{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-12-20T20:35:52.706319Z","iopub.execute_input":"2022-12-20T20:35:52.706695Z","iopub.status.idle":"2022-12-20T20:35:52.717186Z","shell.execute_reply.started":"2022-12-20T20:35:52.706665Z","shell.execute_reply":"2022-12-20T20:35:52.715873Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sd=pd.DataFrame()","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:35:52.721814Z","iopub.execute_input":"2022-12-20T20:35:52.722090Z","iopub.status.idle":"2022-12-20T20:35:52.729134Z","shell.execute_reply.started":"2022-12-20T20:35:52.722065Z","shell.execute_reply":"2022-12-20T20:35:52.728106Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import json\nwith open('/kaggle/input/convolve-epoch1/train.json', 'r') as f:\n    data = json.load(f)","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:35:52.731058Z","iopub.execute_input":"2022-12-20T20:35:52.731759Z","iopub.status.idle":"2022-12-20T20:36:04.158056Z","shell.execute_reply.started":"2022-12-20T20:35:52.731591Z","shell.execute_reply":"2022-12-20T20:36:04.157009Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sd = pd.DataFrame({'count': data})","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:04.159452Z","iopub.execute_input":"2022-12-20T20:36:04.159896Z","iopub.status.idle":"2022-12-20T20:36:07.238827Z","shell.execute_reply.started":"2022-12-20T20:36:04.159856Z","shell.execute_reply":"2022-12-20T20:36:07.237856Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sd.head(100000000000)","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:07.241221Z","iopub.execute_input":"2022-12-20T20:36:07.241690Z","iopub.status.idle":"2022-12-20T20:36:07.263403Z","shell.execute_reply.started":"2022-12-20T20:36:07.241654Z","shell.execute_reply":"2022-12-20T20:36:07.262580Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sd['count'].nunique()","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:07.264922Z","iopub.execute_input":"2022-12-20T20:36:07.265284Z","iopub.status.idle":"2022-12-20T20:36:07.632930Z","shell.execute_reply.started":"2022-12-20T20:36:07.265250Z","shell.execute_reply":"2022-12-20T20:36:07.631847Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sd.isna().values.any()\nsd. rename( columns={1 :'new column name'}, inplace=True )","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:07.634291Z","iopub.execute_input":"2022-12-20T20:36:07.634770Z","iopub.status.idle":"2022-12-20T20:36:07.815725Z","shell.execute_reply.started":"2022-12-20T20:36:07.634713Z","shell.execute_reply":"2022-12-20T20:36:07.814587Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sd.head()","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:07.817546Z","iopub.execute_input":"2022-12-20T20:36:07.817988Z","iopub.status.idle":"2022-12-20T20:36:07.828095Z","shell.execute_reply.started":"2022-12-20T20:36:07.817930Z","shell.execute_reply":"2022-12-20T20:36:07.827062Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\nimport numpy as np\nimport torch\nimport glob\nimport torch.nn as nn\nfrom torchvision.transforms import transforms\nfrom torch.utils.data import DataLoader\nfrom torch.optim import Adam\nfrom torch.autograd import Variable\nfrom tqdm.notebook import tqdm\nimport torchvision\nimport pathlib\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport os.path\n\nfrom sklearn.model_selection import train_test_split\n\nimport tensorflow as tf\n\nfrom sklearn.metrics import r2_score\n","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:07.829808Z","iopub.execute_input":"2022-12-20T20:36:07.830397Z","iopub.status.idle":"2022-12-20T20:36:13.457317Z","shell.execute_reply.started":"2022-12-20T20:36:07.830364Z","shell.execute_reply":"2022-12-20T20:36:13.456340Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sd.head()\n","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:13.461999Z","iopub.execute_input":"2022-12-20T20:36:13.463117Z","iopub.status.idle":"2022-12-20T20:36:13.473634Z","shell.execute_reply.started":"2022-12-20T20:36:13.463077Z","shell.execute_reply":"2022-12-20T20:36:13.472586Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sd.shape","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:13.475185Z","iopub.execute_input":"2022-12-20T20:36:13.475610Z","iopub.status.idle":"2022-12-20T20:36:13.489403Z","shell.execute_reply.started":"2022-12-20T20:36:13.475576Z","shell.execute_reply":"2022-12-20T20:36:13.488452Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(sd['count'][1])","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:13.492328Z","iopub.execute_input":"2022-12-20T20:36:13.492615Z","iopub.status.idle":"2022-12-20T20:36:13.686182Z","shell.execute_reply.started":"2022-12-20T20:36:13.492590Z","shell.execute_reply":"2022-12-20T20:36:13.685139Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sd['index']=sd.index","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:13.687816Z","iopub.execute_input":"2022-12-20T20:36:13.688469Z","iopub.status.idle":"2022-12-20T20:36:13.786886Z","shell.execute_reply.started":"2022-12-20T20:36:13.688434Z","shell.execute_reply":"2022-12-20T20:36:13.785827Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sd['index'][2]","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:13.790683Z","iopub.execute_input":"2022-12-20T20:36:13.790999Z","iopub.status.idle":"2022-12-20T20:36:13.800862Z","shell.execute_reply.started":"2022-12-20T20:36:13.790963Z","shell.execute_reply":"2022-12-20T20:36:13.799995Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sd['c']=0\nfor j in sd.index:\n    sd['c'][j]=sd['index'][j].split('RAS',1)[1]","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:13.802413Z","iopub.execute_input":"2022-12-20T20:36:13.803015Z","iopub.status.idle":"2022-12-20T20:36:17.604899Z","shell.execute_reply.started":"2022-12-20T20:36:13.802982Z","shell.execute_reply":"2022-12-20T20:36:17.602236Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sd.head()","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:22.689896Z","iopub.execute_input":"2022-12-20T20:36:22.690483Z","iopub.status.idle":"2022-12-20T20:36:22.704705Z","shell.execute_reply.started":"2022-12-20T20:36:22.690441Z","shell.execute_reply":"2022-12-20T20:36:22.703678Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sd['c'].nunique()","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:24.939573Z","iopub.execute_input":"2022-12-20T20:36:24.939924Z","iopub.status.idle":"2022-12-20T20:36:25.098654Z","shell.execute_reply.started":"2022-12-20T20:36:24.939893Z","shell.execute_reply":"2022-12-20T20:36:25.095542Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import numpy as np \nimport pandas as pd \nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom nltk.corpus import stopwords \nfrom collections import Counter\nimport string\nimport re\nimport seaborn as sns\nfrom tqdm import tqdm\nimport matplotlib.pyplot as plt\nfrom torch.utils.data import TensorDataset, DataLoader\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder\nfrom transformers import BertTokenizer\nfrom transformers import BertModel","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:25.804573Z","iopub.execute_input":"2022-12-20T20:36:25.804918Z","iopub.status.idle":"2022-12-20T20:36:27.607697Z","shell.execute_reply.started":"2022-12-20T20:36:25.804889Z","shell.execute_reply":"2022-12-20T20:36:27.606533Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df=pd.DataFrame()\ndf['Label']=0\ndf['Text']=0\n\ndf['Label']=sd['count']\ndf['Text']=sd['c']\ndf.index=False","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:27.610688Z","iopub.execute_input":"2022-12-20T20:36:27.611333Z","iopub.status.idle":"2022-12-20T20:36:29.184794Z","shell.execute_reply.started":"2022-12-20T20:36:27.611290Z","shell.execute_reply":"2022-12-20T20:36:29.182962Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.groupby(['Label']).size().plot.bar()","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:29.185900Z","iopub.status.idle":"2022-12-20T20:36:29.186784Z","shell.execute_reply.started":"2022-12-20T20:36:29.186522Z","shell.execute_reply":"2022-12-20T20:36:29.186547Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n\ndf.dropna(inplace = True)","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:29.188651Z","iopub.status.idle":"2022-12-20T20:36:29.189134Z","shell.execute_reply.started":"2022-12-20T20:36:29.188876Z","shell.execute_reply":"2022-12-20T20:36:29.188899Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.head(100000000)","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:29.191002Z","iopub.status.idle":"2022-12-20T20:36:29.191472Z","shell.execute_reply.started":"2022-12-20T20:36:29.191231Z","shell.execute_reply":"2022-12-20T20:36:29.191253Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:29.479452Z","iopub.execute_input":"2022-12-20T20:36:29.480104Z","iopub.status.idle":"2022-12-20T20:36:29.799542Z","shell.execute_reply.started":"2022-12-20T20:36:29.480068Z","shell.execute_reply":"2022-12-20T20:36:29.798459Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df=df.reset_index()","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:30.049231Z","iopub.execute_input":"2022-12-20T20:36:30.049535Z","iopub.status.idle":"2022-12-20T20:36:30.542584Z","shell.execute_reply.started":"2022-12-20T20:36:30.049508Z","shell.execute_reply":"2022-12-20T20:36:30.541581Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.drop(['index'])","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:31.194394Z","iopub.execute_input":"2022-12-20T20:36:31.195276Z","iopub.status.idle":"2022-12-20T20:36:31.691021Z","shell.execute_reply.started":"2022-12-20T20:36:31.195229Z","shell.execute_reply":"2022-12-20T20:36:31.689478Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n\ndef clean_tweet(tweet):\n    if type(tweet) == np.float:\n        return \"\"\n    if type(tweet) == np.int:\n        return \"\"\n    temp = tweet.lower()\n    temp = re.sub(\"'\", \"\", temp) # to avoid removing contractions in english\n    temp = re.sub(\"@[A-Za-z0-9_]+\",\"\", temp)\n    temp = re.sub(\"#\",\"\", temp)\n   \n    \n    \n    temp = re.sub(\"[^a-z0-9]\",\" \", temp)\n    \n    return temp\ndf['Text'] = df['Text'].map(lambda x : clean_tweet(x))","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:31.709188Z","iopub.execute_input":"2022-12-20T20:36:31.709466Z","iopub.status.idle":"2022-12-20T20:36:48.516190Z","shell.execute_reply.started":"2022-12-20T20:36:31.709439Z","shell.execute_reply":"2022-12-20T20:36:48.515130Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tokenizer = BertTokenizer.from_pretrained('bert-base-cased')\nlabels = {'normal':0,\n          'abnormal':1,\n          \n          }","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:48.518387Z","iopub.execute_input":"2022-12-20T20:36:48.518765Z","iopub.status.idle":"2022-12-20T20:36:51.624886Z","shell.execute_reply.started":"2022-12-20T20:36:48.518729Z","shell.execute_reply":"2022-12-20T20:36:51.623865Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class Dataset(torch.utils.data.Dataset):\n\n    def __init__(self, df):\n\n        self.labels = [labels[label] for label in df['Label']]\n        self.texts = [tokenizer(text, \n                               padding='max_length', max_length = 512, truncation=True,\n                                return_tensors=\"pt\") for text in df['Text']]\n\n    def classes(self):\n        return self.labels\n\n    def __len__(self):\n        return len(self.labels)\n\n    def get_batch_labels(self, idx):\n        # Fetch a batch of labels\n        return np.array(self.labels[idx])\n\n    def get_batch_texts(self, idx):\n        # Fetch a batch of inputs\n        return self.texts[idx]\n\n    def __getitem__(self, idx):\n        \n        batch_texts = self.get_batch_texts(idx)\n        batch_y = self.get_batch_labels(idx)\n\n        return batch_texts, batch_y","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:51.626227Z","iopub.execute_input":"2022-12-20T20:36:51.626680Z","iopub.status.idle":"2022-12-20T20:36:51.634811Z","shell.execute_reply.started":"2022-12-20T20:36:51.626643Z","shell.execute_reply":"2022-12-20T20:36:51.633924Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train, df_val, df_test = np.split(df.sample(frac=1, random_state=42), \n                                     [int(.0059*len(df)), int(.019*len(df))])\n\nprint(len(df_train),len(df_val), len(df_test))","metadata":{"execution":{"iopub.status.busy":"2022-12-20T21:12:15.735642Z","iopub.execute_input":"2022-12-20T21:12:15.736073Z","iopub.status.idle":"2022-12-20T21:12:18.420973Z","shell.execute_reply.started":"2022-12-20T21:12:15.736038Z","shell.execute_reply":"2022-12-20T21:12:18.419700Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class BertClassifier(nn.Module):\n\n    def __init__(self, dropout=0.5):\n\n        super(BertClassifier, self).__init__()\n\n        self.bert = BertModel.from_pretrained('bert-base-cased')\n        self.dropout = nn.Dropout(dropout)\n        self.linear = nn.Linear(768, 2)\n        self.relu = nn.ReLU()\n\n    def forward(self, input_id, mask):\n\n        _, out = self.bert(input_ids= input_id, attention_mask=mask,return_dict=False)\n        out = self.dropout(out)\n        out = self.linear(out)\n        out = self.relu(out)\n\n        return out","metadata":{"execution":{"iopub.status.busy":"2022-12-20T21:12:24.775158Z","iopub.execute_input":"2022-12-20T21:12:24.775500Z","iopub.status.idle":"2022-12-20T21:12:24.782768Z","shell.execute_reply.started":"2022-12-20T21:12:24.775471Z","shell.execute_reply":"2022-12-20T21:12:24.781629Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from torch.optim import Adam\nfrom tqdm import tqdm\ndevice=torch.device('cuda' if torch.cuda.is_available() else 'cpu')\ndef train(model, train_data, val_data, learning_rate, epochs):\n\n    train, val = Dataset(train_data), Dataset(val_data)\n\n    train_dataloader = torch.utils.data.DataLoader(train, batch_size=1, shuffle=True)\n    val_dataloader = torch.utils.data.DataLoader(val, batch_size=1)\n\n    \n    use_cuda = torch.cuda.is_available()\n    device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n    criterion = nn.CrossEntropyLoss()\n    optimizer = Adam(model.parameters(), lr= learning_rate)\n\n    if use_cuda:\n\n            model = model.cuda()\n            criterion = criterion.cuda()\n\n    for epoch_num in range(epochs):\n\n            total_acc_train = 0\n            total_loss_train = 0\n\n            for train_input, train_label in tqdm(train_dataloader):\n\n                train_label = train_label.to(device)\n                mask = train_input['attention_mask'].to(device)\n                input_id = train_input['input_ids'].squeeze(1).to(device)\n\n                output = model(input_id, mask)\n                batch_loss = criterion(output, train_label.long())\n                total_loss_train += batch_loss.item()\n                \n                acc = (output.argmax(dim=1) == train_label).sum().item()\n                total_acc_train += acc\n\n                model.zero_grad()\n                batch_loss.backward()\n                optimizer.step()\n            \n            total_acc_val = 0\n            total_loss_val = 0\n\n            with torch.no_grad():\n\n                for val_input, val_label in val_dataloader:\n\n                    val_label = val_label.to(device)\n                    mask = val_input['attention_mask'].to(device)\n                    input_id = val_input['input_ids'].squeeze(1).to(device)\n\n                    output = model(input_id, mask)\n\n                    batch_loss = criterion(output, val_label.long())\n                    total_loss_val += batch_loss.item()\n                    acc = (output.argmax(dim=1) == val_label).sum().item()\n                    total_acc_val += acc\n            \n            print(\n                f'Epochs: {epoch_num + 1} | Train Loss: {total_loss_train / len(train_data): .3f} \\\n                | Train Accuracy: {total_acc_train / len(train_data): .3f} \\\n                | Val Loss: {total_loss_val / len(val_data): .3f} \\\n                | Val Accuracy: {total_acc_val / len(val_data): .3f}')\n                    ","metadata":{"execution":{"iopub.status.busy":"2022-12-20T21:12:25.622686Z","iopub.execute_input":"2022-12-20T21:12:25.623377Z","iopub.status.idle":"2022-12-20T21:12:25.638378Z","shell.execute_reply.started":"2022-12-20T21:12:25.623343Z","shell.execute_reply":"2022-12-20T21:12:25.637466Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(device)","metadata":{"execution":{"iopub.status.busy":"2022-12-20T21:12:26.369395Z","iopub.execute_input":"2022-12-20T21:12:26.369743Z","iopub.status.idle":"2022-12-20T21:12:26.375338Z","shell.execute_reply.started":"2022-12-20T21:12:26.369714Z","shell.execute_reply":"2022-12-20T21:12:26.374245Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"EPOCHS = 2\nmodel = BertClassifier()\nLR = 1e-3\ntrain(model, df_train, df_val, LR, EPOCHS)","metadata":{"execution":{"iopub.status.busy":"2022-12-20T21:12:27.134419Z","iopub.execute_input":"2022-12-20T21:12:27.134771Z","iopub.status.idle":"2022-12-20T22:57:25.277026Z","shell.execute_reply.started":"2022-12-20T21:12:27.134738Z","shell.execute_reply":"2022-12-20T22:57:25.275984Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def evaluate(model, test_data):\n\n    test = Dataset(test_data)\n\n    test_dataloader = torch.utils.data.DataLoader(test, batch_size=2)\n\n    use_cuda = torch.cuda.is_available()\n    device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n\n    if use_cuda:\n\n        model = model.cuda()\n\n    total_acc_test = 0\n    with torch.no_grad():\n\n        for test_input, test_label in test_dataloader:\n\n              test_label = test_label.to(device)\n              mask = test_input['attention_mask'].to(device)\n              input_id = test_input['input_ids'].squeeze(1).to(device)\n\n              output = model(input_id, mask)\n\n              acc = (output.argmax(dim=1) == test_label).sum().item()\n              total_acc_test += acc\n    \n    print(f'Test Accuracy: {total_acc_test / len(test_data): .3f}')\n    \nevaluate(model, df_test)","metadata":{"execution":{"iopub.status.busy":"2022-12-20T22:57:25.279431Z","iopub.execute_input":"2022-12-20T22:57:25.279786Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.index=False","metadata":{"execution":{"iopub.status.busy":"2022-12-20T20:36:17.650563Z","iopub.status.idle":"2022-12-20T20:36:17.652288Z","shell.execute_reply.started":"2022-12-20T20:36:17.652036Z","shell.execute_reply":"2022-12-20T20:36:17.652061Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}