{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ab5e9daf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "77ae8f00",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4f7e5910",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kernel python : /opt/homebrew/Caskroom/miniconda/base/bin/python\n"
     ]
    }
   ],
   "source": [
    "print(\"kernel python :\", sys.executable)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca86e045",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env name      : base\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'タ' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33menv name      :\u001b[39m\u001b[33m\"\u001b[39m, os.environ.get(\u001b[33m\"\u001b[39m\u001b[33mCONDA_DEFAULT_ENV\u001b[39m\u001b[33m\"\u001b[39m))\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[43mタ\u001b[49m\n",
      "\u001b[31mNameError\u001b[39m: name 'タ' is not defined"
     ]
    }
   ],
   "source": [
    "print(\"env name      :\", os.environ.get(\"CONDA_DEFAULT_ENV\"))\n",
    "タ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f67540dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env name      : base\n"
     ]
    }
   ],
   "source": [
    "print(\"env name      :\", os.environ.get(\"CONDA_DEFAULT_ENV\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eb236811",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m df = \u001b[43mpd\u001b[49m.read_csv(\u001b[33m\"\u001b[39m\u001b[33mactivities.csv\u001b[39m\u001b[33m\"\u001b[39m, parse_dates=[\u001b[33m\"\u001b[39m\u001b[33mstart_date\u001b[39m\u001b[33m\"\u001b[39m])\n",
      "\u001b[31mNameError\u001b[39m: name 'pd' is not defined"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"activities.csv\", parse_dates=[\"start_date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d2a0bace",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "75448c16",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'activities.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mFileNotFoundError\u001b[39m                         Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m df = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mactivities.csv\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparse_dates\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstart_date\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/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",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/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",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/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",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/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",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/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",
      "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: 'activities.csv'"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"activities.csv\", parse_dates=[\"start_date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eb71392c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/jitsugen/Documents/SoloPRJ/Strava-Dataset-PRJ'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d31b0b33",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Missing column provided to 'parse_dates': 'start_date'",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m df = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m/Users/jitsugen/Documents/SoloPRJ/Strava-Dataset-PRJ/StravaData/activities.csv\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparse_dates\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstart_date\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/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",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/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",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/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",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1898\u001b[39m, in \u001b[36mTextFileReader._make_engine\u001b[39m\u001b[34m(self, f, engine)\u001b[39m\n\u001b[32m   1895\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[32m   1897\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1898\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmapping\u001b[49m\u001b[43m[\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\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\n\u001b[32m   1899\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[32m   1900\u001b[39m     \u001b[38;5;28;01mif\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[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/site-packages/pandas/io/parsers/c_parser_wrapper.py:161\u001b[39m, in \u001b[36mCParserWrapper.__init__\u001b[39m\u001b[34m(self, src, **kwds)\u001b[39m\n\u001b[32m    155\u001b[39m         \u001b[38;5;28mself\u001b[39m._validate_usecols_names(\n\u001b[32m    156\u001b[39m             usecols,\n\u001b[32m    157\u001b[39m             \u001b[38;5;28mself\u001b[39m.names,  \u001b[38;5;66;03m# type: ignore[has-type]\u001b[39;00m\n\u001b[32m    158\u001b[39m         )\n\u001b[32m    160\u001b[39m \u001b[38;5;66;03m# error: Cannot determine type of 'names'\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m161\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_validate_parse_dates_presence\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mnames\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# type: ignore[has-type]\u001b[39;00m\n\u001b[32m    162\u001b[39m \u001b[38;5;28mself\u001b[39m._set_noconvert_columns()\n\u001b[32m    164\u001b[39m \u001b[38;5;66;03m# error: Cannot determine type of 'names'\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/site-packages/pandas/io/parsers/base_parser.py:243\u001b[39m, in \u001b[36mParserBase._validate_parse_dates_presence\u001b[39m\u001b[34m(self, columns)\u001b[39m\n\u001b[32m    233\u001b[39m missing_cols = \u001b[33m\"\u001b[39m\u001b[33m, \u001b[39m\u001b[33m\"\u001b[39m.join(\n\u001b[32m    234\u001b[39m     \u001b[38;5;28msorted\u001b[39m(\n\u001b[32m    235\u001b[39m         {\n\u001b[32m   (...)\u001b[39m\u001b[32m    240\u001b[39m     )\n\u001b[32m    241\u001b[39m )\n\u001b[32m    242\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m missing_cols:\n\u001b[32m--> \u001b[39m\u001b[32m243\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m    244\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mMissing column provided to \u001b[39m\u001b[33m'\u001b[39m\u001b[33mparse_dates\u001b[39m\u001b[33m'\u001b[39m\u001b[33m: \u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmissing_cols\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    245\u001b[39m     )\n\u001b[32m    246\u001b[39m \u001b[38;5;66;03m# Convert positions to actual column names\u001b[39;00m\n\u001b[32m    247\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m [\n\u001b[32m    248\u001b[39m     col \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28misinstance\u001b[39m(col, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m columns) \u001b[38;5;28;01melse\u001b[39;00m columns[col]\n\u001b[32m    249\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m cols_needed\n\u001b[32m    250\u001b[39m ]\n",
      "\u001b[31mValueError\u001b[39m: Missing column provided to 'parse_dates': 'start_date'"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"/Users/jitsugen/Documents/SoloPRJ/Strava-Dataset-PRJ/StravaData/activities.csv\", parse_dates=[\"start_date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "449763c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ activities.csv -> activities.csv へ変換完了\n"
     ]
    }
   ],
   "source": [
    "# ============================================================\n",
    "# Strava 日本語ヘッダー → 英語ヘッダー　一括変換スクリプト\n",
    "# Jupyter Notebook 用（セル 1 発で完結）\n",
    "# ============================================================\n",
    "\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "\n",
    "# ▼ 変換対象ファイル\n",
    "SRC = Path(\"/Users/jitsugen/Documents/SoloPRJ/Strava-Dataset-PRJ/StravaData/activities.csv\")       # 日本語ヘッダー CSV\n",
    "DST = Path(\"/Users/jitsugen/Documents/SoloPRJ/Strava-Dataset-PRJ/StravaData/activities.csv\")    # 出力先（英語ヘッダー CSV）\n",
    "\n",
    "# ▼ 日本語 → 英語 マッピング辞書\n",
    "JP2EN = {\n",
    "    # ==== 基本情報 ====\n",
    "    \"アクティビティID\": \"Activity ID\",\n",
    "    \"アクティビティ実行日\": \"Activity Date\",\n",
    "    \"アクティビティ名\": \"Activity Name\",\n",
    "    \"アクティビティタイプ\": \"Activity Type\",\n",
    "    \"アクティビティの説明\": \"Description\",\n",
    "    \"経過時間\": \"Elapsed Time\",\n",
    "    \"移動時間\": \"Moving Time\",\n",
    "    \"距離\": \"Distance\",\n",
    "    \"開始時間\": \"Start Time\",\n",
    "    \"タイプ\": \"Device Type\",\n",
    "    \"アクティビティのプライベートメモ\": \"Activity Private Notes\",\n",
    "    \"アクティビティギア\": \"Gear\",\n",
    "    \"ギア\": \"Gear\",\n",
    "    \"ファイル名\": \"Filename\",\n",
    "    # ==== 心拍・パワー ====\n",
    "    \"最大心拍数\": \"Max Heart Rate\",\n",
    "    \"平均心拍数\": \"Average Heart Rate\",\n",
    "    \"最大ワット\": \"Max Power\",\n",
    "    \"平均ワット\": \"Average Power\",\n",
    "    \"加重平均パワー\": \"Weighted Average Power\",\n",
    "    \"パワーカウント\": \"Power Count\",\n",
    "    # ==== スピード・距離 ====\n",
    "    \"最高速度\": \"Max Speed\",\n",
    "    \"平均速度\": \"Average Speed\",\n",
    "    \"平均経過スピード\": \"Average Elapsed Speed\",\n",
    "    \"Grade Adjusted Pace\": \"Grade Adjusted Pace\",\n",
    "    \"Grade Adjusted Pace (平均)\": \"Grade Adjusted Pace (Average)\",\n",
    "    # ==== 標高・勾配 ====\n",
    "    \"獲得標高\": \"Elevation Gain\",\n",
    "    \"獲得標高（下り）\": \"Elevation Loss\",\n",
    "    \"最低標高\": \"Min Elevation\",\n",
    "    \"最高標高\": \"Max Elevation\",\n",
    "    \"最大勾配\": \"Max Grade\",\n",
    "    \"平均勾配\": \"Average Grade\",\n",
    "    \"平均プラス勾配\": \"Average Positive Grade\",\n",
    "    \"平均マイナス勾配\": \"Average Negative Grade\",\n",
    "    # ==== ケイデンス ====\n",
    "    \"最大ケイデンス\": \"Max Cadence\",\n",
    "    \"平均ケイデンス\": \"Average Cadence\",\n",
    "    # ==== エフォート／負荷 ====\n",
    "    \"相対的エフォート\": \"Relative Effort\",\n",
    "    \"主観的相対的エフォート\": \"Perceived Relative Effort\",\n",
    "    \"主観的運動強度\": \"Perceived Exertion\",\n",
    "    \"主観的運動強度を使用する\": \"Perceived Exertion Used\",\n",
    "    \"合計運動量\": \"Total Work\",\n",
    "    \"トレーニングロード\": \"Training Load\",\n",
    "    \"強度\": \"Intensity\",\n",
    "    # ==== 時間内訳 ====\n",
    "    \"上り坂のタイム\": \"Uphill Time\",\n",
    "    \"下り坂のタイム\": \"Downhill Time\",\n",
    "    \"その他のタイム\": \"Other Time\",\n",
    "    \"タイマー時間\": \"Timer Time\",\n",
    "    # ==== 体重・重量 ====\n",
    "    \"アスリート体重\": \"Athlete Weight\",\n",
    "    \"自転車重量\": \"Bike Weight\",\n",
    "    \"総重量\": \"Total Weight\",\n",
    "    # ==== 温度・気象 ====\n",
    "    \"最高気温\": \"Max Temperature\",\n",
    "    \"平均気温\": \"Average Temperature\",\n",
    "    \"体感温度\": \"Apparent Temperature\",\n",
    "    \"露点\": \"Dew Point\",\n",
    "    \"湿度\": \"Humidity\",\n",
    "    \"気圧\": \"Pressure\",\n",
    "    \"天候\": \"Weather\",\n",
    "    \"観測時刻\": \"Observation Time\",\n",
    "    \"風速\": \"Wind Speed\",\n",
    "    \"瞬間風速\": \"Wind Gust\",\n",
    "    \"風向\": \"Wind Bearing\",\n",
    "    \"降水量\": \"Precipitation\",\n",
    "    \"降水確率\": \"Precipitation Probability\",\n",
    "    \"降水タイプ\": \"Precipitation Type\",\n",
    "    \"雲量\": \"Cloud Cover\",\n",
    "    \"視程\": \"Visibility\",\n",
    "    \"UV指数\": \"UV Index\",\n",
    "    \"オゾン情報\": \"Ozone\",\n",
    "    \"日の出\": \"Sunrise Time\",\n",
    "    \"日の入\": \"Sunset Time\",\n",
    "    \"月齢\": \"Moon Phase\",\n",
    "    # ==== ルート特有 ====\n",
    "    \"ジャンプ数\": \"Jump Count\",\n",
    "    \"合計グリット\": \"Total Grit\",\n",
    "    \"平均フロー\": \"Average Flow\",\n",
    "    \"フラグが立てられています\": \"Flagged\",\n",
    "    \"未舗装路の距離\": \"Dirt Distance\",\n",
    "    \"新規探索した距離\": \"Newly Explored Distance\",\n",
    "    \"新規探索した未舗装路の距離\": \"Newly Explored Dirt Distance\",\n",
    "    # ==== カロリー・その他 ====\n",
    "    \"カロリー\": \"Calories\",\n",
    "    \"ラン数\": \"Number of Runs\",\n",
    "    \"通勤\": \"Commute\",\n",
    "    \"CO2の削減量\": \"Carbon Savings\",\n",
    "    \"プールの長さ\": \"Pool Length\",\n",
    "    \"アクティビティ数\": \"Activity Count\",\n",
    "    \"ステップ合計\": \"Step Count\",\n",
    "    \"メディア\": \"Media\",\n",
    "    # ==== その他 ====\n",
    "    \"アップロードより\": \"Weather Observed\",\n",
    "    \"合計サイクル\": \"Total Cycles\",\n",
    "}\n",
    "\n",
    "def build_rename_map(columns):\n",
    "    \"\"\"重複列（例: '距離', '距離.1'）にも対応したリネーム辞書を作成\"\"\"\n",
    "    rename = {}\n",
    "    for col in columns:\n",
    "        base = col.split(\".\")[0]           # 例: '距離'\n",
    "        suffix = col[len(base):]           # '' もしくは '.1'\n",
    "        if base in JP2EN:\n",
    "            rename[col] = JP2EN[base] + suffix\n",
    "    return rename\n",
    "\n",
    "def convert_headers(src: Path, dst: Path):\n",
    "    df = pd.read_csv(src)\n",
    "    df = df.rename(columns=build_rename_map(df.columns))\n",
    "    df.to_csv(dst, index=False)\n",
    "    print(f\"✅ {src.name} -> {dst.name} へ変換完了\")\n",
    "\n",
    "# --- 実行 -------------------------------------------------------\n",
    "if not SRC.exists():\n",
    "    raise FileNotFoundError(f\"CSV not found: {SRC.resolve()}\")\n",
    "\n",
    "convert_headers(SRC, DST)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "48b01972",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"StravaData/activities.csv\", parse_dates=[\"Activity Date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9cda0d2e",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'activities_en.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mFileNotFoundError\u001b[39m                         Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[14]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m df = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m      2\u001b[39m \u001b[43m    \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mactivities_en.csv\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m      3\u001b[39m \u001b[43m    \u001b[49m\u001b[43mparse_dates\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mActivity Date\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m   \u001b[49m\u001b[38;5;66;43;03m# 日付列を datetime へ\u001b[39;49;00m\n\u001b[32m      4\u001b[39m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m=\u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m      5\u001b[39m \u001b[43m        \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mDistance\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;43mfloat32\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m      6\u001b[39m \u001b[43m        \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mElapsed Time\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;43mint32\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m      7\u001b[39m \u001b[43m        \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mMoving Time\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;43mint32\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m      8\u001b[39m \u001b[43m    \u001b[49m\u001b[43m}\u001b[49m\n\u001b[32m      9\u001b[39m \u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/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",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/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",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/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",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/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",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/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",
      "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: 'activities_en.csv'"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\n",
    "    \"activities_en.csv\",\n",
    "    parse_dates=[\"Activity Date\"],   # 日付列を datetime へ\n",
    "    dtype={\n",
    "        \"Distance\": \"float32\",\n",
    "        \"Elapsed Time\": \"int32\",\n",
    "        \"Moving Time\": \"int32\",\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "79d66d2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Activity ID</th>\n",
       "      <th>Activity Date</th>\n",
       "      <th>Activity Name</th>\n",
       "      <th>Activity Type</th>\n",
       "      <th>Description</th>\n",
       "      <th>Elapsed Time</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Max Heart Rate</th>\n",
       "      <th>Relative Effort</th>\n",
       "      <th>Commute</th>\n",
       "      <th>...</th>\n",
       "      <th>Activity Count</th>\n",
       "      <th>Step Count</th>\n",
       "      <th>Carbon Savings</th>\n",
       "      <th>Pool Length</th>\n",
       "      <th>Training Load</th>\n",
       "      <th>Intensity</th>\n",
       "      <th>Grade Adjusted Pace (Average)</th>\n",
       "      <th>Timer Time</th>\n",
       "      <th>Total Cycles</th>\n",
       "      <th>Media</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8280988291</td>\n",
       "      <td>2022-12-23 06:57:23</td>\n",
       "      <td>午後のランニング</td>\n",
       "      <td>ランニング</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1530</td>\n",
       "      <td>4.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8286585787</td>\n",
       "      <td>2022-12-24 09:57:04</td>\n",
       "      <td>夕方の水泳</td>\n",
       "      <td>水泳</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4454</td>\n",
       "      <td>1150.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8293206227</td>\n",
       "      <td>2022-12-26 10:42:37</td>\n",
       "      <td>夕方の水泳</td>\n",
       "      <td>水泳</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4556</td>\n",
       "      <td>550.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8314368113</td>\n",
       "      <td>2022-12-31 06:05:56</td>\n",
       "      <td>午後のライド</td>\n",
       "      <td>ライド</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1897</td>\n",
       "      <td>9.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8330224933</td>\n",
       "      <td>2023-01-01 06:53:06</td>\n",
       "      <td>午後のライド</td>\n",
       "      <td>ライド</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1222</td>\n",
       "      <td>7.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Activity ID       Activity Date Activity Name Activity Type  Description  \\\n",
       "0   8280988291 2022-12-23 06:57:23      午後のランニング         ランニング          NaN   \n",
       "1   8286585787 2022-12-24 09:57:04         夕方の水泳            水泳          NaN   \n",
       "2   8293206227 2022-12-26 10:42:37         夕方の水泳            水泳          NaN   \n",
       "3   8314368113 2022-12-31 06:05:56        午後のライド           ライド          NaN   \n",
       "4   8330224933 2023-01-01 06:53:06        午後のライド           ライド          NaN   \n",
       "\n",
       "   Elapsed Time  Distance  Max Heart Rate  Relative Effort  Commute  ...  \\\n",
       "0          1530      4.62             NaN              NaN    False  ...   \n",
       "1          4454   1150.00             NaN              NaN    False  ...   \n",
       "2          4556    550.00             NaN              NaN    False  ...   \n",
       "3          1897      9.10             NaN              NaN    False  ...   \n",
       "4          1222      7.62             NaN              NaN    False  ...   \n",
       "\n",
       "   Activity Count  Step Count Carbon Savings  Pool Length  Training Load  \\\n",
       "0             NaN         NaN            NaN          NaN            NaN   \n",
       "1             NaN         NaN            NaN          NaN            NaN   \n",
       "2             NaN         NaN            NaN          NaN            NaN   \n",
       "3             NaN         NaN            NaN          NaN            NaN   \n",
       "4             NaN         NaN            NaN          NaN            NaN   \n",
       "\n",
       "   Intensity  Grade Adjusted Pace (Average)  Timer Time  Total Cycles  Media  \n",
       "0        NaN                            NaN         NaN           NaN    NaN  \n",
       "1        NaN                            NaN         NaN           NaN    NaN  \n",
       "2        NaN                            NaN         NaN           NaN    NaN  \n",
       "3        NaN                            NaN         NaN           NaN    NaN  \n",
       "4        NaN                            NaN         NaN           NaN    NaN  \n",
       "\n",
       "[5 rows x 94 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\n",
    "    \"/Users/jitsugen/Documents/SoloPRJ/Strava-Dataset-PRJ/StravaData/activities.csv\",\n",
    "    parse_dates=[\"Activity Date\"],\n",
    "    thousands=\",\",           # ← カンマを数値から自動除去\n",
    "    low_memory=False         # dtype 混在警告を抑制したい場合\n",
    ")\n",
    "\n",
    "# 別セルで型を落とす（省メモリ化）\n",
    "df = df.astype({\n",
    "    \"Distance\": \"float32\",\n",
    "    \"Elapsed Time\": \"int32\",\n",
    "    \"Moving Time\": \"int32\"\n",
    "})\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "67927c66",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'moving_hr'",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/site-packages/pandas/core/indexes/base.py:3805\u001b[39m, in \u001b[36mIndex.get_loc\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   3804\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m3805\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_engine\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   3806\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "\u001b[36mFile \u001b[39m\u001b[32mindex.pyx:167\u001b[39m, in \u001b[36mpandas._libs.index.IndexEngine.get_loc\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mindex.pyx:196\u001b[39m, in \u001b[36mpandas._libs.index.IndexEngine.get_loc\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[39m, in \u001b[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[39m, in \u001b[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[31mKeyError\u001b[39m: 'moving_hr'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[17]\u001b[39m\u001b[32m, line 16\u001b[39m\n\u001b[32m     11\u001b[39m df[\u001b[33m\"\u001b[39m\u001b[33mtraining_category\u001b[39m\u001b[33m\"\u001b[39m] = pd.cut(\n\u001b[32m     12\u001b[39m     df[\u001b[33m\"\u001b[39m\u001b[33mintensity_level\u001b[39m\u001b[33m\"\u001b[39m], bins=bins, labels=labels, right=\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m     13\u001b[39m ).cat.add_categories(\u001b[33m\"\u001b[39m\u001b[33mNoHR\u001b[39m\u001b[33m\"\u001b[39m).fillna(\u001b[33m\"\u001b[39m\u001b[33mNoHR\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m     15\u001b[39m \u001b[38;5;66;03m# 3) TRIMP\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m16\u001b[39m df[\u001b[33m\"\u001b[39m\u001b[33mtrimp\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmoving_hr\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m * df[\u001b[33m\"\u001b[39m\u001b[33mintensity_level\u001b[39m\u001b[33m\"\u001b[39m] * \u001b[32m50\u001b[39m\n\u001b[32m     18\u001b[39m \u001b[38;5;66;03m# 4) 保存\u001b[39;00m\n\u001b[32m     19\u001b[39m df.to_parquet(\u001b[33m\"\u001b[39m\u001b[33mstrava_master_enhanced.parquet\u001b[39m\u001b[33m\"\u001b[39m, index=\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/site-packages/pandas/core/frame.py:4102\u001b[39m, in \u001b[36mDataFrame.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   4100\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.columns.nlevels > \u001b[32m1\u001b[39m:\n\u001b[32m   4101\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._getitem_multilevel(key)\n\u001b[32m-> \u001b[39m\u001b[32m4102\u001b[39m indexer = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   4103\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[32m   4104\u001b[39m     indexer = [indexer]\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/site-packages/pandas/core/indexes/base.py:3812\u001b[39m, in \u001b[36mIndex.get_loc\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   3807\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[32m   3808\u001b[39m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc.Iterable)\n\u001b[32m   3809\u001b[39m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[32m   3810\u001b[39m     ):\n\u001b[32m   3811\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[32m-> \u001b[39m\u001b[32m3812\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[34;01merr\u001b[39;00m\n\u001b[32m   3813\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[32m   3814\u001b[39m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[32m   3815\u001b[39m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[32m   3816\u001b[39m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[32m   3817\u001b[39m     \u001b[38;5;28mself\u001b[39m._check_indexing_error(key)\n",
      "\u001b[31mKeyError\u001b[39m: 'moving_hr'"
     ]
    }
   ],
   "source": [
    "# 0) 前ステップで df に distance_km, moving_hr などが出来ている前提\n",
    "import pandas as pd\n",
    "LTHR = 165          # 自分で設定\n",
    "\n",
    "# 1) intensity_level\n",
    "df[\"intensity_level\"] = (df[\"Average Heart Rate\"] / LTHR).round(2)\n",
    "\n",
    "# 2) training_category\n",
    "bins   = [0, .79, .89, .95, 1.10]\n",
    "labels = [\"Z1-2\", \"Z3\", \"Z4\", \"Z5\"]\n",
    "df[\"training_category\"] = pd.cut(\n",
    "    df[\"intensity_level\"], bins=bins, labels=labels, right=False\n",
    ").cat.add_categories(\"NoHR\").fillna(\"NoHR\")\n",
    "\n",
    "# 3) TRIMP\n",
    "df[\"trimp\"] = df[\"moving_hr\"] * df[\"intensity_level\"] * 50\n",
    "\n",
    "# 4) 保存\n",
    "df.to_parquet(\"strava_master_enhanced.parquet\", index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a95f5212",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Activity ID', 'Activity Date', 'Activity Name', 'Activity Type', 'Description', 'Elapsed Time', 'Distance', 'Max Heart Rate', 'Relative Effort', 'Commute', 'Activity Private Notes', 'Gear', 'Filename', 'Athlete Weight', 'Bike Weight', 'Elapsed Time.1', 'Moving Time', 'Distance.1', 'Max Speed', 'Average Speed', 'Elevation Gain', 'Elevation Loss', 'Min Elevation', 'Max Elevation', 'Max Grade', 'Average Grade', 'Average Positive Grade', 'Average Negative Grade', 'Max Cadence', 'Average Cadence', 'Max Heart Rate.1', 'Average Heart Rate', 'Max Power', 'Average Power', 'Calories', 'Max Temperature', 'Average Temperature', 'Relative Effort.1', 'Total Work', 'Number of Runs', 'Uphill Time', 'Downhill Time', 'Other Time', 'Perceived Exertion', 'Device Type', 'Start Time', 'Weighted Average Power', 'Power Count', 'Perceived Exertion Used', 'Perceived Relative Effort', 'Commute.1', 'Total Weight', 'Weather Observed', 'Grade Adjusted Pace', 'Observation Time', 'Weather', 'Average Temperature.1', 'Apparent Temperature', 'Dew Point', 'Humidity', 'Pressure', 'Wind Speed', 'Wind Gust', 'Wind Bearing', 'Precipitation', 'Sunset Time', 'Sunrise Time', 'Moon Phase', '自転車', 'Gear.1', 'Precipitation Probability', 'Precipitation Type', 'Cloud Cover', 'Visibility', 'UV Index', 'Ozone', 'Jump Count', 'Total Grit', 'Average Flow', 'Flagged', 'Average Elapsed Speed', 'Dirt Distance', 'Newly Explored Distance', 'Newly Explored Dirt Distance', 'Activity Count', 'Step Count', 'Carbon Savings', 'Pool Length', 'Training Load', 'Intensity', 'Grade Adjusted Pace (Average)', 'Timer Time', 'Total Cycles', 'Media', 'intensity_level', 'training_category']\n"
     ]
    }
   ],
   "source": [
    "print(df.columns.tolist())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4ac5e0fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Moving Time は秒なので h に変換\n",
    "df[\"moving_hr\"] = pd.to_numeric(df[\"Moving Time\"], errors=\"coerce\") / 3600\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "23f8cf4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- moving_hr を作ってから一気に ---\n",
    "LTHR = 165\n",
    "\n",
    "df = (\n",
    "    df\n",
    "    .assign(\n",
    "        moving_hr       = pd.to_numeric(df[\"Moving Time\"], errors=\"coerce\") / 3600,\n",
    "        intensity_level = lambda x: (x[\"Average Heart Rate\"] / LTHR).round(2)\n",
    "    )\n",
    ")\n",
    "\n",
    "bins   = [0, .79, .89, .95, 1.10]\n",
    "labels = [\"Z1-2\", \"Z3\", \"Z4\", \"Z5\"]\n",
    "df[\"training_category\"] = pd.cut(\n",
    "    df[\"intensity_level\"], bins=bins, labels=labels, right=False\n",
    ").cat.add_categories(\"NoHR\").fillna(\"NoHR\")\n",
    "\n",
    "df[\"trimp\"] = df[\"moving_hr\"] * df[\"intensity_level\"] * 50\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1a845f19",
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'.\nA suitable version of pyarrow or fastparquet is required for parquet support.\nTrying to import the above resulted in these errors:\n - Missing optional dependency 'pyarrow'. pyarrow is required for parquet support. Use pip or conda to install pyarrow.\n - Missing optional dependency 'fastparquet'. fastparquet is required for parquet support. Use pip or conda to install fastparquet.",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mImportError\u001b[39m                               Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[22]\u001b[39m\u001b[32m, line 11\u001b[39m\n\u001b[32m      8\u001b[39m \u001b[38;5;66;03m# ----------------------------------------\u001b[39;00m\n\u001b[32m      9\u001b[39m \u001b[38;5;66;03m# 1) Parquet 保存（高速・省サイズ）\u001b[39;00m\n\u001b[32m     10\u001b[39m parq_path = out_dir / \u001b[33m\"\u001b[39m\u001b[33mstrava_master_enhanced.parquet\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m11\u001b[39m \u001b[43mdf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto_parquet\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparq_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m     12\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m✅ Parquet 保存完了: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparq_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m     14\u001b[39m \u001b[38;5;66;03m# 2) CSV も欲しい場合（任意）\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/site-packages/pandas/util/_decorators.py:333\u001b[39m, in \u001b[36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m    327\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) > num_allow_args:\n\u001b[32m    328\u001b[39m     warnings.warn(\n\u001b[32m    329\u001b[39m         msg.format(arguments=_format_argument_list(allow_args)),\n\u001b[32m    330\u001b[39m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[32m    331\u001b[39m         stacklevel=find_stack_level(),\n\u001b[32m    332\u001b[39m     )\n\u001b[32m--> \u001b[39m\u001b[32m333\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/site-packages/pandas/core/frame.py:3113\u001b[39m, in \u001b[36mDataFrame.to_parquet\u001b[39m\u001b[34m(self, path, engine, compression, index, partition_cols, storage_options, **kwargs)\u001b[39m\n\u001b[32m   3032\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m   3033\u001b[39m \u001b[33;03mWrite a DataFrame to the binary parquet format.\u001b[39;00m\n\u001b[32m   3034\u001b[39m \n\u001b[32m   (...)\u001b[39m\u001b[32m   3109\u001b[39m \u001b[33;03m>>> content = f.read()\u001b[39;00m\n\u001b[32m   3110\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m   3111\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[34;01mpandas\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mio\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mparquet\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m to_parquet\n\u001b[32m-> \u001b[39m\u001b[32m3113\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mto_parquet\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   3114\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m   3115\u001b[39m \u001b[43m    \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3116\u001b[39m \u001b[43m    \u001b[49m\u001b[43mengine\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3117\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3118\u001b[39m \u001b[43m    \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m=\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3119\u001b[39m \u001b[43m    \u001b[49m\u001b[43mpartition_cols\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpartition_cols\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3120\u001b[39m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3121\u001b[39m \u001b[43m    \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   3122\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/site-packages/pandas/io/parquet.py:476\u001b[39m, in \u001b[36mto_parquet\u001b[39m\u001b[34m(df, path, engine, compression, index, storage_options, partition_cols, filesystem, **kwargs)\u001b[39m\n\u001b[32m    474\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(partition_cols, \u001b[38;5;28mstr\u001b[39m):\n\u001b[32m    475\u001b[39m     partition_cols = [partition_cols]\n\u001b[32m--> \u001b[39m\u001b[32m476\u001b[39m impl = \u001b[43mget_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    478\u001b[39m path_or_buf: FilePath | WriteBuffer[\u001b[38;5;28mbytes\u001b[39m] = io.BytesIO() \u001b[38;5;28;01mif\u001b[39;00m path \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m path\n\u001b[32m    480\u001b[39m impl.write(\n\u001b[32m    481\u001b[39m     df,\n\u001b[32m    482\u001b[39m     path_or_buf,\n\u001b[32m   (...)\u001b[39m\u001b[32m    488\u001b[39m     **kwargs,\n\u001b[32m    489\u001b[39m )\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/site-packages/pandas/io/parquet.py:67\u001b[39m, in \u001b[36mget_engine\u001b[39m\u001b[34m(engine)\u001b[39m\n\u001b[32m     64\u001b[39m         \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[32m     65\u001b[39m             error_msgs += \u001b[33m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m - \u001b[39m\u001b[33m\"\u001b[39m + \u001b[38;5;28mstr\u001b[39m(err)\n\u001b[32m---> \u001b[39m\u001b[32m67\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\n\u001b[32m     68\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mUnable to find a usable engine; \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m     69\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mtried using: \u001b[39m\u001b[33m'\u001b[39m\u001b[33mpyarrow\u001b[39m\u001b[33m'\u001b[39m\u001b[33m, \u001b[39m\u001b[33m'\u001b[39m\u001b[33mfastparquet\u001b[39m\u001b[33m'\u001b[39m\u001b[33m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m     70\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mA suitable version of \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m     71\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mpyarrow or fastparquet is required for parquet \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m     72\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33msupport.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m     73\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mTrying to import the above resulted in these errors:\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m     74\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00merror_msgs\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m     75\u001b[39m     )\n\u001b[32m     77\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m engine == \u001b[33m\"\u001b[39m\u001b[33mpyarrow\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m     78\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m PyArrowImpl()\n",
      "\u001b[31mImportError\u001b[39m: Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'.\nA suitable version of pyarrow or fastparquet is required for parquet support.\nTrying to import the above resulted in these errors:\n - Missing optional dependency 'pyarrow'. pyarrow is required for parquet support. Use pip or conda to install pyarrow.\n - Missing optional dependency 'fastparquet'. fastparquet is required for parquet support. Use pip or conda to install fastparquet."
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "# ----------------------------------------\n",
    "# 0) 保存先ディレクトリを用意（無ければ作成）\n",
    "out_dir = Path(\"/Users/jitsugen/Documents/SoloPRJ/Strava-Dataset-PRJ/my_strava_dataset\")\n",
    "out_dir.mkdir(exist_ok=True)\n",
    "\n",
    "# ----------------------------------------\n",
    "# 1) Parquet 保存（高速・省サイズ）\n",
    "parq_path = out_dir / \"strava_master_enhanced.parquet\"\n",
    "df.to_parquet(parq_path, index=False)\n",
    "print(f\"✅ Parquet 保存完了: {parq_path}\")\n",
    "\n",
    "# 2) CSV も欲しい場合（任意）\n",
    "csv_path = out_dir / \"strava_master_enhanced.csv\"\n",
    "df.to_csv(csv_path, index=False)\n",
    "print(f\"✅ CSV 保存完了: {csv_path}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f01f5685",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install --quiet pyarrow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c689d216",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Parquet 保存完了: /Users/jitsugen/Documents/SoloPRJ/Strava-Dataset-PRJ/my_strava_dataset/strava_master_enhanced.parquet\n",
      "✅ CSV 保存完了: /Users/jitsugen/Documents/SoloPRJ/Strava-Dataset-PRJ/my_strava_dataset/strava_master_enhanced.csv\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "# ----------------------------------------\n",
    "# 0) 保存先ディレクトリを用意（無ければ作成）\n",
    "out_dir = Path(\"/Users/jitsugen/Documents/SoloPRJ/Strava-Dataset-PRJ/my_strava_dataset\")\n",
    "out_dir.mkdir(exist_ok=True)\n",
    "\n",
    "# ----------------------------------------\n",
    "# 1) Parquet 保存（高速・省サイズ）\n",
    "parq_path = out_dir / \"strava_master_enhanced.parquet\"\n",
    "df.to_parquet(parq_path, index=False)\n",
    "print(f\"✅ Parquet 保存完了: {parq_path}\")\n",
    "\n",
    "# 2) CSV も欲しい場合（任意）\n",
    "csv_path = out_dir / \"strava_master_enhanced.csv\"\n",
    "df.to_csv(csv_path, index=False)\n",
    "print(f\"✅ CSV 保存完了: {csv_path}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9bd3bb5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(474, 98) rows x columns\n"
     ]
    },
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Activity ID</th>\n",
       "      <th>Activity Date</th>\n",
       "      <th>Activity Name</th>\n",
       "      <th>Activity Type</th>\n",
       "      <th>Description</th>\n",
       "      <th>Elapsed Time</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Max Heart Rate</th>\n",
       "      <th>Relative Effort</th>\n",
       "      <th>Commute</th>\n",
       "      <th>...</th>\n",
       "      <th>Training Load</th>\n",
       "      <th>Intensity</th>\n",
       "      <th>Grade Adjusted Pace (Average)</th>\n",
       "      <th>Timer Time</th>\n",
       "      <th>Total Cycles</th>\n",
       "      <th>Media</th>\n",
       "      <th>intensity_level</th>\n",
       "      <th>training_category</th>\n",
       "      <th>moving_hr</th>\n",
       "      <th>trimp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8280988291</td>\n",
       "      <td>2022-12-23 06:57:23</td>\n",
       "      <td>午後のランニング</td>\n",
       "      <td>ランニング</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1530</td>\n",
       "      <td>4.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NoHR</td>\n",
       "      <td>0.421944</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8286585787</td>\n",
       "      <td>2022-12-24 09:57:04</td>\n",
       "      <td>夕方の水泳</td>\n",
       "      <td>水泳</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4454</td>\n",
       "      <td>1150.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NoHR</td>\n",
       "      <td>0.446111</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8293206227</td>\n",
       "      <td>2022-12-26 10:42:37</td>\n",
       "      <td>夕方の水泳</td>\n",
       "      <td>水泳</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4556</td>\n",
       "      <td>550.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NoHR</td>\n",
       "      <td>0.400278</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8314368113</td>\n",
       "      <td>2022-12-31 06:05:56</td>\n",
       "      <td>午後のライド</td>\n",
       "      <td>ライド</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1897</td>\n",
       "      <td>9.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NoHR</td>\n",
       "      <td>0.454722</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8330224933</td>\n",
       "      <td>2023-01-01 06:53:06</td>\n",
       "      <td>午後のライド</td>\n",
       "      <td>ライド</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1222</td>\n",
       "      <td>7.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NoHR</td>\n",
       "      <td>0.325278</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 98 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Activity ID       Activity Date Activity Name Activity Type  Description  \\\n",
       "0   8280988291 2022-12-23 06:57:23      午後のランニング         ランニング          NaN   \n",
       "1   8286585787 2022-12-24 09:57:04         夕方の水泳            水泳          NaN   \n",
       "2   8293206227 2022-12-26 10:42:37         夕方の水泳            水泳          NaN   \n",
       "3   8314368113 2022-12-31 06:05:56        午後のライド           ライド          NaN   \n",
       "4   8330224933 2023-01-01 06:53:06        午後のライド           ライド          NaN   \n",
       "\n",
       "   Elapsed Time  Distance  Max Heart Rate  Relative Effort  Commute  ...  \\\n",
       "0          1530      4.62             NaN              NaN    False  ...   \n",
       "1          4454   1150.00             NaN              NaN    False  ...   \n",
       "2          4556    550.00             NaN              NaN    False  ...   \n",
       "3          1897      9.10             NaN              NaN    False  ...   \n",
       "4          1222      7.62             NaN              NaN    False  ...   \n",
       "\n",
       "   Training Load  Intensity Grade Adjusted Pace (Average)  Timer Time  \\\n",
       "0            NaN        NaN                           NaN         NaN   \n",
       "1            NaN        NaN                           NaN         NaN   \n",
       "2            NaN        NaN                           NaN         NaN   \n",
       "3            NaN        NaN                           NaN         NaN   \n",
       "4            NaN        NaN                           NaN         NaN   \n",
       "\n",
       "   Total Cycles  Media  intensity_level  training_category  moving_hr  trimp  \n",
       "0           NaN    NaN              NaN               NoHR   0.421944    NaN  \n",
       "1           NaN    NaN              NaN               NoHR   0.446111    NaN  \n",
       "2           NaN    NaN              NaN               NoHR   0.400278    NaN  \n",
       "3           NaN    NaN              NaN               NoHR   0.454722    NaN  \n",
       "4           NaN    NaN              NaN               NoHR   0.325278    NaN  \n",
       "\n",
       "[5 rows x 98 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_parquet(parq_path)\n",
    "print(test.shape, \"rows x columns\")\n",
    "test.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "98daa8dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "root = Path(\"/Users/jitsugen/Documents/SoloPRJ/Strava-Dataset-PRJ/my_strava_dataset\")\n",
    "df = pd.read_parquet(root / \"strava_master_enhanced.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8ec21e06",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'date'",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/site-packages/pandas/core/indexes/base.py:3805\u001b[39m, in \u001b[36mIndex.get_loc\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   3804\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m3805\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_engine\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   3806\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "\u001b[36mFile \u001b[39m\u001b[32mindex.pyx:167\u001b[39m, in \u001b[36mpandas._libs.index.IndexEngine.get_loc\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mindex.pyx:196\u001b[39m, in \u001b[36mpandas._libs.index.IndexEngine.get_loc\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[39m, in \u001b[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[39m, in \u001b[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[31mKeyError\u001b[39m: 'date'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[27]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m df[\u001b[33m\"\u001b[39m\u001b[33myear\u001b[39m\u001b[33m\"\u001b[39m]  = \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mdate\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m.dt.year\n\u001b[32m      2\u001b[39m df[\u001b[33m\"\u001b[39m\u001b[33mweek\u001b[39m\u001b[33m\"\u001b[39m]  = df[\u001b[33m\"\u001b[39m\u001b[33mdate\u001b[39m\u001b[33m\"\u001b[39m].dt.isocalendar().week\n\u001b[32m      3\u001b[39m df[\u001b[33m\"\u001b[39m\u001b[33mmonth\u001b[39m\u001b[33m\"\u001b[39m] = df[\u001b[33m\"\u001b[39m\u001b[33mdate\u001b[39m\u001b[33m\"\u001b[39m].dt.month\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/site-packages/pandas/core/frame.py:4102\u001b[39m, in \u001b[36mDataFrame.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   4100\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.columns.nlevels > \u001b[32m1\u001b[39m:\n\u001b[32m   4101\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._getitem_multilevel(key)\n\u001b[32m-> \u001b[39m\u001b[32m4102\u001b[39m indexer = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   4103\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[32m   4104\u001b[39m     indexer = [indexer]\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/site-packages/pandas/core/indexes/base.py:3812\u001b[39m, in \u001b[36mIndex.get_loc\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   3807\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[32m   3808\u001b[39m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc.Iterable)\n\u001b[32m   3809\u001b[39m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[32m   3810\u001b[39m     ):\n\u001b[32m   3811\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[32m-> \u001b[39m\u001b[32m3812\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[34;01merr\u001b[39;00m\n\u001b[32m   3813\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[32m   3814\u001b[39m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[32m   3815\u001b[39m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[32m   3816\u001b[39m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[32m   3817\u001b[39m     \u001b[38;5;28mself\u001b[39m._check_indexing_error(key)\n",
      "\u001b[31mKeyError\u001b[39m: 'date'"
     ]
    }
   ],
   "source": [
    "df[\"year\"]  = df[\"date\"].dt.year\n",
    "df[\"week\"]  = df[\"date\"].dt.isocalendar().week\n",
    "df[\"month\"] = df[\"date\"].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0c80a049",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Activity ID', 'Activity Date', 'Activity Name', 'Activity Type', 'Description', 'Elapsed Time', 'Distance', 'Max Heart Rate', 'Relative Effort', 'Commute', 'Activity Private Notes', 'Gear', 'Filename', 'Athlete Weight', 'Bike Weight']\n"
     ]
    }
   ],
   "source": [
    "print(df.columns.tolist()[:15])   # 列名をざっと表示\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3fcb9c20",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={\"Activity Date\": \"date\"})   # 列名が違えば適宜変更\n",
    "df[\"date\"] = pd.to_datetime(df[\"date\"])             # 念のため datetime 型へ\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "78880905",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"year\"]  = df[\"date\"].dt.year\n",
    "df[\"week\"]  = df[\"date\"].dt.isocalendar().week\n",
    "df[\"month\"] = df[\"date\"].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "51c2d9f2",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"Column(s) ['distance_km'] do not exist\"",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[31]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m weekly_sport = (\n\u001b[32m      2\u001b[39m     \u001b[43mdf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43myear\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;43mweek\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;43mActivity Type\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mas_index\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[43m      \u001b[49m\u001b[43m.\u001b[49m\u001b[43magg\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtotal_dur_hr\u001b[49m\u001b[43m=\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmoving_hr\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;43msum\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m      4\u001b[39m \u001b[43m           \u001b[49m\u001b[43mdist_km\u001b[49m\u001b[43m      \u001b[49m\u001b[43m=\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mdistance_km\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;43msum\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m      5\u001b[39m \u001b[43m           \u001b[49m\u001b[43mtrimp_sum\u001b[49m\u001b[43m    \u001b[49m\u001b[43m=\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtrimp\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;43msum\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m      6\u001b[39m )\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/site-packages/pandas/core/groupby/generic.py:1432\u001b[39m, in \u001b[36mDataFrameGroupBy.aggregate\u001b[39m\u001b[34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[39m\n\u001b[32m   1429\u001b[39m     kwargs[\u001b[33m\"\u001b[39m\u001b[33mengine_kwargs\u001b[39m\u001b[33m\"\u001b[39m] = engine_kwargs\n\u001b[32m   1431\u001b[39m op = GroupByApply(\u001b[38;5;28mself\u001b[39m, func, args=args, kwargs=kwargs)\n\u001b[32m-> \u001b[39m\u001b[32m1432\u001b[39m result = \u001b[43mop\u001b[49m\u001b[43m.\u001b[49m\u001b[43magg\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1433\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_dict_like(func) \u001b[38;5;129;01mand\u001b[39;00m result \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   1434\u001b[39m     \u001b[38;5;66;03m# GH #52849\u001b[39;00m\n\u001b[32m   1435\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m.as_index \u001b[38;5;129;01mand\u001b[39;00m is_list_like(func):\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/site-packages/pandas/core/apply.py:190\u001b[39m, in \u001b[36mApply.agg\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    187\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.apply_str()\n\u001b[32m    189\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_dict_like(func):\n\u001b[32m--> \u001b[39m\u001b[32m190\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43magg_dict_like\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    191\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m is_list_like(func):\n\u001b[32m    192\u001b[39m     \u001b[38;5;66;03m# we require a list, but not a 'str'\u001b[39;00m\n\u001b[32m    193\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.agg_list_like()\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/site-packages/pandas/core/apply.py:423\u001b[39m, in \u001b[36mApply.agg_dict_like\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    415\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[34magg_dict_like\u001b[39m(\u001b[38;5;28mself\u001b[39m) -> DataFrame | Series:\n\u001b[32m    416\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m    417\u001b[39m \u001b[33;03m    Compute aggregation in the case of a dict-like argument.\u001b[39;00m\n\u001b[32m    418\u001b[39m \n\u001b[32m   (...)\u001b[39m\u001b[32m    421\u001b[39m \u001b[33;03m    Result of aggregation.\u001b[39;00m\n\u001b[32m    422\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m423\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43magg_or_apply_dict_like\u001b[49m\u001b[43m(\u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43magg\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/site-packages/pandas/core/apply.py:1608\u001b[39m, in \u001b[36mGroupByApply.agg_or_apply_dict_like\u001b[39m\u001b[34m(self, op_name)\u001b[39m\n\u001b[32m   1603\u001b[39m     kwargs.update({\u001b[33m\"\u001b[39m\u001b[33mengine\u001b[39m\u001b[33m\"\u001b[39m: engine, \u001b[33m\"\u001b[39m\u001b[33mengine_kwargs\u001b[39m\u001b[33m\"\u001b[39m: engine_kwargs})\n\u001b[32m   1605\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m com.temp_setattr(\n\u001b[32m   1606\u001b[39m     obj, \u001b[33m\"\u001b[39m\u001b[33mas_index\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m, condition=\u001b[38;5;28mhasattr\u001b[39m(obj, \u001b[33m\"\u001b[39m\u001b[33mas_index\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m   1607\u001b[39m ):\n\u001b[32m-> \u001b[39m\u001b[32m1608\u001b[39m     result_index, result_data = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mcompute_dict_like\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   1609\u001b[39m \u001b[43m        \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mselected_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mselection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\n\u001b[32m   1610\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1611\u001b[39m result = \u001b[38;5;28mself\u001b[39m.wrap_results_dict_like(selected_obj, result_index, result_data)\n\u001b[32m   1612\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/site-packages/pandas/core/apply.py:462\u001b[39m, in \u001b[36mApply.compute_dict_like\u001b[39m\u001b[34m(self, op_name, selected_obj, selection, kwargs)\u001b[39m\n\u001b[32m    460\u001b[39m is_groupby = \u001b[38;5;28misinstance\u001b[39m(obj, (DataFrameGroupBy, SeriesGroupBy))\n\u001b[32m    461\u001b[39m func = cast(AggFuncTypeDict, \u001b[38;5;28mself\u001b[39m.func)\n\u001b[32m--> \u001b[39m\u001b[32m462\u001b[39m func = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mnormalize_dictlike_arg\u001b[49m\u001b[43m(\u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mselected_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    464\u001b[39m is_non_unique_col = (\n\u001b[32m    465\u001b[39m     selected_obj.ndim == \u001b[32m2\u001b[39m\n\u001b[32m    466\u001b[39m     \u001b[38;5;129;01mand\u001b[39;00m selected_obj.columns.nunique() < \u001b[38;5;28mlen\u001b[39m(selected_obj.columns)\n\u001b[32m    467\u001b[39m )\n\u001b[32m    469\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m selected_obj.ndim == \u001b[32m1\u001b[39m:\n\u001b[32m    470\u001b[39m     \u001b[38;5;66;03m# key only used for output\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.12/site-packages/pandas/core/apply.py:663\u001b[39m, in \u001b[36mApply.normalize_dictlike_arg\u001b[39m\u001b[34m(self, how, obj, func)\u001b[39m\n\u001b[32m    661\u001b[39m     cols = Index(\u001b[38;5;28mlist\u001b[39m(func.keys())).difference(obj.columns, sort=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m    662\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(cols) > \u001b[32m0\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m663\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mColumn(s) \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(cols)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m do not exist\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m    665\u001b[39m aggregator_types = (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m, \u001b[38;5;28mdict\u001b[39m)\n\u001b[32m    667\u001b[39m \u001b[38;5;66;03m# if we have a dict of any non-scalars\u001b[39;00m\n\u001b[32m    668\u001b[39m \u001b[38;5;66;03m# eg. {'A' : ['mean']}, normalize all to\u001b[39;00m\n\u001b[32m    669\u001b[39m \u001b[38;5;66;03m# be list-likes\u001b[39;00m\n\u001b[32m    670\u001b[39m \u001b[38;5;66;03m# Cannot use func.values() because arg may be a Series\u001b[39;00m\n",
      "\u001b[31mKeyError\u001b[39m: \"Column(s) ['distance_km'] do not exist\""
     ]
    }
   ],
   "source": [
    "weekly_sport = (\n",
    "    df.groupby([\"year\", \"week\", \"Activity Type\"], as_index=False)\n",
    "      .agg(total_dur_hr=(\"moving_hr\", \"sum\"),\n",
    "           dist_km      =(\"distance_km\", \"sum\"),\n",
    "           trimp_sum    =(\"trimp\", \"sum\"))\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "12b131a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Distance', 'Distance.1', 'Dirt Distance', 'Newly Explored Distance', 'Newly Explored Dirt Distance']\n"
     ]
    }
   ],
   "source": [
    "print([c for c in df.columns if \"Distance\" in c])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2b509864",
   "metadata": {},
   "outputs": [],
   "source": [
    "if \"Distance\" in df.columns:\n",
    "    df[\"distance_km\"] = pd.to_numeric(df[\"Distance\"], errors=\"coerce\") / 1000\n",
    "else:\n",
    "    raise KeyError(\"Distance 列が見つかりません。列名を確認してください。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bc930eeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>week</th>\n",
       "      <th>Activity Type</th>\n",
       "      <th>total_dur_hr</th>\n",
       "      <th>dist_km</th>\n",
       "      <th>trimp_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>51</td>\n",
       "      <td>ランニング</td>\n",
       "      <td>0.421944</td>\n",
       "      <td>0.00462</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>51</td>\n",
       "      <td>水泳</td>\n",
       "      <td>0.446111</td>\n",
       "      <td>1.15000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>52</td>\n",
       "      <td>ライド</td>\n",
       "      <td>0.454722</td>\n",
       "      <td>0.00910</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>52</td>\n",
       "      <td>水泳</td>\n",
       "      <td>0.400278</td>\n",
       "      <td>0.55000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023</td>\n",
       "      <td>1</td>\n",
       "      <td>ウェイトトレーニング</td>\n",
       "      <td>1.250278</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  week Activity Type  total_dur_hr  dist_km  trimp_sum\n",
       "0  2022    51         ランニング      0.421944  0.00462        0.0\n",
       "1  2022    51            水泳      0.446111  1.15000        0.0\n",
       "2  2022    52           ライド      0.454722  0.00910        0.0\n",
       "3  2022    52            水泳      0.400278  0.55000        0.0\n",
       "4  2023     1    ウェイトトレーニング      1.250278  0.00000        0.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekly_sport = (\n",
    "    df.groupby([\"year\", \"week\", \"Activity Type\"], as_index=False)\n",
    "      .agg(total_dur_hr=(\"moving_hr\", \"sum\"),\n",
    "           dist_km      =(\"distance_km\", \"sum\"),\n",
    "           trimp_sum    =(\"trimp\", \"sum\"))\n",
    ")\n",
    "weekly_sport.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "29556526",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[43mdf\u001b[49m.shape)\n",
      "\u001b[31mNameError\u001b[39m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "456de871",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m df = \u001b[43mpd\u001b[49m.read_parquet(\u001b[33m\"\u001b[39m\u001b[33m/Users/jitsugen/Documents/SoloPRJ/Strava-Dataset-PRJ/my_strava_dataset/strava_master_enhanced.parquet\u001b[39m\u001b[33m\"\u001b[39m)\n",
      "\u001b[31mNameError\u001b[39m: name 'pd' is not defined"
     ]
    }
   ],
   "source": [
    "df = pd.read_parquet(\"/Users/jitsugen/Documents/SoloPRJ/Strava-Dataset-PRJ/my_strava_dataset/strava_master_enhanced.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5119551a",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'matplotlib'",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[34;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[34;01mpd\u001b[39;00m\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[34;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[34;01mnp\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[34;01mplt\u001b[39;00m      \u001b[38;5;66;03m# 軽い可視化用\u001b[39;00m\n",
      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'matplotlib'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt      # 軽い可視化用\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0c049c2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4bdfd9b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_parquet(\"/Users/jitsugen/Documents/SoloPRJ/Strava-Dataset-PRJ/my_strava_dataset/strava_master_enhanced.parquet\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7fe05117",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(474, 98)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "29cfcdc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Activity ID', 'Activity Date', 'Activity Name', 'Activity Type', 'Description', 'Elapsed Time', 'Distance', 'Max Heart Rate', 'Relative Effort', 'Commute', 'Activity Private Notes', 'Gear', 'Filename', 'Athlete Weight', 'Bike Weight', 'Elapsed Time.1', 'Moving Time', 'Distance.1', 'Max Speed', 'Average Speed', 'Elevation Gain', 'Elevation Loss', 'Min Elevation', 'Max Elevation', 'Max Grade', 'Average Grade', 'Average Positive Grade', 'Average Negative Grade', 'Max Cadence', 'Average Cadence', 'Max Heart Rate.1', 'Average Heart Rate', 'Max Power', 'Average Power', 'Calories', 'Max Temperature', 'Average Temperature', 'Relative Effort.1', 'Total Work', 'Number of Runs', 'Uphill Time', 'Downhill Time', 'Other Time', 'Perceived Exertion', 'Device Type', 'Start Time', 'Weighted Average Power', 'Power Count', 'Perceived Exertion Used', 'Perceived Relative Effort', 'Commute.1', 'Total Weight', 'Weather Observed', 'Grade Adjusted Pace', 'Observation Time', 'Weather', 'Average Temperature.1', 'Apparent Temperature', 'Dew Point', 'Humidity', 'Pressure', 'Wind Speed', 'Wind Gust', 'Wind Bearing', 'Precipitation', 'Sunset Time', 'Sunrise Time', 'Moon Phase', '自転車', 'Gear.1', 'Precipitation Probability', 'Precipitation Type', 'Cloud Cover', 'Visibility', 'UV Index', 'Ozone', 'Jump Count', 'Total Grit', 'Average Flow', 'Flagged', 'Average Elapsed Speed', 'Dirt Distance', 'Newly Explored Distance', 'Newly Explored Dirt Distance', 'Activity Count', 'Step Count', 'Carbon Savings', 'Pool Length', 'Training Load', 'Intensity', 'Grade Adjusted Pace (Average)', 'Timer Time', 'Total Cycles', 'Media', 'intensity_level', 'training_category', 'moving_hr', 'trimp']\n"
     ]
    }
   ],
   "source": [
    "print(df.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b3e75671",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Activity ID</th>\n",
       "      <th>Activity Date</th>\n",
       "      <th>Activity Name</th>\n",
       "      <th>Activity Type</th>\n",
       "      <th>Description</th>\n",
       "      <th>Elapsed Time</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Max Heart Rate</th>\n",
       "      <th>Relative Effort</th>\n",
       "      <th>Commute</th>\n",
       "      <th>...</th>\n",
       "      <th>Training Load</th>\n",
       "      <th>Intensity</th>\n",
       "      <th>Grade Adjusted Pace (Average)</th>\n",
       "      <th>Timer Time</th>\n",
       "      <th>Total Cycles</th>\n",
       "      <th>Media</th>\n",
       "      <th>intensity_level</th>\n",
       "      <th>training_category</th>\n",
       "      <th>moving_hr</th>\n",
       "      <th>trimp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8280988291</td>\n",
       "      <td>2022-12-23 06:57:23</td>\n",
       "      <td>午後のランニング</td>\n",
       "      <td>ランニング</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1530</td>\n",
       "      <td>4.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NoHR</td>\n",
       "      <td>0.421944</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8286585787</td>\n",
       "      <td>2022-12-24 09:57:04</td>\n",
       "      <td>夕方の水泳</td>\n",
       "      <td>水泳</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4454</td>\n",
       "      <td>1150.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NoHR</td>\n",
       "      <td>0.446111</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8293206227</td>\n",
       "      <td>2022-12-26 10:42:37</td>\n",
       "      <td>夕方の水泳</td>\n",
       "      <td>水泳</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4556</td>\n",
       "      <td>550.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NoHR</td>\n",
       "      <td>0.400278</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8314368113</td>\n",
       "      <td>2022-12-31 06:05:56</td>\n",
       "      <td>午後のライド</td>\n",
       "      <td>ライド</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1897</td>\n",
       "      <td>9.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NoHR</td>\n",
       "      <td>0.454722</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8330224933</td>\n",
       "      <td>2023-01-01 06:53:06</td>\n",
       "      <td>午後のライド</td>\n",
       "      <td>ライド</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1222</td>\n",
       "      <td>7.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NoHR</td>\n",
       "      <td>0.325278</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 98 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Activity ID       Activity Date Activity Name Activity Type  Description  \\\n",
       "0   8280988291 2022-12-23 06:57:23      午後のランニング         ランニング          NaN   \n",
       "1   8286585787 2022-12-24 09:57:04         夕方の水泳            水泳          NaN   \n",
       "2   8293206227 2022-12-26 10:42:37         夕方の水泳            水泳          NaN   \n",
       "3   8314368113 2022-12-31 06:05:56        午後のライド           ライド          NaN   \n",
       "4   8330224933 2023-01-01 06:53:06        午後のライド           ライド          NaN   \n",
       "\n",
       "   Elapsed Time  Distance  Max Heart Rate  Relative Effort  Commute  ...  \\\n",
       "0          1530      4.62             NaN              NaN    False  ...   \n",
       "1          4454   1150.00             NaN              NaN    False  ...   \n",
       "2          4556    550.00             NaN              NaN    False  ...   \n",
       "3          1897      9.10             NaN              NaN    False  ...   \n",
       "4          1222      7.62             NaN              NaN    False  ...   \n",
       "\n",
       "   Training Load  Intensity Grade Adjusted Pace (Average)  Timer Time  \\\n",
       "0            NaN        NaN                           NaN         NaN   \n",
       "1            NaN        NaN                           NaN         NaN   \n",
       "2            NaN        NaN                           NaN         NaN   \n",
       "3            NaN        NaN                           NaN         NaN   \n",
       "4            NaN        NaN                           NaN         NaN   \n",
       "\n",
       "   Total Cycles  Media  intensity_level  training_category  moving_hr  trimp  \n",
       "0           NaN    NaN              NaN               NoHR   0.421944    NaN  \n",
       "1           NaN    NaN              NaN               NoHR   0.446111    NaN  \n",
       "2           NaN    NaN              NaN               NoHR   0.400278    NaN  \n",
       "3           NaN    NaN              NaN               NoHR   0.454722    NaN  \n",
       "4           NaN    NaN              NaN               NoHR   0.325278    NaN  \n",
       "\n",
       "[5 rows x 98 columns]"
      ]
     },
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
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   "execution_count": 10,
   "id": "47335ec0",
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   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 474 entries, 0 to 473\n",
      "Data columns (total 98 columns):\n",
      " #   Column                         Non-Null Count  Dtype         \n",
      "---  ------                         --------------  -----         \n",
      " 0   Activity ID                    474 non-null    int64         \n",
      " 1   Activity Date                  474 non-null    datetime64[ns]\n",
      " 2   Activity Name                  474 non-null    object        \n",
      " 3   Activity Type                  474 non-null    object        \n",
      " 4   Description                    0 non-null      float64       \n",
      " 5   Elapsed Time                   474 non-null    int32         \n",
      " 6   Distance                       474 non-null    float32       \n",
      " 7   Max Heart Rate                 0 non-null      float64       \n",
      " 8   Relative Effort                0 non-null      float64       \n",
      " 9   Commute                        474 non-null    bool          \n",
      " 10  Activity Private Notes         0 non-null      float64       \n",
      " 11  Gear                           0 non-null      float64       \n",
      " 12  Filename                       474 non-null    object        \n",
      " 13  Athlete Weight                 0 non-null      float64       \n",
      " 14  Bike Weight                    0 non-null      float64       \n",
      " 15  Elapsed Time.1                 474 non-null    float64       \n",
      " 16  Moving Time                    474 non-null    int32         \n",
      " 17  Distance.1                     474 non-null    float64       \n",
      " 18  Max Speed                      474 non-null    float64       \n",
      " 19  Average Speed                  474 non-null    float64       \n",
      " 20  Elevation Gain                 474 non-null    float64       \n",
      " 21  Elevation Loss                 372 non-null    float64       \n",
      " 22  Min Elevation                  372 non-null    float64       \n",
      " 23  Max Elevation                  372 non-null    float64       \n",
      " 24  Max Grade                      474 non-null    float64       \n",
      " 25  Average Grade                  474 non-null    float64       \n",
      " 26  Average Positive Grade         0 non-null      float64       \n",
      " 27  Average Negative Grade         0 non-null      float64       \n",
      " 28  Max Cadence                    147 non-null    float64       \n",
      " 29  Average Cadence                260 non-null    float64       \n",
      " 30  Max Heart Rate.1               0 non-null      float64       \n",
      " 31  Average Heart Rate             0 non-null      float64       \n",
      " 32  Max Power                      0 non-null      float64       \n",
      " 33  Average Power                  114 non-null    float64       \n",
      " 34  Calories                       465 non-null    float64       \n",
      " 35  Max Temperature                0 non-null      float64       \n",
      " 36  Average Temperature            20 non-null     float64       \n",
      " 37  Relative Effort.1              0 non-null      float64       \n",
      " 38  Total Work                     115 non-null    float64       \n",
      " 39  Number of Runs                 0 non-null      float64       \n",
      " 40  Uphill Time                    0 non-null      float64       \n",
      " 41  Downhill Time                  0 non-null      float64       \n",
      " 42  Other Time                     0 non-null      float64       \n",
      " 43  Perceived Exertion             0 non-null      float64       \n",
      " 44  Device Type                    0 non-null      float64       \n",
      " 45  Start Time                     0 non-null      float64       \n",
      " 46  Weighted Average Power         114 non-null    float64       \n",
      " 47  Power Count                    114 non-null    float64       \n",
      " 48  Perceived Exertion Used        0 non-null      float64       \n",
      " 49  Perceived Relative Effort      0 non-null      float64       \n",
      " 50  Commute.1                      474 non-null    float64       \n",
      " 51  Total Weight                   0 non-null      float64       \n",
      " 52  Weather Observed               474 non-null    float64       \n",
      " 53  Grade Adjusted Pace            116 non-null    float64       \n",
      " 54  Observation Time               5 non-null      float64       \n",
      " 55  Weather                        5 non-null      float64       \n",
      " 56  Average Temperature.1          5 non-null      float64       \n",
      " 57  Apparent Temperature           5 non-null      float64       \n",
      " 58  Dew Point                      5 non-null      float64       \n",
      " 59  Humidity                       5 non-null      float64       \n",
      " 60  Pressure                       5 non-null      float64       \n",
      " 61  Wind Speed                     5 non-null      float64       \n",
      " 62  Wind Gust                      5 non-null      float64       \n",
      " 63  Wind Bearing                   5 non-null      float64       \n",
      " 64  Precipitation                  5 non-null      float64       \n",
      " 65  Sunset Time                    5 non-null      float64       \n",
      " 66  Sunrise Time                   5 non-null      float64       \n",
      " 67  Moon Phase                     5 non-null      float64       \n",
      " 68  自転車                            0 non-null      float64       \n",
      " 69  Gear.1                         0 non-null      float64       \n",
      " 70  Precipitation Probability      5 non-null      float64       \n",
      " 71  Precipitation Type             5 non-null      float64       \n",
      " 72  Cloud Cover                    5 non-null      float64       \n",
      " 73  Visibility                     5 non-null      float64       \n",
      " 74  UV Index                       5 non-null      float64       \n",
      " 75  Ozone                          0 non-null      float64       \n",
      " 76  Jump Count                     0 non-null      float64       \n",
      " 77  Total Grit                     0 non-null      float64       \n",
      " 78  Average Flow                   0 non-null      float64       \n",
      " 79  Flagged                        474 non-null    float64       \n",
      " 80  Average Elapsed Speed          474 non-null    float64       \n",
      " 81  Dirt Distance                  474 non-null    float64       \n",
      " 82  Newly Explored Distance        0 non-null      float64       \n",
      " 83  Newly Explored Dirt Distance   0 non-null      float64       \n",
      " 84  Activity Count                 0 non-null      float64       \n",
      " 85  Step Count                     28 non-null     float64       \n",
      " 86  Carbon Savings                 0 non-null      float64       \n",
      " 87  Pool Length                    31 non-null     float64       \n",
      " 88  Training Load                  0 non-null      float64       \n",
      " 89  Intensity                      0 non-null      float64       \n",
      " 90  Grade Adjusted Pace (Average)  27 non-null     float64       \n",
      " 91  Timer Time                     0 non-null      float64       \n",
      " 92  Total Cycles                   70 non-null     float64       \n",
      " 93  Media                          0 non-null      float64       \n",
      " 94  intensity_level                0 non-null      float64       \n",
      " 95  training_category              474 non-null    category      \n",
      " 96  moving_hr                      474 non-null    float64       \n",
      " 97  trimp                          0 non-null      float64       \n",
      "dtypes: bool(1), category(1), datetime64[ns](1), float32(1), float64(88), int32(2), int64(1), object(3)\n",
      "memory usage: 351.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eebf9653",
   "metadata": {},
   "outputs": [],
   "source": []
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