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olgabot/singlecell_medline.ipynb

Created April 6, 2017 16:02
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singlecell_medline.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import io\n",
"\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import altair"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Searched MEDLINE for the query `\"single-cell\" transcriptome` [here](http://dan.corlan.net/medline-trend.html) and got the data below. An explanation of the columns is below:\n",
"\n",
"> The first column will be the number of papers in each year, the second column--the year, and the third column the number of papers per 100,000 in the medline database in that year.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'/Users/olga/anaconda3/envs/jupyter-py3/lib/python3.5/io.py'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"io.__file__"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1013"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import sys\n",
"sys.api_version"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Number</th>\n",
" <th>Year</th>\n",
" <th>%000</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>165</td>\n",
" <td>2016</td>\n",
" <td>13.149</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>148</td>\n",
" <td>2015</td>\n",
" <td>11.854</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>86</td>\n",
" <td>2014</td>\n",
" <td>7.171</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>64</td>\n",
" <td>2013</td>\n",
" <td>5.590</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>42</td>\n",
" <td>2012</td>\n",
" <td>3.877</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Number Year %000\n",
"0 165 2016 13.149\n",
"1 148 2015 11.854\n",
"2 86 2014 7.171\n",
"3 64 2013 5.590\n",
"4 42 2012 3.877"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = ''' Number Year %000\n",
" 165 2016 13.149\n",
" 148 2015 11.854\n",
" 86 2014 7.171\n",
" 64 2013 5.590\n",
" 42 2012 3.877\n",
" 29 2011 2.850\n",
" 13 2010 1.383\n",
" 11 2009 1.256\n",
" 10 2008 1.198\n",
" 2 2007 0.255\n",
" 6 2006 0.804\n",
" 1 2005 0.143\n",
" 0 2004 0.000\n",
" 2 2003 0.337\n",
" 0 2002 0.000\n",
" 1 2001 0.183\n",
" 0 2000 0.000\n",
" 0 1999 0.000\n",
" 0 1998 0.000\n",
" 0 1997 0.000\n",
" 0 1996 0.000\n",
" 0 1995 0.000\n",
" 0 1994 0.000\n",
" 0 1993 0.000\n",
" 0 1992 0.000\n",
" 0 1991 0.000\n",
" 0 1990 0.000\n",
" 0 1989 0.000\n",
" 0 1988 0.000\n",
" 0 1987 0.000\n",
" 0 1986 0.000\n",
" 0 1985 0.000\n",
" 0 1984 0.000\n",
" 0 1983 0.000\n",
" 0 1982 0.000\n",
" 0 1981 0.000\n",
" 0 1980 0.000\n",
" 0 1979 0.000\n",
" 0 1978 0.000\n",
" 0 1977 0.000\n",
" 0 1976 0.000\n",
" 0 1975 0.000\n",
" 0 1974 0.000\n",
" 0 1973 0.000\n",
" 0 1972 0.000\n",
" 0 1971 0.000\n",
" 0 1970 0.000\n",
" 0 1969 0.000\n",
" 0 1968 0.000\n",
" 0 1967 0.000\n",
" 0 1966 0.000\n",
" 0 1965 0.000\n",
" 0 1964 0.000\n",
" 0 1963 0.000\n",
" 0 1962 0.000\n",
" 0 1961 0.000\n",
" 0 1960 0.000\n",
" 0 1959 0.000\n",
" 0 1958 0.000\n",
" 0 1957 0.000\n",
" 0 1956 0.000\n",
" 0 1955 0.000\n",
" 0 1954 0.000\n",
" 0 1953 0.000\n",
" 0 1952 0.000\n",
" 0 1951 0.000\n",
" 0 1950 0.000'''\n",
"\n",
"\n",
"data = pd.read_table(io.StringIO(s), sep='\\s+')\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(17, 3)\n"
]
},
{
"data": {
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Number</th>\n",
" <th>Year</th>\n",
" <th>%000</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0</td>\n",
" <td>2004</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2</td>\n",
" <td>2003</td>\n",
" <td>0.337</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0</td>\n",
" <td>2002</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1</td>\n",
" <td>2001</td>\n",
" <td>0.183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0</td>\n",
" <td>2000</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Number Year %000\n",
"12 0 2004 0.000\n",
"13 2 2003 0.337\n",
"14 0 2002 0.000\n",
"15 1 2001 0.183\n",
"16 0 2000 0.000"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"subset = data.query('Year >= 2000')\n",
"print(subset.shape)\n",
"subset.tail()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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".vega-embed svg, .vega-embed canvas {\n",
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"var spec = {\"mark\": \"line\", \"encoding\": {\"y\": {\"field\": \"Number\", \"type\": \"quantitative\"}, \"x\": {\"field\": \"Year\", \"type\": \"quantitative\"}}, \"config\": {\"cell\": {\"width\": 500, \"height\": 350}}, \"data\": {\"values\": [{\"%000\": 13.149000000000001, \"Year\": 2016, \"Number\": 165}, {\"%000\": 11.854000000000001, \"Year\": 2015, \"Number\": 148}, {\"%000\": 7.171, \"Year\": 2014, \"Number\": 86}, {\"%000\": 5.59, \"Year\": 2013, \"Number\": 64}, {\"%000\": 3.877, \"Year\": 2012, \"Number\": 42}, {\"%000\": 2.85, \"Year\": 2011, \"Number\": 29}, {\"%000\": 1.383, \"Year\": 2010, \"Number\": 13}, {\"%000\": 1.256, \"Year\": 2009, \"Number\": 11}, {\"%000\": 1.198, \"Year\": 2008, \"Number\": 10}, {\"%000\": 0.255, \"Year\": 2007, \"Number\": 2}, {\"%000\": 0.804, \"Year\": 2006, \"Number\": 6}, {\"%000\": 0.14300000000000002, \"Year\": 2005, \"Number\": 1}, {\"%000\": 0.0, \"Year\": 2004, \"Number\": 0}, {\"%000\": 0.337, \"Year\": 2003, \"Number\": 2}, {\"%000\": 0.0, \"Year\": 2002, \"Number\": 0}, {\"%000\": 0.183, \"Year\": 2001, \"Number\": 1}, {\"%000\": 0.0, \"Year\": 2000, \"Number\": 0}]}};\n",
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"source": [
"altair.Chart(subset).mark_line().encode(\n",
" x='Year',\n",
" y='Number'\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/olga/anaconda3/envs/jupyter-py3/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
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"data": {
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Number</th>\n",
" <th>Year</th>\n",
" <th>%000</th>\n",
" <th>Year_date</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>165</td>\n",
" <td>2016</td>\n",
" <td>13.149</td>\n",
" <td>2016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>148</td>\n",
" <td>2015</td>\n",
" <td>11.854</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>86</td>\n",
" <td>2014</td>\n",
" <td>7.171</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>64</td>\n",
" <td>2013</td>\n",
" <td>5.590</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>42</td>\n",
" <td>2012</td>\n",
" <td>3.877</td>\n",
" <td>2012</td>\n",
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"text/plain": [
" Number Year %000 Year_date\n",
"0 165 2016 13.149 2016\n",
"1 148 2015 11.854 2015\n",
"2 86 2014 7.171 2014\n",
"3 64 2013 5.590 2013\n",
"4 42 2012 3.877 2012"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"subset['Year_date'] = subset.Year.astype(pd.datetime)\n",
"subset.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Number int64\n",
"Year int64\n",
"%000 float64\n",
"Year_date object\n",
"dtype: object"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"subset.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"
},
"metadata": {
"jupyter-vega": "#8b8bcfe8-342e-4218-90e3-86043f51c70f"
},
"output_type": "display_data"
}
],
"source": [
"altair.Chart(subset).mark_line().encode(\n",
" x='Year',\n",
" y='Number'\n",
")"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python3 (Jupyter)",
"language": "python",
"name": "myenv"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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