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August 1, 2014 14:27
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a notebook to plot citation trends from JCR 2004-2013
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{ | |
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"name": "", | |
"signature": "sha256:d1355cd3916fec7cb8d44c23a9217f3bbff36499b973ea66c47448aa35736365" | |
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"worksheets": [ | |
{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Citation Trends of Molecular & Cell Biology Journals\n", | |
"by Hyeshik\n", | |
"\n", | |
"## Load JCR data between 2000-2012\n", | |
"JCR data between 2000 and 2012 are converted from Excel files to CSV before starting this analysis." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"import pandas as pd" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 2 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"def load_jcr_old(year):\n", | |
" columns_to_load = ['ISSN', 'Impact Factor', '{' + str(year) + '} Articles']\n", | |
" tbl = pd.read_csv('data/SCIE_JCR{}.csv'.format(year), skiprows=2)[columns_to_load]\n", | |
" tbl.columns = ('ISSN', 'IF'+str(year), 'A'+str(year))\n", | |
" return tbl.set_index('ISSN')\n", | |
"\n", | |
"tbl2000 = load_jcr_old(2000)\n", | |
"tbl2000.head()" | |
], | |
"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>IF2000</th>\n", | |
" <th>A2000</th>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>ISSN</th>\n", | |
" <th></th>\n", | |
" <th></th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>0149-1423</th>\n", | |
" <td> 1.494</td>\n", | |
" <td> 81</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>0942-8925</th>\n", | |
" <td> 0.866</td>\n", | |
" <td> 126</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>0025-5858</th>\n", | |
" <td> 0.232</td>\n", | |
" <td> 27</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1069-6563</th>\n", | |
" <td> 1.419</td>\n", | |
" <td> 152</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1040-2446</th>\n", | |
" <td> 1.554</td>\n", | |
" <td> 229</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 3, | |
"text": [ | |
" IF2000 A2000\n", | |
"ISSN \n", | |
"0149-1423 1.494 81\n", | |
"0942-8925 0.866 126\n", | |
"0025-5858 0.232 27\n", | |
"1069-6563 1.419 152\n", | |
"1040-2446 1.554 229" | |
] | |
} | |
], | |
"prompt_number": 3 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"ls data" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"\u001b[0m\u001b[00m2013_IF.csv\u001b[0m \u001b[00mSCIE_JCR2004.csv\u001b[0m \u001b[00mSCIE_JCR2009.csv\u001b[0m \u001b[00mSCIE_JCR2012-category.csv\u001b[0m\r\n", | |
"\u001b[00mSCIE_JCR2000.csv\u001b[0m \u001b[00mSCIE_JCR2005.csv\u001b[0m \u001b[00mSCIE_JCR2010-category.csv\u001b[0m \u001b[00mSCIE_JCR2012.csv\u001b[0m\r\n", | |
"\u001b[00mSCIE_JCR2001.csv\u001b[0m \u001b[00mSCIE_JCR2006.csv\u001b[0m \u001b[00mSCIE_JCR2010.csv\u001b[0m\r\n", | |
"\u001b[00mSCIE_JCR2002.csv\u001b[0m \u001b[00mSCIE_JCR2007.csv\u001b[0m \u001b[00mSCIE_JCR2011-category.csv\u001b[0m\r\n", | |
"\u001b[00mSCIE_JCR2003.csv\u001b[0m \u001b[00mSCIE_JCR2008.csv\u001b[0m \u001b[00mSCIE_JCR2011.csv\u001b[0m\r\n" | |
] | |
} | |
], | |
"prompt_number": 4 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"oldtables = [load_jcr_old(y) for y in range(2000, 2013)]" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 5 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"from functools import reduce\n", | |
"alloldtables = reduce(lambda x, y: pd.merge(x, y, how='outer', left_index=True, right_index=True),\n", | |
" oldtables).dropna(subset=('A2012',))" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 6 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"alloldtables.tail()" | |
], | |
"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>IF2000</th>\n", | |
" <th>A2000</th>\n", | |
" <th>IF2001</th>\n", | |
" <th>A2001</th>\n", | |
" <th>IF2002</th>\n", | |
" <th>A2002</th>\n", | |
" <th>IF2003</th>\n", | |
" <th>A2003</th>\n", | |
" <th>IF2004</th>\n", | |
" <th>A2004</th>\n", | |
" <th>...</th>\n", | |
" <th>IF2008</th>\n", | |
" <th>A2008</th>\n", | |
" <th>IF2009</th>\n", | |
" <th>A2009</th>\n", | |
" <th>IF2010</th>\n", | |
" <th>A2010</th>\n", | |
" <th>IF2011</th>\n", | |
" <th>A2011</th>\n", | |
" <th>IF2012</th>\n", | |
" <th>A2012</th>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>ISSN</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", | |
" <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>8756-3282</th>\n", | |
" <td> 3.998</td>\n", | |
" <td> 199</td>\n", | |
" <td> 3.247</td>\n", | |
" <td> 169</td>\n", | |
" <td> 3.755</td>\n", | |
" <td> 247</td>\n", | |
" <td> 3.572</td>\n", | |
" <td> 191</td>\n", | |
" <td> 3.530</td>\n", | |
" <td> 283</td>\n", | |
" <td>...</td>\n", | |
" <td> 4.145</td>\n", | |
" <td> 275</td>\n", | |
" <td> 4.089</td>\n", | |
" <td> 307</td>\n", | |
" <td> 4.601</td>\n", | |
" <td> 339</td>\n", | |
" <td> 4.023</td>\n", | |
" <td> 340</td>\n", | |
" <td> 3.823</td>\n", | |
" <td> 309</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>8756-5641</th>\n", | |
" <td> 0.871</td>\n", | |
" <td> 25</td>\n", | |
" <td> 0.914</td>\n", | |
" <td> 23</td>\n", | |
" <td> 1.351</td>\n", | |
" <td> 26</td>\n", | |
" <td> 1.672</td>\n", | |
" <td> 33</td>\n", | |
" <td> 1.953</td>\n", | |
" <td> 37</td>\n", | |
" <td>...</td>\n", | |
" <td> 1.964</td>\n", | |
" <td> 36</td>\n", | |
" <td> 2.321</td>\n", | |
" <td> 48</td>\n", | |
" <td> 2.440</td>\n", | |
" <td> 42</td>\n", | |
" <td> 2.556</td>\n", | |
" <td> 57</td>\n", | |
" <td> 2.899</td>\n", | |
" <td> 40</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>8756-758X</th>\n", | |
" <td> 0.675</td>\n", | |
" <td> 99</td>\n", | |
" <td> 0.693</td>\n", | |
" <td> 75</td>\n", | |
" <td> 0.701</td>\n", | |
" <td> 105</td>\n", | |
" <td> 0.706</td>\n", | |
" <td> 106</td>\n", | |
" <td> 0.673</td>\n", | |
" <td> 101</td>\n", | |
" <td>...</td>\n", | |
" <td> 0.934</td>\n", | |
" <td> 91</td>\n", | |
" <td> 0.835</td>\n", | |
" <td> 88</td>\n", | |
" <td> 0.894</td>\n", | |
" <td> 75</td>\n", | |
" <td> 0.847</td>\n", | |
" <td> 91</td>\n", | |
" <td> 0.861</td>\n", | |
" <td> 100</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>8756-7938</th>\n", | |
" <td> 1.897</td>\n", | |
" <td> 163</td>\n", | |
" <td> 1.821</td>\n", | |
" <td> 164</td>\n", | |
" <td> 1.734</td>\n", | |
" <td> 207</td>\n", | |
" <td> 1.488</td>\n", | |
" <td> 267</td>\n", | |
" <td> 1.635</td>\n", | |
" <td> 260</td>\n", | |
" <td>...</td>\n", | |
" <td> 2.108</td>\n", | |
" <td> 173</td>\n", | |
" <td> 2.398</td>\n", | |
" <td> 208</td>\n", | |
" <td> 2.178</td>\n", | |
" <td> 210</td>\n", | |
" <td> 2.340</td>\n", | |
" <td> 199</td>\n", | |
" <td> 1.853</td>\n", | |
" <td> 185</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>8756-971X</th>\n", | |
" <td> 0.763</td>\n", | |
" <td> 60</td>\n", | |
" <td> 0.521</td>\n", | |
" <td> 29</td>\n", | |
" <td> 0.717</td>\n", | |
" <td> 26</td>\n", | |
" <td> 0.811</td>\n", | |
" <td> 78</td>\n", | |
" <td> 0.691</td>\n", | |
" <td> 85</td>\n", | |
" <td>...</td>\n", | |
" <td> 0.894</td>\n", | |
" <td> 100</td>\n", | |
" <td> 0.906</td>\n", | |
" <td> 83</td>\n", | |
" <td> 1.066</td>\n", | |
" <td> 49</td>\n", | |
" <td> NaN</td>\n", | |
" <td> NaN</td>\n", | |
" <td> 0.755</td>\n", | |
" <td> 79</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"<p>5 rows \u00d7 26 columns</p>\n", | |
"</div>" | |
], | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 7, | |
"text": [ | |
" IF2000 A2000 IF2001 A2001 IF2002 A2002 IF2003 A2003 IF2004 \\\n", | |
"ISSN \n", | |
"8756-3282 3.998 199 3.247 169 3.755 247 3.572 191 3.530 \n", | |
"8756-5641 0.871 25 0.914 23 1.351 26 1.672 33 1.953 \n", | |
"8756-758X 0.675 99 0.693 75 0.701 105 0.706 106 0.673 \n", | |
"8756-7938 1.897 163 1.821 164 1.734 207 1.488 267 1.635 \n", | |
"8756-971X 0.763 60 0.521 29 0.717 26 0.811 78 0.691 \n", | |
"\n", | |
" A2004 ... IF2008 A2008 IF2009 A2009 IF2010 A2010 IF2011 \\\n", | |
"ISSN ... \n", | |
"8756-3282 283 ... 4.145 275 4.089 307 4.601 339 4.023 \n", | |
"8756-5641 37 ... 1.964 36 2.321 48 2.440 42 2.556 \n", | |
"8756-758X 101 ... 0.934 91 0.835 88 0.894 75 0.847 \n", | |
"8756-7938 260 ... 2.108 173 2.398 208 2.178 210 2.340 \n", | |
"8756-971X 85 ... 0.894 100 0.906 83 1.066 49 NaN \n", | |
"\n", | |
" A2011 IF2012 A2012 \n", | |
"ISSN \n", | |
"8756-3282 340 3.823 309 \n", | |
"8756-5641 57 2.899 40 \n", | |
"8756-758X 91 0.861 100 \n", | |
"8756-7938 199 1.853 185 \n", | |
"8756-971X NaN 0.755 79 \n", | |
"\n", | |
"[5 rows x 26 columns]" | |
] | |
} | |
], | |
"prompt_number": 7 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## Load JCR data for 2013\n", | |
"Citation report for 2013 was downloaded from Dr. N's dropbox, and converted to CSV using Excel." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"tbl = pd.read_csv('data/2013_IF.csv', skiprows=1, na_values=('Not Available',), thousands=','\n", | |
" ).drop_duplicates()[['Full Journal Title', 'Journal Impact Factor', 'Total Cites']]" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 8 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"tbl['Full Journal Title'] = tbl['Full Journal Title'].apply(lambda x: x.upper() if isinstance(x, str) else '')" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 9 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"tbl.head()" | |
], | |
"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>Full Journal Title</th>\n", | |
" <th>Journal Impact Factor</th>\n", | |
" <th>Total Cites</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>0</th>\n", | |
" <td> CA-A CANCER JOURNAL FOR CLINICIANS</td>\n", | |
" <td> 162.500</td>\n", | |
" <td> 16130</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1</th>\n", | |
" <td> NEW ENGLAND JOURNAL OF MEDICINE</td>\n", | |
" <td> 54.420</td>\n", | |
" <td> 257469</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>2</th>\n", | |
" <td> CHEMICAL REVIEWS</td>\n", | |
" <td> 45.661</td>\n", | |
" <td> 124463</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>3</th>\n", | |
" <td> REVIEWS OF MODERN PHYSICS</td>\n", | |
" <td> 42.860</td>\n", | |
" <td> 37647</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>4</th>\n", | |
" <td> NATURE</td>\n", | |
" <td> 42.351</td>\n", | |
" <td> 590324</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 10, | |
"text": [ | |
" Full Journal Title Journal Impact Factor Total Cites\n", | |
"0 CA-A CANCER JOURNAL FOR CLINICIANS 162.500 16130\n", | |
"1 NEW ENGLAND JOURNAL OF MEDICINE 54.420 257469\n", | |
"2 CHEMICAL REVIEWS 45.661 124463\n", | |
"3 REVIEWS OF MODERN PHYSICS 42.860 37647\n", | |
"4 NATURE 42.351 590324" | |
] | |
} | |
], | |
"prompt_number": 10 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"The 2013 table lacks both ISSN and total 2013 articles. Infer the article count." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"tbl['A2013'] = (tbl['Total Cites'] / tbl['Journal Impact Factor'] / 2).fillna(0).astype(int)\n", | |
"tbl['IF2013'] = tbl['Journal Impact Factor']" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 11 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"tbl.head()" | |
], | |
"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>Full Journal Title</th>\n", | |
" <th>Journal Impact Factor</th>\n", | |
" <th>Total Cites</th>\n", | |
" <th>A2013</th>\n", | |
" <th>IF2013</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>0</th>\n", | |
" <td> CA-A CANCER JOURNAL FOR CLINICIANS</td>\n", | |
" <td> 162.500</td>\n", | |
" <td> 16130</td>\n", | |
" <td> 49</td>\n", | |
" <td> 162.500</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1</th>\n", | |
" <td> NEW ENGLAND JOURNAL OF MEDICINE</td>\n", | |
" <td> 54.420</td>\n", | |
" <td> 257469</td>\n", | |
" <td> 2365</td>\n", | |
" <td> 54.420</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>2</th>\n", | |
" <td> CHEMICAL REVIEWS</td>\n", | |
" <td> 45.661</td>\n", | |
" <td> 124463</td>\n", | |
" <td> 1362</td>\n", | |
" <td> 45.661</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>3</th>\n", | |
" <td> REVIEWS OF MODERN PHYSICS</td>\n", | |
" <td> 42.860</td>\n", | |
" <td> 37647</td>\n", | |
" <td> 439</td>\n", | |
" <td> 42.860</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>4</th>\n", | |
" <td> NATURE</td>\n", | |
" <td> 42.351</td>\n", | |
" <td> 590324</td>\n", | |
" <td> 6969</td>\n", | |
" <td> 42.351</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 12, | |
"text": [ | |
" Full Journal Title Journal Impact Factor Total Cites \\\n", | |
"0 CA-A CANCER JOURNAL FOR CLINICIANS 162.500 16130 \n", | |
"1 NEW ENGLAND JOURNAL OF MEDICINE 54.420 257469 \n", | |
"2 CHEMICAL REVIEWS 45.661 124463 \n", | |
"3 REVIEWS OF MODERN PHYSICS 42.860 37647 \n", | |
"4 NATURE 42.351 590324 \n", | |
"\n", | |
" A2013 IF2013 \n", | |
"0 49 162.500 \n", | |
"1 2365 54.420 \n", | |
"2 1362 45.661 \n", | |
"3 439 42.860 \n", | |
"4 6969 42.351 " | |
] | |
} | |
], | |
"prompt_number": 12 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"jcr2013 = tbl[['A2013', 'IF2013', 'Full Journal Title']]" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 13 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## Load category data from 2012\n", | |
"We need more metadata for category-filtering and matching 2000-2012 and 2013 by journal titles." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"category2012 = pd.read_csv('data/SCIE_JCR2012-category.csv', skiprows=2)[[\n", | |
" 'ISSN', 'Subject Category', 'Journal Title (Full)', 'Abbreviated Journal Title']]" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 14 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"category2012.head()" | |
], | |
"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>ISSN</th>\n", | |
" <th>Subject Category</th>\n", | |
" <th>Journal Title (Full)</th>\n", | |
" <th>Abbreviated Journal Title</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>0</th>\n", | |
" <td> 0814-6039</td>\n", | |
" <td> ACOUSTICS</td>\n", | |
" <td> ACOUSTICS AUSTRALIA</td>\n", | |
" <td> ACOUST AUST</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1</th>\n", | |
" <td> 1063-7710</td>\n", | |
" <td> ACOUSTICS</td>\n", | |
" <td> ACOUSTICAL PHYSICS</td>\n", | |
" <td> ACOUST PHYS+</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>2</th>\n", | |
" <td> 1610-1928</td>\n", | |
" <td> ACOUSTICS</td>\n", | |
" <td> ACTA ACUSTICA UNITED WITH ACUSTICA</td>\n", | |
" <td> ACTA ACUST UNITED AC</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>3</th>\n", | |
" <td> 0003-682X</td>\n", | |
" <td> ACOUSTICS</td>\n", | |
" <td> APPLIED ACOUSTICS</td>\n", | |
" <td> APPL ACOUST</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>4</th>\n", | |
" <td> 0137-5075</td>\n", | |
" <td> ACOUSTICS</td>\n", | |
" <td> ARCHIVES OF ACOUSTICS</td>\n", | |
" <td> ARCH ACOUST</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 15, | |
"text": [ | |
" ISSN Subject Category Journal Title (Full) \\\n", | |
"0 0814-6039 ACOUSTICS ACOUSTICS AUSTRALIA \n", | |
"1 1063-7710 ACOUSTICS ACOUSTICAL PHYSICS \n", | |
"2 1610-1928 ACOUSTICS ACTA ACUSTICA UNITED WITH ACUSTICA \n", | |
"3 0003-682X ACOUSTICS APPLIED ACOUSTICS \n", | |
"4 0137-5075 ACOUSTICS ARCHIVES OF ACOUSTICS \n", | |
"\n", | |
" Abbreviated Journal Title \n", | |
"0 ACOUST AUST \n", | |
"1 ACOUST PHYS+ \n", | |
"2 ACTA ACUST UNITED AC \n", | |
"3 APPL ACOUST \n", | |
"4 ARCH ACOUST " | |
] | |
} | |
], | |
"prompt_number": 15 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"selected_categories = set(['BIOCHEMICAL RESEARCH METHODS',\n", | |
"'BIOCHEMISTRY & MOLECULAR BIOLOGY','BIOLOGY','BIOTECHNOLOGY & APPLIED MICROBIOLOGY',\n", | |
"'CELL BIOLOGY','BIOPHYSICS','CRYSTALLOGRAPHY',\n", | |
"'DEVELOPMENTAL BIOLOGY','GENETICS & HEREDITY','IMMUNOLOGY','MATHEMATICAL & COMPUTATIONAL BIOLOGY',\n", | |
"'MICROBIOLOGY','MYCOLOGY','MULTIDISCIPLINARY SCIENCES',\n", | |
"'NEUROSCIENCES','PLANT SCIENCES','VIROLOGY'])" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 16 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"selectedcategory2012 = category2012[category2012['Subject Category'].isin(selected_categories)]" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 17 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"len(category2012), len(selectedcategory2012)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 18, | |
"text": [ | |
"(13216, 1945)" | |
] | |
} | |
], | |
"prompt_number": 18 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## Merge the tables\n", | |
"We have loaded three tables so far. Merge them into one.\n", | |
"1. Merge the metadata and 2000-2012 by ISSN.\n", | |
"1. Then, merge the first merged table and 2013 by full journal title." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"merge1 = pd.merge(selectedcategory2012, alloldtables, how='inner', left_on='ISSN', right_index=True)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 19 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"merge2 = pd.merge(merge1, jcr2013, how='inner', left_on='Journal Title (Full)',\n", | |
" right_on='Full Journal Title').sort('IF2013', ascending=False)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 20 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"merge2.head()" | |
], | |
"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>ISSN</th>\n", | |
" <th>Subject Category</th>\n", | |
" <th>Journal Title (Full)</th>\n", | |
" <th>Abbreviated Journal Title</th>\n", | |
" <th>IF2000</th>\n", | |
" <th>A2000</th>\n", | |
" <th>IF2001</th>\n", | |
" <th>A2001</th>\n", | |
" <th>IF2002</th>\n", | |
" <th>A2002</th>\n", | |
" <th>...</th>\n", | |
" <th>A2009</th>\n", | |
" <th>IF2010</th>\n", | |
" <th>A2010</th>\n", | |
" <th>IF2011</th>\n", | |
" <th>A2011</th>\n", | |
" <th>IF2012</th>\n", | |
" <th>A2012</th>\n", | |
" <th>A2013</th>\n", | |
" <th>IF2013</th>\n", | |
" <th>Full Journal Title</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>1443</th>\n", | |
" <td> 0028-0836</td>\n", | |
" <td> MULTIDISCIPLINARY SCIENCES</td>\n", | |
" <td> NATURE</td>\n", | |
" <td> NATURE</td>\n", | |
" <td> 25.814</td>\n", | |
" <td> 1315</td>\n", | |
" <td> 27.955</td>\n", | |
" <td> 939</td>\n", | |
" <td> 30.432</td>\n", | |
" <td> 889</td>\n", | |
" <td>...</td>\n", | |
" <td> 866</td>\n", | |
" <td> 36.104</td>\n", | |
" <td> 862</td>\n", | |
" <td> 36.280</td>\n", | |
" <td> 841</td>\n", | |
" <td> 38.597</td>\n", | |
" <td> 869</td>\n", | |
" <td> 6969</td>\n", | |
" <td> 42.351</td>\n", | |
" <td> NATURE</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1192</th>\n", | |
" <td> 0732-0582</td>\n", | |
" <td> IMMUNOLOGY</td>\n", | |
" <td> ANNUAL REVIEW OF IMMUNOLOGY</td>\n", | |
" <td> ANNU REV IMMUNOL</td>\n", | |
" <td> 50.340</td>\n", | |
" <td> 31</td>\n", | |
" <td> 46.233</td>\n", | |
" <td> 24</td>\n", | |
" <td> 54.455</td>\n", | |
" <td> 26</td>\n", | |
" <td>...</td>\n", | |
" <td> 24</td>\n", | |
" <td> 49.271</td>\n", | |
" <td> 22</td>\n", | |
" <td> 52.761</td>\n", | |
" <td> 23</td>\n", | |
" <td> 36.556</td>\n", | |
" <td> 28</td>\n", | |
" <td> 201</td>\n", | |
" <td> 41.392</td>\n", | |
" <td> ANNUAL REVIEW OF IMMUNOLOGY</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1154</th>\n", | |
" <td> 1471-0056</td>\n", | |
" <td> GENETICS & HEREDITY</td>\n", | |
" <td> NATURE REVIEWS GENETICS</td>\n", | |
" <td> NAT REV GENET</td>\n", | |
" <td> NaN</td>\n", | |
" <td> 18</td>\n", | |
" <td> 12.333</td>\n", | |
" <td> 66</td>\n", | |
" <td> 21.762</td>\n", | |
" <td> 83</td>\n", | |
" <td>...</td>\n", | |
" <td> 75</td>\n", | |
" <td> 32.745</td>\n", | |
" <td> 71</td>\n", | |
" <td> 38.075</td>\n", | |
" <td> 71</td>\n", | |
" <td> 41.063</td>\n", | |
" <td> 70</td>\n", | |
" <td> 331</td>\n", | |
" <td> 39.794</td>\n", | |
" <td> NATURE REVIEWS GENETICS</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>827 </th>\n", | |
" <td> 1087-0156</td>\n", | |
" <td> BIOTECHNOLOGY & APPLIED MICROBIOLOGY</td>\n", | |
" <td> NATURE BIOTECHNOLOGY</td>\n", | |
" <td> NAT BIOTECHNOL</td>\n", | |
" <td> 11.542</td>\n", | |
" <td> 195</td>\n", | |
" <td> 11.310</td>\n", | |
" <td> 153</td>\n", | |
" <td> 12.822</td>\n", | |
" <td> 160</td>\n", | |
" <td>...</td>\n", | |
" <td> 103</td>\n", | |
" <td> 31.090</td>\n", | |
" <td> 117</td>\n", | |
" <td> 23.268</td>\n", | |
" <td> 84</td>\n", | |
" <td> 32.438</td>\n", | |
" <td> 92</td>\n", | |
" <td> 539</td>\n", | |
" <td> 39.080</td>\n", | |
" <td> NATURE BIOTECHNOLOGY</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>828 </th>\n", | |
" <td> 1474-1776</td>\n", | |
" <td> BIOTECHNOLOGY & APPLIED MICROBIOLOGY</td>\n", | |
" <td> NATURE REVIEWS DRUG DISCOVERY</td>\n", | |
" <td> NAT REV DRUG DISCOV</td>\n", | |
" <td> NaN</td>\n", | |
" <td> NaN</td>\n", | |
" <td> NaN</td>\n", | |
" <td> NaN</td>\n", | |
" <td> NaN</td>\n", | |
" <td> 91</td>\n", | |
" <td>...</td>\n", | |
" <td> 65</td>\n", | |
" <td> 28.712</td>\n", | |
" <td> 67</td>\n", | |
" <td> 29.008</td>\n", | |
" <td> 61</td>\n", | |
" <td> 33.078</td>\n", | |
" <td> 43</td>\n", | |
" <td> 288</td>\n", | |
" <td> 37.231</td>\n", | |
" <td> NATURE REVIEWS DRUG DISCOVERY</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"<p>5 rows \u00d7 33 columns</p>\n", | |
"</div>" | |
], | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 21, | |
"text": [ | |
" ISSN Subject Category \\\n", | |
"1443 0028-0836 MULTIDISCIPLINARY SCIENCES \n", | |
"1192 0732-0582 IMMUNOLOGY \n", | |
"1154 1471-0056 GENETICS & HEREDITY \n", | |
"827 1087-0156 BIOTECHNOLOGY & APPLIED MICROBIOLOGY \n", | |
"828 1474-1776 BIOTECHNOLOGY & APPLIED MICROBIOLOGY \n", | |
"\n", | |
" Journal Title (Full) Abbreviated Journal Title IF2000 A2000 \\\n", | |
"1443 NATURE NATURE 25.814 1315 \n", | |
"1192 ANNUAL REVIEW OF IMMUNOLOGY ANNU REV IMMUNOL 50.340 31 \n", | |
"1154 NATURE REVIEWS GENETICS NAT REV GENET NaN 18 \n", | |
"827 NATURE BIOTECHNOLOGY NAT BIOTECHNOL 11.542 195 \n", | |
"828 NATURE REVIEWS DRUG DISCOVERY NAT REV DRUG DISCOV NaN NaN \n", | |
"\n", | |
" IF2001 A2001 IF2002 A2002 ... A2009 IF2010 A2010 IF2011 A2011 \\\n", | |
"1443 27.955 939 30.432 889 ... 866 36.104 862 36.280 841 \n", | |
"1192 46.233 24 54.455 26 ... 24 49.271 22 52.761 23 \n", | |
"1154 12.333 66 21.762 83 ... 75 32.745 71 38.075 71 \n", | |
"827 11.310 153 12.822 160 ... 103 31.090 117 23.268 84 \n", | |
"828 NaN NaN NaN 91 ... 65 28.712 67 29.008 61 \n", | |
"\n", | |
" IF2012 A2012 A2013 IF2013 Full Journal Title \n", | |
"1443 38.597 869 6969 42.351 NATURE \n", | |
"1192 36.556 28 201 41.392 ANNUAL REVIEW OF IMMUNOLOGY \n", | |
"1154 41.063 70 331 39.794 NATURE REVIEWS GENETICS \n", | |
"827 32.438 92 539 39.080 NATURE BIOTECHNOLOGY \n", | |
"828 33.078 43 288 37.231 NATURE REVIEWS DRUG DISCOVERY \n", | |
"\n", | |
"[5 rows x 33 columns]" | |
] | |
} | |
], | |
"prompt_number": 21 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"merge2.to_csv('JCRMerged.csv')\n", | |
"gtbl = merge2" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 22 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## Calculate trends by log-linear fitting for 2011-2013 scores" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"trend_first_year = 2011\n", | |
"trend_last_year = 2013\n", | |
"\n", | |
"trendfit_years = range(trend_first_year, trend_last_year+1)\n", | |
"trendfit_if_labels = ['IF{}'.format(y) for y in trendfit_years]" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 23 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"for y in trendfit_years:\n", | |
" gtbl['LOGIF{}'.format(y)] = np.log2(gtbl['IF{}'.format(y)])" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 24 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"logif_labels = ['LOGIF{}'.format(y) for y in trendfit_years]\n", | |
"\n", | |
"gtbl['trend_slope'] = gtbl.apply(\n", | |
" (lambda x: np.polyfit(trendfit_years, x[logif_labels], 1)[0]), axis=1)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 25 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"gtbl.ix[:, -6:].head()" | |
], | |
"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>IF2013</th>\n", | |
" <th>Full Journal Title</th>\n", | |
" <th>LOGIF2011</th>\n", | |
" <th>LOGIF2012</th>\n", | |
" <th>LOGIF2013</th>\n", | |
" <th>trend_slope</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>1443</th>\n", | |
" <td> 42.351</td>\n", | |
" <td> NATURE</td>\n", | |
" <td> 5.181103</td>\n", | |
" <td> 5.270417</td>\n", | |
" <td> 5.404324</td>\n", | |
" <td> 0.111611</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1192</th>\n", | |
" <td> 41.392</td>\n", | |
" <td> ANNUAL REVIEW OF IMMUNOLOGY</td>\n", | |
" <td> 5.721400</td>\n", | |
" <td> 5.192036</td>\n", | |
" <td> 5.371280</td>\n", | |
" <td>-0.175060</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1154</th>\n", | |
" <td> 39.794</td>\n", | |
" <td> NATURE REVIEWS GENETICS</td>\n", | |
" <td> 5.250772</td>\n", | |
" <td> 5.359767</td>\n", | |
" <td> 5.314479</td>\n", | |
" <td> 0.031853</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>827 </th>\n", | |
" <td> 39.080</td>\n", | |
" <td> NATURE BIOTECHNOLOGY</td>\n", | |
" <td> 4.540275</td>\n", | |
" <td> 5.019613</td>\n", | |
" <td> 5.288359</td>\n", | |
" <td> 0.374042</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>828 </th>\n", | |
" <td> 37.231</td>\n", | |
" <td> NATURE REVIEWS DRUG DISCOVERY</td>\n", | |
" <td> 4.858379</td>\n", | |
" <td> 5.047800</td>\n", | |
" <td> 5.218432</td>\n", | |
" <td> 0.180027</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 26, | |
"text": [ | |
" IF2013 Full Journal Title LOGIF2011 LOGIF2012 LOGIF2013 \\\n", | |
"1443 42.351 NATURE 5.181103 5.270417 5.404324 \n", | |
"1192 41.392 ANNUAL REVIEW OF IMMUNOLOGY 5.721400 5.192036 5.371280 \n", | |
"1154 39.794 NATURE REVIEWS GENETICS 5.250772 5.359767 5.314479 \n", | |
"827 39.080 NATURE BIOTECHNOLOGY 4.540275 5.019613 5.288359 \n", | |
"828 37.231 NATURE REVIEWS DRUG DISCOVERY 4.858379 5.047800 5.218432 \n", | |
"\n", | |
" trend_slope \n", | |
"1443 0.111611 \n", | |
"1192 -0.175060 \n", | |
"1154 0.031853 \n", | |
"827 0.374042 \n", | |
"828 0.180027 " | |
] | |
} | |
], | |
"prompt_number": 26 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"gtbl['trend_slope'].hist(range=(-2, 2), bins=50)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 27, | |
"text": [ | |
"<matplotlib.axes.AxesSubplot at 0x7f96be3ba208>" | |
] | |
}, | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
"png": 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| |
"text": [ | |
"<matplotlib.figure.Figure at 0x7f96bdb66668>" | |
] | |
} | |
], | |
"prompt_number": 27 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## Calculate geometric mean of impact factors in 2011-2013" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"gtbl['recent_if'] = (\n", | |
" gtbl[trendfit_if_labels].product(axis=1) ** (1/len(trendfit_years)))" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 28 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## Finally, plot the trends" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"full_if_years = np.arange(2003, 2014)\n", | |
"full_if_labels = ['IF{}'.format(y) for y in full_if_years]\n", | |
"years_zero_centered = full_if_years - full_if_years.mean()" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 29 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"xshrink = 0.1\n", | |
"yshrink = 0.1\n", | |
"xremapscale = 0.75\n", | |
"xlabelcutoff = 15 ** xremapscale\n", | |
"fig = plt.figure(figsize=(8.5, 7))\n", | |
"\n", | |
"plottbl = gtbl[gtbl['recent_if'] > 4]\n", | |
"\n", | |
"for rowi, row in plottbl.iterrows():\n", | |
" # alignment positions of this subplot\n", | |
" xcenter = row['recent_if'] ** xremapscale\n", | |
" ytop = row['trend_slope']\n", | |
"\n", | |
" ifs = np.array(row[full_if_labels])\n", | |
" nmissing = sum(list(map(np.isnan, ifs)))\n", | |
"\n", | |
" yoffsets = np.array(ifs / row['recent_if']) * yshrink\n", | |
" yheight = yoffsets.max() - yoffsets.min()\n", | |
" yoffsets = yoffsets - yoffsets.min() - yheight / 2\n", | |
"\n", | |
" ypoints = (yoffsets + ytop)[nmissing:]\n", | |
" xpoints = (years_zero_centered * xshrink + xcenter)[nmissing:]\n", | |
"\n", | |
" plt.plot(xpoints, ypoints, c='black', alpha=0.7)\n", | |
"\n", | |
" if xcenter > xlabelcutoff or abs(ytop) > 0.2:\n", | |
" plt.annotate(row['Abbreviated Journal Title'].title(),\n", | |
" (xpoints[-1], ypoints[-1]))\n", | |
"\n", | |
"plt.axhline(0, c='black', lw=1)\n", | |
"plt.ylim(-0.8, 0.8)\n", | |
"\n", | |
"xtickpositions = np.array([5, 10, 15, 20, 25, 30, 35, 40, 45])\n", | |
"plt.xticks(xtickpositions ** xremapscale, list(map(str, xtickpositions)))\n", | |
"\n", | |
"plt.grid(True, ls='-', alpha=0.3)\n", | |
"plt.xlabel('Mean Impact Factor 2011-2013')\n", | |
"plt.ylabel('IF Change Trend 2011-2013')\n", | |
"plt.title('All Journals Related to Molecular & Cell Biology')\n", | |
"plt.savefig(DROPBOXHOME+'/Data/2014/jcr-trend-all.pdf')" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
"png": 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1y7D+xIkTzJo1C4CSJUtib29PVFSUYX2ePHk4c+YMISEhuLi40LBhQ5ydnQEl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SCASUkZFB27Zto6ampr+zm+9Fez4Wn7vvr169oilTppCnpydFRka26SDRFhITE8nV1ZWqq6vJ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OPjg5ubGwsXLpRSv0RHR5OUlIS5uTlff/01gwcPlpIFb968mcGDB5cb786dO9SuXRuAzz//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\n5Moa3+G0NdvlUtvi3pY41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iRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJ\nkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJ\nkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJknLS/wMb1x1v9k3msAAAAABJRU5ErkJggg==\n", | |
"text": [ | |
"<matplotlib.figure.Figure at 0x7f96be567cc0>" | |
] | |
} | |
], | |
"prompt_number": 31 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## Done!" | |
] | |
} | |
], | |
"metadata": {} | |
} | |
] | |
} |
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