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   "source": [
    "# MPG Cars"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "The following exercise utilizes data from [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Auto+MPG)\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
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   "source": [
    "### Step 2. Import the first dataset [cars1](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/05_Merge/Auto_MPG/cars1.csv) and [cars2](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/05_Merge/Auto_MPG/cars2.csv).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   ### Step 3. Assign each to a variable called cars1 and cars2"
   ]
  },
  {
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   "execution_count": null,
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Oops, it seems our first dataset has some unnamed blank columns, fix cars1"
   ]
  },
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   "execution_count": null,
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  {
   "cell_type": "markdown",
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   "source": [
    "### Step 5. What is the number of observations in each dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Join cars1 and cars2 into a single DataFrame called cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
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   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Oops, there is a column missing, called owners. Create a random number Series from 15,000 to 73,000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Add the column owners to cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
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