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   "source": [
    "# Iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:\n",
    "\n",
    "This exercise may seem a little bit strange, but keep doing it.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
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   "execution_count": null,
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   "source": [
    "### Step 2. Import the dataset from this [address](https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Create columns for the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 1. sepal_length (in cm)\n",
    "# 2. sepal_width (in cm)\n",
    "# 3. petal_length (in cm)\n",
    "# 4. petal_width (in cm)\n",
    "# 5. class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5.  Is there any missing value in the dataframe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6.  Lets set the values of the rows 10 to 29 of the column 'petal_length' to NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Good, now lets substitute the NaN values to 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Now let's delete the column class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9.  Set the first 3 rows as NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10.  Delete the rows that have NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Reset the index so it begins 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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