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cmrivers/lesson_2

Created November 3, 2015 17:00
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lesson_2
{
"metadata": {
"name": "",
"signature": "sha256:e84e57889637dd138adaa0222510c019330e34f0d641d61e0eda7eaf61a68bba"
},
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"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Python for Data analysis\n",
"\n",
"###Lesson 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What we did last time:\n",
"- Getting a handle on your data\n",
"- Replacing values\n",
"- Summary statistics, with some stratification"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What we will do today:\n",
"- More advanced versions of all of those things"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What we will do next time:\n",
"- Traditional programming in Python\n",
"- Control flows, functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Always import the packages you will rely on first. Pandas and %pylab inline is a must. Numpy and seaborn are helpful."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd #core data analysis package\n",
"import numpy as np #also a useful data analysis package\n",
"import seaborn as sns #make your plots pretty\n",
"#make your plots appear on screen\n",
"%pylab inline "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data = pd.read_csv('C:/Users/caitlin.rivers/Downloads/cases.csv')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Like last time, we need to convert age from a string to a number. Normally you would just re-run your code (or pull up cleaned data that you saved previously), but I'm starting a new notebook to keep it clean."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data.age = data.age.convert_objects(convert_numeric=True)\n",
"data.gender = data.gender.replace(['?', 'M?'], np.nan)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# I. Fancy data manipulation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make a list of where you want your breaks to be. Note it's a list (square brackets) and each category is a string (quotes)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"labels = ['0-50', '51- 99']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"labels = ['0-9', '10-19', '20-29', '30-39', '40-49', '50-59', '60-69', '70-79', '80-89', '90+']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Second, we generate a range that matches those breaks."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"range(0, 101,10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make a new column for age group. Use pandas' cut function to make breaks. Attach labels."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data['age_group'] = pd.cut(data.age, range(0, 101, 10), right=False, labels=labels)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"See if it worked."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data[['age', 'age_group']][:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>age_group</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>25</td>\n",
" <td>20-29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>30</td>\n",
" <td>30-39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>40</td>\n",
" <td>40-49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>60</td>\n",
" <td>60-69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>29</td>\n",
" <td>20-29</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
" age age_group\n",
"0 25 20-29\n",
"1 30 30-39\n",
"2 40 40-49\n",
"3 60 60-69\n",
"4 29 20-29"
]
}
],
"prompt_number": 16
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cool, so how many people are in each age group?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"age_group_counts = data.age_group.value_counts(sort=False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"age_group_counts"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"0-9 9\n",
"10-19 29\n",
"20-29 128\n",
"30-39 209\n",
"40-49 220\n",
"50-59 250\n",
"60-69 198\n",
"70-79 161\n",
"80-89 62\n",
"90+ 11\n",
"dtype: int64"
]
}
],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This would be better as a plot."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"age_group_counts.plot(kind='bar')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 205,
"text": [
"<matplotlib.axes._subplots.AxesSubplot at 0x125f8b00>"
]
},
{
"metadata": {},
"output_type": "display_data",
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"text": [
"<matplotlib.figure.Figure at 0x125f8cf8>"
]
}
],
"prompt_number": 205
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data.age_group.value_counts(sort=False).plot(kind='bar')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": [
"<matplotlib.axes._subplots.AxesSubplot at 0xbc30860>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0xa257588>"
]
}
],
"prompt_number": 20
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or a boxplot could be useful."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data.age.plot(kind='box', )"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 209,
"text": [
"<matplotlib.axes._subplots.AxesSubplot at 0x12aa8160>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAECCAYAAAAMxDf2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": [
"<matplotlib.figure.Figure at 0x129437f0>"
]
}
],
"prompt_number": 209
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can do column-wise math...addition, subtraction, etc."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data.age/100"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 25,
"text": [
"0 0.25\n",
"1 0.30\n",
"2 0.40\n",
"3 0.60\n",
"4 0.29\n",
"5 0.33\n",
"6 0.28\n",
"7 0.45\n",
"8 0.46\n",
"9 0.25\n",
"10 0.53\n",
"11 0.28\n",
"12 0.60\n",
"13 0.60\n",
"14 0.49\n",
"15 0.45\n",
"16 0.45\n",
"17 0.70\n",
"18 0.31\n",
"19 0.39\n",
"20 0.16\n",
"21 0.60\n",
"22 0.39\n",
"23 0.30\n",
"24 0.61\n",
"25 0.69\n",
"26 0.51\n",
"27 0.39\n",
"28 0.40\n",
"29 0.73\n",
" ... \n",
"1261 0.77\n",
"1262 0.60\n",
"1263 0.61\n",
"1264 0.70\n",
"1265 0.75\n",
"1266 0.74\n",
"1267 0.51\n",
"1268 0.50\n",
"1269 0.30\n",
"1270 0.54\n",
"1271 0.41\n",
"1272 0.11\n",
"1273 0.54\n",
"1274 0.26\n",
"1275 0.27\n",
"1276 0.40\n",
"1277 0.65\n",
"1278 0.56\n",
"1279 0.60\n",
"1280 0.76\n",
"1281 0.24\n",
"1282 0.35\n",
"1283 0.24\n",
"1284 0.25\n",
"1285 0.50\n",
"1286 0.36\n",
"1287 0.77\n",
"1288 0.60\n",
"1289 0.93\n",
"1290 0.32\n",
"Name: age, dtype: float64"
]
}
],
"prompt_number": 25
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are lots of ways to do fancy pivot tables and aggregations. You don't really need to memorize it, just remember that this is a possibility."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"country_table = data.groupby('country').agg({'age': [np.min, np.mean, np.median], 'HCW':np.count_nonzero})"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"country_table"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"3\" halign=\"left\">age</th>\n",
" <th>HCW</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>amin</th>\n",
" <th>mean</th>\n",
" <th>median</th>\n",
" <th>count_nonzero</th>\n",
" </tr>\n",
" <tr>\n",
" <th>country</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>France</th>\n",
" <td>51</td>\n",
" <td>51.000000</td>\n",
" <td>51.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iran</th>\n",
" <td>35</td>\n",
" <td>51.500000</td>\n",
" <td>51.0</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Italy</th>\n",
" <td>2</td>\n",
" <td>22.000000</td>\n",
" <td>22.0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jordan</th>\n",
" <td>25</td>\n",
" <td>41.300000</td>\n",
" <td>42.5</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KSA</th>\n",
" <td>0</td>\n",
" <td>51.171908</td>\n",
" <td>52.0</td>\n",
" <td>959</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kuwait</th>\n",
" <td>47</td>\n",
" <td>53.000000</td>\n",
" <td>52.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lebanon</th>\n",
" <td>60</td>\n",
" <td>60.000000</td>\n",
" <td>60.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oman</th>\n",
" <td>31</td>\n",
" <td>55.333333</td>\n",
" <td>59.0</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Qatar</th>\n",
" <td>23</td>\n",
" <td>51.866667</td>\n",
" <td>55.0</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>South Korea</th>\n",
" <td>16</td>\n",
" <td>54.190217</td>\n",
" <td>55.0</td>\n",
" <td>186</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tunisia</th>\n",
" <td>34</td>\n",
" <td>34.500000</td>\n",
" <td>34.5</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>UAE</th>\n",
" <td>4</td>\n",
" <td>44.181818</td>\n",
" <td>42.0</td>\n",
" <td>77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>UK</th>\n",
" <td>30</td>\n",
" <td>34.500000</td>\n",
" <td>34.5</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yemen</th>\n",
" <td>44</td>\n",
" <td>44.000000</td>\n",
" <td>44.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 33,
"text": [
" age HCW\n",
" amin mean median count_nonzero\n",
"country \n",
"France 51 51.000000 51.0 1\n",
"Iran 35 51.500000 51.0 8\n",
"Italy 2 22.000000 22.0 2\n",
"Jordan 25 41.300000 42.5 20\n",
"KSA 0 51.171908 52.0 959\n",
"Kuwait 47 53.000000 52.0 3\n",
"Lebanon 60 60.000000 60.0 1\n",
"Oman 31 55.333333 59.0 9\n",
"Qatar 23 51.866667 55.0 15\n",
"South Korea 16 54.190217 55.0 186\n",
"Tunisia 34 34.500000 34.5 2\n",
"UAE 4 44.181818 42.0 77\n",
"UK 30 34.500000 34.5 2\n",
"Yemen 44 44.000000 44.0 1"
]
}
],
"prompt_number": 33
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Transposing is a cool feature...turn your rows into columns with a single letter."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"country_table.T"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>France</th>\n",
" <th>Iran</th>\n",
" <th>Italy</th>\n",
" <th>Jordan</th>\n",
" <th>KSA</th>\n",
" <th>Kuwait</th>\n",
" <th>Lebanon</th>\n",
" <th>Oman</th>\n",
" <th>Qatar</th>\n",
" <th>South Korea</th>\n",
" <th>Tunisia</th>\n",
" <th>UAE</th>\n",
" <th>UK</th>\n",
" <th>Yemen</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>gender</th>\n",
" <th>count_nonzero</th>\n",
" <td>1</td>\n",
" <td>8.0</td>\n",
" <td>2</td>\n",
" <td>20.0</td>\n",
" <td>959.000000</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>9.000000</td>\n",
" <td>15.000000</td>\n",
" <td>186.000000</td>\n",
" <td>2.0</td>\n",
" <td>77.000000</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">age</th>\n",
" <th>size</th>\n",
" <td>1</td>\n",
" <td>8.0</td>\n",
" <td>2</td>\n",
" <td>20.0</td>\n",
" <td>959.000000</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>9.000000</td>\n",
" <td>15.000000</td>\n",
" <td>186.000000</td>\n",
" <td>2.0</td>\n",
" <td>77.000000</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>51</td>\n",
" <td>51.5</td>\n",
" <td>22</td>\n",
" <td>41.3</td>\n",
" <td>51.115957</td>\n",
" <td>53</td>\n",
" <td>60</td>\n",
" <td>55.333333</td>\n",
" <td>51.866667</td>\n",
" <td>54.190217</td>\n",
" <td>34.5</td>\n",
" <td>44.181818</td>\n",
" <td>34.5</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>median</th>\n",
" <td>51</td>\n",
" <td>51.0</td>\n",
" <td>22</td>\n",
" <td>42.5</td>\n",
" <td>52.000000</td>\n",
" <td>52</td>\n",
" <td>60</td>\n",
" <td>59.000000</td>\n",
" <td>55.000000</td>\n",
" <td>55.000000</td>\n",
" <td>34.5</td>\n",
" <td>42.000000</td>\n",
" <td>34.5</td>\n",
" <td>44</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 196,
"text": [
"country France Iran Italy Jordan KSA Kuwait \\\n",
"gender count_nonzero 1 8.0 2 20.0 959.000000 3 \n",
"age size 1 8.0 2 20.0 959.000000 3 \n",
" mean 51 51.5 22 41.3 51.115957 53 \n",
" median 51 51.0 22 42.5 52.000000 52 \n",
"\n",
"country Lebanon Oman Qatar South Korea Tunisia \\\n",
"gender count_nonzero 1 9.000000 15.000000 186.000000 2.0 \n",
"age size 1 9.000000 15.000000 186.000000 2.0 \n",
" mean 60 55.333333 51.866667 54.190217 34.5 \n",
" median 60 59.000000 55.000000 55.000000 34.5 \n",
"\n",
"country UAE UK Yemen \n",
"gender count_nonzero 77.000000 2.0 1 \n",
"age size 77.000000 2.0 1 \n",
" mean 44.181818 34.5 44 \n",
" median 42.000000 34.5 44 "
]
}
],
"prompt_number": 196
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pivot tables are another great way to make fancy tables. If you can't make groupby work for your issue, you probably need a pivot table."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"gender_grouped = data.pivot_table(index=['country'],\n",
" columns=['gender'],\n",
" values='age',\n",
" fill_value=np.nan)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 40
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nice!"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"gender_grouped.T.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>country</th>\n",
" <th>France</th>\n",
" <th>Iran</th>\n",
" <th>Italy</th>\n",
" <th>Jordan</th>\n",
" <th>KSA</th>\n",
" <th>Kuwait</th>\n",
" <th>Lebanon</th>\n",
" <th>Oman</th>\n",
" <th>Qatar</th>\n",
" <th>South Korea</th>\n",
" <th>Tunisia</th>\n",
" <th>UAE</th>\n",
" <th>UK</th>\n",
" <th>Yemen</th>\n",
" </tr>\n",
" <tr>\n",
" <th>gender</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>F</th>\n",
" <td>NaN</td>\n",
" <td>51.0</td>\n",
" <td>22</td>\n",
" <td>42.666667</td>\n",
" <td>48.590323</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>31.000</td>\n",
" <td>56.000000</td>\n",
" <td>54.578947</td>\n",
" <td>35</td>\n",
" <td>44.789474</td>\n",
" <td>30</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M</th>\n",
" <td>51</td>\n",
" <td>52.5</td>\n",
" <td>NaN</td>\n",
" <td>41.058824</td>\n",
" <td>52.465710</td>\n",
" <td>53</td>\n",
" <td>60</td>\n",
" <td>58.375</td>\n",
" <td>51.571429</td>\n",
" <td>53.916667</td>\n",
" <td>34</td>\n",
" <td>43.982759</td>\n",
" <td>39</td>\n",
" <td>44</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 44,
"text": [
"country France Iran Italy Jordan KSA Kuwait Lebanon Oman \\\n",
"gender \n",
"F NaN 51.0 22 42.666667 48.590323 NaN NaN 31.000 \n",
"M 51 52.5 NaN 41.058824 52.465710 53 60 58.375 \n",
"\n",
"country Qatar South Korea Tunisia UAE UK Yemen \n",
"gender \n",
"F 56.000000 54.578947 35 44.789474 30 NaN \n",
"M 51.571429 53.916667 34 43.982759 39 44 "
]
}
],
"prompt_number": 44
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we plot this, it looks...okay."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"gender_grouped.T.plot(kind='bar')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 43,
"text": [
"<matplotlib.axes._subplots.AxesSubplot at 0xc607f98>"
]
},
{
"metadata": {},
"output_type": "display_data",
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UlBTy8vJo1ao1c+YsYtWq5eUmbzs7Ozp1+gdz576Opp3Ez68pSrUlISHeuLz4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\ncQshhJX5f8vjDHmyacZcAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0xc733710>"
]
}
],
"prompt_number": 43
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It might be better to look at the difference in age means. To do that we will make a new column, age_diff. We do column-wise math again, subtracting M from F."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"gender_grouped['age_diff'] = gender_grouped['M'] - gender_grouped['F']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 45
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can plot the difference and it looks much better. Note the use of barh here instead of bar for a horizontal layout, and the order() argument to make things tidier."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"gender_grouped['age_diff'].order().plot(kind='barh')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 46,
"text": [
"<matplotlib.axes._subplots.AxesSubplot at 0xc6d29b0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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AssDJW4vHjgZ+UsxWfAS4U1Ilw2ulm1cOWVk51GhmBtDV3d05gwlJ44CHIuLF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\n",
"text": [
"<matplotlib.figure.Figure at 0x3c3f048>"
]
}
],
"prompt_number": 46
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# II. Comparing data sets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here I am sampling our data set to pretend like we got new cases for a disease we are already tracking."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"old_list = data.sample(n=500)\n",
"new_list= data.sample(50)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 93
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print len(old_list), len(new_list)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"500 50\n"
]
}
],
"prompt_number": 94
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"new_list.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>number</th>\n",
" <th>FT</th>\n",
" <th>KSA_case</th>\n",
" <th>code</th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>country</th>\n",
" <th>province</th>\n",
" <th>city</th>\n",
" <th>district</th>\n",
" <th>...</th>\n",
" <th>citation</th>\n",
" <th>citation2</th>\n",
" <th>citation3</th>\n",
" <th>citation4</th>\n",
" <th>citation5</th>\n",
" <th>sequence</th>\n",
" <th>accession</th>\n",
" <th>patient</th>\n",
" <th>speculation</th>\n",
" <th>age_group</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>400</th>\n",
" <td>403</td>\n",
" <td>380</td>\n",
" <td>NaN</td>\n",
" <td>38F</td>\n",
" <td>M</td>\n",
" <td>38</td>\n",
" <td>KSA</td>\n",
" <td>NaN</td>\n",
" <td>Jeddah</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>http://www.moh.gov.sa/en/CoronaNew/PressReleas...</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>30-39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>506</th>\n",
" <td>509</td>\n",
" <td>492</td>\n",
" <td>NaN</td>\n",
" <td>47M</td>\n",
" <td>M</td>\n",
" <td>47</td>\n",
" <td>KSA</td>\n",
" <td>NaN</td>\n",
" <td>Jeddah</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>http://www.moh.gov.sa/en/CoronaNew/PressReleas...</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40-49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1001</th>\n",
" <td>1005</td>\n",
" <td>1113</td>\n",
" <td>NaN</td>\n",
" <td>54M</td>\n",
" <td>M</td>\n",
" <td>54</td>\n",
" <td>KSA</td>\n",
" <td>NaN</td>\n",
" <td>Al-Hofuf</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>http://www.who.int/csr/don/9-april-2015-mers-s...</td>\n",
" <td>http://www.moh.gov.sa/en/CCC/PressReleases/Pag...</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>NaN</td>\n",
" <td>50-59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>981</th>\n",
" <td>985</td>\n",
" <td>1109</td>\n",
" <td>NaN</td>\n",
" <td>60M</td>\n",
" <td>M</td>\n",
" <td>60</td>\n",
" <td>KSA</td>\n",
" <td>NaN</td>\n",
" <td>Taimah</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>http://www.who.int/csr/don/26-march-2015-mers-...</td>\n",
" <td>http://www.moh.gov.sa/en/CCC/PressReleases/Pag...</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>NaN</td>\n",
" <td>60-69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>277</th>\n",
" <td>280</td>\n",
" <td>263</td>\n",
" <td>NaN</td>\n",
" <td>52M</td>\n",
" <td>M</td>\n",
" <td>52</td>\n",
" <td>KSA</td>\n",
" <td>NaN</td>\n",
" <td>Jeddah</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>http://www.promedmail.org/direct.php?id=201404...</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>50-59</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 45 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 95,
"text": [
" number FT KSA_case code gender age country province city \\\n",
"400 403 380 NaN 38F M 38 KSA NaN Jeddah \n",
"506 509 492 NaN 47M M 47 KSA NaN Jeddah \n",
"1001 1005 1113 NaN 54M M 54 KSA NaN Al-Hofuf \n",
"981 985 1109 NaN 60M M 60 KSA NaN Taimah \n",
"277 280 263 NaN 52M M 52 KSA NaN Jeddah \n",
"\n",
" district ... citation \\\n",
"400 NaN ... http://www.moh.gov.sa/en/CoronaNew/PressReleas... \n",
"506 NaN ... http://www.moh.gov.sa/en/CoronaNew/PressReleas... \n",
"1001 NaN ... http://www.who.int/csr/don/9-april-2015-mers-s... \n",
"981 NaN ... http://www.who.int/csr/don/26-march-2015-mers-... \n",
"277 NaN ... http://www.promedmail.org/direct.php?id=201404... \n",
"\n",
" citation2 citation3 citation4 \\\n",
"400 NaN NaN NaN \n",
"506 NaN NaN NaN \n",
"1001 http://www.moh.gov.sa/en/CCC/PressReleases/Pag... NaN NaN \n",
"981 http://www.moh.gov.sa/en/CCC/PressReleases/Pag... NaN NaN \n",
"277 NaN NaN NaN \n",
"\n",
" citation5 sequence accession patient speculation age_group \n",
"400 NaN NaN NaN NaN NaN 30-39 \n",
"506 NaN NaN NaN NaN NaN 40-49 \n",
"1001 NaN NaN NaN NaN NaN 50-59 \n",
"981 NaN NaN NaN NaN NaN 60-69 \n",
"277 NaN NaN NaN NaN NaN 50-59 \n",
"\n",
"[5 rows x 45 columns]"
]
}
],
"prompt_number": 95
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Okay here is the magic..I want to find case ids in the new list that also appear in the old list. I just use the isin() method, and feed it a list of the identifiers I want to compare against. It returns True and False."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"new_list.number.isin(old_flu.number)[:10]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 137,
"text": [
"400 True\n",
"506 False\n",
"1001 True\n",
"981 False\n",
"277 False\n",
"376 True\n",
"215 True\n",
"974 True\n",
"1111 False\n",
"223 True\n",
"Name: number, dtype: bool"
]
}
],
"prompt_number": 137
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It looks like there are some! (This number will change each time we run this code because I am sampling randomly.)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"new_list.number.isin(old_list.number).value_counts()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 97,
"text": [
"False 26\n",
"True 24\n",
"dtype: int64"
]
}
],
"prompt_number": 97
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I want to create a new data set that has just new cases that appear in the old data set."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"matches = new_list[new_list.number.isin(old_list.number)]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 138
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"matches.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>number</th>\n",
" <th>FT</th>\n",
" <th>KSA_case</th>\n",
" <th>code</th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>country</th>\n",
" <th>province</th>\n",
" <th>city</th>\n",
" <th>district</th>\n",
" <th>...</th>\n",
" <th>citation</th>\n",
" <th>citation2</th>\n",
" <th>citation3</th>\n",
" <th>citation4</th>\n",
" <th>citation5</th>\n",
" <th>sequence</th>\n",
" <th>accession</th>\n",
" <th>patient</th>\n",
" <th>speculation</th>\n",
" <th>age_group</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>400</th>\n",
" <td>403</td>\n",
" <td>380</td>\n",
" <td>NaN</td>\n",
" <td>38F</td>\n",
" <td>M</td>\n",
" <td>38</td>\n",
" <td>KSA</td>\n",
" <td>NaN</td>\n",
" <td>Jeddah</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>http://www.moh.gov.sa/en/CoronaNew/PressReleas...</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>30-39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>506</th>\n",
" <td>509</td>\n",
" <td>492</td>\n",
" <td>NaN</td>\n",
" <td>47M</td>\n",
" <td>M</td>\n",
" <td>47</td>\n",
" <td>KSA</td>\n",
" <td>NaN</td>\n",
" <td>Jeddah</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>http://www.moh.gov.sa/en/CoronaNew/PressReleas...</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40-49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1001</th>\n",
" <td>1005</td>\n",
" <td>1113</td>\n",
" <td>NaN</td>\n",
" <td>54M</td>\n",
" <td>M</td>\n",
" <td>54</td>\n",
" <td>KSA</td>\n",
" <td>NaN</td>\n",
" <td>Al-Hofuf</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>http://www.who.int/csr/don/9-april-2015-mers-s...</td>\n",
" <td>http://www.moh.gov.sa/en/CCC/PressReleases/Pag...</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>NaN</td>\n",
" <td>50-59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>981</th>\n",
" <td>985</td>\n",
" <td>1109</td>\n",
" <td>NaN</td>\n",
" <td>60M</td>\n",
" <td>M</td>\n",
" <td>60</td>\n",
" <td>KSA</td>\n",
" <td>NaN</td>\n",
" <td>Taimah</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>http://www.who.int/csr/don/26-march-2015-mers-...</td>\n",
" <td>http://www.moh.gov.sa/en/CCC/PressReleases/Pag...</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>NaN</td>\n",
" <td>60-69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>277</th>\n",
" <td>280</td>\n",
" <td>263</td>\n",
" <td>NaN</td>\n",
" <td>52M</td>\n",
" <td>M</td>\n",
" <td>52</td>\n",
" <td>KSA</td>\n",
" <td>NaN</td>\n",
" <td>Jeddah</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>http://www.promedmail.org/direct.php?id=201404...</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>50-59</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 45 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 139,
"text": [
" number FT KSA_case code gender age country province city \\\n",
"400 403 380 NaN 38F M 38 KSA NaN Jeddah \n",
"506 509 492 NaN 47M M 47 KSA NaN Jeddah \n",
"1001 1005 1113 NaN 54M M 54 KSA NaN Al-Hofuf \n",
"981 985 1109 NaN 60M M 60 KSA NaN Taimah \n",
"277 280 263 NaN 52M M 52 KSA NaN Jeddah \n",
"\n",
" district ... citation \\\n",
"400 NaN ... http://www.moh.gov.sa/en/CoronaNew/PressReleas... \n",
"506 NaN ... http://www.moh.gov.sa/en/CoronaNew/PressReleas... \n",
"1001 NaN ... http://www.who.int/csr/don/9-april-2015-mers-s... \n",
"981 NaN ... http://www.who.int/csr/don/26-march-2015-mers-... \n",
"277 NaN ... http://www.promedmail.org/direct.php?id=201404... \n",
"\n",
" citation2 citation3 citation4 \\\n",
"400 NaN NaN NaN \n",
"506 NaN NaN NaN \n",
"1001 http://www.moh.gov.sa/en/CCC/PressReleases/Pag... NaN NaN \n",
"981 http://www.moh.gov.sa/en/CCC/PressReleases/Pag... NaN NaN \n",
"277 NaN NaN NaN \n",
"\n",
" citation5 sequence accession patient speculation age_group \n",
"400 NaN NaN NaN NaN NaN 30-39 \n",
"506 NaN NaN NaN NaN NaN 40-49 \n",
"1001 NaN NaN NaN NaN NaN 50-59 \n",
"981 NaN NaN NaN NaN NaN 60-69 \n",
"277 NaN NaN NaN NaN NaN 50-59 \n",
"\n",
"[5 rows x 45 columns]"
]
}
],
"prompt_number": 139
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(matches)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 140,
"text": [
"24"
]
}
],
"prompt_number": 140
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or maybe I want to create a data set of cases that DON'T appear in the old set."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"novel = new_list[new_list.number.isin(old_list.number) == False]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 141
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(novel)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 142,
"text": [
"26"
]
}
],
"prompt_number": 142
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**This can work on ANY kind of identifier, including strings.**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# III. More data cleaning"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's pretend that 99 is used as a null value, or that we don't believe anyone older than 90 should be in our data set."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data.age.max()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 101,
"text": [
"99.0"
]
}
],
"prompt_number": 101
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can select out records that meet our criteria, and set them to null values."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data.age[data.age > 90] = np.nan"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 143
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data.age.max()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 103,
"text": [
"90.0"
]
}
],
"prompt_number": 103
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Wait I changed my mind, I want to use -9999 as my null value. Here I use the fillna() method to replace np.nan values with my new number."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data.age = data.age.fillna(method='ffill')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 203
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data.age.min()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 204,
"text": [
"-9999.0"
]
}
],
"prompt_number": 204
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also create more complex selection criteria."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data.age[(data.age < 10) & (data.country == 'KSA')] = 'missing'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 144
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data.age.unique()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 148,
"text": [
"array([25.0, 30.0, 40.0, 60.0, 29.0, 33.0, 28.0, 45.0, 46.0, 53.0, 49.0,\n",
" 70.0, 31.0, 39.0, 16.0, 61.0, 69.0, 51.0, 73.0, 56.0, 58.0, 55.0,\n",
" 59.0, 24.0, 87.0, 77.0, 62.0, nan, 50.0, 52.0, 48.0, 43.0, 81.0,\n",
" 64.0, 66.0, 34.0, 35.0, 63.0, 85.0, 76.0, 21.0, 2.0, 42.0, 14.0,\n",
" 83.0, 75.0, 68.0, 'missing', 41.0, 32.0, 12.0, 15.0, 82.0, 26.0,\n",
" 67.0, 54.0, 38.0, 19.0, 79.0, 47.0, 18.0, 74.0, 22.0, 78.0, 23.0,\n",
" 72.0, 65.0, 37.0, 8.0, 57.0, 27.0, 86.0, 71.0, 90.0, 44.0, 13.0,\n",
" 88.0, 17.0, 89.0, 11.0, 80.0, 4.0, 36.0, 10.0, 84.0, 20.0], dtype=object)"
]
}
],
"prompt_number": 148
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another nifty function is splitting up columns into two different columns"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data.code.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 105,
"text": [
"0 25M\n",
"1 30M\n",
"2 40F\n",
"3 60M\n",
"4 29M\n",
"Name: code, dtype: object"
]
}
],
"prompt_number": 105
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Combining apply and lambda is a powerful (but sometimes tough) way to do this. Pandas does most things column-wise. Apply is used to do things row-wise. Here we make sure to use the dropna() method to remove our null values, since our operation will only work on strings. The patient's age is the first two characters of the string, so we select those and put them in a new column. Then we do the same for sex."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data['new_age'] = data.code.dropna().apply(lambda x: x[0:2])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 47
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data['sex'] = data.code.dropna().apply(lambda x: x[-1])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 48
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Look, it worked!"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data[['code', 'new_age', 'sex']].head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>code</th>\n",
" <th>new_age</th>\n",
" <th>sex</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>25M</td>\n",
" <td>25</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>30M</td>\n",
" <td>30</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>40F</td>\n",
" <td>40</td>\n",
" <td>F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>60M</td>\n",
" <td>60</td>\n",
" <td>M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>29M</td>\n",
" <td>29</td>\n",
" <td>M</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 49,
"text": [
" code new_age sex\n",
"0 25M 25 M\n",
"1 30M 30 M\n",
"2 40F 40 F\n",
"3 60M 60 M\n",
"4 29M 29 M"
]
}
],
"prompt_number": 49
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another thing you might want to do is split up names into first name and surname. We don't have names in our data set, so we'll use the country column to exemplify that (keep an eye on South Korea)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data.country.unique()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 125,
"text": [
"array(['Jordan', 'KSA', 'Qatar', 'UK', 'UAE', 'France', 'Tunisia', 'Italy',\n",
" nan, 'Oman', 'Kuwait', 'Yemen', 'Lebanon', 'Iran', 'South Korea'], dtype=object)"
]
}
],
"prompt_number": 125
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We use the .split() method to split the string, and insert a space to tell the computer we want to split on a space."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data.country.dropna().apply(lambda x: x.split(' '))[-10:]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 51,
"text": [
"1281 [South, Korea]\n",
"1282 [KSA]\n",
"1283 [South, Korea]\n",
"1284 [South, Korea]\n",
"1285 [South, Korea]\n",
"1286 [KSA]\n",
"1287 [KSA]\n",
"1288 [KSA]\n",
"1289 [KSA]\n",
"1290 [KSA]\n",
"Name: country, dtype: object"
]
}
],
"prompt_number": 51
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The we use our bracket selections, e.g. [-1] to say we want the last word in the string set."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data.country.dropna().apply(lambda x: x.split(' ')[-1])[-10:]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 136,
"text": [
"1281 Korea\n",
"1282 KSA\n",
"1283 Korea\n",
"1284 Korea\n",
"1285 Korea\n",
"1286 KSA\n",
"1287 KSA\n",
"1288 KSA\n",
"1289 KSA\n",
"1290 KSA\n",
"Name: country, dtype: object"
]
}
],
"prompt_number": 136
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# IV. Homework"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Answer the questions in lesson2_homework. I'll post the answers after class #3."
]
}
],
"metadata": {}
}
]
}
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