{"cells":[{"metadata":{},"cell_type":"markdown","source":"# LANL Earthquake Prediction\n    \n    1. Import libs and dataset\n    2. Prepare the data\n    3. Analysis of Data\n    4. Feature Engineering\n    5. Make a Neural Network model\n    6. Fill submission file\n    \n# 1. Import Libs and dataset"},{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport seaborn as sns\nfrom matplotlib import pyplot as plt\nfrom tqdm import tqdm\nimport os","execution_count":1,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_path='../input/test/'\n# Files in test folder\nlen(os.listdir(test_path))","execution_count":2,"outputs":[{"output_type":"execute_result","execution_count":2,"data":{"text/plain":"2624"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Load the training set\ntrain_path='../input/train.csv'\ntrain=pd.read_csv(train_path,dtype={'acoustic_data':np.int16,'time_to_failure':np.float32})","execution_count":3,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.head(10)","execution_count":4,"outputs":[{"output_type":"execute_result","execution_count":4,"data":{"text/plain":"   acoustic_data  time_to_failure\n0             12           1.4691\n1              6           1.4691\n2              8           1.4691\n3              5           1.4691\n4              8           1.4691\n5              8           1.4691\n6              9           1.4691\n7              7           1.4691\n8             -5           1.4691\n9              3           1.4691","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>acoustic_data</th>\n      <th>time_to_failure</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>12</td>\n      <td>1.4691</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>6</td>\n      <td>1.4691</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8</td>\n      <td>1.4691</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>5</td>\n      <td>1.4691</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>8</td>\n      <td>1.4691</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>8</td>\n      <td>1.4691</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>9</td>\n      <td>1.4691</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>7</td>\n      <td>1.4691</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>-5</td>\n      <td>1.4691</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>3</td>\n      <td>1.4691</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"# Explore the data"},{"metadata":{"trusted":true},"cell_type":"code","source":"ad_sample=train.acoustic_data.values[::100]\nttf_sample=train.time_to_failure.values[::100]","execution_count":5,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Visualize Acoustic- Time to Failure data"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig,ax1=plt.subplots(figsize=(12,8))\nplt.title(\"Acoustic data and time to failure\")\nplt.plot(ad_sample,color='green')\nplt.ylabel('Acoustic data',color='green')\nplt.legend(['acoustic data'],loc=(0.01,0.95))\nax2=ax1.twinx()\nplt.plot(ttf_sample,color='blue')\nplt.ylabel('Time to Failure',color='blue')\nplt.legend(['time to failure'],loc=(0.01,0.9))\nplt.grid(True)\n\ndel ad_sample\ndel ttf_sample","execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x576 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"ad_sample=train.acoustic_data.values[:6000000]\nttf_sample=train.time_to_failure.values[:6000000]\nfig,ax1=plt.subplots(figsize=(12,8))\nplt.title(\"Acoustic data and time to failure\")\nplt.plot(ad_sample,color='green')\nplt.ylabel('Acoustic data',color='green')\nplt.legend(['acoustic data'],loc=(0.01,0.95))\nax2=ax1.twinx()\nplt.plot(ttf_sample,color='blue')\nplt.ylabel('Time to Failure',color='blue')\nplt.legend(['time to failure'],loc=(0.01,0.9))\nplt.grid(True)\n\ndel ad_sample\ndel ttf_sample","execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x576 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"rows = 150000\nsegments = int(np.floor(train.shape[0] / rows))\n\nX_train = pd.DataFrame(index = range(segments),dtype = np.float32,columns = ['mean','std','99quat','50quat','25quat','1quat'])\ny_train = pd.DataFrame(index = range(segments),dtype = np.float32,columns = ['time_to_failure'])","execution_count":8,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for segment in tqdm(range(segments)):\n    x = train.iloc[segment*rows:segment*rows+rows]\n    y = x['time_to_failure'].values[-1]\n    x = x['acoustic_data'].values\n    X_train.loc[segment,'mean'] = np.mean(x)\n    X_train.loc[segment,'std']  = np.std(x)\n    X_train.loc[segment,'99quat'] = np.quantile(x,0.99)\n    X_train.loc[segment,'50quat'] = np.quantile(x,0.5)\n    X_train.loc[segment,'25quat'] = np.quantile(x,0.25)\n    X_train.loc[segment,'1quat'] =  np.quantile(x,0.01)\n    y_train.loc[segment,'time_to_failure'] = y\n    ","execution_count":9,"outputs":[{"output_type":"stream","text":"100%|██████████| 4194/4194 [00:33<00:00, 124.09it/s]\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.layers import Dense\nfrom keras.models import Sequential \nfrom sklearn.preprocessing import StandardScaler\nimport gc\ngc.collect()","execution_count":10,"outputs":[{"output_type":"stream","text":"Using TensorFlow backend.\n","name":"stderr"},{"output_type":"execute_result","execution_count":10,"data":{"text/plain":"0"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Sequential()\nmodel.add(Dense(32,input_shape = (6,),activation = 'relu'))\nmodel.add(Dense(32,activation = 'relu'))\nmodel.add(Dense(32,activation = 'relu'))\nmodel.add(Dense(1))\nmodel.compile(loss = 'mae',optimizer = 'adam',metrics=['accuracy'])","execution_count":11,"outputs":[{"output_type":"stream","text":"WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\nInstructions for updating:\nColocations handled automatically by placer.\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"scaler = StandardScaler()\nX_scaler = scaler.fit_transform(X_train)\ny_train=y_train.values.flatten()","execution_count":12,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.6/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype float32 were all converted to float64 by StandardScaler.\n  return self.partial_fit(X, y)\n/opt/conda/lib/python3.6/site-packages/sklearn/base.py:464: DataConversionWarning: Data with input dtype float32 were all converted to float64 by StandardScaler.\n  return self.fit(X, **fit_params).transform(X)\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"history=model.fit(X_scaler,y_train,epochs = 100)","execution_count":13,"outputs":[{"output_type":"stream","text":"WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\nInstructions for updating:\nUse tf.cast instead.\nEpoch 1/100\n4194/4194 [==============================] - 2s 441us/step - loss: 3.8238 - acc: 0.0000e+00\nEpoch 2/100\n4194/4194 [==============================] - 1s 127us/step - loss: 2.2719 - acc: 0.0000e+00\nEpoch 3/100\n4194/4194 [==============================] - 1s 130us/step - loss: 2.2025 - acc: 0.0000e+00\nEpoch 4/100\n4194/4194 [==============================] - 1s 127us/step - loss: 2.1901 - acc: 0.0000e+00\nEpoch 5/100\n4194/4194 [==============================] - 1s 130us/step - loss: 2.1834 - acc: 0.0000e+00\nEpoch 6/100\n4194/4194 [==============================] - 1s 129us/step - loss: 2.1794 - acc: 0.0000e+00\nEpoch 7/100\n4194/4194 [==============================] - 1s 126us/step - loss: 2.1745 - acc: 0.0000e+00\nEpoch 8/100\n4194/4194 [==============================] - 1s 129us/step - loss: 2.1749 - acc: 0.0000e+00\nEpoch 9/100\n4194/4194 [==============================] - 1s 126us/step - loss: 2.1700 - acc: 0.0000e+00\nEpoch 10/100\n4194/4194 [==============================] - 1s 131us/step - loss: 2.1665 - acc: 0.0000e+00\nEpoch 11/100\n4194/4194 [==============================] - 1s 124us/step - loss: 2.1670 - acc: 0.0000e+00\nEpoch 12/100\n4194/4194 [==============================] - 1s 128us/step - loss: 2.1696 - acc: 0.0000e+00\nEpoch 13/100\n4194/4194 [==============================] - 1s 126us/step - loss: 2.1647 - acc: 0.0000e+00\nEpoch 14/100\n4194/4194 [==============================] - 1s 128us/step - loss: 2.1642 - acc: 0.0000e+00\nEpoch 15/100\n4194/4194 [==============================] - 1s 125us/step - loss: 2.1657 - acc: 0.0000e+00\nEpoch 16/100\n4194/4194 [==============================] - 1s 126us/step - loss: 2.1627 - acc: 0.0000e+00\nEpoch 17/100\n4194/4194 [==============================] - 1s 127us/step - loss: 2.1588 - acc: 0.0000e+00\nEpoch 18/100\n4194/4194 [==============================] - 1s 126us/step - loss: 2.1642 - acc: 0.0000e+00\nEpoch 19/100\n4194/4194 [==============================] - 1s 126us/step - loss: 2.1591 - acc: 0.0000e+00\nEpoch 20/100\n4194/4194 [==============================] - 1s 124us/step - loss: 2.1664 - acc: 0.0000e+00\nEpoch 21/100\n4194/4194 [==============================] - 1s 129us/step - loss: 2.1607 - acc: 0.0000e+00\nEpoch 22/100\n4194/4194 [==============================] - 1s 126us/step - loss: 2.1646 - acc: 0.0000e+00\nEpoch 23/100\n4194/4194 [==============================] - 1s 129us/step - loss: 2.1643 - acc: 0.0000e+00\nEpoch 24/100\n4194/4194 [==============================] - 1s 126us/step - loss: 2.1605 - acc: 0.0000e+00\nEpoch 25/100\n4194/4194 [==============================] - 1s 128us/step - loss: 2.1561 - acc: 0.0000e+00\nEpoch 26/100\n4194/4194 [==============================] - 1s 125us/step - loss: 2.1597 - acc: 0.0000e+00\nEpoch 27/100\n4194/4194 [==============================] - 1s 129us/step - loss: 2.1714 - acc: 0.0000e+00\nEpoch 28/100\n4194/4194 [==============================] - 1s 124us/step - loss: 2.1538 - acc: 0.0000e+00\nEpoch 29/100\n4194/4194 [==============================] - 1s 131us/step - loss: 2.1515 - acc: 0.0000e+00\nEpoch 30/100\n4194/4194 [==============================] - 1s 127us/step - loss: 2.1546 - acc: 0.0000e+00\nEpoch 31/100\n4194/4194 [==============================] - 1s 128us/step - loss: 2.1531 - acc: 0.0000e+00\nEpoch 32/100\n4194/4194 [==============================] - 1s 127us/step - loss: 2.1559 - acc: 0.0000e+00\nEpoch 33/100\n4194/4194 [==============================] - 1s 128us/step - loss: 2.1576 - acc: 0.0000e+00\nEpoch 34/100\n4194/4194 [==============================] - 1s 128us/step - loss: 2.1557 - acc: 0.0000e+00\nEpoch 35/100\n4194/4194 [==============================] - 1s 123us/step - loss: 2.1468 - acc: 0.0000e+00\nEpoch 36/100\n4194/4194 [==============================] - 1s 128us/step - loss: 2.1568 - acc: 0.0000e+00\nEpoch 37/100\n4194/4194 [==============================] - 1s 127us/step - loss: 2.1590 - acc: 0.0000e+00\nEpoch 38/100\n4194/4194 [==============================] - 1s 127us/step - loss: 2.1522 - acc: 0.0000e+00\nEpoch 39/100\n4194/4194 [==============================] - 1s 125us/step - loss: 2.1553 - acc: 0.0000e+00\nEpoch 40/100\n4194/4194 [==============================] - 1s 129us/step - loss: 2.1464 - acc: 0.0000e+00\nEpoch 41/100\n4194/4194 [==============================] - 1s 126us/step - loss: 2.1534 - acc: 0.0000e+00\nEpoch 42/100\n4194/4194 [==============================] - 1s 127us/step - loss: 2.1460 - acc: 0.0000e+00\nEpoch 43/100\n4194/4194 [==============================] - 1s 128us/step - loss: 2.1570 - acc: 0.0000e+00\nEpoch 44/100\n4194/4194 [==============================] - 1s 128us/step - loss: 2.1461 - acc: 0.0000e+00\nEpoch 45/100\n4194/4194 [==============================] - 1s 123us/step - loss: 2.1505 - acc: 0.0000e+00\nEpoch 46/100\n4194/4194 [==============================] - 1s 130us/step - loss: 2.1511 - acc: 0.0000e+00\nEpoch 47/100\n4194/4194 [==============================] - 1s 128us/step - loss: 2.1605 - acc: 0.0000e+00\nEpoch 48/100\n4194/4194 [==============================] - 1s 128us/step - loss: 2.1460 - acc: 0.0000e+00\nEpoch 49/100\n4194/4194 [==============================] - 1s 128us/step - loss: 2.1478 - acc: 0.0000e+00\nEpoch 50/100\n4194/4194 [==============================] - 1s 126us/step - loss: 2.1429 - acc: 0.0000e+00\nEpoch 51/100\n4194/4194 [==============================] - 1s 128us/step - loss: 2.1479 - acc: 0.0000e+00\nEpoch 52/100\n4194/4194 [==============================] - 1s 128us/step - loss: 2.1442 - acc: 0.0000e+00\nEpoch 53/100\n4194/4194 [==============================] - 1s 126us/step - loss: 2.1414 - acc: 0.0000e+00\nEpoch 54/100\n4194/4194 [==============================] - 1s 126us/step - loss: 2.1462 - acc: 0.0000e+00\nEpoch 55/100\n4194/4194 [==============================] - 1s 132us/step - loss: 2.1448 - acc: 0.0000e+00\nEpoch 56/100\n4194/4194 [==============================] - 1s 125us/step - loss: 2.1396 - acc: 0.0000e+00\nEpoch 57/100\n4194/4194 [==============================] - 1s 126us/step - loss: 2.1449 - acc: 0.0000e+00\nEpoch 58/100\n4194/4194 [==============================] - 1s 123us/step - loss: 2.1444 - acc: 0.0000e+00\nEpoch 59/100\n4194/4194 [==============================] - 1s 127us/step - loss: 2.1389 - acc: 0.0000e+00\nEpoch 60/100\n4194/4194 [==============================] - 1s 125us/step - loss: 2.1455 - acc: 0.0000e+00\nEpoch 61/100\n4194/4194 [==============================] - 1s 129us/step - loss: 2.1374 - acc: 0.0000e+00\nEpoch 62/100\n4194/4194 [==============================] - 1s 128us/step - loss: 2.1449 - acc: 0.0000e+00\nEpoch 63/100\n4194/4194 [==============================] - 1s 128us/step - loss: 2.1471 - acc: 0.0000e+00\nEpoch 64/100\n4194/4194 [==============================] - 1s 127us/step - loss: 2.1468 - acc: 0.0000e+00\nEpoch 65/100\n4194/4194 [==============================] - 1s 128us/step - loss: 2.1432 - acc: 0.0000e+00\nEpoch 66/100\n4194/4194 [==============================] - 1s 130us/step - loss: 2.1384 - acc: 0.0000e+00\nEpoch 67/100\n4194/4194 [==============================] - 1s 125us/step - loss: 2.1417 - acc: 0.0000e+00\nEpoch 68/100\n4194/4194 [==============================] - 1s 129us/step - loss: 2.1408 - acc: 0.0000e+00\nEpoch 69/100\n4194/4194 [==============================] - 1s 125us/step - loss: 2.1395 - acc: 0.0000e+00\nEpoch 70/100\n4194/4194 [==============================] - 1s 132us/step - loss: 2.1367 - acc: 0.0000e+00\nEpoch 71/100\n4194/4194 [==============================] - 1s 126us/step - loss: 2.1361 - acc: 0.0000e+00\nEpoch 72/100\n4194/4194 [==============================] - 1s 129us/step - loss: 2.1358 - acc: 0.0000e+00\nEpoch 73/100\n4194/4194 [==============================] - 1s 127us/step - loss: 2.1359 - acc: 0.0000e+00\nEpoch 74/100\n4194/4194 [==============================] - 1s 128us/step - loss: 2.1412 - acc: 0.0000e+00\nEpoch 75/100\n4194/4194 [==============================] - 1s 126us/step - loss: 2.1390 - acc: 0.0000e+00\nEpoch 76/100\n","name":"stdout"},{"output_type":"stream","text":"4194/4194 [==============================] - 1s 125us/step - loss: 2.1358 - acc: 0.0000e+00\nEpoch 77/100\n4194/4194 [==============================] - 1s 124us/step - loss: 2.1355 - acc: 0.0000e+00\nEpoch 78/100\n4194/4194 [==============================] - 1s 127us/step - loss: 2.1360 - acc: 0.0000e+00\nEpoch 79/100\n4194/4194 [==============================] - 1s 127us/step - loss: 2.1340 - acc: 0.0000e+00\nEpoch 80/100\n4194/4194 [==============================] - 1s 125us/step - loss: 2.1335 - acc: 0.0000e+00\nEpoch 81/100\n4194/4194 [==============================] - 1s 126us/step - loss: 2.1383 - acc: 0.0000e+00\nEpoch 82/100\n4194/4194 [==============================] - 1s 127us/step - loss: 2.1384 - acc: 0.0000e+00\nEpoch 83/100\n4194/4194 [==============================] - 1s 132us/step - loss: 2.1342 - acc: 0.0000e+00\nEpoch 84/100\n4194/4194 [==============================] - 1s 126us/step - loss: 2.1360 - acc: 0.0000e+00\nEpoch 85/100\n4194/4194 [==============================] - 1s 131us/step - loss: 2.1366 - acc: 0.0000e+00\nEpoch 86/100\n4194/4194 [==============================] - 1s 135us/step - loss: 2.1372 - acc: 0.0000e+00\nEpoch 87/100\n4194/4194 [==============================] - 1s 129us/step - loss: 2.1369 - acc: 0.0000e+00\nEpoch 88/100\n4194/4194 [==============================] - 1s 127us/step - loss: 2.1342 - acc: 0.0000e+00\nEpoch 89/100\n4194/4194 [==============================] - 1s 128us/step - loss: 2.1401 - acc: 0.0000e+00\nEpoch 90/100\n4194/4194 [==============================] - 1s 128us/step - loss: 2.1329 - acc: 0.0000e+00\nEpoch 91/100\n4194/4194 [==============================] - 1s 127us/step - loss: 2.1352 - acc: 0.0000e+00\nEpoch 92/100\n4194/4194 [==============================] - 1s 128us/step - loss: 2.1297 - acc: 0.0000e+00\nEpoch 93/100\n4194/4194 [==============================] - 1s 127us/step - loss: 2.1319 - acc: 0.0000e+00\nEpoch 94/100\n4194/4194 [==============================] - 1s 128us/step - loss: 2.1311 - acc: 0.0000e+00\nEpoch 95/100\n4194/4194 [==============================] - 1s 123us/step - loss: 2.1324 - acc: 0.0000e+00\nEpoch 96/100\n4194/4194 [==============================] - 1s 126us/step - loss: 2.1302 - acc: 0.0000e+00\nEpoch 97/100\n4194/4194 [==============================] - 1s 124us/step - loss: 2.1293 - acc: 0.0000e+00\nEpoch 98/100\n4194/4194 [==============================] - 1s 127us/step - loss: 2.1297 - acc: 0.0000e+00\nEpoch 99/100\n4194/4194 [==============================] - 1s 124us/step - loss: 2.1317 - acc: 0.0000e+00\nEpoch 100/100\n4194/4194 [==============================] - 1s 128us/step - loss: 2.1306 - acc: 0.0000e+00\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.plot(history.history['loss'])","execution_count":14,"outputs":[{"output_type":"execute_result","execution_count":14,"data":{"text/plain":"[<matplotlib.lines.Line2D at 0x7f62006657b8>]"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"sub_data = pd.read_csv('../input/sample_submission.csv',index_col = 'seg_id')\nX_test = pd.DataFrame(columns = X_train.columns,dtype = np.float32,index = sub_data.index)\n\nfor seq in tqdm(X_test.index):\n    test_data = pd.read_csv('../input/test/'+seq+'.csv')\n    x = test_data['acoustic_data'].values\n    X_test.loc[seq,'mean'] = np.mean(x)\n    X_test.loc[seq,'std']  = np.std(x)\n    X_test.loc[seq,'99quat'] = np.quantile(x,0.99)\n    X_test.loc[seq,'50quat'] = np.quantile(x,0.5)\n    X_test.loc[seq,'25quat'] = np.quantile(x,0.25)\n    X_test.loc[seq,'1quat'] =  np.quantile(x,0.01)\n    ","execution_count":15,"outputs":[{"output_type":"stream","text":"100%|██████████| 2624/2624 [00:59<00:00, 45.14it/s]\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_test_scaler = scaler.transform(X_test)\npred = model.predict(X_test_scaler)","execution_count":16,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:1: DataConversionWarning: Data with input dtype float32 were all converted to float64 by StandardScaler.\n  \"\"\"Entry point for launching an IPython kernel.\n","name":"stderr"},{"output_type":"execute_result","execution_count":16,"data":{"text/plain":"            time_to_failure\nseg_id                     \nseg_00030f                0\nseg_0012b5                0\nseg_00184e                0\nseg_003339                0\nseg_0042cc                0","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>time_to_failure</th>\n    </tr>\n    <tr>\n      <th>seg_id</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>seg_00030f</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>seg_0012b5</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>seg_00184e</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>seg_003339</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>seg_0042cc</th>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"sub_data.to_csv('sub_earthquake.csv',index = False)","execution_count":17,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.2"}},"nbformat":4,"nbformat_minor":1}