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    "# Wine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This exercise is a adaptation from the UCI Wine dataset.\n",
    "The only pupose is to practice deleting data with pandas.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
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   "source": [
    "### Step 2. Import the dataset from this [address](https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Delete the first, fourth, seventh, nineth, eleventh, thirteenth and fourteenth columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Assign the columns as below:\n",
    "\n",
    "The attributes are (dontated by Riccardo Leardi, riclea '@' anchem.unige.it):  \n",
    "1) alcohol  \n",
    "2) malic_acid  \n",
    "3) alcalinity_of_ash  \n",
    "4) magnesium  \n",
    "5) flavanoids  \n",
    "6) proanthocyanins  \n",
    "7) hue "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "cell_type": "markdown",
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   "source": [
    "### Step 6. Set the values of the first 3 rows from alcohol as NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Now set the value of the rows 3 and 4 of magnesium as NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Fill the value of NaN with the number 10 in alcohol and 100 in magnesium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Count the number of missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10.  Create an array of 10 random numbers up until 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11.  Set the rows of the random numbers in the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12.  How many missing values do we have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 14. Print only the non-null values in alcohol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 13. Delete the rows that contain missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 15.  Reset the index, so it starts with 0 again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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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