Initial Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fba09113",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f39b3b7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"DATA.csv\",skiprows = 99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f5d22a27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "      <th>360 angle</th>\n",
       "      <th>Up/down angle</th>\n",
       "      <th>Observed position</th>\n",
       "      <th>Body Rotation</th>\n",
       "      <th>Prone-sit class</th>\n",
       "      <th>Supine-recline class</th>\n",
       "      <th>Overall class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-08-01 11:50:17:000</td>\n",
       "      <td>0.0082</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>-0.9780</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>30.5</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>180.480383</td>\n",
       "      <td>-3.337126</td>\n",
       "      <td>NaN</td>\n",
       "      <td>supine-recline</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left side</td>\n",
       "      <td>left side</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-08-01 11:50:17:010</td>\n",
       "      <td>0.0122</td>\n",
       "      <td>-0.073</td>\n",
       "      <td>-0.9899</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>30.5</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>180.706105</td>\n",
       "      <td>-4.219453</td>\n",
       "      <td>NaN</td>\n",
       "      <td>supine-recline</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left side</td>\n",
       "      <td>left side</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-08-01 11:50:17:020</td>\n",
       "      <td>0.0082</td>\n",
       "      <td>-0.073</td>\n",
       "      <td>-0.9780</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>30.5</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>180.480383</td>\n",
       "      <td>-4.270778</td>\n",
       "      <td>NaN</td>\n",
       "      <td>supine-recline</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left side</td>\n",
       "      <td>left side</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-08-01 11:50:17:030</td>\n",
       "      <td>0.0162</td>\n",
       "      <td>-0.077</td>\n",
       "      <td>-0.9899</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>30.5</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>180.937578</td>\n",
       "      <td>-4.449495</td>\n",
       "      <td>NaN</td>\n",
       "      <td>supine-recline</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left side</td>\n",
       "      <td>left side</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-08-01 11:50:17:040</td>\n",
       "      <td>0.0082</td>\n",
       "      <td>-0.073</td>\n",
       "      <td>-0.9740</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>30.5</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>180.482356</td>\n",
       "      <td>-4.288251</td>\n",
       "      <td>NaN</td>\n",
       "      <td>supine-recline</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left side</td>\n",
       "      <td>left side</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Unnamed: 0  Unnamed: 1  Unnamed: 2  Unnamed: 3  Unnamed: 4  \\\n",
       "0  2024-08-01 11:50:17:000      0.0082      -0.057     -0.9780          74   \n",
       "1  2024-08-01 11:50:17:010      0.0122      -0.073     -0.9899          74   \n",
       "2  2024-08-01 11:50:17:020      0.0082      -0.073     -0.9780          74   \n",
       "3  2024-08-01 11:50:17:030      0.0162      -0.077     -0.9899          74   \n",
       "4  2024-08-01 11:50:17:040      0.0082      -0.073     -0.9740          74   \n",
       "\n",
       "   Unnamed: 5  Unnamed: 6  Unnamed: 7  Unnamed: 8  Unnamed: 9  Unnamed: 10  \\\n",
       "0           0        30.5         NaN         NaN         NaN          NaN   \n",
       "1           0        30.5         NaN         NaN         NaN          NaN   \n",
       "2           0        30.5         NaN         NaN         NaN          NaN   \n",
       "3           0        30.5         NaN         NaN         NaN          NaN   \n",
       "4           0        30.5         NaN         NaN         NaN          NaN   \n",
       "\n",
       "   Unnamed: 11  Unnamed: 12  360 angle   Up/down angle  Observed position  \\\n",
       "0          NaN          NaN  180.480383      -3.337126                NaN   \n",
       "1          NaN          NaN  180.706105      -4.219453                NaN   \n",
       "2          NaN          NaN  180.480383      -4.270778                NaN   \n",
       "3          NaN          NaN  180.937578      -4.449495                NaN   \n",
       "4          NaN          NaN  180.482356      -4.288251                NaN   \n",
       "\n",
       "    Body Rotation Prone-sit class Supine-recline class  Overall class   \n",
       "0  supine-recline             NaN             left side      left side  \n",
       "1  supine-recline             NaN             left side      left side  \n",
       "2  supine-recline             NaN             left side      left side  \n",
       "3  supine-recline             NaN             left side      left side  \n",
       "4  supine-recline             NaN             left side      left side  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3331a252",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df.columns[[5,7,8,9,10,11,12]], axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0ffb9cbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns = {'Unnamed: 0':'Timing'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2454d133",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Timing'] = pd.to_datetime(df['Timing'], format='%Y-%m-%d %H:%M:%S:%f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "69a4d4bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Milliseconds'] = df['Timing'].dt.microsecond // 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a5e46ca4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Time Diff'] = df['Milliseconds'].diff()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fffbd592",
   "metadata": {},
   "source": [
    "# Data Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1a7bb154",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 10.0     19899\n",
       "-990.0      200\n",
       "Name: Milliseconds, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pd.unique(df['Milliseconds'].diff())\n",
    "\n",
    "df['Milliseconds'].diff().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dccd9cd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20100"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Milliseconds'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9c5ed187",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{100}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df[df['Time Diff']<=0].groupby('Overall class ')['Overall class '].count()\n",
    "\n",
    "idx_lst = list(df[df['Time Diff']<=0].index)\n",
    "\n",
    "set([t - s for s, t in zip(idx_lst, idx_lst[1:])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ca12940a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  nan,   10., -990.])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df['Milliseconds']\n",
    "\n",
    "# set(df['Milliseconds'].diff())\n",
    "\n",
    "df['Time Diff'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "63aff473",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df[df['Milliseconds'].diff().isna()] # First Row \n",
    "\n",
    "df[df['Milliseconds'].diff()<0] # Changing from 990 to 0 \n",
    "\n",
    "df[df['Milliseconds'].diff()<0]['Timing'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "32de98b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['Time Diff (s)'].unique()\n",
    "# df[df['Time Diff (s)']==0.01]\n",
    "# df[df['Time Diff (s)']==0.]\n",
    "\n",
    "# If the previous row has the same overall class then add to the sum or \n",
    "\n",
    "# Simply set the Time Diff = 0.01 where the Time Diff = -.099"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "38b01721",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 10.])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Time Diff']<0,'Time Diff'] = 10.0\n",
    "df['Time Diff'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8ad0554b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Time Diff (s)'] = df['Time Diff']/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "41d3d72a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ nan, 0.01])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Time Diff (s)'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f6f4b06",
   "metadata": {},
   "source": [
    "df.loc[df['Time Diff (s)'].isna(),'Time Diff (s)'] = 0.01\n",
    "df['Time Diff (s)'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "271fb053",
   "metadata": {},
   "source": [
    "#### OLD DATA \n",
    "\n",
    "left side : 23.06\n",
    "prone : 20.82\n",
    "prone supported : 62.59\n",
    "reclined : 53.71\n",
    "right side : 2.08\n",
    "supine : 19.82\n",
    "upright : 1.99\n",
    "\n",
    "Old Total 184.07000000000002"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "638577b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Overall class   Time Diff (s)\n",
      "0        left side          23.28\n",
      "1            prone          21.04\n",
      "2  prone supported          63.23\n",
      "3         reclined          54.24\n",
      "4       right side           2.11\n",
      "5           supine          20.02\n",
      "6          upright           2.01\n"
     ]
    }
   ],
   "source": [
    "# df.groupby('Overall class ')['Time Diff'].sum()\n",
    "\n",
    "df['Time Diff (s)'] = df['Time Diff (s)'].fillna(0).clip(lower=0.01)\n",
    "\n",
    "grouped_df = df.groupby('Overall class ')['Time Diff (s)'].sum().reset_index()\n",
    "\n",
    "print(grouped_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "841f1c0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "185.93000000000004"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_df['Time Diff (s)'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "da0e386e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.bar(data = grouped_df, x = 'Overall class ',height = 'Time Diff (s)')\n",
    "\n",
    "plt.xlabel('Overall Class')\n",
    "plt.ylabel('Time Elapsed (s)')\n",
    "\n",
    "sns.despine(bottom = True, left = True)\n",
    "plt.xticks(rotation=45)  # Rotate x-axis labels by 45 degrees\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4235ab2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.groupby('Overall class ')['Milliseconds'].diff().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "701a845b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Body Rotation\n",
       "prone-sit    1.507\n",
       "Name: Timing, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Overall class '].isna()].groupby('Body Rotation')['Timing'].count()/1000 # Missing Seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "05c05e53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200.99\n"
     ]
    }
   ],
   "source": [
    "td = df['Timing'].max() - df['Timing'].min()\n",
    "\n",
    "secs = td.total_seconds()\n",
    "\n",
    "print(secs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c64f47ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_check = df[['Timing','Body Rotation','Overall class ']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "251abc47",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saira\\AppData\\Local\\Temp\\ipykernel_7668\\2484361359.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_check['RN'] = df_check.sort_values('Timing').groupby(['Overall class ']).cumcount()+1\n"
     ]
    }
   ],
   "source": [
    "# df_check.groupby('Overall class ')['Timing'].cumsum()\n",
    "\n",
    "# df['Overall '].sort_values(by='date').cumsum()\n",
    "\n",
    "# df.sort_values('Timing').groupby(['Body Rotation']).cumcount()+1\n",
    "\n",
    "df_check['RN'] = df_check.sort_values('Timing').groupby(['Overall class ']).cumcount()+1\n",
    "\n",
    "# df_check[df_check['RN'] < df_check['RN'].shift(1)]['Timing'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "72945b03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Overall class \n",
       "left side           31\n",
       "prone              123\n",
       "prone supported     17\n",
       "reclined             9\n",
       "right side          11\n",
       "supine              20\n",
       "upright             20\n",
       "Name: Overall class , dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_check[df_check['RN'] < df_check['RN'].shift(1)].groupby('Overall class ')['Overall class '].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "de228751",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Count 1507\n"
     ]
    }
   ],
   "source": [
    "# df.count()\n",
    "\n",
    "# Body Rotation --> 20100\n",
    "\n",
    "# Prone Sit 8628 + Supine-Recline 9965 --> 18593 (Overall Class)\n",
    "\n",
    "# Shift in Class --> 200\n",
    "\n",
    "# Missing --> 1507\n",
    "\n",
    "print(\"Missing Count\",20100-18593)\n",
    "\n",
    "# df.describe()\n",
    "\n",
    "# df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "825a04ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Body Rotation\n",
       "prone-sit         10135\n",
       "supine-recline     9965\n",
       "Name: Body Rotation, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Body Rotation')['Body Rotation'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1c847938",
   "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>Timing</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>360 angle</th>\n",
       "      <th>Up/down angle</th>\n",
       "      <th>Observed position</th>\n",
       "      <th>Body Rotation</th>\n",
       "      <th>Prone-sit class</th>\n",
       "      <th>Supine-recline class</th>\n",
       "      <th>Overall class</th>\n",
       "      <th>Milliseconds</th>\n",
       "      <th>Time Diff</th>\n",
       "      <th>Time Diff (s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>2024-08-01 11:50:17.760</td>\n",
       "      <td>-0.0239</td>\n",
       "      <td>0.0270</td>\n",
       "      <td>-0.0078</td>\n",
       "      <td>130</td>\n",
       "      <td>30.5</td>\n",
       "      <td>108.074603</td>\n",
       "      <td>47.066230</td>\n",
       "      <td>NaN</td>\n",
       "      <td>prone-sit</td>\n",
       "      <td>prone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>prone</td>\n",
       "      <td>760</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2849</th>\n",
       "      <td>2024-08-01 11:50:45.490</td>\n",
       "      <td>-2.1919</td>\n",
       "      <td>2.1906</td>\n",
       "      <td>-0.0039</td>\n",
       "      <td>297</td>\n",
       "      <td>29.5</td>\n",
       "      <td>90.101945</td>\n",
       "      <td>45.005775</td>\n",
       "      <td>NaN</td>\n",
       "      <td>prone-sit</td>\n",
       "      <td>prone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>prone</td>\n",
       "      <td>490</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6238</th>\n",
       "      <td>2024-08-01 11:51:19.380</td>\n",
       "      <td>-0.5619</td>\n",
       "      <td>0.3389</td>\n",
       "      <td>-0.6691</td>\n",
       "      <td>111</td>\n",
       "      <td>29.0</td>\n",
       "      <td>139.976971</td>\n",
       "      <td>21.210611</td>\n",
       "      <td>NaN</td>\n",
       "      <td>prone-sit</td>\n",
       "      <td>prone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>prone</td>\n",
       "      <td>380</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6239</th>\n",
       "      <td>2024-08-01 11:51:19.390</td>\n",
       "      <td>-0.5780</td>\n",
       "      <td>0.3189</td>\n",
       "      <td>-0.6612</td>\n",
       "      <td>111</td>\n",
       "      <td>29.0</td>\n",
       "      <td>138.841079</td>\n",
       "      <td>19.967149</td>\n",
       "      <td>NaN</td>\n",
       "      <td>prone-sit</td>\n",
       "      <td>prone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>prone</td>\n",
       "      <td>390</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6240</th>\n",
       "      <td>2024-08-01 11:51:19.400</td>\n",
       "      <td>-0.6181</td>\n",
       "      <td>0.3189</td>\n",
       "      <td>-0.6691</td>\n",
       "      <td>111</td>\n",
       "      <td>29.0</td>\n",
       "      <td>137.268923</td>\n",
       "      <td>19.304557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>prone-sit</td>\n",
       "      <td>prone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>prone</td>\n",
       "      <td>400</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16899</th>\n",
       "      <td>2024-08-01 11:53:05.990</td>\n",
       "      <td>-0.1685</td>\n",
       "      <td>0.0390</td>\n",
       "      <td>0.0397</td>\n",
       "      <td>726</td>\n",
       "      <td>28.5</td>\n",
       "      <td>76.742424</td>\n",
       "      <td>12.702392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>prone-sit</td>\n",
       "      <td>prone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>prone</td>\n",
       "      <td>990</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16900</th>\n",
       "      <td>2024-08-01 11:53:06.000</td>\n",
       "      <td>-0.2046</td>\n",
       "      <td>0.1149</td>\n",
       "      <td>0.0991</td>\n",
       "      <td>726</td>\n",
       "      <td>28.5</td>\n",
       "      <td>64.156321</td>\n",
       "      <td>26.826444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>prone-sit</td>\n",
       "      <td>prone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>prone</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16901</th>\n",
       "      <td>2024-08-01 11:53:06.010</td>\n",
       "      <td>-0.1323</td>\n",
       "      <td>0.1629</td>\n",
       "      <td>0.0555</td>\n",
       "      <td>726</td>\n",
       "      <td>28.5</td>\n",
       "      <td>67.241896</td>\n",
       "      <td>48.653576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>prone-sit</td>\n",
       "      <td>prone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>prone</td>\n",
       "      <td>10</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16902</th>\n",
       "      <td>2024-08-01 11:53:06.020</td>\n",
       "      <td>-0.0721</td>\n",
       "      <td>0.1149</td>\n",
       "      <td>0.0159</td>\n",
       "      <td>726</td>\n",
       "      <td>28.5</td>\n",
       "      <td>77.563781</td>\n",
       "      <td>57.305085</td>\n",
       "      <td>NaN</td>\n",
       "      <td>prone-sit</td>\n",
       "      <td>prone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>prone</td>\n",
       "      <td>20</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16903</th>\n",
       "      <td>2024-08-01 11:53:06.030</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0829</td>\n",
       "      <td>0.0159</td>\n",
       "      <td>726</td>\n",
       "      <td>28.5</td>\n",
       "      <td>359.279336</td>\n",
       "      <td>79.181977</td>\n",
       "      <td>NaN</td>\n",
       "      <td>prone-sit</td>\n",
       "      <td>prone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>prone</td>\n",
       "      <td>30</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2104 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Timing  Unnamed: 1  Unnamed: 2  Unnamed: 3  Unnamed: 4  \\\n",
       "76    2024-08-01 11:50:17.760     -0.0239      0.0270     -0.0078         130   \n",
       "2849  2024-08-01 11:50:45.490     -2.1919      2.1906     -0.0039         297   \n",
       "6238  2024-08-01 11:51:19.380     -0.5619      0.3389     -0.6691         111   \n",
       "6239  2024-08-01 11:51:19.390     -0.5780      0.3189     -0.6612         111   \n",
       "6240  2024-08-01 11:51:19.400     -0.6181      0.3189     -0.6691         111   \n",
       "...                       ...         ...         ...         ...         ...   \n",
       "16899 2024-08-01 11:53:05.990     -0.1685      0.0390      0.0397         726   \n",
       "16900 2024-08-01 11:53:06.000     -0.2046      0.1149      0.0991         726   \n",
       "16901 2024-08-01 11:53:06.010     -0.1323      0.1629      0.0555         726   \n",
       "16902 2024-08-01 11:53:06.020     -0.0721      0.1149      0.0159         726   \n",
       "16903 2024-08-01 11:53:06.030      0.0002      0.0829      0.0159         726   \n",
       "\n",
       "       Unnamed: 6  360 angle   Up/down angle  Observed position Body Rotation  \\\n",
       "76           30.5  108.074603      47.066230                NaN     prone-sit   \n",
       "2849         29.5   90.101945      45.005775                NaN     prone-sit   \n",
       "6238         29.0  139.976971      21.210611                NaN     prone-sit   \n",
       "6239         29.0  138.841079      19.967149                NaN     prone-sit   \n",
       "6240         29.0  137.268923      19.304557                NaN     prone-sit   \n",
       "...           ...         ...            ...                ...           ...   \n",
       "16899        28.5   76.742424      12.702392                NaN     prone-sit   \n",
       "16900        28.5   64.156321      26.826444                NaN     prone-sit   \n",
       "16901        28.5   67.241896      48.653576                NaN     prone-sit   \n",
       "16902        28.5   77.563781      57.305085                NaN     prone-sit   \n",
       "16903        28.5  359.279336      79.181977                NaN     prone-sit   \n",
       "\n",
       "      Prone-sit class Supine-recline class  Overall class   Milliseconds  \\\n",
       "76              prone                   NaN          prone           760   \n",
       "2849            prone                   NaN          prone           490   \n",
       "6238            prone                   NaN          prone           380   \n",
       "6239            prone                   NaN          prone           390   \n",
       "6240            prone                   NaN          prone           400   \n",
       "...               ...                   ...            ...           ...   \n",
       "16899           prone                   NaN          prone           990   \n",
       "16900           prone                   NaN          prone             0   \n",
       "16901           prone                   NaN          prone            10   \n",
       "16902           prone                   NaN          prone            20   \n",
       "16903           prone                   NaN          prone            30   \n",
       "\n",
       "       Time Diff  Time Diff (s)  \n",
       "76          10.0           0.01  \n",
       "2849        10.0           0.01  \n",
       "6238        10.0           0.01  \n",
       "6239        10.0           0.01  \n",
       "6240        10.0           0.01  \n",
       "...          ...            ...  \n",
       "16899       10.0           0.01  \n",
       "16900       10.0           0.01  \n",
       "16901       10.0           0.01  \n",
       "16902       10.0           0.01  \n",
       "16903       10.0           0.01  \n",
       "\n",
       "[2104 rows x 16 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Body Rotation'] == 'prone-sit') & (df['Overall class ']=='prone')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1617413a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "prone supported    6323\n",
       "prone              2104\n",
       "upright             201\n",
       "Name: Overall class , dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Body Rotation'] == 'prone-sit']['Overall class '].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2bf8d52d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Overall class \n",
       "left side          2328\n",
       "prone              2104\n",
       "prone supported    6323\n",
       "reclined           5424\n",
       "right side          211\n",
       "supine             2002\n",
       "upright             201\n",
       "Name: Overall class , dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Overall class ')['Overall class '].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "70f76355",
   "metadata": {},
   "outputs": [],
   "source": [
    "gp_df = df.groupby(['Body Rotation','Overall class '])['Timing'].count().reset_index().rename(columns = {'Timing':'Row Count'}).sort_values(by = 'Row Count',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4ed207ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Body Rotation   Overall class  \n",
       "prone-sit       prone              2104\n",
       "                prone supported    6323\n",
       "                upright             201\n",
       "supine-recline  left side          2328\n",
       "                reclined           5424\n",
       "                right side          211\n",
       "                supine             2002\n",
       "Name: Timing, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df['Body Rotation'].isna().sum()\n",
    "\n",
    "# df['Body Rotation'].unique() --> supine-recline & prone-sit Total Count = 20100\n",
    " \n",
    "# df.groupby('Body Rotation')['Timing'].count()\n",
    "\n",
    "df.groupby(['Body Rotation','Overall class '])['Timing'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "63b31b04",
   "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>Timing</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>360 angle</th>\n",
       "      <th>Up/down angle</th>\n",
       "      <th>Observed position</th>\n",
       "      <th>Body Rotation</th>\n",
       "      <th>Prone-sit class</th>\n",
       "      <th>Supine-recline class</th>\n",
       "      <th>Milliseconds</th>\n",
       "      <th>Time Diff</th>\n",
       "      <th>Time Diff (s)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overall class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>left side</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2328</td>\n",
       "      <td>0</td>\n",
       "      <td>2328</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prone</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2104</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2104</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prone supported</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6323</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6323</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reclined</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5424</td>\n",
       "      <td>0</td>\n",
       "      <td>5424</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right side</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211</td>\n",
       "      <td>0</td>\n",
       "      <td>211</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>supine</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2002</td>\n",
       "      <td>0</td>\n",
       "      <td>2002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>upright</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>201</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>201</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Timing  Unnamed: 1  Unnamed: 2  Unnamed: 3  Unnamed: 4  \\\n",
       "Overall class                                                             \n",
       "left side             0           0           0           0           0   \n",
       "prone                 0           0           0           0           0   \n",
       "prone supported       0           0           0           0           0   \n",
       "reclined              0           0           0           0           0   \n",
       "right side            0           0           0           0           0   \n",
       "supine                0           0           0           0           0   \n",
       "upright               0           0           0           0           0   \n",
       "\n",
       "                 Unnamed: 6  360 angle   Up/down angle  Observed position  \\\n",
       "Overall class                                                               \n",
       "left side                 0           0              0               2328   \n",
       "prone                     0           0              0               2104   \n",
       "prone supported           0           0              0               6323   \n",
       "reclined                  0           0              0               5424   \n",
       "right side                0           0              0                211   \n",
       "supine                    0           0              0               2002   \n",
       "upright                   0           0              0                201   \n",
       "\n",
       "                 Body Rotation  Prone-sit class  Supine-recline class   \\\n",
       "Overall class                                                            \n",
       "left side                    0             2328                      0   \n",
       "prone                        0                0                   2104   \n",
       "prone supported              0                0                   6323   \n",
       "reclined                     0             5424                      0   \n",
       "right side                   0              211                      0   \n",
       "supine                       0             2002                      0   \n",
       "upright                      0                0                    201   \n",
       "\n",
       "                 Milliseconds  Time Diff  Time Diff (s)  \n",
       "Overall class                                            \n",
       "left side                   0          1              0  \n",
       "prone                       0          0              0  \n",
       "prone supported             0          0              0  \n",
       "reclined                    0          0              0  \n",
       "right side                  0          0              0  \n",
       "supine                      0          0              0  \n",
       "upright                     0          0              0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('Overall class ').isna().groupby(level=0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "27bb87e3",
   "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>Body Rotation</th>\n",
       "      <th>Overall class</th>\n",
       "      <th>Row Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>prone-sit</td>\n",
       "      <td>prone supported</td>\n",
       "      <td>6323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>prone-sit</td>\n",
       "      <td>prone</td>\n",
       "      <td>2104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>prone-sit</td>\n",
       "      <td>upright</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>supine-recline</td>\n",
       "      <td>reclined</td>\n",
       "      <td>5424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>supine-recline</td>\n",
       "      <td>left side</td>\n",
       "      <td>2328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>supine-recline</td>\n",
       "      <td>supine</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>supine-recline</td>\n",
       "      <td>right side</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Body Rotation   Overall class   Row Count\n",
       "1       prone-sit  prone supported       6323\n",
       "0       prone-sit            prone       2104\n",
       "2       prone-sit          upright        201\n",
       "4  supine-recline         reclined       5424\n",
       "3  supine-recline        left side       2328\n",
       "6  supine-recline           supine       2002\n",
       "5  supine-recline       right side        211"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp_df.sort_values(by = 'Body Rotation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "aa03fb38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     6323\n",
       "4    11747\n",
       "3    14075\n",
       "0    16179\n",
       "6    18181\n",
       "5    18392\n",
       "2    18593\n",
       "Name: Row Count, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp_df['Row Count'].cumsum()"
   ]
  }
 ],
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