read_cached_data.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Read Pickle"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle as pkl\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import numpy as np "
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1664563055676
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "play_store = pkl.load(open(\"data/play_store.pkl\", \"rb\"))"
      ],
      "outputs": [],
      "execution_count": 3,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1664563086390
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "play_store[0]"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "{'reviewId': '63e4fd39-a508-48ae-9ee1-6243849ec559',\n 'userName': 'roland roulin',\n 'userImage': 'https://play-lh.googleusercontent.com/a/ALm5wu11he4mh8PKbo4eKXKWDri0i5pAWYSG_nXnWz2P=mo',\n 'content': 'Ich brauche die app regelmässig und finde sie ist einfach anzuwenden.',\n 'score': 4,\n 'thumbsUpCount': 0,\n 'reviewCreatedVersion': '5.6.1',\n 'at': datetime.datetime(2022, 9, 29, 7, 32, 16),\n 'replyContent': 'Vielen Dank für Ihre positive Rückmeldung. Es freut uns sehr, dass Sie mit unserer App zufrieden sind.',\n 'repliedAt': datetime.datetime(2022, 9, 29, 13, 22, 3)}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 4,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1664563087574
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Simple Plots"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\r\n",
        "plt.hist([a['score'] for a in play_store])\r\n",
        "plt.xlabel(\"Score\")\r\n",
        "plt.ylabel(\"Counts\")\r\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 6,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1664563143378
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.dates as mdates\r\n",
        "\r\n",
        "plt.figure()\r\n",
        "ax = plt.gca()\r\n",
        "for score in range(1, 6):\r\n",
        "    vals = [a['at'] for a in play_store if a['score'] == score]\r\n",
        "\r\n",
        "    ax.hist(vals, bins=150, label=\"%i stars\" % score)\r\n",
        "ax.xaxis.set_major_locator(mdates.YearLocator())\r\n",
        "ax.xaxis.set_major_formatter(mdates.DateFormatter('%d.%m.%y'))\r\n",
        "plt.xticks(rotation = 90)\r\n",
        "plt.legend()\r\n",
        "plt.ylabel(\"Counts\")\r\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 11,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1664563418597
        }
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3.8 - AzureML",
      "language": "python",
      "name": "python38-azureml"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.1"
    },
    "microsoft": {
      "host": {
        "AzureML": {
          "notebookHasBeenCompleted": true
        }
      }
    },
    "nteract": {
      "version": "nteract-front-end@1.0.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}