{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Manipulating results from Dimensions is straight forward with python and Jupyter Notebooks\n",
"With pandas, you can quickly load the results from your Dimensions query into table format, and chart the result "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from dslquery import dslquery\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Execution time: 0.43326592445373535\n"
]
}
],
"source": [
"#manipulating results is easy in python\n",
"\n",
"results = dslquery(\"\"\"\n",
" search publications for \"nanotechnology\"\n",
" return year\n",
"\"\"\")\n",
"\n",
"df = pd.DataFrame(results['year'])\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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