371 lines
21 KiB
Plaintext
371 lines
21 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "f3da59fb-eb6b-449f-b8d5-95ddacd456f2",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"from pathlib import Path"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "0c897ead-dfb5-4d18-bcfc-949824a0868f",
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"metadata": {},
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"outputs": [],
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"source": [
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"uber = Path.home() / 'google-drive' / 'Charlie_6013_RECLASS' / 'Data' / 'Raw' / 'Uber Customer Reviews.csv'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "75ad8e81-3f11-4152-9494-b95bbba6fa01",
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"metadata": {},
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"outputs": [
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{
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"ename": "FileNotFoundError",
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"evalue": "[Errno 2] No such file or directory: 'C:\\\\Users\\\\ch\\\\google-drive\\\\Charlie_6013_RECLASS\\\\Data\\\\Raw\\\\Uber Customer Reviews.csv'",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m uber_df = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43muber\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlow_memory\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~\\anaconda3\\envs\\multitag\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[39m, in \u001b[36mread_csv\u001b[39m\u001b[34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[39m\n\u001b[32m 1013\u001b[39m kwds_defaults = _refine_defaults_read(\n\u001b[32m 1014\u001b[39m dialect,\n\u001b[32m 1015\u001b[39m delimiter,\n\u001b[32m (...)\u001b[39m\u001b[32m 1022\u001b[39m dtype_backend=dtype_backend,\n\u001b[32m 1023\u001b[39m )\n\u001b[32m 1024\u001b[39m kwds.update(kwds_defaults)\n\u001b[32m-> \u001b[39m\u001b[32m1026\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~\\anaconda3\\envs\\multitag\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[39m, in \u001b[36m_read\u001b[39m\u001b[34m(filepath_or_buffer, kwds)\u001b[39m\n\u001b[32m 617\u001b[39m _validate_names(kwds.get(\u001b[33m\"\u001b[39m\u001b[33mnames\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[32m 619\u001b[39m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m620\u001b[39m parser = \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 622\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[32m 623\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~\\anaconda3\\envs\\multitag\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[39m, in \u001b[36mTextFileReader.__init__\u001b[39m\u001b[34m(self, f, engine, **kwds)\u001b[39m\n\u001b[32m 1617\u001b[39m \u001b[38;5;28mself\u001b[39m.options[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m] = kwds[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 1619\u001b[39m \u001b[38;5;28mself\u001b[39m.handles: IOHandles | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1620\u001b[39m \u001b[38;5;28mself\u001b[39m._engine = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~\\anaconda3\\envs\\multitag\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1880\u001b[39m, in \u001b[36mTextFileReader._make_engine\u001b[39m\u001b[34m(self, f, engine)\u001b[39m\n\u001b[32m 1878\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[32m 1879\u001b[39m mode += \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m1880\u001b[39m \u001b[38;5;28mself\u001b[39m.handles = \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1881\u001b[39m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1882\u001b[39m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1883\u001b[39m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1884\u001b[39m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcompression\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1885\u001b[39m \u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmemory_map\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1886\u001b[39m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m=\u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1887\u001b[39m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding_errors\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstrict\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1888\u001b[39m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstorage_options\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1889\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1890\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m.handles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 1891\u001b[39m f = \u001b[38;5;28mself\u001b[39m.handles.handle\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~\\anaconda3\\envs\\multitag\\Lib\\site-packages\\pandas\\io\\common.py:873\u001b[39m, in \u001b[36mget_handle\u001b[39m\u001b[34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[39m\n\u001b[32m 868\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[32m 869\u001b[39m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[32m 870\u001b[39m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[32m 871\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ioargs.encoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs.mode:\n\u001b[32m 872\u001b[39m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m873\u001b[39m handle = \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[32m 874\u001b[39m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 875\u001b[39m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 876\u001b[39m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 877\u001b[39m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 878\u001b[39m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 879\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 880\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 881\u001b[39m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[32m 882\u001b[39m handle = \u001b[38;5;28mopen\u001b[39m(handle, ioargs.mode)\n",
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"\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: 'C:\\\\Users\\\\ch\\\\google-drive\\\\Charlie_6013_RECLASS\\\\Data\\\\Raw\\\\Uber Customer Reviews.csv'"
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]
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}
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],
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"source": [
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"uber_df = pd.read_csv(uber, low_memory=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9b8469b3-c606-461f-aaef-9619b7dc1ffd",
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"metadata": {},
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"outputs": [
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{
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"data": {
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" <th></th>\n",
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" <th>source</th>\n",
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" <th>review_id</th>\n",
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" <th>user_name</th>\n",
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" <th>review_title</th>\n",
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" <th>review_description</th>\n",
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" <th>rating</th>\n",
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" <th>thumbs_up</th>\n",
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" <th>review_date</th>\n",
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" <th>developer_response</th>\n",
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" <th>developer_response_date</th>\n",
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" <th>appVersion</th>\n",
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" <th>laguage_code</th>\n",
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" <th>country_code</th>\n",
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" </tr>\n",
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" <tbody>\n",
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" <td>Google Play</td>\n",
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" <td>Milky Way</td>\n",
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" <td>NaN</td>\n",
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" <td>Suddenly, the driver can't have my location an...</td>\n",
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" <td>1</td>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>Google Play</td>\n",
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" <td>50a08f18-cece-4ddf-b617-028844c8aa28</td>\n",
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" <td>Bradlee Severa</td>\n",
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" <td>NaN</td>\n",
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" <td>Very cordial.. And helped with a quick turnaro...</td>\n",
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" <td>5</td>\n",
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" <td>0.0</td>\n",
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" <td>2023-08-10 17:38:35</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>4.485.10000</td>\n",
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" <td>en</td>\n",
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" <td>in</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Google Play</td>\n",
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" <td>b0d8e75a-80a7-4dcd-abaf-72b046dbeeb7</td>\n",
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" <td>Amit Aggarwal</td>\n",
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" <td>NaN</td>\n",
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" <td>Very good experience</td>\n",
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" <td>5</td>\n",
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" <td>4.486.10002</td>\n",
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" <td>en</td>\n",
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" <td>in</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>Google Play</td>\n",
|
|
" <td>502702a9-25ed-4373-a96c-7fa1f06caacd</td>\n",
|
|
" <td>Bryant Inman</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>All I use</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>2023-08-10 17:37:45</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>4.467.10008</td>\n",
|
|
" <td>en</td>\n",
|
|
" <td>in</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>Google Play</td>\n",
|
|
" <td>f47a3fb6-23db-49bd-9e63-f33c8d724d07</td>\n",
|
|
" <td>Addie Whittaker</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>I have enjoyed traveling by Uber my drivers ha...</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>2023-08-10 17:36:56</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>4.486.10002</td>\n",
|
|
" <td>en</td>\n",
|
|
" <td>in</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" source review_id user_name \\\n",
|
|
"0 Google Play 18d6584c-d0e9-4833-a744-f607058aee97 Milky Way \n",
|
|
"1 Google Play 50a08f18-cece-4ddf-b617-028844c8aa28 Bradlee Severa \n",
|
|
"2 Google Play b0d8e75a-80a7-4dcd-abaf-72b046dbeeb7 Amit Aggarwal \n",
|
|
"3 Google Play 502702a9-25ed-4373-a96c-7fa1f06caacd Bryant Inman \n",
|
|
"4 Google Play f47a3fb6-23db-49bd-9e63-f33c8d724d07 Addie Whittaker \n",
|
|
"\n",
|
|
" review_title review_description rating \\\n",
|
|
"0 NaN Suddenly, the driver can't have my location an... 1 \n",
|
|
"1 NaN Very cordial.. And helped with a quick turnaro... 5 \n",
|
|
"2 NaN Very good experience 5 \n",
|
|
"3 NaN All I use 5 \n",
|
|
"4 NaN I have enjoyed traveling by Uber my drivers ha... 5 \n",
|
|
"\n",
|
|
" thumbs_up review_date developer_response developer_response_date \\\n",
|
|
"0 0.0 2023-08-10 17:48:51 NaN NaN \n",
|
|
"1 0.0 2023-08-10 17:38:35 NaN NaN \n",
|
|
"2 0.0 2023-08-10 17:38:17 NaN NaN \n",
|
|
"3 0.0 2023-08-10 17:37:45 NaN NaN \n",
|
|
"4 0.0 2023-08-10 17:36:56 NaN NaN \n",
|
|
"\n",
|
|
" appVersion laguage_code country_code \n",
|
|
"0 NaN en in \n",
|
|
"1 4.485.10000 en in \n",
|
|
"2 4.486.10002 en in \n",
|
|
"3 4.467.10008 en in \n",
|
|
"4 4.486.10002 en in "
|
|
]
|
|
},
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"uber_df.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "1709a2cc-4f7a-4e77-994e-68668612caff",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
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"(1069616, 13)"
|
|
]
|
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},
|
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"execution_count": 15,
|
|
"metadata": {},
|
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"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"np.shape(uber_df)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "06c0c03c-14ba-4451-a6ea-44d36e85327c",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"['source',\n",
|
|
" 'review_id',\n",
|
|
" 'user_name',\n",
|
|
" 'review_title',\n",
|
|
" 'review_description',\n",
|
|
" 'rating',\n",
|
|
" 'thumbs_up',\n",
|
|
" 'review_date',\n",
|
|
" 'developer_response',\n",
|
|
" 'developer_response_date',\n",
|
|
" 'appVersion',\n",
|
|
" 'laguage_code',\n",
|
|
" 'country_code']"
|
|
]
|
|
},
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"uber_df.columns.tolist()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "d22d3bce-eac0-4d02-a4ef-38343f4958ff",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"source object\n",
|
|
"review_id object\n",
|
|
"user_name object\n",
|
|
"review_title object\n",
|
|
"review_description object\n",
|
|
"rating int64\n",
|
|
"thumbs_up float64\n",
|
|
"review_date object\n",
|
|
"developer_response object\n",
|
|
"developer_response_date object\n",
|
|
"appVersion object\n",
|
|
"laguage_code object\n",
|
|
"country_code object\n",
|
|
"dtype: object"
|
|
]
|
|
},
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"uber_df.dtypes"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "e08f5eae-7921-4526-b8fd-29038c55e1bb",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"source 0\n",
|
|
"review_id 0\n",
|
|
"user_name 1\n",
|
|
"review_title 1067436\n",
|
|
"review_description 169\n",
|
|
"rating 0\n",
|
|
"thumbs_up 2180\n",
|
|
"review_date 0\n",
|
|
"developer_response 871352\n",
|
|
"developer_response_date 872338\n",
|
|
"appVersion 241548\n",
|
|
"laguage_code 0\n",
|
|
"country_code 0\n",
|
|
"dtype: int64"
|
|
]
|
|
},
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"uber_df.isnull().sum()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "ea59d211-9958-46f6-bf76-65d8d36c50e4",
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|
"metadata": {},
|
|
"outputs": [],
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"source": []
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|
}
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|
],
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|
"metadata": {
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|
"kernelspec": {
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