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Ferrous Choropleth
Raw
README.md

It's a ferrous choropleth for my friend Wai Keong

Problems....

  1. Are we identifying all iron prescriptions with search string "Ferr"

  2. CCG geographic boundary size varies... choropleth gives too much attention to larger ones... aand that doesn't make sense

  3. ....

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FerrousAnalytics.ipynb
{
"metadata": {
"name": "FerrousAnalytics"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": "import pandas as pd",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 64
},
{
"cell_type": "code",
"collapsed": false,
"input": "from pandas import DataFrame, Series",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 65
},
{
"cell_type": "code",
"collapsed": false,
"input": "data = pd.read_csv('T201304PDPI+BNFT.csv', usecols=[2,3,4,5])",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 66
},
{
"cell_type": "code",
"collapsed": false,
"input": "data.columns",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 67,
"text": "Index([u'PRACTICE', u'BNF CODE', u'BNF NAME ', u'ITEMS '], dtype=object)"
}
],
"prompt_number": 67
},
{
"cell_type": "code",
"collapsed": false,
"input": "data.columns = ['PRACTICE', 'BNF CODE', 'BNF NAME', 'ITEMS'] #rename to cut the white space",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 68
},
{
"cell_type": "code",
"collapsed": false,
"input": "data['ITEMS'] = data['ITEMS'].astype(int) #make the occasional strings in this numerical field into ints",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 69
},
{
"cell_type": "code",
"collapsed": false,
"input": "para = data[data['BNF NAME'].str.contains('Ferr')] #make a new DataFrame that contains rows with partial string 'Ferr'",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 70
},
{
"cell_type": "code",
"collapsed": false,
"input": "para.ITEMS.sum() #total number of items prescribed, with a 60 million pop this gives about 90 items per 10,000 people",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 71,
"text": "543484"
}
],
"prompt_number": 71
},
{
"cell_type": "code",
"collapsed": false,
"input": "para.ITEMS.sum() #total number of items prescribed, with a 60 million pop this gives 90 items per 10,000 people",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 74,
"text": "543484"
}
],
"prompt_number": 74
},
{
"cell_type": "code",
"collapsed": false,
"input": "para # 37045 is the same number returned by grep -c Ferr T201304PDPI+BNFT.csv which is a nice sanity check",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<pre>\n&lt;class 'pandas.core.frame.DataFrame'&gt;\nInt64Index: 37045 entries, 518 to 9603146\nData columns (total 4 columns):\nPRACTICE 37045 non-null values\nBNF CODE 37045 non-null values\nBNF NAME 37045 non-null values\nITEMS 37045 non-null values\ndtypes: int64(1), object(3)\n</pre>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 75,
"text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 37045 entries, 518 to 9603146\nData columns (total 4 columns):\nPRACTICE 37045 non-null values\nBNF CODE 37045 non-null values\nBNF NAME 37045 non-null values\nITEMS 37045 non-null values\ndtypes: int64(1), object(3)"
}
],
"prompt_number": 75
},
{
"cell_type": "code",
"collapsed": false,
"input": "gp2ccg = pd.read_csv('practice_to_ccg_codes.csv')",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 61
},
{
"cell_type": "code",
"collapsed": false,
"input": "gp2ccg[:10]",
"language": "python",
"metadata": {},
"outputs": [
{
"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>PRACTICE</th>\n <th>CCGCODE</th>\n <th>CCG13CD</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td> A81001</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>1</th>\n <td> A81002</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>2</th>\n <td> A81003</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>3</th>\n <td> A81004</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>4</th>\n <td> A81005</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>5</th>\n <td> A81006</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>6</th>\n <td> A81007</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>7</th>\n <td> A81008</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>8</th>\n <td> A81009</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>9</th>\n <td> A81011</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n </tbody>\n</table>\n</div>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 77,
"text": " PRACTICE CCGCODE CCG13CD\n0 A81001 02N E38000001\n1 A81002 02N E38000001\n2 A81003 02N E38000001\n3 A81004 02N E38000001\n4 A81005 02N E38000001\n5 A81006 02N E38000001\n6 A81007 02N E38000001\n7 A81008 02N E38000001\n8 A81009 02N E38000001\n9 A81011 02N E38000001"
}
],
"prompt_number": 77
},
{
"cell_type": "code",
"collapsed": false,
"input": "gp2ccg",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<pre>\n&lt;class 'pandas.core.frame.DataFrame'&gt;\nInt64Index: 8721 entries, 0 to 8720\nData columns (total 3 columns):\nPRACTICE 8721 non-null values\nCCGCODE 8721 non-null values\nCCG13CD 8721 non-null values\ndtypes: object(3)\n</pre>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 78,
"text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 8721 entries, 0 to 8720\nData columns (total 3 columns):\nPRACTICE 8721 non-null values\nCCGCODE 8721 non-null values\nCCG13CD 8721 non-null values\ndtypes: object(3)"
}
],
"prompt_number": 78
},
{
"cell_type": "code",
"collapsed": false,
"input": "para[:10]",
"language": "python",
"metadata": {},
"outputs": [
{
"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>PRACTICE</th>\n <th>BNF CODE</th>\n <th>BNF NAME</th>\n <th>ITEMS</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>518 </th>\n <td> Y03375</td>\n <td> 0901011F0AAACAC</td>\n <td> Ferr Fumar_Oral Soln 140mg/5ml </td>\n <td> 3</td>\n </tr>\n <tr>\n <th>519 </th>\n <td> Y03375</td>\n <td> 0901011F0AAAEAE</td>\n <td> Ferr Fumar_Tab 210mg </td>\n <td> 9</td>\n </tr>\n <tr>\n <th>520 </th>\n <td> Y03375</td>\n <td> 0901011P0AAACAC</td>\n <td> Ferr Sulf_Tab 200mg </td>\n <td> 2</td>\n </tr>\n <tr>\n <th>688 </th>\n <td> Y01120</td>\n <td> 0901011P0AAACAC</td>\n <td> Ferr Sulf_Tab 200mg </td>\n <td> 1</td>\n </tr>\n <tr>\n <th>1962</th>\n <td> Y00688</td>\n <td> 0901011F0AAAEAE</td>\n <td> Ferr Fumar_Tab 210mg </td>\n <td> 1</td>\n </tr>\n <tr>\n <th>2671</th>\n <td> Y02115</td>\n <td> 0901011F0AAAEAE</td>\n <td> Ferr Fumar_Tab 210mg </td>\n <td> 1</td>\n </tr>\n <tr>\n <th>2672</th>\n <td> Y02115</td>\n <td> 0901011F0AAALAL</td>\n <td> Ferr Fumar_Tab 322mg </td>\n <td> 1</td>\n </tr>\n <tr>\n <th>3064</th>\n <td> Y03574</td>\n <td> 0901011F0AAAEAE</td>\n <td> Ferr Fumar_Tab 210mg </td>\n <td> 7</td>\n </tr>\n <tr>\n <th>3065</th>\n <td> Y03574</td>\n <td> 0901011P0AAACAC</td>\n <td> Ferr Sulf_Tab 200mg </td>\n <td> 1</td>\n </tr>\n <tr>\n <th>3377</th>\n <td> Y02299</td>\n <td> 0901011F0AAAEAE</td>\n <td> Ferr Fumar_Tab 210mg </td>\n <td> 6</td>\n </tr>\n </tbody>\n</table>\n</div>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 79,
"text": " PRACTICE BNF CODE BNF NAME ITEMS\n518 Y03375 0901011F0AAACAC Ferr Fumar_Oral Soln 140mg/5ml 3\n519 Y03375 0901011F0AAAEAE Ferr Fumar_Tab 210mg 9\n520 Y03375 0901011P0AAACAC Ferr Sulf_Tab 200mg 2\n688 Y01120 0901011P0AAACAC Ferr Sulf_Tab 200mg 1\n1962 Y00688 0901011F0AAAEAE Ferr Fumar_Tab 210mg 1\n2671 Y02115 0901011F0AAAEAE Ferr Fumar_Tab 210mg 1\n2672 Y02115 0901011F0AAALAL Ferr Fumar_Tab 322mg 1\n3064 Y03574 0901011F0AAAEAE Ferr Fumar_Tab 210mg 7\n3065 Y03574 0901011P0AAACAC Ferr Sulf_Tab 200mg 1\n3377 Y02299 0901011F0AAAEAE Ferr Fumar_Tab 210mg 6"
}
],
"prompt_number": 79
},
{
"cell_type": "code",
"collapsed": false,
"input": "para['PRACTICE']",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 80,
"text": "518 Y03375\n519 Y03375\n520 Y03375\n688 Y01120\n1962 Y00688\n2671 Y02115\n2672 Y02115\n3064 Y03574\n3065 Y03574\n3377 Y02299\n3691 Y02903\n4348 Y02591\n5123 Y00426\n5315 Y01034\n5497 Y01953\n...\n9598333 J82046\n9599993 J82058\n9599994 J82058\n9599996 J82058\n9599997 J82058\n9601591 J82061\n9601592 J82061\n9601593 J82061\n9601594 J82061\n9601595 J82061\n9603142 J82065\n9603143 J82065\n9603144 J82065\n9603145 J82065\n9603146 J82065\nName: PRACTICE, Length: 37045, dtype: object"
}
],
"prompt_number": 80
},
{
"cell_type": "code",
"collapsed": false,
"input": "paragp = para.groupby('PRACTICE')",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 81
},
{
"cell_type": "code",
"collapsed": false,
"input": "sum(paragp.ITEMS.sum()) #still got all our items",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 116,
"text": "543484"
}
],
"prompt_number": 116
},
{
"cell_type": "code",
"collapsed": false,
"input": "practice_items = paragp.ITEMS.sum()",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 92
},
{
"cell_type": "code",
"collapsed": false,
"input": "practice_items.to_csv('practice_items.csv', header=True)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 93
},
{
"cell_type": "code",
"collapsed": false,
"input": "practice_items = pd.read_csv('practice_items.csv')",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 96
},
{
"cell_type": "code",
"collapsed": false,
"input": "paraitems = pd.merge(practice_items, gp2ccg, on='PRACTICE')",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 97
},
{
"cell_type": "code",
"collapsed": false,
"input": "paraitems[:10]",
"language": "python",
"metadata": {},
"outputs": [
{
"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>PRACTICE</th>\n <th>ITEMS</th>\n <th>CCGCODE</th>\n <th>CCG13CD</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td> A81001</td>\n <td> 36</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>1</th>\n <td> A81002</td>\n <td> 240</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>2</th>\n <td> A81003</td>\n <td> 68</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>3</th>\n <td> A81004</td>\n <td> 58</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>4</th>\n <td> A81005</td>\n <td> 71</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>5</th>\n <td> A81006</td>\n <td> 145</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>6</th>\n <td> A81007</td>\n <td> 69</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>7</th>\n <td> A81008</td>\n <td> 37</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>8</th>\n <td> A81009</td>\n <td> 116</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>9</th>\n <td> A81011</td>\n <td> 142</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n </tbody>\n</table>\n</div>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 100,
"text": " PRACTICE ITEMS CCGCODE CCG13CD\n0 A81001 36 02N E38000001\n1 A81002 240 02N E38000001\n2 A81003 68 02N E38000001\n3 A81004 58 02N E38000001\n4 A81005 71 02N E38000001\n5 A81006 145 02N E38000001\n6 A81007 69 02N E38000001\n7 A81008 37 02N E38000001\n8 A81009 116 02N E38000001\n9 A81011 142 02N E38000001"
}
],
"prompt_number": 100
},
{
"cell_type": "code",
"collapsed": false,
"input": "paraitems.ITEMS.sum()",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 117,
"text": "542462"
}
],
"prompt_number": 117
},
{
"cell_type": "code",
"collapsed": false,
"input": "paraitems #some entries have gone missing... this suggests not all practices matched up... so we lost a few items",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<pre>\n&lt;class 'pandas.core.frame.DataFrame'&gt;\nInt64Index: 7767 entries, 0 to 7766\nData columns (total 4 columns):\nPRACTICE 7767 non-null values\nITEMS 7767 non-null values\nCCGCODE 7767 non-null values\nCCG13CD 7767 non-null values\ndtypes: int64(1), object(3)\n</pre>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 101,
"text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 7767 entries, 0 to 7766\nData columns (total 4 columns):\nPRACTICE 7767 non-null values\nITEMS 7767 non-null values\nCCGCODE 7767 non-null values\nCCG13CD 7767 non-null values\ndtypes: int64(1), object(3)"
}
],
"prompt_number": 101
},
{
"cell_type": "code",
"collapsed": false,
"input": "ccg_pop = pd.read_csv('ccgcode_pop.csv')",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 118
},
{
"cell_type": "code",
"collapsed": false,
"input": "ccg_pop",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<pre>\n&lt;class 'pandas.core.frame.DataFrame'&gt;\nInt64Index: 211 entries, 0 to 210\nData columns (total 2 columns):\nCCG13CD 211 non-null values\nPopulation 211 non-null values\ndtypes: int64(1), object(1)\n</pre>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 119,
"text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 211 entries, 0 to 210\nData columns (total 2 columns):\nCCG13CD 211 non-null values\nPopulation 211 non-null values\ndtypes: int64(1), object(1)"
}
],
"prompt_number": 119
},
{
"cell_type": "code",
"collapsed": false,
"input": "ccg_pop['Population'].sum()",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 120,
"text": "55704177"
}
],
"prompt_number": 120
},
{
"cell_type": "code",
"collapsed": false,
"input": "paraitems[:10]",
"language": "python",
"metadata": {},
"outputs": [
{
"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>PRACTICE</th>\n <th>ITEMS</th>\n <th>CCGCODE</th>\n <th>CCG13CD</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td> A81001</td>\n <td> 36</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>1</th>\n <td> A81002</td>\n <td> 240</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>2</th>\n <td> A81003</td>\n <td> 68</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>3</th>\n <td> A81004</td>\n <td> 58</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>4</th>\n <td> A81005</td>\n <td> 71</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>5</th>\n <td> A81006</td>\n <td> 145</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>6</th>\n <td> A81007</td>\n <td> 69</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>7</th>\n <td> A81008</td>\n <td> 37</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>8</th>\n <td> A81009</td>\n <td> 116</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n <tr>\n <th>9</th>\n <td> A81011</td>\n <td> 142</td>\n <td> 02N</td>\n <td> E38000001</td>\n </tr>\n </tbody>\n</table>\n</div>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 147,
"text": " PRACTICE ITEMS CCGCODE CCG13CD\n0 A81001 36 02N E38000001\n1 A81002 240 02N E38000001\n2 A81003 68 02N E38000001\n3 A81004 58 02N E38000001\n4 A81005 71 02N E38000001\n5 A81006 145 02N E38000001\n6 A81007 69 02N E38000001\n7 A81008 37 02N E38000001\n8 A81009 116 02N E38000001\n9 A81011 142 02N E38000001"
}
],
"prompt_number": 147
},
{
"cell_type": "code",
"collapsed": false,
"input": "para_analysis = paraitems.groupby('CCG13CD')",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 158
},
{
"cell_type": "code",
"collapsed": false,
"input": "para_analysis.ITEMS.sum()",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 159,
"text": "CCG13CD\nE38000001 1600\nE38000002 2584\nE38000003 1855\nE38000004 2879\nE38000005 5265\nE38000006 4541\nE38000007 3474\nE38000008 964\nE38000009 2505\nE38000010 5770\nE38000011 2951\nE38000012 13823\nE38000013 5707\nE38000014 2088\nE38000015 1726\n...\nE38000196 324\nE38000197 620\nE38000198 424\nE38000199 671\nE38000200 600\nE38000201 804\nE38000202 607\nE38000203 405\nE38000204 587\nE38000205 1488\nE38000206 1243\nE38000207 49\nE38000208 729\nE38000210 85\nE38000211 160\nName: ITEMS, Length: 209, dtype: int64"
}
],
"prompt_number": 159
},
{
"cell_type": "code",
"collapsed": false,
"input": "sum(para_analysis.ITEMS.sum())",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 185,
"text": "542462"
}
],
"prompt_number": 185
},
{
"cell_type": "code",
"collapsed": false,
"input": "para_analysis",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 168,
"text": "<pandas.core.groupby.DataFrameGroupBy object at 0x19394e90>"
}
],
"prompt_number": 168
},
{
"cell_type": "code",
"collapsed": false,
"input": "para_analysis.ITEMS.sum().to_csv('para_analysis.csv', header=True)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 170
},
{
"cell_type": "code",
"collapsed": false,
"input": "para_analysisdf = pd.read_csv('para_analysis.csv')",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 171
},
{
"cell_type": "code",
"collapsed": false,
"input": "para_analysisdf",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "<pre>\n&lt;class 'pandas.core.frame.DataFrame'&gt;\nInt64Index: 209 entries, 0 to 208\nData columns (total 2 columns):\nCCG13CD 209 non-null values\nITEMS 209 non-null values\ndtypes: int64(1), object(1)\n</pre>",
"metadata": {},
"output_type": "pyout",
"prompt_number": 172,
"text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 209 entries, 0 to 208\nData columns (total 2 columns):\nCCG13CD 209 non-null values\nITEMS 209 non-null values\ndtypes: int64(1), object(1)"
}
],
"prompt_number": 172
},
{
"cell_type": "code",
"collapsed": false,
"input": "para_analysisdf['Per_person_para_by_ccg'] = (para_analysisdf['ITEMS'] / ccg_pop['Population']) * 1000",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 183
},
{
"cell_type": "code",
"collapsed": false,
"input": "para_analysisdf['Per_person_para_by_ccg'].describe()",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 184,
"text": "count 209.000000\nmean 10.761828\nstd 8.015475\nmin 0.045646\n25% 5.892991\n50% 9.751266\n75% 13.633382\nmax 58.441998\ndtype: float64"
}
],
"prompt_number": 184
},
{
"cell_type": "code",
"collapsed": false,
"input": "ccg_geo = 'ccgs.json'",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 189
},
{
"cell_type": "code",
"collapsed": false,
"input": "import folium",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 190
},
{
"cell_type": "code",
"collapsed": false,
"input": "map = folium.Map(location=[54.2, -2.45], zoom_start=5)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 193
},
{
"cell_type": "code",
"collapsed": false,
"input": "map = folium.Map(location=[54.2, -2.45], zoom_start=5)\nmap.geo_json(geo_path=ccg_geo, data_out='data11.json', data=para_analysisdf,\n columns=['CCG13CD', 'Per_person_para_by_ccg'],\n key_on='feature.properties.CCG13CD',\n threshold_scale=[5, 6, 7, 8, 9, 10],\n fill_color='PuBu', fill_opacity=0.7, line_opacity=0.3,\n legend_name='Number of iron containing items prescribed in April per 1000 population by CCG')",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 195
},
{
"cell_type": "code",
"collapsed": false,
"input": "map.create_map(path='map_11.html')",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 196
},
{
"cell_type": "code",
"collapsed": false,
"input": "",
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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