{"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","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport math","metadata":{"execution":{"iopub.status.busy":"2022-07-21T06:46:37.273127Z","iopub.execute_input":"2022-07-21T06:46:37.274641Z","iopub.status.idle":"2022-07-21T06:46:37.280632Z","shell.execute_reply.started":"2022-07-21T06:46:37.274588Z","shell.execute_reply":"2022-07-21T06:46:37.279342Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')\ntest = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')","metadata":{"execution":{"iopub.status.busy":"2022-07-21T06:25:04.513985Z","iopub.execute_input":"2022-07-21T06:25:04.514475Z","iopub.status.idle":"2022-07-21T06:25:04.593380Z","shell.execute_reply.started":"2022-07-21T06:25:04.514429Z","shell.execute_reply":"2022-07-21T06:25:04.591598Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def cubenumber(num):\n    cubenumbers = []\n    if num <= 10 and num >= 0:\n        c = num % 10\n    if num > 10:\n        c = num % 10\n        b = num / 10 % 10\n    if num > 100:\n        c = num % 10\n        b = num / 10 % 10\n        a = num / 100\n    if num == a^3 + b^3 + c^3 or num == b^3 + c^3 or num == c^3:\n        cubenumbers.append(num)","metadata":{"execution":{"iopub.status.busy":"2022-07-21T06:39:54.860551Z","iopub.execute_input":"2022-07-21T06:39:54.861032Z","iopub.status.idle":"2022-07-21T06:39:54.869506Z","shell.execute_reply.started":"2022-07-21T06:39:54.860993Z","shell.execute_reply":"2022-07-21T06:39:54.868343Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def factorialcalc(num1): #num here = num above?\"\n    ans = 1\n    for i in range(1,num1+1):\n        ans = ans * i","metadata":{"execution":{"iopub.status.busy":"2022-07-21T06:48:15.821449Z","iopub.execute_input":"2022-07-21T06:48:15.821940Z","iopub.status.idle":"2022-07-21T06:48:15.828173Z","shell.execute_reply.started":"2022-07-21T06:48:15.821897Z","shell.execute_reply":"2022-07-21T06:48:15.827166Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def primeNumber(num2):\n    for i in range(2,num2):\n        if num2 % i == 0:\n            return false\n        else: \n            return true\n    ","metadata":{"execution":{"iopub.status.busy":"2022-07-21T06:54:27.737206Z","iopub.execute_input":"2022-07-21T06:54:27.737682Z","iopub.status.idle":"2022-07-21T06:54:27.744365Z","shell.execute_reply.started":"2022-07-21T06:54:27.737646Z","shell.execute_reply":"2022-07-21T06:54:27.743288Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}