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Francis-Ngo/US GDP Analysis and Dashboard.ipynb

Created February 16, 2020 13:43
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US GDP Analysis and Dashboard.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": "# US GDP Dataframe"
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>date</th>\n <th>level-current</th>\n <th>level-chained</th>\n <th>change-current</th>\n <th>change-chained</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1930</td>\n <td>92.2</td>\n <td>966.7</td>\n <td>-16.0</td>\n <td>-6.4</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1931</td>\n <td>77.4</td>\n <td>904.8</td>\n <td>-23.1</td>\n <td>-12.9</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1932</td>\n <td>59.5</td>\n <td>788.2</td>\n <td>-4.0</td>\n <td>-1.3</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1933</td>\n <td>57.2</td>\n <td>778.3</td>\n <td>16.9</td>\n <td>10.8</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1934</td>\n <td>66.8</td>\n <td>862.2</td>\n <td>11.1</td>\n <td>8.9</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " date level-current level-chained change-current change-chained\n0 1930 92.2 966.7 -16.0 -6.4\n1 1931 77.4 904.8 -23.1 -12.9\n2 1932 59.5 788.2 -4.0 -1.3\n3 1933 57.2 778.3 16.9 10.8\n4 1934 66.8 862.2 11.1 8.9"
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "# The code was removed by Watson Studio for sharing."
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>date</th>\n <th>level-current</th>\n <th>level-chained</th>\n <th>change-current</th>\n <th>change-chained</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1930</td>\n <td>92.2</td>\n <td>966.7</td>\n <td>-16.0</td>\n <td>-6.4</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1931</td>\n <td>77.4</td>\n <td>904.8</td>\n <td>-23.1</td>\n <td>-12.9</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1932</td>\n <td>59.5</td>\n <td>788.2</td>\n <td>-4.0</td>\n <td>-1.3</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1933</td>\n <td>57.2</td>\n <td>778.3</td>\n <td>16.9</td>\n <td>10.8</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1934</td>\n <td>66.8</td>\n <td>862.2</td>\n <td>11.1</td>\n <td>8.9</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " date level-current level-chained change-current change-chained\n0 1930 92.2 966.7 -16.0 -6.4\n1 1931 77.4 904.8 -23.1 -12.9\n2 1932 59.5 788.2 -4.0 -1.3\n3 1933 57.2 778.3 16.9 10.8\n4 1934 66.8 862.2 11.1 8.9"
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "#US GDP Dataframe head\nbody = client_36f51ccf6a0e4156bc1a5a89bbca0dd8.get_object(Bucket='finalprojectusgdpanalysisanddashb-donotdelete-pr-du26gi8r6br53h',Key='year_csv.csv')['Body']\nif not hasattr(body, \"__iter__\"): body.__iter__ = types.MethodType( __iter__, body )\ndf_data_1 = pd.read_csv(body)\ndf_data_1.head()\n"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": "\n#Unemployment Rate Dataframe"
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>DATE</th>\n <th>UNRATE</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1948-01-01</td>\n <td>3.4</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1948-02-01</td>\n <td>3.8</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1948-03-01</td>\n <td>4.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1948-04-01</td>\n <td>3.9</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1948-05-01</td>\n <td>3.5</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " DATE UNRATE\n0 1948-01-01 3.4\n1 1948-02-01 3.8\n2 1948-03-01 4.0\n3 1948-04-01 3.9\n4 1948-05-01 3.5"
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "\nbody = client_36f51ccf6a0e4156bc1a5a89bbca0dd8.get_object(Bucket='finalprojectusgdpanalysisanddashb-donotdelete-pr-du26gi8r6br53h',Key='unemployment-rate_csv.csv')['Body']\n# add missing __iter__ method, so pandas accepts body as file-like object\nif not hasattr(body, \"__iter__\"): body.__iter__ = types.MethodType( __iter__, body )\n\ndf_data_2 = pd.read_csv(body)\ndf_data_2.head()\n"
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>DATE</th>\n <th>UNRATE</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>326</th>\n <td>1975-03-01</td>\n <td>8.6</td>\n </tr>\n <tr>\n <th>327</th>\n <td>1975-04-01</td>\n <td>8.8</td>\n </tr>\n <tr>\n <th>328</th>\n <td>1975-05-01</td>\n <td>9.0</td>\n </tr>\n <tr>\n <th>329</th>\n <td>1975-06-01</td>\n <td>8.8</td>\n </tr>\n <tr>\n <th>330</th>\n <td>1975-07-01</td>\n <td>8.6</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " DATE UNRATE\n326 1975-03-01 8.6\n327 1975-04-01 8.8\n328 1975-05-01 9.0\n329 1975-06-01 8.8\n330 1975-07-01 8.6"
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "#Dataframe were Unemployment Rate > 8.5%\nUnemployment_sample1 = df_data_2[df_data_2['UNRATE'] > 8.5]\nUnemployment_sample1.head()\n\n "
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": "\n <div class=\"bk-root\">\n <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n <span id=\"1613\">Loading BokehJS ...</span>\n </div>"
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n var JS_MIME_TYPE = 'application/javascript';\n var HTML_MIME_TYPE = 'text/html';\n var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n var CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n var script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n var cell = handle.cell;\n\n var id = cell.output_area._bokeh_element_id;\n var server_id = cell.output_area._bokeh_server_id;\n // Clean up Bokeh references\n if (id != null && id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd, {\n iopub: {\n output: function(msg) {\n var id = msg.content.text.trim();\n if (id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n }\n }\n });\n // Destroy server and session\n var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"<div style='background-color: #fdd'>\\n\"+\n \"<p>\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"1613\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };var element = document.getElementById(\"1613\");\n if (element == null) {\n console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1613' but no matching script tag was found. \")\n return false;\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"1613\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));",
"application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"<div style='background-color: #fdd'>\\n\"+\n \"<p>\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"1613\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };var element = document.getElementById(\"1613\");\n if (element == null) {\n console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1613' but no matching script tag was found. \")\n return false;\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"1613\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "#To my displeasure the make_dashboard was not already defined.\n#I had to look up how to use the fuction properly and sources say that you have to\n#Import pandas and bokeh libraries\n#Define the variables, give the dashboard a title, and file name directory.\nimport pandas as pd\nfrom bokeh.plotting import figure, output_file, show,output_notebook\noutput_notebook()\nfrom bokeh.io import output_file\nfrom bokeh.plotting import figure, show\nfrom bokeh.models import ColumnDataSource, CDSView, GroupFilter\n\n\n\n\n\n"
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [],
"source": "def make_dashboard(x, gdp_change, unemployment, title, file_name):\n output_file(file_name)\n p = figure(title=title, x_axis_label='year', y_axis_label='%')\n p.line(x.squeeze(), gdp_change.squeeze(), color=\"firebrick\", line_width=4, legend=\"% GDP change\")\n p.line(x.squeeze(), unemployment.squeeze(), line_width=4, legend=\"% unemployed\")\n show(p)"
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [],
"source": "links={'GDP':'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/projects/coursera_project/clean_gdp.csv',\\\n 'unemployment':'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/projects/coursera_project/clean_unemployment.csv'}\n\n"
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": "title = 'US GDP and Unemployment Rate'"
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": "time_date = df_data_1[\"date\"]"
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "0 1930\n1 1931\n2 1932\n3 1933\n4 1934\nName: date, dtype: int64"
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "time_date.head()"
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": "change_in_gdp = df_data_1['change-current']"
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "0 -16.0\n1 -23.1\n2 -4.0\n3 16.9\n4 11.1\nName: change-current, dtype: float64"
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "change_in_gdp.head()"
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": "unemployment_rate = df_data_2['UNRATE']"
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "0 3.4\n1 3.8\n2 4.0\n3 3.9\n4 3.5\nName: UNRATE, dtype: float64"
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "unemployment_rate.head()"
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": "file_name= \"US_GDP_UNEMPLOYMENT.html\"\n\n"
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [],
"source": "filename2='https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/projects/coursera_project/clean_gdp.csv'"
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>date</th>\n <th>level-current</th>\n <th>level-chained</th>\n <th>change-current</th>\n <th>change-chained</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1948</td>\n <td>274.8</td>\n <td>2020.0</td>\n <td>-0.7</td>\n <td>-0.6</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1949</td>\n <td>272.8</td>\n <td>2008.9</td>\n <td>10.0</td>\n <td>8.7</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1950</td>\n <td>300.2</td>\n <td>2184.0</td>\n <td>15.7</td>\n <td>8.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1951</td>\n <td>347.3</td>\n <td>2360.0</td>\n <td>5.9</td>\n <td>4.1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1952</td>\n <td>367.7</td>\n <td>2456.1</td>\n <td>6.0</td>\n <td>4.7</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " date level-current level-chained change-current change-chained\n0 1948 274.8 2020.0 -0.7 -0.6\n1 1949 272.8 2008.9 10.0 8.7\n2 1950 300.2 2184.0 15.7 8.0\n3 1951 347.3 2360.0 5.9 4.1\n4 1952 367.7 2456.1 6.0 4.7"
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "x=pd.read_csv(filename2)\n\nx.head()\n\n"
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "unexpected EOF while parsing (<ipython-input-92-7e07242229f6>, line 4)",
"output_type": "error",
"traceback": [
"\u001b[0;36m File \u001b[0;32m\"<ipython-input-92-7e07242229f6>\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m \u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unexpected EOF while parsing\n"
]
}
],
"source": "x[x['date']\n\n"
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "\"None of [Float64Index([-0.7, 10.0, 15.7, 5.9, 6.0, 0.3, 8.9, 5.6, 5.5, 1.5, 8.4,\\n 4.0, 3.7, 7.4, 5.6, 7.4, 8.4, 9.6, 5.7, 9.4, 8.2, 5.5,\\n 8.5, 9.8, 11.4, 8.4, 9.0, 11.2, 11.1, 13.0, 11.7, 8.8, 12.2,\\n 4.3, 8.7, 11.1, 7.5, 5.5, 6.0, 7.9, 7.7, 5.7, 3.3, 5.9,\\n 5.2, 6.3, 4.8, 5.7, 6.2, 5.7, 6.3, 6.5, 3.2, 3.4, 4.8,\\n 6.6, 6.7, 6.0, 4.6, 1.8, -1.8, 3.8, 3.7, 4.2, 3.6, 4.4,\\n 4.0, 2.7, 4.2],\\n dtype='float64')] are in the [columns]\"",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-94-14e2b4afa2e1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgdp_change\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'change-current'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/opt/conda/envs/Python36/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2932\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2933\u001b[0m indexer = self.loc._convert_to_indexer(key, axis=1,\n\u001b[0;32m-> 2934\u001b[0;31m raise_missing=True)\n\u001b[0m\u001b[1;32m 2935\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2936\u001b[0m \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/envs/Python36/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[0;34m(self, obj, axis, is_setter, raise_missing)\u001b[0m\n\u001b[1;32m 1352\u001b[0m kwargs = {'raise_missing': True if is_setter else\n\u001b[1;32m 1353\u001b[0m raise_missing}\n\u001b[0;32m-> 1354\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1355\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1356\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/envs/Python36/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[1;32m 1159\u001b[0m self._validate_read_indexer(keyarr, indexer,\n\u001b[1;32m 1160\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1161\u001b[0;31m raise_missing=raise_missing)\n\u001b[0m\u001b[1;32m 1162\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/envs/Python36/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[1;32m 1244\u001b[0m raise KeyError(\n\u001b[1;32m 1245\u001b[0m u\"None of [{key}] are in the [{axis}]\".format(\n\u001b[0;32m-> 1246\u001b[0;31m key=key, axis=self.obj._get_axis_name(axis)))\n\u001b[0m\u001b[1;32m 1247\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1248\u001b[0m \u001b[0;31m# We (temporarily) allow for some missing keys with .loc, except in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: \"None of [Float64Index([-0.7, 10.0, 15.7, 5.9, 6.0, 0.3, 8.9, 5.6, 5.5, 1.5, 8.4,\\n 4.0, 3.7, 7.4, 5.6, 7.4, 8.4, 9.6, 5.7, 9.4, 8.2, 5.5,\\n 8.5, 9.8, 11.4, 8.4, 9.0, 11.2, 11.1, 13.0, 11.7, 8.8, 12.2,\\n 4.3, 8.7, 11.1, 7.5, 5.5, 6.0, 7.9, 7.7, 5.7, 3.3, 5.9,\\n 5.2, 6.3, 4.8, 5.7, 6.2, 5.7, 6.3, 6.5, 3.2, 3.4, 4.8,\\n 6.6, 6.7, 6.0, 4.6, 1.8, -1.8, 3.8, 3.7, 4.2, 3.6, 4.4,\\n 4.0, 2.7, 4.2],\\n dtype='float64')] are in the [columns]\""
]
}
],
"source": "gdp_change = x[x['change-current']]"
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [],
"source": "filename2='https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/projects/coursera_project/clean_gdp.csv'\n\n"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": ""
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": "BokehUserWarning: ColumnDataSource's columns must be of the same length. Current lengths: ('x', 86), ('y', 851)\n"
},
{
"data": {
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"application/vnd.bokehjs_exec.v0+json": ""
},
"metadata": {
"application/vnd.bokehjs_exec.v0+json": {
"id": "1819"
}
},
"output_type": "display_data"
}
],
"source": "#Now all of the varibles are defined, time to make the Dashboard\nmake_dashboard(title=title, time_date=time_date, change_in_gdp=change_in_gdp, unemployment_rate=unemployment_rate, file_name =file_name)"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": ""
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": ""
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": ""
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": ""
}
],
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},
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}
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