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datageneralist/Trade_Deficit_Notebook.ipynb

Created March 11, 2016 18:45
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Trade Deficit as a % of GDP
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Trade_Deficit_Notebook.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploring the Relationship between U.S Trade Data and GDP\n",
"\n",
"## Why should we do this?\n",
"\n",
"- To combine data from multiple agencies and present it to the public in a more readable format\n",
"- To learn relationships between different high level economic data\n",
"- Exploring economic data is important for policy implications, exploring new markets, expanding business opportunities, etc."
]
},
{
"cell_type": "code",
"execution_count": 273,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.rcParams['figure.figsize'] = (8, 6)\n",
"plt.rcParams['font.size'] = 14"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exports Data Source from the U.S Census Bureau\n",
"\n",
"### Steps to clean it up\n",
"\n",
"- Take out the first few rows that describe the data\n",
"- Delete the country column since it is always the World Total\n",
"- Only keep the quarterly data, delete all of the other rows since GDP is only given quarterly\n",
"- Only want data from 2002-2016 to keep everything consistent. Delete all other rows"
]
},
{
"cell_type": "code",
"execution_count": 274,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"exports = pd.read_csv('Trade Deficit Exports.csv', skiprows = [0, 1])"
]
},
{
"cell_type": "code",
"execution_count": 276,
"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>Time</th>\n",
" <th>Country</th>\n",
" <th>Value ($US)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1992</td>\n",
" <td>World Total</td>\n",
" <td>448,163,612,021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Jan-92</td>\n",
" <td>World Total</td>\n",
" <td>448,163,612,021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1993</td>\n",
" <td>World Total</td>\n",
" <td>465,090,972,324</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Jan-93</td>\n",
" <td>World Total</td>\n",
" <td>465,090,972,324</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1994</td>\n",
" <td>World Total</td>\n",
" <td>512,626,476,328</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Time Country Value ($US)\n",
"0 1992 World Total 448,163,612,021\n",
"1 Jan-92 World Total 448,163,612,021\n",
"2 1993 World Total 465,090,972,324\n",
"3 Jan-93 World Total 465,090,972,324\n",
"4 1994 World Total 512,626,476,328"
]
},
"execution_count": 276,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"exports.head()"
]
},
{
"cell_type": "code",
"execution_count": 277,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"del exports['Country']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a new dataframe that contains only the exports quarterly data from Jan 2002 - Jan 2016"
]
},
{
"cell_type": "code",
"execution_count": 278,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#filter out quarterly data\n",
"monthly_exports = exports[exports.Time.str.contains('Jan|Apr|Jul|Oct')]\n",
"#Filter out the Jan year to date row\n",
"monthly_exports = monthly_exports[monthly_exports.Time.str.contains('-')]\n"
]
},
{
"cell_type": "code",
"execution_count": 280,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#70 total rows. FIrst 13 are not used. Get last 57. Only want 2002-2015 monthly data\n",
"monthly_exports = monthly_exports.tail(57)"
]
},
{
"cell_type": "code",
"execution_count": 283,
"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>Time</th>\n",
" <th>Value ($US)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>Jan-02</td>\n",
" <td>52,667,442,467</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>Apr-02</td>\n",
" <td>58,146,097,985</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>Jul-02</td>\n",
" <td>55,032,210,651</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>Oct-02</td>\n",
" <td>61,975,402,372</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>Jan-03</td>\n",
" <td>54,854,146,362</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Time Value ($US)\n",
"32 Jan-02 52,667,442,467\n",
"35 Apr-02 58,146,097,985\n",
"38 Jul-02 55,032,210,651\n",
"41 Oct-02 61,975,402,372\n",
"45 Jan-03 54,854,146,362"
]
},
"execution_count": 283,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"monthly_exports.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Repeat for the imports data"
]
},
{
"cell_type": "code",
"execution_count": 284,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"imports = pd.read_csv('Trade Deficit Imports.csv', skiprows = [0, 1, 2])\n",
"#Need to rename value all columns to match\n",
"imports.columns = ['Time', 'Im_Value']\n",
"exports.columns = ['Time2', 'Ex_Value']\n",
"monthly_exports.columns = ['Time2', 'Ex_Value']"
]
},
{
"cell_type": "code",
"execution_count": 285,
"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>Time</th>\n",
" <th>Im_Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2002</td>\n",
" <td>1,161,365,969,084</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Jan-02</td>\n",
" <td>85,111,378,458</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Feb-02</td>\n",
" <td>83,472,741,685</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Mar-02</td>\n",
" <td>91,414,901,062</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Apr-02</td>\n",
" <td>96,890,754,064</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Time Im_Value\n",
"0 2002 1,161,365,969,084\n",
"1 Jan-02 85,111,378,458\n",
"2 Feb-02 83,472,741,685\n",
"3 Mar-02 91,414,901,062\n",
"4 Apr-02 96,890,754,064"
]
},
"execution_count": 285,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"imports.head()"
]
},
{
"cell_type": "code",
"execution_count": 286,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"monthly_imports = imports[imports.Time.str.contains('Jan|Apr|Jul|Oct')]\n",
"monthly_imports = monthly_imports[monthly_imports.Time.str.contains('-')]"
]
},
{
"cell_type": "code",
"execution_count": 287,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"monthly_imports = monthly_imports.tail(57)"
]
},
{
"cell_type": "code",
"execution_count": 289,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"monthly_imports.index = monthly_imports['Time']\n",
"monthly_exports.index = monthly_exports['Time2']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Before we can concatenate the monthly imports and exports data, lets check the shape"
]
},
{
"cell_type": "code",
"execution_count": 290,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 290,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Need to be same shape to concatenate\n",
"monthly_imports.shape == monthly_exports.shape"
]
},
{
"cell_type": "code",
"execution_count": 291,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#COncatenate exports and imports based on the index of the date. Then delete the extra time column\n",
"quarter_trade = pd.concat([monthly_imports, monthly_exports], axis = 1)\n",
"del quarter_trade['Time2']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### I concatenated the quarterly imports and exports data into a new data frame called, quaterly trade"
]
},
{
"cell_type": "code",
"execution_count": 292,
"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>Time</th>\n",
" <th>Im_Value</th>\n",
" <th>Ex_Value</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Time</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Jan-02</th>\n",
" <td>Jan-02</td>\n",
" <td>85,111,378,458</td>\n",
" <td>52,667,442,467</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Apr-02</th>\n",
" <td>Apr-02</td>\n",
" <td>96,890,754,064</td>\n",
" <td>58,146,097,985</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jul-02</th>\n",
" <td>Jul-02</td>\n",
" <td>100,472,218,696</td>\n",
" <td>55,032,210,651</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oct-02</th>\n",
" <td>Oct-02</td>\n",
" <td>106,251,232,547</td>\n",
" <td>61,975,402,372</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jan-03</th>\n",
" <td>Jan-03</td>\n",
" <td>97,490,981,313</td>\n",
" <td>54,854,146,362</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Time Im_Value Ex_Value\n",
"Time \n",
"Jan-02 Jan-02 85,111,378,458 52,667,442,467\n",
"Apr-02 Apr-02 96,890,754,064 58,146,097,985\n",
"Jul-02 Jul-02 100,472,218,696 55,032,210,651\n",
"Oct-02 Oct-02 106,251,232,547 61,975,402,372\n",
"Jan-03 Jan-03 97,490,981,313 54,854,146,362"
]
},
"execution_count": 292,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"quarter_trade.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Trade Balance, or deficit since 1992 I believe, is Exports - Imports"
]
},
{
"cell_type": "code",
"execution_count": 293,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Create the trade balance/deficit column\n",
"#CHange the columns to integers. THey are currently strings\n",
"quarter_trade['Trade_Deficit'] = quarter_trade['Ex_Value'].str.replace(',', '').astype(float) - quarter_trade['Im_Value'].str.replace(',', '').astype(float)\n"
]
},
{
"cell_type": "code",
"execution_count": 294,
"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>Time</th>\n",
" <th>Im_Value</th>\n",
" <th>Ex_Value</th>\n",
" <th>Trade_Deficit</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Time</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Jan-02</th>\n",
" <td>Jan-02</td>\n",
" <td>85,111,378,458</td>\n",
" <td>52,667,442,467</td>\n",
" <td>-32443935991</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Apr-02</th>\n",
" <td>Apr-02</td>\n",
" <td>96,890,754,064</td>\n",
" <td>58,146,097,985</td>\n",
" <td>-38744656079</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jul-02</th>\n",
" <td>Jul-02</td>\n",
" <td>100,472,218,696</td>\n",
" <td>55,032,210,651</td>\n",
" <td>-45440008045</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oct-02</th>\n",
" <td>Oct-02</td>\n",
" <td>106,251,232,547</td>\n",
" <td>61,975,402,372</td>\n",
" <td>-44275830175</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jan-03</th>\n",
" <td>Jan-03</td>\n",
" <td>97,490,981,313</td>\n",
" <td>54,854,146,362</td>\n",
" <td>-42636834951</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Time Im_Value Ex_Value Trade_Deficit\n",
"Time \n",
"Jan-02 Jan-02 85,111,378,458 52,667,442,467 -32443935991\n",
"Apr-02 Apr-02 96,890,754,064 58,146,097,985 -38744656079\n",
"Jul-02 Jul-02 100,472,218,696 55,032,210,651 -45440008045\n",
"Oct-02 Oct-02 106,251,232,547 61,975,402,372 -44275830175\n",
"Jan-03 Jan-03 97,490,981,313 54,854,146,362 -42636834951"
]
},
"execution_count": 294,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"quarter_trade.head()"
]
},
{
"cell_type": "code",
"execution_count": 295,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Convert trade deficit to in terms of billions\n",
"quarter_trade['Trade_Deficit'] = (quarter_trade['Trade_Deficit'] / 1000000000).round(0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Read in the Quarterly GDP data from the Bureau of Economic Analysis\n",
"\n",
"### Clean it up by:\n",
"\n",
"- SKipping the first 18 rows that have details on the data\n",
"- Renaming the columns\n",
"- CHanging the date format to match the trade data file"
]
},
{
"cell_type": "code",
"execution_count": 296,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Read in GDP and skip all the junk at the top\n",
"GDP = pd.read_excel('GDP.xls', skiprows = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18])"
]
},
{
"cell_type": "code",
"execution_count": 297,
"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>DATE</th>\n",
" <th>VALUE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2002-01-01</td>\n",
" <td>10834.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2002-04-01</td>\n",
" <td>10934.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2002-07-01</td>\n",
" <td>11037.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2002-10-01</td>\n",
" <td>11103.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2003-01-01</td>\n",
" <td>11230.1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DATE VALUE\n",
"0 2002-01-01 10834.4\n",
"1 2002-04-01 10934.8\n",
"2 2002-07-01 11037.1\n",
"3 2002-10-01 11103.8\n",
"4 2003-01-01 11230.1"
]
},
"execution_count": 297,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"GDP.head()"
]
},
{
"cell_type": "code",
"execution_count": 298,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Rename Columns. Remember GDP is in billions\n",
"GDP.columns = ['Time', 'GDP']"
]
},
{
"cell_type": "code",
"execution_count": 299,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Change date format to the quarter trade dataframe style\n",
"Test = GDP['Time'].astype(str)\n",
"\n",
"Test2 = Test.str.slice(5, 8) + Test.str.slice(2,4)\n",
"\n",
"Test3 = Test2.str.replace('01-', 'Jan-').str.replace('04-', 'Apr-').str.replace('07-', 'Jul-').str.replace('10-', 'Oct-')\n",
"\n",
"GDP['Time'] = Test3"
]
},
{
"cell_type": "code",
"execution_count": 300,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Make the Time COlumn the Index. Then add these columns to the other data set\n",
"GDP.index = GDP['Time']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Add in the second estimate for the Jan 2016 GDP estimate since the final number has yet to be released"
]
},
{
"cell_type": "code",
"execution_count": 301,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#GDP only goes until October 2015. 146.5. Add the estimate for the Jan-15 value\n",
"#res = DataFrame(columns=('lib', 'qty1', 'qty2'))\n",
"New_Data = pd.DataFrame(columns=('Time', 'GDP'))\n",
"New_Data.index = New_Data['Time']"
]
},
{
"cell_type": "code",
"execution_count": 302,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Add new values to the new data frame\n",
"New_Data = New_Data.set_value('Jan-16', 'Time', 'Jan-16')\n",
"New_Data = New_Data.set_value('Jan-16', 'GDP', 18294.9)"
]
},
{
"cell_type": "code",
"execution_count": 303,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#COncatenate the two dataframes by axis 0\n",
"GDP = pd.concat([GDP, New_Data], axis = 0)"
]
},
{
"cell_type": "code",
"execution_count": 304,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 304,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Check shape of GDP and Quarterly Trade Data. Only need same number of rows since we are cocnatenating the columns\n",
"GDP.shape[0] == quarter_trade.shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 305,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"GDP.columns = ['Time2', 'GDP']"
]
},
{
"cell_type": "code",
"execution_count": 306,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"quarter_data = pd.concat([quarter_trade, GDP], axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 307,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"del quarter_data['Time2']"
]
},
{
"cell_type": "code",
"execution_count": 308,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#CHange GDP to float\n",
"quarter_data['GDP'] = quarter_data['GDP'].astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 312,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"quarter_data['Deficit_%_Of_GDP'] = (quarter_data.Trade_Deficit / quarter_data.GDP * -1000).round(2)"
]
},
{
"cell_type": "code",
"execution_count": 313,
"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>Time</th>\n",
" <th>Im_Value</th>\n",
" <th>Ex_Value</th>\n",
" <th>Trade_Deficit</th>\n",
" <th>GDP</th>\n",
" <th>Deficit_%_Of_GDP</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Time</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Jan-02</th>\n",
" <td>Jan-02</td>\n",
" <td>85,111,378,458</td>\n",
" <td>52,667,442,467</td>\n",
" <td>-32</td>\n",
" <td>10834.4</td>\n",
" <td>2.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Apr-02</th>\n",
" <td>Apr-02</td>\n",
" <td>96,890,754,064</td>\n",
" <td>58,146,097,985</td>\n",
" <td>-39</td>\n",
" <td>10934.8</td>\n",
" <td>3.57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jul-02</th>\n",
" <td>Jul-02</td>\n",
" <td>100,472,218,696</td>\n",
" <td>55,032,210,651</td>\n",
" <td>-45</td>\n",
" <td>11037.1</td>\n",
" <td>4.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oct-02</th>\n",
" <td>Oct-02</td>\n",
" <td>106,251,232,547</td>\n",
" <td>61,975,402,372</td>\n",
" <td>-44</td>\n",
" <td>11103.8</td>\n",
" <td>3.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jan-03</th>\n",
" <td>Jan-03</td>\n",
" <td>97,490,981,313</td>\n",
" <td>54,854,146,362</td>\n",
" <td>-43</td>\n",
" <td>11230.1</td>\n",
" <td>3.83</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Time Im_Value Ex_Value Trade_Deficit GDP \\\n",
"Time \n",
"Jan-02 Jan-02 85,111,378,458 52,667,442,467 -32 10834.4 \n",
"Apr-02 Apr-02 96,890,754,064 58,146,097,985 -39 10934.8 \n",
"Jul-02 Jul-02 100,472,218,696 55,032,210,651 -45 11037.1 \n",
"Oct-02 Oct-02 106,251,232,547 61,975,402,372 -44 11103.8 \n",
"Jan-03 Jan-03 97,490,981,313 54,854,146,362 -43 11230.1 \n",
"\n",
" Deficit_%_Of_GDP \n",
"Time \n",
"Jan-02 2.95 \n",
"Apr-02 3.57 \n",
"Jul-02 4.08 \n",
"Oct-02 3.96 \n",
"Jan-03 3.83 "
]
},
"execution_count": 313,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"quarter_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualize the Data"
]
},
{
"cell_type": "code",
"execution_count": 314,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x27d33860940>"
]
},
"execution_count": 314,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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RWe+dnxV3gj4i0gbYDXihQJM3gN+ISKtAnP5oYJ5zruD4QaE3qodRo+Cyy5L3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\npckBB6irPS38inj5+PKXdWniypXpjBX2rRgE5HM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DBvjWtzw+b1ZP4YE+G3N/FPg98E/A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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x27d33cc0d68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"quarter_data['Deficit_%_Of_GDP'].plot(title = 'U.S Trade Deficit as a % of GDP')\n",
"plt.xlabel('Time')\n",
"plt.ylabel('Deficit as a % of GDP')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Future Path\n",
"\n",
"### The next step I will take is to determine if there is any relationship between the defecit as a percent of GDP and the unemployment rate. \n",
"\n",
"### Here is a snapshot of the next few steps."
]
},
{
"cell_type": "code",
"execution_count": 246,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"unemployment = pd.read_excel('Unemployment.xlsx', skiprows = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Original Unemployment Data from the Bureau of Labor Statistics"
]
},
{
"cell_type": "code",
"execution_count": 247,
"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>Year</th>\n",
" <th>Jan</th>\n",
" <th>Feb</th>\n",
" <th>Mar</th>\n",
" <th>Apr</th>\n",
" <th>May</th>\n",
" <th>Jun</th>\n",
" <th>Jul</th>\n",
" <th>Aug</th>\n",
" <th>Sep</th>\n",
" <th>Oct</th>\n",
" <th>Nov</th>\n",
" <th>Dec</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2002</td>\n",
" <td>5.7</td>\n",
" <td>5.7</td>\n",
" <td>5.7</td>\n",
" <td>5.9</td>\n",
" <td>5.8</td>\n",
" <td>5.8</td>\n",
" <td>5.8</td>\n",
" <td>5.7</td>\n",
" <td>5.7</td>\n",
" <td>5.7</td>\n",
" <td>5.9</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2003</td>\n",
" <td>5.8</td>\n",
" <td>5.9</td>\n",
" <td>5.9</td>\n",
" <td>6.0</td>\n",
" <td>6.1</td>\n",
" <td>6.3</td>\n",
" <td>6.2</td>\n",
" <td>6.1</td>\n",
" <td>6.1</td>\n",
" <td>6.0</td>\n",
" <td>5.8</td>\n",
" <td>5.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2004</td>\n",
" <td>5.7</td>\n",
" <td>5.6</td>\n",
" <td>5.8</td>\n",
" <td>5.6</td>\n",
" <td>5.6</td>\n",
" <td>5.6</td>\n",
" <td>5.5</td>\n",
" <td>5.4</td>\n",
" <td>5.4</td>\n",
" <td>5.5</td>\n",
" <td>5.4</td>\n",
" <td>5.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2005</td>\n",
" <td>5.3</td>\n",
" <td>5.4</td>\n",
" <td>5.2</td>\n",
" <td>5.2</td>\n",
" <td>5.1</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>4.9</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>4.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2006</td>\n",
" <td>4.7</td>\n",
" <td>4.8</td>\n",
" <td>4.7</td>\n",
" <td>4.7</td>\n",
" <td>4.6</td>\n",
" <td>4.6</td>\n",
" <td>4.7</td>\n",
" <td>4.7</td>\n",
" <td>4.5</td>\n",
" <td>4.4</td>\n",
" <td>4.5</td>\n",
" <td>4.4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec\n",
"0 2002 5.7 5.7 5.7 5.9 5.8 5.8 5.8 5.7 5.7 5.7 5.9 6.0\n",
"1 2003 5.8 5.9 5.9 6.0 6.1 6.3 6.2 6.1 6.1 6.0 5.8 5.7\n",
"2 2004 5.7 5.6 5.8 5.6 5.6 5.6 5.5 5.4 5.4 5.5 5.4 5.4\n",
"3 2005 5.3 5.4 5.2 5.2 5.1 5.0 5.0 4.9 5.0 5.0 5.0 4.9\n",
"4 2006 4.7 4.8 4.7 4.7 4.6 4.6 4.7 4.7 4.5 4.4 4.5 4.4"
]
},
"execution_count": 247,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unemployment.head()"
]
},
{
"cell_type": "code",
"execution_count": 252,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Delete all nonquarterly data\n",
"del unemployment['Feb']\n",
"del unemployment['Mar']\n",
"del unemployment['May']\n",
"del unemployment['Jun']\n",
"del unemployment['Aug']\n",
"del unemployment['Sep']\n",
"del unemployment['Nov']\n",
"del unemployment['Dec']\n"
]
},
{
"cell_type": "code",
"execution_count": 253,
"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>Year</th>\n",
" <th>Jan</th>\n",
" <th>Apr</th>\n",
" <th>Jul</th>\n",
" <th>Oct</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2002</td>\n",
" <td>5.7</td>\n",
" <td>5.9</td>\n",
" <td>5.8</td>\n",
" <td>5.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2003</td>\n",
" <td>5.8</td>\n",
" <td>6.0</td>\n",
" <td>6.2</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2004</td>\n",
" <td>5.7</td>\n",
" <td>5.6</td>\n",
" <td>5.5</td>\n",
" <td>5.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2005</td>\n",
" <td>5.3</td>\n",
" <td>5.2</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2006</td>\n",
" <td>4.7</td>\n",
" <td>4.7</td>\n",
" <td>4.7</td>\n",
" <td>4.4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Year Jan Apr Jul Oct\n",
"0 2002 5.7 5.9 5.8 5.7\n",
"1 2003 5.8 6.0 6.2 6.0\n",
"2 2004 5.7 5.6 5.5 5.5\n",
"3 2005 5.3 5.2 5.0 5.0\n",
"4 2006 4.7 4.7 4.7 4.4"
]
},
"execution_count": 253,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Current status of the unemployment data\n",
"unemployment.head()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
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"nbformat_minor": 0
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