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   "source": [
    "# Exercises with `pandas` 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1**. Warm up with the `iris` data frame.\n",
    "\n",
    "- Show the first 3 rows\n",
    "- Show the last 3 rows\n",
    "- Show 3 random rows without repetition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2**. Using the `iris` data set,\n",
    "\n",
    "- Find the mean value of all 4 measurements \n",
    "- Find the mean value of all 4 measurements for each Species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3**. Using the `iris` data set,\n",
    "\n",
    "- Sort the observations by Sepal.Width in decreasing order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4**. Using the `iris` data`m set,\n",
    "\n",
    "- Count the number of flowers of each Species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5**. Using the `iris` data set,\n",
    "\n",
    "- Count the number of observations where Petal.Length is longer than Sepal.Width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**6**. Using the `iris` data set, \n",
    "\n",
    "- Find the Species with the most number of observations where the Sepal.Length is less then the mean Sepal.Length of all observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**7**. Using the `iris` data set,\n",
    "\n",
    "- Convert the data frame from the current wide format to a tall format, with just 3 columns: Species, Measurement, Value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**8**. Using the `mtcars` data set, \n",
    "\n",
    "- Find the mean weight of all cars with mpg > 20 and cyl = 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**9**. Using the `mtcars` data set,\n",
    "\n",
    "- Add a new column named `bmi` that is equal to (hp*mpg/wt) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**10**. Using the `mtcars` data set\n",
    "\n",
    "- Find all rows whose car names have numbers in them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**11**. Using the  `iris` data set\n",
    "\n",
    "- Create a new data frame `df` that has only 3 columns (`Species`, `Measure`, `Value`) where `Measure` takes on the values `Sepal.Length`, `Sepal.Width`, `Petal.Length` or `Petal.Width`. Show the first 5 rows.\n",
    "- Show the mean value and counts for each Species and Measure of `df`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**12**. Using the `df` data set,\n",
    "\n",
    "- give each different `treatement` its own column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df <- data.frame(subject=rep(1:4,3),\n",
    "                 treatment = rep(c(\"A\", \"B\", \"C\"), each=4),\n",
    "                 value = rnorm(12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**13**.  Using the  `expt` data set\n",
    "\n",
    "- Find the average blood pressure for each treatment group (A, B or C).\n",
    "\n",
    "Note: You are assumed not to have access to the `pid` and `treat` values separate.y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pid <- rep(1:4, 3)\n",
    "treat <- rep(c('A','B','C'), each = 4)\n",
    "bp <- rnorm(12, 120, 25)\n",
    "expt <- data.frame(name=paste(pid, treat, sep='-'), bp=bp)\n",
    "rm(pid)\n",
    "rm(treat)\n",
    "expt"
   ]
  }
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