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olgabot/singlecell_medline_altair.ipynb

Created April 7, 2017 02:08
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singlecell_medline_altair.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",
"\n",
"How to cite:\n",
"\n",
"> Cite this page as: Alexandru Dan Corlan. Medline trend: automated yearly statistics of PubMed results for any query, 2004. Web resource at URL:http://dan.corlan.net/medline-trend.html. Accessed: 2012-02-14. (Archived by WebCite at http://www.webcitation.org/65RkD48SV)\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": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Number of Papers</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 of Papers 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": 20,
"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 = data.rename(columns={\"Number\": 'Number of Papers'})\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(17, 3)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Number of Papers</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 of Papers 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": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"subset = data.query('Year >= 2000')\n",
"print(subset.shape)\n",
"subset.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## BUG or working as intended?"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"
},
"metadata": {
"jupyter-vega": "#a44b5ea2-305b-47e4-9a40-557fe35c8a63"
},
"output_type": "display_data"
}
],
"source": [
"altair.Chart(subset).mark_line().encode(\n",
" x='Year',\n",
" y='Number of Papers'\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Bug or working as intended? Should the x-axis always start from zero? The minimum x-value in the data is 1950"
]
},
{
"cell_type": "code",
"execution_count": 23,
"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",
" if __name__ == '__main__':\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Number of Papers</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",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Number of Papers 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": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"subset['Year_date'] = subset.Year.astype(pd.datetime)\n",
"subset.head()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Number of Papers int64\n",
"Year int64\n",
"%000 float64\n",
"Year_date object\n",
"dtype: object"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"subset.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from altair import Chart, load_dataset, X, Y, Scale"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"var spec = {\"mark\": \"line\", \"encoding\": {\"y\": {\"field\": \"Number of Papers\", \"type\": \"quantitative\"}, \"x\": {\"scale\": {\"domain\": [2000.0, 2017.0]}, \"field\": \"Year\", \"type\": \"quantitative\"}}, \"config\": {\"cell\": {\"width\": 500, \"height\": 350}}, \"data\": {\"values\": [{\"%000\": 13.149000000000001, \"Year\": 2016, \"Year_date\": 2016, \"Number of Papers\": 165}, {\"%000\": 11.854000000000001, \"Year\": 2015, \"Year_date\": 2015, \"Number of Papers\": 148}, {\"%000\": 7.171, \"Year\": 2014, \"Year_date\": 2014, \"Number of Papers\": 86}, {\"%000\": 5.59, \"Year\": 2013, \"Year_date\": 2013, \"Number of Papers\": 64}, {\"%000\": 3.877, \"Year\": 2012, \"Year_date\": 2012, \"Number of Papers\": 42}, {\"%000\": 2.85, \"Year\": 2011, \"Year_date\": 2011, \"Number of Papers\": 29}, {\"%000\": 1.383, \"Year\": 2010, \"Year_date\": 2010, \"Number of Papers\": 13}, {\"%000\": 1.256, \"Year\": 2009, \"Year_date\": 2009, \"Number of Papers\": 11}, {\"%000\": 1.198, \"Year\": 2008, \"Year_date\": 2008, \"Number of Papers\": 10}, {\"%000\": 0.255, \"Year\": 2007, \"Year_date\": 2007, \"Number of Papers\": 2}, {\"%000\": 0.804, \"Year\": 2006, \"Year_date\": 2006, \"Number of Papers\": 6}, {\"%000\": 0.14300000000000002, \"Year\": 2005, \"Year_date\": 2005, \"Number of Papers\": 1}, {\"%000\": 0.0, \"Year\": 2004, \"Year_date\": 2004, \"Number of Papers\": 0}, {\"%000\": 0.337, \"Year\": 2003, \"Year_date\": 2003, \"Number of Papers\": 2}, {\"%000\": 0.0, \"Year\": 2002, \"Year_date\": 2002, \"Number of Papers\": 0}, {\"%000\": 0.183, \"Year\": 2001, \"Year_date\": 2001, \"Number of Papers\": 1}, {\"%000\": 0.0, \"Year\": 2000, \"Year_date\": 2000, \"Number of Papers\": 0}]}};\n",
"var selector = \"#580c1d4e-2047-4999-8344-f90192c2a433\";\n",
"var type = \"vega-lite\";\n",
"\n",
"var output_area = this;\n",
"require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
" vega.render(selector, spec, type, output_area);\n",
"}, function (err) {\n",
" if (err.requireType !== 'scripterror') {\n",
" throw(err);\n",
" }\n",
"});\n"
]
},
"metadata": {
"jupyter-vega": "#580c1d4e-2047-4999-8344-f90192c2a433"
},
"output_type": "display_data"
},
{
"data": {
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"
},
"metadata": {
"jupyter-vega": "#580c1d4e-2047-4999-8344-f90192c2a433"
},
"output_type": "display_data"
}
],
"source": [
"altair.Chart(subset).mark_line().encode(\n",
" X('Year', scale=Scale(domain=(2000, 2017))),\n",
" y='Number of Papers'\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div class=\"vega-embed\" id=\"c56462a0-a16e-427d-a0f7-b526c0048ecb\"></div>\n",
"\n",
"<style>\n",
".vega-embed svg, .vega-embed canvas {\n",
" border: 1px dotted gray;\n",
"}\n",
"\n",
".vega-embed .vega-actions a {\n",
" margin-right: 6px;\n",
"}\n",
"</style>\n"
]
},
"metadata": {
"jupyter-vega": "#c56462a0-a16e-427d-a0f7-b526c0048ecb"
},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"var spec = {\"mark\": \"line\", \"encoding\": {\"y\": {\"field\": \"Number of Papers\", \"type\": \"quantitative\"}, \"x\": {\"axis\": {\"format\": \"d\"}, \"scale\": {\"domain\": [2000.0, 2017.0]}, \"field\": \"Year\", \"type\": \"quantitative\"}}, \"config\": {\"cell\": {\"width\": 500, \"height\": 350}}, \"data\": {\"values\": [{\"%000\": 13.149000000000001, \"Year\": 2016, \"Year_date\": 2016, \"Number of Papers\": 165}, {\"%000\": 11.854000000000001, \"Year\": 2015, \"Year_date\": 2015, \"Number of Papers\": 148}, {\"%000\": 7.171, \"Year\": 2014, \"Year_date\": 2014, \"Number of Papers\": 86}, {\"%000\": 5.59, \"Year\": 2013, \"Year_date\": 2013, \"Number of Papers\": 64}, {\"%000\": 3.877, \"Year\": 2012, \"Year_date\": 2012, \"Number of Papers\": 42}, {\"%000\": 2.85, \"Year\": 2011, \"Year_date\": 2011, \"Number of Papers\": 29}, {\"%000\": 1.383, \"Year\": 2010, \"Year_date\": 2010, \"Number of Papers\": 13}, {\"%000\": 1.256, \"Year\": 2009, \"Year_date\": 2009, \"Number of Papers\": 11}, {\"%000\": 1.198, \"Year\": 2008, \"Year_date\": 2008, \"Number of Papers\": 10}, {\"%000\": 0.255, \"Year\": 2007, \"Year_date\": 2007, \"Number of Papers\": 2}, {\"%000\": 0.804, \"Year\": 2006, \"Year_date\": 2006, \"Number of Papers\": 6}, {\"%000\": 0.14300000000000002, \"Year\": 2005, \"Year_date\": 2005, \"Number of Papers\": 1}, {\"%000\": 0.0, \"Year\": 2004, \"Year_date\": 2004, \"Number of Papers\": 0}, {\"%000\": 0.337, \"Year\": 2003, \"Year_date\": 2003, \"Number of Papers\": 2}, {\"%000\": 0.0, \"Year\": 2002, \"Year_date\": 2002, \"Number of Papers\": 0}, {\"%000\": 0.183, \"Year\": 2001, \"Year_date\": 2001, \"Number of Papers\": 1}, {\"%000\": 0.0, \"Year\": 2000, \"Year_date\": 2000, \"Number of Papers\": 0}]}};\n",
"var selector = \"#c56462a0-a16e-427d-a0f7-b526c0048ecb\";\n",
"var type = \"vega-lite\";\n",
"\n",
"var output_area = this;\n",
"require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
" vega.render(selector, spec, type, output_area);\n",
"}, function (err) {\n",
" if (err.requireType !== 'scripterror') {\n",
" throw(err);\n",
" }\n",
"});\n"
]
},
"metadata": {
"jupyter-vega": "#c56462a0-a16e-427d-a0f7-b526c0048ecb"
},
"output_type": "display_data"
},
{
"data": {
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"
},
"metadata": {
"jupyter-vega": "#c56462a0-a16e-427d-a0f7-b526c0048ecb"
},
"output_type": "display_data"
}
],
"source": [
"from altair import Axis\n",
"\n",
"altair.Chart(subset).mark_line().encode(\n",
" X('Year', scale=Scale(domain=(2000, 2017)), axis=Axis(format='d')),\n",
" y='Number of Papers'\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Make plot smaller for papers"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div class=\"vega-embed\" id=\"7f473d1c-5b7e-4b74-9a52-e0982f23329e\"></div>\n",
"\n",
"<style>\n",
".vega-embed svg, .vega-embed canvas {\n",
" border: 1px dotted gray;\n",
"}\n",
"\n",
".vega-embed .vega-actions a {\n",
" margin-right: 6px;\n",
"}\n",
"</style>\n"
]
},
"metadata": {
"jupyter-vega": "#7f473d1c-5b7e-4b74-9a52-e0982f23329e"
},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"var spec = {\"mark\": \"line\", \"data\": {\"values\": [{\"%000\": 13.149000000000001, \"Year\": 2016, \"Year_date\": 2016, \"Number of Papers\": 165}, {\"%000\": 11.854000000000001, \"Year\": 2015, \"Year_date\": 2015, \"Number of Papers\": 148}, {\"%000\": 7.171, \"Year\": 2014, \"Year_date\": 2014, \"Number of Papers\": 86}, {\"%000\": 5.59, \"Year\": 2013, \"Year_date\": 2013, \"Number of Papers\": 64}, {\"%000\": 3.877, \"Year\": 2012, \"Year_date\": 2012, \"Number of Papers\": 42}, {\"%000\": 2.85, \"Year\": 2011, \"Year_date\": 2011, \"Number of Papers\": 29}, {\"%000\": 1.383, \"Year\": 2010, \"Year_date\": 2010, \"Number of Papers\": 13}, {\"%000\": 1.256, \"Year\": 2009, \"Year_date\": 2009, \"Number of Papers\": 11}, {\"%000\": 1.198, \"Year\": 2008, \"Year_date\": 2008, \"Number of Papers\": 10}, {\"%000\": 0.255, \"Year\": 2007, \"Year_date\": 2007, \"Number of Papers\": 2}, {\"%000\": 0.804, \"Year\": 2006, \"Year_date\": 2006, \"Number of Papers\": 6}, {\"%000\": 0.14300000000000002, \"Year\": 2005, \"Year_date\": 2005, \"Number of Papers\": 1}, {\"%000\": 0.0, \"Year\": 2004, \"Year_date\": 2004, \"Number of Papers\": 0}, {\"%000\": 0.337, \"Year\": 2003, \"Year_date\": 2003, \"Number of Papers\": 2}, {\"%000\": 0.0, \"Year\": 2002, \"Year_date\": 2002, \"Number of Papers\": 0}, {\"%000\": 0.183, \"Year\": 2001, \"Year_date\": 2001, \"Number of Papers\": 1}, {\"%000\": 0.0, \"Year\": 2000, \"Year_date\": 2000, \"Number of Papers\": 0}]}, \"encoding\": {\"y\": {\"field\": \"Number of Papers\", \"type\": \"quantitative\"}, \"x\": {\"axis\": {\"format\": \"d\"}, \"scale\": {\"domain\": [2000.0, 2017.0]}, \"field\": \"Year\", \"type\": \"quantitative\"}}, \"height\": 100.0, \"width\": 200.0, \"config\": {\"cell\": {\"width\": 500, \"height\": 350}}};\n",
"var selector = \"#7f473d1c-5b7e-4b74-9a52-e0982f23329e\";\n",
"var type = \"vega-lite\";\n",
"\n",
"var output_area = this;\n",
"require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
" vega.render(selector, spec, type, output_area);\n",
"}, function (err) {\n",
" if (err.requireType !== 'scripterror') {\n",
" throw(err);\n",
" }\n",
"});\n"
]
},
"metadata": {
"jupyter-vega": "#7f473d1c-5b7e-4b74-9a52-e0982f23329e"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQMAAACbCAYAAABmk/5RAAAeYUlEQVR4Xu2dCbgcVZXH/9UhEUQw7rtGdBQRNaCO+M1CBnkZMIkExpVBAZeZYVSIy4ijJnlJRkRc2Nw3NmUU1BDzIpqXMbjDqJA4o0ZHIQi4AMqmAyQvXX6/frewqNf9um5Xdfeteud+Hx/Qr+r2Pefe++9zzz3nfyJZMw2YBkwDkiLTgmnANGAaQAMGBrYOTAOmgZYGDAxsIZgGTAMGBrYGTAOmgT9rwCwDWw2mAdOAWQa2BkwDpgGzDGwNmAZMAxkNDPqYME/S+yW9UFLTOTBfLeljkm6UdKSk77gx/pWk8yXtI+klki6y2TMNmAb6p4FBgcED3IZeJekKSUsdGBzhwGGhpIdIWidpX0m7S/qVpEMk/VbSZkkvkvSN/qnCejYNzGwNDBIMFkm6v6S/T4HBcZKeIulkSbMlbZO0QNJBkv5a0kluenjuUZLeObOny6Q3DfRPA4MCg0QCjgn8+h/gLIMHS7pJ0hfcZ7+U9DxJL5P0cEnvcy/y/JkOKDheWDMNmAZK1sCgweAvJH0+BQavlMTRgV9+Nv+nJT1V0jMkPVrSe5y8WA+893RJu9xncQddbJH04pL1FFJ3AOr2kAZU4ljqLBtqCkG+/+s0X8MGgzFJp6SchjgXr5H0e0lPlrTCDRxLAUBI/n+69QdIDFquEvdD164A1I4T2vXtsB+os2xoPmj5fDfNgZL45X2upPmS1jpHX94llrUMPiqJo8G7JD1I0mWSTpR0g6SvS+L7bpG0UdJpkr6U44sMDHIoKdBHgt4sJegsaPl8wABv/iclHS7pW04xP5O0v6SdORWFMugDJyFnf3wGXB/SJ+1Nks5wf3uVpE+4z8+W9EZJEzm+x8Agh5ICfSTozVKCzoKWLy8Y8NxnJV0q6S53lj9e0mckPUvSdQUVNccBQHaz83nDfWferzAwyKup8J4LerOUoK6g5csLBujhcy446BgXIPQOdxXIrzoWQijNwCCUmfAfR9CbxV+cKW8ELZ8PGIxKWunEAwCIBXi5OybcWYKiyurCwKAsTQ6+n6A3SwnqCFo+HzDAwUdw0M0uanCNpA0p/0EeXWXDkXmH6MMLJO0hCT/Bxa6jXsORDQzyzESYzwS9WUpQWVf5FoyunTtn1pyTNi5fxJX7QFteMOC5T0n6y8xdf97BdgpH5kbiq86hyHf8SNITJf1/gXBkA4O8sxLec103S3hD9hrRtPKNjG6Yr6i5OYqiuYriIzcuX3KJV+8FH84LBnxNEiBEmPC1nt8LGLQLRyaqcJOkLzvLgOewPBYXCEc2MPCcnIAen7FgcC8gULx1R3NiwWWjR946yLnxAYO0zyAZ4/XOUiAWIE/LhiNzdcjRIGkfkPQGl9TUaziygUGemQjzmRkJBgvXrF8aN3VOyyKIdd6OeOeyQQMBy8EHDP5d0sHuCjBZSsQJkHiUFwyyQUeAAWP4J0n3cUFHfM9DC4QjM7YnhbnWSxlVCCGtpQjSppM6y4a4U+Q76ISzjtr7Efucyh93/PH2tZe9+2j8cv1spYUjY8Y/021+AIDQ4U45Au0EyoIB4chkIn7XPZxkJxJ7b+HI7ZdEnX896ywbs3kv+UZWbzguUnwOf4jj+MzxlUuW9RMFuvXtYxnAM/AT1+G/uKvF30l6S8ZamO47s2BA4BL/HCZpT0mXS/pHSZyVLBzZwKDb+q3a3+8Bg5HVY+dEkwl6ihUdP75i0bnDFsYHDDDpiQgka/DbLuAIx98TXBpyHlmy4cgcDQg1fo17mSMCOQiEKls4soFBnjVVpWdaYBAiEPj6DACDr7noQ0z4j7jrP+IEthackfs6ACDUOd0sHHmqYutsStdZNj143+c888Cj3/EOKVoaS7fFUbx00/IlJOcF0XwsA5wc0JD9wF0Dkm34z+4cdHsQ0kwOwm4TApoMz6HUFgwIJpq1K/7OrNlzngIQqBktGB9dRAZwMM0HDOAoBMX2S43+pS5nIRiBDAxCmgrvsdQWDEZWjV0VRZofE6PTjJaGBgS+xwSe383dJuDsI1oQslKf1i4cOXkfywM2o++7Dywcub1ma7thQif/8Fno6WcJKIoa8VXN5q4bJtTcfxgxBHnG7mMZ7C0JMhKsARrU5tCL4fXv1jqFIyfvHSppXNKzHRg8wsKRO6rUwKDbagvs7yOr1p8RRdFJd93+u/O+8d5jWzcIITYfMOCXm4AIchSIL3i9E4jF2c1n0CkcmS7Y+JClQHJCngLXixCp9MqObD6DEFdavjHVEugWrhq7RpHm3fD98SN+9KUz87B15dNWyU/5gAG3CYBAQlcOYSmkJiQb5b1NyIYjQ1xCLQSuEWFAxilJERViDSwc2Y4JJS/3wXeXHBHwFYyvWDwSMn+lDxiQm0AaM1FSxBrwy/2fjqj0DznVnA06oi4C7EYflHSspJ86ywAC1F7ZkRmKhSPnnJDAHqtdOPLfvvm8t+++94OOdaHGHw6A2bqUcGQyDCErTfwF5A/QfiwJfwI05t1yFNJgAH9BAiL4H5L+IEzB1LdwZLMMAsMq/+EktwguJRmne7DM1j6WAdGB1EIkxTjb8iYsZS0DfAmMYYek1zpgwZHIL4SFIxsY+O++gN44bPTSec3GrmuIKxhfsXhu6LclPmCQOPs45ycNAeE/7JUdOT116WMCn1s4soFBQFvbfyiHrh5b1pBOJy1548rF3CIE7SD1AYPnO5qztFbucL/iFD3pR7Nw5KlaDXpBFVwEtZItc0SAtSho+fKCAc993HEO4Ni7W9IvJL3AOeuMELXgLvB4PegF5SFHu0drI1ubIwLyBi1fXjBAEJBttaukzHUiDkVSmmFJ7uY4LLhGvF63OAMvdQX1cNCbxUdTbY4ItQKDsyTtI4krRliRCUCCmIFCqdwo5GnZcGTCm7mqpMAqjkO4EZLkDQtHNp9BnjUV5DNtjgi1AgMKn5KohCVAQRVCh/OWV+sUjgxJCpyHUKc9zVVogh8BkPiVy5Ik/2Gzi0okQKlbM8ugm4bC/XstLIPkiICaN65YnLa+g5bP55iAM49NCo05cQVs2p9LwonYrbULR2bTUq4NfnhozxjLNx04YEFYOLJZBt3WVZB/Tx0R1m1cuXhpapCVBwM2Pv4BrkZ8kpPaTVQ2HBmAIcaAxrHgi65uAo5JC0c2MAhys3cb1MJVY5co0hFt6MwqDwaUS39rSgEAAnyIvTgNs0FHdIu1wVEBujPIVq+UZOHInVdc7UJ2U6JWXrYH7/ucvQ88enkrDf/KC9c86+ZtV6ST+EKQr1A4clIWHTpz/AXwHyapxt1AMvv3LBgQtER/X3HHhURxJCpZOLJZBr7ra+jP38N4HCt7RGBslbcMAAPSivk3yEbmok+mYnqCsmDA8QMA4Mpytiu/Tr4CloeFIxsYDH1z+w5gmiNCLcCAK0XqH7JxcRqSYUiewv+6DQxDMkFIeVqaHZlEJdKVSXBKWvoIYuHIBgZ51lQwz7SKpjZmt47PO5o7H9CG0ajylgHXiDAadWqkNVs48uCWZNALqqAaKi1blyNCLSwDnHr371A5Cecf5nxyI1BwLZTyusUZlKLGoXRSaTDockSoBRgMZVUU+FIDgwLKG/KrlQWD9BGh0Zz1+K+MHk6JwGwLWj6foKMy1kk7dmRSlykthb+AUuzfc19k4cjmMyhjzQ2kDyopK47WSvHWjSuW4GBv1wwMJHUKR4YVmfwGuOEeJ+li56S0cOTOSzjoBVVw51VWtoWrxs5VpGOb0hs2rVh8Rl3BgCvFD0la4uoaQN3k2zqxI9M3bLEJYyzxBlwzPsrCkTuquLIbJseiqaxsI6vW3xJF0dxpjgiV9xkkPAbcJsBZQGm161OTyvXgv+bMT+C1bDgyNxXvTR0NSFyiQMtvLBzZwCAHeATxSM4jQuXBAAEorEo9g3bNNzQ5G3QEuzJVmIk3oBF5+EgHOMaO3F7nIYS09msTVlK2BSdf+O45e+595B9uuu6U75x9wnSl1UOQr1A4MhOfWAjnucxCzvRJg+o8b8uCAZYBR5CkKhPgAKMShVUsHLm9VitrSudYJJWULecRoRaWQXoO/07S8ZIeSKq2C1EmpTlvy4IBlgCVnHEgYhFc7QhU7mPhyHZMyLuohvmcxxGhVmDQjhB1vaNPp6hKnpYOR246TkWOCa9xL79C0gXuvy0c2SyDPGtqqM8ktwiK41UbVy6BBWy6FrTl4xNngOef50k3JrmImIALHSEqDr8i7b6uslI2ktHYkadqNegFVWQRhJ7V10625IgQN6MDcpRZD3rufMEAAtT3OaVQOOUmSfu78uwF10Fpr1sEYmmqHHhHQW+WrDYydRRxDnZrQcvnAwaYQCudZfA7Sa902Yw4+nz8Bt0UVvTvBgZFNTi894PeLFPAwJVaj+P4zPGVSyD27daCls8HDCAiwWkIsUnSIDLls5CagUFIs+E3lqA3S1qUVi5CNPsqSq3nPCLwetDy+YBBoovHOMcffoK81Zc7LQm+/9WSPuZyE6jlmMQcWG5Ce60FvaD89v6UpyshG0AwO5q9OYog+Zk2FyErYNDy9QIGBef7Xq8fIen9LrDpIZLWOZaj3Y0qvaOag15QBRdH8LJlgWBHc2JBGxKTTmoIWr5hgwGMy9RjoCALtGfbJC1wXItGlW6WQUFsKff1gkBQq2MCYcn3k7S2A9FJL5pPbiS+IOkASb+U9DzHjmxU6QYGvaypvrxTAhDUCgyIM/gbSftJyhtk1G1iuJGgiAoWApv/065c2zMkWW5Ce+2FEN/ebV57/XuQskF//owXnXzBrNlzntKcuHvblotOOyZDgZ5X3hDkK5ybgLAvknSRq7VI5SNIUDHtcfj1Sns2JumUlNMQ/wHsy3AqWm6CWQZ5N1nfnivJIkjGVwufAb4FSqFxlZhtRQhRP+qOBhRqoR9qOZ4o6QbLTTAHYt92eM6OSwaCWh0TQLW9Mv4Cshepmrwzp36zj+EzOF/S4e4Pb5IESwx5C5abYJZBj8uq+Gt9AIJagQHWAUQmH5BEdSVqMFIzgRDloo0cBAAgmw5tuQlTNRu0qVlwIQQhW5+AoFZgQIISZ3oaacw4+Eg/xqGYpxJzwXWS+3WLQMytquAeHDoY9BEIagUG3Cb8t0tOgqMQIlMiEJ8kqaOHcgjLzcBgCEov6SuHCgZ9BoLagAFHBNKVKalGLAB5Cpsk/VjSYyVdV2AxEL8AhwF8ivgJYEimWTiy+QwKLCu/VwcABLUBAwQhdPiSjIq5YjykzVk/70zALw+/IlGHAA7My9R1JAvyV67v30ra7K42v5GjY7MMcigp0EeGZhmMrB7bHLXWYbzVM8TYR5VDky/PIH3DkRdJOkHSQyVtcJ7/2/J8UYdnKOaKhYEjEssASvWbHXGKhSObZVBgaeV/dWT12DmRdFws3TarOWt+h2pI+Tvs/GStwABhiELkSvHnLi6AW4BeG34IjgZJ46YCR+VLjCq9o0qDXlC9LgT33sBlS4qlAgRqRgtysBUVEXHg8vkM1scyoPrReKbzT7prxl4BIaFS46oSElSCjij3juVh4cjtZzKEkFafNebz7EBle/arTz30AY/dH3Zu3f7rq996+YdP/KLPYHt4dqDydRhf4XDkhCr9qZIodHKrpJdKOtWxGRNC3EsjHPmdkr7rXiZHgZsKilZaOLIdE3pZU7negbJMUXMzVZBykpnm6rfLQ7WxDAgd/h8XdITMlGkHFIrcJhCvwD+HuUpKl7tCKvRLLYUDJd3i2JROS5Vhm07n5kAsY9kOp4+BbJY0S5Finbdx5WJ+hAbRBiJfr4LkOSY8UxKRgGxabhRe5zz9MBRBn47pQ+m1XhpHgzRVOkcENr2FI3fWZtALqpdFkHqn77IVYCkqKFrr9b7LV2SQ3cBgugQlvpfIQ8CALMMiDap0AOCuTCcWjjxVq0EvqCKLYBCbJXVzcO3O5s75HixFBUWrPhggAUxEXPthfmdb0USlMhSc7cOOCf3Q6mD67CvQLVy1flRRtHJANwftNNZX+YpOUTfLIN0/Jv0LXCxAAgzwGXyqzS960XEVed/AoIj2hvtu3zZLcoXYEi+Kj9y4fEk2gG4QkvdNvjIG7wMGSd2E9PeWdUygT24mPi/p++4LLBy5/QwHvaAKLsq+yJYUO2FssaLjx1csmq5SckERpn29L/KVNWAfMCAmAHajNakjA7/CVFVqd4TwGWMSw0BNBsCAKswWjmxg4LOG2j572Oil83ZFE1dNXiEO9Oag1scELAPYiCAgSQcZ+ZRkb6cgNv63HMkJeQpcL0KxZuHIBgaFwODQNesXNJrROa1CJ4q/Pr5iCTkww2y1sQwOdhGCaWXe6OocEAvQS2tIIvmIkOSEHRlORUq1GzuygUEva0qtOILGbEoBupJnfU0+8hljLcAgiUBk0xKCnJCZcCWIpdBrZaWTXMbjByUdK+mnzjJ4mYUjd1xjIYS0+mwAn2cLy0aI8dxH73dq1GjAxKU7b/3t2d98/6uIZQmhFZavBCEKhyMzBnwGW12QUAlj0p4pEMHCIB+B9nLng7BwZLMMcq8zfAPNxsTpUrSUlzgWqNlY1ufEo9zjcw/WwjJAFlKX8Ru80TkN+YyrRZKXeqVKJ2UZq4P3X+vIUugPBLVwZAODXJttZNV6LMxRnITEEMTS6KYViyHWDa3VAgzYsJ+V9OI22i1ClZ7uLn1M4HNjRzYwmHYzt5KNGvHpk6QkLXNg3Y5453EDjir0AZxagAECP14SBVGz14g/y9wu+Cin27MWjjxVQ0EvqG4T2uXvuWVbuGbDSsUxlioL8tooipcNKZDIR+Tc8vl0WtazPnEG/FJDSZYGAzYrE9KrA7EsOdL9WARiP7Q6mD67bpbJ1OP4nMly6FIcx2fujCdGA7YG0prrKt9g1Nz+W3zA4HNtjglFrxb7IbuBQT+0Opg+p90sxA1ETa1tBRAp3tqMtGzT8iUQ4lSl1QYMuEZMgwf0Z1wJ7l8ghZlEJ+6C3+MckW9xFZqYXAtHNp/BPRrASRhF0aRTMHzfQCdwqg0YZAUkmxGqdCom/7BHaIY1Cc5Dajg+TdJnJD3BcSxaOLKBQUsDSdrxJBDEqzauXNLyFVSw1QYMzpL0+swEEHzE5iU/wbclXAmUZIf2jP+Heh1w4GrRwpFnOBikiUhaaceKlg0xych3fbd7vjZgwCaFHooKyTTyEz4uaV0BLeGATGIUOBZASImTklRpC0eewWCQ5ijktkDNaGlgAUS9LPvagEEvwud5B78BQAPdGRRrV0qycOTOmgshpDXPvPbyTEu2g04466i9HjbvbYQUNyfu3rblotOOuXnbFbf30mFg74Qwd4XCkd/tog/bdUKZtWc50tJe9M7735b0FUkcF5IJJ1HJwpFnoGWwcPUYlb4nE4yGn3Lcy5qe7p3KWwb8Yv+bk/B7knZJOsj9f9GrRSoqAQCrXWgzWYzELOxr4cgd11TQC6rX3YN/ILr77otn73E/uC2GTULSqxjd3gt67nziDBCUXAI8uSe6zMV/aFNYpZtCkr+TqES68tNTL6TBxcKRa2IZTIYNR1DrK4piFzock0fQChyKYs2Dc2ASBHRbHMVLKxY/kHfN1wYMjpL0BSc1Jj0WAwVS+9ksHHmqdoeyoFytAajyR5ONW/bE4x/YrXHfw/tY67DsIfv2N5S5yzvIPJYBOQkUUBmRxDHhla40e97vGPRzFoFYosZbIDBrzklxs7lsMvLPv7VuAxRTJYuf/i3RZPEdNRutcnqKdjVudTcFQW8Wf8mnvBG0fHnAIB2GjBl/hSuBhqRFHYgl6HdKFwYGJWi1xR3Y2LWS6sRJdy2OADXO7eNdf9CbpQS1Bi1fHjBIOxCz+rjenfl7pT2bTr8WjtxeO20X1MjqLx8cxU3O3vNbZ/E4bp3HFUX8Em/hFzmOGtvHVzwfnoiOrcUbGOukhCTE/ZqvazbiMwZwjg96sxgYlKCBHrowduQOSnvigmMWPP6Qo+OWIy5uzo/jaF6SwZdXzzGmehRvV9TYEsfRZbOa0bXNaNfBcaTj7uEGmDTpz2vEs0YHeIY3MMg7iX14Lo9l0Iev7dplIXbkkdENB+T1XivW9jhqVX0mHnqLoqh1nmWTtEbZjG9LIt98vOI+/U6a5PHjWmPg170RTXrW45h/t/477XFvr714q+JouxRv4Sy+267dWjJNzJqY12hC/hGR+jtPisgl6djw5kdxfMaOeOKMIaQFGxh03Rr9eyBUMOiZHXnh6rGiNRz6p+0Sem7GzTuiKLoSs7/1yx41t/ua761U4LgxD8uCY0ULKFpOvWh0Z3PHJUMAgUQzBgYlrJFeuwgVDHoOR164ekx33nojrLgtndzx66s1cdcfW//9++1UlJcm7vyj7vjN1a3/3uvh+2i3PQh5kB44j8RJabfd99Rej9in9d97zH2Y9pg7ydU6iH7vuuXPY09/X/rzXifb3jMNSNos6ZB2mggVDCwceVpL/l68EnVa4XW/CQpBvo5jCBUMLBzZwKBOIJfIYmDQ46xaOHJ7xYWwoHqc0q6v1Vk2hA9BvspZBsmqsXDkqfsnhAXVdVf3+ECdZTMw6HFRFHnNFlQR7Q33XZu7/uu/spZBL6qh4CaJVNZMA6aBqRrouD9CdSDaJJoGTAMD1oCBwYAVbl9nGghVAwYGoc6Mjcs0MGANGBgMWOH2daaBUDVQFTCgYAu07KQ1v1XS6Y5inSpPy91nsDBR1p04ZN/Phz0/Zcn3aEnUt6BALu2nkt7Ux8K4efTmK1vSJ3T5j3FVu/is05zmGUM/nylLvqHPXRXAAN7FbRI59q1iK1RdukTSeyW9SxJJBFCtHy3peFfhaY3n5xP9XC1d+i5TvkWSIBR9nwOEu6Aer5hsMGu9WNJ/SHpdCgw6zXXV5q6TfFDKDXXuqgAGIO/nXfk1CrdgHXxI0nMlkXkEweZ1bvFD587nEHjk/fzAHitClbXHypIPOQBDgPNyx0857OrYvrIhw4M0SZTKxrjKgQGWzo/azGnV5q6TfKwl6owOde6qAAYo6j6S7nY1GL8q6QJJn5W01dG2w7SELJskrZD0KY/POXbA7TjMVoZ8yIFFRN1KdMS/CelGF8NsPrKl5wLmbbgcEiun3VxXbe46yZeUGhzq3FUFDFjM+0la7wqucGSgElN2gQAQH3YErtR2SEBius+p3QBl+7BbUfk+IIm6E1hLv5fErzIELSR99YOWzkdfeWVLz0Wa0wLLoN1cV23uOsnHWsbiHercVQUMXuIciIelNi4kBJRiozQ8RK2JKYl5SYWmvJ8zCb/xWdl9eLYM+ZADJ9skUYM0y1XJTkCxD8PO1aWPbOm5SINBp7mu2tx1kg9F4vsa6txVAQwwFa+RBAsOzjDMTqo6cWy4UNIPnDMRKvePuF/C8z0/35lrWffnobLkg86MqtjPk/Q1SQudlbQ/vCz9GXrXXnuRLZmLNBiwTjvNddXmrp18/JAxR0OduyqAwQslXZxZdomXGeJU/ASYoWwEnIZYC76fd13VfXygTPme45yHDBdrCYD8YR/H3q3rXmRL+oTt6pHOZ8Bnnea02xj6+fcy5Rv63FUBDPJMJmYk1Z2y/Ie+n+f5rmE84ysHz09yvYXfOsnWaeS+zw9bA77jHdrc1QUMhj3h9v2mgcprwMCg8lNoApgGytGAgUE5erReTAOV14CBQeWn0AQwDZSjAQODcvRovZgGKq8BA4PKT6EJYBooRwMGBuXo0XoxDVReAwYGlZ/CgQtAuPe4pNMcjwSxHUQ5EldP7shRVLAb+KjsCwtrwMCgsApnXAckQ/2Xi/Z8tov4TP6f1GMSiqxVUAMGBhWctACGTCbkTyRtkLRW0ickrXY5Im+XdLILh4akBG4J1tkrnDVBFVvCy98mCb4F8khgsTpW0qUpMpMAxJxZQzAwmFnzXaa05ObDPkS7XtLTHQnNkyQdI+lgV7/iyS55bJ0DhJ+7zFOOFIDGj10fJFeR4vulMgdpfeXXgIFBfl3Zk/fWADH08EAAAoc7lh6yS7EUSCGHceo4SW+UdLb7/4c4FiN4Ky9y/JXwNKb9D6bnIWnAwGBIiq/J10K+Cl8B6eWY/9DOQbl2iqTZkh4o6Wfu34AEoMBRAP7KX0h6syNtfY07atRELdUUw8CgmvMWyqgTMMCHQAo5lgKp0zBRwbTEUQALAbJPmKtJ0wUkcDKmLYM08Wkoss24cRgYzLgpL1VgwOBId1SAWg3m3zHHL8EXwVqNHwC/AY5ErAdAAyKPKxwJKI5IswxKnZbeOjMw6E1v9tb0GsCfABsVVO1Jg4YNRp+q8CzMuDk2MJhxU24Cmwbaa8DAwFaGacA00NKAgYEtBNOAacDAwNaAacA08GcNmGVgq8E0YBowy8DWgGnANGCWga0B04BpIKOBPwGV3HoycxMIyQAAAABJRU5ErkJggg=="
},
"metadata": {
"jupyter-vega": "#7f473d1c-5b7e-4b74-9a52-e0982f23329e"
},
"output_type": "display_data"
}
],
"source": [
"from altair import Axis\n",
"\n",
"altair.Chart(subset, height=100, width=200).mark_line().encode(\n",
" X('Year', scale=Scale(domain=(2000, 2017)), axis=Axis(format='d')),\n",
" y='Number of Papers'\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Try Altair's filtering\n",
"\n",
"And use fewer y-ticks"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div class=\"vega-embed\" id=\"60b0b2c9-9b85-4fc8-8979-a596bead8641\"></div>\n",
"\n",
"<style>\n",
".vega-embed svg, .vega-embed canvas {\n",
" border: 1px dotted gray;\n",
"}\n",
"\n",
".vega-embed .vega-actions a {\n",
" margin-right: 6px;\n",
"}\n",
"</style>\n"
]
},
"metadata": {
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"
},
"metadata": {
"jupyter-vega": "#60b0b2c9-9b85-4fc8-8979-a596bead8641"
},
"output_type": "display_data"
}
],
"source": [
"from altair import Axis\n",
"\n",
"altair.Chart(data, height=100, width=200).mark_line().encode(\n",
" X('Year', scale=Scale(domain=(2000, 2017)), \n",
" axis=Axis(format='d'), ),\n",
" Y('Number of Papers', axis=Axis(ticks=5))\n",
").transform_data(filter='datum.Year >= 2000')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Try Layered Chart for both dots and lines\n",
"\n",
"## BUG or working as intended? \n",
"\n",
"x-scale applied in `X()` object but not viewed in chart"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div class=\"vega-embed\" id=\"65d1f75a-4107-4718-a79e-995a527c5375\"></div>\n",
"\n",
"<style>\n",
".vega-embed svg, .vega-embed canvas {\n",
" border: 1px dotted gray;\n",
"}\n",
"\n",
".vega-embed .vega-actions a {\n",
" margin-right: 6px;\n",
"}\n",
"</style>\n"
]
},
"metadata": {
"jupyter-vega": "#65d1f75a-4107-4718-a79e-995a527c5375"
},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
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"
},
"metadata": {
"jupyter-vega": "#65d1f75a-4107-4718-a79e-995a527c5375"
},
"output_type": "display_data"
}
],
"source": [
"from altair import LayeredChart\n",
"\n",
"chart = LayeredChart(data, height=100, width=200)\n",
"\n",
"x = X('Year', scale=Scale(domain=(2000, 2017)), \n",
" axis=Axis(format='d'))\n",
"y = Y('Number of Papers', axis=Axis(ticks=5))\n",
"\n",
"chart += Chart().mark_line().encode(x, y )\n",
"chart += Chart().mark_circle().encode(x, y)\n",
"chart"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Ermmm filter the chart\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div class=\"vega-embed\" id=\"226aba74-334a-4b30-a160-bb51fe11b30b\"></div>\n",
"\n",
"<style>\n",
".vega-embed svg, .vega-embed canvas {\n",
" border: 1px dotted gray;\n",
"}\n",
"\n",
".vega-embed .vega-actions a {\n",
" margin-right: 6px;\n",
"}\n",
"</style>\n"
]
},
"metadata": {
"jupyter-vega": "#226aba74-334a-4b30-a160-bb51fe11b30b"
},
"output_type": "display_data"
},
{
"data": {
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"
},
"metadata": {
"jupyter-vega": "#226aba74-334a-4b30-a160-bb51fe11b30b"
},
"output_type": "display_data"
}
],
"source": [
"from altair import LayeredChart\n",
"\n",
"chart = LayeredChart(data, height=100, width=150)\n",
"\n",
"x = X('Year', scale=Scale(domain=(2000, 2016)), \n",
" axis=Axis(format='d'))\n",
"y = Y('Number of Papers', axis=Axis(ticks=5))\n",
"\n",
"chart += Chart().mark_line().encode(x, y ).transform_data(filter='datum.Year >= 2000')\n",
"chart += Chart().mark_circle().encode(x, y).transform_data(filter='datum.Year >= 2000')\n",
"chart\n",
"\n",
"\n",
"# chart.savechart('single_cell_transcriptome_papers_per_year.png')\n",
"# chart.savechart('single_cell_transcriptome_papers_per_year.svg')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## New query: all `\"single cell\"` papers \n",
"\n",
"Same [website](http://dan.corlan.net/medline-trend.html) to view trends in medline, but this time the query was `\"single cell\"`."
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Number of single cell papers</th>\n",
" <th>Year</th>\n",
" <th>%000</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2730</td>\n",
" <td>2016</td>\n",
" <td>217.556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2693</td>\n",
" <td>2015</td>\n",
" <td>215.689</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2446</td>\n",
" <td>2014</td>\n",
" <td>203.959</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2275</td>\n",
" <td>2013</td>\n",
" <td>198.715</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2023</td>\n",
" <td>2012</td>\n",
" <td>186.724</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Number of single cell papers Year %000\n",
"0 2730 2016 217.556\n",
"1 2693 2015 215.689\n",
"2 2446 2014 203.959\n",
"3 2275 2013 198.715\n",
"4 2023 2012 186.724"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = ''' Number Year %000\n",
" 2730 2016 217.556\n",
" 2693 2015 215.689\n",
" 2446 2014 203.959\n",
" 2275 2013 198.715\n",
" 2023 2012 186.724\n",
" 1811 2011 178.000\n",
" 1469 2010 156.314\n",
" 1356 2009 154.863\n",
" 1234 2008 147.785\n",
" 1144 2007 145.795\n",
" 1165 2006 156.035\n",
" 1086 2005 155.344\n",
" 923 2004 144.595\n",
" 833 2003 140.406\n",
" 809 2002 143.424\n",
" 778 2001 142.488\n",
" 820 2000 154.223\n",
" 663 1999 134.559\n",
" 656 1998 138.761\n",
" 630 1997 138.377\n",
" 594 1996 129.950\n",
" 516 1995 115.247\n",
" 481 1994 110.042\n",
" 433 1993 101.588\n",
" 413 1992 99.075\n",
" 422 1991 102.387\n",
" 372 1990 90.738\n",
" 359 1989 89.313\n",
" 314 1988 81.268\n",
" 292 1987 79.511\n",
" 289 1986 82.837\n",
" 250 1985 74.704\n",
" 261 1984 82.164\n",
" 258 1983 83.517\n",
" 199 1982 67.484\n",
" 164 1981 57.942\n",
" 132 1980 47.145\n",
" 117 1979 41.566\n",
" 88 1978 32.337\n",
" 92 1977 35.144\n",
" 119 1976 46.712\n",
" 80 1975 32.149\n",
" 40 1974 17.030\n",
" 30 1973 12.990\n",
" 27 1972 11.864\n",
" 22 1971 9.849\n",
" 13 1970 5.931\n",
" 20 1969 9.300\n",
" 20 1968 9.621\n",
" 22 1967 11.473\n",
" 17 1966 9.447\n",
" 17 1965 9.629\n",
" 11 1964 6.793\n",
" 10 1963 7.077\n",
" 3 1962 2.389\n",
" 6 1961 4.998\n",
" 6 1960 5.351\n",
" 9 1959 8.186\n",
" 9 1958 8.225\n",
" 7 1957 6.279\n",
" 9 1956 8.413\n",
" 5 1955 4.610\n",
" 3 1954 2.839\n",
" 3 1953 2.760\n",
" 3 1952 2.778\n",
" 6 1951 5.789\n",
" 5 1950 5.841'''\n",
"singlecell = pd.read_table(io.StringIO(s), sep='\\s+')\n",
"singlecell = singlecell.rename(columns={'Number': 'Number of single cell papers'})\n",
"singlecell.head()"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Number of single cell papers int64\n",
"Year int64\n",
"%000 float64\n",
"dtype: object"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"singlecell.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"
},
"metadata": {
"jupyter-vega": "#2197f7f6-7a5b-49a6-accc-0a0ff3cf57ca"
},
"output_type": "display_data"
}
],
"source": [
"altair.Chart(singlecell).mark_line().encode(\n",
" x='Year',\n",
" y='Number of single cell papers'\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Number of single cell papers</th>\n",
" <th>Year</th>\n",
" <th>%000</th>\n",
" <th>Number of \"single cell\" papers</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2730</td>\n",
" <td>2016</td>\n",
" <td>217.556</td>\n",
" <td>2730</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2693</td>\n",
" <td>2015</td>\n",
" <td>215.689</td>\n",
" <td>2693</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2446</td>\n",
" <td>2014</td>\n",
" <td>203.959</td>\n",
" <td>2446</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2275</td>\n",
" <td>2013</td>\n",
" <td>198.715</td>\n",
" <td>2275</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2023</td>\n",
" <td>2012</td>\n",
" <td>186.724</td>\n",
" <td>2023</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Number of single cell papers Year %000 Number of \"single cell\" papers\n",
"0 2730 2016 217.556 2730\n",
"1 2693 2015 215.689 2693\n",
"2 2446 2014 203.959 2446\n",
"3 2275 2013 198.715 2275\n",
"4 2023 2012 186.724 2023"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"singlecell['Number of \"single cell\" papers'] = singlecell['Number of single cell papers']\n",
"singlecell.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## BUG? Can't handle quotes in y-axis name"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div class=\"vega-embed\" id=\"d37080b2-f941-4741-948c-380cce7ec8bb\"></div>\n",
"\n",
"<style>\n",
".vega-embed svg, .vega-embed canvas {\n",
" border: 1px dotted gray;\n",
"}\n",
"\n",
".vega-embed .vega-actions a {\n",
" margin-right: 6px;\n",
"}\n",
"</style>\n"
]
},
"metadata": {
"jupyter-vega": "#d37080b2-f941-4741-948c-380cce7ec8bb"
},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
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"
},
"metadata": {
"jupyter-vega": "#310ffea2-0b56-4788-9c79-72b1c6fdb3cb"
},
"output_type": "display_data"
}
],
"source": [
"from altair import LayeredChart\n",
"\n",
"chart = LayeredChart(singlecell, height=200, width=300)\n",
"\n",
"min_year = 1950\n",
"\n",
"x = X('Year', scale=Scale(domain=(min_year, 2016)), \n",
" axis=Axis(format='d'))\n",
"y = Y('Number of single cell papers', axis=Axis(ticks=5, format='d'))\n",
"\n",
"chart += Chart().mark_line().encode(x, y ).transform_data(filter='datum.Year >= {min_year}'.format(min_year=min_year))\n",
"# chart += Chart().mark_circle().encode(x, y).transform_data(filter='datum.Year >= {min_year}'.format(min_year=min_year))\n",
"chart\n",
"\n",
"\n",
"# chart.savechart('single_cell_transcriptome_papers_per_year.png')\n",
"# chart.savechart('single_cell_transcriptome_papers_per_year.svg')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## New query: Only `\"single cell\" sequencing` papers \n",
"\n",
"Same [website](http://dan.corlan.net/medline-trend.html) to view trends in medline, but this time the query was `\"single cell\" sequencing`, to limit it to only RNA- or DNA-sequencing (hopefully)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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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