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AakashVasudevan/Python for Data Science.ipynb

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Python for Data Science.ipynb
{
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"cell_type": "markdown",
"metadata": {},
"source": "<center>\n <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\" />\n</center>\n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "<h1>Analyzing US Economic Data and Building a Dashboard </h1>\n<h2>Description</h2>\n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Extracting essential data from a dataset and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data. In this assignment, you will extract some essential economic indicators from some data, you will then display these economic indicators in a Dashboard. You can then share the dashboard via an URL.\n\n<p>\n<a href=\"https://en.wikipedia.org/wiki/Gross_domestic_product\"> Gross domestic product (GDP)</a> is a measure of the market value of all the final goods and services produced in a period. GDP is an indicator of how well the economy is doing. A drop in GDP indicates the economy is producing less; similarly an increase in GDP suggests the economy is performing better. In this lab, you will examine how changes in GDP impact the unemployment rate. You will take screen shots of every step, you will share the notebook and the URL pointing to the dashboard.</p>\n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "<h2>Table of Contents</h2>\n<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n <ul>\n <li><a href=\"#Section_1\"> Define a Function that Makes a Dashboard </a></li>\n <li><a href=\"#Section_2\">Question 1: Create a dataframe that contains the GDP data and display it</a> </li>\n <li><a href=\"#Section_3\">Question 2: Create a dataframe that contains the unemployment data and display it</a></li>\n <li><a href=\"#Section_4\">Question 3: Display a dataframe where unemployment was greater than 8.5%</a></li>\n <li><a href=\"#Section_5\">Question 4: Use the function make_dashboard to make a dashboard</a></li>\n <li><a href=\"#Section_6\"><b>(Optional not marked)</b> Save the dashboard on IBM cloud and display it</a></li>\n </ul>\n<p>\n Estimated Time Needed: <strong>180 min</strong></p>\n</div>\n\n<hr>\n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "<h2 id=\"Section_1\"> Define Function that Makes a Dashboard </h2>\n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "We will import the following libraries.\n"
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
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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(\"1001\");\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) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"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.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\": \"kLr4fYcqcSpbuI95brIH3vnnYCquzzSxHPU6XGQCIkQRGJwhg0StNbj1eegrHs12\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\": \"xIGPmVtaOm+z0BqfSOMn4lOR6ciex448GIKG4eE61LsAvmGj48XcMQZtKcE/UXZe\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\": \"Dc9u1wF/0zApGIWoBbH77iWEHtdmkuYWG839Uzmv8y8yBLXebjO9ZnERsde5Ln/P\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\": \"cT9JaBz7GiRXdENrJLZNSC6eMNF3nh3fa5fTF51Svp+ukxPdwcU5kGXGPBgDCa2j\"};\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n if (url in hashes) {\n element.crossOrigin = \"anonymous\";\n element.integrity = \"sha384-\" + hashes[url];\n }\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n \n var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\"];\n var css_urls = [];\n \n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n function(Bokeh) {\n \n \n }\n ];\n\n function run_inline_js() {\n \n if (root.Bokeh !== undefined || force === true) {\n \n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\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(\"1001\")).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.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "import pandas as pd\nfrom bokeh.plotting import figure, output_file, show,output_notebook\noutput_notebook()"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "In this section, we define the function <code>make_dashboard</code>. \nYou don't have to know how the function works, you should only care about the inputs. The function will produce a dashboard as well as an html file. You can then use this html file to share your dashboard. If you do not know what an html file is don't worry everything you need to know will be provided in the lab. \n"
},
{
"cell_type": "code",
"execution_count": 33,
"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": "markdown",
"metadata": {},
"source": "The dictionary <code>links</code> contain the CSV files with all the data. The value for the key <code>GDP</code> is the file that contains the GDP data. The value for the key <code>unemployment</code> contains the unemployment data.\n"
},
{
"cell_type": "code",
"execution_count": 2,
"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'}"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "<h3 id=\"Section_2\"> Question 1: Create a dataframe that contains the GDP data and display the first five rows of the dataframe.</h3>\n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Use the dictionary <code>links</code> and the function <code>pd.read_csv</code> to create a Pandas dataframes that contains the GDP data.\n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "<b>Hint: <code>links[\"GDP\"]</code> contains the path or name of the file.</b>\n"
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": "# Type your code here\nimport pandas as pd\ngdp_data = pd.read_csv(links['GDP'])"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Use the method <code>head()</code> to display the first five rows of the GDP data, then take a screen-shot.\n"
},
{
"cell_type": "code",
"execution_count": 4,
"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>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": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "# Type your code here\ngdp_data.head()"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "<h3 id=\"Section_2\"> Question 2: Create a dataframe that contains the unemployment data. Display the first five rows of the dataframe. </h3>\n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Use the dictionary <code>links</code> and the function <code>pd.read_csv</code> to create a Pandas dataframes that contains the unemployment data.\n"
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": "# Type your code here\nunemployment_data = pd.read_csv(links['unemployment'])"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Use the method <code>head()</code> to display the first five rows of the unemployment data, then take a screen-shot.\n"
},
{
"cell_type": "code",
"execution_count": 6,
"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>unemployment</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1948</td>\n <td>3.750000</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1949</td>\n <td>6.050000</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1950</td>\n <td>5.208333</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1951</td>\n <td>3.283333</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1952</td>\n <td>3.025000</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " date unemployment\n0 1948 3.750000\n1 1949 6.050000\n2 1950 5.208333\n3 1951 3.283333\n4 1952 3.025000"
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "# Type your code here\nunemployment_data.head()"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "<h3 id=\"Section_3\">Question 3: Display a dataframe where unemployment was greater than 8.5%. Take a screen-shot.</h3>\n"
},
{
"cell_type": "code",
"execution_count": 26,
"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>unemployment</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>34</th>\n <td>1982</td>\n <td>9.708333</td>\n </tr>\n <tr>\n <th>35</th>\n <td>1983</td>\n <td>9.600000</td>\n </tr>\n <tr>\n <th>61</th>\n <td>2009</td>\n <td>9.283333</td>\n </tr>\n <tr>\n <th>62</th>\n <td>2010</td>\n <td>9.608333</td>\n </tr>\n <tr>\n <th>63</th>\n <td>2011</td>\n <td>8.933333</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " date unemployment\n34 1982 9.708333\n35 1983 9.600000\n61 2009 9.283333\n62 2010 9.608333\n63 2011 8.933333"
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "# Type your code here\n\nunemployment_data.query('unemployment > 8.5')\n \n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "<h3 id=\"Section_4\">Question 4: Use the function make_dashboard to make a dashboard</h3>\n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "In this section, you will call the function <code>make_dashboard</code> , to produce a dashboard. We will use the convention of giving each variable the same name as the function parameter.\n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Create a new dataframe with the column <code>'date'</code> called <code>x</code> from the dataframe that contains the GDP data.\n"
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": "x = gdp_data['date']"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Create a new dataframe with the column <code>'change-current' </code> called <code>gdp_change</code> from the dataframe that contains the GDP data.\n"
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": "gdp_change = gdp_data['change-current']"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Create a new dataframe with the column <code>'unemployment' </code> called <code>unemployment</code> from the dataframe that contains the unemployment data.\n"
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": "unemployment = unemployment_data['unemployment']"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Give your dashboard a string title, and assign it to the variable <code>title</code>\n"
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": "title = \"GDP vs Unemployment\""
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Finally, the function <code>make_dashboard</code> will output an <code>.html</code> in your direictory, just like a <code>csv</code> file. The name of the file is <code>\"index.html\"</code> and it will be stored in the varable <code>file_name</code>.\n"
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": "file_name = \"index.html\""
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Call the function <code>make_dashboard</code> , to produce a dashboard. Assign the parameter values accordingly take a the <b>, take a screen shot of the dashboard and submit it</b>.\n"
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": "BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\nBokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\n"
},
{
"data": {
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"metadata": {},
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},
{
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"output_type": "display_data"
}
],
"source": "# Fill up the parameters in the following function:\nmake_dashboard(x=x, gdp_change=gdp_change, unemployment=unemployment, title=title, file_name=file_name)"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "<h2 id=\"Section_5\"> How to submit </h2>\n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "<p>Once you complete your notebook you will have to share it to be marked. Select the icon on the top right a marked in red in the image below, a dialogue box should open, select the option all&nbsp;content excluding sensitive code cells.</p>\n\n<p><img height=\"440\" width=\"700\" src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/projects/EdX/ReadMe%20files/share_noteook1.png\" alt=\"share notebook\" /></p>\n<p></p>\n\n<p>You can then share the notebook&nbsp; via a&nbsp; URL by scrolling down as shown in the following image:</p>\n<p style=\"text-align: center;\"> <img height=\"308\" width=\"350\" src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/projects/EdX/ReadMe%20files/link2.png\" alt=\"share notebook\" /> </p>\n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "<hr>\n<p>Copyright &copy; 2019 IBM Developer Skills Network. This notebook and its source code are released under the terms of the <a href=\"https://cognitiveclass.ai/mit-license/\">MIT License</a>.</p>\n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "<h2>About the Authors:</h2> \n\n<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n\n<p>\nOther contributors: <a href=\"https://www.linkedin.com/in/yi-leng-yao-84451275/\">Yi leng Yao</a>, <a href=\"www.linkedin.com/in/jiahui-mavis-zhou-a4537814a\">Mavis Zhou</a> \n</p>\n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "<h2>References :</h2> \n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "<ul>\n <il>\n 1) <a href=\"https://research.stlouisfed.org/\">Economic Research at the St. Louis Fed </a>:<a href=\"https://fred.stlouisfed.org/series/UNRATE/\"> Civilian Unemployment Rate</a>\n </il> \n <p>\n <il>\n 2) <a href=\"https://github.com/datasets\">Data Packaged Core Datasets\n </a>\n </il> \n </p>\n \n</ul>\n</div>\n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "## Change Log\n\n| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n| ----------------- | ------- | ------------- | ------------------------- |\n| 2020-11-10 | 1.1 | Malika Singla | Deleted the Optional part |\n| 2020-08-27 | 1.0 | Malika Singla | Added lab to GitLab |\n\n<hr>\n\n## <h3 align=\"center\"> \u00a9 IBM Corporation 2020. All rights reserved. <h3/>\n\n<p>\n"
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.7",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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