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    "# Getting Financial Data - Google Finance"
   ]
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   "source": [
    "### Introduction:\n",
    "\n",
    "This time you will get data from a website.\n",
    "\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
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    "### 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)"
   ]
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   "execution_count": null,
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    "### Step 3. Select the Apple, Tesla, Twitter, IBM, LinkedIn stocks symbols and assign them to a variable called stocks"
   ]
  },
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   "execution_count": null,
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   "source": [
    "### Step 4. Read the data from google, assign to df and print it"
   ]
  },
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   "source": [
    "### Step 5.  What is the type of structure of df ?"
   ]
  },
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    "### 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) "
   ]
  },
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   "source": [
    "### Step 7. Good, now we know  the data avaiable. Create a dataFrame called vol, with the Volume values."
   ]
  },
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   "execution_count": null,
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   "cell_type": "markdown",
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   "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."
   ]
  },
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   "execution_count": null,
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    "collapsed": false,
    "scrolled": true
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   "source": [
    "### Step 9. Find all the volume traded in the year of 2015"
   ]
  },
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   "execution_count": null,
   "metadata": {
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
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   "execution_count": null,
   "metadata": {
    "collapsed": true
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