{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Marc's_Reporting_DB_Python_Code_Template.ipynb", "provenance": [], "include_colab_link": true }, "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": "3.7.0" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "FIC36jIvNO38", "outputId": "cd08ab5d-ed91-4272-aac1-921842e63c6c", "colab": { "base_uri": "https://localhost:8080/", "height": 122 } }, "source": [ "#SQL Connection\n", "!pip install psycopg2-binary" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Collecting psycopg2-binary\n", "\u001b[?25l Downloading https://files.pythonhosted.org/packages/13/c3/050b8da85b02886f2c649da42cf195a4dc8cc5770a5524cd5b34c36c03a7/psycopg2_binary-2.8.4-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (1.5MB)\n", "\u001b[K |████████████████████████████████| 1.6MB 4.0MB/s \n", "\u001b[?25hInstalling collected packages: psycopg2-binary\n", "Successfully installed psycopg2-binary-2.8.4\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "t668c0dcNMu8", "colab": {} }, "source": [ "import psycopg2\n", "import numpy as np\n", "import pandas as pd\n", "con=psycopg2.connect(dbname= 'smaredshiftdb', host='reporting-db.smeanalyticsapps.com', port= '5439', user= 'mfridson', password='MbtW7DN9HVnT')\n", "cur = con.cursor()" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "JM7EUWTgNWd5", "colab": {} }, "source": [ "#Query you would like to run\n", "cur.execute(\"\"\"\n", "SELECT * FROM spotify.dim_consumer_attributes ORDER BY age_group_name LIMIT 100;\n", "\"\"\")\n", "#Save query result to a Dataframe\n", "col_names = []\n", "for elt in cur.description:\n", " col_names.append(elt[0])\n", "df=pd.DataFrame(cur.fetchall(),columns=col_names)\n", "cur.close() \n", "con.close()" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "G8gDawBSnWY5", "colab_type": "code", "outputId": "2f1829e8-9fff-48e4-e2ed-2505c27df014", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "df" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " consumer_attribute_key gender_code age_group_code age_group_intl_code \\\n", "0 29 u 0-12 0-14 \n", "1 162 m 0-12 0-14 \n", "2 209 f 0-12 0-14 \n", "3 225 u 0-12 0-14 \n", "4 350 m 0-12 0-14 \n", "5 383 f 0-12 0-14 \n", "6 2667 female 0-12 0-14 \n", "7 2684 0-12 0-14 \n", "8 2754 male 0-12 0-14 \n", "9 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