Code-Example-002-1-Pandas-Basics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 2: Introduction to Numpy and Pandas\n",
    "\n",
    "The following tutorial contains examples of using the numpy and pandas library modules. The notebook can be downloaded from http://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial2/tutorial2.ipynb. Read the step-by-step instructions below carefully. To execute the code, click on the cell and press the SHIFT-ENTER keys simultaneously."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Introduction to Numpy\n",
    "\n",
    "Numpy, which stands for numerical Python, is a Python library package to support numerical computations. The basic data structure in numpy is a multi-dimensional array object called ndarray. Numpy provides a suite of functions that can efficiently manipulate elements of the ndarray. \n",
    "\n",
    "### 2.1.1 Creating ndarray\n",
    "\n",
    "An ndarray can be created from a list or tuple object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 2. 3. 4. 5.]\n",
      "#Dimensions = 1\n",
      "Dimension = (5,)\n",
      "Size = 5\n",
      "Array type = float64\n",
      "[[1 2]\n",
      " [3 4]\n",
      " [5 6]\n",
      " [7 8]]\n",
      "#Dimensions = 2\n",
      "Dimension = (4, 2)\n",
      "Size = 8\n",
      "Array type = int64\n",
      "[['1' 'a' '3.0']\n",
      " ['2' 'b' '3.5']]\n",
      "#Dimensions = 2\n",
      "Dimension = (2, 3)\n",
      "Size = 6\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "oneDim = np.array([1.0,2,3,4,5])   # a 1-dimensional array (vector)\n",
    "print(oneDim)\n",
    "print(\"#Dimensions =\", oneDim.ndim)\n",
    "print(\"Dimension =\", oneDim.shape)\n",
    "print(\"Size =\", oneDim.size)\n",
    "print(\"Array type =\", oneDim.dtype)\n",
    "\n",
    "twoDim = np.array([[1,2],[3,4],[5,6],[7,8]])  # a two-dimensional array (matrix)\n",
    "print(twoDim)\n",
    "print(\"#Dimensions =\", twoDim.ndim)\n",
    "print(\"Dimension =\", twoDim.shape)\n",
    "print(\"Size =\", twoDim.size)\n",
    "print(\"Array type =\", twoDim.dtype)\n",
    "\n",
    "arrFromTuple = np.array([(1,'a',3.0),(2,'b',3.5)])  # create ndarray from tuple\n",
    "print(arrFromTuple)\n",
    "print(\"#Dimensions =\", arrFromTuple.ndim)\n",
    "print(\"Dimension =\", arrFromTuple.shape)\n",
    "print(\"Size =\", arrFromTuple.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several built-in functions in numpy that can be used to create ndarrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.76771699 0.90676971 0.16464249 0.02056386 0.51935759]\n",
      "[ 2.18994559 -0.77519377  0.05072361 -0.18790099  0.45781793]\n",
      "[-10  -8  -6  -4  -2   0   2   4   6   8]\n",
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n",
      "[0.         0.11111111 0.22222222 0.33333333 0.44444444 0.55555556\n",
      " 0.66666667 0.77777778 0.88888889 1.        ]\n",
      "[1.e-03 1.e-02 1.e-01 1.e+00 1.e+01 1.e+02 1.e+03]\n"
     ]
    }
   ],
   "source": [
    "print(np.random.rand(5))      # random numbers from a uniform distribution between [0,1]\n",
    "print(np.random.randn(5))     # random numbers from a normal distribution\n",
    "print(np.arange(-10,10,2))    # similar to range, but returns ndarray instead of list\n",
    "print(np.arange(12).reshape(3,4))  # reshape to a matrix\n",
    "print(np.linspace(0,1,10))    # split interval [0,1] into 10 equally separated values\n",
    "print(np.logspace(-3,3,7))    # create ndarray with values from 10^-3 to 10^3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0.]\n",
      " [0. 0. 0.]]\n",
      "[[1. 1.]\n",
      " [1. 1.]\n",
      " [1. 1.]]\n",
      "[[1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "print(np.zeros((2,3)))        # a matrix of zeros\n",
    "print(np.ones((3,2)))         # a matrix of ones\n",
    "print(np.eye(3))              # a 3 x 3 identity matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1.2 Element-wise Operations\n",
    "\n",
    "You can apply standard operators such as addition and multiplication on each element of the ndarray."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 3 4 5 6]\n",
      "[0 1 2 3 4]\n",
      "[ 2  4  6  8 10]\n",
      "[0 1 1 2 2]\n",
      "[ 1  4  9 16 25]\n",
      "[1 0 1 0 1]\n",
      "[1.         0.5        0.33333333 0.25       0.2       ]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([1,2,3,4,5])\n",
    "\n",
    "print(x + 1)      # addition\n",
    "print(x - 1)      # subtraction\n",
    "print(x * 2)      # multiplication\n",
    "print(x // 2)     # integer division\n",
    "print(x ** 2)     # square\n",
    "print(x % 2)      # modulo  \n",
    "print(1 / x)      # division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3  6  9 12 15]\n",
      "[1 2 3 4 5]\n",
      "[ 2  8 18 32 50]\n",
      "[2. 2. 2. 2. 2.]\n",
      "[2 2 2 2 2]\n",
      "[     2     16    216   4096 100000]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([2,4,6,8,10])\n",
    "y = np.array([1,2,3,4,5])\n",
    "\n",
    "print(x + y)\n",
    "print(x - y)\n",
    "print(x * y)\n",
    "print(x / y)\n",
    "print(x // y)\n",
    "print(x ** y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1.3 Indexing and Slicing\n",
    "\n",
    "There are various ways to select certain elements with an ndarray."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-5 -4 -3 -2 -1  0  1  2  3  4]\n",
      "[-2 -1]\n",
      "[1000 1000]\n",
      "[  -5   -4   -3 1000 1000    0    1    2    3    4]\n",
      "[1000 1000]\n",
      "[500 500]\n",
      "[  -5   -4   -3 1000 1000    0    1    2    3    4]\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(-5,5)\n",
    "print(x)\n",
    "\n",
    "y = x[3:5]     # y is a slice, i.e., pointer to a subarray in x\n",
    "print(y)\n",
    "y[:] = 1000    # modifying the value of y will change x\n",
    "print(y)\n",
    "print(x)\n",
    "\n",
    "z = x[3:5].copy()   # makes a copy of the subarray\n",
    "print(z)\n",
    "z[:] = 500          # modifying the value of z will not affect x\n",
    "print(z)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]\n",
      "[9, 10, 11, 12]\n",
      "[9, 10, 11, 12]\n",
      "[[ 1  2  3  4]\n",
      " [ 5  6  7  8]\n",
      " [ 9 10 11 12]]\n",
      "[ 9 10 11 12]\n",
      "[ 9 10 11 12]\n",
      "[ 9 10 11 12]\n",
      "[ 3  7 11]\n",
      "[[3 4]\n",
      " [7 8]]\n"
     ]
    }
   ],
   "source": [
    "my2dlist = [[1,2,3,4],[5,6,7,8],[9,10,11,12]]   # a 2-dim list\n",
    "print(my2dlist)\n",
    "print(my2dlist[2])        # access the third sublist\n",
    "print(my2dlist[:][2])     # can't access third element of each sublist\n",
    "# print(my2dlist[:,2])    # this will cause syntax error\n",
    "\n",
    "my2darr = np.array(my2dlist)\n",
    "print(my2darr)\n",
    "print(my2darr[2][:])      # access the third row\n",
    "print(my2darr[2,:])       # access the third row\n",
    "print(my2darr[:][2])      # access the third row (similar to 2d list)\n",
    "print(my2darr[:,2])       # access the third column\n",
    "print(my2darr[:2,2:])     # access the first two rows & last two columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ndarray also supports boolean indexing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  3  4]\n",
      " [ 5  6  7  8]\n",
      " [ 9 10 11 12]]\n",
      "[ 3  6  9 12] <class 'numpy.ndarray'>\n",
      "[[ 9 12]]\n"
     ]
    }
   ],
   "source": [
    "my2darr = np.arange(1,13,1).reshape(3,4)\n",
    "print(my2darr)\n",
    "\n",
    "divBy3 = my2darr[my2darr % 3 == 0]\n",
    "print(divBy3, type(divBy3))\n",
    "\n",
    "divBy3LastRow = my2darr[2:, my2darr[2,:] % 3 == 0]\n",
    "print(divBy3LastRow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More indexing examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  3]\n",
      " [ 4  5  6]\n",
      " [ 7  8  9]\n",
      " [10 11 12]]\n",
      "[[ 7  8  9]\n",
      " [ 4  5  6]\n",
      " [ 1  2  3]\n",
      " [10 11 12]]\n",
      "[ 1  3  4  8 12]\n"
     ]
    }
   ],
   "source": [
    "my2darr = np.arange(1,13,1).reshape(4,3)\n",
    "print(my2darr)\n",
    "\n",
    "indices = [2,1,0,3]    # selected row indices\n",
    "print(my2darr[indices,:])\n",
    "\n",
    "rowIndex = [0,0,1,2,3]     # row index into my2darr\n",
    "columnIndex = [0,2,0,1,2]  # column index into my2darr\n",
    "print(my2darr[rowIndex,columnIndex])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1.4 Numpy Arithmetic and Statistical Functions\n",
    "\n",
    "There are many built-in mathematical functions available for manipulating elements of nd-array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.4  0.4 -3.2  2.5  3.4]\n",
      "[1.4 0.4 3.2 2.5 3.4]\n",
      "[1.18321596 0.63245553 1.78885438 1.58113883 1.84390889]\n",
      "[-1.  1. -1.  1.  1.]\n",
      "[ 0.24659696  1.4918247   0.0407622  12.18249396 29.96410005]\n",
      "[-3.2 -1.4  0.4  2.5  3.4]\n"
     ]
    }
   ],
   "source": [
    "y = np.array([-1.4, 0.4, -3.2, 2.5, 3.4])    # generate a random vector\n",
    "print(y)\n",
    "\n",
    "print(np.abs(y))          # convert to absolute values\n",
    "print(np.sqrt(abs(y)))    # apply square root to each element\n",
    "print(np.sign(y))         # get the sign of each element\n",
    "print(np.exp(y))          # apply exponentiation\n",
    "print(np.sort(y))         # sort array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-2 -1  0  1  2]\n",
      "[ 1.01069183  0.97920437  1.23348486  0.16382707 -0.41290651]\n",
      "[-0.98930817 -0.02079563  1.23348486  1.16382707  1.58709349]\n",
      "[-3.01069183 -1.97920437 -1.23348486  0.83617293  2.41290651]\n",
      "[-2.02138365 -0.97920437  0.          0.16382707 -0.82581303]\n",
      "[-1.97884256 -1.02123727  0.          6.10399738 -4.84371143]\n",
      "[1.01069183 0.97920437 1.23348486 1.         2.        ]\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(-2,3)\n",
    "y = np.random.randn(5)\n",
    "print(x)\n",
    "print(y)\n",
    "\n",
    "print(np.add(x,y))           # element-wise addition       x + y\n",
    "print(np.subtract(x,y))      # element-wise subtraction    x - y\n",
    "print(np.multiply(x,y))      # element-wise multiplication x * y\n",
    "print(np.divide(x,y))        # element-wise division       x / y\n",
    "print(np.maximum(x,y))       # element-wise maximum        max(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-3.2 -1.4  0.4  2.5  3.4]\n",
      "Min = -3.2\n",
      "Max = 3.4\n",
      "Average = 0.34000000000000014\n",
      "Std deviation = 2.432776191925595\n",
      "Sum = 1.7000000000000006\n"
     ]
    }
   ],
   "source": [
    "y = np.array([-3.2, -1.4, 0.4, 2.5, 3.4])    # generate a random vector\n",
    "print(y)\n",
    "\n",
    "print(\"Min =\", np.min(y))             # min \n",
    "print(\"Max =\", np.max(y))             # max \n",
    "print(\"Average =\", np.mean(y))        # mean/average\n",
    "print(\"Std deviation =\", np.std(y))   # standard deviation\n",
    "print(\"Sum =\", np.sum(y))             # sum "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1.5 Numpy linear algebra\n",
    "\n",
    "Numpy provides many functions to support linear algebra operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.35494422 -1.08348296  1.22180751]\n",
      " [-1.47681087 -0.50399437 -0.65891549]]\n",
      "[[ 0.35494422 -1.47681087]\n",
      " [-1.08348296 -0.50399437]\n",
      " [ 1.22180751 -0.65891549]]\n",
      "[ 1.27304957 -0.8480265   0.06200353]\n",
      "[ 1.44644022 -1.49350794]\n",
      "[[ 2.79273429 -0.78318405]\n",
      " [-0.78318405  2.8691503 ]]\n",
      "[[ 2.30695575  0.35972836  1.40676706]\n",
      " [ 0.35972836  1.42794564 -0.99171791]\n",
      " [ 1.40676706 -0.99171791  1.9269832 ]]\n"
     ]
    }
   ],
   "source": [
    "X = np.random.randn(2,3)    # create a 2 x 3 random matrix\n",
    "print(X)\n",
    "print(X.T)             # matrix transpose operation X^T\n",
    "\n",
    "y = np.random.randn(3) # random vector \n",
    "print(y)\n",
    "print(X.dot(y))        # matrix-vector multiplication  X * y\n",
    "print(X.dot(X.T))      # matrix-matrix multiplication  X * X^T\n",
    "print(X.T.dot(X))      # matrix-matrix multiplication  X^T * X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.44680179  1.78814411 -0.75215335]\n",
      " [-1.08862455 -0.39503941 -1.42728752]\n",
      " [ 1.31540796  1.1133248   1.21141149]\n",
      " [-2.40914718 -0.33099591 -1.14987401]\n",
      " [-1.55433392 -0.00749438  1.38776604]]\n",
      "[[ 0.10039519  0.01105928 -0.06793098]\n",
      " [ 0.01105928  0.21944175 -0.03544568]\n",
      " [-0.06793098 -0.03544568  0.18618748]]\n",
      "334.04740462458915\n",
      "[15.97974012  5.39567694  3.8742929 ]\n",
      "[[-0.8691772  -0.37063931 -0.32734918]\n",
      " [-0.04989084  0.72432881 -0.6876472 ]\n",
      " [-0.49197753  0.58135554  0.6480616 ]]\n"
     ]
    }
   ],
   "source": [
    "X = np.random.randn(5,3)\n",
    "print(X)\n",
    "\n",
    "C = X.T.dot(X)               # C = X^T * X is a square matrix\n",
    "\n",
    "invC = np.linalg.inv(C)      # inverse of a square matrix\n",
    "print(invC)\n",
    "detC = np.linalg.det(C)      # determinant of a square matrix\n",
    "print(detC)\n",
    "S, U = np.linalg.eig(C)      # eigenvalue S and eigenvector U of a square matrix\n",
    "print(S)\n",
    "print(U)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Introduction to Pandas\n",
    "\n",
    "Pandas provide two convenient data structures for storing and manipulating data--Series and DataFrame. A Series is similar to a one-dimensional array whereas a DataFrame is more similar to representing a matrix or a spreadsheet table.  \n",
    "\n",
    "### 2.2.1 Series\n",
    "\n",
    "A Series object consists of a one-dimensional array of values, whose elements can be referenced using an index array. A Series object can be created from a list, a numpy array, or a Python dictionary. You can apply most of the numpy functions on the Series object.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    3.1\n",
      "1    2.4\n",
      "2   -1.7\n",
      "3    0.2\n",
      "4   -2.9\n",
      "5    4.5\n",
      "dtype: float64\n",
      "Values= [ 3.1  2.4 -1.7  0.2 -2.9  4.5]\n",
      "Index= RangeIndex(start=0, stop=6, step=1)\n"
     ]
    }
   ],
   "source": [
    "from pandas import Series\n",
    "\n",
    "s = Series([3.1, 2.4, -1.7, 0.2, -2.9, 4.5])   # creating a series from a list\n",
    "print(s)\n",
    "print('Values=', s.values)     # display values of the Series\n",
    "print('Index=', s.index)       # display indices of the Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0   -0.878850\n",
      "1   -0.587288\n",
      "2    1.384642\n",
      "3   -0.727591\n",
      "4   -0.944580\n",
      "5   -1.307259\n",
      "dtype: float64\n",
      "Values= [-0.87884988 -0.58728776  1.38464165 -0.72759097 -0.94458016 -1.30725858]\n",
      "Index= RangeIndex(start=0, stop=6, step=1)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "s2 = Series(np.random.randn(6))  # creating a series from a numpy ndarray\n",
    "print(s2)\n",
    "print('Values=', s2.values)   # display values of the Series\n",
    "print('Index=', s2.index)     # display indices of the Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jan 1    1.2\n",
      "Jan 2    2.5\n",
      "Jan 3   -2.2\n",
      "Jan 4    3.1\n",
      "Jan 5   -0.8\n",
      "Jan 6   -3.2\n",
      "dtype: float64\n",
      "Values= [ 1.2  2.5 -2.2  3.1 -0.8 -3.2]\n",
      "Index= Index(['Jan 1', 'Jan 2', 'Jan 3', 'Jan 4', 'Jan 5', 'Jan 6'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "s3 = Series([1.2,2.5,-2.2,3.1,-0.8,-3.2], \n",
    "            index = ['Jan 1','Jan 2','Jan 3','Jan 4','Jan 5','Jan 6',])\n",
    "print(s3)\n",
    "print('Values=', s3.values)   # display values of the Series\n",
    "print('Index=', s3.index)     # display indices of the Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MI       Lansing\n",
      "CA    Sacramento\n",
      "TX        Austin\n",
      "MN       St Paul\n",
      "dtype: object\n",
      "Values= ['Lansing' 'Sacramento' 'Austin' 'St Paul']\n",
      "Index= Index(['MI', 'CA', 'TX', 'MN'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "capitals = {'MI': 'Lansing', 'CA': 'Sacramento', 'TX': 'Austin', 'MN': 'St Paul'}\n",
    "\n",
    "s4 = Series(capitals)   # creating a series from dictionary object\n",
    "print(s4)\n",
    "print('Values=', s4.values)   # display values of the Series\n",
    "print('Index=', s4.index)     # display indices of the Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jan 1    1.2\n",
      "Jan 2    2.5\n",
      "Jan 3   -2.2\n",
      "Jan 4    3.1\n",
      "Jan 5   -0.8\n",
      "Jan 6   -3.2\n",
      "dtype: float64\n",
      "\n",
      "s3[2]= -2.2\n",
      "s3['Jan 3']= -2.2\n",
      "\n",
      "s3[1:3]=\n",
      "Jan 2    2.5\n",
      "Jan 3   -2.2\n",
      "dtype: float64\n",
      "s3.iloc([1:3])=\n",
      "Jan 2    2.5\n",
      "Jan 3   -2.2\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "s3 = Series([1.2,2.5,-2.2,3.1,-0.8,-3.2], \n",
    "            index = ['Jan 1','Jan 2','Jan 3','Jan 4','Jan 5','Jan 6',])\n",
    "print(s3)\n",
    "\n",
    "# Accessing elements of a Series\n",
    "\n",
    "print('\\ns3[2]=', s3[2])        # display third element of the Series\n",
    "print('s3[\\'Jan 3\\']=', s3['Jan 3'])   # indexing element of a Series \n",
    "\n",
    "print('\\ns3[1:3]=')             # display a slice of the Series\n",
    "print(s3[1:3])\n",
    "print('s3.iloc([1:3])=')      # display a slice of the Series\n",
    "print(s3.iloc[1:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape = (6,)\n",
      "size = 6\n"
     ]
    }
   ],
   "source": [
    "print('shape =', s3.shape)  # get the dimension of the Series\n",
    "print('size =', s3.size)    # get the # of elements of the Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jan 1    1.2\n",
      "Jan 2    2.5\n",
      "Jan 4    3.1\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(s3[s3 > 0])   # applying filter to select elements of the Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jan 1    5.2\n",
      "Jan 2    6.5\n",
      "Jan 3    1.8\n",
      "Jan 4    7.1\n",
      "Jan 5    3.2\n",
      "Jan 6    0.8\n",
      "dtype: float64\n",
      "Jan 1    0.300\n",
      "Jan 2    0.625\n",
      "Jan 3   -0.550\n",
      "Jan 4    0.775\n",
      "Jan 5   -0.200\n",
      "Jan 6   -0.800\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(s3 + 4)       # applying scalar operation on a numeric Series\n",
    "print(s3 / 4)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jan 1    1.648659\n",
      "Jan 2    1.871802\n",
      "Jan 3    0.587787\n",
      "Jan 4    1.960095\n",
      "Jan 5    1.163151\n",
      "Jan 6   -0.223144\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(np.log(s3 + 4))    # applying numpy math functions to a numeric Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.2 DataFrame\n",
    "\n",
    "A DataFrame object is a tabular, spreadsheet-like data structure containing a collection of columns, each of which can be of different types (numeric, string, boolean, etc). Unlike Series, a DataFrame has distinct row and column indices. There are many ways to create a DataFrame object (e.g., from a dictionary, list of tuples, or even numpy's ndarrays)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>make</th>\n",
       "      <th>model</th>\n",
       "      <th>MSRP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ford</td>\n",
       "      <td>Taurus</td>\n",
       "      <td>27595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Accord</td>\n",
       "      <td>23570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Camry</td>\n",
       "      <td>23495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tesla</td>\n",
       "      <td>Model S</td>\n",
       "      <td>68000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     make    model   MSRP\n",
       "0    Ford   Taurus  27595\n",
       "1   Honda   Accord  23570\n",
       "2  Toyota    Camry  23495\n",
       "3   Tesla  Model S  68000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas import DataFrame\n",
    "\n",
    "cars = {'make': ['Ford', 'Honda', 'Toyota', 'Tesla'],\n",
    "       'model': ['Taurus', 'Accord', 'Camry', 'Model S'],\n",
    "       'MSRP': [27595, 23570, 23495, 68000]}          \n",
    "carData = DataFrame(cars)   # creating DataFrame from dictionary\n",
    "carData                     # display the table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=4, step=1)\n",
      "Index(['make', 'model', 'MSRP'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(carData.index)       # print the row indices\n",
    "print(carData.columns)     # print the column indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>make</th>\n",
       "      <th>model</th>\n",
       "      <th>MSRP</th>\n",
       "      <th>year</th>\n",
       "      <th>dealership</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ford</td>\n",
       "      <td>Taurus</td>\n",
       "      <td>27595</td>\n",
       "      <td>2018</td>\n",
       "      <td>Courtesy Ford</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Accord</td>\n",
       "      <td>23570</td>\n",
       "      <td>2018</td>\n",
       "      <td>Capital Honda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Camry</td>\n",
       "      <td>23495</td>\n",
       "      <td>2018</td>\n",
       "      <td>Spartan Toyota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tesla</td>\n",
       "      <td>Model S</td>\n",
       "      <td>68000</td>\n",
       "      <td>2018</td>\n",
       "      <td>N/A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     make    model   MSRP  year      dealership\n",
       "1    Ford   Taurus  27595  2018   Courtesy Ford\n",
       "2   Honda   Accord  23570  2018   Capital Honda\n",
       "3  Toyota    Camry  23495  2018  Spartan Toyota\n",
       "4   Tesla  Model S  68000  2018             N/A"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carData2 = DataFrame(cars, index = [1,2,3,4])  # change the row index\n",
    "carData2['year'] = 2018    # add column with same value\n",
    "carData2['dealership'] = ['Courtesy Ford','Capital Honda','Spartan Toyota','N/A']\n",
    "carData2                   # display table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating DataFrame from a list of tuples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>temp</th>\n",
       "      <th>precip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>45.1</td>\n",
       "      <td>32.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>42.4</td>\n",
       "      <td>34.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>47.2</td>\n",
       "      <td>39.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>44.2</td>\n",
       "      <td>31.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>39.9</td>\n",
       "      <td>29.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016</td>\n",
       "      <td>41.5</td>\n",
       "      <td>36.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  temp  precip\n",
       "0  2011  45.1    32.4\n",
       "1  2012  42.4    34.5\n",
       "2  2013  47.2    39.2\n",
       "3  2014  44.2    31.4\n",
       "4  2015  39.9    29.8\n",
       "5  2016  41.5    36.7"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuplelist = [(2011,45.1,32.4),(2012,42.4,34.5),(2013,47.2,39.2),\n",
    "              (2014,44.2,31.4),(2015,39.9,29.8),(2016,41.5,36.7)]\n",
    "columnNames = ['year','temp','precip']\n",
    "weatherData = DataFrame(tuplelist, columns=columnNames)\n",
    "weatherData"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating DataFrame from numpy ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.649251</td>\n",
       "      <td>-1.526916</td>\n",
       "      <td>-0.333483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.108550</td>\n",
       "      <td>1.397225</td>\n",
       "      <td>0.457765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.964795</td>\n",
       "      <td>-0.338937</td>\n",
       "      <td>0.714107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.779310</td>\n",
       "      <td>-0.267505</td>\n",
       "      <td>-1.537886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.183949</td>\n",
       "      <td>-0.689601</td>\n",
       "      <td>-0.644631</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         x1        x2        x3\n",
       "0  0.649251 -1.526916 -0.333483\n",
       "1 -0.108550  1.397225  0.457765\n",
       "2 -0.964795 -0.338937  0.714107\n",
       "3 -0.779310 -0.267505 -1.537886\n",
       "4  1.183949 -0.689601 -0.644631"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "npdata = np.random.randn(5,3)  # create a 5 by 3 random matrix\n",
    "columnNames = ['x1','x2','x3']\n",
    "data = DataFrame(npdata, columns=columnNames)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The elements of a DataFrame can be accessed in many ways."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0   -1.526916\n",
      "1    1.397225\n",
      "2   -0.338937\n",
      "3   -0.267505\n",
      "4   -0.689601\n",
      "Name: x2, dtype: float64\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "# accessing an entire column will return a Series object\n",
    "\n",
    "print(data['x2'])\n",
    "print(type(data['x2']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Row 3 of data table:\n",
      "x1   -0.964795\n",
      "x2   -0.338937\n",
      "x3    0.714107\n",
      "Name: 2, dtype: float64\n",
      "<class 'pandas.core.series.Series'>\n",
      "\n",
      "Row 3 of car data table:\n",
      "make                  Toyota\n",
      "model                  Camry\n",
      "MSRP                   23495\n",
      "year                    2018\n",
      "dealership    Spartan Toyota\n",
      "Name: 3, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# accessing an entire row will return a Series object\n",
    "\n",
    "print('Row 3 of data table:')\n",
    "print(data.iloc[2])       # returns the 3rd row of DataFrame\n",
    "print(type(data.iloc[2]))\n",
    "print('\\nRow 3 of car data table:')\n",
    "print(carData2.iloc[2])   # row contains objects of different types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23570\n",
      "Taurus\n",
      "carData2.iloc[1:3,1:3]=\n",
      "    model   MSRP\n",
      "2  Accord  23570\n",
      "3   Camry  23495\n"
     ]
    }
   ],
   "source": [
    "# accessing a specific element of the DataFrame\n",
    "\n",
    "print(carData2.iloc[1,2])      # retrieving second row, third column\n",
    "print(carData2.loc[1,'model']) # retrieving second row, column named 'model'\n",
    "\n",
    "# accessing a slice of the DataFrame\n",
    "\n",
    "print('carData2.iloc[1:3,1:3]=')\n",
    "print(carData2.iloc[1:3,1:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "carData2.shape = (4, 5)\n",
      "carData2.size = 20\n"
     ]
    }
   ],
   "source": [
    "print('carData2.shape =', carData2.shape)\n",
    "print('carData2.size =', carData2.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "carData2[carData2.MSRP > 25000]\n",
      "    make    model   MSRP  year     dealership\n",
      "1   Ford   Taurus  27595  2018  Courtesy Ford\n",
      "4  Tesla  Model S  68000  2018            N/A\n"
     ]
    }
   ],
   "source": [
    "# selection and filtering\n",
    "\n",
    "print('carData2[carData2.MSRP > 25000]')  \n",
    "print(carData2[carData2.MSRP > 25000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.3 Arithmetic Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         x1        x2        x3\n",
      "0  0.649251 -1.526916 -0.333483\n",
      "1 -0.108550  1.397225  0.457765\n",
      "2 -0.964795 -0.338937  0.714107\n",
      "3 -0.779310 -0.267505 -1.537886\n",
      "4  1.183949 -0.689601 -0.644631\n",
      "Data transpose operation:\n",
      "           0         1         2         3         4\n",
      "x1  0.649251 -0.108550 -0.964795 -0.779310  1.183949\n",
      "x2 -1.526916  1.397225 -0.338937 -0.267505 -0.689601\n",
      "x3 -0.333483  0.457765  0.714107 -1.537886 -0.644631\n",
      "Addition:\n",
      "         x1        x2        x3\n",
      "0  4.649251  2.473084  3.666517\n",
      "1  3.891450  5.397225  4.457765\n",
      "2  3.035205  3.661063  4.714107\n",
      "3  3.220690  3.732495  2.462114\n",
      "4  5.183949  3.310399  3.355369\n",
      "Multiplication:\n",
      "          x1         x2         x3\n",
      "0   6.492508 -15.269157  -3.334827\n",
      "1  -1.085500  13.972250   4.577645\n",
      "2  -9.647946  -3.389365   7.141070\n",
      "3  -7.793095  -2.675047 -15.378857\n",
      "4  11.839491  -6.896008  -6.446305\n"
     ]
    }
   ],
   "source": [
    "print(data)\n",
    "\n",
    "print('Data transpose operation:')\n",
    "print(data.T)    # transpose operation\n",
    "\n",
    "print('Addition:')\n",
    "print(data + 4)    # addition operation\n",
    "\n",
    "print('Multiplication:')\n",
    "print(data * 10)   # multiplication operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data =\n",
      "         x1        x2        x3\n",
      "0  0.649251 -1.526916 -0.333483\n",
      "1 -0.108550  1.397225  0.457765\n",
      "2 -0.964795 -0.338937  0.714107\n",
      "3 -0.779310 -0.267505 -1.537886\n",
      "4  1.183949 -0.689601 -0.644631\n",
      "\n",
      "data2 =\n",
      "         x1        x2        x3\n",
      "0  1.004179 -1.173473 -1.237750\n",
      "1 -0.144540  0.194380  0.197815\n",
      "2 -0.007113 -1.393603 -2.674410\n",
      "3  0.221448 -0.190930 -0.950504\n",
      "4 -1.903745  0.151535 -0.723407\n",
      "\n",
      "data + data2 = \n",
      "         x1        x2        x3\n",
      "0  1.653430 -2.700388 -1.571233\n",
      "1 -0.253090  1.591605  0.655579\n",
      "2 -0.971908 -1.732540 -1.960303\n",
      "3 -0.557862 -0.458435 -2.488389\n",
      "4 -0.719796 -0.538066 -1.368038\n",
      "\n",
      "data * data2 = \n",
      "         x1        x2        x3\n",
      "0  0.651964  1.791794  0.412768\n",
      "1  0.015690  0.271592  0.090553\n",
      "2  0.006863  0.472343 -1.909815\n",
      "3 -0.172576  0.051075  1.461766\n",
      "4 -2.253937 -0.104499  0.466330\n"
     ]
    }
   ],
   "source": [
    "print('data =')\n",
    "print(data)\n",
    "\n",
    "columnNames = ['x1','x2','x3']\n",
    "data2 = DataFrame(np.random.randn(5,3), columns=columnNames)\n",
    "print('\\ndata2 =')\n",
    "print(data2)\n",
    "\n",
    "print('\\ndata + data2 = ')\n",
    "print(data.add(data2))\n",
    "\n",
    "print('\\ndata * data2 = ')\n",
    "print(data.mul(data2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         x1        x2        x3\n",
      "0  0.649251  1.526916  0.333483\n",
      "1  0.108550  1.397225  0.457765\n",
      "2  0.964795  0.338937  0.714107\n",
      "3  0.779310  0.267505  1.537886\n",
      "4  1.183949  0.689601  0.644631\n",
      "\n",
      "Maximum value per column:\n",
      "x1    1.183949\n",
      "x2    1.397225\n",
      "x3    0.714107\n",
      "dtype: float64\n",
      "\n",
      "Minimum value per row:\n",
      "0   -1.526916\n",
      "1   -0.108550\n",
      "2   -0.964795\n",
      "3   -1.537886\n",
      "4   -0.689601\n",
      "dtype: float64\n",
      "\n",
      "Sum of values per column:\n",
      "x1   -0.019454\n",
      "x2   -1.425733\n",
      "x3   -1.344127\n",
      "dtype: float64\n",
      "\n",
      "Average value per row:\n",
      "0   -0.403716\n",
      "1    0.582147\n",
      "2   -0.196541\n",
      "3   -0.861567\n",
      "4   -0.050094\n",
      "dtype: float64\n",
      "\n",
      "Calculate max - min per column\n",
      "x1    2.148744\n",
      "x2    2.924141\n",
      "x3    2.251993\n",
      "dtype: float64\n",
      "\n",
      "Calculate max - min per row\n",
      "0    2.176166\n",
      "1    1.505775\n",
      "2    1.678902\n",
      "3    1.270381\n",
      "4    1.873550\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(data.abs())    # get the absolute value for each element\n",
    "\n",
    "print('\\nMaximum value per column:')\n",
    "print(data.max())    # get maximum value for each column\n",
    "\n",
    "print('\\nMinimum value per row:')\n",
    "print(data.min(axis=1))    # get minimum value for each row\n",
    "\n",
    "print('\\nSum of values per column:')\n",
    "print(data.sum())    # get sum of values for each column\n",
    "\n",
    "print('\\nAverage value per row:')\n",
    "print(data.mean(axis=1))    # get average value for each row\n",
    "\n",
    "print('\\nCalculate max - min per column')\n",
    "f = lambda x: x.max() - x.min()\n",
    "print(data.apply(f))\n",
    "\n",
    "print('\\nCalculate max - min per row')\n",
    "f = lambda x: x.max() - x.min()\n",
    "print(data.apply(f, axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.4 Plotting Series and DataFrame\n",
    "\n",
    "There are built-in functions you can use to plot the data stored in a Series or a DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7feda6ada190>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "s3 = Series([1.2,2.5,-2.2,3.1,-0.8,-3.2,1.4], \n",
    "            index = ['Jan 1','Jan 2','Jan 3','Jan 4','Jan 5','Jan 6','Jan 7'])\n",
    "s3.plot(kind='line', title='Line plot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7feda6b75a30>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s3.plot(kind='bar', title='Bar plot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7feda6c836d0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s3.plot(kind='hist', title = 'Histogram')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7feda6d84610>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuplelist = [(2011,45.1,32.4),(2012,42.4,34.5),(2013,47.2,39.2),\n",
    "              (2014,44.2,31.4),(2015,39.9,29.8),(2016,41.5,36.7)]\n",
    "columnNames = ['year','temp','precip']\n",
    "weatherData = DataFrame(tuplelist, columns=columnNames)\n",
    "weatherData[['temp','precip']].plot(kind='box', title='Box plot')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}