{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Global Carbon Budget: Fossil Fuel and Cement Production"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import math\n",
"\n",
"import matplotlib.pyplot as plt\n",
"plt.rcParams.update({'mathtext.default': 'regular'})\n",
"plt.style.use(\"ggplot\")\n",
"\n",
"from pandas_datapackage_reader import read_datapackage"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's load the global fossil fuel and cement production emissions data from the [Global Carbon Budget](http://www.globalcarbonproject.org/carbonbudget/index.htm). It has been pre-processed for easy reading in as a [data package](https://github.com/openclimatedata/global-carbon-budget)."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"ffc = read_datapackage(\"https://github.com/openclimatedata/global-carbon-budget\",\n",
" \"fossil-fuel-cement\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" | \n",
" Value | \n",
" Source | \n",
"
\n",
" \n",
" | Year | \n",
" Category | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" | 1959 | \n",
" Total | \n",
" 2453 | \n",
" CDIAC | \n",
"
\n",
" \n",
" | 1960 | \n",
" Total | \n",
" 2568 | \n",
" CDIAC | \n",
"
\n",
" \n",
" | 1961 | \n",
" Total | \n",
" 2579 | \n",
" CDIAC | \n",
"
\n",
" \n",
" | 1962 | \n",
" Total | \n",
" 2685 | \n",
" CDIAC | \n",
"
\n",
" \n",
" | 1963 | \n",
" Total | \n",
" 2832 | \n",
" CDIAC | \n",
"
\n",
" \n",
"
\n",
"