231015_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from processing.data_pipeline import pipeline\n",
    "from processing.data_processing import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = \"/Users/nicolas/Workspace/School/V2023/Big Data/Exercise 3/tdt4225_assignment3/dataset\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nicolas/Workspace/School/V2023/Big Data/Exercise 3/tdt4225_assignment3/processing/data_processing.py:161: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  user_df.rename(columns={'id': '_id'}, inplace=True)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Must specify a fill 'value' or 'method'.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/Users/nicolas/Workspace/School/V2023/Big Data/Exercise 3/tdt4225_assignment3/231015_test.ipynb Cell 3\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/nicolas/Workspace/School/V2023/Big%20Data/Exercise%203/tdt4225_assignment3/231015_test.ipynb#W2sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m users, activities, track_points \u001b[39m=\u001b[39m pipeline(data_path)\n",
      "File \u001b[0;32m~/Workspace/School/V2023/Big Data/Exercise 3/tdt4225_assignment3/processing/data_pipeline.py:14\u001b[0m, in \u001b[0;36mpipeline\u001b[0;34m(data_path)\u001b[0m\n\u001b[1;32m     11\u001b[0m removed_users \u001b[39m=\u001b[39m find_removed_users(track_points_df, users_df)\n\u001b[1;32m     13\u001b[0m users_table \u001b[39m=\u001b[39m make_user_df(users_df, removed_users)\n\u001b[0;32m---> 14\u001b[0m activities_table \u001b[39m=\u001b[39m make_activity_df(track_points_df, labels_df)\n\u001b[1;32m     15\u001b[0m track_points_table \u001b[39m=\u001b[39m make_track_point_df(track_points_df, activities_table)\n\u001b[1;32m     17\u001b[0m users_dict \u001b[39m=\u001b[39m make_user_dict(users_table, activities_table)\n",
      "File \u001b[0;32m~/Workspace/School/V2023/Big Data/Exercise 3/tdt4225_assignment3/processing/data_processing.py:174\u001b[0m, in \u001b[0;36mmake_activity_df\u001b[0;34m(track_points_df, labels_df)\u001b[0m\n\u001b[1;32m    172\u001b[0m result_df \u001b[39m=\u001b[39m result_df\u001b[39m.\u001b[39mdrop_duplicates(subset\u001b[39m=\u001b[39m[\u001b[39m'\u001b[39m\u001b[39mid\u001b[39m\u001b[39m'\u001b[39m], keep\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mlast\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m    173\u001b[0m result_df\u001b[39m.\u001b[39mrename(columns\u001b[39m=\u001b[39m{\u001b[39m'\u001b[39m\u001b[39mid\u001b[39m\u001b[39m'\u001b[39m: \u001b[39m'\u001b[39m\u001b[39m_id\u001b[39m\u001b[39m'\u001b[39m}, inplace\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[0;32m--> 174\u001b[0m result_df[\u001b[39m'\u001b[39m\u001b[39mtransportation_mode\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m result_df[\u001b[39m'\u001b[39;49m\u001b[39mtransportation_mode\u001b[39;49m\u001b[39m'\u001b[39;49m]\u001b[39m.\u001b[39;49mfillna(\u001b[39mNone\u001b[39;49;00m)\n\u001b[1;32m    176\u001b[0m \u001b[39mreturn\u001b[39;00m result_df\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/mongo/lib/python3.11/site-packages/pandas/core/generic.py:7156\u001b[0m, in \u001b[0;36mNDFrame.fillna\u001b[0;34m(self, value, method, axis, inplace, limit, downcast)\u001b[0m\n\u001b[1;32m   7149\u001b[0m         \u001b[39mif\u001b[39;00m sys\u001b[39m.\u001b[39mgetrefcount(\u001b[39mself\u001b[39m) \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m REF_COUNT:\n\u001b[1;32m   7150\u001b[0m             warnings\u001b[39m.\u001b[39mwarn(\n\u001b[1;32m   7151\u001b[0m                 _chained_assignment_method_msg,\n\u001b[1;32m   7152\u001b[0m                 ChainedAssignmentError,\n\u001b[1;32m   7153\u001b[0m                 stacklevel\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m,\n\u001b[1;32m   7154\u001b[0m             )\n\u001b[0;32m-> 7156\u001b[0m value, method \u001b[39m=\u001b[39m validate_fillna_kwargs(value, method)\n\u001b[1;32m   7157\u001b[0m \u001b[39mif\u001b[39;00m method \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   7158\u001b[0m     warnings\u001b[39m.\u001b[39mwarn(\n\u001b[1;32m   7159\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mtype\u001b[39m(\u001b[39mself\u001b[39m)\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m.fillna with \u001b[39m\u001b[39m'\u001b[39m\u001b[39mmethod\u001b[39m\u001b[39m'\u001b[39m\u001b[39m is deprecated and \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   7160\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mwill raise in a future version. Use obj.ffill() or obj.bfill() \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   7163\u001b[0m         stacklevel\u001b[39m=\u001b[39mfind_stack_level(),\n\u001b[1;32m   7164\u001b[0m     )\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/mongo/lib/python3.11/site-packages/pandas/util/_validators.py:293\u001b[0m, in \u001b[0;36mvalidate_fillna_kwargs\u001b[0;34m(value, method, validate_scalar_dict_value)\u001b[0m\n\u001b[1;32m    290\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mpandas\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mcore\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mmissing\u001b[39;00m \u001b[39mimport\u001b[39;00m clean_fill_method\n\u001b[1;32m    292\u001b[0m \u001b[39mif\u001b[39;00m value \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m method \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 293\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mMust specify a fill \u001b[39m\u001b[39m'\u001b[39m\u001b[39mvalue\u001b[39m\u001b[39m'\u001b[39m\u001b[39m or \u001b[39m\u001b[39m'\u001b[39m\u001b[39mmethod\u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    294\u001b[0m \u001b[39mif\u001b[39;00m value \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m method \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    295\u001b[0m     method \u001b[39m=\u001b[39m clean_fill_method(method)\n",
      "\u001b[0;31mValueError\u001b[0m: Must specify a fill 'value' or 'method'."
     ]
    }
   ],
   "source": [
    "users, activities, track_points = pipeline(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('data/users.pkl', 'wb') as fp:\n",
    "    pickle.dump(users, fp)\n",
    "    print('dictionary saved successfully to file')\n",
    "\n",
    "with open('data/activities.pkl', 'wb') as fp:\n",
    "    pickle.dump(activities, fp)\n",
    "    print('dictionary saved successfully to file')\n",
    "\n",
    "with open('data/track_points.pkl', 'wb') as fp:\n",
    "    pickle.dump(track_points, fp)\n",
    "    print('dictionary saved successfully to file')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/users.pkl', 'rb') as fp:\n",
    "    _users = pickle.load(fp)\n",
    "    print('dictionary loaded successfully from file')\n",
    "_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/activities.pkl', 'rb') as fp:\n",
    "    _activities = pickle.load(fp)\n",
    "    print('dictionary loaded successfully from file')\n",
    "_activities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/track_points.pkl', 'rb') as fp:\n",
    "    _track_points = pickle.load(fp)\n",
    "    print('dictionary loaded successfully from file')\n",
    "_track_points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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