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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Getting Financial Data - Google Finance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Introduction:\n",
"\n",
"This time you will get data from a website.\n",
"\n",
"\n",
"### Step 1. Import the necessary libraries"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"# package to extract data from various Internet sources into a DataFrame\n",
"# make sure you have it installed\n",
"from pandas_datareader import data, wb\n",
"\n",
"# package for dates\n",
"import datetime as dt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 2. Create your time range (start and end variables). The start date should be 01/01/2015 and the end should today (whatever your today is)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"datetime.datetime(2015, 1, 1, 0, 0)"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 3. Select the Apple, Tesla, Twitter, IBM, LinkedIn stocks symbols and assign them to a variable called stocks"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['AAPL', 'TSLA', 'IBM', 'LNKD']"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 4. Read the data from google, assign to df and print it"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<class 'pandas.core.panel.Panel'>\n",
"Dimensions: 5 (items) x 399 (major_axis) x 4 (minor_axis)\n",
"Items axis: Open to Volume\n",
"Major_axis axis: 2015-01-02 00:00:00 to 2016-08-02 00:00:00\n",
"Minor_axis axis: AAPL to TSLA"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 5. What is the type of structure of df ?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# 'pandas.core.panel.Panel'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 6. Print all the Items axis values\n",
"#### To learn more about the Panel structure go to [documentation](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#panel) "
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index([u'Open', u'High', u'Low', u'Close', u'Volume'], dtype='object')"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 7. Good, now we know the data avaiable. Create a dataFrame called vol, with the Volume values."
]
},
{
"cell_type": "code",
"execution_count": 122,
"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>AAPL</th>\n",
" <th>IBM</th>\n",
" <th>LNKD</th>\n",
" <th>TSLA</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2015-01-02</th>\n",
" <td>53204626.0</td>\n",
" <td>5525466.0</td>\n",
" <td>1203743.0</td>\n",
" <td>4764443.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-01-05</th>\n",
" <td>64285491.0</td>\n",
" <td>4880389.0</td>\n",
" <td>1400562.0</td>\n",
" <td>5368477.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-01-06</th>\n",
" <td>65797116.0</td>\n",
" <td>6146712.0</td>\n",
" <td>2006546.0</td>\n",
" <td>6261936.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-01-07</th>\n",
" <td>40105934.0</td>\n",
" <td>4701839.0</td>\n",
" <td>985016.0</td>\n",
" <td>2968390.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-01-08</th>\n",
" <td>59364547.0</td>\n",
" <td>4241113.0</td>\n",
" <td>1293955.0</td>\n",
" <td>3442509.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AAPL IBM LNKD TSLA\n",
"Date \n",
"2015-01-02 53204626.0 5525466.0 1203743.0 4764443.0\n",
"2015-01-05 64285491.0 4880389.0 1400562.0 5368477.0\n",
"2015-01-06 65797116.0 6146712.0 2006546.0 6261936.0\n",
"2015-01-07 40105934.0 4701839.0 985016.0 2968390.0\n",
"2015-01-08 59364547.0 4241113.0 1293955.0 3442509.0"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 8. Aggregate the data of Volume to weekly\n",
"#### Hint: Be careful to not sum data from the same week of 2015 and other years."
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>AAPL</th>\n",
" <th>IBM</th>\n",
" <th>LNKD</th>\n",
" <th>TSLA</th>\n",
" </tr>\n",
" <tr>\n",
" <th>week</th>\n",
" <th>year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">1</th>\n",
" <th>2015</th>\n",
" <td>53204626.0</td>\n",
" <td>5525466.0</td>\n",
" <td>1203743.0</td>\n",
" <td>4764443.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016</th>\n",
" <td>343422014.0</td>\n",
" <td>25233098.0</td>\n",
" <td>6630485.0</td>\n",
" <td>20967926.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">2</th>\n",
" <th>2015</th>\n",
" <td>283252615.0</td>\n",
" <td>24458400.0</td>\n",
" <td>7203125.0</td>\n",
" <td>22709607.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016</th>\n",
" <td>302072797.0</td>\n",
" <td>29379214.0</td>\n",
" <td>9160521.0</td>\n",
" <td>22997290.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <th>2015</th>\n",
" <td>304226647.0</td>\n",
" <td>23263206.0</td>\n",
" <td>7084168.0</td>\n",
" <td>30799137.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AAPL IBM LNKD TSLA\n",
"week year \n",
"1 2015 53204626.0 5525466.0 1203743.0 4764443.0\n",
" 2016 343422014.0 25233098.0 6630485.0 20967926.0\n",
"2 2015 283252615.0 24458400.0 7203125.0 22709607.0\n",
" 2016 302072797.0 29379214.0 9160521.0 22997290.0\n",
"3 2015 304226647.0 23263206.0 7084168.0 30799137.0"
]
},
"execution_count": 132,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 9. Find all the volume traded in the year of 2015"
]
},
{
"cell_type": "code",
"execution_count": 131,
"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>AAPL</th>\n",
" <th>IBM</th>\n",
" <th>LNKD</th>\n",
" <th>TSLA</th>\n",
" </tr>\n",
" <tr>\n",
" <th>year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2015</th>\n",
" <td>1.301994e+10</td>\n",
" <td>1.100959e+09</td>\n",
" <td>440376163.0</td>\n",
" <td>1.085839e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016</th>\n",
" <td>6.081474e+09</td>\n",
" <td>6.585250e+08</td>\n",
" <td>453233878.0</td>\n",
" <td>7.540962e+08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AAPL IBM LNKD TSLA\n",
"year \n",
"2015 1.301994e+10 1.100959e+09 440376163.0 1.085839e+09\n",
"2016 6.081474e+09 6.585250e+08 453233878.0 7.540962e+08"
]
},
"execution_count": 131,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### BONUS: Create your own question and answer it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
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"display_name": "Python 2",
"language": "python",
"name": "python2"
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