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   "source": [
    "# Housing Market"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This time we will create our own dataset with fictional numbers to describe a house market. As we are going to create random data don't try to reason of the numbers.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
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    "### Step 2. Create 3 differents Series, each of length 100, as follows: \n",
    "1. The first a random number from 1 to 4 \n",
    "2. The second a random number from 1 to 3\n",
    "3. The third a random number from 10,000 to 30,000"
   ]
  },
  {
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   "execution_count": null,
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   "source": [
    "### Step 3. Let's create a DataFrame by joinning the Series by column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "cell_type": "markdown",
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   "source": [
    "### Step 4. Change the name of the columns to bedrs, bathrs, price_sqr_meter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Create a one column DataFrame with the values of the 3 Series and assign it to 'bigcolumn'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Ops it seems it is going only until index 99. Is it true?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Reindex the DataFrame so it goes from 0 to 299"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
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