test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "XLRDError",
     "evalue": "Unsupported format, or corrupt file: Expected BOF record; found b'{\\n \"cell'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mXLRDError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\kapta\\Documents\\Flatiron\\Capstone\\sentimentpredictor\\test.ipynb Cell 1\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/kapta/Documents/Flatiron/Capstone/sentimentpredictor/test.ipynb#W2sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mseaborn\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39msns\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/kapta/Documents/Flatiron/Capstone/sentimentpredictor/test.ipynb#W2sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mdatetime\u001b[39;00m \u001b[39mimport\u001b[39;00m datetime\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/kapta/Documents/Flatiron/Capstone/sentimentpredictor/test.ipynb#W2sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m df \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39;49mread_excel(\u001b[39m'\u001b[39;49m\u001b[39mtest.ipynb\u001b[39;49m\u001b[39m'\u001b[39;49m,engine\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mxlrd\u001b[39;49m\u001b[39m'\u001b[39;49m)\n",
      "File \u001b[1;32mc:\\Users\\kapta\\anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py:311\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    305\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(args) \u001b[39m>\u001b[39m num_allow_args:\n\u001b[0;32m    306\u001b[0m     warnings\u001b[39m.\u001b[39mwarn(\n\u001b[0;32m    307\u001b[0m         msg\u001b[39m.\u001b[39mformat(arguments\u001b[39m=\u001b[39marguments),\n\u001b[0;32m    308\u001b[0m         \u001b[39mFutureWarning\u001b[39;00m,\n\u001b[0;32m    309\u001b[0m         stacklevel\u001b[39m=\u001b[39mstacklevel,\n\u001b[0;32m    310\u001b[0m     )\n\u001b[1;32m--> 311\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\kapta\\anaconda3\\lib\\site-packages\\pandas\\io\\excel\\_base.py:457\u001b[0m, in \u001b[0;36mread_excel\u001b[1;34m(io, sheet_name, header, names, index_col, usecols, squeeze, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, parse_dates, date_parser, thousands, decimal, comment, skipfooter, convert_float, mangle_dupe_cols, storage_options)\u001b[0m\n\u001b[0;32m    455\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(io, ExcelFile):\n\u001b[0;32m    456\u001b[0m     should_close \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m--> 457\u001b[0m     io \u001b[39m=\u001b[39m ExcelFile(io, storage_options\u001b[39m=\u001b[39;49mstorage_options, engine\u001b[39m=\u001b[39;49mengine)\n\u001b[0;32m    458\u001b[0m \u001b[39melif\u001b[39;00m engine \u001b[39mand\u001b[39;00m engine \u001b[39m!=\u001b[39m io\u001b[39m.\u001b[39mengine:\n\u001b[0;32m    459\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m    460\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mEngine should not be specified when passing \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    461\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39man ExcelFile - ExcelFile already has the engine set\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    462\u001b[0m     )\n",
      "File \u001b[1;32mc:\\Users\\kapta\\anaconda3\\lib\\site-packages\\pandas\\io\\excel\\_base.py:1419\u001b[0m, in \u001b[0;36mExcelFile.__init__\u001b[1;34m(self, path_or_buffer, engine, storage_options)\u001b[0m\n\u001b[0;32m   1416\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mengine \u001b[39m=\u001b[39m engine\n\u001b[0;32m   1417\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstorage_options \u001b[39m=\u001b[39m storage_options\n\u001b[1;32m-> 1419\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reader \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engines[engine](\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_io, storage_options\u001b[39m=\u001b[39;49mstorage_options)\n",
      "File \u001b[1;32mc:\\Users\\kapta\\anaconda3\\lib\\site-packages\\pandas\\io\\excel\\_xlrd.py:25\u001b[0m, in \u001b[0;36mXlrdReader.__init__\u001b[1;34m(self, filepath_or_buffer, storage_options)\u001b[0m\n\u001b[0;32m     23\u001b[0m err_msg \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mInstall xlrd >= 1.0.0 for Excel support\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m     24\u001b[0m import_optional_dependency(\u001b[39m\"\u001b[39m\u001b[39mxlrd\u001b[39m\u001b[39m\"\u001b[39m, extra\u001b[39m=\u001b[39merr_msg)\n\u001b[1;32m---> 25\u001b[0m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__init__\u001b[39;49m(filepath_or_buffer, storage_options\u001b[39m=\u001b[39;49mstorage_options)\n",
      "File \u001b[1;32mc:\\Users\\kapta\\anaconda3\\lib\\site-packages\\pandas\\io\\excel\\_base.py:518\u001b[0m, in \u001b[0;36mBaseExcelReader.__init__\u001b[1;34m(self, filepath_or_buffer, storage_options)\u001b[0m\n\u001b[0;32m    516\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles\u001b[39m.\u001b[39mhandle\u001b[39m.\u001b[39mseek(\u001b[39m0\u001b[39m)\n\u001b[0;32m    517\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 518\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbook \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mload_workbook(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mhandles\u001b[39m.\u001b[39;49mhandle)\n\u001b[0;32m    519\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m:\n\u001b[0;32m    520\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mclose()\n",
      "File \u001b[1;32mc:\\Users\\kapta\\anaconda3\\lib\\site-packages\\pandas\\io\\excel\\_xlrd.py:38\u001b[0m, in \u001b[0;36mXlrdReader.load_workbook\u001b[1;34m(self, filepath_or_buffer)\u001b[0m\n\u001b[0;32m     36\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(filepath_or_buffer, \u001b[39m\"\u001b[39m\u001b[39mread\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m     37\u001b[0m     data \u001b[39m=\u001b[39m filepath_or_buffer\u001b[39m.\u001b[39mread()\n\u001b[1;32m---> 38\u001b[0m     \u001b[39mreturn\u001b[39;00m open_workbook(file_contents\u001b[39m=\u001b[39;49mdata)\n\u001b[0;32m     39\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m     40\u001b[0m     \u001b[39mreturn\u001b[39;00m open_workbook(filepath_or_buffer)\n",
      "File \u001b[1;32mc:\\Users\\kapta\\anaconda3\\lib\\site-packages\\xlrd\\__init__.py:172\u001b[0m, in \u001b[0;36mopen_workbook\u001b[1;34m(filename, logfile, verbosity, use_mmap, file_contents, encoding_override, formatting_info, on_demand, ragged_rows, ignore_workbook_corruption)\u001b[0m\n\u001b[0;32m    169\u001b[0m \u001b[39mif\u001b[39;00m file_format \u001b[39mand\u001b[39;00m file_format \u001b[39m!=\u001b[39m \u001b[39m'\u001b[39m\u001b[39mxls\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[0;32m    170\u001b[0m     \u001b[39mraise\u001b[39;00m XLRDError(FILE_FORMAT_DESCRIPTIONS[file_format]\u001b[39m+\u001b[39m\u001b[39m'\u001b[39m\u001b[39m; not supported\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m--> 172\u001b[0m bk \u001b[39m=\u001b[39m open_workbook_xls(\n\u001b[0;32m    173\u001b[0m     filename\u001b[39m=\u001b[39;49mfilename,\n\u001b[0;32m    174\u001b[0m     logfile\u001b[39m=\u001b[39;49mlogfile,\n\u001b[0;32m    175\u001b[0m     verbosity\u001b[39m=\u001b[39;49mverbosity,\n\u001b[0;32m    176\u001b[0m     use_mmap\u001b[39m=\u001b[39;49muse_mmap,\n\u001b[0;32m    177\u001b[0m     file_contents\u001b[39m=\u001b[39;49mfile_contents,\n\u001b[0;32m    178\u001b[0m     encoding_override\u001b[39m=\u001b[39;49mencoding_override,\n\u001b[0;32m    179\u001b[0m     formatting_info\u001b[39m=\u001b[39;49mformatting_info,\n\u001b[0;32m    180\u001b[0m     on_demand\u001b[39m=\u001b[39;49mon_demand,\n\u001b[0;32m    181\u001b[0m     ragged_rows\u001b[39m=\u001b[39;49mragged_rows,\n\u001b[0;32m    182\u001b[0m     ignore_workbook_corruption\u001b[39m=\u001b[39;49mignore_workbook_corruption,\n\u001b[0;32m    183\u001b[0m )\n\u001b[0;32m    185\u001b[0m \u001b[39mreturn\u001b[39;00m bk\n",
      "File \u001b[1;32mc:\\Users\\kapta\\anaconda3\\lib\\site-packages\\xlrd\\book.py:79\u001b[0m, in \u001b[0;36mopen_workbook_xls\u001b[1;34m(filename, logfile, verbosity, use_mmap, file_contents, encoding_override, formatting_info, on_demand, ragged_rows, ignore_workbook_corruption)\u001b[0m\n\u001b[0;32m     77\u001b[0m t1 \u001b[39m=\u001b[39m perf_counter()\n\u001b[0;32m     78\u001b[0m bk\u001b[39m.\u001b[39mload_time_stage_1 \u001b[39m=\u001b[39m t1 \u001b[39m-\u001b[39m t0\n\u001b[1;32m---> 79\u001b[0m biff_version \u001b[39m=\u001b[39m bk\u001b[39m.\u001b[39;49mgetbof(XL_WORKBOOK_GLOBALS)\n\u001b[0;32m     80\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m biff_version:\n\u001b[0;32m     81\u001b[0m     \u001b[39mraise\u001b[39;00m XLRDError(\u001b[39m\"\u001b[39m\u001b[39mCan\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt determine file\u001b[39m\u001b[39m'\u001b[39m\u001b[39ms BIFF version\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\kapta\\anaconda3\\lib\\site-packages\\xlrd\\book.py:1284\u001b[0m, in \u001b[0;36mBook.getbof\u001b[1;34m(self, rqd_stream)\u001b[0m\n\u001b[0;32m   1282\u001b[0m     bof_error(\u001b[39m'\u001b[39m\u001b[39mExpected BOF record; met end of file\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m   1283\u001b[0m \u001b[39mif\u001b[39;00m opcode \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m bofcodes:\n\u001b[1;32m-> 1284\u001b[0m     bof_error(\u001b[39m'\u001b[39;49m\u001b[39mExpected BOF record; found \u001b[39;49m\u001b[39m%r\u001b[39;49;00m\u001b[39m'\u001b[39;49m \u001b[39m%\u001b[39;49m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmem[savpos:savpos\u001b[39m+\u001b[39;49m\u001b[39m8\u001b[39;49m])\n\u001b[0;32m   1285\u001b[0m length \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget2bytes()\n\u001b[0;32m   1286\u001b[0m \u001b[39mif\u001b[39;00m length \u001b[39m==\u001b[39m MY_EOF:\n",
      "File \u001b[1;32mc:\\Users\\kapta\\anaconda3\\lib\\site-packages\\xlrd\\book.py:1278\u001b[0m, in \u001b[0;36mBook.getbof.<locals>.bof_error\u001b[1;34m(msg)\u001b[0m\n\u001b[0;32m   1277\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mbof_error\u001b[39m(msg):\n\u001b[1;32m-> 1278\u001b[0m     \u001b[39mraise\u001b[39;00m XLRDError(\u001b[39m'\u001b[39m\u001b[39mUnsupported format, or corrupt file: \u001b[39m\u001b[39m'\u001b[39m \u001b[39m+\u001b[39m msg)\n",
      "\u001b[1;31mXLRDError\u001b[0m: Unsupported format, or corrupt file: Expected BOF record; found b'{\\n \"cell'"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from datetime import datetime\n",
    "df = pd.read_excel('test.ipynb',engine='xlrd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\kapta\\Documents\\Flatiron\\Capstone\\sentimentpredictor\\test.ipynb Cell 1\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/kapta/Documents/Flatiron/Capstone/sentimentpredictor/test.ipynb#W0sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# (1) Adding day of the week.\u001b[39;00m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/kapta/Documents/Flatiron/Capstone/sentimentpredictor/test.ipynb#W0sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m df[\u001b[39m'\u001b[39m\u001b[39mWeekday\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m df\u001b[39m.\u001b[39mtimestamp\u001b[39m.\u001b[39mdt\u001b[39m.\u001b[39mweekday\u001b[39m# (2) Adding the average of rides grouped by the weekday and hour\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/kapta/Documents/Flatiron/Capstone/sentimentpredictor/test.ipynb#W0sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39m# 7 Days, 24 Hours = 168 values\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/kapta/Documents/Flatiron/Capstone/sentimentpredictor/test.ipynb#W0sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m df\u001b[39m.\u001b[39mavg_hour_day \u001b[39m=\u001b[39m df\u001b[39m.\u001b[39mavg_hour_day\u001b[39m.\u001b[39mreplace(df[:\u001b[39m7344\u001b[39m]\u001b[39m.\u001b[39mgroupby(df\u001b[39m.\u001b[39mWeekday\u001b[39m.\u001b[39mastype(\u001b[39mstr\u001b[39m) \u001b[39m+\u001b[39m \u001b[39m'\u001b[39m\u001b[39m \u001b[39m\u001b[39m'\u001b[39m \u001b[39m+\u001b[39m df\u001b[39m.\u001b[39mHour\u001b[39m.\u001b[39mastype(\u001b[39mstr\u001b[39m))[\u001b[39m'\u001b[39m\u001b[39mvalue\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mmean()\u001b[39m.\u001b[39mto_dict())\u001b[39m# (3) Featuring the number of rides during the day and during the night.\u001b[39;00m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "# (1) Adding day of the week.\n",
    "df['Weekday'] = df.timestamp.dt.weekday# (2) Adding the average of rides grouped by the weekday and hour\n",
    "# 7 Days, 24 Hours = 168 values\n",
    "df.avg_hour_day = df.avg_hour_day.replace(df[:7344].groupby(df.Weekday.astype(str) + ' ' + df.Hour.astype(str))['value'].mean().to_dict())# (3) Featuring the number of rides during the day and during the night.\n",
    "# We define the day time to be any hours between 6 AM and 10 PM while Night time where usually there is less \n",
    "# demand is any time between 10:00 PM and 6:00 AM\n",
    "df['day_time'] = ((df['Hour'] >= 6) & (df['Hour'] <= 22)).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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