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mcs07/pandas_examples.ipynb

Created September 2, 2015 21:49
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pandas_examples.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set up a simple DataFrame with some dummy data:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pd.DataFrame({\n",
" 'apKa': [9.82, 6.34, 10.38, 10.39, 5.12, 13.25],\n",
" 'bpKa': [-8.43, -6.55, -5.18, 8.45, 9.21, 1.79]\n",
"})"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>apKa</th>\n",
" <th>bpKa</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 9.82</td>\n",
" <td>-8.43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 6.34</td>\n",
" <td>-6.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 10.38</td>\n",
" <td>-5.18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 10.39</td>\n",
" <td> 8.45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 5.12</td>\n",
" <td> 9.21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td> 13.25</td>\n",
" <td> 1.79</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" apKa bpKa\n",
"0 9.82 -8.43\n",
"1 6.34 -6.55\n",
"2 10.38 -5.18\n",
"3 10.39 8.45\n",
"4 5.12 9.21\n",
"5 13.25 1.79"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's very easy to generate columns that are simple mathematical operations or thresholds:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df['Base'] = df['bpKa'] > 8\n",
"df['Acid'] = df['apKa'] < 6.5"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>apKa</th>\n",
" <th>bpKa</th>\n",
" <th>Base</th>\n",
" <th>Acid</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 9.82</td>\n",
" <td>-8.43</td>\n",
" <td> False</td>\n",
" <td> False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 6.34</td>\n",
" <td>-6.55</td>\n",
" <td> False</td>\n",
" <td> True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 10.38</td>\n",
" <td>-5.18</td>\n",
" <td> False</td>\n",
" <td> False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 10.39</td>\n",
" <td> 8.45</td>\n",
" <td> True</td>\n",
" <td> False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 5.12</td>\n",
" <td> 9.21</td>\n",
" <td> True</td>\n",
" <td> True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td> 13.25</td>\n",
" <td> 1.79</td>\n",
" <td> False</td>\n",
" <td> False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" apKa bpKa Base Acid\n",
"0 9.82 -8.43 False False\n",
"1 6.34 -6.55 False True\n",
"2 10.38 -5.18 False False\n",
"3 10.39 8.45 True False\n",
"4 5.12 9.21 True True\n",
"5 13.25 1.79 False False"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For more complex situations, we can use the the `apply` method with `axis=1`. This allows us to generate a value using an arbitrary function on the existing row:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>apKa</th>\n",
" <th>bpKa</th>\n",
" <th>Base</th>\n",
" <th>Acid</th>\n",
" <th>ABZN</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 9.82</td>\n",
" <td>-8.43</td>\n",
" <td> False</td>\n",
" <td> False</td>\n",
" <td> Neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 6.34</td>\n",
" <td>-6.55</td>\n",
" <td> False</td>\n",
" <td> True</td>\n",
" <td> Acid</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 10.38</td>\n",
" <td>-5.18</td>\n",
" <td> False</td>\n",
" <td> False</td>\n",
" <td> Neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 10.39</td>\n",
" <td> 8.45</td>\n",
" <td> True</td>\n",
" <td> False</td>\n",
" <td> Base</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 5.12</td>\n",
" <td> 9.21</td>\n",
" <td> True</td>\n",
" <td> True</td>\n",
" <td> Zwitterion</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td> 13.25</td>\n",
" <td> 1.79</td>\n",
" <td> False</td>\n",
" <td> False</td>\n",
" <td> Neutral</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" apKa bpKa Base Acid ABZN\n",
"0 9.82 -8.43 False False Neutral\n",
"1 6.34 -6.55 False True Acid\n",
"2 10.38 -5.18 False False Neutral\n",
"3 10.39 8.45 True False Base\n",
"4 5.12 9.21 True True Zwitterion\n",
"5 13.25 1.79 False False Neutral"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def calculate_ABZN(row):\n",
" if row['Acid'] and row['Base']:\n",
" return 'Zwitterion'\n",
" elif row['Acid'] and not row['Base']:\n",
" return 'Acid'\n",
" elif not row['Acid'] and row['Base']:\n",
" return 'Base'\n",
" elif not row['Acid'] and not row['Base']:\n",
" return 'Neutral'\n",
" \n",
"df['ABZN'] = df.apply(calculate_ABZN, axis=1)\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you don't actually need the `Acid` and `Base` columns, you could calculate `ABZN` straight from the `apKa` and `bpKa` columns in a similar way."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively we could use the `map` function:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>apKa</th>\n",
" <th>bpKa</th>\n",
" <th>Base</th>\n",
" <th>Acid</th>\n",
" <th>ABZN</th>\n",
" <th>ABZN2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 9.82</td>\n",
" <td>-8.43</td>\n",
" <td> False</td>\n",
" <td> False</td>\n",
" <td> Neutral</td>\n",
" <td> Neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 6.34</td>\n",
" <td>-6.55</td>\n",
" <td> False</td>\n",
" <td> True</td>\n",
" <td> Acid</td>\n",
" <td> Acid</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 10.38</td>\n",
" <td>-5.18</td>\n",
" <td> False</td>\n",
" <td> False</td>\n",
" <td> Neutral</td>\n",
" <td> Neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 10.39</td>\n",
" <td> 8.45</td>\n",
" <td> True</td>\n",
" <td> False</td>\n",
" <td> Base</td>\n",
" <td> Base</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 5.12</td>\n",
" <td> 9.21</td>\n",
" <td> True</td>\n",
" <td> True</td>\n",
" <td> Zwitterion</td>\n",
" <td> Zwitterion</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td> 13.25</td>\n",
" <td> 1.79</td>\n",
" <td> False</td>\n",
" <td> False</td>\n",
" <td> Neutral</td>\n",
" <td> Neutral</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" apKa bpKa Base Acid ABZN ABZN2\n",
"0 9.82 -8.43 False False Neutral Neutral\n",
"1 6.34 -6.55 False True Acid Acid\n",
"2 10.38 -5.18 False False Neutral Neutral\n",
"3 10.39 8.45 True False Base Base\n",
"4 5.12 9.21 True True Zwitterion Zwitterion\n",
"5 13.25 1.79 False False Neutral Neutral"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lookup = {(True, True): 'Zwitterion', (True, False): 'Acid', (False, True): 'Base', (False, False): 'Neutral'}\n",
"\n",
"def calculate_ABZN2(acid, base):\n",
" return lookup[(acid, base)]\n",
" \n",
"df['ABZN2'] = map(calculate_ABZN2, df['Acid'], df['Base'])\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `ABZN` column contains a string and therefore is an object type:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"apKa float64\n",
"bpKa float64\n",
"Base bool\n",
"Acid bool\n",
"ABZN object\n",
"ABZN2 object\n",
"dtype: object"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It may be useful to use the pandas categorical functionality: <http://pandas.pydata.org/pandas-docs/stable/categorical.html>"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df['ABZN'] = df['ABZN'].astype('category')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"apKa float64\n",
"bpKa float64\n",
"Base bool\n",
"Acid bool\n",
"ABZN category\n",
"ABZN2 object\n",
"dtype: object"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index([u'Acid', u'Base', u'Neutral', u'Zwitterion'], dtype='object')"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['ABZN'].cat.categories"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
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},
"nbformat": 4,
"nbformat_minor": 0
}
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