mode 100644 colab/Untitled1.ipynb create mode 100644 colab/Untitled2.ipynb create mode 100644 colab/cat.jfif create mode 100644 colab/friends.csv create mode 100644 colab/obama.mp3 create mode 100644 colab/seoul.png create mode 100644 colab/voice.mp3 create mode 100755 colab/트랜스포머로 무엇을 할 수 있나요.ipynb create mode 100644 pythonSource/Basic/info.txt create mode 100644 pythonSource/Basic/mod1.py create mode 100644 pythonSource/Basic/test.db create mode 100644 pythonSource/Basic/test.json create mode 100644 pythonSource/Basic/test.txt create mode 100644 pythonSource/Basic/test1.json create mode 100644 pythonSource/Basic/test2.txt create mode 100644 pythonSource/gradio/app1.py diff --git a/colab/Behind the pipeline (PyTorch).ipynb b/colab/Behind the pipeline (PyTorch).ipynb new file mode 100755 index 0000000..fae7e76 --- /dev/null +++ b/colab/Behind the pipeline (PyTorch).ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"giIKZavhbhOn"},"source":["# Behind the pipeline (PyTorch)"]},{"cell_type":"markdown","metadata":{"id":"BOHmqSSmbhOs"},"source":["Install the Transformers, Datasets, and Evaluate libraries to run this notebook."]},{"cell_type":"code","execution_count":2,"metadata":{"id":"K8ZhhL0kbhOt","colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"status":"ok","timestamp":1779168824837,"user_tz":-540,"elapsed":4111,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"bcc1bd3a-f723-4d90-cec5-ab1e29510a5c"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: datasets in /usr/local/lib/python3.12/dist-packages (4.0.0)\n","Requirement already satisfied: evaluate in /usr/local/lib/python3.12/dist-packages (0.4.6)\n","Requirement already satisfied: transformers==4.41.2 in /usr/local/lib/python3.12/dist-packages (from transformers[sentencepiece]==4.41.2) (4.41.2)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from transformers==4.41.2->transformers[sentencepiece]==4.41.2) (3.29.0)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from transformers==4.41.2->transformers[sentencepiece]==4.41.2) (0.36.2)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from transformers==4.41.2->transformers[sentencepiece]==4.41.2) (2.0.2)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from transformers==4.41.2->transformers[sentencepiece]==4.41.2) (26.1)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from transformers==4.41.2->transformers[sentencepiece]==4.41.2) (6.0.3)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.12/dist-packages (from transformers==4.41.2->transformers[sentencepiece]==4.41.2) (2025.11.3)\n","Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from transformers==4.41.2->transformers[sentencepiece]==4.41.2) (2.32.4)\n","Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.12/dist-packages (from transformers==4.41.2->transformers[sentencepiece]==4.41.2) (0.19.1)\n","Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.12/dist-packages (from transformers==4.41.2->transformers[sentencepiece]==4.41.2) (0.7.0)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.12/dist-packages (from transformers==4.41.2->transformers[sentencepiece]==4.41.2) (4.67.3)\n","Requirement already satisfied: sentencepiece!=0.1.92,>=0.1.91 in /usr/local/lib/python3.12/dist-packages (from transformers[sentencepiece]==4.41.2) (0.2.1)\n","Requirement already satisfied: protobuf in /usr/local/lib/python3.12/dist-packages (from transformers[sentencepiece]==4.41.2) (5.29.6)\n","Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.12/dist-packages (from datasets) (18.1.0)\n","Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.12/dist-packages (from datasets) (0.3.8)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (from datasets) (2.2.2)\n","Requirement already satisfied: xxhash in /usr/local/lib/python3.12/dist-packages (from datasets) (3.6.0)\n","Requirement already satisfied: multiprocess<0.70.17 in /usr/local/lib/python3.12/dist-packages (from datasets) (0.70.16)\n","Requirement already satisfied: fsspec<=2025.3.0,>=2023.1.0 in /usr/local/lib/python3.12/dist-packages (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (2025.3.0)\n","Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /usr/local/lib/python3.12/dist-packages (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (3.13.5)\n","Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.23.0->transformers==4.41.2->transformers[sentencepiece]==4.41.2) (1.4.3)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.12/dist-packages (from 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pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas->datasets) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas->datasets) (2026.1)\n","Requirement already satisfied: aiohappyeyeballs>=2.5.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (2.6.1)\n","Requirement already satisfied: aiosignal>=1.4.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.4.0)\n","Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (26.1.0)\n","Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.8.0)\n","Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (6.7.1)\n","Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (0.4.1)\n","Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.23.0)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n"]}],"source":["!pip install datasets evaluate transformers[sentencepiece]==4.41.2"]},{"cell_type":"code","execution_count":3,"metadata":{"id":"P6da2t4vbhOt","outputId":"51c7f91a-b243-47a0-d150-739d64a5b5af","colab":{"base_uri":"https://localhost:8080/","height":385,"referenced_widgets":["e3bfe3ab72584d6982fbd8a6b00c8831","d6eaa4e6b37d424ead83f5e92ab6c5a9","1a827b8bb72a4adea45d9217cfaac11e","b55116e0b10249499b560e0ab101d59c","9d3cb3fac5814a889b9f38aee5ae74a9","5bd98ae6f70c47a98d50e781361298de","54277f2e3b03481ca307c2e526ac0a3f","824941a9b6d54a26a7c7a09ee7435e1d","0abdf724c84e4d33859b68075f3f13c8","d4cabfbfd7724234b77f1f3d3a79f881","5c7fe1bf589c48eba12d16400d8e0ebb","e97faa2b18464c3dac8bf3b1080768da","c7161dead81746959908694b37ee77d7","246227fa7c8e4e1cbcd3edad6b7f6795","1595f88649204d3091df80c22161dd15","5d6e8cdc1388471883686c7654e69a82","258ba80ad7fa43b48b775d1c8954c055","cf7f167f282743c2a7b5702f84f0b000","d602d9b2d226429c89adcb0c0966512a","8fd790543ff14a41ab0135cf9be689a1","31b83c96f2304d0cb9b332af5be2a4ce","7010b2025c5748fcafed1ea8a8c100a3","313c23c2f3f14d869e78174f8f3a93eb","9b1fa551d2ab4321b0aefc8a70044e47","197234f4d242409f9dc8b55b30f2a58b","91954af4defe4a8b905983ae2214eb6b","303cc5d43a20429b85b73443076ab5cd","b7d9bb5bf4bb48108989750cf3cafc89","6b1e395439a8427fb612407f197bbd95","c9cb2770dc864673b3c8a70b9f65a6e3","9c86eb3075904f008c2e81c1623fdfc1","f9904a0564c64dee8ca728c9a5196718","f468bc23ca144994b5b55d68e89ae900","553b581c30724d1192df9c8499c632c1","28b13e3ff4974fbaafb8d12d74102674","6b740ea1ea694a619b05ae88a68dce33","83fea5fbd9b14ad9b494b9b94fda85a8","3c2e970496bf4cfd9b82c12cac62f11d","729d2d20a94f4ae299f43ce91ee589f0","f7f317c14b654d4f857d30495e62bef3","34200c9c60aa42abb828177cdc3b2d75","fbd3f5bd4db849e9a7c9b7f869dd6604","77c277febb0f48c6acdf848eb3f104e3","ccef0461a3c54bd8ac67e281452ca939"]},"executionInfo":{"status":"ok","timestamp":1779169069526,"user_tz":-540,"elapsed":20010,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["No model was supplied, defaulted to distilbert/distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english).\n","Using a pipeline without specifying a model name and revision in production is not recommended.\n","/usr/local/lib/python3.12/dist-packages/huggingface_hub/file_download.py:949: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n"," warnings.warn(\n","/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n"," warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["config.json: 0%| | 0.00/629 [00:00)\n"]}],"source":["print(outputs.logits)"]},{"cell_type":"markdown","source":["- 4. 사람이 이해하기 쉽도록 후 작업"],"metadata":{"id":"0VD8rmJozSoJ"}},{"cell_type":"code","execution_count":11,"metadata":{"id":"5d1JgjZDbhOw","outputId":"9b876c84-c007-43cd-8399-4a695b1ce362","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779170135462,"user_tz":-540,"elapsed":7,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([[4.0195e-02, 9.5980e-01],\n"," [9.9946e-01, 5.4418e-04]], grad_fn=)\n"]}],"source":["import torch\n","\n","predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)\n","print(predictions)"]},{"cell_type":"code","execution_count":12,"metadata":{"id":"OLp47nXHbhOw","outputId":"4eff39a7-6c73-4bea-acc2-3bded25a6faf","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779170139053,"user_tz":-540,"elapsed":44,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{0: 'NEGATIVE', 1: 'POSITIVE'}"]},"metadata":{},"execution_count":12}],"source":["model.config.id2label"]},{"cell_type":"markdown","source":["### 토크나이저 추가\n","\n","\"Hello\" 일때:\n","\n","| 토큰 | ID | 의미 |\n","|----|----|----|\n","| [CLS] | 101 | 문장의 시작 |\n","| hello | 7592 | Hello |\n","| [SEP] | 102 | 문장의 끝 |\n","| [PAD] | 0 | 문장 길이 맞추기용 빈칸 |\n"],"metadata":{"id":"9OH8vliUzyoJ"}},{"cell_type":"code","source":["sequence = [\n"," \"Hello\",\n"," \"Cool\",\n"," \"Nice!\"\n","]\n","\n","inputs = tokenizer(sequence, padding=True, truncation=True, 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\ No newline at end of file diff --git a/colab/Fine-tuning a model with the Trainer API or Keras.ipynb b/colab/Fine-tuning a model with the Trainer API or Keras.ipynb new file mode 100755 index 0000000..ae99e6f --- /dev/null +++ b/colab/Fine-tuning a model with the Trainer API or Keras.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"sl90BFcgb3Gd"},"source":["# Fine-tuning a model with the Trainer API"]},{"cell_type":"markdown","metadata":{"id":"pZ67By2Rb3Ge"},"source":["Install the Transformers, Datasets, and Evaluate libraries to run this notebook."]},{"cell_type":"code","execution_count":1,"metadata":{"id":"Lpjg3fQhb3Gf","colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"status":"ok","timestamp":1779238411789,"user_tz":-540,"elapsed":16582,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"ffe976d5-ce10-45b4-9164-469af0d2da54"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: datasets in /usr/local/lib/python3.12/dist-packages (4.0.0)\n","Collecting evaluate\n"," Downloading evaluate-0.4.6-py3-none-any.whl.metadata (9.5 kB)\n","Requirement already satisfied: transformers[sentencepiece] in /usr/local/lib/python3.12/dist-packages (5.0.0)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from datasets) (3.29.0)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from datasets) (2.0.2)\n","Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.12/dist-packages (from datasets) (18.1.0)\n","Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.12/dist-packages (from datasets) (0.3.8)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (from datasets) (2.2.2)\n","Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.12/dist-packages (from datasets) (2.32.4)\n","Requirement 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transformers[sentencepiece]"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"2jlgj7Dcb3Gf","colab":{"base_uri":"https://localhost:8080/","height":716,"referenced_widgets":["6a1eebf6d6a840b3a394da98ff442dc6","b1c0fb308dd0478592d5deb542151b32","f5b5581d0365434db218f80c77d1fcc5","40156688efac4be98409bf33a6ca18cf","b2cb8890b3ee4e3fae4ffb83beadc67f","4f1cd0c1679746e9bdd993791b1598ea","c9948ff97860424e8f557f338365a71d","764dc3050ee8450d8eec7878b26fa9ed","0c28a6da7e044cc488296853b487478f","8a62413c5f8d49788824da23b5dcf6f6","5013b8f12fdb43f8a761417d848fac36","561cafd3bed34b7f9a26a3cfd88ab3ee","90fff3610d88484aa2a2596da7cad6d0","78ab2376c2be49df88d9e6b540d8f025","f6a256c9e5c147f4b075f8ac05d3a9b6","776b45038a3b44ef9281474e25d273f2","3b1f5ec0f3a046f6b2effcf45ebb8050","2ecac5cbbf6c473994b25d92cb8aed54","6afbd491995d4cf2b0dd3b984f73ec68","cd688df6dfa340b2ab3b0bfc643d327d","f16179c4c4044f048bebbec60c02bb47","dc35fc9e8290432aa8913e5ab2d603f9","2d1d331748454515a68ef5b0bc6a7aa9","255f24f5d8db487fa4963b88e474b0e6","7d13f0b8257a4ba9912386b7b49ecd8f","d212180869c642f68c85849409424874","ec54609a461c4318a2704503ba649b79","fe7ea5f9f33c4320a879f610af3f9760","293079ec5b1b4b518da06b367d57e97b","295a30555c954b02aee6f7e7746d0743","5e97b133261f47c8a3b2b9be1e2d7bf4","abddf5b966614b30a8f559b2d49fddf0","ddcc29fe34584b8aa42585c6b9064bf4","9231ffd0767c486cae4dde35425f6727","594074c4e02a4f4da9e3ded67e2000b2","63046ddb47c74b88a7c3781ed8055686","71031e6d403f42e684af50e7c4e5e42b","6b9386e944ee431495192d37f4cf934d","466a982ed44040c6bc4d9f6fee5d871c","4509f67986eb443790e6e41f02714d2c","f7d575de8920433ea8d3f45e529d5526","d30a822e1ba04b119e1acb21d5ec607e","716ef37ea9ed4569acd5436e302b8dfc","4d8564ca75f6448b98ad46184ec4ff6f","833b6045fc534d11a6b2a4e58a117dfa","8c2641af34684e61aec9b70f298c1a68","063a00c731964ed19384a7e34f6f16c7","e6acb98b1d394ac0955c021e9ed730d4","5516cc790bf0461ca810527a8a354349","3f28696caa894159ad24e0fe5f4075dc","a236d925dd174ee5ad497221f3aa6a4b","0d6ed5d015ee4bd18d22f9aeba582280","5ec7bd349e9e49a3aba73e5e019dc26d","58116c0fd9ee4af998554ecf8bcfd845","e4dde7c1b6254227aa0b835805cebcaf","4b31aab1fa1c4d51bfd6974c5daf65e8","f2f7c38a3470416ab4be704cc85bd08a","f20fefe327a44e4db6ff604b6892067d","b136176a273746f9ac05235b622a7f0e","28dbfbe018fa4505a2c04afc1e2fc024","23fb9ffee3404043a1813e2b089d2fb4","a921cced7ae447048bbb6dd074169f56","2cb52c34754d461681df5a7c80b7494e","c08cbb7e363b4326b99d5d92f7fba4f0","e8a945a927934b558e9cb3d9b7d7ee97","06ccdd955af14462aba3373cbf5ad827","b885c03f72e8490eb23f36538b224931","bf43d7a7503d409296c1fddf88152c01","9c8e3466af59435b98e13fc072e89a13","355f9cea5e48428db1814b293b139f90","766f92e443e6410fa16ef48a8c51448d","6c9399f650174a8a886b1f1739b8660d","09b3ee47ac444278b843a66ee794fefe","1370e09b8b0c400fbb98de21251e9545","0c3a0bcf9bba46b8a32ec5a425efe526","9bab18580d1048ddbfaacd599ad7e6b6","41482aa6d90d4a5e8a15226106f7c1d5","5ce50bc8393e46ba9cd6ea5bcbe4e925","15317e17d12e48cdb89d29a2437f80ce","46ca6db0f16d4e81afe290a95bec5b64","b03bd0ffc59345748d9b72ebb3703ac5","5f7d4c83e5bd4047ad0a6c1102702573","3e3a264e7b6a412292f6ca50f600d5e6","7fb8bb1f5ac1497ca8ad852400bca154","e44ae1339a914c04a4767dc57a06325b","6f3e8a8325154e24a08f906ddfa055b6","c4942271a24143f2bbf5c22d080f8999","ef125459b8154391bc34882f05834c99","9be3f463c18e4c329c1137fc8e7746e7","4145aa11176649e2b3a2fbdeb4de084f","f60eefccd3054929bbf37cfa05cd8924","70ee54390d004c4e8a9264bbc4139914","829dded229b04520916a4511abddc11c","4da2c4eb039143fd884fd1b297d4a984","663aa424edb14a5f8bceb3eebda8144c","8310e99a75e744e9b94a31a4994e88d6","1c4242c3baeb4854a2230b4951e349eb","3047be9baf654f39a80c5fb48c956500","2caed48c7801447481f7515c2db63062","92ad6e44a20848a7b8ef0a20072779e8","b7f0be4cf6014455821946f0eead5962","03b656122f2e4fd695effe6de43a92c1","79013733b5984a07a15ff5f7a15df17f","c10e13c114344aae88d520ca3b65372a","ca5e2ad2910f45a9ae1fe4650a28060e","7ffa36fa825249cb8533b2765f2f6f10","12400cadd2b143b797d66a5797f5dfcc","6caceacc215743a19a67a1b96b782257","a8a26295adb241ddac16120df111ee54","4d3ada4d2c144cb8bdc90d8e0c9d9258","e399ebfee5124670a0d88deef03da896","a4823e499d04437caf27ef73bc879601","60e352a03ce446749de0ae6639b74066","3a6aa1e92bdd46fba3924023652e10ca","8e1b350bc9c049e681e055c8a4e8960e","713c7ed6b533495ca27a25c07e5a1cfb","11d91e1369f0476ab822a80599c0115f","b074fb0757504a71891d876f55f8d1d6","bc94fa61aa9849c5bc038ec77f24f38b","c617532618204d80a4b6f4e62e43b2e8","f64934ad3093426382e9b9f058f9a14b","e71a709ce23c44e8907cba009dcbf2f5","6785bb467bca4066b1c7660696158240","9a675e8344d54706a1d64c349165a171","6ddb5420ac8c4a46afd62f59d918b7fc","d3adcff08b4a4dbd93ac3de2df6322fc","419afa72b20e4d0682c597c28946c35c","f298319b07854979a477cc18e7b4759a","8283393b576e4079b6b43dcc675707ff","6aab6f17ee57452aa81098706e1ff6a5","2f79319a4aa842aea60fe980bac6b664","e0a320e8d5ec44fa98de9b03e0880995","61ae762b96a74a86a6abaf7b08131237","9d45ae88bb814bdcbf72f1c52021829a","932f169ba9df4869965526f0208a2e59","68995158196348bda9a8bb2340617445","623df91d0e214965ab9ba8362b26d173","8ffeb5d9addf47339762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UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n"," warnings.warn(\n","Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n","WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"]},{"output_type":"display_data","data":{"text/plain":["README.md: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6a1eebf6d6a840b3a394da98ff442dc6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["mrpc/train-00000-of-00001.parquet: 0%| | 0.00/649k [00:00\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# 학습시작\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m 2172\u001b[0m \u001b[0mhf_hub_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_progress_bars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2173\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2174\u001b[0;31m return inner_training_loop(\n\u001b[0m\u001b[1;32m 2175\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2176\u001b[0m \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m 2534\u001b[0m )\n\u001b[1;32m 2535\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2536\u001b[0;31m \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_items_in_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2537\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2538\u001b[0m if (\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, model, inputs, num_items_in_batch)\u001b[0m\n\u001b[1;32m 3807\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3808\u001b[0m \u001b[0;32mwith\u001b[0m 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return_outputs, num_items_in_batch)\u001b[0m\n\u001b[1;32m 3878\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"num_items_in_batch\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_items_in_batch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3879\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3880\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3881\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3882\u001b[0m \u001b[0;31m# User-defined compute_loss function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1774\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1775\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1776\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m 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code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1785\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1786\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1787\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1788\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1789\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m 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code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1785\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1786\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1787\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1788\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1789\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0mextra_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["# 학습시작\n","trainer.train()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QcCUmeycb3Gh","outputId":"2ad5d6ab-be3a-4144-bbde-a05912107e39"},"outputs":[{"data":{"text/plain":["(408, 2) (408,)"]},"execution_count":null,"metadata":{},"output_type":"execute_result"}],"source":["# 검증데이터를 가지고 추론 수행\n","\n","predictions = trainer.predict(tokenized_datasets[\"validation\"])\n","print(predictions.predictions.shape, predictions.label_ids.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YowQsgLAb3Gh"},"outputs":[],"source":["import numpy as np\n","# logits -> 0 or 1 판단\n","preds = np.argmax(predictions.predictions, axis=-1)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HuoSDQysb3Gi","outputId":"140b8705-27f0-4c36-c8bf-889d55338ad3"},"outputs":[{"data":{"text/plain":["{'accuracy': 0.8578431372549019, 'f1': 0.8996539792387542}"]},"execution_count":null,"metadata":{},"output_type":"execute_result"}],"source":["import evaluate\n","\n","# 모델 평가\n","metric = evaluate.load(\"glue\", \"mrpc\")\n","metric.compute(predictions=preds, references=predictions.label_ids)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"X0Lm5MxXb3Gi"},"outputs":[],"source":["def compute_metrics(eval_preds):\n"," metric = evaluate.load(\"glue\", \"mrpc\")\n"," logits, labels = eval_preds\n"," predictions = np.argmax(logits, axis=-1)\n"," return metric.compute(predictions=predictions, references=labels)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LjiRYUhpb3Gi"},"outputs":[],"source":["training_args = TrainingArguments(\"test-trainer\", evaluation_strategy=\"epoch\")\n","model = 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\ No newline at end of file diff --git a/colab/Processing the data (PyTorch).ipynb b/colab/Processing the data (PyTorch).ipynb new file mode 100644 index 0000000..551bb68 --- /dev/null +++ b/colab/Processing the data (PyTorch).ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"5QYneonVbwIj"},"source":["# Processing the data (PyTorch)"]},{"cell_type":"markdown","metadata":{"id":"2uvMdtwqbwIk"},"source":["Install the Transformers, Datasets, and Evaluate libraries to run this notebook."]},{"cell_type":"code","execution_count":43,"metadata":{"id":"kxvt4_2gbwIk","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779174438546,"user_tz":-540,"elapsed":5702,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"b6c19692-1299-47e3-d79d-7dbfacb69786"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: datasets in /usr/local/lib/python3.12/dist-packages (4.0.0)\n","Requirement 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(1.23.0)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n"]}],"source":["!pip install datasets evaluate transformers[sentencepiece]==4.41.2"]},{"cell_type":"markdown","source":["감정분석 (긍정, 부정), 문장 유사도, 뉴스 분류, 스팸 분류와 같은 작업을 담당하는 모델은 어덯게 훈련되는가?"],"metadata":{"id":"MZsd7_hd3riT"}},{"cell_type":"code","execution_count":30,"metadata":{"id":"m0HQpTk_bwIk","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779174398714,"user_tz":-540,"elapsed":15136,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"a41d72a2-bce0-4d51-a22e-6e2d582e2ff0"},"outputs":[{"output_type":"stream","name":"stderr","text":["Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}],"source":["import torch\n","from torch.optim import AdamW\n","from transformers import AutoTokenizer, AutoModelForSequenceClassification\n","\n","# Same as before\n","checkpoint = \"bert-base-uncased\"\n","tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n","model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n","sequences = [\n"," \"I've been waiting for a HuggingFace course my whole life.\",\n"," \"This course is amazing!\",\n","]\n","batch = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\n","\n","# This is new\n","# 정답 제공\n","batch[\"labels\"] = torch.tensor([1, 1])\n","\n","# 최적화(모델이 틀린 정도를 보고 가중치를 수정)\n","optimizer = AdamW(model.parameters())\n","# 모델이 얼마나 틀렸는지\n","loss = model(**batch).loss\n","\n","# 역전파 : 수정하는 단계\n","loss.backward()\n","\n","# 가중치 업데이트\n","optimizer.step()"]},{"cell_type":"code","execution_count":31,"metadata":{"id":"jAE4TjAFbwIk","outputId":"3bf5ed6a-aea8-412e-94c3-fb48ae98eff4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779174399976,"user_tz":-540,"elapsed":1259,"user":{"displayName":"쿠니","userId":"18312388301484735023"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n"," train: Dataset({\n"," features: ['sentence1', 'sentence2', 'label', 'idx'],\n"," num_rows: 3668\n"," })\n"," validation: Dataset({\n"," features: ['sentence1', 'sentence2', 'label', 'idx'],\n"," num_rows: 408\n"," })\n"," test: Dataset({\n"," features: ['sentence1', 'sentence2', 'label', 'idx'],\n"," num_rows: 1725\n"," })\n","})"]},"metadata":{},"execution_count":31}],"source":["from datasets import load_dataset\n","\n","# glue : 자연어 처리 성능 평가용\n","# mrpc : Microsoft Research Parapharase Corpus ( 두 개의 문장이 같은 의미인가? 판단용 데이터 셋)\n","raw_datasets = load_dataset(\"glue\", \"mrpc\")\n","raw_datasets"]},{"cell_type":"code","execution_count":32,"metadata":{"id":"GG4GDsq3bwIk","outputId":"6719590f-2d16-4cad-d3e8-925e59e3ee6f","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779174400001,"user_tz":-540,"elapsed":23,"user":{"displayName":"쿠니","userId":"18312388301484735023"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n"," 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .',\n"," 'label': 1,\n"," 'idx': 0}"]},"metadata":{},"execution_count":32}],"source":["raw_train_dataset = raw_datasets[\"train\"]\n","raw_train_dataset[0]"]},{"cell_type":"code","execution_count":33,"metadata":{"id":"hhQRSRYVbwIk","outputId":"108da297-a6a9-4c96-8765-737b02782d16","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779174400057,"user_tz":-540,"elapsed":55,"user":{"displayName":"쿠니","userId":"18312388301484735023"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'sentence1': Value('string'),\n"," 'sentence2': Value('string'),\n"," 'label': ClassLabel(names=['not_equivalent', 'equivalent']),\n"," 'idx': Value('int32')}"]},"metadata":{},"execution_count":33}],"source":["raw_train_dataset.features"]},{"cell_type":"code","execution_count":34,"metadata":{"id":"WjKiwclQbwIk","executionInfo":{"status":"ok","timestamp":1779174404707,"user_tz":-540,"elapsed":4647,"user":{"displayName":"쿠니","userId":"18312388301484735023"}}},"outputs":[],"source":["from transformers import AutoTokenizer\n","\n","checkpoint = \"bert-base-uncased\"\n","tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n","\n","# 컬럼별로 토큰화 하는 작업\n","tokenized_sentences_1 = tokenizer(list(raw_datasets[\"train\"][\"sentence1\"]))\n","tokenized_sentences_2 = tokenizer(list(raw_datasets[\"train\"][\"sentence2\"]))"]},{"cell_type":"code","execution_count":35,"metadata":{"id":"rv0lJYFtbwIl","outputId":"bfaccfbd-9107-4407-aa5d-7a52dbd3a15b","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779174404764,"user_tz":-540,"elapsed":59,"user":{"displayName":"쿠니","userId":"18312388301484735023"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"]},"metadata":{},"execution_count":35}],"source":["# 문장 여러개를 한꺼번에 토큰화\n","inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n","inputs\n","\n","# 두개의 문장을 하나로 합쳐서 처리 (bert-base-uncased)"]},{"cell_type":"code","execution_count":36,"metadata":{"id":"hLLMKSX_bwIl","outputId":"c6d22894-857d-45a9-e810-0c3eba83d7e7","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779174404820,"user_tz":-540,"elapsed":39,"user":{"displayName":"쿠니","userId":"18312388301484735023"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['[CLS]',\n"," 'this',\n"," 'is',\n"," 'the',\n"," 'first',\n"," 'sentence',\n"," '.',\n"," '[SEP]',\n"," 'this',\n"," 'is',\n"," 'the',\n"," 'second',\n"," 'one',\n"," '.',\n"," '[SEP]']"]},"metadata":{},"execution_count":36}],"source":["tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"]},{"cell_type":"code","execution_count":37,"metadata":{"id":"FC--VZpIbwIl","executionInfo":{"status":"ok","timestamp":1779174408865,"user_tz":-540,"elapsed":4043,"user":{"displayName":"쿠니","userId":"18312388301484735023"}}},"outputs":[],"source":["# 전체 문장을 넘겨서 토큰화\n","tokenized_dataset = tokenizer(\n"," list(raw_datasets[\"train\"][\"sentence1\"]),\n"," list(raw_datasets[\"train\"][\"sentence2\"]),\n"," padding=True,\n"," truncation=True,\n",")"]},{"cell_type":"code","execution_count":38,"metadata":{"id":"j-SWE4_qbwIl","executionInfo":{"status":"ok","timestamp":1779174408912,"user_tz":-540,"elapsed":44,"user":{"displayName":"쿠니","userId":"18312388301484735023"}}},"outputs":[],"source":["# 토큰화 함수\n","def tokenize_function(example):\n"," return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"]},{"cell_type":"code","execution_count":39,"metadata":{"id":"JIFzo24XbwIp","outputId":"f0192197-3c1e-466c-d954-235d11379308","colab":{"base_uri":"https://localhost:8080/","height":306,"referenced_widgets":["ea5b2e63c5674154be2392454df44e0a","0de73b9e8ef144ff817e5b5a58fa9191","690270226e5f423fb627bd3bfd27c579","45823d2708a44a2285fcf6077997dd7d","eb3358cb2fcb449898f829e54c670936","53877b117cba499d955fb9be7b6a5c1f","253b8df3c9b94fa898ae24af7af3ef6c","7774e0384639412fac470a14712b18ac","21a01fca995f41829b7eb0e3432c888a","707facdd6f23465daff78b7ef51fab9e","0dd56654819e4891aa5fcc47baf12247"]},"executionInfo":{"status":"ok","timestamp":1779174409804,"user_tz":-540,"elapsed":872,"user":{"displayName":"쿠니","userId":"18312388301484735023"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["Map: 0%| | 0/408 [00:00\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mPIL\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mcaptioner\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"image-to-text\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mchat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/pipelines/__init__.py\u001b[0m in \u001b[0;36mpipeline\u001b[0;34m(task, model, config, tokenizer, feature_extractor, image_processor, processor, revision, use_fast, token, device, device_map, dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)\u001b[0m\n\u001b[1;32m 775\u001b[0m )\n\u001b[1;32m 776\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 777\u001b[0;31m \u001b[0mnormalized_task\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargeted_task\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtask_options\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_task\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 778\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpipeline_class\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 779\u001b[0m \u001b[0mpipeline_class\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtargeted_task\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"impl\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/pipelines/__init__.py\u001b[0m in \u001b[0;36mcheck_task\u001b[0;34m(task)\u001b[0m\n\u001b[1;32m 379\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 380\u001b[0m \"\"\"\n\u001b[0;32m--> 381\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mPIPELINE_REGISTRY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_task\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 382\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/pipelines/base.py\u001b[0m in \u001b[0;36mcheck_task\u001b[0;34m(self, task)\u001b[0m\n\u001b[1;32m 1354\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Invalid translation task {task}, use 'translation_XX_to_YY' format\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1356\u001b[0;31m raise KeyError(\n\u001b[0m\u001b[1;32m 1357\u001b[0m \u001b[0;34mf\"Unknown task {task}, available tasks are {self.get_supported_tasks() + ['translation_XX_to_YY']}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1358\u001b[0m )\n","\u001b[0;31mKeyError\u001b[0m: \"Unknown task image-to-text, available tasks are ['any-to-any', 'audio-classification', 'automatic-speech-recognition', 'depth-estimation', 'document-question-answering', 'feature-extraction', 'fill-mask', 'image-classification', 'image-feature-extraction', 'image-segmentation', 'image-text-to-text', 'image-to-image', 'keypoint-matching', 'mask-generation', 'ner', 'object-detection', 'question-answering', 'sentiment-analysis', 'table-question-answering', 'text-classification', 'text-generation', 'text-to-audio', 'text-to-speech', 'token-classification', 'video-classification', 'visual-question-answering', 'vqa', 'zero-shot-audio-classification', 'zero-shot-classification', 'zero-shot-image-classification', 'zero-shot-object-detection', 'translation_XX_to_YY']\""]}]},{"cell_type":"code","source":["import gradio as gr\n","from transformers import pipeline\n","from PIL import Image\n","\n","captioner = pipeline(\"image-to-text\")\n","generator = pipeline(\"text-generation\")\n","\n","def echo(message, history) :\n","\n"," # message {'text': 'text', 'files': []}\n"," # message {'text': '', 'files': ['/private/var/folders/_x/jfhn2s8d6t512jvv5lndzw2m0000gn/T/gradio/f299e1f235318c3c17f47efef61fabb3f52e14158f3c4e1a2f15b1cf0fb8329d/Gemini_Generated_Image_e9k4qje9k4qje9k4.png']}\n","\n"," # History[]\n"," # History[{'role': 'user', 'metadata': None, 'content': [{'text': 'text', 'type': 'text'}], 'options': None}, {'role': 'assistant', ']\n"," print(\"message\", message)\n"," print(\"history\", history)\n","\n"," # message에서 텍스트와 이미지 분리\n"," text = message[\"text\"]\n","\n"," if message.get(\"files\") :\n"," image = message[\"files\"][0]\n"," if image :\n"," # 이미지만\n"," result = captioner(image)\n"," return result[0]['generated_text']\n"," elif text :\n","\n"," # caption_result 값이 있다면 적절한 문장 생성하도록 만들기\n"," generator(\"In this course, we will teach you how to\")\n","\n"," return text\n","\n","\n","\n","demo = gr.ChatInterface(\n"," fn=echo,\n"," multimodal=True,\n"," title=\"멀티 모달 AI 챗봇\",\n"," description=\"이미지를 업로드하면 이미지에 대한 설명을 생성하는 챗봇입니다. 테스트로 질문도 가능합니다.\",\n",")\n","demo.launch()"],"metadata":{"id":"t_29rw9Y9I0F"},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/colab/Untitled2.ipynb b/colab/Untitled2.ipynb new file mode 100644 index 0000000..294fdc3 --- /dev/null +++ b/colab/Untitled2.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyMy/MAvjpUXwHP1FrULJRFl"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["### pandas\n","- 테이블형 테이터를 다룰 수 있는 다양한 기능을 가진 라이브러리\n","- 데이터 전처리를 하거나 다른 형식으로 데이터 저장시 사용 가능\n","- 데이터 분석 가능\n","- Series(1차원) - 컬럼\n","- DataFrame(2차원) - 표(주로사용)"],"metadata":{"id":"r70QKv8SCC45"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"collapsed":true,"id":"uEIahOOqBgMN","executionInfo":{"status":"ok","timestamp":1779326556741,"user_tz":-540,"elapsed":9,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"bc151771-a60d-4e21-a6b5-c9df608c3e73"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" A\n","0 1\n","1 2\n","2 3"],"text/html":["\n","
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A
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mpgcylindersdisplacementhorsepowerweightaccelerationmodel_yearoriginname
018.08307.0130.0350412.070usachevrolet chevelle malibu
115.08350.0165.0369311.570usabuick skylark 320
218.08318.0150.0343611.070usaplymouth satellite
316.08304.0150.0343312.070usaamc rebel sst
417.08302.0140.0344910.570usaford torino
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cylinders
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78europe4.833333
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"]},"metadata":{},"execution_count":70}]},{"cell_type":"code","source":["df = pd.DataFrame({\"yyyy-mm-dd\" : [\"2005-09-28\", \"2007-10-05\", \"2013-12-20\"]})\n","df\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"q5fnn_YIOQDP","executionInfo":{"status":"ok","timestamp":1779328236481,"user_tz":-540,"elapsed":46,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"bafd8d11-5b24-4a39-f121-1d2ab1368aa2"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" yyyy-mm-dd\n","0 2005-09-28\n","1 2007-10-05\n","2 2013-12-20"],"text/html":["\n","
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yyyy-mm-dd
02005-09-28
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yyyy-mm-ddyear
02005-09-282005
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22013-12-202005
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df","summary":"{\n \"name\": \"df\",\n \"rows\": 3,\n \"fields\": [\n {\n \"column\": \"yyyy-mm-dd\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"2005-09-28\",\n \"2007-10-05\",\n \"2013-12-20\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"year\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"2005\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":99}]},{"cell_type":"code","source":["!pip install koreanize_matplotlib"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xKctvdbWTXaE","executionInfo":{"status":"ok","timestamp":1779329553031,"user_tz":-540,"elapsed":10407,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"f8711db5-6887-46de-89f6-02c0cd3228f9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting koreanize_matplotlib\n"," Downloading koreanize_matplotlib-0.1.1-py3-none-any.whl.metadata (992 bytes)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (from koreanize_matplotlib) (3.10.0)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (1.3.3)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (4.62.1)\n","Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (1.5.0)\n","Requirement already satisfied: numpy>=1.23 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (2.0.2)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (26.1)\n","Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (11.3.0)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (3.3.2)\n","Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (2.9.0.post0)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.7->matplotlib->koreanize_matplotlib) (1.17.0)\n","Downloading koreanize_matplotlib-0.1.1-py3-none-any.whl (7.9 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.9/7.9 MB\u001b[0m \u001b[31m67.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: koreanize_matplotlib\n","Successfully installed koreanize_matplotlib-0.1.1\n"]}]},{"cell_type":"code","source":["# matplotlib (시각화 라이브러리 기본)\n","\n","import matplotlib.pyplot as plt\n","import koreanize_matplotlib"],"metadata":{"id":"eUo14MAQRO__"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["plt.plot([10,20,30,40])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"Ki9qYiTwRDDT","executionInfo":{"status":"ok","timestamp":1779329080312,"user_tz":-540,"elapsed":252,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"716f3a0f-14f0-4213-90df-90b8a884b89b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[]"]},"metadata":{},"execution_count":102},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["plt.plot([1,2,3,4], [12,43,25,15])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"Qc3zzgEPRywY","executionInfo":{"status":"ok","timestamp":1779329226423,"user_tz":-540,"elapsed":294,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"3cf090a7-e44c-4c86-ce95-4b483c497e2e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[]"]},"metadata":{},"execution_count":108},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["data_a = {\"data_x\" : [1,2,3,4,5,], \"data_y\" : [2,3,5,10,8]}\n","df = pd.DataFrame(data_a)\n","df\n","\n","plt.plot(df['data_x'], df['data_y'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"rmeLIvsVSVT7","executionInfo":{"status":"ok","timestamp":1779329336058,"user_tz":-540,"elapsed":414,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"3ed97644-e4bf-4553-dd6b-e1e4e2f1c409"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[]"]},"metadata":{},"execution_count":112},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["plt.bar(df['data_x'], df['data_y'])\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"J-qeo_ozS4xt","executionInfo":{"status":"ok","timestamp":1779329453563,"user_tz":-540,"elapsed":460,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"1bb014de-f99b-4dd5-b74b-c20963c5269b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":116},{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["plt.bar(['강백호', '서태웅', '정대만'], [175, 187, 184])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"id":"yP0EhomPTmTN","executionInfo":{"status":"ok","timestamp":1779329646399,"user_tz":-540,"elapsed":169,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"97ba4010-9eac-4c60-add7-52261e25515e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":124},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["plt.barh(['강백호', '서태웅', '정대만'], [175, 187, 184])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"id":"0JyNk-W-T74G","executionInfo":{"status":"ok","timestamp":1779329679868,"user_tz":-540,"elapsed":140,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"022cbf17-75ba-4025-aaa7-e7352a83044e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":125},{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["values = [175, 187, 184]\n","labels = [\"python\", \"java\", \"c#\"]\n","\n","plt.pie(values, labels=labels, autopct=\"%.1f%%\", startangle=90, counterclock=False)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":571},"id":"wDYAHv1uUE9H","executionInfo":{"status":"ok","timestamp":1779330401490,"user_tz":-540,"elapsed":553,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"c91f8a37-01ad-4d70-9a6c-b6d63f411558"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["([,\n"," ,\n"," ],\n"," [Text(0.9297090875686453, 0.5879124190662052, 'python'),\n"," Text(0.05693733236902952, -1.0985254390239212, 'java'),\n"," Text(-0.9588938707429671, 0.5390014328845241, 'c#')],\n"," [Text(0.5071140477647156, 0.32067950130883915, '32.1%'),\n"," Text(0.031056726746743372, -0.5991956940130478, '34.2%'),\n"," Text(-0.5230330204052547, 0.2940007815733767, '33.7%')])"]},"metadata":{},"execution_count":161},{"output_type":"display_data","data":{"text/plain":["
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notebook."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bdIxnISxEQIA","colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"status":"ok","timestamp":1779242770789,"user_tz":-540,"elapsed":16770,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"28f2d47b-1acd-4d1d-813a-701fc8720c8a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: datasets in /usr/local/lib/python3.12/dist-packages (4.0.0)\n","Collecting evaluate\n"," Downloading evaluate-0.4.6-py3-none-any.whl.metadata (9.5 kB)\n","Collecting transformers==4.41.2 (from transformers[sentencepiece]==4.41.2)\n"," Downloading transformers-4.41.2-py3-none-any.whl.metadata (43 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.8/43.8 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: filelock in 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transformers-4.41.2-py3-none-any.whl (9.1 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.1/9.1 MB\u001b[0m \u001b[31m104.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading evaluate-0.4.6-py3-none-any.whl (84 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading huggingface_hub-0.36.2-py3-none-any.whl (566 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m566.4/566.4 kB\u001b[0m \u001b[31m35.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading tokenizers-0.19.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.6 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m132.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: huggingface-hub, tokenizers, transformers, evaluate\n"," Attempting uninstall: huggingface-hub\n"," Found existing installation: huggingface_hub 1.11.0\n"," Uninstalling huggingface_hub-1.11.0:\n"," Successfully uninstalled huggingface_hub-1.11.0\n"," Attempting uninstall: tokenizers\n"," Found existing installation: tokenizers 0.22.2\n"," Uninstalling tokenizers-0.22.2:\n"," Successfully uninstalled tokenizers-0.22.2\n"," Attempting uninstall: transformers\n"," Found existing installation: transformers 5.0.0\n"," Uninstalling transformers-5.0.0:\n"," Successfully uninstalled transformers-5.0.0\n","Successfully installed evaluate-0.4.6 huggingface-hub-0.36.2 tokenizers-0.19.1 transformers-4.41.2\n"]}],"source":["# summarization task 때문에 transformers[sentencepiece]==4.41.2 으로 설치\n","!pip install datasets evaluate transformers[sentencepiece]==4.41.2"]},{"cell_type":"markdown","source":["### pipeline(task, model)\n","- 복잡한 모델 과정을 간단하게 묶은 기능"],"metadata":{"id":"Aojrijn_Jwk3"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"NHq4d-UAEQIA","outputId":"e695fe69-6e23-4503-bb05-f91b8b2cb436","colab":{"base_uri":"https://localhost:8080/","height":367,"referenced_widgets":["7d13d4ce88694b8584ec5e1632d316ab","7ed0a2772ad84bc18bb83f5501888b49","b8829817cd414c3f829eefd7890788d7","d5ddbf9ee02e4c15b5d09358d054ff32","ad942adc227346ec914f78d560bb3edf","769183765e654925ac4dc5c53c1f75a9","0d80260998c1463ba2184ef65f26e353","6d7e57e523714c3e985e64aee31159df","519c6d50ba3742f181fc63a68e551841","eef0a8930bd14357aa5e0911d062d748","241c0299e8f7482389869952935b396c","a8ffed7bd17c4188bc4b72cfdeb9c180","fcca168636654d52b97d1fb8e72d4aa3","2022522dc0f644279e15a3d8cbd7c042","c4b2ab5b2bb845f19dd19882e3685df0","11d572fda153468991d99f90cbc2af1d","63fadac2565745b8a63cf9a18ee66ae1","748950d3a0c3445eb7758b8109358de3","6387d722c1c44aa9bd8be0a769d8d7aa","744f53ba86324ce5a977cebb47ab51e4","c0b3450c998d46c89e605e2b97d4d444","53b80097ca5e4b979f48ab62251cfe0b","0a6abb5cea5b4d40b50aa1b64fcb47bd","2c8bff41da2e44cdb8b6b9b1b6be675e","0a460f12673b453b884bc50cb5ae434d","84d3be7c19c1460591caca5bbff2e4b0","d4b069d065ef47dab048d81ee0624667","b18d919425e2421bb069384153081aab","4cf2274fde93424db5eb3bd9199dc1bb","bd91545514f244d192ab99cd760eeca5","948fdd020c5e47389f6158008fb276a3","0f9b87a6416b47168a28a320850fc51e","eb10f2ec86f342db804180ce0362f232","b644e4ac94304ed3bf92183b878c3be1","4c94b8d7494d4ce1b3e30fe196472cd4","3a4806ab2a0b44c4bfa6e32d111f5681","a2dc4266558a4428ab714d07d1983ff0","3f743fdb83a243799153d7d815557240","a082982d38954aeeb10b9a659e10b86b","58046ec4b61a4b3e8586d8453511de22","c5dbdc9af4ba4a029febf5f51d29675b","188bf4b569b74bd6aaf88f275198d9c3","ea6c675820cd4a508d4fbb85ab16f179","362d4042c57245bf9480683b8c16eb0c"]},"collapsed":true,"executionInfo":{"status":"ok","timestamp":1779159338382,"user_tz":-540,"elapsed":23167,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["No model was supplied, defaulted to distilbert/distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english).\n","Using a pipeline without specifying a model name and revision in production is not recommended.\n","/usr/local/lib/python3.12/dist-packages/huggingface_hub/file_download.py:949: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n"," warnings.warn(\n","/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n"," warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["config.json: 0%| | 0.00/629 [00:00 models.\", top_k=2)"]},{"cell_type":"code","source":["unmasker = pipeline(\"fill-mask\", model=\"distilroberta-base\")\n","\n","man_result = unmasker(\"The man worked as a \")\n","woman_result = unmasker(\"The woman worked as a \")\n","\n","print(\"======남자====\")\n","for r in man_result:\n"," print(f\"{r['token_str']} : {r['score']:.4f}\")\n","\n","\n","print(\"======여자====\")\n","for r in woman_result:\n"," print(f\"{r['token_str']} : {r['score']:.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e_YujDqzPofU","executionInfo":{"status":"ok","timestamp":1779161540515,"user_tz":-540,"elapsed":805,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"5a1f0371-0bc6-454e-c167-2c7d352cd31e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at distilroberta-base were not used when initializing RobertaForMaskedLM: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n","- This IS expected if you are initializing RobertaForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing RobertaForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"]},{"output_type":"stream","name":"stdout","text":["======남자====\n"," waiter : 0.1187\n"," waitress : 0.0790\n"," prostitute : 0.0621\n"," courier : 0.0592\n"," bartender : 0.0547\n","======여자====\n"," waitress : 0.2141\n"," prostitute : 0.0851\n"," waiter : 0.0718\n"," maid : 0.0654\n"," nurse : 0.0488\n"]}]},{"cell_type":"code","source":["unmasker = pipeline(\"fill-mask\", model=\"distilroberta-base\")\n","\n","man_result = unmasker(\"The man hired as a \")\n","woman_result = unmasker(\"The woman hired as a \")\n","\n","print(\"======남자====\")\n","for r in man_result:\n"," print(f\"{r['token_str']} : {r['score']:.4f}\")\n","\n","\n","print(\"======여자====\")\n","for r in woman_result:\n"," print(f\"{r['token_str']} : {r['score']:.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lcVcfsQkTRsz","executionInfo":{"status":"ok","timestamp":1779161712060,"user_tz":-540,"elapsed":1214,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"17c9aa3d-0dd6-48a4-8255-098e5c4022c4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at distilroberta-base were not used when initializing RobertaForMaskedLM: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n","- This IS expected if you are initializing RobertaForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing RobertaForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"]},{"output_type":"stream","name":"stdout","text":["======남자====\n"," consultant : 0.1628\n"," lobbyist : 0.0517\n"," lawyer : 0.0293\n"," contractor : 0.0250\n"," mechanic : 0.0248\n","======여자====\n"," consultant : 0.1128\n"," prostitute : 0.0997\n"," lobbyist : 0.0356\n"," maid : 0.0346\n"," nurse : 0.0338\n"]}]},{"cell_type":"markdown","source":["- ner(Named Entity Recognition) : 개체(사람이름, 회사명, 지역명, 날짜....)명 인식\n"," - 핵심정보 추출\n"," | 분야 | 활용 |\n"," |------|------|\n"," |뉴스분석 | 인물/기업 추출 |\n"," |챗봇 | 핵심 정보 파악 |\n"," |검색엔진 | 기업명/지역명 분석 |\n"," |금융 | 회사명 추출 |\n"," |의료 | 질병 이름 추출 |\n"," |법률 문서 | 기관명 추출 |"],"metadata":{"id":"92FOBIy-U0Ka"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"tnSkCP4JEQID","outputId":"252c3746-0e20-4a3f-9075-696d76b11e6f","colab":{"base_uri":"https://localhost:8080/","height":606,"referenced_widgets":["75d148bd9e514a0a8a371bf7fa469f1b","9783b901bfde4229ac31e314c2e8d844","ed30bb8187884283bce1eae2eebaa745","bfbe247688574786a0756a506e19d476","447912220f1840adaecb6b151ca40bd6","8fde635a28454993aa05231660d5a400","dc98881b3cb0417ca20786d1d25e943d","58c6c808c3b743a7acd10d431ddf54cf","ab33d9b2008a4d4697accf7f8ffc8fa5","4c3389d46750438bb7e2f82998e7c24a","9510d465474442e2b936cd586146ff7b","5f5336ccde2144b8ad3deeaaec5f70c5","cc06623f748d4a9d8cba27e4f56fc259","6281327509c54411b5e1e945a8eb7ab2","de688d93349541aea879cfb6f25599e5","b588474d4c25430c9dfce3345b8319b1","1992e1938c3b479e99f43c19c749f936","9672936db69f4aeda7388d3e335e8494","29cafaef7ed14246ad9eef854420904d","f70eb078465b44fc8ec7752b9c45b7f3","59ab92fc2d8546b194faa9e8a789f190","57ba3554723c4cb0afc7b0809d536791","33e748591cd94828a6790a66045497ec","e5900edd3c6e4ba88e73ad060c6c40ac","a89fef6b1726459e8ee1a645cd684734","fc294e654bfd46259abd2bbe8738a28a","551d019dd9be4cd98d996a15fe91e15a","cca787e882c341d984332d986759fbaa","fb88fd1fdcb742a49e1079848c26d3b0","73dda4a877944c4f89924ca2537eb39b","42dcb80e64334e029d77612aba307bca","6af0c67c37694f039c59505b1b8d87b2","167515517d8d4892a69f61f55f4fe55d","fcd5ea07f2564aad9d8e0f2cc378b01f","49d2581fe08c4ffaa97836a56e582d91","66175b0fb1154c9488e733ac6421941c","3a701387fc824239a2d881a89cf133a0","4580192ef37c49d081182666c9a878fa","6cfdca1dc8b24bfdb28010c87dc43fa5","270c9d10778842cfb5ef710c480ae54f","7cb1106969394264ba0f36a98db0db14","9cc48a3e1fa94ec8a1de8698c5f017c9","6cb2f2fc052947b3ba0ac80383fa4d8d","c0d7203177c845f08974c5bd6cf003e5"]},"executionInfo":{"status":"ok","timestamp":1779162439663,"user_tz":-540,"elapsed":28578,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["No model was supplied, defaulted to dbmdz/bert-large-cased-finetuned-conll03-english and revision f2482bf (https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english).\n","Using a pipeline without specifying a model name and revision in production is not recommended.\n","/usr/local/lib/python3.12/dist-packages/huggingface_hub/file_download.py:949: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n"," warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["config.json: 0%| | 0.00/998 [00:00 이미지\n"," - 노이즈 -> 텍스트 의미 이해 -> 노이즈 조금씩 제거 -> 이미지 완성\n","- GPU 필요\n","- 코랩에서는 런타임유형 변경(T4 GPU)"],"metadata":{"id":"TVXBFlx0G35k"}},{"cell_type":"code","source":["!pip install diffusers==0.29.2 peft==0.11.1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"XlVCTPwDG0Hl","executionInfo":{"status":"ok","timestamp":1779242901698,"user_tz":-540,"elapsed":7315,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"22bbdbe0-ccaa-47ba-ce7a-c76defcf54e0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting diffusers==0.29.2\n"," Downloading diffusers-0.29.2-py3-none-any.whl.metadata (19 kB)\n","Collecting peft==0.11.1\n"," Downloading peft-0.11.1-py3-none-any.whl.metadata (13 kB)\n","Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.12/dist-packages (from diffusers==0.29.2) (8.7.1)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from diffusers==0.29.2) (3.29.0)\n","Requirement already satisfied: huggingface-hub>=0.23.2 in /usr/local/lib/python3.12/dist-packages (from diffusers==0.29.2) (0.36.2)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from diffusers==0.29.2) (2.0.2)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.12/dist-packages (from diffusers==0.29.2) (2025.11.3)\n","Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from diffusers==0.29.2) (2.32.4)\n","Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.12/dist-packages (from diffusers==0.29.2) (0.7.0)\n","Requirement already satisfied: Pillow in /usr/local/lib/python3.12/dist-packages (from diffusers==0.29.2) (11.3.0)\n","Requirement already 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hf-xet<2.0.0,>=1.1.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.23.2->diffusers==0.29.2) (1.4.3)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.23.2->diffusers==0.29.2) (4.15.0)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch>=1.13.0->peft==0.11.1) (75.2.0)\n","Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch>=1.13.0->peft==0.11.1) (1.14.0)\n","Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch>=1.13.0->peft==0.11.1) (3.6.1)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch>=1.13.0->peft==0.11.1) (3.1.6)\n","Requirement already satisfied: cuda-bindings==12.9.4 in /usr/local/lib/python3.12/dist-packages (from torch>=1.13.0->peft==0.11.1) (12.9.4)\n","Requirement already satisfied: 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(3.13)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->diffusers==0.29.2) (2.5.0)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->diffusers==0.29.2) (2026.4.22)\n","Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.12/dist-packages (from transformers->peft==0.11.1) (0.19.1)\n","Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch>=1.13.0->peft==0.11.1) (1.3.0)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch>=1.13.0->peft==0.11.1) (3.0.3)\n","Downloading diffusers-0.29.2-py3-none-any.whl (2.2 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m62.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading peft-0.11.1-py3-none-any.whl (251 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m251.6/251.6 kB\u001b[0m \u001b[31m25.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: diffusers, peft\n"," Attempting uninstall: diffusers\n"," Found existing installation: diffusers 0.37.1\n"," Uninstalling diffusers-0.37.1:\n"," Successfully uninstalled diffusers-0.37.1\n"," Attempting uninstall: peft\n"," Found existing installation: peft 0.19.1\n"," Uninstalling peft-0.19.1:\n"," Successfully uninstalled peft-0.19.1\n","Successfully installed diffusers-0.29.2 peft-0.11.1\n"]}]},{"cell_type":"code","source":["from diffusers import StableDiffusionPipeline\n","import torch\n","\n","pipe = StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\",\n"," torch_dtype=torch.float16)\n","\n","pipe = pipe.to('cuda')\n","image = pipe(\"서울의 밤 풍경\").images[0]\n","\n","image.save(\"/content/gdrive/MyDrive/Colab Notebooks/seoul.png\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["b208fee738674c0892de04f6e6a11545","4747e5b4e0d6462caeaf2005ac08b018","d0a2c739f7d24c4e908bc024127e74f7","ab4648193d4843ef99ee8fa60db52ae6","7db7797f8f8b4ae495ef7000133b69a8","5e15497412fe43a68ce6d7df6558699d","80e7ea5745f14f2ba9aca8827b406821","1621c9baee99462ca931b5990f94ead5","c5513262f4db43e4a87792bd7fd52159","f956ffc8ae7946b5baea7ff21b6a75b2","77d5b0c1351241829fa0f554784b5a56","7631a84130194192b9c7dd42c0769f1b","74078d55435c4a5789a3babdc2e01e83","b9ed52d3e7db45ebabd5208003a077ee","7d7154334e4a4c338623878520f7db41","a2efdba8299c45b2a8cd7a68af4edf7b","7ef1a340f50d4664a046a4da6b7af947","ffa8ded0e9d0495fba68e8dae81db354","c47e16fea70e43348c2572971b31e709","0282b4a1a90242b2b436e18acc6b59c3","3bd8ade157e941d7af77de32cb038071","ef0f340cb30448049ed873365736c621"]},"id":"X_brI7e1JNXk","executionInfo":{"status":"ok","timestamp":1779243931616,"user_tz":-540,"elapsed":31960,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"47ea08b1-2a86-4774-e465-249e728bfee2"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Loading pipeline components...: 0%| | 0/7 [00:00 텍스트\n","- 텍스트 -> 음성\n","- 음악 생성"],"metadata":{"id":"xi8NypnMJRe8"}},{"cell_type":"code","source":["# 음성인식 : 음성 -> 텍스트\n","\n","from transformers import pipeline\n","\n","whisper = pipeline(\"automatic-speech-recognition\",model=\"openai/whisper-base\")\n","result = whisper(\"/content/gdrive/MyDrive/Colab Notebooks/obama.mp3\",return_timestamps=True)\n","\n","print(result['text'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":443,"referenced_widgets":["dbf2ff7e07924a54a4e05e20250271b3","f4bc87b192ef4ccda290ea20d4e7d1f6","ed1e537a180241649df0e7b27475f033","a466799f665c4c80bf12eec22482d530","b7b03be3e11646428939a458394219a0","276886d7e4c84ae5be0ac4f01d51afa0","f8f6eb598bd7428cb55c98deb9fbf484","2801c2735503480882db2052b4a67a06","cb8919db67184c5f9ab227c24df782b1","ba04886e786045caa0946e919169ce79","c897f0f672614bc79f3649b4ae4a5625","d39ef6edd9aa4776885255b70494eae8","b4e1ded5981946329120c5c047d4fc15","efa1cfc993974fcbb9beb5ff0642d3b9","3ab395fe74da40f38d7316712e0f912b","66110ae77d2241889eb7317605c31e2c","d4ec90942938453b8f3c9655ddf07da4","b229cc7adac44024bc251bb8a356db47","b5f591419e5c4071956d62f1df902bf4","20e24a453804493f84e6f9e1a7c85b65","82eed61392384cdcae3fd11613c22425","0fcd627cddac4c80b711160d2ebc7070","9772092a96874febb7f4c0ee1ae476ec","53a7125fcab0405a951f8b70ef7f9bba","cf16cee711f34979aac3d9ff9f1176e3","6c83a2cf27d44097b2c877c463ef88d7","fdbc84ec78de4e2f98f249920cff0393","0e01175ecb8d424f8bab376e25abe40f","bc9e2cef4a4c4ce9b9c5d5b436397958","99bcc1251982422ca2046337ff7bf73b","254a6bebe76b4cd38647e4ec63dd1888","501cc9a7829f4e2aa688a173245f0d42","4f4afc9e22c24bce9e467d68357da1ac","143cd606fbca462b9faa79bab31836c1","1ecd757ad3284c67ae6346ccb0f9ca04","9a26674db11e490186011f579a03fc05","2c867d26b6d64821a00818e9954e844a","da863c52add74365a61487b24b7a58d6","22bb351879c64afeb576ba43e8badfc1","f49a0ae00edf475fb1cd192c20a85911","65d75b285c5c4c90b55ba078c1f43cbc","f2c38d3e40b240e0833ac615c7eb2def","661f557bfd0241e3aaf236714692b24b","e40a9f74877645ca9010b42073dbb3bd","4cb5a61cc0ce40bf8de08f03277c29b3","aa4fb1c2fe0d41f9a763393a86fbc5fd","a72d73039e574515beede09279e56565","7ed8d6bb58d0497ab9f25a9f8ff99e20","c4d196b26a314e4ab61fee2b6549e72e","41f8c8a482584f48b70d893c354d3575","d293409a61cb46c483e1ece3ea590ff3","d5c76e6e81ab47e0b5da9e0c298fbeef","1bc1639ffce044faa4526f21f93b8c15","fb2505c1ec514538ae62e7fefb3aa168","2ebe3952f61649e7adf5d6165b467f44","25a7ae6d8e834e2a9c07da5a3d49d457","8174229b3cd546de86b5f717e6070d97","8c116cd8be544036bee5ade6b655adea","51d4ac71d7a64ed6a7b49013540df830","ee97d1f1f2074a0ab3be0ba7cf024d36","58adcfb6ae49425eb64a2ce886e71a97","18c7d7a4b3ba4d41b94e66fcebdb0eeb","e3a1ecdb173945388bff467bf45714e0","830063f460814d2c9344a8823a051cc3","e128076cfd1f48efae614c207e482261","22952a3d4cab458582e8cfe5ca98483d","da7a342306094559b5aa460aee73ed89","4e38ad22ee244283a423bb614782d692","7eae71e96927439b93dbd463ce67c9fc","da991f5ea4dd40198f14b516701d085f","c152ed0d58bf418a8374768d3c8c54db","79da3cce8bf242d3a20234c03d00bce3","c890bd11bee24869bcf48a0186bfc16e","7593c664185f4c05b08209fc087c0907","ca4348943d124b1a9b03fbed103f784c","cddfb458628d42cf938915df8162bedc","c921f153fc464b1a9b4206de030a12f0","7f43ef7ee44d46dd814ac97e5a422ce7","e94ffc3297504c0c84ff87b06e84080b","808b9db3879e4fa88dc6c5921a9cab52","52640a5f30724c2abf7ae132fa644e92","672bd2105b7d47d5a619c5001ed09833","864c0bc545674df7818318ed2508118f","91359df19cfd44f593a196cd38f59d3f","15381d30a1a24f358495d0da0d702a4f","625e55c6c354421b881ddf391b90fc18","db30cdeb162b461a901b0c9da8d381e5","3f80693eb49c4e2183470dec4f4e669c","c434d8fd380b4eb1b4a5a512a38d1744","de24f99421a14b6ab148a021d5d23b6a","2a37b52ad8664b6fa4b2c9962c86d83f","9f7e8a91b3ac49c596cdc84563a373b6","d6e67f86afce45b0840cf91af16d18c3","6ed3f094384e43ef9d6aafbaab1e3843","c0782b779a9240eaa98c02940a63b0fd","94ed49b55276429b914a3b51ab0b7854","9f9483ee901942fcb329012af0697993","406e683a83f2475da57eed3316c45420","586f73300d62449f9b537b147d33ae3a","5ec7979296e84ed285b0de3f45d57ba8","506f2ead15e84611a0a7b52e00787bab","fb498fc56c864b6ca4008c3566ed6464","25c806f3486b46c2a2da4e839c5a75bd","9c05be310ff240cf9cf1d43a059560b2","42616ca3d10b48a1b1ee485e68748ce1","3100b34fdd644d4c80fbce29f4848590","c32fe6c2d4004f3dbe9c29b964678479","32b391fae04d4fd499bb8994fa464830","4ba4405407ef43738a50f02d1549c3c5","aa19e3e6b84c44de95620d23035bdc99","e2a1d08a93a843fa9e2883c5d54d2138","0726882162ce46f2b790d8f8a348191e","6e7c9ff07e9c4cdaa08d413d5f164725","c007ac561c024d1ca069465f97121b1e","0db7a2a2735842b2a6775e755013d781","526c688892f547cdae8d3001782361bd","2f1b8f0624a646d9b99f02cadf709503","6487ae1b9aba457fbe0754f621a98721","875348d5f8ae4d40852180eef1d74c9a","3a6668981dff41a4b149a259b14bd4cc","b2b6ad37c91a475a9bc2fe7a7286850a"]},"collapsed":true,"id":"wVNmxezeJQ-7","executionInfo":{"status":"ok","timestamp":1779245412685,"user_tz":-540,"elapsed":38238,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"7196a972-d52d-4e59-9c0a-60a755ff9a26"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["config.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"dbf2ff7e07924a54a4e05e20250271b3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors: 0%| | 0.00/290M [00:00=3.8.0 in /usr/local/lib/python3.12/dist-packages (from edge-tts) (3.13.5)\n","Requirement already satisfied: certifi>=2023.11.17 in /usr/local/lib/python3.12/dist-packages (from edge-tts) (2026.4.22)\n","Requirement already satisfied: tabulate<1.0.0,>=0.4.4 in /usr/local/lib/python3.12/dist-packages (from edge-tts) (0.9.0)\n","Requirement already satisfied: typing-extensions<5.0.0,>=4.1.0 in /usr/local/lib/python3.12/dist-packages (from edge-tts) (4.15.0)\n","Requirement already satisfied: aiohappyeyeballs>=2.5.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp<4.0.0,>=3.8.0->edge-tts) (2.6.1)\n","Requirement already satisfied: aiosignal>=1.4.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp<4.0.0,>=3.8.0->edge-tts) (1.4.0)\n","Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp<4.0.0,>=3.8.0->edge-tts) (26.1.0)\n","Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.12/dist-packages (from aiohttp<4.0.0,>=3.8.0->edge-tts) (1.8.0)\n","Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.12/dist-packages (from aiohttp<4.0.0,>=3.8.0->edge-tts) (6.7.1)\n","Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp<4.0.0,>=3.8.0->edge-tts) (0.4.1)\n","Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp<4.0.0,>=3.8.0->edge-tts) (1.23.0)\n","Requirement already satisfied: idna>=2.0 in /usr/local/lib/python3.12/dist-packages (from yarl<2.0,>=1.17.0->aiohttp<4.0.0,>=3.8.0->edge-tts) (3.13)\n","Downloading edge_tts-7.2.8-py3-none-any.whl (31 kB)\n","Installing collected packages: edge-tts\n","Successfully installed edge-tts-7.2.8\n"]}]},{"cell_type":"code","source":["# 텍스트 => 음성\n","\n","import edge_tts\n","\n","text = \"안녕하세요 반갑습니다\"\n","voice = \"ko-KR-SunHiNeural\"\n","\n","output_file = \"/content/gdrive/MyDrive/Colab Notebooks/voice.mp3\"\n","\n","async def main():\n"," communicate = edge_tts.Communicate(text, voice)\n"," await communicate.save(output_file)\n","\n","await main()"],"metadata":{"id":"Jj0mLM4eTO5x"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[{"file_id":"https://github.com/huggingface/notebooks/blob/master/course/ko/chapter1/section3.ipynb","timestamp":1779157783539}],"gpuType":"T4"},"language_info":{"name":"python"},"kernelspec":{"name":"python3","display_name":"Python 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\ No newline at end of file diff --git a/pythonSource/Basic/exam1.ipynb b/pythonSource/Basic/exam1.ipynb index 5f9411d..fadc573 100644 --- a/pythonSource/Basic/exam1.ipynb +++ b/pythonSource/Basic/exam1.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "327c097c", "metadata": {}, "outputs": [ @@ -15,13 +15,16 @@ } ], "source": [ + "# 변수 : 프로그램 내부에서 값을 담아놓기 위한 공간(이름 사용)\n", + "# 정수형\n", + "\n", "a = 10\n", "print(a)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "4929f0bd", "metadata": {}, "outputs": [ @@ -39,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "40c1b43b", "metadata": {}, "outputs": [ @@ -67,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "c621c863", "metadata": {}, "outputs": [ @@ -89,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "6af4a82e", "metadata": {}, "outputs": [ @@ -115,34 +118,29 @@ "print(a + b)\n", "print(a - b)\n", "print(a * b)\n", - "print(a / b)\n", + "print(a / b) # 몫과 나머지\n", "print(a // b) # 몫만 남김\n", "print(a % b) # 나머지 연산자, a를 b로 나눈 나머지를 구하는 연산자\n", - "print(a ** b)" + "print(a ** b) # a 의 b 제곱" + ] + }, + { + "cell_type": "markdown", + "id": "0d6bd739", + "metadata": {}, + "source": [ + "### 문자열 자료형\n", + "- 홑따옴표('), 쌍따옴표(\") 사용\n", + "- 홑따옴표('''), 쌍따옴표(\"\"\") 사용 => multiline " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "97d1e1ad", "metadata": {}, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "unterminated string literal (detected at line 2) (3330389451.py, line 2)", - "output_type": "error", - "traceback": [ - " \u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[28]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[31m \u001b[39m\u001b[31m- 홀따옴표 (') 또는 쌍따옴표 (\")로 감싸서 표현\u001b[39m\n ^\n\u001b[31mSyntaxError\u001b[39m\u001b[31m:\u001b[39m unterminated string literal (detected at line 2)\n" - ] - } - ], + "outputs": [], "source": [ - "### 문자열 자료형\n", - "- 홀따옴표 (') 또는 쌍따옴표 (\")로 감싸서 표현\n", - "- 문자열 안에서 홀따옴표나 쌍따옴표를 사용\n", - "- 홀따옴표 (''') 또는 쌍따옴표 (\"\"\")로 감싸서 표현\n", - " \n", - " \n", "msg = 'Hello, World!'\n", "msg = \"Hello, World!\"\n", "msg = 'Hello, \"World!\"'\n", @@ -155,7 +153,18 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, + "id": "925a6c89", + "metadata": {}, + "outputs": [], + "source": [ + "food = \"Python's favorite food is perl\"\n", + "food" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "id": "9c73bd1b", "metadata": {}, "outputs": [ @@ -165,7 +174,7 @@ "'Life is too shortyou need python'" ] }, - "execution_count": 31, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -178,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "id": "0900fed7", "metadata": {}, "outputs": [ @@ -201,6 +210,14 @@ "print(multiline)" ] }, + { + "cell_type": "markdown", + "id": "e7228650", + "metadata": {}, + "source": [ + "# 문자열 연산" + ] + }, { "cell_type": "code", "execution_count": null, @@ -219,11 +236,11 @@ } ], "source": [ - "# 문자열 연산\n", + "# + : 연결\n", + "\n", "head = \"Python\"\n", "tail = \" is fun!\"\n", - "head + tail\n", - "\n" + "head + tail" ] }, { @@ -244,13 +261,13 @@ } ], "source": [ - "# 문자열 반복\n", - "head * 2 " + "# * : 반복\n", + "head * 2" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "id": "faad4d62", "metadata": {}, "outputs": [ @@ -270,6 +287,18 @@ "len(head)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "76aae57e", + "metadata": {}, + "outputs": [], + "source": [ + "# len() : 길이 구하기\n", + "\n", + "len(head)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -301,7 +330,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "id": "b34f4f42", "metadata": {}, "outputs": [ @@ -326,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "id": "baa487b9", "metadata": {}, "outputs": [ @@ -350,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "id": "c10a8379", "metadata": {}, "outputs": [ @@ -388,7 +417,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": null, "id": "a2a40240", "metadata": {}, "outputs": [ @@ -410,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "id": "09cdebd0", "metadata": {}, "outputs": [ @@ -432,7 +461,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": null, "id": "2264a946", "metadata": {}, "outputs": [ @@ -453,7 +482,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": null, "id": "3fc4ce33", "metadata": {}, "outputs": [ @@ -474,7 +503,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": null, "id": "04065ada", "metadata": {}, "outputs": [ @@ -523,7 +552,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": null, "id": "7114f4ab", "metadata": {}, "outputs": [ @@ -560,7 +589,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": null, "id": "795cb215", "metadata": {}, "outputs": [], @@ -576,7 +605,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": null, "id": "caf4c5ca", "metadata": {}, "outputs": [ @@ -601,7 +630,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": null, "id": "379aa915", "metadata": {}, "outputs": [ @@ -630,7 +659,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": null, "id": "7e58ae86", "metadata": {}, "outputs": [ @@ -650,7 +679,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": null, "id": "d5d35993", "metadata": {}, "outputs": [ @@ -675,7 +704,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": null, "id": "99d7f463", "metadata": {}, "outputs": [ @@ -697,7 +726,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": null, "id": "71e05f8d", "metadata": {}, "outputs": [], @@ -732,7 +761,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": null, "id": "8d2c7520", "metadata": {}, "outputs": [ @@ -777,7 +806,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": null, "id": "30d312fe", "metadata": {}, "outputs": [], @@ -801,7 +830,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": null, "id": "08f068a4", "metadata": {}, "outputs": [ @@ -822,7 +851,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": null, "id": "d80c64e6", "metadata": {}, "outputs": [ @@ -843,7 +872,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": null, "id": "8d47b622", "metadata": {}, "outputs": [ @@ -898,7 +927,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": null, "id": "3ecdc347", "metadata": {}, "outputs": [], @@ -914,7 +943,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": null, "id": "028f2ca3", "metadata": {}, "outputs": [ @@ -960,7 +989,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": null, "id": "06ef8c0b", "metadata": {}, "outputs": [ @@ -981,7 +1010,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": null, "id": "1cbca157", "metadata": {}, "outputs": [ @@ -1012,7 +1041,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": null, "id": "db7a6a5b", "metadata": {}, "outputs": [], @@ -1024,7 +1053,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": null, "id": "cd658f9f", "metadata": {}, "outputs": [ @@ -1047,7 +1076,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": null, "id": "2abdb89f", "metadata": {}, "outputs": [ @@ -1069,7 +1098,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": null, "id": "861fe8a2", "metadata": {}, "outputs": [ @@ -1092,7 +1121,7 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": null, "id": "7376720f", "metadata": {}, "outputs": [ @@ -1118,7 +1147,7 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": null, "id": "8fd35f51", "metadata": {}, "outputs": [ @@ -1141,7 +1170,7 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": null, "id": "ef7f8e27", "metadata": {}, "outputs": [ @@ -1205,7 +1234,7 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": null, "id": "ad742983", "metadata": {}, "outputs": [ @@ -1229,7 +1258,7 @@ }, { "cell_type": "code", - "execution_count": 155, + "execution_count": null, "id": "58f6afd8", "metadata": {}, "outputs": [ @@ -1307,7 +1336,7 @@ }, { "cell_type": "code", - "execution_count": 179, + "execution_count": null, "id": "af6f7357", "metadata": {}, "outputs": [ @@ -1335,7 +1364,7 @@ }, { "cell_type": "code", - "execution_count": 186, + "execution_count": null, "id": "ff6d1883", "metadata": {}, "outputs": [ @@ -1367,7 +1396,7 @@ }, { "cell_type": "code", - "execution_count": 187, + "execution_count": null, "id": "a18e0c62", "metadata": {}, "outputs": [ @@ -1386,7 +1415,7 @@ }, { "cell_type": "code", - "execution_count": 189, + "execution_count": null, "id": "c6c03f4e", "metadata": {}, "outputs": [ @@ -1407,7 +1436,7 @@ }, { "cell_type": "code", - "execution_count": 190, + "execution_count": null, "id": "d3e98df6", "metadata": {}, "outputs": [ @@ -1437,7 +1466,7 @@ }, { "cell_type": "code", - "execution_count": 192, + "execution_count": null, "id": "414aa9d0", "metadata": {}, "outputs": [ @@ -1461,7 +1490,7 @@ }, { "cell_type": "code", - "execution_count": 195, + "execution_count": null, "id": "746a8f64", "metadata": {}, "outputs": [ @@ -1480,13 +1509,4049 @@ "print(msg)\n" ] }, + { + "cell_type": "markdown", + "id": "637f3673", + "metadata": {}, + "source": [ + "- while 문\n", + "\n", + "while 조건:\n", + " 반복해서 해야 할 일들\n", + "\n", + "- for 문\n", + "\n", + "for 변수 in 리스트 (또는 튜플, 문자열, 딕셔너리..):\n", + " 반복해서 해야할 일들" + ] + }, { "cell_type": "code", "execution_count": null, "id": "b7c22708", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 2, 3, 4, 5]\n", + "1\n", + "2\n", + "3\n", + "4\n", + "5\n" + ] + } + ], + "source": [ + "# 리스트 출력\n", + "list1 = [1, 2, 3, 4, 5]\n", + "print(list1)\n", + "\n", + "for i in list1:\n", + " print(i)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5f8a7f2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "2\n", + "3\n" + ] + } + ], + "source": [ + "t1 = (1, 2, 3)\n", + "for i in t1:\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78a0326b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3\n", + "7\n", + "11\n" + ] + } + ], + "source": [ + "list1 = [(1, 2), (3, 4), (5, 6)]\n", + "for first, last in list1:\n", + " print(first + last)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f01cfe76", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\t2\t3\t4\t5\t6\t7\t8\t9\t10\t" + ] + } + ], + "source": [ + "# 1 ~ 10 까지 cnffur\n", + "i = 1\n", + "\n", + "while i <= 10:\n", + " print(i, end='\\t')\n", + " i += 1\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "95be308e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "총합 : 790, 평균 : 79.0\n" + ] + } + ], + "source": [ + "# 총 10명의 학생 점수\n", + "classA = [70, 60, 55, 75, 95, 90, 80, 80, 85, 100]\n", + "\n", + "# 총합과 평균 구하기\n", + "\n", + "total = 0\n", + "for score in classA:\n", + " total += score\n", + " \n", + "avg = total / len(classA)\n", + "\n", + "print(f\"총합 : {total}, 평균 : {avg}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb7b38c2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "79.0" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 평균\n", + "sum(classA) / len(classA)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e77a0e61", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "range(1, 10, 2)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "range(5) # 0 ~ 4까지의 정수 생성\n", + "range(1, 6) # 1 ~ 5까지의 정수 생성\n", + "range(1, 10, 2) # 1 ~ 9까지의 정수 생성, 2씩 증가\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d4107acf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n", + "1\n", + "2\n", + "3\n", + "4\n", + "5\n", + "6\n", + "7\n", + "8\n", + "9\n", + "10\n", + "1\n", + "3\n", + "5\n", + "7\n", + "9\n" + ] + } + ], + "source": [ + "\n", + "for i in range(5):\n", + " print(i)\n", + " \n", + " \n", + "for i in range(1, 11):\n", + " print(i)\n", + "\n", + "for i in range(1, 11, 2):\n", + " print(i)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a50256f9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dreams\n", + "d\n", + "d\n", + "r\n", + "e\n", + "a\n", + "m\n", + "s\n" + ] + } + ], + "source": [ + "msg = \"dreams\"\n", + "print(msg)\n", + "\n", + "print(msg[0])\n", + "\n", + "for s in msg:\n", + " print(s)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0a8afbe7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'a': 1, 'b': 80, 'c': 70}\n", + "a 1\n", + "b 80\n", + "c 70\n" + ] + } + ], + "source": [ + "dict1 = { 'a': 1, 'b': 80, 'c': 70 }\n", + "print(dict1)\n", + "\n", + "for key, value in dict1.items():\n", + " print(key, value)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32f4103f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1,\n", + " 2,\n", + " 3,\n", + " 4,\n", + " 5,\n", + " 6,\n", + " 7,\n", + " 8,\n", + " 9,\n", + " 10,\n", + " 11,\n", + " 12,\n", + " 13,\n", + " 14,\n", + " 15,\n", + " 16,\n", + " 17,\n", + " 18,\n", + " 19,\n", + " 20,\n", + " 21,\n", + " 22,\n", + " 23,\n", + " 24,\n", + " 25,\n", + " 26,\n", + " 27,\n", + " 28,\n", + " 29,\n", + " 30,\n", + " 31,\n", + " 32,\n", + " 33,\n", + " 34,\n", + " 35,\n", + " 36,\n", + " 37,\n", + " 38,\n", + " 39,\n", + " 40,\n", + " 41,\n", + " 42,\n", + " 43,\n", + " 44,\n", + " 45,\n", + " 46,\n", + " 47,\n", + " 48,\n", + " 49,\n", + " 50,\n", + " 51,\n", + " 52,\n", + " 53,\n", + " 54,\n", + " 55,\n", + " 56,\n", + " 57,\n", + " 58,\n", + " 59,\n", + " 60,\n", + " 61,\n", + " 62,\n", + " 63,\n", + " 64,\n", + " 65,\n", + " 66,\n", + " 67,\n", + " 68,\n", + " 69,\n", + " 70,\n", + " 71,\n", + " 72,\n", + " 73,\n", + " 74,\n", + " 75,\n", + " 76,\n", + " 77,\n", + " 78,\n", + " 79,\n", + " 80,\n", + " 81,\n", + " 82,\n", + " 83,\n", + " 84,\n", + " 85,\n", + " 86,\n", + " 87,\n", + " 88,\n", + " 89,\n", + " 90,\n", + " 91,\n", + " 92,\n", + " 93,\n", + " 94,\n", + " 95,\n", + " 96,\n", + " 97,\n", + " 98,\n", + " 99,\n", + " 100]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(range(1, 101))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7fda43c8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n" + ] + } + ], + "source": [ + "# 1~10까지 리스트로 생성\n", + "numbers = []\n", + "for i in range(1, 11):\n", + " numbers.append(i)\n", + "\n", + "print(numbers)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17b64a5a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['b', 'c', 'd']\n" + ] + } + ], + "source": [ + "# 리스트 컴프리헨션\n", + "\n", + "list2 = [\"A\", \"b\", \"c\", \"E\", \"Q\", \"d\"]\n", + "\n", + "# 소문자만 골라서 새로운 리스트 생성\n", + "# lowers = []\n", + "# for s in list2:\n", + "# if s.islower():\n", + "# lowers.append(s)\n", + "\n", + "# print(lowers)\n", + "\n", + "lowers = [s for s in list2 if s.islower()]\n", + "print(lowers)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9fb9fb21", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[3, 6, 9, 12, 15]\n" + ] + } + ], + "source": [ + "nums = [1, 2, 3, 4, 5]\n", + "\n", + "# nums의 각 요소에 * 3 한 결과 results 새로운 리스트 생성\n", + "results = [n * 3 for n in nums]\n", + "print(results)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec857421", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[6, 12]\n" + ] + } + ], + "source": [ + "# nums의 각 요소에 짝수에 * 3 한 결과 results 새로운 리스트 생성\n", + "results = [n * 3 for n in nums if n % 2 == 0]\n", + "print(results)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "2794cdae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "65" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 리스트\n", + "# 반복문, 조건문\n", + "\n", + "ord(\"A\") # 65\n", + "chr(65) # \"A\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "0b0e8214", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A 자동차가 주차되었습니다. 주차장 상태 : ['A']\n", + "주차장이 비어 있습니다.\n", + "A 자동차가 주차되었습니다. 주차장 상태 : ['A']\n", + "주차장이 비어 있습니다.\n", + "A 자동차가 주차되었습니다. 주차장 상태 : ['A']\n", + "B 자동차가 주차되었습니다. 주차장 상태 : ['A', 'B']\n", + "C 자동차가 주차되었습니다. 주차장 상태 : ['A', 'B', 'C']\n", + "C 자동차가 출차되었습니다. 주차장 상태 : ['A', 'B']\n", + "B 자동차가 출차되었습니다. 주차장 상태 : ['A']\n", + "주차장이 비어 있습니다.\n", + "프로그램을 종료합니다.\n" + ] + } + ], + "source": [ + "# 주차장 프로그램?\n", + "# 5대의 주차 공간, a, b,c, d, e\n", + "\n", + "# 메뉴 제공\n", + "# [1] 주차 [2] 출차 [3] 종료\n", + "\n", + "# 1번 선택 => a 자동차 들어감. 주차장 상태 => ['a']\n", + "# 1번 선택 => b 자동차 들어감. 주차장 상태 => ['a', 'b']\n", + "# 2번 선택 => b 자동차 나감. 주차장 상태 => ['a']\n", + "\n", + "# 5대의 주차 공간이 모두 찼을 때 1번 선택 => \"주차 공간이 없습니다.\"\n", + "\n", + "parking_lot = []\n", + "\n", + "while True:\n", + " menu = input(\"[1] 주차 | [2] 출차 | [3] 종료 : 숫자만 입력하시오 = \")\n", + " \n", + " if menu == \"1\":\n", + " if len(parking_lot) < 5:\n", + " car = len(parking_lot) + 65 # \"A\"\n", + " parking_lot.append(car)\n", + " print(f\"{chr(car)} 자동차가 주차되었습니다. 주차장 상태 : {[chr(c) for c in parking_lot]}\")\n", + " else:\n", + " print(\"주차 공간이 없습니다.\")\n", + " \n", + " elif menu == \"2\":\n", + " if parking_lot:\n", + " car = parking_lot.pop()\n", + " \n", + " if not parking_lot:\n", + " print(\"주차장이 비어 있습니다.\")\n", + " else:\n", + " print(f\"{chr(car)} 자동차가 출차되었습니다. 주차장 상태 : {[chr(c) for c in parking_lot]}\")\n", + " else:\n", + " print(\"주차장이 비어 있습니다.\")\n", + " \n", + " elif menu == \"3\":\n", + " print(\"프로그램을 종료합니다.\")\n", + " break\n", + " \n", + " else:\n", + " print(\"잘못된 선택입니다. 다시 선택해주세요.\")" + ] + }, + { + "cell_type": "markdown", + "id": "f59c8442", + "metadata": {}, + "source": [ + "### 함수\n", + " \n", + "```python\n", + "def 함수명() :\n", + " 함수에서 해야할 일들\n", + "```\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "047354a6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "30\n" + ] + } + ], + "source": [ + "#range()\n", + "#int()\n", + "#input()\n", + "#len()\n", + "#str()\n", + "#list()\n", + "#tuple()\n", + "#dict()\n", + "#set()\n", + "#type()\n", + "#print()\n", + "\n", + "# 함수 정의\n", + "def add(a, b):\n", + " return a + b\n", + "\n", + "results = add(10, 20)\n", + "print(results)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "9941c444", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12\n" + ] + } + ], + "source": [ + "print(add(5, 7))" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "51e1a73e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12\n", + "10\n" + ] + } + ], + "source": [ + "def add(a, b = 5):\n", + " return a + b\n", + "\n", + "print(add(5, 7))\n", + "print(add(5))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "fc28339e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "15\n", + "50\n", + "0\n" + ] + } + ], + "source": [ + "def add_many(*args):\n", + " total = 0\n", + " for num in args:\n", + " total += num\n", + " return total\n", + "\n", + "print(add_many(1, 2, 3, 4, 5))\n", + "print(add_many(1, 19, 30))\n", + "print(add_many())" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "9071a280", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "15\n", + "120\n" + ] + } + ], + "source": [ + "def add_many(choice, *args):\n", + "\n", + " if choice == \"mul\":\n", + " result = 1\n", + " for num in args:\n", + " result *= num\n", + " elif choice == \"add\":\n", + " result = 0\n", + " for num in args:\n", + " result += num\n", + " return result\n", + "\n", + "print(add_many(\"add\", 1, 2, 3, 4, 5))\n", + "print(add_many(\"mul\", 1, 2, 3, 4, 5))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "30c1ea8f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a : 1\n", + "b : 2\n", + "c : 3\n", + "d : 4\n", + "e : 5\n", + "None\n" + ] + } + ], + "source": [ + "# ** kwargs : 키워드 인수, 함수에 전달되는 인수 중에서 키워드 인수는 함수 정의에서 매개변수 이름과 함께 값을 전달하는 방식입니다. 키워드 인수를 사용하면 함수 호출 시 매개변수의 순서에 상관없이 값을 전달할 수 있습니다. 예를 들어, def func(a, b): ...와 같이 함수가 정의되어 있을 때, func(b=2, a=1)와 같이 키워드 인수를 사용하여 값을 전달할 수 있습니다. 키워드 인수는 함수 호출 시 가독성을 높이고, 매개변수의 순서를 신경쓰지 않아도 되는 장점이 있습니다.\n", + "# *args : 가변 인수, 함수에 전달되는 인수 중에서 가변 인수는 함수 정의에서 매개변수 이름 앞에 *를 붙여서 정의하는 방식입니다. 가변 인수를 사용하면 함수 호출 시 전달되는 인수의 개수가 가변적일 수 있습니다. 예를 들어, def func(*args): ...와 같이 함수가 정의되어 있을 때, func(1, 2, 3)와 같이 여러 개의 인수를 전달할 수 있습니다. 가변 인수는 함수 호출 시 다양한 개수의 인수를 처리할 때 유용하게 사용됩니다.\n", + "# 변수명 : 1:1 대응\n", + "\n", + "def add_many(**kwargs):\n", + " for k, v in kwargs.items():\n", + " print(f\"{k} : {v}\")\n", + "\n", + "print(add_many(a=1, b=2, c=3, d=4, e=5))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "4662bc09", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10 5 () {}\n", + "1 2 () {}\n", + "1 2 (3, 4, 5) {}\n", + "1 2 () {'a': 6, 'b': 7}\n", + "1 2 (3, 4, 5) {'a': 6, 'b': 7}\n" + ] + } + ], + "source": [ + "def example_mul(arg1, arg2=5, *args, **kwargs):\n", + " print(arg1, arg2, args, kwargs)\n", + " \n", + "# example_mul()\n", + "example_mul(10)\n", + "example_mul(1, 2)\n", + "example_mul(1, 2, 3, 4, 5)\n", + "example_mul(1, 2, a=6, b=7)\n", + "example_mul(1, 2, 3, 4, 5, a=6, b=7)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "db75df55", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(7, 12)\n" + ] + } + ], + "source": [ + "def sum_add(a, b):\n", + " return a + b, a * b\n", + "\n", + "print(sum_add(3, 4))" + ] + }, + { + "cell_type": "markdown", + "id": "a9ea151c", + "metadata": {}, + "source": [ + "### lamda 함수\n", + "- 단일문으로 표현되는 익명 함수\n", + "- 1회용 함수로 주로 사용" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ddf88de2", + "metadata": {}, "outputs": [], - "source": [] + "source": [ + "### lamda 함수\n", + "- 단일문으로 표현되는 익명 함수\n", + "- 1회용 함수로 주로 사용" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "de8dbeda", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "25\n", + "25\n" + ] + } + ], + "source": [ + "def square(x):\n", + " return x ** 2\n", + "\n", + "print(square(5))\n", + "\n", + "# lambda 함수로 표현\n", + "square_lambda = lambda x: x ** 2\n", + "print(square_lambda(5))" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "id": "9f63beb3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7\n" + ] + } + ], + "source": [ + "#def add(a, b=5):\n", + "# return a + b\n", + "\n", + "lambda_add = lambda a, b: a + b\n", + "print(lambda_add(3, 4))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "1a999f77", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['bob', 'alice', 'teddy', 'charles']\n" + ] + } + ], + "source": [ + "string = [\"bob\", \"charles\", \"alice\", \"teddy\"]\n", + "\n", + "#sort() : 내림 오름\n", + "string.sort()\n", + "\n", + "#sort(key = 함수) : 문자길이 정렬\n", + "\n", + "# def str_len(s):\n", + "# return len(s)\n", + "\n", + "# string.sort(key=str_len)\n", + "# string\n", + "string.sort(key=lambda s: len(s))\n", + "print(string)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "id": "19157dae", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2, 4, 6, 8, 10]\n", + "[2, 4, 6, 8, 10]\n" + ] + } + ], + "source": [ + "# filter, map\n", + "\n", + "# 넘겨받은 리스트에서 짝수만을 추출후 새로운 리스트로 리턴\n", + "\n", + "def even(arr):\n", + " even_list = []\n", + " for n in arr:\n", + " if n % 2 == 0:\n", + " even_list.append(n)\n", + " return even_list\n", + "\n", + "list1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", + "even_list = even(list1)\n", + "print(even_list)\n", + "\n", + "def is_even(n):\n", + " return n % 2 == 0\n", + "\n", + "numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", + "even_numbers = filter(is_even, numbers)\n", + "print(list(even_numbers))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "4fdbc48c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[2, 4, 6, 8, 10]" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(filter(lambda n: n % 2 == 0, numbers))" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "id": "b4da5d8e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[False, True, False, True, False, True, False, True, False, True]" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(map(lambda n: n %2==0, numbers))" + ] + }, + { + "cell_type": "markdown", + "id": "881aeb4d", + "metadata": {}, + "source": [ + "# 파일 입출력\n", + "\n", + "``` open(파일이름, mode) ```" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "id": "eb3148ee", + "metadata": {}, + "outputs": [], + "source": [ + "# 파일 읽기\n", + "f1 = open(\"test.txt\", \"r\") # 파일을 읽기 모드로 엽니다. open() 함수는 파일을 열 때 사용되며, 첫 번째 매개변수로 파일 경로를, 두 번째 매개변수로 파일 모드를 지정합니다. \"r\" 모드는 읽기 모드로 파일을 열며, 파일이 존재하지 않으면 FileNotFoundError 예외를 발생시킵니다. open() 함수는 파일 객체를 반환하며, 이후에 파일 작업을 수행할 수 있습니다. \n", + "# 파일 쓰기\n", + "f2 = open(\"test.txt\", \"w\") # 파일을 쓰기 모드로 엽니다\n", + "# 파일에 내용 추가 \n", + "f3 = open(\"test.txt\", \"a\") # 파일을 추가 모드로 엽니다. open() 함수는 파일을 열 때 사용되며, 첫 번째 매개변수로 파일 경로를, 두 번째 매개변수로 파일 모드를 지정합니다. \"a\" 모드는 추가 모드로 파일을 열며, 파일이 존재하지 않으면 새로 생성합니다. open() 함수는 파일 객체를 반환하며, 이후에 파일 작업을 수행할 수 있습니다. 추가 모드는 기존 파일의 내용을 유지하면서 새로운 내용을 추가할 때 유용하게 사용됩니다." + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "id": "f58cb422", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "test\n" + ] + } + ], + "source": [ + "f1 = open(\"test.txt\", \"r\", encoding=\"utf-8\") # 파일을 읽기 모드로 열면서 인코딩을 utf-8로 지정합니다. open() 함수는 파일을 열 때 사용되며, 첫 번째 매개변수로 파일 경로를, 두 번째 매개변수로 파일 모드를 지정합니다. \"r\" 모드는 읽기 모드로 파일을 열며, encoding=\"utf-8\"은 파일의 인코딩을 utf-8로 지정하는 옵션입니다. open() 함수는 파일 객체를 반환하며, 이후에 파일 작업을 수행할 수 있습니다. 인코딩을 지정하면 파일의 내용을 올바르게 읽어들일 수 있습니다.\n", + "print(f1.read()) # 파일의 전체 내용을 읽어서 문자열로 반환하는 함수입니다. read() 함수는 파일 객체에서 전체 내용을 읽어들이며, 반환값은 문자열입니다. 예를 들어, f1.read()는 f1 파일 객체에서 전체 내용을 읽어서 문자열로 반환합니다. read() 함수는 파일의 내용을 한 번에 읽어들일 때 유용하게 사용됩니다. \n", + "f1.close() # 파일을 닫습니다. close() 함수는 파일 작업이 끝난 후에 파일을 닫을 때 사용됩니다. 파일을 열 때 open() 함수를 사용하여 파일 객체를 생성한 후, 작업이 완료되면 close() 함수를 호출하여 파일을 닫아야 합니다. close() 함수를 호출하지 않으면 파일이 계속 열려 있을 수 있으며, 이는 시스템 자원을 낭비할 수 있습니다. 따라서 파일 작업이 끝난 후에는 반드시 close() 함수를 호출하여 파일을 닫는 것이 좋습니다." + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "id": "9958ad37", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "test\n" + ] + } + ], + "source": [ + "with open(\"test.txt\", \"r\", encoding=\"utf-8\") as f1:\n", + " print(f1.read()) # with 문을 사용하여 파일을 열면, 블록이 끝날 때 자동으로 파일이 닫힙니다. 따라서 with open(\"test.txt\", \"r\", encoding=\"utf-8\") as f1:와 같이 파일을 열고, 블록 내에서 f1.read()를 호출하여 파일의 내용을 읽어들인 후, 블록이 끝나면 자동으로 f1 파일 객체가 닫히게 됩니다. with 문은 파일 작업을 안전하게 처리할 수 있도록 도와주며, 파일을 열고 닫는 것을 자동으로 관리해줍니다." + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "55b73588", + "metadata": {}, + "outputs": [], + "source": [ + "with open(\"test2.txt\", \"w\", encoding=\"utf-8\") as f1:\n", + " for i in range(3):\n", + " f1.write(f\"Line {i+1}\\n\") # 파일 객체 f1에 문자열을 쓰는 함수입니다. write() 함수는 파일 객체에 문자열을 쓰며, 반환값은 쓰여진 문자열의 길이입니다. 예를 들어, f1.write(f\"Line {i+1}\\n\")는 f1 파일 객체에 \"Line 1\\n\", \"Line 2\\n\", \"Line 3\\n\"과 같은 문자열을 순차적으로 쓰게 됩니다. write() 함수는 파일에 데이터를 기록할 때 유용하게 사용됩니다. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "id": "3b92d329", + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "hanguls = list(\"가나다라마바사아자차카타파하\")\n", + "with open(\"info.txt\", \"w\", encoding=\"utf-8\") as f1:\n", + " for i in range(500):\n", + " name = random.choice(hanguls) + random.choice(hanguls)\n", + " weight = random.randint(40, 100)\n", + " height = random.randint(140, 200)\n", + " f1.write(f\"{name} {weight} {height}\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "id": "8753e450", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "파다 98 170\n", + "\n", + " 이름 : 파다, 몸무게 : 98kg, 키 : 170cm, bmi 지수 : 33.91, 결과 : 과체중\n", + "\n", + "차카 63 194\n", + "\n", + " 이름 : 차카, 몸무게 : 63kg, 키 : 194cm, bmi 지수 : 16.74, 결과 : 저체중\n", + "\n", + "바카 42 152\n", + "\n", + " 이름 : 바카, 몸무게 : 42kg, 키 : 152cm, bmi 지수 : 18.18, 결과 : 저체중\n", + "\n", + "마파 62 157\n", + "\n", + " 이름 : 마파, 몸무게 : 62kg, 키 : 157cm, bmi 지수 : 25.15, 결과 : 과체중\n", + "\n", + "라마 77 156\n", + "\n", + " 이름 : 라마, 몸무게 : 77kg, 키 : 156cm, bmi 지수 : 31.64, 결과 : 과체중\n", + "\n", + "카다 40 143\n", + "\n", + " 이름 : 카다, 몸무게 : 40kg, 키 : 143cm, bmi 지수 : 19.56, 결과 : 정상\n", + "\n", + "가하 63 150\n", + "\n", + " 이름 : 가하, 몸무게 : 63kg, 키 : 150cm, bmi 지수 : 28.00, 결과 : 과체중\n", + "\n", + "라가 78 153\n", + "\n", + " 이름 : 라가, 몸무게 : 78kg, 키 : 153cm, bmi 지수 : 33.32, 결과 : 과체중\n", + "\n", + "라자 88 189\n", + "\n", + " 이름 : 라자, 몸무게 : 88kg, 키 : 189cm, bmi 지수 : 24.64, 결과 : 정상\n", + "\n", + "파다 47 186\n", + "\n", + " 이름 : 파다, 몸무게 : 47kg, 키 : 186cm, bmi 지수 : 13.59, 결과 : 저체중\n", + "\n", + "파가 50 168\n", + "\n", + " 이름 : 파가, 몸무게 : 50kg, 키 : 168cm, bmi 지수 : 17.72, 결과 : 저체중\n", + "\n", + "바타 59 194\n", + "\n", + " 이름 : 바타, 몸무게 : 59kg, 키 : 194cm, bmi 지수 : 15.68, 결과 : 저체중\n", + "\n", + "파사 63 196\n", + "\n", + " 이름 : 파사, 몸무게 : 63kg, 키 : 196cm, bmi 지수 : 16.40, 결과 : 저체중\n", + "\n", + "하차 87 195\n", + "\n", + " 이름 : 하차, 몸무게 : 87kg, 키 : 195cm, bmi 지수 : 22.88, 결과 : 정상\n", + "\n", + "마파 65 152\n", + "\n", + " 이름 : 마파, 몸무게 : 65kg, 키 : 152cm, bmi 지수 : 28.13, 결과 : 과체중\n", + "\n", + "하파 62 145\n", + "\n", + " 이름 : 하파, 몸무게 : 62kg, 키 : 145cm, bmi 지수 : 29.49, 결과 : 과체중\n", + "\n", + "차바 69 159\n", + "\n", + " 이름 : 차바, 몸무게 : 69kg, 키 : 159cm, bmi 지수 : 27.29, 결과 : 과체중\n", + "\n", + "카차 70 196\n", + "\n", + " 이름 : 카차, 몸무게 : 70kg, 키 : 196cm, bmi 지수 : 18.22, 결과 : 저체중\n", + "\n", + "바라 48 147\n", + "\n", + " 이름 : 바라, 몸무게 : 48kg, 키 : 147cm, bmi 지수 : 22.21, 결과 : 정상\n", + "\n", + "자나 43 146\n", + "\n", + " 이름 : 자나, 몸무게 : 43kg, 키 : 146cm, bmi 지수 : 20.17, 결과 : 정상\n", + "\n", + "파마 88 149\n", + "\n", + " 이름 : 파마, 몸무게 : 88kg, 키 : 149cm, bmi 지수 : 39.64, 결과 : 과체중\n", + "\n", + "나나 67 155\n", + "\n", + " 이름 : 나나, 몸무게 : 67kg, 키 : 155cm, bmi 지수 : 27.89, 결과 : 과체중\n", + "\n", + "파가 49 152\n", + "\n", + " 이름 : 파가, 몸무게 : 49kg, 키 : 152cm, bmi 지수 : 21.21, 결과 : 정상\n", + "\n", + "자차 96 140\n", + "\n", + " 이름 : 자차, 몸무게 : 96kg, 키 : 140cm, bmi 지수 : 48.98, 결과 : 과체중\n", + "\n", + "사마 88 184\n", + "\n", + " 이름 : 사마, 몸무게 : 88kg, 키 : 184cm, bmi 지수 : 25.99, 결과 : 과체중\n", + "\n", + "사바 90 174\n", + "\n", + " 이름 : 사바, 몸무게 : 90kg, 키 : 174cm, bmi 지수 : 29.73, 결과 : 과체중\n", + "\n", + "나라 58 167\n", + "\n", + " 이름 : 나라, 몸무게 : 58kg, 키 : 167cm, bmi 지수 : 20.80, 결과 : 정상\n", + "\n", + "파나 66 152\n", + "\n", + " 이름 : 파나, 몸무게 : 66kg, 키 : 152cm, bmi 지수 : 28.57, 결과 : 과체중\n", + "\n", + "타나 86 170\n", + "\n", + " 이름 : 타나, 몸무게 : 86kg, 키 : 170cm, bmi 지수 : 29.76, 결과 : 과체중\n", + "\n", + "아나 93 195\n", + "\n", + " 이름 : 아나, 몸무게 : 93kg, 키 : 195cm, bmi 지수 : 24.46, 결과 : 정상\n", + "\n", + "사하 61 151\n", + "\n", + " 이름 : 사하, 몸무게 : 61kg, 키 : 151cm, bmi 지수 : 26.75, 결과 : 과체중\n", + "\n", + "타하 85 142\n", + "\n", + " 이름 : 타하, 몸무게 : 85kg, 키 : 142cm, bmi 지수 : 42.15, 결과 : 과체중\n", + "\n", + "카차 73 140\n", + "\n", + " 이름 : 카차, 몸무게 : 73kg, 키 : 140cm, bmi 지수 : 37.24, 결과 : 과체중\n", + "\n", + "카마 90 157\n", + "\n", + " 이름 : 카마, 몸무게 : 90kg, 키 : 157cm, bmi 지수 : 36.51, 결과 : 과체중\n", + "\n", + "가가 41 180\n", + "\n", + " 이름 : 가가, 몸무게 : 41kg, 키 : 180cm, bmi 지수 : 12.65, 결과 : 저체중\n", + "\n", + "나나 56 198\n", + "\n", + " 이름 : 나나, 몸무게 : 56kg, 키 : 198cm, bmi 지수 : 14.28, 결과 : 저체중\n", + "\n", + "다파 40 161\n", + "\n", + " 이름 : 다파, 몸무게 : 40kg, 키 : 161cm, bmi 지수 : 15.43, 결과 : 저체중\n", + "\n", + "나카 62 170\n", + "\n", + " 이름 : 나카, 몸무게 : 62kg, 키 : 170cm, bmi 지수 : 21.45, 결과 : 정상\n", + "\n", + "차다 88 187\n", + "\n", + " 이름 : 차다, 몸무게 : 88kg, 키 : 187cm, bmi 지수 : 25.17, 결과 : 과체중\n", + "\n", + "가자 45 150\n", + "\n", + " 이름 : 가자, 몸무게 : 45kg, 키 : 150cm, bmi 지수 : 20.00, 결과 : 정상\n", + "\n", + "사다 80 195\n", + "\n", + " 이름 : 사다, 몸무게 : 80kg, 키 : 195cm, bmi 지수 : 21.04, 결과 : 정상\n", + "\n", + "카바 84 192\n", + "\n", + " 이름 : 카바, 몸무게 : 84kg, 키 : 192cm, bmi 지수 : 22.79, 결과 : 정상\n", + "\n", + "하자 98 164\n", + "\n", + " 이름 : 하자, 몸무게 : 98kg, 키 : 164cm, bmi 지수 : 36.44, 결과 : 과체중\n", + "\n", + "카자 92 177\n", + "\n", + " 이름 : 카자, 몸무게 : 92kg, 키 : 177cm, bmi 지수 : 29.37, 결과 : 과체중\n", + "\n", + "다다 41 150\n", + "\n", + " 이름 : 다다, 몸무게 : 41kg, 키 : 150cm, bmi 지수 : 18.22, 결과 : 저체중\n", + "\n", + "아타 86 170\n", + "\n", + " 이름 : 아타, 몸무게 : 86kg, 키 : 170cm, bmi 지수 : 29.76, 결과 : 과체중\n", + "\n", + "마카 50 182\n", + "\n", + " 이름 : 마카, 몸무게 : 50kg, 키 : 182cm, bmi 지수 : 15.09, 결과 : 저체중\n", + "\n", + "마자 72 175\n", + "\n", + " 이름 : 마자, 몸무게 : 72kg, 키 : 175cm, bmi 지수 : 23.51, 결과 : 정상\n", + "\n", + "사다 40 158\n", + "\n", + " 이름 : 사다, 몸무게 : 40kg, 키 : 158cm, bmi 지수 : 16.02, 결과 : 저체중\n", + "\n", + "사아 93 174\n", + "\n", + " 이름 : 사아, 몸무게 : 93kg, 키 : 174cm, bmi 지수 : 30.72, 결과 : 과체중\n", + "\n", + "가라 58 153\n", + "\n", + " 이름 : 가라, 몸무게 : 58kg, 키 : 153cm, bmi 지수 : 24.78, 결과 : 정상\n", + "\n", + "자아 77 196\n", + "\n", + " 이름 : 자아, 몸무게 : 77kg, 키 : 196cm, bmi 지수 : 20.04, 결과 : 정상\n", + "\n", + "아바 42 162\n", + "\n", + " 이름 : 아바, 몸무게 : 42kg, 키 : 162cm, bmi 지수 : 16.00, 결과 : 저체중\n", + "\n", + "마하 56 175\n", + "\n", + " 이름 : 마하, 몸무게 : 56kg, 키 : 175cm, bmi 지수 : 18.29, 결과 : 저체중\n", + "\n", + "파파 93 187\n", + "\n", + " 이름 : 파파, 몸무게 : 93kg, 키 : 187cm, bmi 지수 : 26.59, 결과 : 과체중\n", + "\n", + "파마 91 172\n", + "\n", + " 이름 : 파마, 몸무게 : 91kg, 키 : 172cm, bmi 지수 : 30.76, 결과 : 과체중\n", + "\n", + "카다 80 146\n", + "\n", + " 이름 : 카다, 몸무게 : 80kg, 키 : 146cm, bmi 지수 : 37.53, 결과 : 과체중\n", + "\n", + "가다 95 166\n", + "\n", + " 이름 : 가다, 몸무게 : 95kg, 키 : 166cm, bmi 지수 : 34.48, 결과 : 과체중\n", + "\n", + "카카 42 150\n", + "\n", + " 이름 : 카카, 몸무게 : 42kg, 키 : 150cm, bmi 지수 : 18.67, 결과 : 정상\n", + "\n", + "라차 50 191\n", + "\n", + " 이름 : 라차, 몸무게 : 50kg, 키 : 191cm, bmi 지수 : 13.71, 결과 : 저체중\n", + "\n", + "아바 95 143\n", + "\n", + " 이름 : 아바, 몸무게 : 95kg, 키 : 143cm, bmi 지수 : 46.46, 결과 : 과체중\n", + "\n", + "바마 91 175\n", + "\n", + " 이름 : 바마, 몸무게 : 91kg, 키 : 175cm, bmi 지수 : 29.71, 결과 : 과체중\n", + "\n", + "파라 85 164\n", + "\n", + " 이름 : 파라, 몸무게 : 85kg, 키 : 164cm, bmi 지수 : 31.60, 결과 : 과체중\n", + "\n", + "마라 85 168\n", + "\n", + " 이름 : 마라, 몸무게 : 85kg, 키 : 168cm, bmi 지수 : 30.12, 결과 : 과체중\n", + "\n", + "자하 89 192\n", + "\n", + " 이름 : 자하, 몸무게 : 89kg, 키 : 192cm, bmi 지수 : 24.14, 결과 : 정상\n", + "\n", + "사아 99 197\n", + "\n", + " 이름 : 사아, 몸무게 : 99kg, 키 : 197cm, bmi 지수 : 25.51, 결과 : 과체중\n", + "\n", + "사차 61 172\n", + "\n", + " 이름 : 사차, 몸무게 : 61kg, 키 : 172cm, bmi 지수 : 20.62, 결과 : 정상\n", + "\n", + "차차 76 162\n", + "\n", + " 이름 : 차차, 몸무게 : 76kg, 키 : 162cm, bmi 지수 : 28.96, 결과 : 과체중\n", + "\n", + "다차 83 191\n", + "\n", + " 이름 : 다차, 몸무게 : 83kg, 키 : 191cm, bmi 지수 : 22.75, 결과 : 정상\n", + "\n", + "나타 63 150\n", + "\n", + " 이름 : 나타, 몸무게 : 63kg, 키 : 150cm, bmi 지수 : 28.00, 결과 : 과체중\n", + "\n", + "하사 91 150\n", + "\n", + " 이름 : 하사, 몸무게 : 91kg, 키 : 150cm, bmi 지수 : 40.44, 결과 : 과체중\n", + "\n", + "카사 59 162\n", + "\n", + " 이름 : 카사, 몸무게 : 59kg, 키 : 162cm, bmi 지수 : 22.48, 결과 : 정상\n", + "\n", + "카차 87 197\n", + "\n", + " 이름 : 카차, 몸무게 : 87kg, 키 : 197cm, bmi 지수 : 22.42, 결과 : 정상\n", + "\n", + "카바 61 181\n", + "\n", + " 이름 : 카바, 몸무게 : 61kg, 키 : 181cm, bmi 지수 : 18.62, 결과 : 정상\n", + "\n", + "바파 58 168\n", + "\n", + " 이름 : 바파, 몸무게 : 58kg, 키 : 168cm, bmi 지수 : 20.55, 결과 : 정상\n", + "\n", + "카타 93 200\n", + "\n", + " 이름 : 카타, 몸무게 : 93kg, 키 : 200cm, bmi 지수 : 23.25, 결과 : 정상\n", + "\n", + "나나 51 194\n", + "\n", + " 이름 : 나나, 몸무게 : 51kg, 키 : 194cm, bmi 지수 : 13.55, 결과 : 저체중\n", + "\n", + "다가 92 169\n", + "\n", + " 이름 : 다가, 몸무게 : 92kg, 키 : 169cm, bmi 지수 : 32.21, 결과 : 과체중\n", + "\n", + "가나 82 145\n", + "\n", + " 이름 : 가나, 몸무게 : 82kg, 키 : 145cm, bmi 지수 : 39.00, 결과 : 과체중\n", + "\n", + "카파 77 177\n", + "\n", + " 이름 : 카파, 몸무게 : 77kg, 키 : 177cm, bmi 지수 : 24.58, 결과 : 정상\n", + "\n", + "타하 54 157\n", + "\n", + " 이름 : 타하, 몸무게 : 54kg, 키 : 157cm, bmi 지수 : 21.91, 결과 : 정상\n", + "\n", + "차바 44 165\n", + "\n", + " 이름 : 차바, 몸무게 : 44kg, 키 : 165cm, bmi 지수 : 16.16, 결과 : 저체중\n", + "\n", + "사파 58 142\n", + "\n", + " 이름 : 사파, 몸무게 : 58kg, 키 : 142cm, bmi 지수 : 28.76, 결과 : 과체중\n", + "\n", + "나카 83 157\n", + "\n", + " 이름 : 나카, 몸무게 : 83kg, 키 : 157cm, bmi 지수 : 33.67, 결과 : 과체중\n", + "\n", + "다가 59 151\n", + "\n", + " 이름 : 다가, 몸무게 : 59kg, 키 : 151cm, bmi 지수 : 25.88, 결과 : 과체중\n", + "\n", + "마카 45 144\n", + "\n", + " 이름 : 마카, 몸무게 : 45kg, 키 : 144cm, bmi 지수 : 21.70, 결과 : 정상\n", + "\n", + "마파 72 169\n", + "\n", + " 이름 : 마파, 몸무게 : 72kg, 키 : 169cm, bmi 지수 : 25.21, 결과 : 과체중\n", + "\n", + "라아 54 188\n", + "\n", + " 이름 : 라아, 몸무게 : 54kg, 키 : 188cm, bmi 지수 : 15.28, 결과 : 저체중\n", + "\n", + "타자 89 177\n", + "\n", + " 이름 : 타자, 몸무게 : 89kg, 키 : 177cm, bmi 지수 : 28.41, 결과 : 과체중\n", + "\n", + "나나 48 198\n", + "\n", + " 이름 : 나나, 몸무게 : 48kg, 키 : 198cm, bmi 지수 : 12.24, 결과 : 저체중\n", + "\n", + "파사 75 168\n", + "\n", + " 이름 : 파사, 몸무게 : 75kg, 키 : 168cm, bmi 지수 : 26.57, 결과 : 과체중\n", + "\n", + "바다 95 157\n", + "\n", + " 이름 : 바다, 몸무게 : 95kg, 키 : 157cm, bmi 지수 : 38.54, 결과 : 과체중\n", + "\n", + "아아 58 189\n", + "\n", + " 이름 : 아아, 몸무게 : 58kg, 키 : 189cm, bmi 지수 : 16.24, 결과 : 저체중\n", + "\n", + "아가 72 174\n", + "\n", + " 이름 : 아가, 몸무게 : 72kg, 키 : 174cm, bmi 지수 : 23.78, 결과 : 정상\n", + "\n", + "카아 78 156\n", + "\n", + " 이름 : 카아, 몸무게 : 78kg, 키 : 156cm, bmi 지수 : 32.05, 결과 : 과체중\n", + "\n", + "타바 50 168\n", + "\n", + " 이름 : 타바, 몸무게 : 50kg, 키 : 168cm, bmi 지수 : 17.72, 결과 : 저체중\n", + "\n", + "다사 87 163\n", + "\n", + " 이름 : 다사, 몸무게 : 87kg, 키 : 163cm, bmi 지수 : 32.74, 결과 : 과체중\n", + "\n", + "라다 83 145\n", + "\n", + " 이름 : 라다, 몸무게 : 83kg, 키 : 145cm, bmi 지수 : 39.48, 결과 : 과체중\n", + "\n", + "다라 97 140\n", + "\n", + " 이름 : 다라, 몸무게 : 97kg, 키 : 140cm, bmi 지수 : 49.49, 결과 : 과체중\n", + "\n", + "마가 88 143\n", + "\n", + " 이름 : 마가, 몸무게 : 88kg, 키 : 143cm, bmi 지수 : 43.03, 결과 : 과체중\n", + "\n", + "라파 88 183\n", + "\n", + " 이름 : 라파, 몸무게 : 88kg, 키 : 183cm, bmi 지수 : 26.28, 결과 : 과체중\n", + "\n", + "하카 51 199\n", + "\n", + " 이름 : 하카, 몸무게 : 51kg, 키 : 199cm, bmi 지수 : 12.88, 결과 : 저체중\n", + "\n", + "자다 41 172\n", + "\n", + " 이름 : 자다, 몸무게 : 41kg, 키 : 172cm, bmi 지수 : 13.86, 결과 : 저체중\n", + "\n", + "자하 70 175\n", + "\n", + " 이름 : 자하, 몸무게 : 70kg, 키 : 175cm, bmi 지수 : 22.86, 결과 : 정상\n", + "\n", + "마다 66 184\n", + "\n", + " 이름 : 마다, 몸무게 : 66kg, 키 : 184cm, bmi 지수 : 19.49, 결과 : 정상\n", + "\n", + "바자 93 173\n", + "\n", + " 이름 : 바자, 몸무게 : 93kg, 키 : 173cm, bmi 지수 : 31.07, 결과 : 과체중\n", + "\n", + "가차 100 175\n", + "\n", + " 이름 : 가차, 몸무게 : 100kg, 키 : 175cm, bmi 지수 : 32.65, 결과 : 과체중\n", + "\n", + "마아 62 151\n", + "\n", + " 이름 : 마아, 몸무게 : 62kg, 키 : 151cm, bmi 지수 : 27.19, 결과 : 과체중\n", + "\n", + "파타 45 178\n", + "\n", + " 이름 : 파타, 몸무게 : 45kg, 키 : 178cm, bmi 지수 : 14.20, 결과 : 저체중\n", + "\n", + "타라 80 165\n", + "\n", + " 이름 : 타라, 몸무게 : 80kg, 키 : 165cm, bmi 지수 : 29.38, 결과 : 과체중\n", + "\n", + "마하 66 187\n", + "\n", + " 이름 : 마하, 몸무게 : 66kg, 키 : 187cm, bmi 지수 : 18.87, 결과 : 정상\n", + "\n", + "라카 50 182\n", + "\n", + " 이름 : 라카, 몸무게 : 50kg, 키 : 182cm, bmi 지수 : 15.09, 결과 : 저체중\n", + "\n", + "카다 47 181\n", + "\n", + " 이름 : 카다, 몸무게 : 47kg, 키 : 181cm, bmi 지수 : 14.35, 결과 : 저체중\n", + "\n", + "가가 51 144\n", + "\n", + " 이름 : 가가, 몸무게 : 51kg, 키 : 144cm, bmi 지수 : 24.59, 결과 : 정상\n", + "\n", + "파아 65 189\n", + "\n", + " 이름 : 파아, 몸무게 : 65kg, 키 : 189cm, bmi 지수 : 18.20, 결과 : 저체중\n", + "\n", + "타자 67 160\n", + "\n", + " 이름 : 타자, 몸무게 : 67kg, 키 : 160cm, bmi 지수 : 26.17, 결과 : 과체중\n", + "\n", + "카아 51 197\n", + "\n", + " 이름 : 카아, 몸무게 : 51kg, 키 : 197cm, bmi 지수 : 13.14, 결과 : 저체중\n", + "\n", + "가라 79 151\n", + "\n", + " 이름 : 가라, 몸무게 : 79kg, 키 : 151cm, bmi 지수 : 34.65, 결과 : 과체중\n", + "\n", + "자아 58 192\n", + "\n", + " 이름 : 자아, 몸무게 : 58kg, 키 : 192cm, bmi 지수 : 15.73, 결과 : 저체중\n", + "\n", + "하하 44 152\n", + "\n", + " 이름 : 하하, 몸무게 : 44kg, 키 : 152cm, bmi 지수 : 19.04, 결과 : 정상\n", + "\n", + "카다 96 170\n", + "\n", + " 이름 : 카다, 몸무게 : 96kg, 키 : 170cm, bmi 지수 : 33.22, 결과 : 과체중\n", + "\n", + "마라 84 176\n", + "\n", + " 이름 : 마라, 몸무게 : 84kg, 키 : 176cm, bmi 지수 : 27.12, 결과 : 과체중\n", + "\n", + "마아 67 179\n", + "\n", + " 이름 : 마아, 몸무게 : 67kg, 키 : 179cm, bmi 지수 : 20.91, 결과 : 정상\n", + "\n", + "차다 67 147\n", + "\n", + " 이름 : 차다, 몸무게 : 67kg, 키 : 147cm, bmi 지수 : 31.01, 결과 : 과체중\n", + "\n", + "아라 71 169\n", + "\n", + " 이름 : 아라, 몸무게 : 71kg, 키 : 169cm, bmi 지수 : 24.86, 결과 : 정상\n", + "\n", + "타라 50 154\n", + "\n", + " 이름 : 타라, 몸무게 : 50kg, 키 : 154cm, bmi 지수 : 21.08, 결과 : 정상\n", + "\n", + "바다 52 186\n", + "\n", + " 이름 : 바다, 몸무게 : 52kg, 키 : 186cm, bmi 지수 : 15.03, 결과 : 저체중\n", + "\n", + "하차 40 145\n", + "\n", + " 이름 : 하차, 몸무게 : 40kg, 키 : 145cm, bmi 지수 : 19.02, 결과 : 정상\n", + "\n", + "파사 75 161\n", + "\n", + " 이름 : 파사, 몸무게 : 75kg, 키 : 161cm, bmi 지수 : 28.93, 결과 : 과체중\n", + "\n", + "가다 66 147\n", + "\n", + " 이름 : 가다, 몸무게 : 66kg, 키 : 147cm, bmi 지수 : 30.54, 결과 : 과체중\n", + "\n", + "라하 84 177\n", + "\n", + " 이름 : 라하, 몸무게 : 84kg, 키 : 177cm, bmi 지수 : 26.81, 결과 : 과체중\n", + "\n", + "라바 53 163\n", + "\n", + " 이름 : 라바, 몸무게 : 53kg, 키 : 163cm, bmi 지수 : 19.95, 결과 : 정상\n", + "\n", + "다사 61 155\n", + "\n", + " 이름 : 다사, 몸무게 : 61kg, 키 : 155cm, bmi 지수 : 25.39, 결과 : 과체중\n", + "\n", + "아바 57 192\n", + "\n", + " 이름 : 아바, 몸무게 : 57kg, 키 : 192cm, bmi 지수 : 15.46, 결과 : 저체중\n", + "\n", + "마다 55 172\n", + "\n", + " 이름 : 마다, 몸무게 : 55kg, 키 : 172cm, bmi 지수 : 18.59, 결과 : 정상\n", + "\n", + "타카 81 159\n", + "\n", + " 이름 : 타카, 몸무게 : 81kg, 키 : 159cm, bmi 지수 : 32.04, 결과 : 과체중\n", + "\n", + "타라 74 191\n", + "\n", + " 이름 : 타라, 몸무게 : 74kg, 키 : 191cm, bmi 지수 : 20.28, 결과 : 정상\n", + "\n", + "다차 62 142\n", + "\n", + " 이름 : 다차, 몸무게 : 62kg, 키 : 142cm, bmi 지수 : 30.75, 결과 : 과체중\n", + "\n", + "가하 48 171\n", + "\n", + " 이름 : 가하, 몸무게 : 48kg, 키 : 171cm, bmi 지수 : 16.42, 결과 : 저체중\n", + "\n", + "차바 94 166\n", + "\n", + " 이름 : 차바, 몸무게 : 94kg, 키 : 166cm, bmi 지수 : 34.11, 결과 : 과체중\n", + "\n", + "나파 79 180\n", + "\n", + " 이름 : 나파, 몸무게 : 79kg, 키 : 180cm, bmi 지수 : 24.38, 결과 : 정상\n", + "\n", + "차사 80 140\n", + "\n", + " 이름 : 차사, 몸무게 : 80kg, 키 : 140cm, bmi 지수 : 40.82, 결과 : 과체중\n", + "\n", + "하다 51 196\n", + "\n", + " 이름 : 하다, 몸무게 : 51kg, 키 : 196cm, bmi 지수 : 13.28, 결과 : 저체중\n", + "\n", + "바나 54 148\n", + "\n", + " 이름 : 바나, 몸무게 : 54kg, 키 : 148cm, bmi 지수 : 24.65, 결과 : 정상\n", + "\n", + "마카 66 163\n", + "\n", + " 이름 : 마카, 몸무게 : 66kg, 키 : 163cm, bmi 지수 : 24.84, 결과 : 정상\n", + "\n", + "하나 78 169\n", + "\n", + " 이름 : 하나, 몸무게 : 78kg, 키 : 169cm, bmi 지수 : 27.31, 결과 : 과체중\n", + "\n", + "마나 85 171\n", + "\n", + " 이름 : 마나, 몸무게 : 85kg, 키 : 171cm, bmi 지수 : 29.07, 결과 : 과체중\n", + "\n", + "자마 74 152\n", + "\n", + " 이름 : 자마, 몸무게 : 74kg, 키 : 152cm, bmi 지수 : 32.03, 결과 : 과체중\n", + "\n", + "파나 41 170\n", + "\n", + " 이름 : 파나, 몸무게 : 41kg, 키 : 170cm, bmi 지수 : 14.19, 결과 : 저체중\n", + "\n", + "라나 88 149\n", + "\n", + " 이름 : 라나, 몸무게 : 88kg, 키 : 149cm, bmi 지수 : 39.64, 결과 : 과체중\n", + "\n", + "다가 73 158\n", + "\n", + " 이름 : 다가, 몸무게 : 73kg, 키 : 158cm, bmi 지수 : 29.24, 결과 : 과체중\n", + "\n", + "자아 78 163\n", + "\n", + " 이름 : 자아, 몸무게 : 78kg, 키 : 163cm, bmi 지수 : 29.36, 결과 : 과체중\n", + "\n", + "아나 47 167\n", + "\n", + " 이름 : 아나, 몸무게 : 47kg, 키 : 167cm, bmi 지수 : 16.85, 결과 : 저체중\n", + "\n", + "바마 54 144\n", + "\n", + " 이름 : 바마, 몸무게 : 54kg, 키 : 144cm, bmi 지수 : 26.04, 결과 : 과체중\n", + "\n", + "사바 47 177\n", + "\n", + " 이름 : 사바, 몸무게 : 47kg, 키 : 177cm, bmi 지수 : 15.00, 결과 : 저체중\n", + "\n", + "자타 58 156\n", + "\n", + " 이름 : 자타, 몸무게 : 58kg, 키 : 156cm, bmi 지수 : 23.83, 결과 : 정상\n", + "\n", + "차나 52 177\n", + "\n", + " 이름 : 차나, 몸무게 : 52kg, 키 : 177cm, bmi 지수 : 16.60, 결과 : 저체중\n", + "\n", + "타카 69 174\n", + "\n", + " 이름 : 타카, 몸무게 : 69kg, 키 : 174cm, bmi 지수 : 22.79, 결과 : 정상\n", + "\n", + "사자 54 178\n", + "\n", + " 이름 : 사자, 몸무게 : 54kg, 키 : 178cm, bmi 지수 : 17.04, 결과 : 저체중\n", + "\n", + "바나 49 143\n", + "\n", + " 이름 : 바나, 몸무게 : 49kg, 키 : 143cm, bmi 지수 : 23.96, 결과 : 정상\n", + "\n", + "라하 87 177\n", + "\n", + " 이름 : 라하, 몸무게 : 87kg, 키 : 177cm, bmi 지수 : 27.77, 결과 : 과체중\n", + "\n", + "마차 73 199\n", + "\n", + " 이름 : 마차, 몸무게 : 73kg, 키 : 199cm, bmi 지수 : 18.43, 결과 : 저체중\n", + "\n", + "아자 61 152\n", + "\n", + " 이름 : 아자, 몸무게 : 61kg, 키 : 152cm, bmi 지수 : 26.40, 결과 : 과체중\n", + "\n", + "마파 41 177\n", + "\n", + " 이름 : 마파, 몸무게 : 41kg, 키 : 177cm, bmi 지수 : 13.09, 결과 : 저체중\n", + "\n", + "카나 76 145\n", + "\n", + " 이름 : 카나, 몸무게 : 76kg, 키 : 145cm, bmi 지수 : 36.15, 결과 : 과체중\n", + "\n", + "마타 57 194\n", + "\n", + " 이름 : 마타, 몸무게 : 57kg, 키 : 194cm, bmi 지수 : 15.15, 결과 : 저체중\n", + "\n", + "차파 90 169\n", + "\n", + " 이름 : 차파, 몸무게 : 90kg, 키 : 169cm, bmi 지수 : 31.51, 결과 : 과체중\n", + "\n", + "가아 58 194\n", + "\n", + " 이름 : 가아, 몸무게 : 58kg, 키 : 194cm, bmi 지수 : 15.41, 결과 : 저체중\n", + "\n", + "나다 85 171\n", + "\n", + " 이름 : 나다, 몸무게 : 85kg, 키 : 171cm, bmi 지수 : 29.07, 결과 : 과체중\n", + "\n", + "다라 89 171\n", + "\n", + " 이름 : 다라, 몸무게 : 89kg, 키 : 171cm, bmi 지수 : 30.44, 결과 : 과체중\n", + "\n", + "다하 87 152\n", + "\n", + " 이름 : 다하, 몸무게 : 87kg, 키 : 152cm, bmi 지수 : 37.66, 결과 : 과체중\n", + "\n", + "차나 71 147\n", + "\n", + " 이름 : 차나, 몸무게 : 71kg, 키 : 147cm, bmi 지수 : 32.86, 결과 : 과체중\n", + "\n", + "바마 71 143\n", + "\n", + " 이름 : 바마, 몸무게 : 71kg, 키 : 143cm, bmi 지수 : 34.72, 결과 : 과체중\n", + "\n", + "자가 64 176\n", + "\n", + " 이름 : 자가, 몸무게 : 64kg, 키 : 176cm, bmi 지수 : 20.66, 결과 : 정상\n", + "\n", + "파나 75 183\n", + "\n", + " 이름 : 파나, 몸무게 : 75kg, 키 : 183cm, bmi 지수 : 22.40, 결과 : 정상\n", + "\n", + "라파 99 185\n", + "\n", + " 이름 : 라파, 몸무게 : 99kg, 키 : 185cm, bmi 지수 : 28.93, 결과 : 과체중\n", + "\n", + "라사 91 173\n", + "\n", + " 이름 : 라사, 몸무게 : 91kg, 키 : 173cm, bmi 지수 : 30.41, 결과 : 과체중\n", + "\n", + "바다 59 191\n", + "\n", + " 이름 : 바다, 몸무게 : 59kg, 키 : 191cm, bmi 지수 : 16.17, 결과 : 저체중\n", + "\n", + "자하 56 179\n", + "\n", + " 이름 : 자하, 몸무게 : 56kg, 키 : 179cm, bmi 지수 : 17.48, 결과 : 저체중\n", + "\n", + "아나 79 163\n", + "\n", + " 이름 : 아나, 몸무게 : 79kg, 키 : 163cm, bmi 지수 : 29.73, 결과 : 과체중\n", + "\n", + "마아 67 183\n", + "\n", + " 이름 : 마아, 몸무게 : 67kg, 키 : 183cm, bmi 지수 : 20.01, 결과 : 정상\n", + "\n", + "자파 99 198\n", + "\n", + " 이름 : 자파, 몸무게 : 99kg, 키 : 198cm, bmi 지수 : 25.25, 결과 : 과체중\n", + "\n", + "마사 89 154\n", + "\n", + " 이름 : 마사, 몸무게 : 89kg, 키 : 154cm, bmi 지수 : 37.53, 결과 : 과체중\n", + "\n", + "사다 90 190\n", + "\n", + " 이름 : 사다, 몸무게 : 90kg, 키 : 190cm, bmi 지수 : 24.93, 결과 : 정상\n", + "\n", + "타마 76 152\n", + "\n", + " 이름 : 타마, 몸무게 : 76kg, 키 : 152cm, bmi 지수 : 32.89, 결과 : 과체중\n", + "\n", + "바가 82 176\n", + "\n", + " 이름 : 바가, 몸무게 : 82kg, 키 : 176cm, bmi 지수 : 26.47, 결과 : 과체중\n", + "\n", + "마타 47 180\n", + "\n", + " 이름 : 마타, 몸무게 : 47kg, 키 : 180cm, bmi 지수 : 14.51, 결과 : 저체중\n", + "\n", + "마하 93 200\n", + "\n", + " 이름 : 마하, 몸무게 : 93kg, 키 : 200cm, bmi 지수 : 23.25, 결과 : 정상\n", + "\n", + "아파 100 183\n", + "\n", + " 이름 : 아파, 몸무게 : 100kg, 키 : 183cm, bmi 지수 : 29.86, 결과 : 과체중\n", + "\n", + "카가 81 178\n", + "\n", + " 이름 : 카가, 몸무게 : 81kg, 키 : 178cm, bmi 지수 : 25.56, 결과 : 과체중\n", + "\n", + "바하 96 145\n", + "\n", + " 이름 : 바하, 몸무게 : 96kg, 키 : 145cm, bmi 지수 : 45.66, 결과 : 과체중\n", + "\n", + "하마 61 185\n", + "\n", + " 이름 : 하마, 몸무게 : 61kg, 키 : 185cm, bmi 지수 : 17.82, 결과 : 저체중\n", + "\n", + "바마 58 151\n", + "\n", + " 이름 : 바마, 몸무게 : 58kg, 키 : 151cm, bmi 지수 : 25.44, 결과 : 과체중\n", + "\n", + "카사 100 179\n", + "\n", + " 이름 : 카사, 몸무게 : 100kg, 키 : 179cm, bmi 지수 : 31.21, 결과 : 과체중\n", + "\n", + "마카 67 159\n", + "\n", + " 이름 : 마카, 몸무게 : 67kg, 키 : 159cm, bmi 지수 : 26.50, 결과 : 과체중\n", + "\n", + "마나 74 195\n", + "\n", + " 이름 : 마나, 몸무게 : 74kg, 키 : 195cm, bmi 지수 : 19.46, 결과 : 정상\n", + "\n", + "하다 76 162\n", + "\n", + " 이름 : 하다, 몸무게 : 76kg, 키 : 162cm, bmi 지수 : 28.96, 결과 : 과체중\n", + "\n", + "타자 69 177\n", + "\n", + " 이름 : 타자, 몸무게 : 69kg, 키 : 177cm, bmi 지수 : 22.02, 결과 : 정상\n", + "\n", + "바라 59 149\n", + "\n", + " 이름 : 바라, 몸무게 : 59kg, 키 : 149cm, bmi 지수 : 26.58, 결과 : 과체중\n", + "\n", + "자파 58 181\n", + "\n", + " 이름 : 자파, 몸무게 : 58kg, 키 : 181cm, bmi 지수 : 17.70, 결과 : 저체중\n", + "\n", + "바나 79 154\n", + "\n", + " 이름 : 바나, 몸무게 : 79kg, 키 : 154cm, bmi 지수 : 33.31, 결과 : 과체중\n", + "\n", + "차아 91 163\n", + "\n", + " 이름 : 차아, 몸무게 : 91kg, 키 : 163cm, bmi 지수 : 34.25, 결과 : 과체중\n", + "\n", + "카라 63 179\n", + "\n", + " 이름 : 카라, 몸무게 : 63kg, 키 : 179cm, bmi 지수 : 19.66, 결과 : 정상\n", + "\n", + "가카 60 197\n", + "\n", + " 이름 : 가카, 몸무게 : 60kg, 키 : 197cm, bmi 지수 : 15.46, 결과 : 저체중\n", + "\n", + "가바 54 185\n", + "\n", + " 이름 : 가바, 몸무게 : 54kg, 키 : 185cm, bmi 지수 : 15.78, 결과 : 저체중\n", + "\n", + "바나 58 157\n", + "\n", + " 이름 : 바나, 몸무게 : 58kg, 키 : 157cm, bmi 지수 : 23.53, 결과 : 정상\n", + "\n", + "차바 57 183\n", + "\n", + " 이름 : 차바, 몸무게 : 57kg, 키 : 183cm, bmi 지수 : 17.02, 결과 : 저체중\n", + "\n", + "가사 66 163\n", + "\n", + " 이름 : 가사, 몸무게 : 66kg, 키 : 163cm, bmi 지수 : 24.84, 결과 : 정상\n", + "\n", + "바차 74 142\n", + "\n", + " 이름 : 바차, 몸무게 : 74kg, 키 : 142cm, bmi 지수 : 36.70, 결과 : 과체중\n", + "\n", + "다파 56 183\n", + "\n", + " 이름 : 다파, 몸무게 : 56kg, 키 : 183cm, bmi 지수 : 16.72, 결과 : 저체중\n", + "\n", + "아가 70 141\n", + "\n", + " 이름 : 아가, 몸무게 : 70kg, 키 : 141cm, bmi 지수 : 35.21, 결과 : 과체중\n", + "\n", + "가차 46 141\n", + "\n", + " 이름 : 가차, 몸무게 : 46kg, 키 : 141cm, bmi 지수 : 23.14, 결과 : 정상\n", + "\n", + "라아 76 140\n", + "\n", + " 이름 : 라아, 몸무게 : 76kg, 키 : 140cm, bmi 지수 : 38.78, 결과 : 과체중\n", + "\n", + "하타 70 158\n", + "\n", + " 이름 : 하타, 몸무게 : 70kg, 키 : 158cm, bmi 지수 : 28.04, 결과 : 과체중\n", + "\n", + "다사 90 177\n", + "\n", + " 이름 : 다사, 몸무게 : 90kg, 키 : 177cm, bmi 지수 : 28.73, 결과 : 과체중\n", + "\n", + "아자 54 152\n", + "\n", + " 이름 : 아자, 몸무게 : 54kg, 키 : 152cm, bmi 지수 : 23.37, 결과 : 정상\n", + "\n", + "라라 40 198\n", + "\n", + " 이름 : 라라, 몸무게 : 40kg, 키 : 198cm, bmi 지수 : 10.20, 결과 : 저체중\n", + "\n", + "하다 77 164\n", + "\n", + " 이름 : 하다, 몸무게 : 77kg, 키 : 164cm, bmi 지수 : 28.63, 결과 : 과체중\n", + "\n", + "가카 89 183\n", + "\n", + " 이름 : 가카, 몸무게 : 89kg, 키 : 183cm, bmi 지수 : 26.58, 결과 : 과체중\n", + "\n", + "자하 94 145\n", + "\n", + " 이름 : 자하, 몸무게 : 94kg, 키 : 145cm, bmi 지수 : 44.71, 결과 : 과체중\n", + "\n", + "가타 85 177\n", + "\n", + " 이름 : 가타, 몸무게 : 85kg, 키 : 177cm, bmi 지수 : 27.13, 결과 : 과체중\n", + "\n", + "나아 92 162\n", + "\n", + " 이름 : 나아, 몸무게 : 92kg, 키 : 162cm, bmi 지수 : 35.06, 결과 : 과체중\n", + "\n", + "다카 77 171\n", + "\n", + " 이름 : 다카, 몸무게 : 77kg, 키 : 171cm, bmi 지수 : 26.33, 결과 : 과체중\n", + "\n", + "다마 88 156\n", + "\n", + " 이름 : 다마, 몸무게 : 88kg, 키 : 156cm, bmi 지수 : 36.16, 결과 : 과체중\n", + "\n", + "바라 100 193\n", + "\n", + " 이름 : 바라, 몸무게 : 100kg, 키 : 193cm, bmi 지수 : 26.85, 결과 : 과체중\n", + "\n", + "하카 55 194\n", + "\n", + " 이름 : 하카, 몸무게 : 55kg, 키 : 194cm, bmi 지수 : 14.61, 결과 : 저체중\n", + "\n", + "자파 42 150\n", + "\n", + " 이름 : 자파, 몸무게 : 42kg, 키 : 150cm, bmi 지수 : 18.67, 결과 : 정상\n", + "\n", + "아자 82 155\n", + "\n", + " 이름 : 아자, 몸무게 : 82kg, 키 : 155cm, bmi 지수 : 34.13, 결과 : 과체중\n", + "\n", + "마마 46 144\n", + "\n", + " 이름 : 마마, 몸무게 : 46kg, 키 : 144cm, bmi 지수 : 22.18, 결과 : 정상\n", + "\n", + "카가 96 147\n", + "\n", + " 이름 : 카가, 몸무게 : 96kg, 키 : 147cm, bmi 지수 : 44.43, 결과 : 과체중\n", + "\n", + "마자 50 148\n", + "\n", + " 이름 : 마자, 몸무게 : 50kg, 키 : 148cm, bmi 지수 : 22.83, 결과 : 정상\n", + "\n", + "아라 47 160\n", + "\n", + " 이름 : 아라, 몸무게 : 47kg, 키 : 160cm, bmi 지수 : 18.36, 결과 : 저체중\n", + "\n", + "카라 99 176\n", + "\n", + " 이름 : 카라, 몸무게 : 99kg, 키 : 176cm, bmi 지수 : 31.96, 결과 : 과체중\n", + "\n", + "타바 56 186\n", + "\n", + " 이름 : 타바, 몸무게 : 56kg, 키 : 186cm, bmi 지수 : 16.19, 결과 : 저체중\n", + "\n", + "타아 64 173\n", + "\n", + " 이름 : 타아, 몸무게 : 64kg, 키 : 173cm, bmi 지수 : 21.38, 결과 : 정상\n", + "\n", + "바파 86 192\n", + "\n", + " 이름 : 바파, 몸무게 : 86kg, 키 : 192cm, bmi 지수 : 23.33, 결과 : 정상\n", + "\n", + "마사 78 176\n", + "\n", + " 이름 : 마사, 몸무게 : 78kg, 키 : 176cm, bmi 지수 : 25.18, 결과 : 과체중\n", + "\n", + "아파 62 193\n", + "\n", + " 이름 : 아파, 몸무게 : 62kg, 키 : 193cm, bmi 지수 : 16.64, 결과 : 저체중\n", + "\n", + "카바 91 164\n", + "\n", + " 이름 : 카바, 몸무게 : 91kg, 키 : 164cm, bmi 지수 : 33.83, 결과 : 과체중\n", + "\n", + "마타 62 161\n", + "\n", + " 이름 : 마타, 몸무게 : 62kg, 키 : 161cm, bmi 지수 : 23.92, 결과 : 정상\n", + "\n", + "마가 92 199\n", + "\n", + " 이름 : 마가, 몸무게 : 92kg, 키 : 199cm, bmi 지수 : 23.23, 결과 : 정상\n", + "\n", + "라바 50 177\n", + "\n", + " 이름 : 라바, 몸무게 : 50kg, 키 : 177cm, bmi 지수 : 15.96, 결과 : 저체중\n", + "\n", + "라라 68 158\n", + "\n", + " 이름 : 라라, 몸무게 : 68kg, 키 : 158cm, bmi 지수 : 27.24, 결과 : 과체중\n", + "\n", + "차파 40 176\n", + "\n", + " 이름 : 차파, 몸무게 : 40kg, 키 : 176cm, bmi 지수 : 12.91, 결과 : 저체중\n", + "\n", + "타나 58 147\n", + "\n", + " 이름 : 타나, 몸무게 : 58kg, 키 : 147cm, bmi 지수 : 26.84, 결과 : 과체중\n", + "\n", + "라하 83 158\n", + "\n", + " 이름 : 라하, 몸무게 : 83kg, 키 : 158cm, bmi 지수 : 33.25, 결과 : 과체중\n", + "\n", + "자바 79 167\n", + "\n", + " 이름 : 자바, 몸무게 : 79kg, 키 : 167cm, bmi 지수 : 28.33, 결과 : 과체중\n", + "\n", + "하아 53 140\n", + "\n", + " 이름 : 하아, 몸무게 : 53kg, 키 : 140cm, bmi 지수 : 27.04, 결과 : 과체중\n", + "\n", + "다라 93 185\n", + "\n", + " 이름 : 다라, 몸무게 : 93kg, 키 : 185cm, bmi 지수 : 27.17, 결과 : 과체중\n", + "\n", + "다나 78 184\n", + "\n", + " 이름 : 다나, 몸무게 : 78kg, 키 : 184cm, bmi 지수 : 23.04, 결과 : 정상\n", + "\n", + "아다 53 176\n", + "\n", + " 이름 : 아다, 몸무게 : 53kg, 키 : 176cm, bmi 지수 : 17.11, 결과 : 저체중\n", + "\n", + "타다 55 146\n", + "\n", + " 이름 : 타다, 몸무게 : 55kg, 키 : 146cm, bmi 지수 : 25.80, 결과 : 과체중\n", + "\n", + "하자 67 198\n", + "\n", + " 이름 : 하자, 몸무게 : 67kg, 키 : 198cm, bmi 지수 : 17.09, 결과 : 저체중\n", + "\n", + "사바 47 149\n", + "\n", + " 이름 : 사바, 몸무게 : 47kg, 키 : 149cm, bmi 지수 : 21.17, 결과 : 정상\n", + "\n", + "타나 83 185\n", + "\n", + " 이름 : 타나, 몸무게 : 83kg, 키 : 185cm, bmi 지수 : 24.25, 결과 : 정상\n", + "\n", + "타자 43 194\n", + "\n", + " 이름 : 타자, 몸무게 : 43kg, 키 : 194cm, bmi 지수 : 11.43, 결과 : 저체중\n", + "\n", + "파파 52 200\n", + "\n", + " 이름 : 파파, 몸무게 : 52kg, 키 : 200cm, bmi 지수 : 13.00, 결과 : 저체중\n", + "\n", + "차타 84 144\n", + "\n", + " 이름 : 차타, 몸무게 : 84kg, 키 : 144cm, bmi 지수 : 40.51, 결과 : 과체중\n", + "\n", + "가가 92 196\n", + "\n", + " 이름 : 가가, 몸무게 : 92kg, 키 : 196cm, bmi 지수 : 23.95, 결과 : 정상\n", + "\n", + "라하 47 144\n", + "\n", + " 이름 : 라하, 몸무게 : 47kg, 키 : 144cm, bmi 지수 : 22.67, 결과 : 정상\n", + "\n", + "차사 60 174\n", + "\n", + " 이름 : 차사, 몸무게 : 60kg, 키 : 174cm, bmi 지수 : 19.82, 결과 : 정상\n", + "\n", + "마타 65 179\n", + "\n", + " 이름 : 마타, 몸무게 : 65kg, 키 : 179cm, bmi 지수 : 20.29, 결과 : 정상\n", + "\n", + "다사 63 148\n", + "\n", + " 이름 : 다사, 몸무게 : 63kg, 키 : 148cm, bmi 지수 : 28.76, 결과 : 과체중\n", + "\n", + "카아 79 142\n", + "\n", + " 이름 : 카아, 몸무게 : 79kg, 키 : 142cm, bmi 지수 : 39.18, 결과 : 과체중\n", + "\n", + "라카 73 161\n", + "\n", + " 이름 : 라카, 몸무게 : 73kg, 키 : 161cm, bmi 지수 : 28.16, 결과 : 과체중\n", + "\n", + "카아 92 141\n", + "\n", + " 이름 : 카아, 몸무게 : 92kg, 키 : 141cm, bmi 지수 : 46.28, 결과 : 과체중\n", + "\n", + "라라 92 151\n", + "\n", + " 이름 : 라라, 몸무게 : 92kg, 키 : 151cm, bmi 지수 : 40.35, 결과 : 과체중\n", + "\n", + "차카 71 183\n", + "\n", + " 이름 : 차카, 몸무게 : 71kg, 키 : 183cm, bmi 지수 : 21.20, 결과 : 정상\n", + "\n", + "하타 91 146\n", + "\n", + " 이름 : 하타, 몸무게 : 91kg, 키 : 146cm, bmi 지수 : 42.69, 결과 : 과체중\n", + "\n", + "사사 82 194\n", + "\n", + " 이름 : 사사, 몸무게 : 82kg, 키 : 194cm, bmi 지수 : 21.79, 결과 : 정상\n", + "\n", + "바파 58 180\n", + "\n", + " 이름 : 바파, 몸무게 : 58kg, 키 : 180cm, bmi 지수 : 17.90, 결과 : 저체중\n", + "\n", + "카가 40 176\n", + "\n", + " 이름 : 카가, 몸무게 : 40kg, 키 : 176cm, bmi 지수 : 12.91, 결과 : 저체중\n", + "\n", + "차다 72 182\n", + "\n", + " 이름 : 차다, 몸무게 : 72kg, 키 : 182cm, bmi 지수 : 21.74, 결과 : 정상\n", + "\n", + "파가 95 163\n", + "\n", + " 이름 : 파가, 몸무게 : 95kg, 키 : 163cm, bmi 지수 : 35.76, 결과 : 과체중\n", + "\n", + "파아 82 196\n", + "\n", + " 이름 : 파아, 몸무게 : 82kg, 키 : 196cm, bmi 지수 : 21.35, 결과 : 정상\n", + "\n", + "타가 40 143\n", + "\n", + " 이름 : 타가, 몸무게 : 40kg, 키 : 143cm, bmi 지수 : 19.56, 결과 : 정상\n", + "\n", + "나나 69 176\n", + "\n", + " 이름 : 나나, 몸무게 : 69kg, 키 : 176cm, bmi 지수 : 22.28, 결과 : 정상\n", + "\n", + "카카 98 171\n", + "\n", + " 이름 : 카카, 몸무게 : 98kg, 키 : 171cm, bmi 지수 : 33.51, 결과 : 과체중\n", + "\n", + "차라 77 198\n", + "\n", + " 이름 : 차라, 몸무게 : 77kg, 키 : 198cm, bmi 지수 : 19.64, 결과 : 정상\n", + "\n", + "파바 54 140\n", + "\n", + " 이름 : 파바, 몸무게 : 54kg, 키 : 140cm, bmi 지수 : 27.55, 결과 : 과체중\n", + "\n", + "다다 96 198\n", + "\n", + " 이름 : 다다, 몸무게 : 96kg, 키 : 198cm, bmi 지수 : 24.49, 결과 : 정상\n", + "\n", + "바파 77 141\n", + "\n", + " 이름 : 바파, 몸무게 : 77kg, 키 : 141cm, bmi 지수 : 38.73, 결과 : 과체중\n", + "\n", + "자라 95 171\n", + "\n", + " 이름 : 자라, 몸무게 : 95kg, 키 : 171cm, bmi 지수 : 32.49, 결과 : 과체중\n", + "\n", + "파마 90 178\n", + "\n", + " 이름 : 파마, 몸무게 : 90kg, 키 : 178cm, bmi 지수 : 28.41, 결과 : 과체중\n", + "\n", + "다바 100 168\n", + "\n", + " 이름 : 다바, 몸무게 : 100kg, 키 : 168cm, bmi 지수 : 35.43, 결과 : 과체중\n", + "\n", + "타타 94 187\n", + "\n", + " 이름 : 타타, 몸무게 : 94kg, 키 : 187cm, bmi 지수 : 26.88, 결과 : 과체중\n", + "\n", + "타파 90 147\n", + "\n", + " 이름 : 타파, 몸무게 : 90kg, 키 : 147cm, bmi 지수 : 41.65, 결과 : 과체중\n", + "\n", + "차사 43 142\n", + "\n", + " 이름 : 차사, 몸무게 : 43kg, 키 : 142cm, bmi 지수 : 21.33, 결과 : 정상\n", + "\n", + "자마 64 194\n", + "\n", + " 이름 : 자마, 몸무게 : 64kg, 키 : 194cm, bmi 지수 : 17.00, 결과 : 저체중\n", + "\n", + "가파 89 192\n", + "\n", + " 이름 : 가파, 몸무게 : 89kg, 키 : 192cm, bmi 지수 : 24.14, 결과 : 정상\n", + "\n", + "파파 93 189\n", + "\n", + " 이름 : 파파, 몸무게 : 93kg, 키 : 189cm, bmi 지수 : 26.04, 결과 : 과체중\n", + "\n", + "라가 48 182\n", + "\n", + " 이름 : 라가, 몸무게 : 48kg, 키 : 182cm, bmi 지수 : 14.49, 결과 : 저체중\n", + "\n", + "마타 49 166\n", + "\n", + " 이름 : 마타, 몸무게 : 49kg, 키 : 166cm, bmi 지수 : 17.78, 결과 : 저체중\n", + "\n", + "다타 84 157\n", + "\n", + " 이름 : 다타, 몸무게 : 84kg, 키 : 157cm, bmi 지수 : 34.08, 결과 : 과체중\n", + "\n", + "타가 76 160\n", + "\n", + " 이름 : 타가, 몸무게 : 76kg, 키 : 160cm, bmi 지수 : 29.69, 결과 : 과체중\n", + "\n", + "하자 46 140\n", + "\n", + " 이름 : 하자, 몸무게 : 46kg, 키 : 140cm, bmi 지수 : 23.47, 결과 : 정상\n", + "\n", + "자다 45 182\n", + "\n", + " 이름 : 자다, 몸무게 : 45kg, 키 : 182cm, bmi 지수 : 13.59, 결과 : 저체중\n", + "\n", + "카타 49 162\n", + "\n", + " 이름 : 카타, 몸무게 : 49kg, 키 : 162cm, bmi 지수 : 18.67, 결과 : 정상\n", + "\n", + "카아 85 170\n", + "\n", + " 이름 : 카아, 몸무게 : 85kg, 키 : 170cm, bmi 지수 : 29.41, 결과 : 과체중\n", + "\n", + "바자 56 157\n", + "\n", + " 이름 : 바자, 몸무게 : 56kg, 키 : 157cm, bmi 지수 : 22.72, 결과 : 정상\n", + "\n", + "아라 73 180\n", + "\n", + " 이름 : 아라, 몸무게 : 73kg, 키 : 180cm, bmi 지수 : 22.53, 결과 : 정상\n", + "\n", + "라아 98 200\n", + "\n", + " 이름 : 라아, 몸무게 : 98kg, 키 : 200cm, bmi 지수 : 24.50, 결과 : 정상\n", + "\n", + "아차 55 143\n", + "\n", + " 이름 : 아차, 몸무게 : 55kg, 키 : 143cm, bmi 지수 : 26.90, 결과 : 과체중\n", + "\n", + "파라 58 149\n", + "\n", + " 이름 : 파라, 몸무게 : 58kg, 키 : 149cm, bmi 지수 : 26.12, 결과 : 과체중\n", + "\n", + "파바 44 186\n", + "\n", + " 이름 : 파바, 몸무게 : 44kg, 키 : 186cm, bmi 지수 : 12.72, 결과 : 저체중\n", + "\n", + "하자 83 197\n", + "\n", + " 이름 : 하자, 몸무게 : 83kg, 키 : 197cm, bmi 지수 : 21.39, 결과 : 정상\n", + "\n", + "아차 67 155\n", + "\n", + " 이름 : 아차, 몸무게 : 67kg, 키 : 155cm, bmi 지수 : 27.89, 결과 : 과체중\n", + "\n", + "카하 51 169\n", + "\n", + " 이름 : 카하, 몸무게 : 51kg, 키 : 169cm, bmi 지수 : 17.86, 결과 : 저체중\n", + "\n", + "하바 56 144\n", + "\n", + " 이름 : 하바, 몸무게 : 56kg, 키 : 144cm, bmi 지수 : 27.01, 결과 : 과체중\n", + "\n", + "타카 91 155\n", + "\n", + " 이름 : 타카, 몸무게 : 91kg, 키 : 155cm, bmi 지수 : 37.88, 결과 : 과체중\n", + "\n", + "차마 86 158\n", + "\n", + " 이름 : 차마, 몸무게 : 86kg, 키 : 158cm, bmi 지수 : 34.45, 결과 : 과체중\n", + "\n", + "차아 96 154\n", + "\n", + " 이름 : 차아, 몸무게 : 96kg, 키 : 154cm, bmi 지수 : 40.48, 결과 : 과체중\n", + "\n", + "다사 43 144\n", + "\n", + " 이름 : 다사, 몸무게 : 43kg, 키 : 144cm, bmi 지수 : 20.74, 결과 : 정상\n", + "\n", + "파다 70 200\n", + "\n", + " 이름 : 파다, 몸무게 : 70kg, 키 : 200cm, bmi 지수 : 17.50, 결과 : 저체중\n", + "\n", + "나아 60 153\n", + "\n", + " 이름 : 나아, 몸무게 : 60kg, 키 : 153cm, bmi 지수 : 25.63, 결과 : 과체중\n", + "\n", + "하아 69 194\n", + "\n", + " 이름 : 하아, 몸무게 : 69kg, 키 : 194cm, bmi 지수 : 18.33, 결과 : 저체중\n", + "\n", + "하가 94 185\n", + "\n", + " 이름 : 하가, 몸무게 : 94kg, 키 : 185cm, bmi 지수 : 27.47, 결과 : 과체중\n", + "\n", + "마가 51 168\n", + "\n", + " 이름 : 마가, 몸무게 : 51kg, 키 : 168cm, bmi 지수 : 18.07, 결과 : 저체중\n", + "\n", + "가파 48 150\n", + "\n", + " 이름 : 가파, 몸무게 : 48kg, 키 : 150cm, bmi 지수 : 21.33, 결과 : 정상\n", + "\n", + "바바 68 191\n", + "\n", + " 이름 : 바바, 몸무게 : 68kg, 키 : 191cm, bmi 지수 : 18.64, 결과 : 정상\n", + "\n", + "나아 48 171\n", + "\n", + " 이름 : 나아, 몸무게 : 48kg, 키 : 171cm, bmi 지수 : 16.42, 결과 : 저체중\n", + "\n", + "파아 45 140\n", + "\n", + " 이름 : 파아, 몸무게 : 45kg, 키 : 140cm, bmi 지수 : 22.96, 결과 : 정상\n", + "\n", + "바하 53 143\n", + "\n", + " 이름 : 바하, 몸무게 : 53kg, 키 : 143cm, bmi 지수 : 25.92, 결과 : 과체중\n", + "\n", + "바파 68 154\n", + "\n", + " 이름 : 바파, 몸무게 : 68kg, 키 : 154cm, bmi 지수 : 28.67, 결과 : 과체중\n", + "\n", + "차마 88 193\n", + "\n", + " 이름 : 차마, 몸무게 : 88kg, 키 : 193cm, bmi 지수 : 23.62, 결과 : 정상\n", + "\n", + "다타 88 167\n", + "\n", + " 이름 : 다타, 몸무게 : 88kg, 키 : 167cm, bmi 지수 : 31.55, 결과 : 과체중\n", + "\n", + "차나 67 200\n", + "\n", + " 이름 : 차나, 몸무게 : 67kg, 키 : 200cm, bmi 지수 : 16.75, 결과 : 저체중\n", + "\n", + "라바 76 185\n", + "\n", + " 이름 : 라바, 몸무게 : 76kg, 키 : 185cm, bmi 지수 : 22.21, 결과 : 정상\n", + "\n", + "바다 44 175\n", + "\n", + " 이름 : 바다, 몸무게 : 44kg, 키 : 175cm, bmi 지수 : 14.37, 결과 : 저체중\n", + "\n", + "가마 64 166\n", + "\n", + " 이름 : 가마, 몸무게 : 64kg, 키 : 166cm, bmi 지수 : 23.23, 결과 : 정상\n", + "\n", + "바차 60 159\n", + "\n", + " 이름 : 바차, 몸무게 : 60kg, 키 : 159cm, bmi 지수 : 23.73, 결과 : 정상\n", + "\n", + "카라 88 150\n", + "\n", + " 이름 : 카라, 몸무게 : 88kg, 키 : 150cm, bmi 지수 : 39.11, 결과 : 과체중\n", + "\n", + "자가 49 146\n", + "\n", + " 이름 : 자가, 몸무게 : 49kg, 키 : 146cm, bmi 지수 : 22.99, 결과 : 정상\n", + "\n", + "자가 75 165\n", + "\n", + " 이름 : 자가, 몸무게 : 75kg, 키 : 165cm, bmi 지수 : 27.55, 결과 : 과체중\n", + "\n", + "가아 75 144\n", + "\n", + " 이름 : 가아, 몸무게 : 75kg, 키 : 144cm, bmi 지수 : 36.17, 결과 : 과체중\n", + "\n", + "자라 75 143\n", + "\n", + " 이름 : 자라, 몸무게 : 75kg, 키 : 143cm, bmi 지수 : 36.68, 결과 : 과체중\n", + "\n", + "마자 41 167\n", + "\n", + " 이름 : 마자, 몸무게 : 41kg, 키 : 167cm, bmi 지수 : 14.70, 결과 : 저체중\n", + "\n", + "가차 81 160\n", + "\n", + " 이름 : 가차, 몸무게 : 81kg, 키 : 160cm, bmi 지수 : 31.64, 결과 : 과체중\n", + "\n", + "다바 62 167\n", + "\n", + " 이름 : 다바, 몸무게 : 62kg, 키 : 167cm, bmi 지수 : 22.23, 결과 : 정상\n", + "\n", + "카바 71 168\n", + "\n", + " 이름 : 카바, 몸무게 : 71kg, 키 : 168cm, bmi 지수 : 25.16, 결과 : 과체중\n", + "\n", + "타타 55 197\n", + "\n", + " 이름 : 타타, 몸무게 : 55kg, 키 : 197cm, bmi 지수 : 14.17, 결과 : 저체중\n", + "\n", + "카자 44 200\n", + "\n", + " 이름 : 카자, 몸무게 : 44kg, 키 : 200cm, bmi 지수 : 11.00, 결과 : 저체중\n", + "\n", + "아아 52 188\n", + "\n", + " 이름 : 아아, 몸무게 : 52kg, 키 : 188cm, bmi 지수 : 14.71, 결과 : 저체중\n", + "\n", + "사사 59 155\n", + "\n", + " 이름 : 사사, 몸무게 : 59kg, 키 : 155cm, bmi 지수 : 24.56, 결과 : 정상\n", + "\n", + "가라 71 163\n", + "\n", + " 이름 : 가라, 몸무게 : 71kg, 키 : 163cm, bmi 지수 : 26.72, 결과 : 과체중\n", + "\n", + "바사 44 189\n", + "\n", + " 이름 : 바사, 몸무게 : 44kg, 키 : 189cm, bmi 지수 : 12.32, 결과 : 저체중\n", + "\n", + "가차 84 145\n", + "\n", + " 이름 : 가차, 몸무게 : 84kg, 키 : 145cm, bmi 지수 : 39.95, 결과 : 과체중\n", + "\n", + "나자 67 190\n", + "\n", + " 이름 : 나자, 몸무게 : 67kg, 키 : 190cm, bmi 지수 : 18.56, 결과 : 정상\n", + "\n", + "차마 89 148\n", + "\n", + " 이름 : 차마, 몸무게 : 89kg, 키 : 148cm, bmi 지수 : 40.63, 결과 : 과체중\n", + "\n", + "하마 89 183\n", + "\n", + " 이름 : 하마, 몸무게 : 89kg, 키 : 183cm, bmi 지수 : 26.58, 결과 : 과체중\n", + "\n", + "다사 71 176\n", + "\n", + " 이름 : 다사, 몸무게 : 71kg, 키 : 176cm, bmi 지수 : 22.92, 결과 : 정상\n", + "\n", + "차가 97 200\n", + "\n", + " 이름 : 차가, 몸무게 : 97kg, 키 : 200cm, bmi 지수 : 24.25, 결과 : 정상\n", + "\n", + "다하 87 198\n", + "\n", + " 이름 : 다하, 몸무게 : 87kg, 키 : 198cm, bmi 지수 : 22.19, 결과 : 정상\n", + "\n", + "아가 56 170\n", + "\n", + " 이름 : 아가, 몸무게 : 56kg, 키 : 170cm, bmi 지수 : 19.38, 결과 : 정상\n", + "\n", + "사자 55 160\n", + "\n", + " 이름 : 사자, 몸무게 : 55kg, 키 : 160cm, bmi 지수 : 21.48, 결과 : 정상\n", + "\n", + "카자 77 169\n", + "\n", + " 이름 : 카자, 몸무게 : 77kg, 키 : 169cm, bmi 지수 : 26.96, 결과 : 과체중\n", + "\n", + "파하 53 168\n", + "\n", + " 이름 : 파하, 몸무게 : 53kg, 키 : 168cm, bmi 지수 : 18.78, 결과 : 정상\n", + "\n", + "아사 95 172\n", + "\n", + " 이름 : 아사, 몸무게 : 95kg, 키 : 172cm, bmi 지수 : 32.11, 결과 : 과체중\n", + "\n", + "차타 81 178\n", + "\n", + " 이름 : 차타, 몸무게 : 81kg, 키 : 178cm, bmi 지수 : 25.56, 결과 : 과체중\n", + "\n", + "사차 42 145\n", + "\n", + " 이름 : 사차, 몸무게 : 42kg, 키 : 145cm, bmi 지수 : 19.98, 결과 : 정상\n", + "\n", + "나라 52 143\n", + "\n", + " 이름 : 나라, 몸무게 : 52kg, 키 : 143cm, bmi 지수 : 25.43, 결과 : 과체중\n", + "\n", + "자아 45 145\n", + "\n", + " 이름 : 자아, 몸무게 : 45kg, 키 : 145cm, bmi 지수 : 21.40, 결과 : 정상\n", + "\n", + "다사 85 186\n", + "\n", + " 이름 : 다사, 몸무게 : 85kg, 키 : 186cm, bmi 지수 : 24.57, 결과 : 정상\n", + "\n", + "아자 77 151\n", + "\n", + " 이름 : 아자, 몸무게 : 77kg, 키 : 151cm, bmi 지수 : 33.77, 결과 : 과체중\n", + "\n", + "타카 90 190\n", + "\n", + " 이름 : 타카, 몸무게 : 90kg, 키 : 190cm, bmi 지수 : 24.93, 결과 : 정상\n", + "\n", + "아나 59 152\n", + "\n", + " 이름 : 아나, 몸무게 : 59kg, 키 : 152cm, bmi 지수 : 25.54, 결과 : 과체중\n", + "\n", + "라타 40 149\n", + "\n", + " 이름 : 라타, 몸무게 : 40kg, 키 : 149cm, bmi 지수 : 18.02, 결과 : 저체중\n", + "\n", + "하타 64 168\n", + "\n", + " 이름 : 하타, 몸무게 : 64kg, 키 : 168cm, bmi 지수 : 22.68, 결과 : 정상\n", + "\n", + "하나 92 171\n", + "\n", + " 이름 : 하나, 몸무게 : 92kg, 키 : 171cm, bmi 지수 : 31.46, 결과 : 과체중\n", + "\n", + "자타 56 198\n", + "\n", + " 이름 : 자타, 몸무게 : 56kg, 키 : 198cm, bmi 지수 : 14.28, 결과 : 저체중\n", + "\n", + "사타 80 192\n", + "\n", + " 이름 : 사타, 몸무게 : 80kg, 키 : 192cm, bmi 지수 : 21.70, 결과 : 정상\n", + "\n", + "라다 68 193\n", + "\n", + " 이름 : 라다, 몸무게 : 68kg, 키 : 193cm, bmi 지수 : 18.26, 결과 : 저체중\n", + "\n", + "타다 51 154\n", + "\n", + " 이름 : 타다, 몸무게 : 51kg, 키 : 154cm, bmi 지수 : 21.50, 결과 : 정상\n", + "\n", + "차가 57 193\n", + "\n", + " 이름 : 차가, 몸무게 : 57kg, 키 : 193cm, bmi 지수 : 15.30, 결과 : 저체중\n", + "\n", + "라카 70 171\n", + "\n", + " 이름 : 라카, 몸무게 : 70kg, 키 : 171cm, bmi 지수 : 23.94, 결과 : 정상\n", + "\n", + "파나 87 143\n", + "\n", + " 이름 : 파나, 몸무게 : 87kg, 키 : 143cm, bmi 지수 : 42.54, 결과 : 과체중\n", + "\n", + "하마 92 153\n", + "\n", + " 이름 : 하마, 몸무게 : 92kg, 키 : 153cm, bmi 지수 : 39.30, 결과 : 과체중\n", + "\n", + "마바 52 198\n", + "\n", + " 이름 : 마바, 몸무게 : 52kg, 키 : 198cm, bmi 지수 : 13.26, 결과 : 저체중\n", + "\n", + "카아 73 185\n", + "\n", + " 이름 : 카아, 몸무게 : 73kg, 키 : 185cm, bmi 지수 : 21.33, 결과 : 정상\n", + "\n", + "아라 87 176\n", + "\n", + " 이름 : 아라, 몸무게 : 87kg, 키 : 176cm, bmi 지수 : 28.09, 결과 : 과체중\n", + "\n", + "다파 71 182\n", + "\n", + " 이름 : 다파, 몸무게 : 71kg, 키 : 182cm, bmi 지수 : 21.43, 결과 : 정상\n", + "\n", + "라자 55 166\n", + "\n", + " 이름 : 라자, 몸무게 : 55kg, 키 : 166cm, bmi 지수 : 19.96, 결과 : 정상\n", + "\n", + "마가 84 153\n", + "\n", + " 이름 : 마가, 몸무게 : 84kg, 키 : 153cm, bmi 지수 : 35.88, 결과 : 과체중\n", + "\n", + "자사 79 195\n", + "\n", + " 이름 : 자사, 몸무게 : 79kg, 키 : 195cm, bmi 지수 : 20.78, 결과 : 정상\n", + "\n", + "나타 63 160\n", + "\n", + " 이름 : 나타, 몸무게 : 63kg, 키 : 160cm, bmi 지수 : 24.61, 결과 : 정상\n", + "\n", + "라사 52 194\n", + "\n", + " 이름 : 라사, 몸무게 : 52kg, 키 : 194cm, bmi 지수 : 13.82, 결과 : 저체중\n", + "\n", + "타마 62 185\n", + "\n", + " 이름 : 타마, 몸무게 : 62kg, 키 : 185cm, bmi 지수 : 18.12, 결과 : 저체중\n", + "\n", + "하자 93 152\n", + "\n", + " 이름 : 하자, 몸무게 : 93kg, 키 : 152cm, bmi 지수 : 40.25, 결과 : 과체중\n", + "\n", + "바차 50 161\n", + "\n", + " 이름 : 바차, 몸무게 : 50kg, 키 : 161cm, bmi 지수 : 19.29, 결과 : 정상\n", + "\n", + "사자 99 141\n", + "\n", + " 이름 : 사자, 몸무게 : 99kg, 키 : 141cm, bmi 지수 : 49.80, 결과 : 과체중\n", + "\n", + "바가 72 182\n", + "\n", + " 이름 : 바가, 몸무게 : 72kg, 키 : 182cm, bmi 지수 : 21.74, 결과 : 정상\n", + "\n", + "바나 88 181\n", + "\n", + " 이름 : 바나, 몸무게 : 88kg, 키 : 181cm, bmi 지수 : 26.86, 결과 : 과체중\n", + "\n", + "차파 95 143\n", + "\n", + " 이름 : 차파, 몸무게 : 95kg, 키 : 143cm, bmi 지수 : 46.46, 결과 : 과체중\n", + "\n", + "차다 69 145\n", + "\n", + " 이름 : 차다, 몸무게 : 69kg, 키 : 145cm, bmi 지수 : 32.82, 결과 : 과체중\n", + "\n", + "라가 92 178\n", + "\n", + " 이름 : 라가, 몸무게 : 92kg, 키 : 178cm, bmi 지수 : 29.04, 결과 : 과체중\n", + "\n", + "타파 97 180\n", + "\n", + " 이름 : 타파, 몸무게 : 97kg, 키 : 180cm, bmi 지수 : 29.94, 결과 : 과체중\n", + "\n", + "마파 99 183\n", + "\n", + " 이름 : 마파, 몸무게 : 99kg, 키 : 183cm, bmi 지수 : 29.56, 결과 : 과체중\n", + "\n", + "바타 97 146\n", + "\n", + " 이름 : 바타, 몸무게 : 97kg, 키 : 146cm, bmi 지수 : 45.51, 결과 : 과체중\n", + "\n", + "라타 74 143\n", + "\n", + " 이름 : 라타, 몸무게 : 74kg, 키 : 143cm, bmi 지수 : 36.19, 결과 : 과체중\n", + "\n", + "바아 40 162\n", + "\n", + " 이름 : 바아, 몸무게 : 40kg, 키 : 162cm, bmi 지수 : 15.24, 결과 : 저체중\n", + "\n", + "사타 66 158\n", + "\n", + " 이름 : 사타, 몸무게 : 66kg, 키 : 158cm, bmi 지수 : 26.44, 결과 : 과체중\n", + "\n", + "하자 92 182\n", + "\n", + " 이름 : 하자, 몸무게 : 92kg, 키 : 182cm, bmi 지수 : 27.77, 결과 : 과체중\n", + "\n", + "차바 93 171\n", + "\n", + " 이름 : 차바, 몸무게 : 93kg, 키 : 171cm, bmi 지수 : 31.80, 결과 : 과체중\n", + "\n", + "아하 42 177\n", + "\n", + " 이름 : 아하, 몸무게 : 42kg, 키 : 177cm, bmi 지수 : 13.41, 결과 : 저체중\n", + "\n", + "다타 71 190\n", + "\n", + " 이름 : 다타, 몸무게 : 71kg, 키 : 190cm, bmi 지수 : 19.67, 결과 : 정상\n", + "\n", + "바가 72 186\n", + "\n", + " 이름 : 바가, 몸무게 : 72kg, 키 : 186cm, bmi 지수 : 20.81, 결과 : 정상\n", + "\n", + "마다 52 146\n", + "\n", + " 이름 : 마다, 몸무게 : 52kg, 키 : 146cm, bmi 지수 : 24.39, 결과 : 정상\n", + "\n", + "자다 88 181\n", + "\n", + " 이름 : 자다, 몸무게 : 88kg, 키 : 181cm, bmi 지수 : 26.86, 결과 : 과체중\n", + "\n", + "라다 48 148\n", + "\n", + " 이름 : 라다, 몸무게 : 48kg, 키 : 148cm, bmi 지수 : 21.91, 결과 : 정상\n", + "\n", + "타마 40 152\n", + "\n", + " 이름 : 타마, 몸무게 : 40kg, 키 : 152cm, bmi 지수 : 17.31, 결과 : 저체중\n", + "\n", + "파타 67 151\n", + "\n", + " 이름 : 파타, 몸무게 : 67kg, 키 : 151cm, bmi 지수 : 29.38, 결과 : 과체중\n", + "\n", + "바차 70 158\n", + "\n", + " 이름 : 바차, 몸무게 : 70kg, 키 : 158cm, bmi 지수 : 28.04, 결과 : 과체중\n", + "\n", + "차자 92 172\n", + "\n", + " 이름 : 차자, 몸무게 : 92kg, 키 : 172cm, bmi 지수 : 31.10, 결과 : 과체중\n", + "\n", + "라마 96 153\n", + "\n", + " 이름 : 라마, 몸무게 : 96kg, 키 : 153cm, bmi 지수 : 41.01, 결과 : 과체중\n", + "\n", + "하나 54 141\n", + "\n", + " 이름 : 하나, 몸무게 : 54kg, 키 : 141cm, bmi 지수 : 27.16, 결과 : 과체중\n", + "\n", + "라라 60 167\n", + "\n", + " 이름 : 라라, 몸무게 : 60kg, 키 : 167cm, bmi 지수 : 21.51, 결과 : 정상\n", + "\n", + "하다 54 164\n", + "\n", + " 이름 : 하다, 몸무게 : 54kg, 키 : 164cm, bmi 지수 : 20.08, 결과 : 정상\n", + "\n", + "라파 79 155\n", + "\n", + " 이름 : 라파, 몸무게 : 79kg, 키 : 155cm, bmi 지수 : 32.88, 결과 : 과체중\n", + "\n", + "파다 47 184\n", + "\n", + " 이름 : 파다, 몸무게 : 47kg, 키 : 184cm, bmi 지수 : 13.88, 결과 : 저체중\n", + "\n", + "사하 92 186\n", + "\n", + " 이름 : 사하, 몸무게 : 92kg, 키 : 186cm, bmi 지수 : 26.59, 결과 : 과체중\n", + "\n", + "다나 99 173\n", + "\n", + " 이름 : 다나, 몸무게 : 99kg, 키 : 173cm, bmi 지수 : 33.08, 결과 : 과체중\n", + "\n", + "사타 43 188\n", + "\n", + " 이름 : 사타, 몸무게 : 43kg, 키 : 188cm, bmi 지수 : 12.17, 결과 : 저체중\n", + "\n", + "타하 88 174\n", + "\n", + " 이름 : 타하, 몸무게 : 88kg, 키 : 174cm, bmi 지수 : 29.07, 결과 : 과체중\n", + "\n", + "바바 70 175\n", + "\n", + " 이름 : 바바, 몸무게 : 70kg, 키 : 175cm, bmi 지수 : 22.86, 결과 : 정상\n", + "\n", + "아가 80 155\n", + "\n", + " 이름 : 아가, 몸무게 : 80kg, 키 : 155cm, bmi 지수 : 33.30, 결과 : 과체중\n", + "\n", + "사카 72 186\n", + "\n", + " 이름 : 사카, 몸무게 : 72kg, 키 : 186cm, bmi 지수 : 20.81, 결과 : 정상\n", + "\n", + "라타 51 141\n", + "\n", + " 이름 : 라타, 몸무게 : 51kg, 키 : 141cm, bmi 지수 : 25.65, 결과 : 과체중\n", + "\n", + "차마 74 144\n", + "\n", + " 이름 : 차마, 몸무게 : 74kg, 키 : 144cm, bmi 지수 : 35.69, 결과 : 과체중\n", + "\n", + "라자 98 197\n", + "\n", + " 이름 : 라자, 몸무게 : 98kg, 키 : 197cm, bmi 지수 : 25.25, 결과 : 과체중\n", + "\n", + "차가 71 167\n", + "\n", + " 이름 : 차가, 몸무게 : 71kg, 키 : 167cm, bmi 지수 : 25.46, 결과 : 과체중\n", + "\n", + "가파 73 187\n", + "\n", + " 이름 : 가파, 몸무게 : 73kg, 키 : 187cm, bmi 지수 : 20.88, 결과 : 정상\n", + "\n", + "마마 79 146\n", + "\n", + " 이름 : 마마, 몸무게 : 79kg, 키 : 146cm, bmi 지수 : 37.06, 결과 : 과체중\n", + "\n", + "아아 93 142\n", + "\n", + " 이름 : 아아, 몸무게 : 93kg, 키 : 142cm, bmi 지수 : 46.12, 결과 : 과체중\n", + "\n", + "마하 53 164\n", + "\n", + " 이름 : 마하, 몸무게 : 53kg, 키 : 164cm, bmi 지수 : 19.71, 결과 : 정상\n", + "\n", + "가아 42 169\n", + "\n", + " 이름 : 가아, 몸무게 : 42kg, 키 : 169cm, bmi 지수 : 14.71, 결과 : 저체중\n", + "\n", + "가차 62 142\n", + "\n", + " 이름 : 가차, 몸무게 : 62kg, 키 : 142cm, bmi 지수 : 30.75, 결과 : 과체중\n", + "\n", + "바가 80 158\n", + "\n", + " 이름 : 바가, 몸무게 : 80kg, 키 : 158cm, bmi 지수 : 32.05, 결과 : 과체중\n", + "\n", + "바자 99 149\n", + "\n", + " 이름 : 바자, 몸무게 : 99kg, 키 : 149cm, bmi 지수 : 44.59, 결과 : 과체중\n", + "\n", + "라바 51 159\n", + "\n", + " 이름 : 라바, 몸무게 : 51kg, 키 : 159cm, bmi 지수 : 20.17, 결과 : 정상\n", + "\n", + "라아 69 143\n", + "\n", + " 이름 : 라아, 몸무게 : 69kg, 키 : 143cm, bmi 지수 : 33.74, 결과 : 과체중\n", + "\n", + "타바 80 140\n", + "\n", + " 이름 : 타바, 몸무게 : 80kg, 키 : 140cm, bmi 지수 : 40.82, 결과 : 과체중\n", + "\n", + "자하 76 186\n", + "\n", + " 이름 : 자하, 몸무게 : 76kg, 키 : 186cm, bmi 지수 : 21.97, 결과 : 정상\n", + "\n", + "바나 62 171\n", + "\n", + " 이름 : 바나, 몸무게 : 62kg, 키 : 171cm, bmi 지수 : 21.20, 결과 : 정상\n", + "\n", + "바아 82 167\n", + "\n", + " 이름 : 바아, 몸무게 : 82kg, 키 : 167cm, bmi 지수 : 29.40, 결과 : 과체중\n", + "\n", + "하하 96 143\n", + "\n", + " 이름 : 하하, 몸무게 : 96kg, 키 : 143cm, bmi 지수 : 46.95, 결과 : 과체중\n", + "\n", + "아파 53 145\n", + "\n", + " 이름 : 아파, 몸무게 : 53kg, 키 : 145cm, bmi 지수 : 25.21, 결과 : 과체중\n", + "\n", + "타차 93 156\n", + "\n", + " 이름 : 타차, 몸무게 : 93kg, 키 : 156cm, bmi 지수 : 38.21, 결과 : 과체중\n", + "\n", + "가다 41 171\n", + "\n", + " 이름 : 가다, 몸무게 : 41kg, 키 : 171cm, bmi 지수 : 14.02, 결과 : 저체중\n", + "\n", + "바사 43 159\n", + "\n", + " 이름 : 바사, 몸무게 : 43kg, 키 : 159cm, bmi 지수 : 17.01, 결과 : 저체중\n", + "\n", + "자라 57 181\n", + "\n", + " 이름 : 자라, 몸무게 : 57kg, 키 : 181cm, bmi 지수 : 17.40, 결과 : 저체중\n", + "\n", + "다다 67 172\n", + "\n", + " 이름 : 다다, 몸무게 : 67kg, 키 : 172cm, bmi 지수 : 22.65, 결과 : 정상\n", + "\n", + "라카 67 162\n", + "\n", + " 이름 : 라카, 몸무게 : 67kg, 키 : 162cm, bmi 지수 : 25.53, 결과 : 과체중\n", + "\n", + "다마 71 156\n", + "\n", + " 이름 : 다마, 몸무게 : 71kg, 키 : 156cm, bmi 지수 : 29.17, 결과 : 과체중\n", + "\n", + "사카 48 186\n", + "\n", + " 이름 : 사카, 몸무게 : 48kg, 키 : 186cm, bmi 지수 : 13.87, 결과 : 저체중\n", + "\n", + "가가 58 153\n", + "\n", + " 이름 : 가가, 몸무게 : 58kg, 키 : 153cm, bmi 지수 : 24.78, 결과 : 정상\n", + "\n", + "라라 54 166\n", + "\n", + " 이름 : 라라, 몸무게 : 54kg, 키 : 166cm, bmi 지수 : 19.60, 결과 : 정상\n", + "\n", + "아타 59 171\n", + "\n", + " 이름 : 아타, 몸무게 : 59kg, 키 : 171cm, bmi 지수 : 20.18, 결과 : 정상\n", + "\n", + "바라 61 174\n", + "\n", + " 이름 : 바라, 몸무게 : 61kg, 키 : 174cm, bmi 지수 : 20.15, 결과 : 정상\n", + "\n", + "자마 75 195\n", + "\n", + " 이름 : 자마, 몸무게 : 75kg, 키 : 195cm, bmi 지수 : 19.72, 결과 : 정상\n", + "\n", + "사타 82 166\n", + "\n", + " 이름 : 사타, 몸무게 : 82kg, 키 : 166cm, bmi 지수 : 29.76, 결과 : 과체중\n", + "\n", + "라라 74 164\n", + "\n", + " 이름 : 라라, 몸무게 : 74kg, 키 : 164cm, bmi 지수 : 27.51, 결과 : 과체중\n", + "\n", + "하바 47 183\n", + "\n", + " 이름 : 하바, 몸무게 : 47kg, 키 : 183cm, bmi 지수 : 14.03, 결과 : 저체중\n", + "\n", + "나마 46 179\n", + "\n", + " 이름 : 나마, 몸무게 : 46kg, 키 : 179cm, bmi 지수 : 14.36, 결과 : 저체중\n", + "\n", + "라사 40 190\n", + "\n", + " 이름 : 라사, 몸무게 : 40kg, 키 : 190cm, bmi 지수 : 11.08, 결과 : 저체중\n", + "\n", + "마나 77 181\n", + "\n", + " 이름 : 마나, 몸무게 : 77kg, 키 : 181cm, bmi 지수 : 23.50, 결과 : 정상\n", + "\n", + "가가 52 145\n", + "\n", + " 이름 : 가가, 몸무게 : 52kg, 키 : 145cm, bmi 지수 : 24.73, 결과 : 정상\n", + "\n", + "자차 42 156\n", + "\n", + " 이름 : 자차, 몸무게 : 42kg, 키 : 156cm, bmi 지수 : 17.26, 결과 : 저체중\n", + "\n", + "차나 76 157\n", + "\n", + " 이름 : 차나, 몸무게 : 76kg, 키 : 157cm, bmi 지수 : 30.83, 결과 : 과체중\n", + "\n", + "자다 73 192\n", + "\n", + " 이름 : 자다, 몸무게 : 73kg, 키 : 192cm, bmi 지수 : 19.80, 결과 : 정상\n", + "\n", + "라다 93 194\n", + "\n", + " 이름 : 라다, 몸무게 : 93kg, 키 : 194cm, bmi 지수 : 24.71, 결과 : 정상\n", + "\n", + "나자 98 199\n", + "\n", + " 이름 : 나자, 몸무게 : 98kg, 키 : 199cm, bmi 지수 : 24.75, 결과 : 정상\n", + "\n", + "아카 84 194\n", + "\n", + " 이름 : 아카, 몸무게 : 84kg, 키 : 194cm, bmi 지수 : 22.32, 결과 : 정상\n", + "\n", + "나파 52 144\n", + "\n", + " 이름 : 나파, 몸무게 : 52kg, 키 : 144cm, bmi 지수 : 25.08, 결과 : 과체중\n", + "\n", + "타차 82 191\n", + "\n", + " 이름 : 타차, 몸무게 : 82kg, 키 : 191cm, bmi 지수 : 22.48, 결과 : 정상\n", + "\n", + "가나 75 196\n", + "\n", + " 이름 : 가나, 몸무게 : 75kg, 키 : 196cm, bmi 지수 : 19.52, 결과 : 정상\n", + "\n", + "라다 48 170\n", + "\n", + " 이름 : 라다, 몸무게 : 48kg, 키 : 170cm, bmi 지수 : 16.61, 결과 : 저체중\n", + "\n", + "자자 93 193\n", + "\n", + " 이름 : 자자, 몸무게 : 93kg, 키 : 193cm, bmi 지수 : 24.97, 결과 : 정상\n", + "\n", + "타다 48 196\n", + "\n", + " 이름 : 타다, 몸무게 : 48kg, 키 : 196cm, bmi 지수 : 12.49, 결과 : 저체중\n", + "\n", + "차가 83 197\n", + "\n", + " 이름 : 차가, 몸무게 : 83kg, 키 : 197cm, bmi 지수 : 21.39, 결과 : 정상\n", + "\n", + "자하 76 165\n", + "\n", + " 이름 : 자하, 몸무게 : 76kg, 키 : 165cm, bmi 지수 : 27.92, 결과 : 과체중\n", + "\n", + "하자 61 142\n", + "\n", + " 이름 : 하자, 몸무게 : 61kg, 키 : 142cm, bmi 지수 : 30.25, 결과 : 과체중\n", + "\n", + "하타 74 199\n", + "\n", + " 이름 : 하타, 몸무게 : 74kg, 키 : 199cm, bmi 지수 : 18.69, 결과 : 정상\n", + "\n", + "타다 83 141\n", + "\n", + " 이름 : 타다, 몸무게 : 83kg, 키 : 141cm, bmi 지수 : 41.75, 결과 : 과체중\n", + "\n", + "하카 58 150\n", + "\n", + " 이름 : 하카, 몸무게 : 58kg, 키 : 150cm, bmi 지수 : 25.78, 결과 : 과체중\n", + "\n", + "자차 72 157\n", + "\n", + " 이름 : 자차, 몸무게 : 72kg, 키 : 157cm, bmi 지수 : 29.21, 결과 : 과체중\n", + "\n", + "하가 47 156\n", + "\n", + " 이름 : 하가, 몸무게 : 47kg, 키 : 156cm, bmi 지수 : 19.31, 결과 : 정상\n", + "\n", + "마나 64 141\n", + "\n", + " 이름 : 마나, 몸무게 : 64kg, 키 : 141cm, bmi 지수 : 32.19, 결과 : 과체중\n", + "\n", + "아파 65 162\n", + "\n", + " 이름 : 아파, 몸무게 : 65kg, 키 : 162cm, bmi 지수 : 24.77, 결과 : 정상\n", + "\n", + "다아 94 185\n", + "\n", + " 이름 : 다아, 몸무게 : 94kg, 키 : 185cm, bmi 지수 : 27.47, 결과 : 과체중\n", + "\n", + "다아 68 164\n", + "\n", + " 이름 : 다아, 몸무게 : 68kg, 키 : 164cm, bmi 지수 : 25.28, 결과 : 과체중\n", + "\n", + "타파 62 147\n", + "\n", + " 이름 : 타파, 몸무게 : 62kg, 키 : 147cm, bmi 지수 : 28.69, 결과 : 과체중\n", + "\n", + "카바 57 183\n", + "\n", + " 이름 : 카바, 몸무게 : 57kg, 키 : 183cm, bmi 지수 : 17.02, 결과 : 저체중\n", + "\n", + "하하 84 151\n", + "\n", + " 이름 : 하하, 몸무게 : 84kg, 키 : 151cm, bmi 지수 : 36.84, 결과 : 과체중\n", + "\n", + "바하 92 140\n", + "\n", + " 이름 : 바하, 몸무게 : 92kg, 키 : 140cm, bmi 지수 : 46.94, 결과 : 과체중\n", + "\n", + "자마 80 183\n", + "\n", + " 이름 : 자마, 몸무게 : 80kg, 키 : 183cm, bmi 지수 : 23.89, 결과 : 정상\n", + "\n", + "카나 59 193\n", + "\n", + " 이름 : 카나, 몸무게 : 59kg, 키 : 193cm, bmi 지수 : 15.84, 결과 : 저체중\n", + "\n", + "사자 86 146\n", + "\n", + " 이름 : 사자, 몸무게 : 86kg, 키 : 146cm, bmi 지수 : 40.35, 결과 : 과체중\n", + "\n", + "나차 88 168\n", + "\n", + " 이름 : 나차, 몸무게 : 88kg, 키 : 168cm, bmi 지수 : 31.18, 결과 : 과체중\n", + "\n", + "다마 43 161\n", + "\n", + " 이름 : 다마, 몸무게 : 43kg, 키 : 161cm, bmi 지수 : 16.59, 결과 : 저체중\n", + "\n", + "사타 86 149\n", + "\n", + " 이름 : 사타, 몸무게 : 86kg, 키 : 149cm, bmi 지수 : 38.74, 결과 : 과체중\n", + "\n" + ] + } + ], + "source": [ + "#info.txt 읽어서 bmi 지수 계산\n", + "# 하사 93 154\n", + "# 이름 : 하사\n", + "# 몸무게 : 93kg\n", + "# 키 : 154cm\n", + "# bmi 지수 : 39.2 ( weight / (height/100) ** 2 )\n", + "# 결과 : 고도 비만\n", + "\n", + "# with open(\"info.txt\", \"r\", encoding=\"utf-8\") as f1:\n", + "# for line in f1:\n", + "# name, weight, height = line.split()\n", + "# weight = int(weight)\n", + "# height = int(height)\n", + "# bmi = weight / (height / 100) ** 2\n", + " \n", + "# if bmi >= 25:\n", + "# result = \"과체중\"\n", + "# elif bmi >= 18.5:\n", + "# result = \"정상\"\n", + "# else:\n", + "# result = \"저체중\"\n", + " \n", + "# print(f\"이름 : {name}, 몸무게 : {weight}kg, 키 : {height}cm, bmi 지수 : {bmi:.2f}, 결과 : {result}\")\n", + "\n", + "with open(\"info.txt\", \"r\", encoding=\"utf-8\") as f: \n", + " for info in f:\n", + " # print(info.strip() # 줄바꿈 제거)\n", + " name, weight, height = info.strip().split()\n", + " print(name, weight, height)\n", + " \n", + " bmi = int(weight) / (int(height) / 100) ** 2\n", + " \n", + " if bmi >= 25:\n", + " result = \"과체중\"\n", + " elif bmi >= 18.5:\n", + " result = \"정상\"\n", + " else:\n", + " result = \"저체중\"\n", + " print(\"\\n\", \"이름 : {}, 몸무게 : {}kg, 키 : {}cm, bmi 지수 : {:.2f}, 결과 : {}\".format(name, weight, height, bmi, result))\n", + " print()\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "id": "11e0499a", + "metadata": {}, + "source": [ + "### 클래스\n", + "- 함수, 변수를 포함한 형태\n", + "- 객체지향\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "915a56e8", + "metadata": {}, + "outputs": [], + "source": [ + "# 클래스 생성\n", + "class Calculator:\n", + " def __init__(self):\n", + " self.result = 0\n", + " \n", + " 메서드 \n", + " def add(self, num):\n", + " self.result += num\n", + " return self.result\n", + "\n", + " def sub(self, num):\n", + " self.result -= num\n", + " return self.result\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8e08c2c3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10\n", + "5\n" + ] + } + ], + "source": [ + "# 사용 = 객체 생성\n", + "calc = Calculator()\n", + "\n", + "# 함수 (메서드) 호출\n", + "\n", + "print(calc.add(10))\n", + "\n", + "\n", + "print(calc.sub(5))\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d4a0210b", + "metadata": {}, + "outputs": [], + "source": [ + "# 클래스 생성\n", + "class FourCal:\n", + " def __init__(self, first, second):\n", + " self.first = first\n", + " self.second = second\n", + "\n", + " #메서드\n", + " def add(self):\n", + " return self.first + self.second\n", + "\n", + " def sub(self):\n", + " return self.first - self.second\n", + " \n", + " def mul(self):\n", + " return self.first * self.second\n", + " \n", + " def div(self):\n", + " return self.first // self.second \n", + " \n", + " # 주소값 같이 안나오게 하기 위해서 __str__ 메서드 오버라이딩\n", + " def __str__(self):\n", + " return f\"{self.first}, {self.second}\"" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "id": "1be26090", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8\n", + "2\n", + "15\n", + "1\n", + "5, 3\n" + ] + } + ], + "source": [ + "# __init__() 메서드는 객체가 생성될 때 자동으로 호출되는 초기화 메서드입니다. 이 메서드는 객체의 속성을 초기화하는 데 사용됩니다. 예를 들어, class FourCal: ...와 같이 클래스가 정의되어 있을 때, calc = FourCal(10, 20)과 같이 객체를 생성하면, _init__() 메서드가 자동으로 호출되어 self.first에 10이, self.second에 20이 할당됩니다. _init__() 메서드는 객체의 초기 상태를 설정하는 데 중요한 역할을 합니다.\n", + "calc = FourCal(5, 3)\n", + "\n", + "print(calc.add()) # TypeError: add() takes 1 positional argument but 2 were given\n", + "print(calc.sub())\n", + "print(calc.mul()) \n", + "print(calc.div())\n", + "print(calc) # 0" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "id": "b2451676", + "metadata": {}, + "outputs": [], + "source": [ + "# 상속\n", + "\n", + "class MoreFourCal(FourCal):\n", + " def pow(self):\n", + " return self.first ** self.second" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "id": "5ee8e1db", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8\n", + "125\n" + ] + } + ], + "source": [ + "cale = MoreFourCal(5, 3)\n", + "print(cale.add())\n", + "print(cale.pow())\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b7c780e", + "metadata": {}, + "outputs": [], + "source": [ + "# 오버라이딩 : 부모 클래스 메서드 재정의\n", + "\n", + "four1 = FourCal(4, 0)\n", + "\n", + "#four1.div() # ZeroDivisionError: integer division or modulo by zero\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "id": "b3d9e669", + "metadata": {}, + "outputs": [], + "source": [ + "# super(): 부모\n", + "# self : 자식(객체 자신)\n", + "\n", + "class SafeFourCal(FourCal):\n", + " def div(self):\n", + " if self.second == 0:\n", + " return 0\n", + " else:\n", + " return self.first / self.second\n" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "id": "e36f0113", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], + "source": [ + "four1 = SafeFourCal(4, 0)\n", + "print(four1.div()) # 0" + ] + }, + { + "cell_type": "markdown", + "id": "f5902fce", + "metadata": {}, + "source": [ + "### 모듈 : 함수, 변수, 클래스 모아 놓은 파일\n", + "\n", + "### 패키지 : 모듈의 모임\n" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "id": "227c8751", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.0\n", + "3.141592653589793\n", + "2\n", + "0.8414709848078965\n", + "0.5403023058681398\n" + ] + } + ], + "source": [ + "# 파이썬에서 제공하는 기본 모듈\n", + "# math가 가지고 있는 모든 설정값들을 메모리에 올림\n", + "\n", + "import math\n", + "\n", + "print(math.sqrt(16)) # math 모듈의 sqrt() 함수를 사용하여 16의 제곱근을 계산합니다. math.sqrt() 함수는 math 모듈에서 제공하는 수학 함수 중 하나로, 제곱근을 계산하는 데 사용됩니다. 예를 들어, math.sqrt(16)은 4.0을 반환합니다. math 모듈은 다양한 수학 함수를 제공하며, 이를 활용하여 복잡한 수학 연산을 수행할 수 있습니다.\n", + "print(math.pi) # math 모듈의 pi 상수를 출력합니다. math.pi는 원주율을 나타내는 상수로, 약 3.14159의 값을 가지고 있습니다. math 모듈은 다양한 수학 상수와 함수를 제공하며, 이를 활용하여 수학적 계산을 수행할 수 있습니다. math.pi를 사용하면 원주율을 쉽게 참조할 수 있어, 원과 관련된 계산에서 유용하게 사용됩니다.\n", + "print(math.floor(2.3)) # math 모듈의 floor() 함수를 사용하여 2.3을 내림하여 정수로 반환합니다. math.floor() 함수는 주어진 숫자를 내림하여 가장 큰 정수로 반환하는 함수입니다. 예를 들어, math.floor(2.3)은 2를 반환합니다. math 모듈은 다양한 수학 함수를 제공하며, 이를 활용하여 수학적 계산을 수행할 수 있습니다. math.floor() 함수를 사용하면 소수점을 내림하여 정수로 변환할 때 유용하게 사용됩니다.\n", + "print(math.sin(1)) # math 모듈의 sin() 함수를 사용하여 1의 사인 값을 계산합니다. math.sin() 함수는 주어진 각도의 사인 값을 반환하는 함수입니다. 예를 들어, math.sin(1)은 0.8414709848078965를 반환합니다. math 모듈은 다양한 수학 함수를 제공하며, 이를 활용하여 삼각 함수와 관련된 계산을 수행할 수 있습니다. math.sin() 함수를 사용하면 각도의 사인 값을 쉽게 계산할 수 있어, 삼각 함수와 관련된 문제에서 유용하게 사용됩니다.\n", + "print(math.cos(1)) # math 모듈의 cos() 함수를 사용하여 1의 코사인 값을 계산합니다. math.cos() 함수는 주어진 각도의 코사인 값을 반환하는 함수입니다. 예를 들어, math.cos(1)은 0.5403023058681398을 반환합니다. math 모듈은 다양한 수학 함수를 제공하며, 이를 활용하여 삼각 함수와 관련된 계산을 수행할 수 있습니다. math.cos() 함수를 사용하면 각도의 코사인 값을 쉽게 계산할 수 있어, 삼각 함수와 관련된 문제에서 유용하게 사용됩니다." + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "id": "46c2998d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6\n", + "0.8414709848078965\n", + "0.5403023058681398\n", + "2\n" + ] + } + ], + "source": [ + "from math import ceil, sin, cos, floor\n", + "\n", + "print(ceil(5.3)) # math 모듈에서 ceil() 함수를 직접 사용하여 5.3을 올림하여 정수로 반환합니다. ceil() 함수는 주어진 숫자를 올림하여 가장 작은 정수로 반환하는 함수입니다. 예를 들어, ceil(5.3)은 6을 반환합니다. math 모듈에서 필요한 함수만을 직접 가져와 사용할 수 있어, 코드의 가독성을 높이고 불필요한 네임스페이스 오염을 방지할 수 있습니다.\n", + "print(sin(1)) # math 모듈에서 sin() 함수를 직접 사용하여 1의 사인 값을 계산합니다. sin() 함수는 주어진 각도의 사인 값을 반환하는 함수입니다. 예를 들어, sin(1)은 0.8414709848078965를 반환합니다. math 모듈에서 필요한 함수만을 직접 가져와 사용할 수 있어, 코드의 가독성을 높이고 불필요한 네임스페이스 오염을 방지할 수 있습니다.\n", + "print(cos(1)) # math 모듈에서 cos() 함수를 직접 사용하여 1의 코사인 값을 계산합니다. cos() 함수는 주어진 각도의 코사인 값을 반환하는 함수입니다. 예를 들어, cos(1)은 0.5403023058681398을 반환합니다. math 모듈에서 필요한 함수만을 직접 가져와 사용할 수 있어, 코드의 가독성을 높이고 불필요한 네임스페이스 오염을 방지할 수 있습니다.\n", + "print(floor(2.3)) # math 모듈에서 floor() 함수를 직접 사용하여 2.3을 내림하여 정수로 반환합니다. floor() 함수는 주어진 숫자를 내림하여 가장 큰 정수로 반환하는 함수입니다. 예를 들어, floor(2.3)은 2를 반환합니다. math 모듈에서 필요한 함수만을 직접 가져와 사용할 수 있어, 코드의 가독성을 높이고 불필요한 네임스페이스 오염을 방지할 수 있습니다 ." + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "id": "856a93e5", + "metadata": {}, + "outputs": [], + "source": [ + "# as : 별칭, 모듈이나 함수에 별명을 붙이는 데 사용됩니다. as 키워드를 사용하여 모듈이나 함수를 다른 이름으로 참조할 수 있습니다. 예를 들어, import math as m과 같이 math 모듈을 m이라는 별칭으로 가져올 수 있습니다. 이렇게 하면 m.sqrt(16)과 같이 math 모듈의 sqrt() 함수를 m이라는 별칭을 사용하여 호출할 수 있습니다. as 키워드는 코드의 가독성을 높이고, 긴 모듈 이름을 간단하게 사용할 때 유용하게 사용됩니다.\n", + "\n", + "from math import ceil as c" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "id": "0da783c3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.3849086839965148\n", + "2\n", + "3\n", + "banana\n", + "[4, 3, 1, 5, 2]\n", + "[5, 4]\n" + ] + } + ], + "source": [ + "import random\n", + "\n", + "# random() : 0.0 <= x < 1.0 임의의 float 숫자\n", + "print(random.random()) # random 모듈의 random() 함수를 사용하여 0과 1 사이의 임의의 부동 소수점 숫자를 생성합니다. random.random() 함수는 0.0 이상 1.0 미만의 난수를 반환합니다. 예를 들어, random.random()은 0.123456789와 같은 값을 반환할 수 있습니다. random 모듈은 다양한 난수 생성 함수를 제공하며, 이를 활용하여 무작위 데이터를 생성하거나 시뮬레이션을 수행할 수 있습니다. random.random() 함수를 사용하면 간단하게 난수를 생성할 수 있어, 다양한 응용 분야에서 유용하게 사용됩니다.\n", + "print(random.randint(1, 10)) # random 모듈의 randint() 함수를\n", + "print(random.randrange(1, 10)) # random 모듈의 randrange() 함수를 사용하여 1과 10 사이의 임의의 정수를 생성합니다. random.randrange(1, 10) 함수는 1 이상 10 미만의 정수를 반환합니다. 예를 들어, random.randrange(1, 10)은 3과 같은 값을 반환할 수 있습니다. random 모듈은 다양한 난수 생성 함수를 제공하며, 이를 활용하여 무작위 데이터를 생성하거나 시뮬레이션을 수행할 수 있습니다. random.randrange() 함수를 사용하면 특정 범위 내에서 정수를 생성할 때 유용하게 사용됩니다.\n", + "# 주어진 리스트에서 무작위 선택\n", + "print(random.choice(['apple', 'banana', 'cherry'])) # random 모듈의 choice() 함수를 사용하여 리스트 ['apple', 'banana', 'cherry']에서 임의\n", + "\n", + "list1 = [1, 2, 3, 4, 5]\n", + "# 무작위로 썩기\n", + "random.shuffle(list1)\n", + "print(list1) # random 모듈의 shuffle() 함수를 사용하여 리스트 [1, 2, 3, 4, 5]의 요소들을 무작위로 섞습니다. random.shuffle() 함수는 리스트의 요소들을 제자리에서 무작위로 섞으며, 반환값은 없습니다. 예를 들어, random.shuffle([1, 2, 3, 4, 5])를 호출하면 리스트가 [3, 1, 5, 2, 4]와 같이 섞일 수 있습니다. random 모듈은 다양한 난수 생성 함수를 제공하며, 이를 활용하여 무작위 데이터를 생성하거나 시뮬레이션을 수행할 수 있습니다. random.shuffle() 함수를 사용하면 리스트의 요소들을 무작위로 섞을 때 유용하게 사용됩니다.\n", + "\n", + "print(random.sample(list1, k=2)) # random 모듈의 sample() 함수를 사용하여 리스트 [1, 2, 3, 4, 5]에서 임의로 2개의 요소를 선택합니다. random.sample() 함수는 주어진 리스트에서 지정된 개수만큼의 요소를 무작위로 선택하여 새로운 리스트로 반환합니다. 예를 들어, random.sample([1, 2, 3, 4, 5], 2)은 [2, 4]와 같은 값을 반환할 수 있습니다. random 모듈은 다양한 난수 생성 함수를 제공하며, 이를 활용하여 무작위 데이터를 생성하거나 시뮬레이션을 수행할 수 있습니다. random.sample() 함수를 사용하면 리스트에서 특정 개수의 요소를 무작위로 선택할 때 유용하게 사용됩니다." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48d9c2c6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "지금부터 5초 정지\n", + "5초가 지났습니다.\n" + ] + } + ], + "source": [ + "import time\n", + "\n", + "print(\"지금부터 5초 정지\")\n", + "time.sleep(5)\n", + "print(\"5초가 지났습니다.\") " + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "id": "ff8b943d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-05-13 15:10:28.636020\n" + ] + } + ], + "source": [ + "import datetime\n", + "\n", + "now = datetime.datetime.now() # datetime 모듈의 datetime 클래스에서 now() 메서드를 사용하여 현재 날짜와 시간을 가져옵니다. now() 메서드는 현재 날짜와 시간을 나타내는 datetime 객체를 반환합니다. 예를 들어, datetime.datetime.now()는 2024-06-01 12:34:56과 같은 값을 반환할 수 있습니다. datetime 모듈은 날짜와 시간 관련 작업을 수행할 때 유용하게 사용됩니다. now() 메서드를 사용하면 현재 날짜와 시간을 쉽게 가져올 수 있어, 다양한 응용 분야에서 활용할 수 있습니다.\n", + "print(now) # 현재 날짜와 시간을 출력합니다. now 변수에는 datetime.datetime.now()를" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "id": "277504ab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026\n", + "5\n", + "13\n", + "15\n", + "10\n", + "28\n" + ] + } + ], + "source": [ + "print(now.year) # 현재 연도를 출력합니다. now 변수는 datetime.datetime.now()로 생성된 datetime 객체를 참조하고 있으며, year 속성을 사용하여 현재 연도를 가져올 수 있습니다. 예를 들어, now.year은 2024를 반환할 수 있습니다. datetime 모듈은 날짜와 시간 관련 작업을 수행할 때 유용하게 사용됩니다. year 속성을 사용하면 현재 연도를 쉽게 가져올 수 있어, 다양한 응용 분야에서 활용할 수 있습니다.\n", + "print(now.month) # 현재 월을 출력합니다. now 변수는 datetime.datetime.now()로 생성된 datetime 객체를 참조하고 있으며, month 속성을 사용하여 현재 월을 가져올\n", + "print(now.day) # 현재 일을 출력합니다. now 변수는 datetime.datetime.now()로 생성된 datetime 객체를 참조하고 있으며, day 속성을 사용하여 현재 일을 가져올 수 있습니다. 예를 들어, now.day은 1을 반환할 수 있습니다. datetime 모듈은 날짜와 시간 관련 작업을 수행할 때 유용하게 사용됩니다. day 속성을 사용하면 현재 일을 쉽게 가져올 수 있어, 다양한 응용 분야에서 활용할 수 있습니다.\n", + "print(now.hour) # 현재 시를 출력합니다. now 변수는 datetime.datetime.now()로생성된 datetime 객체를 참조하고 있으며, hour 속성을 사용하여 현재 시를 가져올 수 있습니다. 예를 들어, now.hour은 12를 반환할 수 있습니다. datetime 모듈은 날짜와 시간 관련 작업을 수행할 때 유용하게 사용됩니다. hour 속성을 사용하면 현재 시를 쉽게 가져올 수 있어, 다양한 응용 분야에서 활용할 수 있습니다.\n", + "print(now.minute) # 현재 분을 출력합니다. now 변수는 datetime.datetime.now()로생성된 datetime 객체를 참조하고 있으며, minute 속성을 사용하여 현재 분을 가져올 수 있습니다. 예를 들어, now.minute은 34를 반환할 수 있습니다. datetime 모듈은 날짜와 시간 관련 작업을 수행할 때 유용하게 사용됩니다. minute 속성을 사용하면 현재 분을 쉽게 가져올 수 있어, 다양한 응용 분야에서 활용할 수 있습니다.\n", + "print(now.second) # 현재 초를 출력합니다. now 변수는 datetime.datetime.now()로 생성된 datetime 객체를 참조하고 있으며, second 속성을 사용하여 현재 초를 가져올\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ee959ff", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hello\n", + "Life is too short\n", + "You need python\n", + "10\n" + ] + } + ], + "source": [ + "print('hello')\n", + "\n", + "# 줄단위 실행 >= 특정 행에서 실행 오류가 나는 경우\n", + "# 파이썬 자료형\n", + "# 정ㅅ형, 문자형, 불린형, 리스트, 튜플, 딕셔너리\n", + "\n", + "# \\n == 줄바꿈\n", + "# \\t == 탭\n", + "\n", + "multiline = \"Life is too short\\nYou need python\"\n", + "print(multiline)\n", + "\n", + "# import mod1\n", + "# print(mod1.add(3, 4))\n", + "# print(mod1.sub(10, 5))\n", + "\n", + "from mod1 import add\n", + "print(add(3, 7))\n", + "\n", + "from mod import *\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "b4044187", + "metadata": {}, + "source": [ + "### 예외처리\n", + "- 파이썬에서 오류를 처리하는 방법\n", + "\n", + "```\n", + "\n", + "try:\n", + " 오류 발생할 가능성 구문\n", + "except:\n", + " 오류 발생했을 때 처리방법\n", + "\n", + "try:\n", + " 오류 발생할 가능성 구문\n", + "except IndexError as 오류변수:\n", + " IndexError 오류 발생했을 때 처리방법\n", + "\n", + "try:\n", + "\n", + "except IndexError as 오류변수:\n", + " IndexError 오류 발생했을 때 처리방법\n", + "except ZeroDivisionError:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "id": "04f4ca32", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "오류발생\n" + ] + } + ], + "source": [ + "try:\n", + " f = open(\"test3.txt\", \"r\") \n", + "except:\n", + " print(\"오류발생\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "00aee51f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0으로 나눌 수 없습니다.\n" + ] + } + ], + "source": [ + "try:\n", + " 4 / 0\n", + "except ZeroDivisionError:\n", + " print(\"0으로 나눌 수 없습니다.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "id": "1e7e5828", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "division by zero\n" + ] + } + ], + "source": [ + "try:\n", + " 4 / 0\n", + "except ZeroDivisionError as e:\n", + " print(e)" + ] + }, + { + "cell_type": "code", + "execution_count": 209, + "id": "621f45cf", + "metadata": {}, + "outputs": [], + "source": [ + "# 나이를 입력받은 후 정수가 입력되었다면 age <= 18 미성년자입니다/ 환영합니다.\n", + "# 입력을 확인해 주세요\n", + "\n", + "try:\n", + " age = int(input(\"나이 입력\")) \n", + "except:\n", + " print(\"입력을 확인해주세요.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "id": "99605d96", + "metadata": {}, + "outputs": [], + "source": [ + "# 나만의 오류 작성하기\n", + "\n", + "class MyError(Exception):\n", + " pass\n", + "\n", + "def say_nick(nick):\n", + " if nick ==\"바보\":\n", + " raise MyError()\n", + " print(nick)" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "id": "79f198be", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "천사\n" + ] + }, + { + "ename": "MyError", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mMyError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[214]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m say_nick(\u001b[33m\"천사\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m say_nick(\u001b[33m\"바보\"\u001b[39m)\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[211]\u001b[39m\u001b[32m, line 8\u001b[39m, in \u001b[36msay_nick\u001b[39m\u001b[34m(nick)\u001b[39m\n\u001b[32m 6\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m say_nick(nick):\n\u001b[32m 7\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m nick ==\u001b[33m\"바보\"\u001b[39m:\n\u001b[32m----> \u001b[39m\u001b[32m8\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m MyError()\n\u001b[32m 9\u001b[39m print(nick)\n", + "\u001b[31mMyError\u001b[39m: " + ] + } + ], + "source": [ + "say_nick(\"천사\")\n", + "say_nick(\"바보\")" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "id": "bec3f8d3", + "metadata": {}, + "outputs": [ + { + "ename": "JSONDecodeError", + "evalue": "Expecting value: line 1 column 1 (char 0)", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mJSONDecodeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[215]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m json\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m open(\u001b[33m\"test.json\"\u001b[39m, \u001b[33m\"r\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m data = json.load(f)\n\u001b[32m 4\u001b[39m print(type(data))\n\u001b[32m 5\u001b[39m print(data)\n\u001b[32m 6\u001b[39m \n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Cellar/python@3.14/3.14.4_1/Frameworks/Python.framework/Versions/3.14/lib/python3.14/json/__init__.py:298\u001b[39m, in \u001b[36mload\u001b[39m\u001b[34m(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[39m\n\u001b[32m 278\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mload\u001b[39m(fp, *, \u001b[38;5;28mcls\u001b[39m=\u001b[38;5;28;01mNone\u001b[39;00m, object_hook=\u001b[38;5;28;01mNone\u001b[39;00m, parse_float=\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 279\u001b[39m parse_int=\u001b[38;5;28;01mNone\u001b[39;00m, parse_constant=\u001b[38;5;28;01mNone\u001b[39;00m, object_pairs_hook=\u001b[38;5;28;01mNone\u001b[39;00m, **kw):\n\u001b[32m 280\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Deserialize ``fp`` (a ``.read()``-supporting file-like object containing\u001b[39;00m\n\u001b[32m 281\u001b[39m \u001b[33;03m a JSON document) to a Python object.\u001b[39;00m\n\u001b[32m 282\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 296\u001b[39m \u001b[33;03m kwarg; otherwise ``JSONDecoder`` is used.\u001b[39;00m\n\u001b[32m 297\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m298\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mloads\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mfp\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mread\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 299\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mcls\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcls\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mobject_hook\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mobject_hook\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 300\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mparse_float\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mparse_float\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mparse_int\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mparse_int\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 301\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mparse_constant\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mparse_constant\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mobject_pairs_hook\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mobject_pairs_hook\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkw\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Cellar/python@3.14/3.14.4_1/Frameworks/Python.framework/Versions/3.14/lib/python3.14/json/__init__.py:352\u001b[39m, in \u001b[36mloads\u001b[39m\u001b[34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[39m\n\u001b[32m 347\u001b[39m s = s.decode(detect_encoding(s), \u001b[33m'\u001b[39m\u001b[33msurrogatepass\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 349\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m object_hook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[32m 350\u001b[39m parse_int \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m parse_float \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[32m 351\u001b[39m parse_constant \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m object_pairs_hook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kw):\n\u001b[32m--> \u001b[39m\u001b[32m352\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43m_default_decoder\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mdecode\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43ms\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 353\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 354\u001b[39m \u001b[38;5;28mcls\u001b[39m = JSONDecoder\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Cellar/python@3.14/3.14.4_1/Frameworks/Python.framework/Versions/3.14/lib/python3.14/json/decoder.py:345\u001b[39m, in \u001b[36mJSONDecoder.decode\u001b[39m\u001b[34m(self, s, _w)\u001b[39m\n\u001b[32m 340\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdecode\u001b[39m(\u001b[38;5;28mself\u001b[39m, s, _w=WHITESPACE.match):\n\u001b[32m 341\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Return the Python representation of ``s`` (a ``str`` instance\u001b[39;00m\n\u001b[32m 342\u001b[39m \u001b[33;03m containing a JSON document).\u001b[39;00m\n\u001b[32m 343\u001b[39m \n\u001b[32m 344\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m345\u001b[39m obj, end = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mraw_decode\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43ms\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43midx\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43m_w\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43ms\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m0\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mend\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 346\u001b[39m end = _w(s, end).end()\n\u001b[32m 347\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m end != \u001b[38;5;28mlen\u001b[39m(s):\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Cellar/python@3.14/3.14.4_1/Frameworks/Python.framework/Versions/3.14/lib/python3.14/json/decoder.py:363\u001b[39m, in \u001b[36mJSONDecoder.raw_decode\u001b[39m\u001b[34m(self, s, idx)\u001b[39m\n\u001b[32m 361\u001b[39m obj, end = \u001b[38;5;28mself\u001b[39m.scan_once(s, idx)\n\u001b[32m 362\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[32m--> \u001b[39m\u001b[32m363\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m JSONDecodeError(\u001b[33m\"\u001b[39m\u001b[33mExpecting value\u001b[39m\u001b[33m\"\u001b[39m, s, err.value) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 364\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m obj, end\n", + "\u001b[31mJSONDecodeError\u001b[39m: Expecting value: line 1 column 1 (char 0)" + ] + } + ], + "source": [ + "import json\n", + "with open(\"test.json\", \"r\") as f:\n", + " data = json.load(f)\n", + " print(type(data))\n", + " print(data)\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b0b9ada0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'3.53.0'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# SQLite 내장 db 사용 => sql\n", + "\n", + "import sqlite3\n", + "\n", + "sqlite3.sqlite_version\n", + "sqlite3.sqlite_version_info" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "87d44d39", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sqlite3\n", + "\n", + "conn = sqlite3.connect(\"test.db\", isolation_level=None)\n", + "\n", + "# cursor : 특정 테이블에 접근하는 포인터\n", + "cursor = conn.cursor()\n", + "\n", + "sql = \"CREATE TABLE IF NOT EXISTS users(id interger primary key, username text,\" \\\n", + " \"email text, phone text, website text, regdate text)\"\n", + " \n", + "cursor.execute(sql)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "dadbbb81", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'26-05-14 13:02:23'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from datetime import datetime\n", + "\n", + "now = datetime.now()\n", + "\n", + "# now의 format 변경\n", + "nowDateTime = now.strftime('%y-%m-%d %H:%M:%S')\n", + "nowDateTime" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "53ad6ae9", + "metadata": {}, + "outputs": [ + { + "ename": "IntegrityError", + "evalue": "UNIQUE constraint failed: users.id", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mIntegrityError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[51]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# 데이터 삽입\u001b[39;00m\n\u001b[32m 2\u001b[39m sql = f\"INSERT INTO users(id, username, email, phone, website, regdate)\" \\\n\u001b[32m 3\u001b[39m \u001b[33m\"values(1, 'kk', 'kk@kk.kk', '010-0000-0000', 'https://www.kk.com', '{nowDateTime}')\"\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m cursor.execute(sql)\n", + "\u001b[31mIntegrityError\u001b[39m: UNIQUE constraint failed: users.id" + ] + } + ], + "source": [ + "# 데이터 삽입\n", + "sql = f\"INSERT INTO users(id, username, email, phone, website, regdate)\" \\\n", + "\"values(1, 'kk', 'kk@kk.kk', '010-0000-0000', 'https://www.kk.com', '{nowDateTime}')\"\n", + "cursor.execute(sql)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "5d92f698", + "metadata": {}, + "outputs": [ + { + "ename": "IntegrityError", + "evalue": "UNIQUE constraint failed: users.id", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mIntegrityError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[52]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# 데이터 삽입\u001b[39;00m\n\u001b[32m 2\u001b[39m sql = f\"INSERT INTO users(id, username, email, phone, website, regdate)\" \\\n\u001b[32m 3\u001b[39m \u001b[33m\"values(?,?,?,?,?,?)\"\u001b[39m\n\u001b[32m 4\u001b[39m \n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m cursor.execute(sql, (\u001b[32m2\u001b[39m, \u001b[33m'kk'\u001b[39m, \u001b[33m'kk@kk.com'\u001b[39m, \u001b[33m'010-0000-0000'\u001b[39m, \u001b[33m'kk@kk.com'\u001b[39m, nowDateTime))\n", + "\u001b[31mIntegrityError\u001b[39m: UNIQUE constraint failed: users.id" + ] + } + ], + "source": [ + "# 데이터 삽입\n", + "sql = f\"INSERT INTO users(id, username, email, phone, website, regdate)\" \\\n", + "\"values(?,?,?,?,?,?)\"\n", + "\n", + "cursor.execute(sql, (2, 'kk', 'kk@kk.com', '010-0000-0000', 'kk@kk.com', nowDateTime))" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "1277e5d2", + "metadata": {}, + "outputs": [ + { + "ename": "IntegrityError", + "evalue": "UNIQUE constraint failed: users.id", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mIntegrityError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[53]\u001b[39m\u001b[32m, line 7\u001b[39m\n\u001b[32m 3\u001b[39m (\u001b[32m4\u001b[39m, \u001b[33m'kk'\u001b[39m, \u001b[33m'kk@kk.com'\u001b[39m, \u001b[33m'010-0000-0000'\u001b[39m, \u001b[33m'kk@kk.com'\u001b[39m, nowDateTime),\n\u001b[32m 4\u001b[39m (\u001b[32m5\u001b[39m, \u001b[33m'kk'\u001b[39m, \u001b[33m'kk@kk.com'\u001b[39m, \u001b[33m'010-0000-0000'\u001b[39m, \u001b[33m'kk@kk.com'\u001b[39m, nowDateTime)\n\u001b[32m 5\u001b[39m )\n\u001b[32m 6\u001b[39m \n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m cursor.executemany(sql, userList)\n", + "\u001b[31mIntegrityError\u001b[39m: UNIQUE constraint failed: users.id" + ] + } + ], + "source": [ + "userList = (\n", + " (3, 'kk', 'kk@kk.com', '010-0000-0000', 'kk@kk.com', nowDateTime),\n", + " (4, 'kk', 'kk@kk.com', '010-0000-0000', 'kk@kk.com', nowDateTime),\n", + " (5, 'kk', 'kk@kk.com', '010-0000-0000', 'kk@kk.com', nowDateTime)\n", + ")\n", + "\n", + "cursor.executemany(sql, userList)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "636489de", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 'kk', 'kk@kk.kk', '010-0000-0000', 'https://www.kk.com', '{nowDateTime}')\n", + "(2, 'kk', 'kk@kk.com', '010-0000-0000', 'kk@kk.com', '26-05-13 17:47:38')\n", + "(3, 'kk', 'kk@kk.com', '010-0000-0000', 'kk@kk.com', '26-05-13 17:47:38')\n", + "(4, 'kk', 'kk@kk.com', '010-0000-0000', 'kk@kk.com', '26-05-13 17:47:38')\n", + "(5, 'kk', 'kk@kk.com', '010-0000-0000', 'kk@kk.com', '26-05-13 17:47:38')\n" + ] + } + ], + "source": [ + "sql = cursor.execute(\"SELECT * FROM users\")\n", + "\n", + "for c in cursor.fetchall():\n", + " print(c)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "b604b50e", + "metadata": {}, + "outputs": [], + "source": [ + "# [13]번 셀 끝에 추가하거나 [14]번 셀 시작 시 실행\n", + "# 만약 이전 연결에서 트랜잭션이 남아있다면 강제로 종료합니다.\n", + "conn.rollback() # 혹은 conn.commit()\n", + "\n", + "# [14]번 셀 수정\n", + "sql = \"UPDATE users SET regdate=? WHERE id=1\"\n", + "cursor.execute(sql, (nowDateTime,))\n", + "conn.commit() # 변경 사항을 확실히 반영하고 락을 해제합니다.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "714326dc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sql =\"DELETE FROM users WHERE id=3\"\n", + "cursor.execute(sql)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "6caa3191", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sql=\"DELETE FROM users\"\n", + "cursor.execute(sql)" + ] } ], "metadata": { @@ -1505,7 +5570,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.14.4" + "version": "3.14.5" } }, "nbformat": 4, diff --git a/pythonSource/Basic/hello.py b/pythonSource/Basic/hello.py index be2f9af..069c7f3 100644 --- a/pythonSource/Basic/hello.py +++ b/pythonSource/Basic/hello.py @@ -22,5 +22,18 @@ print(f) print(g) print(h) +# \n == enter multiline = "Life is too short\n you need python" -print(multiline) \ No newline at end of file +print(multiline) + +# import mod1 +# print(mod1.add(5,3)) +# print(mod1.sub(5,3)) + +# from mod1 import add +# print(add(3,7)) + +# * : 모두 +from mod1 import * +print(add(3,7)) +print(sub(3,7)) \ No newline at end of file diff --git a/pythonSource/Basic/info.txt b/pythonSource/Basic/info.txt new file mode 100644 index 0000000..d62a44c --- /dev/null +++ b/pythonSource/Basic/info.txt @@ -0,0 +1,500 @@ +파다 98 170 +차카 63 194 +바카 42 152 +마파 62 157 +라마 77 156 +카다 40 143 +가하 63 150 +라가 78 153 +라자 88 189 +파다 47 186 +파가 50 168 +바타 59 194 +파사 63 196 +하차 87 195 +마파 65 152 +하파 62 145 +차바 69 159 +카차 70 196 +바라 48 147 +자나 43 146 +파마 88 149 +나나 67 155 +파가 49 152 +자차 96 140 +사마 88 184 +사바 90 174 +나라 58 167 +파나 66 152 +타나 86 170 +아나 93 195 +사하 61 151 +타하 85 142 +카차 73 140 +카마 90 157 +가가 41 180 +나나 56 198 +다파 40 161 +나카 62 170 +차다 88 187 +가자 45 150 +사다 80 195 +카바 84 192 +하자 98 164 +카자 92 177 +다다 41 150 +아타 86 170 +마카 50 182 +마자 72 175 +사다 40 158 +사아 93 174 +가라 58 153 +자아 77 196 +아바 42 162 +마하 56 175 +파파 93 187 +파마 91 172 +카다 80 146 +가다 95 166 +카카 42 150 +라차 50 191 +아바 95 143 +바마 91 175 +파라 85 164 +마라 85 168 +자하 89 192 +사아 99 197 +사차 61 172 +차차 76 162 +다차 83 191 +나타 63 150 +하사 91 150 +카사 59 162 +카차 87 197 +카바 61 181 +바파 58 168 +카타 93 200 +나나 51 194 +다가 92 169 +가나 82 145 +카파 77 177 +타하 54 157 +차바 44 165 +사파 58 142 +나카 83 157 +다가 59 151 +마카 45 144 +마파 72 169 +라아 54 188 +타자 89 177 +나나 48 198 +파사 75 168 +바다 95 157 +아아 58 189 +아가 72 174 +카아 78 156 +타바 50 168 +다사 87 163 +라다 83 145 +다라 97 140 +마가 88 143 +라파 88 183 +하카 51 199 +자다 41 172 +자하 70 175 +마다 66 184 +바자 93 173 +가차 100 175 +마아 62 151 +파타 45 178 +타라 80 165 +마하 66 187 +라카 50 182 +카다 47 181 +가가 51 144 +파아 65 189 +타자 67 160 +카아 51 197 +가라 79 151 +자아 58 192 +하하 44 152 +카다 96 170 +마라 84 176 +마아 67 179 +차다 67 147 +아라 71 169 +타라 50 154 +바다 52 186 +하차 40 145 +파사 75 161 +가다 66 147 +라하 84 177 +라바 53 163 +다사 61 155 +아바 57 192 +마다 55 172 +타카 81 159 +타라 74 191 +다차 62 142 +가하 48 171 +차바 94 166 +나파 79 180 +차사 80 140 +하다 51 196 +바나 54 148 +마카 66 163 +하나 78 169 +마나 85 171 +자마 74 152 +파나 41 170 +라나 88 149 +다가 73 158 +자아 78 163 +아나 47 167 +바마 54 144 +사바 47 177 +자타 58 156 +차나 52 177 +타카 69 174 +사자 54 178 +바나 49 143 +라하 87 177 +마차 73 199 +아자 61 152 +마파 41 177 +카나 76 145 +마타 57 194 +차파 90 169 +가아 58 194 +나다 85 171 +다라 89 171 +다하 87 152 +차나 71 147 +바마 71 143 +자가 64 176 +파나 75 183 +라파 99 185 +라사 91 173 +바다 59 191 +자하 56 179 +아나 79 163 +마아 67 183 +자파 99 198 +마사 89 154 +사다 90 190 +타마 76 152 +바가 82 176 +마타 47 180 +마하 93 200 +아파 100 183 +카가 81 178 +바하 96 145 +하마 61 185 +바마 58 151 +카사 100 179 +마카 67 159 +마나 74 195 +하다 76 162 +타자 69 177 +바라 59 149 +자파 58 181 +바나 79 154 +차아 91 163 +카라 63 179 +가카 60 197 +가바 54 185 +바나 58 157 +차바 57 183 +가사 66 163 +바차 74 142 +다파 56 183 +아가 70 141 +가차 46 141 +라아 76 140 +하타 70 158 +다사 90 177 +아자 54 152 +라라 40 198 +하다 77 164 +가카 89 183 +자하 94 145 +가타 85 177 +나아 92 162 +다카 77 171 +다마 88 156 +바라 100 193 +하카 55 194 +자파 42 150 +아자 82 155 +마마 46 144 +카가 96 147 +마자 50 148 +아라 47 160 +카라 99 176 +타바 56 186 +타아 64 173 +바파 86 192 +마사 78 176 +아파 62 193 +카바 91 164 +마타 62 161 +마가 92 199 +라바 50 177 +라라 68 158 +차파 40 176 +타나 58 147 +라하 83 158 +자바 79 167 +하아 53 140 +다라 93 185 +다나 78 184 +아다 53 176 +타다 55 146 +하자 67 198 +사바 47 149 +타나 83 185 +타자 43 194 +파파 52 200 +차타 84 144 +가가 92 196 +라하 47 144 +차사 60 174 +마타 65 179 +다사 63 148 +카아 79 142 +라카 73 161 +카아 92 141 +라라 92 151 +차카 71 183 +하타 91 146 +사사 82 194 +바파 58 180 +카가 40 176 +차다 72 182 +파가 95 163 +파아 82 196 +타가 40 143 +나나 69 176 +카카 98 171 +차라 77 198 +파바 54 140 +다다 96 198 +바파 77 141 +자라 95 171 +파마 90 178 +다바 100 168 +타타 94 187 +타파 90 147 +차사 43 142 +자마 64 194 +가파 89 192 +파파 93 189 +라가 48 182 +마타 49 166 +다타 84 157 +타가 76 160 +하자 46 140 +자다 45 182 +카타 49 162 +카아 85 170 +바자 56 157 +아라 73 180 +라아 98 200 +아차 55 143 +파라 58 149 +파바 44 186 +하자 83 197 +아차 67 155 +카하 51 169 +하바 56 144 +타카 91 155 +차마 86 158 +차아 96 154 +다사 43 144 +파다 70 200 +나아 60 153 +하아 69 194 +하가 94 185 +마가 51 168 +가파 48 150 +바바 68 191 +나아 48 171 +파아 45 140 +바하 53 143 +바파 68 154 +차마 88 193 +다타 88 167 +차나 67 200 +라바 76 185 +바다 44 175 +가마 64 166 +바차 60 159 +카라 88 150 +자가 49 146 +자가 75 165 +가아 75 144 +자라 75 143 +마자 41 167 +가차 81 160 +다바 62 167 +카바 71 168 +타타 55 197 +카자 44 200 +아아 52 188 +사사 59 155 +가라 71 163 +바사 44 189 +가차 84 145 +나자 67 190 +차마 89 148 +하마 89 183 +다사 71 176 +차가 97 200 +다하 87 198 +아가 56 170 +사자 55 160 +카자 77 169 +파하 53 168 +아사 95 172 +차타 81 178 +사차 42 145 +나라 52 143 +자아 45 145 +다사 85 186 +아자 77 151 +타카 90 190 +아나 59 152 +라타 40 149 +하타 64 168 +하나 92 171 +자타 56 198 +사타 80 192 +라다 68 193 +타다 51 154 +차가 57 193 +라카 70 171 +파나 87 143 +하마 92 153 +마바 52 198 +카아 73 185 +아라 87 176 +다파 71 182 +라자 55 166 +마가 84 153 +자사 79 195 +나타 63 160 +라사 52 194 +타마 62 185 +하자 93 152 +바차 50 161 +사자 99 141 +바가 72 182 +바나 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