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walkerh/fillna.ipynb

Created March 31, 2023 21:21
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fillna.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "17062f26",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"id": "9def47f6",
"metadata": {},
"source": [
"# A simple set"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d1fd9c97",
"metadata": {},
"outputs": [],
"source": [
"df1 = pd.DataFrame([[1, 2], [3, 4], [5, 6]], columns=['A', 'B'])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4a7e7c0d",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" .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",
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" <td>5</td>\n",
" <td>6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B\n",
"0 1 2\n",
"1 3 4\n",
"2 5 6"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1"
]
},
{
"cell_type": "markdown",
"id": "cf31a951",
"metadata": {},
"source": [
"## Introducing `DataFrame.apply`\n",
"\n",
"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.apply.html#pandas.DataFrame.apply"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ab3fb7df",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"A 9\n",
"B 12\n",
"dtype: int64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.apply(np.sum, axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "1873d275",
"metadata": {},
"outputs": [],
"source": [
"result = df1.apply(np.sum, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "dbd88fd8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.series.Series"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(result)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "6d016f95",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 3\n",
"1 7\n",
"2 11\n",
"dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "4b5a4dd7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 (Series, A 1\\nB 2\\nName: 0, dtype: int64)\n",
"1 (Series, A 3\\nB 4\\nName: 1, dtype: int64)\n",
"2 (Series, A 5\\nB 6\\nName: 2, dtype: int64)\n",
"dtype: object"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def identify(input_series):\n",
" return type(input_series).__name__, str(input_series)\n",
"\n",
"df1.apply(identify, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "c679aaee",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 4\n",
"1 16\n",
"2 36\n",
"dtype: int64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.apply(lambda s: (s[\"A\"] + 1) * s[\"B\"], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "d05a759b",
"metadata": {},
"outputs": [],
"source": [
"result = df1.apply(lambda s: pd.Series({\"C\": s.A*2, \"D\": s.B+1}), axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "91e11df0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.frame.DataFrame"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(result)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "27324fd3",
"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",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <th></th>\n",
" <th>C</th>\n",
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" <tr>\n",
" <th>0</th>\n",
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" <td>6</td>\n",
" <td>5</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" C D\n",
"0 2 3\n",
"1 6 5\n",
"2 10 7"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result"
]
},
{
"cell_type": "markdown",
"id": "7a069ca7",
"metadata": {},
"source": [
"If the return value of `apply` is scalar, you get a Series. I the return value is a `Series`, you get a `DataFrame`."
]
},
{
"cell_type": "markdown",
"id": "2620785c",
"metadata": {},
"source": [
"# A more complex set\n",
"\n",
"This is a modified version of this StackOverflow answer: https://stackoverflow.com/a/62159000/642372"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "e2c83114",
"metadata": {},
"outputs": [],
"source": [
"header = [\"year\", \"name\", \"miles\"]\n",
"data = [\n",
" ('2010', 'Paul', 6.0),\n",
" ('2010', 'Paul', 4.0),\n",
" ('2010', 'Paul', np.nan),\n",
" ('2011', 'Paul', 7.0),\n",
" ('2011', 'Paul', 8.0),\n",
" ('2011', 'Paul', np.nan),\n",
" ('2012', 'Paul', 9.0),\n",
" ('2012', 'Paul', 10.9),\n",
" ('2012', 'Paul', 12.0),\n",
"]\n",
"df2 = pd.DataFrame(data, columns=header)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "e39b85fa",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <tbody>\n",
" <tr>\n",
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" <td>NaN</td>\n",
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" <th>3</th>\n",
" <td>2011</td>\n",
" <td>Paul</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2011</td>\n",
" <td>Paul</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2011</td>\n",
" <td>Paul</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2012</td>\n",
" <td>Paul</td>\n",
" <td>9.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2012</td>\n",
" <td>Paul</td>\n",
" <td>10.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2012</td>\n",
" <td>Paul</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year name miles\n",
"0 2010 Paul 6.0\n",
"1 2010 Paul 4.0\n",
"2 2010 Paul NaN\n",
"3 2011 Paul 7.0\n",
"4 2011 Paul 8.0\n",
"5 2011 Paul NaN\n",
"6 2012 Paul 9.0\n",
"7 2012 Paul 10.9\n",
"8 2012 Paul 12.0"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "9a63e832",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"\n",
" .dataframe thead th {\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>miles</th>\n",
" </tr>\n",
" <tr>\n",
" <th>year</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2010</th>\n",
" <td>5.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011</th>\n",
" <td>7.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012</th>\n",
" <td>10.633333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" miles\n",
"year \n",
"2010 5.000000\n",
"2011 7.500000\n",
"2012 10.633333"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2.groupby(['year']).mean(numeric_only=True)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "82730846",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"df2['miles_new'] = df2['miles'].fillna(\n",
" df2.groupby(['year'])['miles'].transform('mean')\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "0e10c31d",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <tbody>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2010</td>\n",
" <td>Paul</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2010</td>\n",
" <td>Paul</td>\n",
" <td>NaN</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2011</td>\n",
" <td>Paul</td>\n",
" <td>7.0</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2011</td>\n",
" <td>Paul</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2011</td>\n",
" <td>Paul</td>\n",
" <td>NaN</td>\n",
" <td>7.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2012</td>\n",
" <td>Paul</td>\n",
" <td>9.0</td>\n",
" <td>9.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2012</td>\n",
" <td>Paul</td>\n",
" <td>10.9</td>\n",
" <td>10.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2012</td>\n",
" <td>Paul</td>\n",
" <td>12.0</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year name miles miles_new\n",
"0 2010 Paul 6.0 6.0\n",
"1 2010 Paul 4.0 4.0\n",
"2 2010 Paul NaN 5.0\n",
"3 2011 Paul 7.0 7.0\n",
"4 2011 Paul 8.0 8.0\n",
"5 2011 Paul NaN 7.5\n",
"6 2012 Paul 9.0 9.0\n",
"7 2012 Paul 10.9 10.9\n",
"8 2012 Paul 12.0 12.0"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "c4574c57",
"metadata": {},
"outputs": [],
"source": [
"defaults = {\"2010\": 5.1, \"2011\": 8.1, \"2012\": 10.6}"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "2717675a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 (Series, year 2010\\nname Paul\\...\n",
"1 (Series, year 2010\\nname Paul\\...\n",
"2 (Series, year 2010\\nname Paul\\...\n",
"3 (Series, year 2011\\nname Paul\\...\n",
"4 (Series, year 2011\\nname Paul\\...\n",
"5 (Series, year 2011\\nname Paul\\...\n",
"6 (Series, year 2012\\nname Paul\\...\n",
"7 (Series, year 2012\\nname Paul\\...\n",
"8 (Series, year 2012\\nname Paul\\...\n",
"dtype: object"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2.apply(identify, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "5e2160a9",
"metadata": {},
"outputs": [
{
"data": {
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" <tr>\n",
" <th>1</th>\n",
" <td>2010</td>\n",
" <td>Paul</td>\n",
" <td>4.0</td>\n",
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" <td>2010</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2010</td>\n",
" <td>Paul</td>\n",
" <td>NaN</td>\n",
" <td>5.0</td>\n",
" <td>2010</td>\n",
" <td>5.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2011</td>\n",
" <td>Paul</td>\n",
" <td>7.0</td>\n",
" <td>7.0</td>\n",
" <td>2011</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2011</td>\n",
" <td>Paul</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>2011</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2011</td>\n",
" <td>Paul</td>\n",
" <td>NaN</td>\n",
" <td>7.5</td>\n",
" <td>2011</td>\n",
" <td>8.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2012</td>\n",
" <td>Paul</td>\n",
" <td>9.0</td>\n",
" <td>9.0</td>\n",
" <td>2012</td>\n",
" <td>9.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2012</td>\n",
" <td>Paul</td>\n",
" <td>10.9</td>\n",
" <td>10.9</td>\n",
" <td>2012</td>\n",
" <td>10.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2012</td>\n",
" <td>Paul</td>\n",
" <td>12.0</td>\n",
" <td>12.0</td>\n",
" <td>2012</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year name miles miles_new A B\n",
"0 2010 Paul 6.0 6.0 2010 6.0\n",
"1 2010 Paul 4.0 4.0 2010 4.0\n",
"2 2010 Paul NaN 5.0 2010 5.1\n",
"3 2011 Paul 7.0 7.0 2011 7.0\n",
"4 2011 Paul 8.0 8.0 2011 8.0\n",
"5 2011 Paul NaN 7.5 2011 8.1\n",
"6 2012 Paul 9.0 9.0 2012 9.0\n",
"7 2012 Paul 10.9 10.9 2012 10.9\n",
"8 2012 Paul 12.0 12.0 2012 12.0"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.concat(\n",
" [\n",
" df2, \n",
" df2.apply(\n",
" lambda s: pd.Series(\n",
" {\n",
" \"A\": s[\"year\"], \n",
" \"B\": defaults[s[\"year\"]] if np.isnan(s[\"miles\"]) else s[\"miles\"]}\n",
" ), \n",
" axis=1\n",
" )\n",
" ], \n",
" axis=1\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5cc74700",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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