leitner.ipynb
{
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{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from datetime import datetime as dt\n",
"from datetime import timedelta"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>card_id</th>\n",
" <th>starred</th>\n",
" <th>comfort_level</th>\n",
" <th>next_due</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td></td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" card_id starred comfort_level next_due\n",
"0 0 0 3 \n",
"1 1 0 3 \n",
"2 2 1 2 \n",
"3 3 1 5 \n",
"4 4 0 5 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"matrix = [(0, 0, 3, ''),\n",
" (1, 0, 3, ''),\n",
" (2, 1, 2, ''),\n",
" (3, 1, 5, ''),\n",
" (4, 0, 5, '')]\n",
"\n",
"df = pd.DataFrame(matrix, columns=['card_id', 'starred', 'comfort_level', 'next_due'])\n",
"\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"def leitner_dates(row):\n",
"\n",
" if (row['starred'] == 0) & (row['comfort_level'] < 5):\n",
" row['comfort_level'] += 1\n",
" row = update_next_due(row)\n",
" \n",
" elif (row['starred'] == 0) & (row['comfort_level'] == 5):\n",
" row = update_next_due(row)\n",
" \n",
" else:\n",
" row['comfort_level'] = 1\n",
" row = update_next_due(row)\n",
" \n",
" return row\n",
"\n",
"def update_next_due(row):\n",
" \n",
" comfort_dict = {1 : 0, 2 : 2, 3 : 4, 4 : 9, 5 : 14}\n",
" \n",
" next_due = dt.now() + timedelta(days=comfort_dict[row['comfort_level']])\n",
" row['next_due'] = next_due.strftime('%m-%d-%Y, %H:%M')\n",
" return row"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>card_id</th>\n",
" <th>starred</th>\n",
" <th>comfort_level</th>\n",
" <th>next_due</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>08-07-2020, 23:30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>08-07-2020, 23:30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>07-29-2020, 23:30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>07-29-2020, 23:30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>08-12-2020, 23:30</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" card_id starred comfort_level next_due\n",
"0 0 0 4 08-07-2020, 23:30\n",
"1 1 0 4 08-07-2020, 23:30\n",
"2 2 1 1 07-29-2020, 23:30\n",
"3 3 1 1 07-29-2020, 23:30\n",
"4 4 0 5 08-12-2020, 23:30"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.apply(leitner_dates, axis=1)"
]
}
],
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