{"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":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:07:37.907427Z","iopub.execute_input":"2022-01-10T12:07:37.908664Z","iopub.status.idle":"2022-01-10T12:07:37.932881Z","shell.execute_reply.started":"2022-01-10T12:07:37.908521Z","shell.execute_reply":"2022-01-10T12:07:37.931759Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"DATASET_PATH  = '../input/ashrae-energy-prediction/'","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:07:40.679395Z","iopub.execute_input":"2022-01-10T12:07:40.679713Z","iopub.status.idle":"2022-01-10T12:07:40.684236Z","shell.execute_reply.started":"2022-01-10T12:07:40.679682Z","shell.execute_reply":"2022-01-10T12:07:40.683390Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data = pd.read_csv(DATASET_PATH+'train.csv')\n","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:07:43.149054Z","iopub.execute_input":"2022-01-10T12:07:43.149358Z","iopub.status.idle":"2022-01-10T12:07:58.480205Z","shell.execute_reply.started":"2022-01-10T12:07:43.149329Z","shell.execute_reply":"2022-01-10T12:07:58.479295Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"buildingData = pd.read_csv(DATASET_PATH+'building_metadata.csv')","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:08:42.170386Z","iopub.execute_input":"2022-01-10T12:08:42.171209Z","iopub.status.idle":"2022-01-10T12:08:42.188276Z","shell.execute_reply.started":"2022-01-10T12:08:42.171152Z","shell.execute_reply":"2022-01-10T12:08:42.187492Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"buildingData.isnull().sum()","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Merging building data with training data","metadata":{}},{"cell_type":"code","source":"data = data.merge(buildingData,on = 'building_id',how = 'left')","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:08:46.011300Z","iopub.execute_input":"2022-01-10T12:08:46.012375Z","iopub.status.idle":"2022-01-10T12:08:50.236731Z","shell.execute_reply.started":"2022-01-10T12:08:46.012328Z","shell.execute_reply":"2022-01-10T12:08:50.235520Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Removing features having a lot of nulls**","metadata":{}},{"cell_type":"code","source":"data = data.drop(['year_built','floor_count'],axis = 1)","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:09:09.840575Z","iopub.execute_input":"2022-01-10T12:09:09.840910Z","iopub.status.idle":"2022-01-10T12:09:12.583620Z","shell.execute_reply.started":"2022-01-10T12:09:09.840872Z","shell.execute_reply":"2022-01-10T12:09:12.582848Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#DATA_PATH = \"D:Study/Machine Learning/ML Project/\"\nweather_train = pd.read_csv(DATASET_PATH + 'weather_train.csv')\nweather_test  = pd.read_csv(DATASET_PATH + 'weather_test.csv')","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:09:14.720101Z","iopub.execute_input":"2022-01-10T12:09:14.720683Z","iopub.status.idle":"2022-01-10T12:09:15.425718Z","shell.execute_reply.started":"2022-01-10T12:09:14.720647Z","shell.execute_reply":"2022-01-10T12:09:15.424626Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Concatenating weather train and test data","metadata":{}},{"cell_type":"code","source":"import datetime as dt\nframes = [weather_train,weather_test]\nweather_data = pd.concat(frames)\ndel weather_train\ndel weather_test","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:09:19.508200Z","iopub.execute_input":"2022-01-10T12:09:19.508497Z","iopub.status.idle":"2022-01-10T12:09:19.530767Z","shell.execute_reply.started":"2022-01-10T12:09:19.508468Z","shell.execute_reply":"2022-01-10T12:09:19.529802Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Merging data with weather data","metadata":{}},{"cell_type":"code","source":"data = data.merge(weather_data, on=['site_id', 'timestamp'], how='left')\n","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:09:22.494714Z","iopub.execute_input":"2022-01-10T12:09:22.495547Z","iopub.status.idle":"2022-01-10T12:09:28.847402Z","shell.execute_reply.started":"2022-01-10T12:09:22.495505Z","shell.execute_reply":"2022-01-10T12:09:28.846664Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Converting timestamp feature to datetime to extract another features like day,month,hour,and year","metadata":{}},{"cell_type":"code","source":"data[\"timestamp\"] = pd.to_datetime(data[\"timestamp\"])\ndata[\"day\"]       = data[\"timestamp\"].dt.day\ndata[\"hour\"]      = data[\"timestamp\"].dt.hour\ndata[\"week\"]      = data[\"timestamp\"].dt.weekday\ndata[\"month\"]     = data[\"timestamp\"].dt.month\ndata[\"year\"]      = data[\"timestamp\"].dt.year\n#data              = data.drop(\"timestamp\", axis = 1)\n#del weather_data","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:09:47.769031Z","iopub.execute_input":"2022-01-10T12:09:47.769505Z","iopub.status.idle":"2022-01-10T12:10:01.032496Z","shell.execute_reply.started":"2022-01-10T12:09:47.769472Z","shell.execute_reply":"2022-01-10T12:10:01.031586Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Deleting useless variables to reduce memory usage","metadata":{}},{"cell_type":"code","source":"del frames","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:10:11.659086Z","iopub.execute_input":"2022-01-10T12:10:11.659418Z","iopub.status.idle":"2022-01-10T12:10:11.668217Z","shell.execute_reply.started":"2022-01-10T12:10:11.659385Z","shell.execute_reply":"2022-01-10T12:10:11.667280Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2022-01-10T11:41:21.530046Z","iopub.execute_input":"2022-01-10T11:41:21.530659Z","iopub.status.idle":"2022-01-10T11:41:24.360864Z","shell.execute_reply.started":"2022-01-10T11:41:21.530609Z","shell.execute_reply":"2022-01-10T11:41:24.359940Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Removing unimportant features","metadata":{}},{"cell_type":"code","source":"data = data.drop(['cloud_coverage','wind_direction','sea_level_pressure'],axis=1)\ndata","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:10:22.240641Z","iopub.execute_input":"2022-01-10T12:10:22.241351Z","iopub.status.idle":"2022-01-10T12:10:25.494879Z","shell.execute_reply.started":"2022-01-10T12:10:22.241315Z","shell.execute_reply":"2022-01-10T12:10:25.493975Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Filling nulls","metadata":{}},{"cell_type":"code","source":"data['air_temperature'] = data['air_temperature'].fillna(data['air_temperature'].mean())\ndata['wind_speed'] = data['wind_speed'].fillna(data['wind_speed'].mean())\ndata['dew_temperature'] = data['dew_temperature'].fillna(data['dew_temperature'].mean())\ndata['precip_depth_1_hr'] = data['precip_depth_1_hr'].fillna(data['precip_depth_1_hr'].mean())","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:10:31.115237Z","iopub.execute_input":"2022-01-10T12:10:31.115573Z","iopub.status.idle":"2022-01-10T12:10:32.042143Z","shell.execute_reply.started":"2022-01-10T12:10:31.115538Z","shell.execute_reply":"2022-01-10T12:10:32.041306Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.describe()","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Fixing the site_id 0 error","metadata":{}},{"cell_type":"code","source":"data['meter_reading'] = [j*0.2931 if i == 0 else j for i,j in zip(data['site_id'],data['meter_reading'])]\ndata","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:10:37.239031Z","iopub.execute_input":"2022-01-10T12:10:37.239970Z","iopub.status.idle":"2022-01-10T12:10:49.691045Z","shell.execute_reply.started":"2022-01-10T12:10:37.239926Z","shell.execute_reply":"2022-01-10T12:10:49.690192Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Removing Outliers","metadata":{}},{"cell_type":"code","source":"mask1 = data[\"meter\"] == 0\nmask2 = data[\"meter_reading\"] > 40000\nmask = np.logical_and(mask1, mask2)\nprint(data.shape)\ndata[mask][\"meter_reading\"] = data[mask1][\"meter_reading\"].mean()","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:10:52.926492Z","iopub.execute_input":"2022-01-10T12:10:52.927108Z","iopub.status.idle":"2022-01-10T12:10:54.094812Z","shell.execute_reply.started":"2022-01-10T12:10:52.927068Z","shell.execute_reply":"2022-01-10T12:10:54.093830Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mask1 = data[\"meter\"] == 0\nmask2 = data[\"meter_reading\"] > 40000\nmask = np.logical_and(mask1, mask2)\nprint(data.shape)\ndata[mask][\"meter_reading\"] = data[mask1][\"meter_reading\"].mean()","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:11:13.630166Z","iopub.execute_input":"2022-01-10T12:11:13.630910Z","iopub.status.idle":"2022-01-10T12:11:14.784479Z","shell.execute_reply.started":"2022-01-10T12:11:13.630849Z","shell.execute_reply":"2022-01-10T12:11:14.783559Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mask1 = data[\"meter\"] == 3\nmask2 = data[\"meter_reading\"] > 140000\nmask = np.logical_and(mask1, mask2)\nprint(data.shape)\ndata[mask][\"meter_reading\"] = data[mask1][\"meter_reading\"].mean()\nprint(data.shape)\ndel mask1\ndel mask2","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:11:27.681017Z","iopub.execute_input":"2022-01-10T12:11:27.681745Z","iopub.status.idle":"2022-01-10T12:11:27.948060Z","shell.execute_reply.started":"2022-01-10T12:11:27.681696Z","shell.execute_reply":"2022-01-10T12:11:27.946893Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Converting primary use to numiric","metadata":{}},{"cell_type":"code","source":"from sklearn.preprocessing import LabelEncoder","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:11:37.527731Z","iopub.execute_input":"2022-01-10T12:11:37.528196Z","iopub.status.idle":"2022-01-10T12:11:38.403058Z","shell.execute_reply.started":"2022-01-10T12:11:37.528150Z","shell.execute_reply":"2022-01-10T12:11:38.402015Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"le = LabelEncoder()\ndata['primary_use'] = le.fit_transform(data['primary_use'])","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:11:40.094149Z","iopub.execute_input":"2022-01-10T12:11:40.094483Z","iopub.status.idle":"2022-01-10T12:11:46.643007Z","shell.execute_reply.started":"2022-01-10T12:11:40.094451Z","shell.execute_reply":"2022-01-10T12:11:46.642028Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Removing timestamp to reduce features size","metadata":{}},{"cell_type":"code","source":"data = data.drop('timestamp',axis = 1)","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:11:48.297846Z","iopub.execute_input":"2022-01-10T12:11:48.298644Z","iopub.status.idle":"2022-01-10T12:11:50.393826Z","shell.execute_reply.started":"2022-01-10T12:11:48.298609Z","shell.execute_reply":"2022-01-10T12:11:50.392694Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Test data section","metadata":{}},{"cell_type":"markdown","source":"> **we will do the same operations that we did on training data**","metadata":{}},{"cell_type":"code","source":"testData = pd.read_csv(DATASET_PATH+'test.csv')","metadata":{"execution":{"iopub.status.busy":"2022-01-10T11:48:53.213130Z","iopub.execute_input":"2022-01-10T11:48:53.213499Z","iopub.status.idle":"2022-01-10T11:49:23.218652Z","shell.execute_reply.started":"2022-01-10T11:48:53.213458Z","shell.execute_reply":"2022-01-10T11:49:23.217750Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\ntestData = testData.merge(buildingData, on='building_id', how='left')\ntestData = testData.merge(weather_data, on=['site_id', 'timestamp'], how='left')\ntestData","metadata":{"execution":{"iopub.status.busy":"2022-01-10T11:50:07.046565Z","iopub.execute_input":"2022-01-10T11:50:07.046914Z","iopub.status.idle":"2022-01-10T11:50:26.925971Z","shell.execute_reply.started":"2022-01-10T11:50:07.046877Z","shell.execute_reply":"2022-01-10T11:50:26.924887Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"testData = testData.drop(['row_id','year_built','floor_count','cloud_coverage','sea_level_pressure','wind_direction'],axis=1)","metadata":{"execution":{"iopub.status.busy":"2022-01-10T11:51:51.223360Z","iopub.execute_input":"2022-01-10T11:51:51.223925Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del weather_data\ndel buildingData","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:11:53.932690Z","iopub.execute_input":"2022-01-10T12:11:53.933029Z","iopub.status.idle":"2022-01-10T12:11:53.943777Z","shell.execute_reply.started":"2022-01-10T12:11:53.932994Z","shell.execute_reply":"2022-01-10T12:11:53.943066Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"testData[\"timestamp\"] = pd.to_datetime(testData[\"timestamp\"])\ntestData[\"day\"]       = testData[\"timestamp\"].dt.day\ntestData[\"hour\"]      = testData[\"timestamp\"].dt.hour\ntestData[\"week\"]      = testData[\"timestamp\"].dt.weekday\ntestData[\"month\"]     = testData[\"timestamp\"].dt.month\ntestData[\"year\"]      = testData[\"timestamp\"].dt.year\ntestData              = testData.drop(\"timestamp\", axis = 1)\n","metadata":{"execution":{"iopub.status.busy":"2022-01-10T11:44:01.182008Z","iopub.execute_input":"2022-01-10T11:44:01.182588Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"testData['air_temperature'] = testData['air_temperature'].fillna(testData['air_temperature'].mean())\ntestData['wind_speed'] = testData['wind_speed'].fillna(testData['wind_speed'].mean())\ntestData['dew_temperature'] = testData['dew_temperature'].fillna(testData['dew_temperature'].mean())\ntestData['precip_depth_1_hr'] = testData['precip_depth_1_hr'].fillna(testData['precip_depth_1_hr'].mean())","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"testData","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"testData['primary_use'] = le.fit_transform(testData['primary_use'])","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Algorithms Section","metadata":{}},{"cell_type":"code","source":"from sklearn.linear_model import LinearRegression","metadata":{"execution":{"iopub.status.busy":"2022-01-10T11:58:20.940362Z","iopub.execute_input":"2022-01-10T11:58:20.941052Z","iopub.status.idle":"2022-01-10T11:58:21.072099Z","shell.execute_reply.started":"2022-01-10T11:58:20.941009Z","shell.execute_reply":"2022-01-10T11:58:21.071130Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Preparing X train and Y train for pridection","metadata":{}},{"cell_type":"markdown","source":"# **We could not use test data because of memory leak**> > ","metadata":{}},{"cell_type":"code","source":"x_train = data[['building_id','meter','primary_use','square_feet','air_temperature','dew_temperature','precip_depth_1_hr','wind_speed','day','hour','week','month','year']]\ny_train = data['meter_reading']\n#x_test = testData\nx_train","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:12:01.215964Z","iopub.execute_input":"2022-01-10T12:12:01.216338Z","iopub.status.idle":"2022-01-10T12:12:02.073140Z","shell.execute_reply.started":"2022-01-10T12:12:01.216306Z","shell.execute_reply":"2022-01-10T12:12:02.072233Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_train","metadata":{"execution":{"iopub.status.busy":"2022-01-10T11:58:47.508377Z","iopub.execute_input":"2022-01-10T11:58:47.508662Z","iopub.status.idle":"2022-01-10T11:58:47.517734Z","shell.execute_reply.started":"2022-01-10T11:58:47.508633Z","shell.execute_reply":"2022-01-10T11:58:47.517072Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = LinearRegression()","metadata":{"execution":{"iopub.status.busy":"2022-01-10T11:58:53.697993Z","iopub.execute_input":"2022-01-10T11:58:53.698318Z","iopub.status.idle":"2022-01-10T11:58:53.704980Z","shell.execute_reply.started":"2022-01-10T11:58:53.698286Z","shell.execute_reply":"2022-01-10T11:58:53.704349Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.fit(x_train.values,y_train.values)","metadata":{"execution":{"iopub.status.busy":"2022-01-10T11:59:14.944437Z","iopub.execute_input":"2022-01-10T11:59:14.944736Z","iopub.status.idle":"2022-01-10T11:59:23.628052Z","shell.execute_reply.started":"2022-01-10T11:59:14.944706Z","shell.execute_reply":"2022-01-10T11:59:23.627052Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.svm import SVR","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:04:53.298951Z","iopub.execute_input":"2022-01-10T12:04:53.299846Z","iopub.status.idle":"2022-01-10T12:04:53.433426Z","shell.execute_reply.started":"2022-01-10T12:04:53.299804Z","shell.execute_reply":"2022-01-10T12:04:53.432702Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"svrModel = SVR(C = 5)\nsvrModel.fit(x_train.values,y_train.values)","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:04:59.945354Z","iopub.execute_input":"2022-01-10T12:04:59.946281Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.linear_model import SGDRegressor\n","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:12:11.805993Z","iopub.execute_input":"2022-01-10T12:12:11.806837Z","iopub.status.idle":"2022-01-10T12:12:11.934152Z","shell.execute_reply.started":"2022-01-10T12:12:11.806796Z","shell.execute_reply":"2022-01-10T12:12:11.933463Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import StandardScaler\n","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:14:14.911817Z","iopub.execute_input":"2022-01-10T12:14:14.912155Z","iopub.status.idle":"2022-01-10T12:14:14.916684Z","shell.execute_reply.started":"2022-01-10T12:14:14.912120Z","shell.execute_reply":"2022-01-10T12:14:14.915992Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"reg = make_pipeline(StandardScaler(),\n                     SGDRegressor(max_iter=1000, tol=1e-3))\nreg.fit(x_train.values, y_train.values)","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:14:19.950889Z","iopub.execute_input":"2022-01-10T12:14:19.951575Z","iopub.status.idle":"2022-01-10T12:21:26.871714Z","shell.execute_reply.started":"2022-01-10T12:14:19.951536Z","shell.execute_reply":"2022-01-10T12:21:26.870342Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"reg.score(x_train.values, y_train.values)","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:43:49.875222Z","iopub.execute_input":"2022-01-10T12:43:49.876272Z","iopub.status.idle":"2022-01-10T12:43:53.121738Z","shell.execute_reply.started":"2022-01-10T12:43:49.876227Z","shell.execute_reply":"2022-01-10T12:43:53.120677Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.linear_model import Ridge\nclf = Ridge(alpha=1.0)\nclf.fit(x_train.values, y_train.values)\n","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:25:14.092872Z","iopub.execute_input":"2022-01-10T12:25:14.093249Z","iopub.status.idle":"2022-01-10T12:25:17.949064Z","shell.execute_reply.started":"2022-01-10T12:25:14.093210Z","shell.execute_reply":"2022-01-10T12:25:17.948104Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"clf.score(x_train.values, y_train.values)","metadata":{"execution":{"iopub.status.busy":"2022-01-10T12:43:32.184875Z","iopub.execute_input":"2022-01-10T12:43:32.185222Z","iopub.status.idle":"2022-01-10T12:43:34.424064Z","shell.execute_reply.started":"2022-01-10T12:43:32.185188Z","shell.execute_reply":"2022-01-10T12:43:34.423415Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}