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Update allocation notebook to deal with incoming data format

parent 2bc103f2
Inline Side-by-side
Showing with 615 additions and 1 deletions
2023__housing_allocations.ipynb
...@@ -17,6 +17,620 @@ ...@@ -17,6 +17,620 @@
] ]
}, },
{ {
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"raw_responses = pd.read_csv(\n",
" \"/home/prayush/Documents/ICTS/icts-housing/Housing allotment request form (Responses) - Form Responses 1.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 41,
"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>Timestamp</th>\n",
" <th>Email Address</th>\n",
" <th>Name</th>\n",
" <th>Please indicate your order of preference for campus housing [On-campus accommodation]</th>\n",
" <th>Please indicate your order of preference for campus housing [Hostel 1]</th>\n",
" <th>Please indicate your order of preference for campus housing [Hostel 2]</th>\n",
" <th>Please indicate your order of preference for campus housing [Hostel 3]</th>\n",
" <th>Please indicate your order of preference for campus housing [Hostel 4]</th>\n",
" <th>Please indicate your order of preference for campus housing [Hostel 5]</th>\n",
" <th>Year of Study</th>\n",
" <th>Program enrolled</th>\n",
" <th>Are you a female student?</th>\n",
" <th>Please indicate your order of preference for campus housing [On-campus studio apartment (only applicable for postdoctoral fellows)]</th>\n",
" <th>Please indicate your order of preference for campus housing [Off-campus accommodation with spouse/partner (applicable only if the partner stays for at least 220 days out of 365 days)]</th>\n",
" <th>Please indicate your order of preference for campus housing [On-campus accommodation with spouse/partner (applicable only if the partner stays for at least 220 days out of 365 days)]</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>8/4/2023 9:06:23</td>\n",
" <td>neha.sharma@icts.res.in</td>\n",
" <td>Neha Sharma</td>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>Second year</td>\n",
" <td>IPhD</td>\n",
" <td>Yes</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>8/1/2023 15:07:58</td>\n",
" <td>aiswarya.ns@icts.res.in</td>\n",
" <td>Aiswarya N S</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>Second year</td>\n",
" <td>IPhD</td>\n",
" <td>Yes</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7/31/2023 10:37:46</td>\n",
" <td>bikram.pain@icts.res.in</td>\n",
" <td>Bikram Pain</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Third year</td>\n",
" <td>IPhD</td>\n",
" <td>No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>7/31/2023 11:36:55</td>\n",
" <td>alorika.kar@icts.res.in</td>\n",
" <td>Alorika Kar</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>Second year</td>\n",
" <td>PhD</td>\n",
" <td>Yes</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>7/31/2023 11:14:02</td>\n",
" <td>ankur.barsode@icts.res.in</td>\n",
" <td>Ankur Barsode</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" <td>5.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>Third year</td>\n",
" <td>PhD</td>\n",
" <td>No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Timestamp Email Address Name \\\n",
"0 8/4/2023 9:06:23 neha.sharma@icts.res.in Neha Sharma \n",
"1 8/1/2023 15:07:58 aiswarya.ns@icts.res.in Aiswarya N S \n",
"2 7/31/2023 10:37:46 bikram.pain@icts.res.in Bikram Pain \n",
"3 7/31/2023 11:36:55 alorika.kar@icts.res.in Alorika Kar \n",
"4 7/31/2023 11:14:02 ankur.barsode@icts.res.in Ankur Barsode \n",
"\n",
" Please indicate your order of preference for campus housing [On-campus accommodation] \\\n",
"0 NaN \n",
"1 5.0 \n",
"2 NaN \n",
"3 1.0 \n",
"4 1.0 \n",
"\n",
" Please indicate your order of preference for campus housing [Hostel 1] \\\n",
"0 4.0 \n",
"1 NaN \n",
"2 NaN \n",
"3 5.0 \n",
"4 NaN \n",
"\n",
" Please indicate your order of preference for campus housing [Hostel 2] \\\n",
"0 1.0 \n",
"1 1.0 \n",
"2 1.0 \n",
"3 2.0 \n",
"4 2.0 \n",
"\n",
" Please indicate your order of preference for campus housing [Hostel 3] \\\n",
"0 5.0 \n",
"1 3.0 \n",
"2 NaN \n",
"3 4.0 \n",
"4 5.0 \n",
"\n",
" Please indicate your order of preference for campus housing [Hostel 4] \\\n",
"0 2.0 \n",
"1 2.0 \n",
"2 NaN \n",
"3 1.0 \n",
"4 3.0 \n",
"\n",
" Please indicate your order of preference for campus housing [Hostel 5] \\\n",
"0 3.0 \n",
"1 4.0 \n",
"2 NaN \n",
"3 3.0 \n",
"4 4.0 \n",
"\n",
" Year of Study Program enrolled Are you a female student? \\\n",
"0 Second year IPhD Yes \n",
"1 Second year IPhD Yes \n",
"2 Third year IPhD No \n",
"3 Second year PhD Yes \n",
"4 Third year PhD No \n",
"\n",
" Please indicate your order of preference for campus housing [On-campus studio apartment (only applicable for postdoctoral fellows)] \\\n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"\n",
" Please indicate your order of preference for campus housing [Off-campus accommodation with spouse/partner (applicable only if the partner stays for at least 220 days out of 365 days)] \\\n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"\n",
" Please indicate your order of preference for campus housing [On-campus accommodation with spouse/partner (applicable only if the partner stays for at least 220 days out of 365 days)] \n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN "
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_responses.head()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"# Replacing empty entries with a constant number that is large enough,\n",
"# This amounts to treating those choices for which students have filled\n",
"# no preferences as being equally preferred by them\n",
"import numpy as np\n",
"\n",
"proc_responses = raw_responses.replace(np.nan, 99)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 Timestamp\n",
"1 Email Address\n",
"2 name\n",
"3 Please indicate your order of preference for campus housing [On-campus accommodation]\n",
"4 Please indicate your order of preference for campus housing [Hostel 1]\n",
"5 Please indicate your order of preference for campus housing [Hostel 2]\n",
"6 Please indicate your order of preference for campus housing [Hostel 3]\n",
"7 Please indicate your order of preference for campus housing [Hostel 4]\n",
"8 Please indicate your order of preference for campus housing [Hostel 5]\n",
"9 Year of Study\n",
"10 Program enrolled\n",
"11 Are you a female student?\n",
"12 Please indicate your order of preference for campus housing [On-campus studio apartment (only applicable for postdoctoral fellows)]\n",
"13 Please indicate your order of preference for campus housing [Off-campus accommodation with spouse/partner (applicable only if the partner stays for at least 220 days out of 365 days)]\n",
"14 Please indicate your order of preference for campus housing [On-campus accommodation with spouse/partner (applicable only if the partner stays for at least 220 days out of 365 days)]\n"
]
}
],
"source": [
"# Standardizing column names to be used in rest of the notebook\n",
"proc_responses.columns\n",
"\n",
"for idx, cname in enumerate(proc_responses.columns):\n",
" print(idx, cname)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"col_names = list(proc_responses.columns)\n",
"\n",
"proc_responses = proc_responses.rename(columns={\n",
" col_names[1]: 'email',\n",
" col_names[2]: 'name',\n",
" col_names[9]: 'year',\n",
" col_names[10]: 'course',\n",
" col_names[11]: 'female',\n",
" \n",
" col_names[3]: 'housing0',\n",
" col_names[4]: 'housing1',\n",
" col_names[5]: 'housing2',\n",
" col_names[6]: 'housing3',\n",
" col_names[7]: 'housing4',\n",
" col_names[8]: 'housing5',\n",
"})"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"new_col = []\n",
"for idx in range(len(proc_responses)):\n",
" new_col.append(\n",
" proc_responses[[\n",
" 'housing0',\n",
" 'housing1',\n",
" 'housing2',\n",
" 'housing3',\n",
" 'housing4',\n",
" 'housing5',\n",
" ]].iloc[idx].to_list())\n",
"\n",
"proc_responses['preferences'] = new_col"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"final_responses = proc_responses[[\n",
" 'email', 'name', 'year', 'course', 'female', 'responses'\n",
"]]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'email': {0: 'neha.sharma@icts.res.in',\n",
" 1: 'aiswarya.ns@icts.res.in',\n",
" 2: 'bikram.pain@icts.res.in',\n",
" 3: 'alorika.kar@icts.res.in',\n",
" 4: 'ankur.barsode@icts.res.in',\n",
" 5: 'mrinal.jyoti@icts.res.in',\n",
" 6: 'saumav.kapoor@icts.res.in',\n",
" 7: 'uddeepta.deka@icts.res.in',\n",
" 8: 'souvik.jana@icts.res.in',\n",
" 9: 'mukesh.singh@icts.res.in',\n",
" 10: 'priyadarshi.paul@icts.res.in',\n",
" 11: 'basudeb.mondal@icts.res.in',\n",
" 12: 'babli.khatun@icts.res.in',\n",
" 13: 'debanjan.karan@icts.res.in',\n",
" 14: 'anjali.kundalpady@icts.res.in',\n",
" 15: 'sam.mathew@icts.res.in',\n",
" 16: 'omkar.shetye@icts.res.in',\n",
" 17: 'ritesh.harshe@icts.res.in',\n",
" 18: 'naveen.kumard@icts.res.in',\n",
" 19: 'anwesha.dey@icts.res.in',\n",
" 20: 'anwesha.dey@icts.res.in',\n",
" 21: 'rajarshi.chattopadhyay@icts.res.in',\n",
" 22: 'godwin.martin@icts.res.in',\n",
" 23: 'pradeeptha.jain@icts.res.in',\n",
" 24: 'seema.s@icts.res.in',\n",
" 25: 'harsh.nigam@icts.res.in',\n",
" 26: 'sam.mathew@icts.res.in',\n",
" 27: 'barmanjyotirmoy2016@gmail.com',\n",
" 28: 'saptarshi.mandal@icts.res.in',\n",
" 29: 'rahul.metya@icts.res.in',\n",
" 30: 'rukmani.r@icts.res.in',\n",
" 31: 'tuneer.chakraborty@icts.res.in',\n",
" 32: 'sourabh.saini@icts.res.in',\n",
" 33: 'tirthankar.mondal@icts.res.in',\n",
" 34: 'devadevan.mm@icts.res.in',\n",
" 35: 'santhiya.ps@icts.res.in',\n",
" 36: 'vinay.kumar@icts.res.in',\n",
" 37: 'ritvik.vantipalli@icts.res.in',\n",
" 38: 'priyadharshini.v@icts.res.in',\n",
" 39: 'shaibal.karmakar@icts.res.in',\n",
" 40: 'tirthankar.mondal@icts.res.in',\n",
" 41: 'koustav.narayan@icts.res.in'},\n",
" 'name': {0: 'Neha Sharma ',\n",
" 1: 'Aiswarya N S',\n",
" 2: 'Bikram Pain',\n",
" 3: 'Alorika Kar',\n",
" 4: 'Ankur Barsode',\n",
" 5: 'Mrinal Jyoti Powdel',\n",
" 6: 'Saumav Kapoor',\n",
" 7: 'Uddeepta Deka',\n",
" 8: 'Souvik Jana',\n",
" 9: 'Mukesh Kumar Singh',\n",
" 10: 'Priyadarshi Paul',\n",
" 11: 'Basudeb Mondal ',\n",
" 12: 'Babli Khatun',\n",
" 13: 'Debanjan Karan',\n",
" 14: 'Anjali Kundalpady ',\n",
" 15: 'SAM K MATHEW',\n",
" 16: 'Omkar Shetye',\n",
" 17: 'Ritesh Purushottam Harshe ',\n",
" 18: 'Naveen Kumar D',\n",
" 19: 'Anwesha Dey',\n",
" 20: 'Anwesha Dey',\n",
" 21: 'Rajarshi',\n",
" 22: 'Godwin Martin',\n",
" 23: 'Pradeeptha R Jain',\n",
" 24: 'Seema',\n",
" 25: 'Harsh Nigam',\n",
" 26: 'SAM K MATHEW ',\n",
" 27: 'Jyotirmoy Barman ',\n",
" 28: 'Saptarshi Mandal',\n",
" 29: 'Rahul Metya ',\n",
" 30: 'Rukmani R ',\n",
" 31: 'Tuneer Chakraborty ',\n",
" 32: 'Sourabh Saini',\n",
" 33: 'Tirthankar Mondal',\n",
" 34: 'DEVADEVAN M M',\n",
" 35: 'Santhiya P S',\n",
" 36: 'Vinay',\n",
" 37: 'Vantipalli Ritvik',\n",
" 38: 'Priyadharshini V',\n",
" 39: 'Shaibal Karmakar ',\n",
" 40: 'Tirthankar Mondal ',\n",
" 41: 'Koustav Narayan Maity'},\n",
" 'year': {0: 'Second year',\n",
" 1: 'Second year',\n",
" 2: 'Third year',\n",
" 3: 'Second year',\n",
" 4: 'Third year',\n",
" 5: 'Third year',\n",
" 6: 'Sixth year',\n",
" 7: 'Fifth year',\n",
" 8: 'Fifth year',\n",
" 9: 'Fifth year',\n",
" 10: 'Fifth year',\n",
" 11: 'Sixth year',\n",
" 12: 'First year',\n",
" 13: 'First year',\n",
" 14: 'First year',\n",
" 15: 'First year',\n",
" 16: 'Fifth year',\n",
" 17: 'First year',\n",
" 18: 'First year',\n",
" 19: 'First year',\n",
" 20: 'First year',\n",
" 21: 'Fourth year',\n",
" 22: 'Fourth year',\n",
" 23: 'First year',\n",
" 24: 'First year',\n",
" 25: 'Second year',\n",
" 26: 'First year',\n",
" 27: 'First year',\n",
" 28: 'First year',\n",
" 29: 'First year',\n",
" 30: 'First year',\n",
" 31: 'Fifth year',\n",
" 32: 'First year',\n",
" 33: 'First year',\n",
" 34: 'First year',\n",
" 35: 'Second year',\n",
" 36: 'Third year',\n",
" 37: 'First year',\n",
" 38: 'Second year',\n",
" 39: 'First year',\n",
" 40: 'First year',\n",
" 41: 'Second year'},\n",
" 'course': {0: 'IPhD',\n",
" 1: 'IPhD',\n",
" 2: 'IPhD',\n",
" 3: 'PhD',\n",
" 4: 'PhD',\n",
" 5: 'IPhD',\n",
" 6: 'IPhD',\n",
" 7: 'PhD',\n",
" 8: 'PhD',\n",
" 9: 'PhD',\n",
" 10: 'PhD',\n",
" 11: 'PhD',\n",
" 12: 'PhD',\n",
" 13: 'PhD',\n",
" 14: 'PhD',\n",
" 15: 'PhD',\n",
" 16: 'PhD',\n",
" 17: 'PhD',\n",
" 18: 'PhD',\n",
" 19: 'PhD',\n",
" 20: 'PhD',\n",
" 21: 'IPhD',\n",
" 22: 'IPhD',\n",
" 23: 'PhD',\n",
" 24: 'PhD',\n",
" 25: 'PhD',\n",
" 26: 'PhD',\n",
" 27: 'PhD',\n",
" 28: 'PhD',\n",
" 29: 'PhD',\n",
" 30: 'PhD',\n",
" 31: 'PhD',\n",
" 32: 'PhD',\n",
" 33: 'IPhD',\n",
" 34: 'PhD',\n",
" 35: 'PhD',\n",
" 36: 'IPhD',\n",
" 37: 'PhD',\n",
" 38: 'PhD',\n",
" 39: 'PhD',\n",
" 40: 'IPhD',\n",
" 41: 'PhD'},\n",
" 'female': {0: 'Yes',\n",
" 1: 'Yes',\n",
" 2: 'No',\n",
" 3: 'Yes',\n",
" 4: 'No',\n",
" 5: 'No',\n",
" 6: 'No',\n",
" 7: 'No',\n",
" 8: 'No',\n",
" 9: 'No',\n",
" 10: 'No',\n",
" 11: 'No',\n",
" 12: 'Yes',\n",
" 13: 'No',\n",
" 14: 'Yes',\n",
" 15: 'No',\n",
" 16: 'No',\n",
" 17: 'No',\n",
" 18: 'No',\n",
" 19: 'Yes',\n",
" 20: 'Yes',\n",
" 21: 'No',\n",
" 22: 'No',\n",
" 23: 'Yes',\n",
" 24: 'Yes',\n",
" 25: 'No',\n",
" 26: 'No',\n",
" 27: 'No',\n",
" 28: 'No',\n",
" 29: 'No',\n",
" 30: 'Yes',\n",
" 31: 'No',\n",
" 32: 'No',\n",
" 33: 'No',\n",
" 34: 'No',\n",
" 35: 'Yes',\n",
" 36: 'No',\n",
" 37: 'No',\n",
" 38: 'Yes',\n",
" 39: 'No',\n",
" 40: 'No',\n",
" 41: 'No'},\n",
" 'responses': {0: [99.0, 4.0, 1.0, 5.0, 2.0, 3.0],\n",
" 1: [5.0, 99.0, 1.0, 3.0, 2.0, 4.0],\n",
" 2: [99.0, 99.0, 1.0, 99.0, 99.0, 99.0],\n",
" 3: [1.0, 5.0, 2.0, 4.0, 1.0, 3.0],\n",
" 4: [1.0, 99.0, 2.0, 5.0, 3.0, 4.0],\n",
" 5: [99.0, 4.0, 1.0, 5.0, 2.0, 3.0],\n",
" 6: [1.0, 4.0, 2.0, 99.0, 3.0, 5.0],\n",
" 7: [1.0, 99.0, 2.0, 99.0, 3.0, 4.0],\n",
" 8: [1.0, 3.0, 2.0, 99.0, 4.0, 5.0],\n",
" 9: [1.0, 2.0, 3.0, 99.0, 4.0, 5.0],\n",
" 10: [1.0, 99.0, 4.0, 99.0, 2.0, 3.0],\n",
" 11: [99.0, 2.0, 1.0, 4.0, 3.0, 5.0],\n",
" 12: [1.0, 99.0, 4.0, 3.0, 2.0, 5.0],\n",
" 13: [1.0, 99.0, 4.0, 3.0, 2.0, 5.0],\n",
" 14: [1.0, 3.0, 2.0, 99.0, 99.0, 99.0],\n",
" 15: [1.0, 4.0, 99.0, 5.0, 3.0, 2.0],\n",
" 16: [99.0, 5.0, 4.0, 3.0, 2.0, 1.0],\n",
" 17: [1.0, 99.0, 99.0, 4.0, 3.0, 2.0],\n",
" 18: [1.0, 3.0, 2.0, 99.0, 3.0, 4.0],\n",
" 19: [1.0, 99.0, 99.0, 99.0, 99.0, 99.0],\n",
" 20: [1.0, 99.0, 99.0, 99.0, 99.0, 99.0],\n",
" 21: [2.0, 99.0, 1.0, 5.0, 4.0, 3.0],\n",
" 22: [99.0, 2.0, 1.0, 5.0, 3.0, 4.0],\n",
" 23: [1.0, 99.0, 99.0, 99.0, 99.0, 99.0],\n",
" 24: [1.0, 4.0, 2.0, 3.0, 99.0, 5.0],\n",
" 25: [1.0, 99.0, 99.0, 99.0, 99.0, 99.0],\n",
" 26: [1.0, 99.0, 4.0, 5.0, 3.0, 2.0],\n",
" 27: [2.0, 99.0, 5.0, 3.0, 4.0, 1.0],\n",
" 28: [2.0, 99.0, 5.0, 3.0, 4.0, 1.0],\n",
" 29: [2.0, 99.0, 5.0, 3.0, 4.0, 1.0],\n",
" 30: [1.0, 99.0, 99.0, 99.0, 99.0, 99.0],\n",
" 31: [1.0, 99.0, 4.0, 5.0, 2.0, 3.0],\n",
" 32: [1.0, 99.0, 4.0, 99.0, 3.0, 2.0],\n",
" 33: [99.0, 99.0, 1.0, 99.0, 2.0, 3.0],\n",
" 34: [1.0, 99.0, 2.0, 5.0, 3.0, 4.0],\n",
" 35: [1.0, 4.0, 3.0, 99.0, 5.0, 2.0],\n",
" 36: [1.0, 4.0, 3.0, 5.0, 5.0, 2.0],\n",
" 37: [4.0, 99.0, 1.0, 2.0, 3.0, 5.0],\n",
" 38: [3.0, 99.0, 2.0, 5.0, 4.0, 1.0],\n",
" 39: [5.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
" 40: [99.0, 99.0, 1.0, 99.0, 2.0, 3.0],\n",
" 41: [1.0, 5.0, 4.0, 99.0, 3.0, 2.0]}}"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final_responses.to_dict()"
]
},
{
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
...@@ -382,7 +996,7 @@ ...@@ -382,7 +996,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.11.0" "version": "3.8.16"
}, },
"orig_nbformat": 4 "orig_nbformat": 4
}, },
......
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