2 - Pandas Series exercises.ipynb

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   "source": [
    "![rmotr](https://user-images.githubusercontent.com/7065401/52071918-bda15380-2562-11e9-828c-7f95297e4a82.png)\n",
    "<hr style=\"margin-bottom: 40px;\">\n",
    "\n",
    "# Pandas Series exercises\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.25.3\n"
     ]
    }
   ],
   "source": [
    "# Import the numpy package under the name np\n",
    "import numpy as np\n",
    "\n",
    "# Import the pandas package under the name pd\n",
    "import pandas as pd\n",
    "\n",
    "# Print the pandas version and the configuration\n",
    "print(pd.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![purple-divider](https://user-images.githubusercontent.com/7065401/52071927-c1cd7100-2562-11e9-908a-dde91ba14e59.png)\n",
    "\n",
    "## Series creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create an empty pandas Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [],
   "source": [
    "pd.Series()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Given the X python list convert it to an Y pandas Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [],
   "source": [
    "X = ['A','B','C']\n",
    "print(X, type(X))\n",
    "\n",
    "Y = pd.Series(X)\n",
    "print(Y, type(Y)) # different type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Given the X pandas Series, name it 'My letters'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [],
   "source": [
    "X = pd.Series(['A','B','C'])\n",
    "\n",
    "X.name = 'My letters'\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Given the X pandas Series, show its values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [],
   "source": [
    "X = pd.Series(['A','B','C'])\n",
    "\n",
    "X.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![purple-divider](https://user-images.githubusercontent.com/7065401/52071927-c1cd7100-2562-11e9-908a-dde91ba14e59.png)\n",
    "\n",
    "## Series indexation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assign index names to the given X pandas Series\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [],
   "source": [
    "X = pd.Series(['A','B','C'])\n",
    "index_names = ['first', 'second', 'third']\n",
    "\n",
    "X.index = index_names\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Given the X pandas Series, show its first element\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [],
   "source": [
    "X = pd.Series(['A','B','C'], index=['first', 'second', 'third'])\n",
    "\n",
    "#X[0] # by position\n",
    "#X.iloc[0] # by position\n",
    "X['first'] # by index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Given the X pandas Series, show its last element\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [],
   "source": [
    "X = pd.Series(['A','B','C'], index=['first', 'second', 'third'])\n",
    "\n",
    "#X[-1] # by position\n",
    "#X.iloc[-1] # by position\n",
    "X['third'] # by index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Given the X pandas Series, show all middle elements\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [],
   "source": [
    "X = pd.Series(['A','B','C','D','E'],\n",
    "              index=['first','second','third','forth','fifth'])\n",
    "\n",
    "#X[['second', 'third', 'forth']]\n",
    "#X.iloc[1:-1] # by position\n",
    "X[1:-1] # by position"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Given the X pandas Series, show the elements in reverse position\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [],
   "source": [
    "X = pd.Series(['A','B','C','D','E'],\n",
    "              index=['first','second','third','forth','fifth'])\n",
    "\n",
    "#X.iloc[::-1]\n",
    "X[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Given the X pandas Series, show the first and last elements\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [],
   "source": [
    "X = pd.Series(['A','B','C','D','E'],\n",
    "              index=['first','second','third','forth','fifth'])\n",
    "\n",
    "#X[['first', 'fifth']]\n",
    "#X.iloc[[0, -1]]\n",
    "X[[0, -1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![purple-divider](https://user-images.githubusercontent.com/7065401/52071927-c1cd7100-2562-11e9-908a-dde91ba14e59.png)\n",
    "\n",
    "## Series manipulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert the given integer pandas Series to float\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [],
   "source": [
    "X = pd.Series([1,2,3,4,5],\n",
    "              index=['first','second','third','forth','fifth'])\n",
    "\n",
    "pd.Series(X, dtype=np.float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Reverse the given pandas Series (first element becomes last)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [],
   "source": [
    "X = pd.Series([1,2,3,4,5],\n",
    "              index=['first','second','third','forth','fifth'])\n",
    "\n",
    "X[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Order (sort) the given pandas Series\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [],
   "source": [
    "X = pd.Series([4,2,5,1,3],\n",
    "              index=['forth','second','fifth','first','third'])\n",
    "\n",
    "X = X.sort_values()\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Given the X pandas Series, set the fifth element equal to 10\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [],
   "source": [
    "X = pd.Series([1,2,3,4,5],\n",
    "              index=['A','B','C','D','E'])\n",
    "\n",
    "X[4] = 10\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Given the X pandas Series, change all the middle elements to 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "X = pd.Series([1,2,3,4,5],\n",
    "              index=['A','B','C','D','E'])\n",
    "\n",
    "X[1:-1] = 0\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Given the X pandas Series, add 5 to every element\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [],
   "source": [
    "X = pd.Series([1,2,3,4,5])\n",
    "\n",
    "X + 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![purple-divider](https://user-images.githubusercontent.com/7065401/52071927-c1cd7100-2562-11e9-908a-dde91ba14e59.png)\n",
    "\n",
    "## Series boolean arrays (also called masks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Given the X pandas Series, make a mask showing negative elements\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [],
   "source": [
    "X = pd.Series([-1,2,0,-4,5,6,0,0,-9,10])\n",
    "\n",
    "mask = X <= 0\n",
    "mask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Given the X pandas Series, get the negative elements\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [],
   "source": [
    "X = pd.Series([-1,2,0,-4,5,6,0,0,-9,10])\n",
    "\n",
    "mask = X <= 0\n",
    "X[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Given the X pandas Series, get numbers higher than 5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [],
   "source": [
    "X = pd.Series([-1,2,0,-4,5,6,0,0,-9,10])\n",
    "\n",
    "mask = X > 5\n",
    "X[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Given the X pandas Series, get numbers higher than the elements mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [],
   "source": [
    "X = pd.Series([-1,2,0,-4,5,6,0,0,-9,10])\n",
    "\n",
    "mask = X > X.mean()\n",
    "X[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Given the X pandas Series, get numbers equal to 2 or 10\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X = pd.Series([-1,2,0,-4,5,6,0,0,-9,10])\n",
    "\n",
    "mask = (X == 2) | (X == 10)\n",
    "X[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![purple-divider](https://user-images.githubusercontent.com/7065401/52071927-c1cd7100-2562-11e9-908a-dde91ba14e59.png)\n",
    "\n",
    "## Logic functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Given the X pandas Series, return True if none of its elements is zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [],
   "source": [
    "X = pd.Series([-1,2,0,-4,5,6,0,0,-9,10])\n",
    "\n",
    "X.all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Given the X pandas Series, return True if any of its elements is zero\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [],
   "source": [
    "X = pd.Series([-1,2,0,-4,5,6,0,0,-9,10])\n",
    "\n",
    "X.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![purple-divider](https://user-images.githubusercontent.com/7065401/52071927-c1cd7100-2562-11e9-908a-dde91ba14e59.png)\n",
    "\n",
    "## Summary statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Given the X pandas Series, show the sum of its elements\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [],
   "source": [
    "X = pd.Series([3,5,6,7,2,3,4,9,4])\n",
    "\n",
    "#np.sum(X)\n",
    "X.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Given the X pandas Series, show the mean value of its elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [],
   "source": [
    "X = pd.Series([1,2,0,4,5,6,0,0,9,10])\n",
    "\n",
    "#np.mean(X)\n",
    "X.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Given the X pandas Series, show the max value of its elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code goes here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [],
   "source": [
    "X = pd.Series([1,2,0,4,5,6,0,0,9,10])\n",
    "\n",
    "#np.max(X)\n",
    "X.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![purple-divider](https://user-images.githubusercontent.com/7065401/52071927-c1cd7100-2562-11e9-908a-dde91ba14e59.png)"
   ]
  }
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