Data_Analysis_with_Pandas_and_Numpy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PyData Global 2021 : Data Analysis with Pandas and NumPy\n",
    "\n",
    "### Instructor : Nimrita Koul\n",
    "                  Assistant Professor, Computer Science and Engineering. \n",
    "                  From Bangalore, India\n",
    "### Webpage: nimritakoul.github.io \n",
    "\n",
    "### Github Repository URL  for this Notebook: \n",
    "### https://github.com/NimritaKoul/PyData_Global2021_DataAnalysisPandasandNumPy\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contents\n",
    "1. Introduction to NumPy \n",
    "2. Very Brief Introduction to Matplotlib.pyplot\n",
    "3. Introduction to pandas \n",
    "4. Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sources and References\n",
    "\n",
    "[1] https://numpy.org/doc/stable/user/basics.html\n",
    "\n",
    "[2] https://pandas.pydata.org/docs/getting_started/intro_tutorials/\n",
    "\n",
    "[3] https://matplotlib.org/stable/gallery/index.html\n",
    "\n",
    "[4] https://pandas.pydata.org/docs/pandas.pdf\n",
    "\n",
    "[5] Python for Data Analysis by Wes McKinney (O'Reilly). Copyright 2017 Wes McKinney, 978-1-491-95766-0\n",
    "\n",
    "[6] Guide to NumPy , Travis E. Oliphant https://web.mit.edu/dvp/Public/numpybook.pdf\n",
    "\n",
    "[7] https://cs231n.github.io/python-numpy-tutorial/#numpy\n",
    "\n",
    "[8] https://www.kaggle.com/lavanyashukla01/pandas-numpy-python-cheatsheet\n",
    "\n",
    "[9] https://cs231n.github.io/python-numpy-tutorial/#numpy\n",
    "\n",
    "[10] https://www.geeksforgeeks.org/how-to-set-the-spacing-between-subplots-in-matplotlib-in-python/\n",
    "\n",
    "[11] https://blog.jovian.ai/5-numpy-functions-you-didnt-know-you-needed-8e68624e1d69\n",
    "\n",
    "[12] https://nbviewer.org/github/justmarkham/pandas-videos/blob/master/pandas.ipynb\n",
    "\n",
    "[13] https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises.ipynb\n",
    "\n",
    "[14] https://www.hackerearth.com/practice/machine-learning/data-manipulation-visualisation-r-python/tutorial-data-manipulation-numpy-pandas-python/tutorial/\n",
    "\n",
    "[15] https://github.com/datasciencedojo/datasets/blob/master/titanic.csv\n",
    "\n",
    "[16] https://towardsdatascience.com/all-pandas-groupby-you-should-know-for-grouping-data-and-performing-operations-2a8ec1327b5\n",
    "\n",
    "[17] **Case Study -This case study is heavily inspired by the data analysis case study from NVIDIA Deep Learning Institute's Course \"Fundamentals of Accelerated Data Science with Rapids\". https://www.nvidia.com/en-in/training/instructor-led-workshops/**\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-hPR4jtYQ2K2"
   },
   "source": [
    "# NumPy \n",
    "###  Numpy is the fundamental Python library for high performance numerical computing. \n",
    "\n",
    "1. NumPy provides a multi-dimensional (ndarray) array object as a data structure and comprehensive numerical computing tools for fast manipulation of these arrays. ndarray elements are stored in contiguous memory locations, making it faster to operate on. Its core is written in optimized C.\n",
    "\n",
    "2. The operations include mathematical, statistical, logical, reshaping, sorting and selection, I/O, linear algebra, random simulations etc. It has functionality for performing linear algebra, Fourier transform, and random number related operations.\n",
    "\n",
    "3. ndarray is a multidimensional array of homogeneous datatypes. The size of the array is fixed during creation. Operations are implemented in compiled C code for speed and performance.\n",
    " \n",
    "4. NumPy is fast due to vectorization. i.e., there is no need for explicit looping and indexing. It provides implicit element-by-element behaviour for efficiency in all the operations. This behaviour is called as broadcasting. \n",
    "\n",
    "5. NumPy supports a wide range of hardware platforms including distributed and GPU based platforms. \n",
    "\n",
    "6. NumPy code can be integrated with C, C++ and Fortran Code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Speed comparison array vs list from Wes McKinney Book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 401 ms\n",
      "Wall time: 23.2 s\n"
     ]
    }
   ],
   "source": [
    "# Source Credit - Python for Data Analysis by Wes McKinney (O'Reilly). Copyright 2017 Wes McKinney, 978-1-491-95766-0\n",
    "import numpy as np\n",
    "\n",
    "# creating an ndarray of 10 million integers\n",
    "arr1 = np.arange(10000000) # from 0 to 9,999,999\n",
    "\n",
    "#creating a list of same 10 million integers\n",
    "list1 = list(range(10000000))\n",
    "\n",
    "#time requied to multiply each of the elements by 2 in ndarray , repeated 10 times ( 1 billion million multiplications)\n",
    "%time for _ in range(10): arr1 = arr1 * 2\n",
    "    \n",
    "#time requied to multiply each of the elements by 2 in list, repeated 10 times ( 1 billion multiplications)\n",
    "%time for _ in range(10): list1 = [x * 2 for x in list1]\n",
    "\n",
    "#57.8 times faster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gEE4nGCuMkiJ"
   },
   "source": [
    "### Creating N-D array \n",
    "In NumPy, we can create an N-dimensional (ND) array using array() function. There are many other built-in functions to create ND array. ND array has some attributes and builtin functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "A0GxxckZlgcL",
    "outputId": "d920dcfb-ad58-45ac-dc13-c6fa21b497ec"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "arr1 = np.array([1,2,3,4,5])\n",
    "arr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JPhI2XPDgtSJ",
    "outputId": "eeef43bf-90b4-4c4b-a770-9a09bf139a7d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Array created using np.zeros(): \n",
      " [[0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0.]]\n",
      "\n",
      "Array created using np.ones(): \n",
      " [[1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]]\n",
      "\n",
      "Array created using np.full(): \n",
      " [[3 3 3 3]\n",
      " [3 3 3 3]\n",
      " [3 3 3 3]\n",
      " [3 3 3 3]\n",
      " [3 3 3 3]\n",
      " [3 3 3 3]]\n",
      "\n",
      "Array created using np.eye(): \n",
      " [[1. 0. 0. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [0. 0. 1. 0.]\n",
      " [0. 0. 0. 1.]]\n",
      "\n",
      "Array created using np.diag(): \n",
      " [[1 0 0 0 0 0]\n",
      " [0 2 0 0 0 0]\n",
      " [0 0 3 0 0 0]\n",
      " [0 0 0 4 0 0]\n",
      " [0 0 0 0 5 0]\n",
      " [0 0 0 0 0 6]]\n",
      "\n",
      "Array created using np.arange(): \n",
      " [0 1 2 3 4 5 6 7 8 9]\n",
      "\n",
      "Array created using np.linspace(): \n",
      " [ 1.          3.05263158  5.10526316  7.15789474  9.21052632 11.26315789\n",
      " 13.31578947 15.36842105 17.42105263 19.47368421 21.52631579 23.57894737\n",
      " 25.63157895 27.68421053 29.73684211 31.78947368 33.84210526 35.89473684\n",
      " 37.94736842 40.        ]\n",
      "\n",
      "Array created using np.arange(): \n",
      " [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14]\n",
      "\n",
      "Array reshaped into a 3x5 matrix using np.reshape(): \n",
      " [[ 0  1  2  3  4]\n",
      " [ 5  6  7  8  9]\n",
      " [10 11 12 13 14]]\n",
      "\n",
      "Array created using np.random.random(): \n",
      " [[0.52777988 0.64591985 0.15783217]\n",
      " [0.97102495 0.89100027 0.36457393]\n",
      " [0.99634737 0.79778417 0.24116177]]\n",
      "\n",
      "Array created using np.random.randint(): \n",
      " [[ 4  6]\n",
      " [13 10]\n",
      " [18 12]]\n",
      "\n",
      "Array created using np.random.normal(), mean 0, sd=0.1: \n",
      " [[-0.01183062 -0.10486019  0.06710506  0.08734172  0.01129673]\n",
      " [-0.05499249  0.1998238  -0.02701191  0.03328722  0.06936844]\n",
      " [ 0.1176453  -0.11019566 -0.04350208 -0.19733461 -0.14227196]\n",
      " [-0.08058268  0.12879554  0.0544927   0.0618522  -0.08556055]\n",
      " [-0.04841946  0.19219804 -0.03194427  0.04541208 -0.02765047]]\n"
     ]
    }
   ],
   "source": [
    "# Other methods for creating nd arrays\n",
    "import numpy as np\n",
    "arr = np.zeros((3,5))\n",
    "print(\"\\nArray created using np.zeros(): \\n\", arr)\n",
    "\n",
    "arr = np.ones((5,4))\n",
    "print(\"\\nArray created using np.ones(): \\n\", arr)\n",
    "\n",
    "arr = np.full((6,4), 3)\n",
    "print(\"\\nArray created using np.full(): \\n\", arr)\n",
    "\n",
    "arr = np.eye(4)\n",
    "print(\"\\nArray created using np.eye(): \\n\", arr)\n",
    "\n",
    "arr = np.diag([1,2,3,4,5,6])\n",
    "print(\"\\nArray created using np.diag(): \\n\", arr)\n",
    "\n",
    "arr = np.arange(10)\n",
    "print(\"\\nArray created using np.arange(): \\n\", arr)\n",
    "\n",
    "arr = np.linspace(1, 40, 20)\n",
    "print(\"\\nArray created using np.linspace(): \\n\", arr)\n",
    "\n",
    "# Reshaping an array\n",
    "arr = np.arange(15)\n",
    "print(\"\\nArray created using np.arange(): \\n\", arr)\n",
    "\n",
    "arr = np.reshape(arr, (3,5))\n",
    "print(\"\\nArray reshaped into a 3x5 matrix using np.reshape(): \\n\", arr)\n",
    "\n",
    "arr = np.random.random((3,3))\n",
    "print(\"\\nArray created using np.random.random(): \\n\", arr)\n",
    "\n",
    "arr = np.random.randint(3,25, size=(3,2))\n",
    "print(\"\\nArray created using np.random.randint(): \\n\", arr)\n",
    "\n",
    "arr = np.random.normal(0, 0.1, size=(5,5))\n",
    "print(\"\\nArray created using np.random.normal(), mean 0, sd=0.1: \\n\", arr)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kJADqGQYeIE9"
   },
   "source": [
    "# Array Datatypes\n",
    "- numpy.bool_           \n",
    "- numpy.byte\n",
    "- numpy.ubyte\n",
    "- numpy.short\n",
    "- numpy.ushort\n",
    "- numpy.intc\n",
    "- numpy.uintc\n",
    "- numpy.int_\n",
    "- numpy.uint\n",
    "- numpy.longlong\n",
    "- numpy.ulonglong\n",
    "- numpy.half / numpy.float16\n",
    "- numpy.single\n",
    "- numpy.double\n",
    "- numpy.longdouble\n",
    "- numpy.csingle\n",
    "- numpy.cdouble\n",
    "- numpy.clongdouble"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_Fxw_ZYfd9T9"
   },
   "source": [
    "# Array Attributes\n",
    "- shape\n",
    "- ndim\n",
    "- dtype\n",
    "- itemsize\n",
    "- size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "m01eNeACl8H5",
    "outputId": "fc8b2674-e184-44f0-bd76-8b6c58a16f42"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The array is: \n",
      " [[ 1  2  3  4  5]\n",
      " [ 6  7  8  9 10]]\n",
      "\n",
      "Datatype of the array :  <class 'numpy.ndarray'>\n",
      "\n",
      "Shape / size in each dimension of the array :  (2, 5)\n",
      "\n",
      "Number of Dimensions/Axes of the array :  2\n",
      "\n",
      "Datatype of the array elements :  int32\n",
      "\n",
      "Size of the array :  10\n",
      "\n",
      "Size of each element of the array :  4\n",
      "\n",
      "Element at index 1,2 :  8\n",
      "\n",
      "\n",
      "After reassignment at index 1,2\n",
      " [[ 1  2  3  4  5]\n",
      " [ 6  7 20  9 10]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "arr1 = np.array([[1,2,3,4,5],[6,7,8,9,10]])\n",
    "print(\"The array is: \\n\", arr1)\n",
    "\n",
    "print(\"\\nDatatype of the array : \", type(arr1))\n",
    "print(\"\\nShape / size in each dimension of the array : \",arr1.shape)\n",
    "print(\"\\nNumber of Dimensions/Axes of the array : \",arr1.ndim)\n",
    "print(\"\\nDatatype of the array elements : \",arr1.dtype)\n",
    "print(\"\\nSize of the array : \", arr1.size)\n",
    "print(\"\\nSize of each element of the array : \", arr1.itemsize)\n",
    "\n",
    "# Reassign value at some index\n",
    "print(\"\\nElement at index 1,2 : \",arr1[1,2])\n",
    "arr1[1,2] = 20\n",
    "print(\"\\n\\nAfter reassignment at index 1,2\\n\",arr1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving and loading ND array "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4O5uebXugyMG",
    "outputId": "e5953ddf-72b7-4b1d-d6c9-d6218e7a94f8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Saving an nd array and loading \n",
    "import numpy as np\n",
    "arr1 = np.array([1,2,3,4,5])\n",
    "np.save('saved_arr1', arr1)\n",
    "loaded_arr1=np.load('saved_arr1.npy')\n",
    "loaded_arr1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deleting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "Kmb16RXXpeou"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5]\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'x' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m-------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0mTraceback (most recent call last)",
      "\u001b[1;32m<ipython-input-35-73f5d2182fc6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'x' is not defined"
     ]
    }
   ],
   "source": [
    "# Deleting\n",
    "x = np.array([1, 2, 3, 4, 5])\n",
    "print(x)\n",
    "del x\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x: \n",
      " [1 2 3 4 5]\n",
      "x now: \n",
      " [2 3 4]\n",
      "y: \n",
      " [[1 2 3]\n",
      " [4 5 6]\n",
      " [7 8 9]]\n",
      "y now : \n",
      " [[4 5 6]\n",
      " [7 8 9]]\n",
      "y now :\n",
      " [[2 3]\n",
      " [5 6]\n",
      " [8 9]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.array([1, 2, 3, 4, 5])\n",
    "print(\"x: \\n\", x)\n",
    "# delete the first and fifth element of x\n",
    "x = np.delete(x, [0,4])\n",
    "print(\"x now: \\n\", x)\n",
    "\n",
    "y = np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
    "print(\"y: \\n\", y)\n",
    "# delete the first row of Y\n",
    "w = np.delete(y, 0, axis=0)\n",
    "print(\"y now : \\n\", w)\n",
    "\n",
    "# delete the first column of y\n",
    "v = np.delete(y, 0, axis=1)\n",
    "print(\"y now :\\n\", v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Append, insert, copy , diagonal elements, unique elements, set operations, math functions, statistical operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9QEuOvfuptrE",
    "outputId": "f9adaa2c-7fea-4d59-b71d-a3391472ae8f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original x: \n",
      " [1 2 3 4 5]\n",
      "original y: \n",
      " [[1 2]\n",
      " [3 4]]\n",
      "x with appended values: \n",
      " [1 2 3 4 5 6 7 8]\n",
      "y with appended values on axis = 0 : \n",
      " [[ 1  2]\n",
      " [ 3  4]\n",
      " [ 9 10]]\n",
      "y with appended values on axis = 1 : \n",
      " [[ 1  2 11]\n",
      " [ 3  4 12]\n",
      " [ 9 10 13]]\n"
     ]
    }
   ],
   "source": [
    "# Append, insert, copy , diagonal elements, unique elements, set operations, math functions, statistical operations\n",
    "import numpy as np\n",
    "x=np.array([1,2,3,4,5])\n",
    "y=np.array([[1,2],[3,4]])\n",
    "print(\"original x: \\n\",  x)\n",
    "print(\"original y: \\n\" , y)\n",
    "\n",
    "#np.append(ndarray, elements, axis)\n",
    "x = np.append(x, [6,7,8]) # default axis is 0\n",
    "print(\"x with appended values: \\n\", x)\n",
    "\n",
    "\n",
    "y = np.append(y, [[9,10]], axis=0) # default axis is 0\n",
    "print(\"y with appended values on axis = 0 : \\n\", y)\n",
    "\n",
    "\n",
    "y = np.append(y, [[11],[12], [13]], axis=1) # default axis is 0\n",
    "print(\"y with appended values on axis = 1 : \\n\", y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original x: \n",
      " [1 2 3 4 5]\n",
      "original y: \n",
      " [[1 2]\n",
      " [3 4]]\n",
      "x after inserting :\n",
      " [ 1  2  3 89 90  4  5]\n",
      "y after inserting :\n",
      " [[1 5 2]\n",
      " [3 5 4]]\n"
     ]
    }
   ],
   "source": [
    "# np.insert(ndarray, index, elements, axis) - The insert method inserts the given list of elements just before\n",
    "# the index specified in the method\n",
    "import numpy as np\n",
    "x=np.array([1,2,3,4,5])\n",
    "y=np.array([[1,2],[3,4]])\n",
    "print(\"original x: \\n\",  x)\n",
    "print(\"original y: \\n\" , y)\n",
    "\n",
    "x=np.insert(x, 3, [89,90])\n",
    "print(\"x after inserting :\\n\", x)\n",
    "\n",
    "y = np.insert(y,1,5, axis=1)# This will insert 5 in both rows in column 1\n",
    "print(\"y after inserting :\\n\", y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Horizontal and vertical stacking\n",
      "\n",
      "original x: \n",
      " [1 2] \n",
      "\n",
      "original y: \n",
      " [[3 4]\n",
      " [5 6]] \n",
      "\n",
      "Vertical stacking of x and y in z: \n",
      " [[1 2]\n",
      " [3 4]\n",
      " [5 6]] \n",
      "\n",
      "Horizontal stacking of x and y in w: \n",
      " [[3 4 1]\n",
      " [5 6 2]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Horizontal and vertical stacking of the arrays of compatible dimensions\n",
    "import numpy as np\n",
    "print(\"Horizontal and vertical stacking\\n\")\n",
    "x = np.array([1,2])\n",
    "print(\"original x: \\n\",x,  \"\\n\")\n",
    "y = np.array([[3,4],[5,6]])\n",
    "print(\"original y: \\n\",y, \"\\n\")\n",
    "\n",
    "z = np.vstack((x,y)) \n",
    "print(\"Vertical stacking of x and y in z: \\n\", z, \"\\n\")\n",
    "w = np.hstack((y,x.reshape(2,1))) \n",
    "print(\"Horizontal stacking of x and y in w: \\n\",w, \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Copying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " original x: [1 2] \n",
      "\n",
      "p is the copy of x:\n",
      " [1 2]\n",
      "p1 is the copy of x:\n",
      " [1 2]\n"
     ]
    }
   ],
   "source": [
    "# Copying\n",
    "x = np.array([1,2])\n",
    "print(\" original x:\",x,  \"\\n\")\n",
    "\n",
    "p=x.copy()\n",
    "print(\"p is the copy of x:\\n\", p)\n",
    "p1=np.copy(x)\n",
    "print(\"p1 is the copy of x:\\n\", p1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diagonal Elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original y: \n",
      " [[  3   2   1]\n",
      " [124  35  16]\n",
      " [ 37  81  94]]\n",
      "Diagonal d1:\n",
      " [ 3 35 94]\n",
      "Diagonal d2:\n",
      " [ 2 16]\n",
      "Diagonal d3:\n",
      " [124  81]\n",
      "Diagonal d3:\n",
      " [1]\n",
      "Diagonal d3:\n",
      " [37]\n",
      "Diagonal d3:\n",
      " []\n",
      "Unique elements in y: \n",
      " [  1   2   3  16  35  37  81  94 124]\n",
      "y sorted row wise: \n",
      " [[  3   2   1]\n",
      " [ 37  35  16]\n",
      " [124  81  94]]\n",
      "y sorted column wise: \n",
      " [[  1   2   3]\n",
      " [ 16  35  37]\n",
      " [ 81  94 124]]\n"
     ]
    }
   ],
   "source": [
    "# Diagnol Elements\n",
    "y = np.array([[3,2,1],[124,35,16],[37,81,94]])\n",
    "print(\"original y: \\n\" , y)\n",
    "\n",
    "d1=np.diag(y)\n",
    "print(\"Diagonal d1:\\n\", d1)\n",
    "d2=np.diag(y, k=1)\n",
    "print(\"Diagonal d2:\\n\", d2)\n",
    "d3=np.diag(y, k=-1)\n",
    "print(\"Diagonal d3:\\n\", d3)\n",
    "d4=np.diag(y, k=2)\n",
    "print(\"Diagonal d3:\\n\", d4)\n",
    "d5=np.diag(y, k=-2)\n",
    "print(\"Diagonal d3:\\n\", d5)\n",
    "d6=np.diag(y, k=3)\n",
    "print(\"Diagonal d3:\\n\", d6)\n",
    "\n",
    "# Identify unique elements in an array\n",
    "el= np.unique(y)\n",
    "print(\"Unique elements in y: \\n\", el)\n",
    "\n",
    "# Sorting\n",
    "y.sort(axis=0)\n",
    "print('y sorted row wise: \\n',y )\n",
    "y.sort(axis=1)\n",
    "print('y sorted column wise: \\n',y )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wKRvwTA6Oc4Y"
   },
   "source": [
    "### Accessing Elements : Indexing and slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "H2wrVxClmNxh",
    "outputId": "763f67d2-554d-4583-e096-1b195572366e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array arr2\n",
      " [[1 2 3 4 5 6]\n",
      " [4 5 6 7 8 9]\n",
      " [2 3 4 5 7 9]\n",
      " [1 4 7 9 2 3]]\n",
      "Shape \n",
      " (4, 6)\n",
      "Element at [1,0]\n",
      " 4\n",
      "Slice of the array, rows 2 ro 4, and columns 3 onwards\n",
      " [[5 7 9]\n",
      " [9 2 3]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "arr2 = np.array([[1,2,3,4,5,6], [4,5,6,7,8,9], [2,3,4,5,7,9] , [1,4,7,9,2,3]],  dtype= \"object\")\n",
    "print(\"Array arr2\\n\",arr2)\n",
    "print(\"Shape \\n\",arr2.shape)\n",
    "print(\"Element at [1,0]\\n\", arr2 [1,0])\n",
    "print(\"Slice of the array, rows 2 ro 4, and columns 3 onwards\\n\", arr2[2:5,3:])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZMoyVhafpYaX",
    "outputId": "82609c2f-33f0-4bcf-c993-5c9f12db9192"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rank x :  [5 6 7 8]  Shape of x:  (4,)  ndim of x:  1\n",
      "Rank y :  [[5 6 7 8]] Shape of y:  (1, 4)  ndim of y:  2\n"
     ]
    }
   ],
   "source": [
    "#Extracting a single row, resulting in different ranked results\n",
    "import numpy as np\n",
    "arr3 = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n",
    "x = arr3[1, :]    \n",
    "y = arr3[1:2, :]  \n",
    "print(\"Rank x : \", x, \" Shape of x: \" , x.shape , \" ndim of x: \" , x.ndim)  \n",
    "print(\"Rank y : \", y, \"Shape of y: \",  y.shape , \" ndim of y: \" , y.ndim)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VSiyONDmoJg6",
    "outputId": "aa378105-c1b3-4a24-f436-d8234a4de814"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3 4]]\n",
      "   0  1  2  3\n",
      "0  1  2  3  4\n"
     ]
    }
   ],
   "source": [
    "# Specifying ndim parameter to array() function\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "arr4 = np.array([1, 2, 3, 4], ndmin=2)\n",
    "print(arr4)\n",
    "df_arr4 = pd.DataFrame(arr4)\n",
    "print(df_arr4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zeerUYFFoBJp",
    "outputId": "5878bddc-8435-47ea-ace3-0142b12f923c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array a : \n",
      " [[1 2 3]\n",
      " [3 4 5]\n",
      " [5 6 7]]\n",
      "Using Integer Indexing Form 1: \n",
      " [1 4 5]\n",
      "Using Integer Indexing Form 2: \n",
      " [1 4 5]\n",
      "Select one element from each row of a using the indices in b: \n",
      " [1 5 6]\n",
      "Changed elements in a: \n",
      " [[ 3  2  3]\n",
      " [ 3  4 15]\n",
      " [ 5 18  7]]\n"
     ]
    }
   ],
   "source": [
    "# Fancy Indexing - Different ways of accessing elements using integer indexing\n",
    "import numpy as np\n",
    "#A 3x3 array.\n",
    "a = np.array([[1, 2, 3], [3, 4, 5], [5, 6, 7]])\n",
    "print(\"Array a : \\n\", a)\n",
    "\n",
    "# Accessing 3 elements at a[0,0], a[1,1] and a[2,0], The returned array will have shape (3,) \n",
    "print(\"Using Integer Indexing Form 1: \\n\", a[[0, 1, 2], [0, 1, 0]])  \n",
    "\n",
    "# This line also accesses the same elements\n",
    "print(\"Using Integer Indexing Form 2: \\n\",np.array([a[0, 0], a[1, 1], a[2, 0]])) \n",
    "\n",
    "# Create an array of indices\n",
    "b = np.array([0, 2, 1])\n",
    "\n",
    "# Select one element from each row of a using the indices in b\n",
    "print(\"Select one element from each row of a using the indices in b: \\n\", a[[0,1,2], b])  #Row 0,1,2 ,Columns 0,2,1\n",
    "\n",
    "# Mutate one element from each row of a using the indices in b\n",
    "a[[0,1,2], b] *= 3\n",
    "\n",
    "print(\"Changed elements in a: \\n\", a) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "74_dNiPvqxHF",
    "outputId": "68d521db-52bd-4976-8ede-b1ba1fd4462f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array a: \n",
      " [[1 2 3 4]\n",
      " [3 4 6 7]\n",
      " [5 6 8 9]]\n",
      "Elements of a that are greater than 4 : \n",
      " [6 7 5 6 8 9]\n"
     ]
    }
   ],
   "source": [
    "#Boolean array indexing: Boolean array indexing lets you pick out arbitrary elements of an array. Frequently this type of indexing is used to select the elements of an array that satisfy some condition. Here is an example:\n",
    "import numpy as np\n",
    "\n",
    "a = np.array([[1,2,3,4], [3, 4 ,6, 7], [5, 6 , 8, 9]])\n",
    "\n",
    "print(\"Array a: \\n\", a)\n",
    "\n",
    "print(\"Elements of a that are greater than 4 : \\n\", a[a > 4])     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original arr1:  [1.2 3.5 0.  2.2]\n",
      "32 bytes\n",
      "Reversed : [2.2 0.  3.5 1.2]\n",
      "Index of non zero elements in arr1: (array([0, 1, 3], dtype=int64),)\n",
      "0.0 3.5\n"
     ]
    }
   ],
   "source": [
    "#Finding Size of an array\n",
    "import numpy as np\n",
    "\n",
    "arr1 = np.array([1.2, 3.5, 0.0, 2.2])\n",
    "print(\"Original arr1: \", arr1)\n",
    "print(\"%d bytes\" % (arr1.size * arr1.itemsize))\n",
    "\n",
    "# reversing an array\n",
    "print(\"Reversed :\", arr1[::-1])\n",
    "\n",
    "#index of non-zero elements\n",
    "nz_idx = np.nonzero(arr1)\n",
    "print('Index of non zero elements in arr1:', nz_idx)\n",
    "\n",
    "#min and max\n",
    "print(arr1.min(), arr1.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2 3 4 5]\n",
      " [3 6 7 8]]\n",
      "[[0 0 0 0 0 0]\n",
      " [0 2 3 4 5 0]\n",
      " [0 3 6 7 8 0]\n",
      " [0 0 0 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "# padding an array with zeros\n",
    "import numpy as np\n",
    "arr1 = np.array([[2,3,4,5], [3,6,7,8]])\n",
    "print(arr1)\n",
    "arr1 = np.pad(arr1, pad_width=1) #, mode='constant', constant_values=0)\n",
    "print(arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index of 50th element in a 6x7x8 array is (0, 6, 1)\n"
     ]
    }
   ],
   "source": [
    "#Find the index of a specific element in an array - print the index of 50th element in a 6x7x8 array\n",
    "import numpy as np\n",
    "print(\"Index of 50th element in a 6x7x8 array is\", np.unravel_index(49,(6,7,8)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arr1 : \n",
      " [[ 9  4  4  8]\n",
      " [11  3  5  2]\n",
      " [ 5  3  3 10]]\n",
      "Arr2 : \n",
      " [[15  2]\n",
      " [ 3 18]\n",
      " [10  6]\n",
      " [11 18]]\n",
      "Product Arr : \n",
      " [[275 258]\n",
      " [246 142]\n",
      " [224 262]]\n"
     ]
    }
   ],
   "source": [
    "# matrix multiplication\n",
    "arr1 = np.random.randint(1,12,(3,4))\n",
    "print(\"Arr1 : \\n\", arr1)\n",
    "\n",
    "arr2 = np.random.randint(2,21, (4,2))\n",
    "print(\"Arr2 : \\n\", arr2)\n",
    "\n",
    "prod_arr = arr1 @ arr2\n",
    "print(\"Product Arr : \\n\",prod_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o38HR5_WVNNk"
   },
   "source": [
    "### Arithmetic Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CsuhId1Aq-8k",
    "outputId": "2a5004c2-2960-43fe-c463-6d0915ea58d5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dot Product : \n",
      "\n",
      "[[19 22]\n",
      " [43 50]]\n",
      "[[19 22]\n",
      " [43 50]]\n",
      "Sum of all elements in a: \n",
      " 10\n",
      "Sum of all elements column wise: \n",
      " [4 6]\n",
      "Sum of all elements row wise: \n",
      " [3 7]\n",
      "Elementwise Difference of all elements in a and b: \n",
      " [[-4 -4]\n",
      " [-4 -4]]\n",
      "Elementwise Product of all elements in a and b: \n",
      " [[ 5 12]\n",
      " [21 32]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.array([[1,2],[3,4]])\n",
    "b = np.array([[5,6],[7,8]])\n",
    "print(\"Dot Product : \\n\")\n",
    "print(a.dot(b))\n",
    "print(np.dot(a, b))\n",
    "\n",
    "print(\"Sum of all elements in a: \\n\", np.sum(a))  \n",
    "print(\"Sum of all elements column wise: \\n\", np.sum(a, axis=0))  \n",
    "print(\"Sum of all elements row wise: \\n\", np.sum(a, axis=1))  \n",
    "\n",
    "print(\"Elementwise Difference of all elements in a and b: \\n\", np.subtract(a,b))\n",
    "print(\"Elementwise Product of all elements in a and b: \\n\", np.multiply(a,b))  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Product of a vector with an array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fv8PhPl0YNRd",
    "outputId": "4c348eeb-34d2-4421-be66-f3dceda2f135"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original v: \n",
      " [1 2 3] Shape of v : (3,)\n",
      "Original w: \n",
      " [4 5] Shape of w : (2,)\n",
      "Now the Shape of v : (3, 1)\n",
      "Outer product v x w (After reshaping v): \n",
      " [[ 4  5]\n",
      " [ 8 10]\n",
      " [12 15]] Shape of product :  (3, 2)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "# Compute outer product of vectors\n",
    "v = np.array([1,2,3])  # v has shape (3,)\n",
    "w = np.array([4,5])    # w has shape (2,)\n",
    "\n",
    "print(\"Original v: \\n\" , v , \"Shape of v :\", v.shape)\n",
    "print(\"Original w: \\n\" , w , \"Shape of w :\", w.shape)\n",
    "\n",
    "# To compute an outer product, we first reshape v to be a column vector of shape (3, 1); we can then broadcast it against w to yield an output of shape (3, 2), which is the outer product of v and w:\n",
    "v1=np.reshape(v,(3,1))\n",
    "print(\"Now the Shape of v :\", v1.shape)\n",
    "print(\"Outer product v x w (After reshaping v): \\n\" , v1 * w, \"Shape of product : \", (v1*w).shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original x: \n",
      " [1 2 3]\n",
      "Original y: \n",
      " [4 5] \n",
      " Shape of y:  (2,) \n",
      " Dimensions of y: 1\n",
      "Original z : \n",
      " [[1 2 3]\n",
      " [4 5 6]]\n",
      "Sum of z and x: \n",
      " [[2 4 6]\n",
      " [5 7 9]]\n",
      "Sum of y and z by reshaping y to a column vector : \n",
      " [[ 5  6  7]\n",
      " [ 9 10 11]]\n"
     ]
    }
   ],
   "source": [
    "# Add a vector to each row of a matrix\n",
    "import numpy as np\n",
    "\n",
    "# Compute outer product of vectors\n",
    "x = np.array([1,2,3])  # v has shape (3,)\n",
    "y = np.array([4,5])    # w has shape (2,)\n",
    "z = np.array([[1,2,3], [4,5,6]])\n",
    "\n",
    "print(\"Original x: \\n\" , x)\n",
    "print(\"Original y: \\n\" , y , \"\\n Shape of y: \", y.shape, '\\n Dimensions of y:' , y.ndim)\n",
    "print('Original z : \\n', z)\n",
    "# z has shape (2, 3) and x has shape (3,) so they broadcast to (2, 3),\n",
    "# Add a vector to each row of a matrix\n",
    "print(\"Sum of z and x: \\n\" , z + x)\n",
    "print('Sum of y and z by reshaping y to a column vector : \\n', z + np.reshape(y, (2, 1)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "r-K8zXUDrJDk",
    "outputId": "7d1e8bd2-cc10-48ae-e901-c5d68d41cd8e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original x: \n",
      " [[ 1  2  3]\n",
      " [ 4  5  6]\n",
      " [ 7  8  9]\n",
      " [10 11 12]]\n",
      "Original y: \n",
      " [1 0 1]\n",
      "Original z: \n",
      " [[ 1  2  3]\n",
      " [ 4  5  6]\n",
      " [ 7  8  9]\n",
      " [10 11 12]]\n",
      "\n",
      "Adding vector y to array x using a loop: \n",
      " [[ 2  2  4]\n",
      " [ 5  5  7]\n",
      " [ 8  8 10]\n",
      " [11 11 13]]\n",
      "\n",
      "Adding v to x using broadcasting: \n",
      " [[ 2  2  4]\n",
      " [ 5  5  7]\n",
      " [ 8  8 10]\n",
      " [11 11 13]]\n"
     ]
    }
   ],
   "source": [
    "# Broadcasting\n",
    "import numpy as np\n",
    "\n",
    "# We will add the vector v to each row of the matrix x,\n",
    "x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\n",
    "y = np.array([1, 0, 1])\n",
    "z = np.empty_like(x)   # Create an empty matrix with the same shape as x\n",
    "\n",
    "print(\"Original x: \\n\", x)\n",
    "print(\"Original y: \\n\", y)\n",
    "print(\"Original z: \\n\", z)\n",
    "\n",
    "# Add the vector y to each row of the matrix x with an explicit loop\n",
    "for i in range(4):\n",
    "    z[i, :] = x[i, :] + y\n",
    "print(\"\\nAdding vector y to array x using a loop: \\n\", z)\n",
    "\n",
    "z=x+y\n",
    "print(\"\\nAdding v to x using broadcasting: \\n\", z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshaping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original arr1: \n",
      " [43  9  8 29 10 34 15 22 36 42 39 32]\n",
      "Reshaping arr1 to (6x2): \n",
      " [[43  9]\n",
      " [ 8 29]\n",
      " [10 34]\n",
      " [15 22]\n",
      " [36 42]\n",
      " [39 32]]\n",
      "Reshaping arr1 to (3x2x2): \n",
      " [[[43  9]\n",
      "  [ 8 29]]\n",
      "\n",
      " [[10 34]\n",
      "  [15 22]]\n",
      "\n",
      " [[36 42]\n",
      "  [39 32]]]\n",
      "\n",
      "Original arr2: \n",
      " [[30 19 31  8 42 32 33 31 39]\n",
      " [15 10 20 20 15 17 28  4 42]\n",
      " [ 7 15  2 43 31  7 29 22 43]]\n",
      "Reshaping arr2 to (3x3x3): \n",
      " [[[30 19 31]\n",
      "  [ 8 42 32]\n",
      "  [33 31 39]]\n",
      "\n",
      " [[15 10 20]\n",
      "  [20 15 17]\n",
      "  [28  4 42]]\n",
      "\n",
      " [[ 7 15  2]\n",
      "  [43 31  7]\n",
      "  [29 22 43]]]\n",
      "Reshaping arr2 to (3x3x3x1): \n",
      " [[[[30]\n",
      "   [19]\n",
      "   [31]]\n",
      "\n",
      "  [[ 8]\n",
      "   [42]\n",
      "   [32]]\n",
      "\n",
      "  [[33]\n",
      "   [31]\n",
      "   [39]]]\n",
      "\n",
      "\n",
      " [[[15]\n",
      "   [10]\n",
      "   [20]]\n",
      "\n",
      "  [[20]\n",
      "   [15]\n",
      "   [17]]\n",
      "\n",
      "  [[28]\n",
      "   [ 4]\n",
      "   [42]]]\n",
      "\n",
      "\n",
      " [[[ 7]\n",
      "   [15]\n",
      "   [ 2]]\n",
      "\n",
      "  [[43]\n",
      "   [31]\n",
      "   [ 7]]\n",
      "\n",
      "  [[29]\n",
      "   [22]\n",
      "   [43]]]]\n"
     ]
    }
   ],
   "source": [
    "# Reshaping\n",
    "import numpy as np\n",
    "#random.randint(low, high=None, size=None, dtype=int)\n",
    "arr1 = np.random.randint(2,44,size = 12)\n",
    "print(\"Original arr1: \\n\", arr1)\n",
    "print('Reshaping arr1 to (6x2): \\n' , arr1.reshape(6,2))\n",
    "print('Reshaping arr1 to (3x2x2): \\n' , arr1.reshape(3,2,2))\n",
    "\n",
    "arr2 = np.random.randint(2,44,size = (3,9))\n",
    "print(\"\\nOriginal arr2: \\n\", arr2)\n",
    "print('Reshaping arr2 to (3x3x3): \\n' , arr2.reshape(3,3,3))\n",
    "print('Reshaping arr2 to (3x3x3x1): \\n' , arr2.reshape(3,3,3,1) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original arr1 : \n",
      " [[1 2]\n",
      " [3 4]\n",
      " [5 6]]\n",
      "Shape of arr1 : \n",
      " (3, 2)\n",
      "Reshaped to flat vector :  [1 2 3 4 5 6]  \n",
      "New shape of arr1:  (6,)\n",
      "   0\n",
      "0  1\n",
      "1  2\n",
      "2  3\n",
      "3  4\n",
      "4  5\n",
      "5  6\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "arr1 = np.array([[1 , 2], [3, 4], [5, 6]])\n",
    "print(\"Original arr1 : \\n\", arr1)\n",
    "print(\"Shape of arr1 : \\n\", arr1.shape)\n",
    "# Reshape to flat array\n",
    "print(\"Reshaped to flat vector : \", arr1.reshape(-1), \" \\nNew shape of arr1: \", arr1.reshape(-1).shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Special functions in NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5 6]\n",
      "[ 2  4  6  7  8 10]\n",
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# Checking elementwise similarity within a tolerance limit\n",
    "arr1 = np.arange(1, 7)\n",
    "print(arr1)\n",
    "arr2 = np.array([2, 4, 6, 7, 8, 10])\n",
    "print(arr2)\n",
    "\n",
    "tol = 0.5\n",
    "print(np.allclose(arr1, arr2, tol))\n",
    "\n",
    "tol = 0.1\n",
    "print(np.allclose(arr1, arr2, tol))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original arr :\n",
      " [[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]\n",
      " [12 13 14 15]\n",
      " [16 17 18 19]]\n",
      "Indexes where the elements in arr are greated than 15:  (array([4, 4, 4, 4], dtype=int64), array([0, 1, 2, 3], dtype=int64)) \n",
      "\n",
      "Find the elements less than 3, replace them by five times their value, retain the remaining values: \n",
      " [[ 0  5 10  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]\n",
      " [12 13 14 15]\n",
      " [16 17 18 19]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "arr = np.arange(20).reshape(5, 4)\n",
    "print(\"Original arr :\\n\", arr)\n",
    "print(\"Indexes where the elements in arr are greated than 15: \", np.where(arr > 15), \"\\n\")\n",
    "print(\"Find the elements less than 3, replace them by five times their value, retain the remaining values: \\n\", np.where(arr<3,arr*5,arr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original arr  :\n",
      " [0 1 2 3 4 5 6 7 8 9]\n",
      "Values from arr clipped between range 4 and 5 : [4 4 4 4 4 5 5 5 5 5]\n"
     ]
    }
   ],
   "source": [
    "# Clipping values to keep the values of an array within an interval between a defined upper and lower range.\n",
    "arr = np.arange(10)\n",
    "print(\"Original arr  :\\n\", arr)\n",
    "print(\"Values from arr clipped between range 4 and 5 :\" , arr.clip(4, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 2 4]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([23, 29, 32])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# argpartition() - Returns the indices of 'n' largest elements in an array \n",
    "arr = np.array([23, 1, 32, 4, 29])\n",
    "indexes = np.argpartition (arr, -3)[-3:]\n",
    "print(indexes)\n",
    "np.sort(arr[indexes])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True False  True False  True False  True False  True False]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4, 6, 8])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract() - To extract particular members of an array.\n",
    "arr1 = np.arange(10)\n",
    "mask1 = np.mod(arr1, 2) == 0\n",
    "print(mask1)\n",
    "np.extract(mask1, arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12  1]\n",
      "1110\n",
      "[ 2  4 16]\n",
      "[0 0 2]\n"
     ]
    }
   ],
   "source": [
    "# Bitwise Operations\n",
    "import numpy as np\n",
    "print(np.bitwise_and([14 , 3], 13))\n",
    "print(np.binary_repr(14))\n",
    "print(np.bitwise_and(np.array([2,5,255]), np.array([3,14,16])))\n",
    "\n",
    "arr1 = np.array([1,2,3])\n",
    "arr2 = np.array([4,5,6])\n",
    "print(arr1 & arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " d: ['2002-10-27T04:30' '2002-10-27T05:00' '2002-10-27T05:30'\n",
      " '2002-10-27T06:00']\n",
      "\n",
      " with UTC:  ['2002-10-27T04:30Z' '2002-10-27T05:00Z' '2002-10-27T05:30Z'\n",
      " '2002-10-27T06:00Z']\n",
      "\n",
      " with US\\Eastern Timezone ['2002-10-27T00:30-0400' '2002-10-27T01:00-0400' '2002-10-27T01:30-0400'\n",
      " '2002-10-27T01:00-0500']\n"
     ]
    }
   ],
   "source": [
    "# Datatime support functions\n",
    "import pytz\n",
    "d = np.arange('2002-10-27T04:30', 2*60, 30, dtype='M8[m]')\n",
    "print(\" d:\", d)\n",
    "print(\"\\n with UTC: \", np.datetime_as_string(d, timezone='UTC'))\n",
    "print(\"\\n with US\\Eastern Timezone\", np.datetime_as_string(d, timezone=pytz.timezone('US/Eastern')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True  True  True False False]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Business days in calendar\n",
    "import numpy as np\n",
    "busdays = np.busdaycalendar(holidays = ['2021-10-30', '2021-10-29', '20-10-28'])\n",
    "print(busdays.weekmask)\n",
    "np.is_busday(['2021-10-27'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2  3  4]\n",
      " [ 6  7  8]\n",
      " [ 9 10 11]]\n",
      "determinant: \n",
      " -2.467162276944798e-15\n",
      "determinant: \n",
      " -2.0000000000000004\n",
      "determinant: \n",
      " [-2. -3. -8.]\n",
      "rank: \n",
      " [2 2 2]\n"
     ]
    }
   ],
   "source": [
    "# Linear Algebra \n",
    "import numpy as np\n",
    "arr1 = np.array([[2,3,4],[6,7,8],[9,10,11]])\n",
    "print(arr1)\n",
    "print('determinant: \\n', np.linalg.det(arr1))\n",
    "\n",
    "a = np.array([[1, 2], [3, 4]])\n",
    "print('determinant: \\n', np.linalg.det(a))\n",
    "\n",
    "\n",
    "a = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ])\n",
    "print('determinant: \\n', np.linalg.det(a))\n",
    "#Rank of the array is the number of singular values of the array that are greater than tolerance parameter.\n",
    "print('rank: \\n', np.linalg.matrix_rank(a))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['abid' '70' 'F' 'Delhi' '28.679079' '77.06971']\n",
      " ['Aabida' '54' 'M' 'Delhi' '28.679079' '77.06971']\n",
      " ['Achal' '65' 'M' 'Delhi' '28.679079' '77.06971']\n",
      " ['aadesh' '76' 'M' 'Delhi' '28.679079' '77.06971']\n",
      " ['aadil' '34' 'M' 'Delhi' '28.679079' '77.06971']\n",
      " ['aadish' '23' 'M' 'Delhi' '28.679079' '77.06971']\n",
      " ['aaditya' '65' 'M' 'Delhi' '28.679079' '77.06971']\n",
      " ['aaenab' '74' 'M' 'Delhi' '28.679079' '77.06971']\n",
      " ['aafreen' '13' 'M' 'Delhi' '28.679079' '77.06971']]\n"
     ]
    }
   ],
   "source": [
    "# Reading the contents of a csv file into an ndarray\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import csv\n",
    "f = open('population.csv')\n",
    "population_data = list(csv.reader(f))\n",
    "arr_pop = np.array(population_data[1:10])\n",
    "print(arr_pop)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Universal Functions\n",
    "Functions that operate on ndarrays element by element. They are vectorized wrappers for functions with specific number of inputs and outputs.\n",
    "\n",
    "\n",
    "### Partial list of important numpy functions\n",
    "### Source credit - https://numpy.org/doc/stable/reference/generated/numpy.linalg.eigvals.html#numpy.linalg.eigvals\n",
    "\n",
    "### Linear Algebra\n",
    "- np.dot\n",
    "- np.linalg.multi_dot\n",
    "- np.inner\n",
    "- np.outer\n",
    "- np.matmul\n",
    "- np.tensordot\n",
    "- np.linalg.svd\n",
    "- np.linalg.eigvals\n",
    "- np.linalg.norm\n",
    "- np.linale.det\n",
    "\n",
    "### Polynomials\n",
    "- np.polynomial.polynomial\n",
    "### Sorting\n",
    "- np.sort\n",
    "- np.lexsort\n",
    "- np.argsort\n",
    "- np.ndarray.sort\n",
    "- np.partition\n",
    "- np.argwhere\n",
    "- np.nonzero\n",
    "- np.count_nonzero\n",
    "\n",
    "### Statistical Functions\n",
    "- np.amin\n",
    "- np.amax\n",
    "- np.nanmin\n",
    "- np.percentile\n",
    "- np.quantile\n",
    "- np.histogram\n",
    "- np.digitize\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-k2TmGY-rlFP"
   },
   "source": [
    "# Matplotlib\n",
    "Matplotlib is a plotting library for 2D and 3D plots in Python. Here we will see the brief introduction to matpotlib.pyplot module that provides MATLAB like plotting functionality in Python.\n",
    "\n",
    "### Some 2D plot types in Matplotlib\n",
    "- Line Plot\n",
    "- Multiple subplots in one figure\n",
    "- Images\n",
    "- Contouring and pseudocolor\n",
    "- Histograms\n",
    "- Paths\n",
    "- Streamplot\n",
    "- Ellipses\n",
    "- Bar charts\n",
    "- Pie charts\n",
    "- Tables\n",
    "- Scatter plots\n",
    "\n",
    "Additional Features of Matplotlib plots\n",
    "- GUI widgets\n",
    "- Filled curves\n",
    "- Date handling\n",
    "- Log plots\n",
    "- Polar plots\n",
    "- Legends\n",
    "- TeX-notation for text objects\n",
    "- Native TeX rendering\n",
    "- EEG GUI\n",
    "- XKCD-style sketch plots\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = np.arange(16, 626)\n",
    "y = np.sqrt(x)\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Square Root X')\n",
    "plt.title(\"Square Root Curve\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 251
    },
    "id": "tZObzt2vJR-V",
    "outputId": "c4a25318-0664-4ba0-8509-28407ff666c0"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Source Credit: https://www.geeksforgeeks.org/how-to-set-the-spacing-between-subplots-in-matplotlib-in-python/\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# create data\n",
    "x=np.array([1, 2, 3, 4, 5])\n",
    "# making subplots\n",
    "fig, ax = plt.subplots(3, 3)\n",
    "# set data with subplots and plot\n",
    "ax[0, 0].plot(x, x)\n",
    "ax[0, 1].plot(x, x*x)\n",
    "ax[0, 2].plot(x, x*x*x)\n",
    "ax[1, 0].plot(x, x*x*x*x)\n",
    "ax[1, 1].plot(x, x*x*x*x*x)\n",
    "ax[1, 2].plot(x, x*x*x*x*x)\n",
    "ax[2, 0].plot(x, x*x*x*x*x*x)\n",
    "ax[2, 1].plot(x, x*x*x*x*x*x*x)\n",
    "ax[2, 2].plot(x, x*x*x*x*x*x*x*x)\n",
    "# set the spacing between subplots\n",
    "plt.subplots_adjust(left=0.2,bottom=0.2,right=0.9,top=0.9,wspace=0.7,hspace=0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Source Credit - https://matplotlib.org/stable/gallery/shapes_and_collections/scatter.html\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "N = 10\n",
    "x = np.arange(1, 11)\n",
    "y = np.arange(40, 50)\n",
    "plt.scatter(x, y, alpha=0.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASUAAAD7CAYAAAAsJIKcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOy9abAlx3Ue+GVm1V3e1svrFd1AN4DGRiwNgAT3BYQoiRJNcSzLEql1bI8Ys9AjW9aI9NjhcdjjCI09IWscjpgYhWWHQuPQMmFRpiiJEsVNokAR3AmAIPal9+X1299dqjJzfpwls+59DcgEMfMkVv5A491btyorKyvzO+d85zsmxoi2ta1tbdspzf7/3YG2ta1tbctbuyi1rW1t21GtXZTa1ra27ajWLkpta1vbdlRrF6W2ta1tO6q1i1Lb2ta2HdVesUXJGPNOY8zjxpinjDEfeqWu07a2te2vVjOvBE/JGOMAPAHguwGcBvAFAO+LMX7j236xtrWtbX+lWvEKnfe1AJ6KMT4DAMaY3wDwHgDbLkoLc/14YHEBaKyPpnFMRJz4RI6JjeOj/B0nDtMPJ89i0nfbrc9TF03XM5NfGkAWeTNx9PYni9k3k/e7bQ+v2mgk5NrTI6XfmW2+m9iYjDFIH03/Tr4zJv1OR0W/My/a3xdrL3a/jXsz0p/JUTYA91efx/RtY7v9WK9twjbjmI3Bi4z1dM/tVY7afoSaYzc5n7c50/RrkL7absrDZONi+N/0Xf7/eszEQ5HfxZeYqUaf0XS3v/nUqcsxxv2Tn79Si9IRAKeyv08DeF1+gDHm/QDeDwD79s7jX37wx+ACEOEBANZQ1zzfaAgBjm/W6p1a+Bjo/53l42r62we9VpCxshGTt6zX8R6IdG2XPUX5bdTBpWMKW2YP1Ogxvqob5wgm8HcWMI7PIRM66HQ1cI1+xWwiGWMbv6P75HHi31trEeKYz1XKzaXjY63HTTa5p8Dj65wDn15nk3NOr6/9d0Hv33s+jjteGIto6Z7kUcg9WROhF+BjYjDyCPU51zGk3/GDKFwn9cFFHouaP6NjnC31Pr336b7lWXD/Q/6m8Pkdj3WwA50bct58YZaxMtHqdxYyPnJv8mw6cDzvQuAxs1Hn7uQCYa3V11reBx0uOJ0P+vLbOHUO8cyYSNeiT9J7U/MJXVk07ts5p2Nni0I/c645d+VvH/MFiM9voo5t4WRM0nss8+AN7/qZ57FNe6UWpZfcPmKMvwzglwHgxHWHoomWHxj9tOaXCC4tGpEHy/Ast4g6gPWoovPq4hGmHlSsI6xtToTIE9UZo9eOfP4QAoy8KTLR+HfeBJ2Q+csv5+XeQ9YAA4s6VI0BcSYiqFuv+eYam85li7JxbkeQTO8TAHwdFKZEvnq+aEY+f6zTQqIvJ4+BjGUMEZFfHseLpYkBRia6vLg+TTRBDJEne4UIw+cNE67L2gbtm/Pyu4Cah1jGTt89BBhe0OtaFqAII781zfP76OH9xAIU0jXTs4d+py8sP0vvDYzMH3nxrKCDbAEx6fyyjsvt2kh9DlVADXr2uin4hB78JJrzHs7J2Mr5eUwKIMhc1GeavVo8X2224Jkoi13B95M27GpEG1ns8BwzAQUfJwu1tQZ1mNiAuQ/GGHjZ8Pg5WFPoE48TiMoYpwvt1dortSidBnBt9vdRAGevdnCMEaGqJ2A4D5wgnxDgg0wYXuVN1MkkO1DaxS1kHQx6Ko9omghjeodpvsx6fe2s5z4klCVdNc5mLzp/JhPPj2EFDdm0oHj+sZxLl6gQ0iLhm4tMzPqqSMA46IJe0wtQlqWcTc8rL7VzTsdMJ7f2q4bj/sjCUo3SS22LCVRXB118pW919CgU4cmY8WR0Ni32gvQA1CHhAxom3r2NQRVGPC7IxosXNtdclMI2u3e+kCjKkeecuqFjEqyFkYU5yCIsm4TTMfMhLfKChC2PduANINR1uk8ZO5tsLtkEE9oPAASZNG4N3vt0T7wBxBjTiqZ7W7rHhBb5JDGdA7FpYRjj0hzJNvCiI+ipiZiAhJoSCjR6fpnfsoFba3SRvFp7paJvXwBwkzHmemNMB8B7AXzkFbpW29rWtr9C7RVBSjHG2hjzAQB/CFry/32M8dEX+03ateUc+g0A3ol4J++wrQs1+ZIZJn/X3usKLrugt5lPRnZJ8SPGZOOLz8pGIKotIbtG6qPCevELxYCKUY36xsTkjAAYwgvQ9zHAWUIzTvwjmXmlO+mULyfbSxhdFEWByjdRoB8nkzbIOMpWWldTPrHAvzcxoDBNdBZChv5CE2U6GHk06Rhnk0+o9o3jjXVqcsRsh05IRm6NvqsBePaXFbar5xIztR43zc/cJ7YdAmY3FjwjytyUFTPROKuOHEF6Mi987ROKlWdDfwCAPoeS559BciXIGOdOQzHzovQHZILSh83rOOd0/uR9cLGJXn1Iz0/8lYEREKyBlfkcmnMr2Kj+JfB7ZrsZwi47fAV54EBQc1DM/6DITVCjmG8xXN3BL+2VMt8QY/x9AL//Sp2/bW1r21/N9ootSv9lLQIIqOsazhFymERO1lpUY3Eo8K7R7cpGot59tXV9pWhIvysyhzTb8eJ3CaFGKbuk7Ni1R6E7LSOx3BzOIi/Sx1gPGn206ij22leNDIWoO7PuhN2EXuqKrlnIjmslKpX8b4JQrLEoxA8UJiJDSL4hREZrdQ03ER0rCkFFXpFVcgrnaKKJ2HyMqKU/4gSvvKI4RTDyXcgcpeonHGVRAelr8jsF9Q8zAojpuElKQI6UBP0656b644MgpZieYRbyjtY0jtf7DynaJYGYGGMjagskBNFAYvyd9zGNI5ooP4QAI76nCZ9niD5F5GwaJ+8FScoLIeMVBfDA8ntQ+6rhlwQAVBwwikCH7ylFMGsYK/5KRkyCpoxJ/w/9aKpZfRe3jwA3jn3Rb9vWtra17f/jtkOQEvM8bJEiNNnnABCNw5XlFQDAQ1/+OgBg78FrMPKyEzKnYhsCl+xA1iauTSHbR1Coke3oEgkzcOJ0ck3/S1mWqNlXUpZF+s4IH4gvLrfj0m+Lguxy8ScB0LBqUaZdOYbmTp2+81O7U8d1UJtRs//Zrh+M+H9kBwbGwinisQhekFXQ37qCd1ATdLcTX4lgWZchgZwfpBws9d8JRSA9GwlT0+e+cbzsmQ4OI0Y1RYZaJGKkACvzfxl2fYinxWWEQXnmZTHHv4uoqqrxnXEJaSoXziSOUWqprxkzDgAwzqKJcukGJUN+ayTCnFBREN+kMBWysdQol0vWgUzT4QSPCDAYVU0qirGh4aOS80rzno6vx9SHDjroSmQ28HnVoon6AATBmWh0DsqNhwbie/Ho245ZlEKgCZqIcHSDY4alc/1ZVOzM+4OPfwUAcPtdAyyvrvHvm+Yekf3oHJ2CnKPep4ksC8OkgxZI5k+nKHWy6kTMiGj6MusksQpdBeaKeZhDVuHQ9DrdxJUx8lJz/2GmYK5MnE63QOmKxmfGuG1MRl7MCptN7uTQzSkDNGalXlvMSumCLW3WHwkPS0g7+61O8phIe1kwQY4R80TOGWLinCmHSRZjUyhvTc9vrfKY0guY/paXQJzazlh0+A8hYIbM/CvLHn0n99icTvw7+m7sx7pZdQoZQ5s56iXg0OgVfaZUi0QrSItc6k+cXAh1wZ3mWzlnUFW0IemmIBsS8hC/hOUTRWSK8Z4NpZj1dRUQMeYe0gFujgMO8BjzAmqZTOuiS0TciWdKv3nx1ppvbWtb23ZU2xFIKUaG+9YoXlWTi5fVuq7V8Xn82GEAwMGDRzA3uxdA5myWk1qrq7U4tTuu1N11MswOazMzQ8LzPp1DwsFsRjgYFLzzCmU/ZqQxCYMHYQHHBFtlF6zrWk0iN8GUjSYoepImqRx+aDHwTdNlVG8p2XCSEFqFKpEb1UFuUYXmcdJMMKgYuiv5zUEdv+KcHvsh96FQpCSxBO+TQ1YQpSC3cT1SdFN0UtrIkCkMkyiwV3bUdB3XyXGtrOdtUmjU5GVE1usUauJIXzUdBBYd7kfB3xlj1EyV/lubxrPka3fL9ArFLFUDUIsfrrAoxWSXkHpI80e7zaiotBYe4hooG//GmJzyDXPeTZjimr5kmFgLlAIbTa2uA3GXyLk6tsz6z2NoLYy4Rzoz9FmXTN9Ot6u0DvHNmxCxNaK5EXiONObYdp7wrLVIqW1ta9uOajsCKQERIYSUTJs1QQej0QijEdnN4ucZDrfgK1mR6bPR5iYA2ill15NzbEWDXt81ziEEvxCj7lTipHNxOm1EbWVjp3xK0STynebP8QgXRUdTJMSRmGfZy+/SjpjcgU4dzAlBSLKr7JD9Xm+KaKd5gJYoF81moXvShI+iKDpT9+1DSKF6bjHzyRSZ055+mDuzr773pcTOoGH8qhLkk/oVOfw9o/6yPMWG0Q37fKqxV19Gxf4UX9fa/3q0wbedAgnVmImP3G8XU5qPmSRKxqiIOTnbbSJeSjhenrMFirLpc4s+KrIVpCfICVlOpyTMCpJzxqrPSs5Vx1pRXfqd+DKLFLQQn5Jz6scS/5rMZWetzsEcfZUl+ZBcl3xvJfen1+ujJ4iP6SYzsyWuu/4oAGC2T8eNOTcV1kznzky0HbEoxcjZw40seHpQ4kQrxh41Jw92utnLyQ9ozxwN1uFrD9Ex1qiDL72sgA90Dk3kDcmhKAtVqQ7RHqqMIS7XBGhslT3Ob1MIQVcCsQzGhhMxYTSR1Th2vEeva4X0teYXstvtpuRedvZLHl5dJ56MC5JlX6tZJTlUVZ3uTe5XHKIUWBAvdqaUAGCrqjQJWDk61uhLlrLgU8St1hcyOcMn+UPyt/c+44dJH02KoPILJux4ugaPqy7GDm4i37Ho0BzoFInp79QcixAvrpvv8TVT/42+ChLxS2Mc7dXNDVn4G1Gx2FzsQ7aYa1+t1Tkim5MGXULaLKXJs4kZs37IC6iPBoaniCgsRCsRtwpbW1uN85dlmSln8LiOpe8p2ihBJuNS8CRM5K3Fqsb+PeRC+eqXHgIA3Pfau/DffeCn6T55sdsaEHjolv1twUfeWvOtbW1r245qOwIpAZz9nUFybzL0AdptBEIK8hn5GsMxreZ7DtBqff1+0owKo01ljkjuT6fowWM0ceG0m0/mP0VkjmujXjztl5nYqX0mIidoa8iOPudcOq8Q0zOzRpCSZsHbYiqLvyzZ1PE+oUBuPqb+WCcU3sRB8RNIL8aYcsyUx5V4OWLyJpMlC/1OOM2995kpJblRNukboYk4okkoTiyWuq4T/UJNkWQeD4e82zOCM7AJXUrIm++o8l7NNsldzE1MQbaCBGyRMcD5vjs2ouL/l+MnKQv5GMAVGeuaTV8ZiTyIkuVcSn9kasm5yrKboSg2oeSaNsKKPImoYCAFGkrbdOKbwmFmZqY5Tplm0mQrreN5nJnnhoI+QHJ+Kw2vCpjr0/lvvvUmAMD8rjn9rZ/IWJik7mzXWqTUtra1bUe1HYOUEE1TjCzboQFgPB6njHh2svnM7nY1IxSxvesKRanxaTpmGFCUE7lEdXIaarhcdIAQdQeNdiLsHILu8vkukJyz9MNemdCKCKeJIJpFCu+ibPo0jDEY8z2Jo7XkcwcbYDqCSORnBSKjMs31k13NFgjCxLXNHTvva06OVI2fnNAn/hPR/VG0ZhC139MMYW2ibmCcoi4NP8OgqgnFip9PSZSuRF0zeU9cUM7Ci1NaHL51oloI2nLZuMYJPS6vtxtRVYzMeT4Y6/V4eSZJKdJjksBY+ahh+EksQEhV+ib0lELHfaT3Rucaj2sUXSZ4qk+TjqnqOvGhJffQRww31um3E/6vbq9ErEWETSasx2jcpGtIG3uvSAlZoKEoxB8qkRumgBQd1KzgUEs2Q2Eb6htAIgfH6BMx9CqtRUpta1vbdlTbEUgpgkl6lU8Z0+wMED9DgSKFNIV6j4BC0APvNmLzRpdo/KJMGOERfHOVdkbyuEKm8yJaNFZ9SPKdRB+cTeL6Ke8rIrAvTM8lPg1nkwKC2Nu+npJhVReFc5m+Mf+LhNLQyL8CEGLSLhdJV1VHHGYZ7gp39KeaBiIoM/opFOisnaYcJNFflKJImD2/pO4pfhTR2xmjK5uxEE99wAzvxsE2UQhMjV5H0JkgW6hzS0mm4vbrdGAl9URTsKKGygzzNHLtJ/W5ySWzdAtNhQnpXHmKBwDUAclBpqqOPOYx6PkFTTnnFI1KpC0pMuZYIfl1APJb5ikndDmTfGJMVZDZ0fD3WVE08EmNFRPPNOaIT/qfouE1D9DyBqHypc0het1Z6VwaMx6KkOk6ydC8BCNgZyxKAA2GzWCu8C4qhZyJ96HysCFoOFWE9DXHyxaIDCdFVsNYo/AzOae5WaNsb5kkNpqUiKsTk1/0mHSdNQKczSV9n7K8LDmvK+V3UXOUBNKKEzO6CF/LQsj9lxfHmUY4G6AJXWuVg+a1bWGRaypL/8Q8qWWhFikMRE32NJJYabYpHCDmMUwW9ub7MBFeBe9kIWcuU4jJzJUE7IxikXLC5PxexcisE0qGVyla6KZAfS2NRWTqh8qi5/KtaDqPo48ohAMmY+fDVFi+yGgDavLKvQFJpE2oHyrrG3R8NKfQBJUekY2uyDS09UnIvTUkiJnOIhuxszD6JlMwKEn+JrNQNgVXWtSSHaCFJ/i9CDkrXCZn0LEe82fnerQoLa+dRhQtcs9cO9dNpq8GgyTroUD0L74qteZb29rWth3VdgRSMiDWsrEGoeLdW0WxxEFbJJgu5MaiRJQqJpMZ9dbCTuSmOVsmNKBVIiR0ibTj8jny8GVuVmnT3TuTGxGBdNkMrIjmJ1EvVSW10K1ZMIFUqqDKK4KC5LZziQgxARVqZfSFZqdNtHpP0YvNkqwMRTzKYyyTqZaLc6kzm8+VWZNJQI2PD4m4qI5in4IKSsXIQuWTDGoTE7pQSRtBT8YoI1qaMPF9CNpXq3mGfkqxIadJiNlpGCXUdb0NBYDNGh8SMzuTGbY2RzMJvVehbqgn0LUrZeqrZKwiy6ZqBV1zGl04EaEzyBBJImdKXzsq7C9IN2gVFjVNa6au2ELH3UkBBF/pQmEY7fY5gFBah5pzFoV1HhAVZSUTU6gBZsq5PtlapNS2trVtR7UdgZQAIsMZWJVkHQ4oPylku7LYv1F33qjObw3zqph9Zo9nrgex84NvhsEL65J0qhLEovoHvIY0hTZglESodbusS/Y731fgfKCuK1QsX8vPeD9VsiZqmDs50lXsX4plWqtSqzFIZnryc0BrdIHvP2TnZ59PUSBaSV2o0gCB0KmkXWhGgI0JWRnxRfHfwSSCpBdCX0wBBkF6gnpD2klFkjfzQ2cSYIxUfK1FLmNeeih7FvShIIdSc7tkpy7LjNwoiEeybKxNwQEBnrbQkHtUgq34Ai28+ogSFSLEhEQAwBQCJQtNw9GyUzYqZSNXqpAW1B8lQQXosYrc+B6Nc1AAHOU+hLBqU96mIKaYRN/Ep6fFFOIIzqT8SwDw8EmtQ1Q82H9W+xF6rKslqhGuk/SyVAAuo83kBSy3aztiUYowqOEAeAR+QSw/UFXq8xFQbkvig2g0Rrk/WfRHhb7EGelRMaeiZMdeXdHf62sDuI6InDHHJRiNHKn4lUxkGBipB5aqDyZRNEmatD3us9OXX2B315aQ2STOTo3qxKwgdP7ygPlQ4PytfFHrSHSp+dCjNVoHLK8CbPnx15I1bDNtZmF0y8ttEj9JeEQFOzidNSqaJ/wjihCKQzk3O2kxFnG0VHUk8bfEMa4RJxMSPyZMT2jl1Wjwa7pQZWX8FIdMIqtVqNWE0sXJGq0oIhufLjpIQmvIHfyyQMliIV11UDkc4UaZaHRh02YliJKpZAqvTDaoGDJ+Hp8zDDOzmY/PWPQpXY0d8SGZmWJySjUUm/GnZK4UtkCQaikiapfpg6sCDM8nYzswysZn8TmtnOxQ+6YS5mRrzbe2ta1tO6rtCKSEGBF9gDPM94CCItRB4HFWMTbbRWQX0HC7OBS9STBa6qAhqmkzFJmFhT4AYO7wbqxurDa6ZeBS2Jmb8kDqCF81dZSjMcpylt1GTEE/jIkbos6/qKimyBysdP/jKbG2vIkuuMh8UD5Tk1Gs9b5CmJKq8D6ryDEhy2pNoQixZHRUdhxkZ7aZxAZAu7iYMYlqY3TXnpSrBZDY2BmPQhzjFs37ddYgmOndNYWu+ZKCUAKFveneM7giCgCFjIHk2hmYidLoORdJypWnijmJkCPdz2VqEzM+OZ+NEr8EGQdFXoloIDIuGVdIS9TzuaODhA+8BhdsqmAjlkJWBViCFcJRq4KfcrxrLUIfU8VbucmQZKQl/C95d9aYRvABAAprUEzWLNR8yYCr5d1Ja5FS29rWth3VdgZSAvmCclKVEclSL3lBIfmSst1YnGaKooQI6CtdneWYoijQiXTeixuXAADH7rgeAHDizldhwP6lkvOO6nGlO7/u9kI4rGv1X4lD1yNq6FZ32SwkrXXiChGfq1CPOV9N8pPEPdJxKe+pFrIofVmFKjkOo2TDe62AEdhHFDlEHn2dqQrwblknMqQz4kOQ30cVjRc/WPQjjEcD/p53by9jkdBkYi6nXCt1mLKjv/ZjxEqo7gl1CBKR8xf8e1/VmePXZcc3xyBVC+40/G96fDWJZHhEjEkaVJwD1y1NVmBA/JtJuUJzwaQAQubqEmnZvOKv+Clt5rfTbPw4iSiNEked0h6a8xzIAjLWJrCiDPCsQ4LwpMJ02cmc5fTvmOfHONTKqEetWgCwTs7XtATy+5TcxRij9mNSHeEv0r5lpGSMudYY8yljzGPGmEeNMT/Dn+81xnzcGPMk/7vnW71G29rWtu+89nKQUg3gH8QYv2yMmQfwJWPMxwH81wA+EWP8BWPMhwB8CMAHX/p0RLnS2leSdiFpC7ZAJa4kDTEbJQ+KPo+oFRYmao6cxrJDqr9ls+gEAKz7gMdeOAcAGI4kw7+jdWZEy0lzlwqrYXBNsbBOd1fZF2RHBQxKFo0XHR3reghMOLPFRNZ2UeguVkpaBH/XMzGljQg6cAbqw9DHmlQavWgV8XdldwbSDCMNk7wzGW1BlDM9rEm7I33JvzfTPqPcJyMI0Wr4P6h8sfgqQkyFIVK2icyFpP0k1yJExvcrdcrqhJgm9aZI/0p8Qs17C6HOlB0ZnYYqkSvFN6dky6gITHIzQwh6vmqizhrxHXKmKSmZyhiPeM6OWDOKrAK+T5mndZofXu8t6PHq85EQv146atRUmvcheyY0/xbmdwMA+v0ZVT/VPNLs/71UymWfbB0q9CXqyI+7sNB3Tv2nQlbernbVRPuWF6UY4zkA5/j/140xjwE4AuA9AO7nw34VwKfxF1iUjDEw1iJwyD5VBU78Gq3KILC6Tk45fZmd5B1ZFZpSay+mumwplEvnH40jPv6pLwMAfus/fQoA8NbX3I3VlSt0lDizVWDLaGjTK/enm/SNNUmUju90Ohn0Tk7IbikCdMkpLddTx69Il3BYfHa2j15fnLU8WU3KPxMTQZzPDkb7pVrMrtTzSni+zMo1K2OZz1mUTkXmZI6XXE+v0+kk8TXeTAqbBySaZaCNTex2KREdTDKb0xjSzw2ijrssSq6wKDNZZACIzC/oOItym6RVuSd9DjIHgs/6zc8hW+SVYiEa7pnjPi8SmbTRJcGWfxYzeV4Juniv/CqZP7JB0iLT3JQ13zMvGskLkRQypeP4eF5YQl1pRoMcZ7yDZBxb5ihcPHURAHD51HkszC0AAKqxCCLGZFqLpZ94KqkIZeZ4l3sRVnjF87QsCmSG5bbt2+LoNsYcB3APgM8DOMgLlixcB67ym/cbY75ojPnixtbw29GNtrWtbX8F2st2dBtj5gD8JwB/L8a49hd1aMUYfxnALwPA8Wv2R89cXXVe+qajzBYF/KhZ6dN735BkzY+nFVzikGLW+Ay2mvQZiHQ22yVJ3RuP3ggA2LP3OpRd2jVk9xOipAkxyXvwbl/7qDuVk2sWad0XeK8h1xAwYsfqcNTMXaKqD2IuCapjiL60pbW2tABCXWcSKonxTtepFemVmTCY55wluJTHJOcSEySvmCHnKGwThZDp0jQ/s9vWXTPJ7Xr0u1LvLSEHMVd7PTFzk2Cclfw8rfpboNcjAqmw4mV8Z/oddMpUv41usZhSOXCcPRB9QIfHpRBxuKLQ5ysmuNxbp99TQqiG1mO6v06PEGSX76PjOpmYnZAUE6FSkIyev9PJUB31IXcHJMUA+n3Zm0kohZ3sknZYWoeu9LEQukxQ87/kPiwvrQAg09NMYBljjJqwxQTdAS4q8VfM7rIsE91Cav8J8g4pv/Nq7WUhJWNMCVqQ/mOM8bf54wvGmMP8/WEAF1/ONdrWtrZ9Z7VvGSkZ2oJ+BcBjMcZfzL76CICfAvAL/O9//gucC6Ut4auUmS3VUrVEToxaayqvU5aQ2bQDTRUGuFkHcEn0VL5HK9RalBxuHq5T7TjvK4wChcFldxJHsY9e7WWhLUSkXUAr3mp9sNQPyRIoiwI+NBFbkYnli7NWyx1JHXobEX2T7Nfp9JL/TcTMUk5MQoQZPULIjerHyr6TUVWHcZ7FL3K46gR3Gfqr9VypAAO03/SDtBOrMzUaTOopxQwtu4kae/ARa6ucwlA0r72xOkSlWkOS75X5kqZSVZL8r/TRwUxl6kuZrTxNIhUCSMUZhLSr1ZUbU1Oc+Uk5oM+Ib8xFMGKMmnEv01uOLUuHIft6cv9gKjogftf0as/2iSAsAZayNBiPiCj8Iz/wvQCAhd0UJI/wGtyRfhtn9Rkq9UYc/eMknysSvuQrZToKv295vpt5CSz0csy3NwH4CQAPG2O+yp/9z6DF6LeMMX8HwAsA/uZLnSjGqCWsrZR/LppmmTUeY4kgpczQJD3imy8dORelGCALVPkafUcqecpKFse1MZqvNh7RolTHISpOMuyIc12dihFVLSqT4D530JHy0jxxhAFurc3yvMRkSQ5leVErzQgNcJL/JxrI25RAllppwXs1q4Tp61XxMZma8j6OxynCkwjU8tLVmT62vEReJ2kQWROR2jA14JsmAj2H5jPJMjwzU67WPmpAQnPNkq+u+F8AACAASURBVIO/Hjfz0GKM6InZLE5kTiStfY1uQS9ih2vBqfQJgE6nucAZ41KlFVnkjdMXaXK7CyGg6DSd7HVVqRMbmZQNIPlkE3w6k1wBQgHSBTck2Rqo6Ut/1nWNbn/CiQ+Xxlb6mHG+VtZkkecgkvFYuUIGjAizVVXiA6q5LWZxSO+QRkslGmidLo7isPeIyjnM31VpL+XieTnRt88im84T7bu+1fO2rW1t+85uO4bRbYyBz/gWYooUQjGqPQrmVGgoFQnWyuquq3DwKmSlSeSubLBy6SS8K7hUNdR2hWZg1UEXRJo2D3tq/k+hx2h5OCfh+FQJ1k44fGECakFSaIbxQ8zywqTemtyOSccJSxqAVvMVE0HkUsuy1ONSxVub6rcxXPd1JuciPBkvTGqjIXFVYshzEUVBQBXsomaGT1b+kPHIv6uz3LGU8ycMcJ+VDxehuDKrYyaogE0vl2RolLdWFLq7K1NecyMTE1/dB9Gro93XTawUY8R4LLZNymOUPTpUQs1gZz7qFOJXxGpUyLDQ6jPpGoWw8ZWrJb8LyjnTPMYwVmqC9FQlYeAV1emLEAp0u93G/WrffTKvkkxPSNr24vTvpFLpUnVZKq4QcprIU/0vaG3uW9va1rYd1XYQUiJCXcrHos/FE2Ct1XCqsWktTSFx3u01bypmYXO5zSKhFWFJ825jbaGVZQUd+Gi14qugInWdG6u7mAjvu6LQnDGo45d9Vi45inVHcR2t6urVBuf7Mh1UErKXvmp2vlMR/qjOba/oQEPLQjR0biqvLIQ6MbE190qc50Zz/HKJV5GWFWewpioaqGCa+KwsbFZ8oOnrQoi6k2ugwabxEeQWdJf1qs+jRWJCpfdeTzp9gKzemxyf9JQqUZRgGO5jQmkSDvcxiZHFiRC2cbZRc5AOCioE6KTWn+aJGRXUqwX5GJOE6yYQtLUFfFZZGUh0ByBRFKpqk/8udN5rYQIe3ipWqKHVE+j40INiqonCG84VWXVkr7+bdAOlisIeqTbjIA2HDougxpQP+FLoqUVKbWtb23ZU2xFIyRgmaGWpAFUlGfK8+7tuCi1v472f1B4ydNLGMcEnXRtBGuOKdiTvvaKn0Wikn3nNwWvuarlQfy5pmyJNvNtLJnfWF/FBxRiVOiCpG0m2dpxKFHHkaMg+lOCT70Z3WQSN3BmhJShScepT6vR7+YjwcUw50MwYO1VSalRX6HfLxmeqfRXrLN9L0jXKhJC0AAD9Lf4Mvjnqlyumnmu+u8pYqK6QtVPRunwOSMpJo0aa5i4l8q0c45QuIKg0Ux3VZ8M+Ke9T6o/kRIZa/aFS0EAQdDS5byWNDzS3rEnJCHWFeiKtRFFtiNrHVJjBwUzkJYqfJyIiiBqlojSn/jotZBASElNy5lii1yl3bxJxW6R3VvyveRGIIMqlk9G4F2k7YlHK22Sn5WGEEBAl941zngbDGj1jt/0dtSYQjJl56KWQokJmqwmbKeE3oqoIkorcqzMd/Zlmz4lJZGJWgFFC2HxO41MuFP9yPBxjc52YtLESrhP9bm52AQsLxCbfe2gfAGB+lpJou93+tk5hNVOlaoUId9VRzQaB+VVVYXPIcFuExDLOljrBJaLrrFYLkWsXkk/nDCaLYzY5ZNytjA+lcidiAtcpKVblTzK6h5jFwyFLvXiTpHe5qd66sbpoj1hLOt8wgjLdE59Iju+WtGjXoVI6QW9mjs+friNrhCSmGuNgRdtaqrZIdZUYdGzzBVQpIkL9ELMpRJ1ncotK34hJN17OWdc1Cs5DFJa48gy8V1tKROSKVCRONy55enVM5phK8ToLp9LJEwEla/S9kYCAc9MUC5Ppkb/UwtSab21rW9t2VNsRSClG3jmysLCS5ERMvcHCnci/QYLMiTltEkQVrzliVhq5eSYgdzRyON9YzaGyKpkhzt4CxjSdeDY722TJahsDlpeXAAAb62sAgBPXH8dr7nwdAODAfsq727t3r55ztkcEQMOO8ZpRW1WPFcLIeA0Gg1TWfML0isGg7NKuL+gmhKBM3E4hv6O+hzrJaoiJUHY7qKumc7focc4crNIPhMXsbWK1a2lu7vNwNMaYx1FUAqIPGr6vNBU95RlGdt6LMgHNl+0dpkWW55aLvE3KmWglZJcE78R0X9tYw8YW/f8WJ4yvrq7y31sYbdGzWB/SMZubm+iyabxnD0uIicKCtSlfU816A5SSDyaseei/IvI2hSlsIgwL7aQs0/1KHp1W57WpJLm8S8NqmIQDJ5QKnDOZyUiX9HVURBg4MCTXyce0tIKcO0kuJU7jnlYOt21ta9tfqrYjkBK74wCTbHvNH5K0jujUkSZ+iI4rlDowmW5iS5tWafW/BA19aqEsceAFTMmTGmNVEtQWIjHLP4uA7GNKeKwzSgPvgsMNQkVrK1dw3333AgDuOXk7AGB+Zkbzx1ZXlwEAy5fOAwBGWwNs8m9Hm+sAgG6Hd9tQqbN8fn4XAKDf6eLM2VMAgNnZWe5PQihDRgDCtex0OpqNr/XrQpbGM+HYdC5VqRU0IX6kwjpFRSNFO5XGjY04qbnPMwu7sXc/+ckkNePs6TOYmaN+S3qMSNMWxqLg/LBc5E123EqDIZIflnLBVA8qQ0+CYqW4QNHtqPN91zyh0/2LBzVw0O32dQwAYDgYo+IxCOyn2RoMcPkKIeErV0iD69y5CwCAtdUt9Yl1Zulc3V4/0Vd0miYfjvh1krM/+eOMkkVZz8sHTAZ6fOKmaEGFpLdlVI9rMkhgbdIcC3k4n/NCKx47QZn5WGuNPedSGpFaDELE/Evm6M6dYMlBSd8Z41KFBh7kelzBiYBYVvNLz8XnVbZrNFOVMlJ+ksmiVeIotho908iKvBQhqLNTFyJnNEn34vkzAIDDBxYBAO9+5w/h2LXXAACefeYpAMDzjz+CmqMkai5x//r9Hvo8WxcPH2x8F2KtQmhiUi3M7UL/BobWUhHFC39kjLnY19EAKDcwOePB581E3kqRFkmTPalWkONX6qLpZgEyzQDAI8mZzM3NAwBmWTzs0NFrMTtDi8ypZ58BAHRLqxUwerwI+CKJh0miszw9Z1O1GudkwvMiPPbIHj6NQTVSh75G3QqRahkn8bgsyqpmeZEWO+qD1XFZXKRnc3jPXhxk01sqgwzH9By2RiNcvEKbzlPP0caxtLSkDveKn/3MDAUybNnJZFZkAUrRtUk2PGULNAM+ZdmMlAJZ/b3MhZFMNVmwp2WAqAqQyNxkkdOJa3c5ibgorIrmiQs9D8xMFAiaaq351ra2tW1HtR2DlEwETAzqMBUEJDQAH20GMek3eZhX9hF1sNmA6AXKcgjbWEyG0jVnLgZd3RWOGqiJkigrjIpiVGefhE6dK/C1L30RAPDO7/luAMDb3kaO7NHmBgYc/hcT7dX3nsTzzz4NANjaIHZuKSHaGJLEL7cB80bG4zHmZ5q5ckvLV9Rsq/l+5fgQvHJhJHJvQpKjUKqCIKZggZpNqCwXUcwMpzQMaH9EdkPypg4cOohZNi3LPiGr3gL9HQF8+WskLHGGUeONx45hOCDUOB4Im1mE2oAi9wKjyUMLKigmvKjURylvbmLQ+mcdNgXlOffLjubpSRg/hFrHX8wl1eM2QQMga2yyXb64hDGLEGoNNXYsu34fe/YfBgC85drjAIDRcIwL50kT/qnHaQ5cvEiZ+72ZOUVNTioJayAnJPdCehF03K2iIeGqOVSMXgvlhxUaxk+5oilooO+IcvL8VI29XOROx0VkcQ10UqmsiRxvrercX621SKltbWvbjmo7AilR0QAHE4IKUQkTVDLXYwgwRkStmFBpRpqMVnHYXHd2Z5IfQn3aLgluibwt16qqQ9ptFAn4KmkGlUKES4zfko3jfod8IJ/9zGfxY+8l+ag3vuHVAIALT3+Ff9/HwuFjAICbbyFHd6fbU4Qh/iwR2Q81UDFaKZWIxuznTqH3rUigMBiMNxvjk7LnkTmuBW163a9SviA7fp2F5xCwoKPCWgQeY3Fg1+zDqbzD/muuAwAsHjwEAOj2k+ic6AaJw3V5aQk9HrM3vvHtAMifcmWVHMRnTj1PfWW/IoKBnyDJFkWpuWyC9FSbyUTIni6KBlQcuYmSpRWGfU4AfMG+w9KhksIBItkr8ygEnStBw/kB83PCsGZ/H6OEjeEIzz7+NR4D+t1cfw633ngCAHDnLfTvc6fJD/mlr30dl88Rml6YIfQrqHPLeESRKOa52XEOUUL2UarbpmREIf4qI91GBMf+NSOaWNOOHpXYNU5JnJLf5zNKihaqgCDbAgX/vxN5B5kLLr4kEmqRUtva1rYd1XYEUoqRoiuS/wZAa2yl1ASrqACZdlKqCMZefrWNjUZUGrT2CcJdkjPNa5zLzu6mtYCEpOYj5ufI7n/k6+Qf+b7vfQDf+z33AwDOnH4WAPAkR5de/6b7FQksLXPZJiwmG71MZaMAijAWGl1pEt3yEj9yvw5mSkNIo0YmqC/JS+md4DFZmVXJcmGcFAmkzlddQeKZWxyqF5LjyVffjQMHyWeyvkm1y6pqrM9OdnkhO15z+AgO7qciN8tXyM92aeUKeqzR0+uQ72PAWfDkRJTwtNx76r/cXO4nSaRA9vs5h9JNR6Tkb0VgGm2NCEZyDSUNh18XYxThqqKliajV35UUGwFgoTeDeQ7BD0bkL1teW8MXvkQlvfYfpKjskSP074+8+/vx7HOnAQCf+Myf0vHniV5wzZFrE5ERGnKGs81oqYS4QohaviuqVLHV90CKRQxUmTNkkb/0rliJTGdqpgCra0yQdi1iI5cOSL5eY5z6Lq/WdsSiBLDTGlG9rQKPxQQAgGBD4zPiHTUdoOmhxCmYHuDVBNScriLVjVNzRhfENMGsLlTMLC4dVleZP8Q2zt98z/eh59jEYqmKY9dTZRRXFgrPb771FgBANdxUbeThxrr2EeCFQmveNR381gJjLvddZkmQRikDvJDLy2StSq5oMnDIQ8N8vPwbvJozoku9vrmFffzyXHPkKABgcT+ZaosHFrG5SQuIVtMIBqMRTcwhs54ll2+0NcDKygqPMV1zbm4O509TuHzA53LZ7FXtaQl11zHl8dVNzWqqLCIPWAIlRu9TE6k1FzGmDAJlNpeohQYyObfkIoBKFjvnNPerllLqUuI9+iRayAvV4twupZtUrAn/JDv/9+/fjxO3vorG+sffBwD45GceBAA8/PCjOH78OPch1cwDphcSgJ69iuGZ5J6QxUjr1mVjk3IIRea2grxfeZAJoE2uKxVaNMnXKG9NaTyZbvzkpjDZWvOtbW1r245qOwMpGVqB6zrqDqRi5Jn4mZp26hN2cGKiSYZyVkJaS1vneFFNP/oz5RGlD9Nu4BGYolBw9rhIYvTn5vDnnyVo/cG//98CADoOuMykyXpEZowwhWfmF3Bpicy2u2cJMVwZbOLItYQ6nnjk69R/JyaV1V1GmLupjLVPGl06Tj5VYZ2oeUY11QSBRj2/gAkx+8T0Gg0rZVzvmaNw/tF+D4sHyETbd+Ag/47OubG1gZIz6gdbG9xnYG5hN48nPZPl5WXtj1TrkMjD+vIVVGzaaCWMrCac3IuEn01M9A5BT9JiCFN160pnEAQxCN0kQxdBSaJJlULQcZzIQ7MxVefQuYKECqLm1AkqN1pkYpbz45yxagL2etT//h4a37W1NfzpH/8RAOBVd94FAPjBd5Hs/dxMD5/6U0JNtzCaKrICGg3pHpDpr/LRDKJKlyomS8aEVs7J8tJyeWFFfVptV9wMKW9Q5IAMMtKzkp8ZbSKpFlyttUipbW1r245qOwIpGRaKImlN+mwyo9vEqHKsJa/m4/EYvS6FTJWOr/tZKlOTO4ZDogzSf4OgEcCwP0ToBUDM6pJxHhQ7pC+dO4e3vvm1AIBDnEry9JOPwzCRb5ZznCI7rv3IY3mJ89uWKdt8ce9+hJrQiTghO1yjq/YBYw6Ji9O/LCTHLmSkUt6BjIP6hkTPh9FLCClNQUXSnFXUtL5B6KbH6SAnbr9B0ZBUoYWDCuIPmORYMYIrXQdbjLIuXyY0uLi4mHLTWCtK/Dy+8hpvWL5EhMGlC+e1/6U6ZlPemlYfqiXnyiW5XHEfiU/DZuoM6iuKmgsozqRU/XeaOIiQifipfK6k4RRK8rWK6iJMR3SR6N+6To5yy9fSem6IGLEKwezCfromEyVdZwb7F+i3X/2zz/B90/i++50PoOLufuXLDwMAjh8/jsDOb7mXVGE3iecJYhoNxygnxiBvgpCS/lSyLDQNLLAqhEtIW/W4bJmqU8szET+naVUC2ta2tv0lazsCKQFRE/tSRVbeDiTpNStXmqRLo6KJGTO94k9mLxc2ef7TDpEiK0Lbj6IvVDiN0klErujT777+8MP42Q/8NwCALY6cPf/c0ziwl6JpFas6rnM2eTUeoub/P3+W/E5dexjjjRXubEJ/ADCuvPpFNKFYthDjUukjm0XYNBVAdmomZhZG0yhkN6vGFZZX6dr7j1JU7Z7XUEpMf3YBV9bonkb6GFIN+0J8LbWoBgw0BH30GjpXMEHTLqT+/Ih9RtVwU/WINtYINXY6BYIQVbeVNk56P3SMzzAxtVzZUpUbtFClnUbOWREJGbuY+UqgqRr8OxnfaFSVUqZiVY+09JGgoS77lKrROFVAlvJUxmKDo5PHDtOYLR6iaGbhSlSsufXEo4ReVy4Qory09wV8/3e/jf7/IhEs19dWMDtPvj8Zw3Tf2wj1uxS5TJpVQkrtJHKylmRK9odKGzvRh0pVrXNVAfVpqVoD9LuXcirtkEUJAAIvTPJ34osAQEStFVDzJpMjDJPWNgCgTCBQHko19uizg0/rpclDMSYtQBLujBk3SCpI8EO/7toj2L+fYPdglXKYZrsdlPyw1tfpZVvbpH6NRiN0WMb3j/7w9wEAf+8D/wPWLtP3Y8bkMpFN9OgxnJfFqRqLnEVsOGSlab/V/GTGuSm0/+ucYzeoPG68/U4AwK133Mn3Rue/eGVZw/cScNjcXMeAF9Vej/hZgR3YZWHhuB663vfairLzhZqwvkyL4Nr6FR1XqTxsjNMcNumrURZ31HlsXdpE9CUQeVipFhwCZP7k4mcpL7IZ4jfG6mItWQARHkWZKAY0rhxwKMok6BdFrM5m90Lf9bo0TpUb4fyFs41r33Dbnbj13jcAAHbtI3UBocFUVYXDzFm6uHSZrn2WeEtPff1ruPVe6tcP/sD3AwD+7f/1KyowZ11zgYBzSLZvEs8rG2OVNrAY/ZSz3IeglZjlrRK+W4wRXiohy8ZRGFXyEPNW6wJm9Q+v1lrzrW1ta9uOai8bKRnasr8I4EyM8a8ZY64H8BsA9gL4MoCfiDGOX+wcqaX69iryJiHgEFO5XK0CYaYg+eQq3/j/wmamXFMwzhgHI3BbqLg2q9TAiOH8eYLMN9x0A7ocyl05T6aIiWFKs0fM0fF4rE7jW266CQCJsZ0+SyjrhuOUFyc779LZsyi5sqlIwQqMjDFq2FydqSGZEkorMMnhurzG5EZGlne/9vU4dv0NAIDNAVMBGAnt2jWvImHD4ZaeQ539XNVkNGLHd7WJ9VUyNyKjiW7Z0+z0jRWWkWXRuq4ttMKqmKGIFgUzuTXfMNPiyUXI5F+Ved1G+lbGos4qCE8ylcUs9r5GJXlhIiOLCB+ahRKSuv4YjmV5xTwZbA0wHnEhBp1vZALv2r0PM0ynWGCp3Jm9h1A5ehZrA6mXllDOjMxLRsubm3TMwcV9uHSKSKZ3vuHNAIDXvPpuPPLoEwCAxUVC7yJKWIegREbD9r+xKXtBwaNQRuI0AdNlom1mgglOpjJ/VkrOZRKGluNjo7DEK4+UfgbAY9nf/xuAfx1jvAnAMoC/8224Rtva1rbvkPaykJIx5iiAdwH4FwB+1tCW8gCAH+VDfhXAPwXwf/5FzykSqEF3UFlhbVKsk0z6kHZH9SWlvk05TGEtopx/G/U+8SmNJQs+jhWJzPSIevDcc88BAF59710YcEVQkbK1VYVxh527vOuPRhQi73b7eOv9RIBbWyPEsLS8omku4uRcYU0dY0xCWRLOl1pjzsBzaoXsSZWvMFZU2cyeH/kaC/vo/LewH+nI0evU/7O6Qn2c3zXP5wQ2OcTfn6H72LNrQVHjQPSOeOzmZxew0Kffim8CMajW0PIFQpcxq0cvJZYErZRlKWXG4CfKNSGrLaYSo9ZqWo2WiJKjbUQ9UWfMGJORT/W0AAgdJU0pCWvbVJ1W5HOl0EJZalrNBSbE7t23iAPHKKVo1y7K9XPs5xn5AMeqCLMz5Kv7yiNP4F/+q/8DAPBL/+afAwDuuP0kAKDX6WNzhcbu3POUQznbo+fgq4D+Ajm1L7Me07133I7PP0g6Xot7icqhkznUmrOoVZWrYYZqElqUNok8c+QiVAsp/GCMy6ouJ0d3OgePmVBwXoIOALx88+2XAPw8gHn+exHASowirI3TAI5s90NjzPsBvB8AFnfN8mdOql2jljpoYdoMk2iOhVF6d0NPGDSw8tIkvoVHqbl1IkuRzAKlqPD1rLXppZFcPH4Y+/btm5Kd9ZXHEpsqM/M0JAK/C9fB5gotRn/yp58DANxz9+249TaSMZGFaoMjeZ1uoY5J4dfUkusVgpp5JS9+8/PzahKJE1Py9Lq9Gew5SAmwi5w4OxiMsLGxpb8FgDGvCjEMlfE7Glbcv1VdVJJcKpsAMGoqSvTw9KkXsHL5EgBgls/VZXO09l45SBJerapKQb1K/UokllTbqG8Zd0Zy/ArnGt/FGFEUk4GAoKuRsJI1iuvT3JJAhXUOwXNOnRTr5HMur6xRrTsA7/yedwAAdh86hMiO7f2HaMpfPEuL8Sc+/nEt3PnNJ0nQ7cO/9yf44b/xbhofcRswX2xl7Rwe+ypL3vDGV5RSxcVisEGfLT32OADgjle/Bsevu5bOIVFi7nNhjZrIYmqORzUw05SCyc1j5bJl8U1d3CdyCm0W0c4j25NJunl7xeq+GWP+GoCLMcYv5R9vc+i28b8Y4y/HGF8TY3zN/Gxvu0Pa1ra2fQe2l4OU3gTgB4wx3w+gB2ABhJx2G2MKRktHAZx9qRNFECw0WbazciWEThESxyat5NnuOIFyYDPzjT9yzsGJUpewi1XCM2WK68qa5VfpOfhCM92enl/QQd85XYGlqkfJDO3hcIgvPPjnAICvfImywXfvXsB115E42ibLmajeRXZPIuURMk6VOA6FC3R4927MzpNpIHQHsXScc7h8gaC+ILI9+/ajxw7r2VlCSiXfR1XXWol2xI5u7+vMASqogv4aDEZYukSoaJXD/n5caT25SShvrVW+i5yLZF4nnhfvxtEZFTFT3WOXchsT10wx7jYBD4Mg4viFVEShb7r9Hrr82ZDN1rXBEPNc167H6Okyc7c2aoN33k8I6ehhciyvVxUwQ+O4MaZ+fOHBLwAA/sMv/gfcchuhp288Rxy1X/inP4+bbr4VALB5gUy0L3zyEwCArbVV9DiQ0WX0Kwio7JbYu5tyCs8/8Q0AQMda3HiCghZf/NqjAIBd5V4ek5jGKUM3RSE8I/Bx8q/Rd0NrC2bOb0FdQhGAddM5fxaYrJicI7JXDCnFGP9hjPFojPE4gPcC+GSM8ccAfArAD/FhPwXgP3+r12hb29r2nddeCfLkBwH8hjHmfwXwFQC/8pK/4JXY+6i5TbJeio/Jx7STiv/C+yRoXg+ZBKa7sVMyYdISmg535n4IKa8jOyOdp7nbC/ooC4tqgrBpXKEIr8vh/y320xTO4dAi7V5vfsMbAQArGwMssZ/p8H5yUD7/OO1+w8019PuEkCyHn3usOOBjUP9S2aX+ra6u4vIS+bPU18W5aaGuUHthd3f4d30cZCZxlyvx9hnVFUVHw7xCPfA2ohRyII/P2jIR+y6cO4cNdppb3kl73SLTKOJdUpQNbO5YzvPO+F9MoxzRlBJmNxWaoI+mav7ZqD6rHCXHCd+Q3ON4PMbSFWGW03PbfeAguoF8SpcYZa6O6fh3/chPYO8uQqU/87M/AwD40Z/8SbzjPbQXf/4h8gf90v/+rwEAx4/sUob/P/nQzwEArr/pBjz9FClDnH7qSQDAHD+Ha3bvVmWCtQE51AXRjEYjbG5RX+c5v3JtbQUHFvfp9wCUQQ5rtHq0NHqXhDbCH2YoVq2TDLlqsQL+V+szImQFFqz+q6RVYcWHzAf14kDp27MoxRg/DeDT/P/PAHjtt+O8bWtb277z2s5IMzEkXWuQsqlD3czhCbGGhSCHFLERL1KKtEmhgVJVFrVaZ6jQsd3GZ1olCU4JlWWmTCiRmhGH4PcfOsj9SlpLKTJhtYzPcEjfDcdcSbUao+QdYsi16f/4oS/juhuINHnrjfSv1Ku/9poDWGVFQvEFSE7VcLgFL0UseXcabG7qPc30RB2A/TWlRR2bKTrj0RDPPfFNut8unbffpyhof3ZWryWRsE5hp/x8G+tEhUD0mOMySp6jptV4BMPRP4kcCQ3AWMCwKkLFY9jt9KZQrKhwWlfA+GauonOl6kzlxRUBQsRuIt1ifX0N46qpPLHFKCTEiGM3khqoRM527dkNVBzlWiM/0z333AYAuO7YMVURfd9Pvx8AcNfJk3js4UcAAP/Lh/4nAMD9DxAiPnnyJG6+7Q4AwBzTBD73xx/D1hJF5w5fd4MMIwBgdXUtRRu1fK7k2UQMGXV1OimquZcLYaqOFIf/fRhpqaok57upEVpBi0pEzQpt6vPYxgektAof1CIRMyKahFAnyc0hIJOs3r7tjEWJG/EbjP4/kLgtNmZs3iIXchOI2QxLAtOhxxiMSmZIZVl5qW3hkqNbnX5eHeFDZirPMWyPMWpF147U5kLUxbTm6qgSYu52u8pp+crDBNt/+IfehZP33E2/Zbgu5alvvOk2fPGLDzXuUZzP49Ew5Q9pEipVjQWAMTO0DRuupnRwjDSrAgAAIABJREFUtslM7xY1wC+I5HtJtZKNlbGOy2yfwtx1t4sxO72XWIKlz7rTM705eB5YWXgCIqw4uEXit0wlt8XR3WVzqaqqxObXWESanpMVe71PDlMVcuMFdGNjDSvMHSv4s3379uMAa4XL70S29uZX3Y7F/WTKrg9Ye3u0haUV+n7xOqo2cvMd9KxW1lZx8DDxvm68lZzVX/rzh/AHH/4wAOCDP/c/AgBOvIpE2Ex/FjPM43rwY38IALhw6hRuv45oGkNeEDY3hny3FpB8R97cHAc7XFGiw/9/6RxxmZaWllDOk/kmJrg072NKZhbpXmNTNWgRQkwU7MQl4leJysTLGbmiENLYpwrRIotbJEAg648Ixhm3beWUvLW5b21rW9t2VNsZSIkr3VrrMB43zTZZooO3iYHLZMKisKnuVsjCkSBzT4mRRuRhfarXNVHjnLoxkRtl7ZSjW3bjsiggSePSx6IolIAoplAnkpm1srGJN33X/QCAt//1HwAA7N6/B5cvE4P7/AVyGt9yO+3Gw9FYq+Ua0D0KkzpEjyImx6Hch7CphwNGdXOE6vYtHsDSynJjXJ1x8EEyw+k2BCkWnUJpDpU4TqsasnUu7qGQtED+qqqUpqG15jKTSnZZyfA/f3EJd997D/WVhc6Gm1sYcA6Ycm+FgW1Nkgipkph/j5HexhaZYRIjmd21D0fYHJuZo+eAaLDM+XnHbiRz6cTNxMCuvMdwyMi2Ytb6yiWcv0xI5O77KJt/H2fub60t4dIlqi7ykd/5qPb/J3/yxwEAx28kmscqi+GNPHCGKyGffopM5iOHD2KNian7D5NI4MwMHX/hwgWtxCxjIDXwnCswYNrCHs6jG2yNcOAAISR5N2JWgEJVDuQctoOooX0x66dlR1IiRBLUE9Rbx+z4CfkTonI0gxU2I7O+YpSAtrWtbW17JdrOQErcYlZzKpU5Et+PyzLiWTrWOl1VhUzoMwK51vASJ56FKqXJDlGpEHqdGcDiNDcZeZL9OpuUCuBsgSAOd+7PjCnUZyOKAOurdPzZ8+dwG++4l1YIFT3+uYfx0Y/8HgDg7pNUUfeBt1Dm9+mzp1AwxBhsceoJh4ydKVHLrsfjVRQlZkWov8OIiivsbm1tYSROXRm7Tk99TtqkCAOsFjzwlejtVEmPSIILQhcYjpSiIA5mlGWzoi+ASyyVe+LmGzHDKC6A0NHc/G6sMIF0g8XnhCxbjyvUhgs4qMO0xrmzlC1vGb0ePcbqC3v2YsgE0kefegEAPY8eV5vd5PD0w8+SRtEzzzyDHj/6rSUmf44u487Xvo1uhRHZY09SJv6T3/gafve3PwIA+LH3/RQA4HXfex8+++efAQA89FWq5/bm+98OANg9s4BTXD6qW9Oz6cwuYFhTv9fW6fkaJtwW8Dr/U8oN0x6qGqssAPf6t1D/7MwCqlICOHQfjaCBhPu1hJbTQEaOdgFKHQpaaVjegxqW68oJuVl8ggEGUdKBslxTQcrbKTiINPPV2o5YlKTWVAwZ3LMSORNt3yRTInCxsKWKtAmPQ1nPxiEY5ulI3qZJPCM1+6QP0cLGZrTOwajGdqckh+/q8hk+v8X8AjlOd83xYlDXMAUdX4HNHh7hWFc49wRN6l/8JcpPHgXg9W+8FwDw9S8Tt+XkreRUve7IfnyVmb69Lr0UhvlKc70+NlkGRKbe7MIulMxA3mAht1V+uQfVJoyW5qbj69FARdGUX8ImoTN9rY/HAU9YF5KeOZsIoldeugJbw2aduJneDCw7NJ/mgpzXXnc9AODg4Wv1ee3eS2M33NqUNRGRH5JUaoFPAQRwVZlBVeGGu+4DABxl2ZdVzhtcX1nHiE3ZO+4kZ/NgfQ01v/xLj1IA4eI5Gt9RNcZgTGOwdz9Vl7ntrQ9gLz/Xj/27/xsA8Ju/8Vt0vAH+2S/9KwDAq99I7JfN1WVcukybzeGDdI6De4jtffqZp/DsU8S03r/IgRKfGNCbS5ca4+pcqRrsQmHvdThIUI1UEmbEQaADi3txmTe/GRYSdD6LTIp5pd7qTRWw06oyKopn0sauypxWhfQ0Wie6cSGvfSdR6ACYZjFKmzH3rXtxA60139rWtrbtqLYjkBLAUiOISS5Csp2lrlld6SotaMfHVNMKfsJ5ZoLu2qmcMBDEoSf5c6p1bRVG1LzD15n+sFR+NbwbjMdD9ETpUzgldUzs7oqd347OvzlYw4KlMPJr3/R6AOQ0LHgHlH+fP0MmxW03HcfFy4R03vHA/QCAC+eI11J7jzmux6awO3gVXdu9izgrB/cRp2ow2MQqO7qTE98pB0ucqom7Vancrtxb6TooOKQ/ZlNqwDt2v9tDv0PXHrFz19fApTXq/5Hj5Fg+eR/xdsYh5QuKqTbc3FL6hxPzgf+e6ZfosvTHEpf5PnTgGrz6PtIUP81yL+fPEVKpRgM1P4VGsTmusL5JSGmFa/OJFXHwmuO44zpyenc40//i6dNY73I+4gJde5WP/38+/B9x460303Esz2KDxw++m+Rptzi/8JGH/gwA8I1HH0FXas1JPmCo4UTVQeZ1FOE7ozyuoGJvNPazM3OoeM4vnae5Usca6BOqm5llvpgyto2yqmETdaVwgpCoyXtXhTFKsTqyV0r5ghNOalskRYBosnmUSQ3Td3S8cVbZ6ldrLVJqW9vatqPajkFKkWkBEvLNK1NIM5qfwxn+3ifnbHJI8MFGk97UPRsiomuiApvVuPLsv9qqUn0wYXlL/paQKWs/wnggTkIOrfs67Qy+2df1lXUcn6VdeDfnwD377LPoMM372LUURv70p/8EAHDyrjtwhJm+Y762qAAsX7qI7gznqfH4bGxsoOBs/2uuJc2kdSYQzs3NaZWLFQ6L+zqRFa3SLjhnLkLpCJanSD2u0Juja5bsVN3aYpJmJqi3zIz0olPiJhYtu+0k+X4KdtQvX7mCqibEI4imMEZlYYUI69mPtLG2rjlyY342K2srePZZ8glJfqEEFwYbK1hjlHaJneczC7tw+Bj5nm6/m4TuJMxedme0lt1zT5P/a3l5GSusW3T7SaJp/Nrv/CoAYPHQbjz6CDmzN3g8x1ubqAeExFbZR3SJEVyv28cCP3NBg7UfMs0C2LWXnqsQDtdXN+FZaWA8Eo0rfkbOaE5kPaBrf/XzT+P2N5BTvcNO+c2tVLtQmiDjgEwJQEmvXD3YuRSyFxkFlwJItRfERF9ZazHicczpKZO+4ewtbCkBbWtb2/5ytR2DlIDtiVU5kVFAU5cz3cejGoZD+6qnlGWHWw1pMhpCqhk/GaoMoVYUpJqIzmmYoWSfUqjSjuIkMlVKjleJatwMp5bcV2eHamfvktpwTz+FbuQIFke+bjpOvo2P/cEf4YF30O73zCnygVzDu621Nilz6r2lGmpao579I+cvnEXB/dBQcFVrNn7U/EJJ2Sm02qtq5YSoyEjQaMnOB1s4XLxAu/aBYxQ93Lv/IK47Qbli6JGfY8wRscH6CtYvE/kQmpu2jtUrhGrW+T5kzzxw8DB28b0LGjLGYLhBx+3bR1GuhVkpPDDSslFHWJFxayQpHMD8PFEDxB8Xqhqf/cjv0lhxTturbr8dHU6xufUmQliDAV3vYx99EEP2G0Fq3w22UI/pGgX7Vg7so9SP4IEFrj7cY4R74fy5pObAk7fD0sNhY0O1nzqMJEccTau3osryKsKNMeUo8vPd2CTkB5NQkSsyTSMFUBO4xKYlIUW78/eySeb0wSVtJUmFKQvFZ4mQLLQco6Tmq7UdsShFcM0yE1U7WJzaQ66H5usalh11yRmey15MMEhzZqpUOole4aQ40PNcTqOVTfiDEHVhK/jAIecnGWPgjehjM/en7GaF/Di3i6kEc/2ADTZtdrH873g8RF/0nBkPf+ELlBe377p9uIHzqqSPp54hVvCcC5hhE2rIpostHHo8v55n9vD1zGpe3H9YGchHjzLbePmKCr5pSW9N0jQpsbNMNAlbiNObnsPyFTrnyuYAd77uTXQuSy/dVx/9Jh569DkAwFvfRGHzMKLrvfDEo5jviZlBC91zL5zBgQOiI34XAGD3HlqI6mBx+QqZRB3hLo1G2LWbXvTHv0FjNsO16nq9WVxzhBJr5T5iXekLsrZCL6wktK4tr+CuV9EC+ge/Q/yjG269GSdvp/E/8yTJzn75iySyunthThNaJV+2Pz+DENKCCQBD3qBsTAut5JgVRYE+s807TPkQXfqF3QsYcrHOQur7cTWT8Xis+ZEDNmXvuPserdOX5HTEpCrh+DW3Ih0TUx8Tl091YBBEHDD7ThLehScm7H8XLSqhB8iClRUPlS1e3CsvvhxRa823trWtbTuq7QikBIh55rWqQhTWsDjYotW6bFKW2nXKqaokImxmzMzUdzBNEhf4bAChLzFxkkpATI72Caa5MUZ3ozHD9q4xWjki8E4hsri9Xgerq+T4PHKcmMcHD+7Hc88T43j1Ejml3/vjVAjmLQ+8GefPEToYjOiat91JDtcHP/57OHGMkICQCq3rQPiOkj948RwpES/s2av3q1V2ixJdNk9kDARVeO+TcFqW0D3Lcq+SoyaM9te+8S3Y5JyxTz/4eQDAr/367+Af/YOfpmsNyQF85qnHuYM1zvNYOEZ8b/+udyjDfH2diIBXlghZmaJUGRHjhSC6ofl8lk2W4yfI9O3NzGKZq4F0uZpvVVXosAk+ZpG0S2fEXNzAvkXKa/u7H/j7AICZ3V08/ThVDls6RyJv+7nAQgTQ42uu8HViqJWmIRH4pHoQsbxMfRUpm/FwpE5+1a/jeTozM4MtsAwxT919B7ga8+aW0hwEQVdVpSxsqcIc15Pbo2aXg5h4VFiB3yV5NURxoSwSkzt7f/QzyapgUnFhrZI4c0f3ZGaGXNvBKXXgaq1FSm1rW9t2VNshSClSVr+ziFLTXBBJViBFdh5xEBZFkfSEJFSpO3zQXzbQjlTVUYGwLCdnIiWHVvxmnfQ8+7onGj+qVFAnYTO+zoAdrGW3i1UuOXT9DRTqX5jr48ARCt//LRYLO3IN7dhPPf0CPvoHvw8AGHHl1R9/H8mtvu6tb8cjX6JUiUWmGeyZ7WN1SAigxxpFkqc3Gm5p+aXLS4RQrCnUv9FjNFGWTHcYV6prJWNXlKXWtZ9nXaJ7Xk95es8/exb//F+Q9Cu7a/BzH/hJnLydnN4XniaEtM7IZlwD19/6qsZYjDa3cOqF5wEAmyzQP8O5atZa7O5T/8+cpmOWNwe49V5K0TnKlX7FyesQsc4CeZs1oa3Z+RkYLj6wfJEQ5DLfT7c/hw0ub3TDbeSH+9wf/64K7i1yKsz6CvXr8JFr0Z8nVFSwJG3HApfOkJO83xVRNRqLOkObY07HiTEq4ikZ2UrY3SDJzgoqFV+oKR1296g/8j48d/YsDhw/wffS5+NX9Tqa/uGS4sVkGauaUX/HdaYKOABpjsv7kny4DoGDLr0eixEWSTkgoaesou5L6OHuiEXJwKAwVIZ5MvqmTlhYNT1U1SQGdYjrufjhOeeSg5vPWcW0aBUpc5H+8QFWqOLSBes08VCuI1UgQu3hLUfibFJ1FNEsa+gF6XL5qOhrOD7X+RfIZHvg7W9DbzfJVlQ8cz/7Z8QC/tQffQp791L0ptsjs+Hf/9qvAwB+5G+8B697KxW2/ObXmC8zHCNwBE+kPIRj1Sk6yhDuSCWPuoLn8axFcZMXM9vtpDwsdm5vDQaw7ODed5gW0oe+SkqLH/ntj+I9f51qmL35rW8FAMzNlnjyMXJAn2EH/TUHiGF+1y2vgmPT8dTztECsXL6Aip3eTG5HhzeAwXCIzz1MuWOHrqVI2Nve8T3o76Gx6/DCcIkrqhQmmTZzcyxSNx7izJnnAABjXoAOHdjH4zXG4h5aaL/40IN0zPoajl1DjvfLq7QY7T9COW17Dh9SLfhbr6d8vjPPPZnE6VR2xOtzkMRvUQM1JmNC87yoeIFYX19P5hVvzsvMfEe0OLhP5gwtcN1uV3PlpOS89MW5zJSSPNKsoshkAq+NCQjUXuZ3WlwKVcIUNwWgLhBI7qRN+Xam+Q7GGJGt0du21nxrW9vatqPazkBKhpANSaJyfk7d5DdYa3WFlcU6hKBiU2Mx4ySEGoIyUoVmkMf/kxNchMQiYJr2m8/4GeK41ooZhVMnqoipdZE4QupI99TZftnDMRYJO3OJnKN37prHC2cJ8n/qkyR7cf4MmVeHDx+BEfOQ2cwdLh3+G7/9EbztDRRmv+UGCmXPdgyWWfNZEINk219ZXVXJXqu5bAVmOBQtDvvBFpvA1qEUJjvzUUIIuPUkheotl54+yBII//Cf/GM1N+S+nz91FldWaSe/703Et9rDIfClC+ex9AyxsSX0HeoR5hjxlIzmxNS8sraOG+8maZc3veV+GteixBVGD2U3Ob8BYGF+N/YwyhyyU/vCqWcxlOrDUo2F6R279uxFj53g3/w6lb++9+YTWGUzcpErzRw+dpwv3dWQ9/nzNOb9/iyOHiUkdYHzF0VKOKedCPKPSCXqJeyvNJUYMGaHuKB3fR4x4hJzvGRuDmqj/KftMiEkVC/0llAnqovI2qrsdGm1w8K/s7HOKsagce3cUslNtaBKA03cEzOn+dVai5Ta1ra27ai2I5BSjGLnOrXV6wlCn/dNdjdA6EYWYuOaq6+1hWZYK7pxHbWX03dCmIyYcDMh+lRfTegItfignMXCbmJmb14hh2lReXW2DrgGfCWEzBCVJCeVJ1aWLuMZ1liSShi33Hw79c+alGfH6EtC8s45fOLT5Pt49hg5fm85cQPuezU5aQ8ePQ4AuHiR+nX0+r5K5Ypg3NrKFZznDHdxSoq8qkWEEX8OO1oX9h1Alx3cA3Fisg/qsW98ExfYyXvmDPmIPvmZL+PvfuBvAQBmGFk9mYfYuT972GEcbIRndPDcaUJI84vkO/mv3vujqGYJZa3zWHQj5cQB0FywPQs0Pp1OiZLh9Oe/RMhn31yJec4z3GAHs+mwVK7r4exZ6vceRhynLy/h0BHyX916F+XwzfHz9uMKW0yGrNgB5iKAGUJG+5gEKs+trsc6Z6WN66DEVIm1CBqxjZA6Uz4ELXe7WOYadSduIqWCy6tbsJkjOf+3DslPK+Tg8XiMTilET5HcSIEiETQU32quyaSMbs2IqFP2AjvPc+JyqmBt8lO+aGuRUtva1rYd1XYEUgJS3ptkTsvK70WuFkGRzHbZyBKqT6H+mJTzOCJXhRpGqrxK9EGRU1KjTCt18imN2Le1xZuINxaeyYabnK5hfa0lfVKZIM5Zik5rl8k5ly5fxE0njgMAHmF/ROB7q2LQLVQihaov1J1BuZ/QwfmLRAA8d+4cXvN6Ilc+8fRTAICPf+xTAICTJ+9Rv9refRROPnjiDhzlMbt4mnwgl88TWpjvd9Bj1KH5YR7Y5FSHj/4h1bz/9Q//MQDgnW+8E2uXCN2ceoH8Hf/o5/977N1NyOqZR74GAFhjZcZep8T8Ljr/5sYKjwmwNiCf0M13U1GBozcQGXLXgYN4/iIRGL1oOO1dVDH6Ta6DduQw+X5OP/s0HmMEupvzyXqFQVRiLY3/4sHDPF6nVYr2EKfhHL7+BA7w90dZD+oK5+ZduXgBy+epP1vr9Nl4OEC3xwUqJAol5MlYIvL5JSUJISolw2t6BqOjGOGUUNnUIxoMtzDLvrcVrlBc1RZDLqsVtyEyOmbVKqmzKJCH9IGkk2SM0TpuQp60MRXQkFp7iurKQk0L9aMaRyE7pHcpFfFI579ae1mLkjFmN4B/B+AOEK772wAeB/CbAI4DeA7AD8cYl1/qXDEaxPjisgbqsCsT30Ic4SaKg4//Lhz8xO3l7IG8JHRqfF79KyUzWikPnkFtYXIPtuhlIkY3tYrd8qWRJN8ODGfw7mGJkaXVFc1Fu/MkFSv8zOcov+rwoWsBXiQnCbDBp+Kbch/33vMa2AVy7n7mwd+kAx1B9G88/gwGzCUR5u++PXtwC1f1uJkXxutPENP8hacfV0b0DCeSbm5u4govWt/3trfQ7zg379EvfBEFm3Z/+x//BABg1655PPs0laNeYzNyN5s3g9EQq6tski6Q+fbc6VO49ibi2tz92vt4fGhcL1xewiwnCK8PU8h7/34y72RDOneWTNlnn3gEPcsyshwsGFRjPHeK+n//A+8CAPy/7L1psCXHdR74ZWbVvffdt7/XKxrdaGwECQIgQVDcRhQpcZFISUOFQrtjTFPUaKw1xg47JMc4RrZnYsYxS0zItiybYVmiJmTRGs0ijSVR1EKJIimSAAgSxL52N3pf336XqsqcH3m+k1n3Ndg04Yl4DN4TgXiNe+vWklWVec53vvOdx548AQA4f+Ea3vGOd8R/S+LhC488iu/57ljMe+rFuN1Dn4t0DVOPESphVcskaU3AcEvCGAnTNa1fN5oA0dS6LVXDufEEtV/+ddSGqtUQh4SqQAb5Y0+9iB15BrWFNp+PgARcW7K4U9+91FJdeTC7Jiw0STufIZrPJqzJulOYtP1k+n+y4eh1r/WGW3x1+2UAHw8hvBrA6wA8CeAXAfxZCOFOAH8m/z+1qU1tal+Tfd2ekjFmAcC3AfhbABBCGAMYG2M+AOCdstlHAfwFgF/4GvYHa1wmV0sio4RPfqRNBGgRwKa8QpyBmeJEk7wu9noLobkOEFjrcazUvuXkyd3qA/Gr0WiEph8/U6Eyn1YIrk5ePbgaI+nbRld+tixx/nQkUr75jZGd/PAjkSSIulFJCAqcEfwPJq0mJ09G7+C/+NEfwki6Y3zyUxHc/d73vgdATJXPCCM6CClyuFPj1379NwEAP/pjPwIA+K73fgcA4MAxg5uOxNX72aefAABs71xTIt9YwtUDIrd7zw/+AFYXIwj84tMRsH/o03+FRWGbd8WVH4m3VnYMGllpnz95AgDwtm9/F0Ivjn9F7qF4QDuDAY4cibVfrozewaWr6zh0MIaiG0Id+MpDnwMALM50sbgQ78kVSZ+vDxvc/+ZITXjudASK/9F/F1noH/2Nf60daH/u7/8PAIC/+9M/ivWL3G+s5+sKUN6bmUHRFZCcHWkbr1X1ep8o7+tS9xCm9q0t9DnoSY/ADaFH1HWtHreGgjbzXiinI/s6cOAAZiVhABX4pzKG1YYB9LY6nU7LqwGSB2SMQdC22hldRoDueqJVvTeJUmKMaPkAGmHkqh2A1FXeAO1+JZ7SbQAuAfh1Y8wjxph/Y4yZBXAwhHAOAOTvgev92Bjzk8aYh4wxD21uD6+3ydSmNrVvQnslmFIB4A0Afi6E8HljzC/jPyJUCyF8BMBHAODWI/uDMQbeN0kriS11xKwpds26eZqVK4pXzCdJupB8CGs0ltaq5aw3nFEvKh2T0zrrddRZa7zWDbWE6GR/BY/Ntk0+oKnYMTX+nZ2dxZqsxvtWo9fxY98fu+f+43/yv+Kt3xqF9kdUAmB/JCTZ4CM3R9zj0PHjePLZE60xaHl5svp1xPvqFg6H9kdsYkaq37fERflXv/a/4wPvj17WG94W8aPTZ07gkqT7WRaxJKtzv9/Do1/5EgDg7AsRYJ7rdhRgHch1z86JON94BxevRczqjW+L9XO33n23StdyXA/ujxjZ5avXcFLwoJV98ZxvvvlmnHgmtqUarUXQ2UhPNVfM4Mq1CKA/KyTN7//hD8F3YzLhB/5mrCH8uz/zEwCAxbk5/MNf+m8BAD/ztyMm9p99y3148ksR35sVp31e6AIonN5n7XdXlkpQ7QsAP5Ree0VRYEY8MV7bcLCNgrVoAh4vz4tqg3XaBVdxHZPqytakhpKifkPbUTUH1lpqKZbNBdZYBgL0pD4P2jZL5HO7CaAn0dM3jbZYUnImHS1ndwksWmu1hRa/q9NDmXoDvoy9kknpNIDTIYTPy///LuKkdMEYcziEcM4YcxjAxa91hwFeWa6cGKpMykTnIAkj6rpOhaOsW8tzZ1phm3SCyZCdPE5hLKxhETDPp0mTHKmsYsYG7cqwI9mfotNFR3grWjekD+9Iw7EgrvB4XGOuH0HRBz/7FwCA977v+wAAP/tTH8KvfOTXAQDf+m1RQG1bunEURQdra/Gle/0bRf+6P49PfzJmxd5yf2Rej4dJiiTVUnEitSi6cZKYETCbYceJl07hP/zhx+O/T0fe1P1vegCv/pYIypfygiytRKD5yQcfxGMPRt7ULYfI0WngXRy8BZn0KgHZL21t430/GEPGkUxww7rCorQDPycs6SOdyIBfXV1Vbe5SVoWzLzyGpx+Nk8btx6OMy5FDcWI/e+EiOgKqv/u7fyiORdXHl6W33oHZeN3fKuJzf/6pP8Xb3hLD57e9JY7nEw9/FuNhnBjmRPea5+A8EGziFMXx9Bg3HO+kTsrxJxqx0JcwK1Ra5FpIP78B+Wi9Hgq0Gdo1ldRgVTKGi5wtC90XZWuSiGGKldgNva5Gqe+qWF7hwBq8pJ5aoJEKC2YKm0zQjddZsOOPCQqjXK9t943A7q87fAshnAfwkjHmLvnoXQCeAPD7AD4on30QwO99vceY2tSm9s1nr5Sn9HMAfssY0wHwAoAPIU50v2OM+TCAUwB+8GvblRcmqLiH5CTJP1wT1NUkJcB1iqRdKx7TOAv7WJfDNLh1he6jtAR+lb6trFndZZPYquw0IYsCRlUFIK6gJd1wNIknJYD9WGrHur2Oinlx9Yqp/bjdoQPR6/jcX8cauLe84zvxQz/8/QCAP/3TyAd67WtiCn40qrCxFl33+9/wRgDA+UvX8Jk/i7yk7/j2dwIAtgfikrsi6z4h49TUAOvhSLEAl9IhbjoUocCnnolp/S8//7zKtzJW+JEf+mEAwOryIpaFUb/Si6v+GmrVlz59KnYIoTb529/zXiwfi2zpS8IqP3/xAo4fPw4A2H8o8oMuSo3g6tKidoB55LNxfMJoHXfeFkPXgciBXJPwr9fr4a3fGkFjaEhnAAAgAElEQVT7Lz8WEwFPP/XXWJmNntjf/tDfiNcd4vis7F/B6u3xfD7/l3Gs57oWc7PSWpzgMb2EegTLMAYyZL5R2ojVtHn8zhiDbeERkS/W63cx2KJHJQC6eK6jwY7KzlbCcqcAYdNUmJF91OLZX7h0HndoqRm11Rs9L3rrPNlQNwpfpIqGVMu2S6kjq6bQnWQcwCHpDojeqQnQCCYJJqbtb+QpvaJJKYTwJQBvvM5X73ol+53a1Kb2zWt7gtEdADTBwjiraX9jiSVJGroJCpxSk6aua1jHFUt+F9LMbxQQj9s3AFxJBndbC8kVHQUvaS5jjHtHjZn4nQUwoxozqbqbTFbPtK3E0qHxWndH7Mq6AkKYhrPRq5gt43k9+YW/wH/+HW8FAAw2Ivnw8w9FZvRdr74Hd9x2HABwy80RT/mrB7+AA0viyYj3VYnnY1DoiugsEwkNZCgwW0T8ohrHcz+9CYy60TPpzsXtZ8IY7Kn6pa/E89j8zvcBAA4dPIjuUkzZb14TYbTleZy8HImIvcMRZ3rn90W8bHOwhQtnXwIA7DsYcaPBzghX+Vthgvf3x3PY3B7ghS//VbwW6Rc3O9/D5SvRk9oexOu941WRCb4wu4TP/vmDAIAXnxXgfWkZB49Ez+rem6LA3Lp4VnZjG08/FVnwy6L44Ir0jHTEg2HDhMoCICYJ6mcZyD81mUImvm8a1OJBX5ZGDyurC3Azcb870nOuK9SAjjXwkgwhwDIj9Ip6FGDpmcjzurrUUwWJ9c2Ib3alx54NjdZQkibuTdbJmZSVhu9P5m3R1XNJraAoGGHEr4xPTQWIGxlnwQYnSSGB1Ji2asL1bFr7NrWpTW1P2Z7wlAyAwpp237fAqZZp+gq+ZrucOE13C7erBCNXgdTMHKn0Nmg9D3ttJeH0bF8Z9sNeaMxWMAcSGq/ZK+1bv7mRBNg1hSrbe68eFRUcvfdae1dJKpeZsJfOXcC5T0T85H3fHXvUb0tm5RN/8hn8mDQYoMrUw3/1Kdz+mugBjLSynCNRK/nOOqoYjHVl64s6JuVZWwuZ6kI16ItKwepiTNU/81QkVt5+00HMHoye0vNPR/Ln6Mo5vPGtMd1/0/FbAaTsVeMttuXfqyYRAJ96IhIv960uynZx0J9/9gnM9gQLE3VNO/LYtyItmYQmcG0t4jafe+TT2JFM5cGDMSM3HI1wQmoCicdduRwTw6PtbfR7JM6m3n/0JryQXk3Wc87LeBaWdWVel/iUNk8eBMtRRkKQDIuzWJiNmbiBIx4q+GNZAgWpAO2SFQSPkTwH10ReuFjcnxQAJGPYl+yjg4HxlNKIf4fDoWJbTFonnMcmjSVSCZoA69qUBijp2CeMlAPgrI4dTSV2A5TM+XK2NyYlEwG2HAALKhrGD6wWMJYdhlJjDduUoU0diKyIUOe5kNKR2v0kpN+7CWEFj9AaTADo6iSWJiyVbOj3lS81JPNV9hXbIbdpC03TwEj4STC+L62533zbXVgXrgrFy378wzGpefnqNdxySwxFyFl59HMP4+3fGcFdbadts2udEP8aj8eYETkQFhGvb0amtoNmsLXfXVl2sH4lhjsvvRB5Qfe9Kb5gi8tLeODtUQa3EFnZffv34847Yy3dhmhbr8n+e7N91FvxfNjlY2VpCUekVxtlRK5ein+7HYt56QF36FDcpguHbamNe+yROJmdPROpBAsLC5iV8GUs8XG321UayEXRAu8LHaPodTVBwdpGmwcRsnKRYlF0nNInyEx3MJqUYQgPbS+fFiSyn9fWNrJW2SxepSzuSJ831sxtbMZrK8uuShofe1Uc36ZYSHrfWmhOUbYq8flkknFlkaoE6jaFIH/m9XeZeD2f7+Q8eIxGMdQ0Gf8oKPUk7ovSzFEa5atPStPwbWpTm9qesj3hKYUQ3V/vvVYyswJfwyHn1P2c9HaAxHZWD6hJGgFpVgecENXY3UOBPj/OZFB4XkHFwor24hfDuYneVtZaBdWdZy2RdCqFVZCZR/DBq9gcKQeXRBJjbn4BR0WKg6vytlTu//3/+qe1McH/+X/Hjq79eafXorQHR2jaaE1hR8JbP6yxuhLT/ovSpffCpQioz5QGs3LB566JtK4f4u5XRUraP//l/yn+7qZIY9jc2MLZUxHUfuLR2Llk38GrOHHqJdl/DPuOHo1ezsED+zArXkqdpaRJAOxLndjKLTHsW1+7qu3S2en3xedPaNcT7otdR+q61sXd0QPwHlbcDzKzGZ54+DRWmcCf1l+iHYrXdY15IYSyU+76tSsZaVLGODBZYzSJ0p+L5zgajbC+yU69bd+gGo3Qk+dzTsJ5HyI7fGZmBgvzcR9GxumWV92LU5ejd8wOLIurkX4RjEtMaxb9X7dBRyJn0rRO1PuUSJoUb0PQd6mQMYzPstB22EW6Ysdeu+vYkzb1lKY2tantKdsTnhIQVyFnC9R1G/vgDO590FS3camiOX0vqwHTkoVBtSM0/Gz7qmG8HI9rwY63XuPsiTOT79vESmstjLQvZd2QDQ2CkWOJno/VmDqgMG0Pz5UW46zjLpA6Ar/w1KP40sNx9evJarkq8rArKys4IiL2b3jgXgDA2csX8YJ0oD184LBcm4CwzumK5QQjagYjLEgKmsS8zY2I77gm4MXnI4h992ui5OprX38vjt0U9zsv3ubpM9ETunDuEh78TGz1dPLZSLbc2ryCe94QNaLe+b73AgBqWS2//PCDmjFw3QjIWldifi56PIcFND9/JiooPP34M7h8LuJAnTJ6ObOzi9i/P25HjyR1L3ZKhM17/jGpUE/UVRrrdJUPLNoylVJPtGNSICExYZPcV4OAsmy/TvTWvPcKAnP/rjuDwVb0fg7fHImbfSlBGW5vYXMjUh8KGZ8FF++VMwYCS+GaNIhYWLmKkXQoJr6p56AeUFbTVjfqGRpVBEiicGPFkORcFQtNtXiN5vVtS8YXiLQcR9z0OnWq+EYAuoHo4iIEBQl7kg3Z3t6WLay6ygQU68onhcq6HUoZY3YJXiGbc1jwmxjYDYout5fN844Q8lmjH1ndf1fcVz8eQ6XCZb+NPMidootKWk/zwel0SziZ2AgWWskuzc7NY0HCKrq+o+0IFD9+5jQ+/3CcBN79PR8AAPz8z/4UHvxClO74xB98AgCwsS61W0srOlmXIsZ27vwJ3H1/ZIg7eYFPn4gTyt2vOYif+q9iYeqy9Gp79uRJXBQZkJckjCwkw3Pp/Hl8+tN/DQD4/u/9TgDAd7/vvThyLE5iFwSMP3k2KlyOhju4VfqlHZMQrfbAukyKz0kRbUce5JuPHYcTRrTjS2cLlZ3hpEQCjDE+KTZmUjX1xAKgGSJrVKecciAVUiNSTto6kRaF1h5yUup0OhgKkD4jcibkydXDKmuxLTsZNbpoPP5E7ItXCDgPQBs8DrZPtc5rdqaPo4fiZEwBu2eefBLzR2+X8ZF3RLOCdXqOyUXyQScoTkY5qJ3mD1nIrEVoCNS3YRUYo4qW+iuTphVOWDyetVbH5eVsGr5NbWpT21O2Zzwla60AlG23m6AhkFioBJi7nY66k6zATysAdqXzTUgcDHVls3Mgg5fmnMuoCQzjZF8mANIGmt6RCY3WLHVkZCsuxi5zb1W4uFYvpSPhHjucjpsCJrDbhYDUIrP6wJvv0W6tF0X65NkzD+EN98eK/gdeH/lKzzwZV+BTZy/gsuhjc2V/9X134LbbI/C8IO2Wv+Nb3wIAmH/P22FdPJ9npANJf3YRG1ejx9OVc97ejN7Cpz/z5/g7v/DTAID7H4jV9s1oiN/+9x8DAHz8//0jAMDV9The/80//nu49bYofUumOepGPeC+yAU3wt2y1mm8PawoK1treprpew35m0ZrKLmi11lPwdSZlpXuQHoSEuM/ddKh18tEQpG6CTt2vk0h2lXxUElB8HWqJ6NIWrcoMSuUjCBM6wuXpQuuNegIj+jqlQhc331X9Gr3LS9hRzogz3Sljq7b1XejyIB9ACic0yQBEyDtDrkE8RM1oGnakEjTJK+O7evrJl6HCyGJx5Vd3af2ayTfyibv1GYRyPVs6ilNbWpT21O2ZzwlQFjVhjpH7dk0hAZg7RtrebJOnJz5tUNtPYZTYqWk9c3uOTgBoVZ7oWsI3jRpxZFjK+vbeHgjKeBCcKOZDoYC1NeyPYmeIXiNpVkXVBRGK8+NYAC2yXvCsw6QsX2p53dScJct8VZ6FvjcX0Q9pUUhGvaEgX3Pa27H6mrUDhpL//my6Gqftx3BcsabcVV+6sWLOHpLBF8Jbp85cQrrFyOmdM890RM7cT7SAD78Ex/C8r5Yw/aJP/8TAMCffPw/4HV3xNX9x/+m4FOrEQspZ7t46MEow3XhTNxnxxVYXo2pbrg2taHe2VGJXMUrQqpzVFPv2u2SPY7idrJaZ2JkNN5nwZDhnFO6Brl+rP+qg1fPnF2SB4MRNjejB7O5LSL+BM+9V2+FnszWYIgLGxFj3L8UAW5SP1ZX92FBat0uX4ikyfPSLWZzZxs1G1UIftRbmMd+qT3sdvmMUHPMg75HwfegSJX69LBS55Wg3AejSRqnSgmJ8CmkWmc0OUUcK1JjkgeZ7z+SiKeUgKlNbWrfQLZnPKVEiGx3+tT6IxOJWkBWB4RU11Y37VXTFA6YKEGBMzqbKwksq9EJWeoTiLN6tyDJUvZr0nes9u8U9NIq7DsUsZ7QjVmu9TMxTV9VPpMBlVjdV5iXbNis/D19Mmaouh0Hw+1CPIeRdN197OGHVB2BmbwQDGZFIfHy2bi6ooyez+tedy8qkWbdlpKVqqpU7nUg5R/sTnzfA9+CwyKz+8XPxaza+qXzWBR9oeF29LbuuTvSES5duqrV+EeEtPf3fuZn0WNpgdRqbYsHceHFU4pNMDXdDAY4+0LErGrJprHK3sJkGAgZgNAMGz+iRxmsSalrPj9VnVbtMq3oANUQ2+RbY1JmV71xVXwcap80UkAuX7uMoWgfBfGihoJF1YORZuSGIjOx7/BN2B7F61xYiPtYFknk06dP49qF6EHOL0TcaUs6Gy/vX8HNxyIWeOFcLMO5+w1vxNWBKE9KFpfPVYWE+XjSQRBa3WyB3ZFJPhYIVr1FviOakcRuMqQrjKonIG0l/0jlWS9ne2ZSio0l0wMwOUjee5ii/VlVVbsAO31C4/+0vqu9T50UJpv2OYtCdJSZvg0hKHeJ0iXK6rBWB3okRZDj0QBvenOUGzlxKb6Al178CoAoecoJlOFnU4+xM4q/7cpDO6rjxDPrOuDcSy4J+SKdpW5iojOcMV3lS813477uvDvyhGZ7fTz1ZKwP29na0LHjJNYTkbcV0ew+uG8/Hn8kSsduiAjbvpVlDEV24+yZOHFeEmrATHcGqzMx3BgKCHvhxRdVo7qqCdQXsn1HZTcMtaGDx2yHdVUEmMm4LhDAYuzERSIVoJRi0VL2Hzk07XsfjMWIBd0THLgQgk52TLT4yMOO38czVGig8gE70phyOIhh1WBYwzC7IfdmQ7q+9Ls9zElzz+ekh9yr77sXKxKlPvXlh+Q8jG4/Pyf8LZUuiffq+PHjSmOZFwZ7pz8DW8XniLK5nJQLCwWZueZaaxUK8DKe9SgleVhgS0kS+N2675RbmZs3OsYM6SI7v/1e6juV8aZezqbh29SmNrU9ZXvCUzIwcFaYoaY9kyp4hqCrKz2U0hVKeiPYpgzWcD1Qu0mpysm0cDDaH87o9kYTxRS93xGvtNvtqqgXj7mwsIAnHo8CaOevUf40rnDdzoyu/EORsTDWqpTFC5dj+HPs2DE936uXr8g+mGplLdUYvHVkMTQBqCXGpLj+k4/GFXgwGKAr3hNr+WZ7HTT0HOSC6UV9/q8+qeOyshRDtsFwqKsk+58NBBjfunpVu71wxAprMCvekwrlyYBVVaVeATuAGB80JKJ8CFfbsnCo6TkLQ6S0CczmCp3uqWl5QTyHVKkvnmqT4AACsaQS7IzG0BssXtSgZpfhgGrEkDR6g8YynElhEnu9zc/P63O8shKTEJ/68z+FEWB7375Yg7gk4nbdwqHbaYeYbKDxwnPPYUbagw8G8dn5y0/+GY696vVyzJ6chEirGKuej4a71mrSZ9KjiVUSciFkXvsGmOjeq33cglEQX5kuoR1tADmlxkyB7qlNbWrfWLYnPCWAM6rX2i8yErVMKdiM3Ji8Ik3Zs/Mo63UKk3RtxPIZOpHZUizNLqm0pmkU46KV2QrgZCVcXI0rnfUjnD8X8RaKqbHfVdelGN0PpP4ORtXj5iQFPBbCYKfb09TyvlWpvg7EZpx2MdV+d3XADlc46U0/K3SEhdllXREr6gBlleLEU9jBt3AWRZersXgCrqtV/LUAuhRGczNFhv/E86qqSsmGXttZ8d6WWQ2hJB7QqPdETSN6d957FK79qBprYUg0FaxHe5EVKZ1Pop4Puz0krTe0hd5lkjOHWfsuekz0OKq6xlURiisop2ytlgPdfkckhm6sR6rFmVNnUBQbrfM/uG8ZM4Kh9aShgRIf6zG8Cq3F7cfimZWFRTOM57h2JRJiX/fmt8FIyc/2dvSeVldSUiWwWE7cTBsyrS0KuWXE4eQ1URwuaN2fEiqRIoz0O62xwqRd7917OdszkxJMrH0jI3o8ZFM9KtwFbezopObHOKsupiroUVyqrlSqguNmTaHAIVm6IQi47RMXKdFTnIaKHbkJMzIr7ezsYP+h4wAASmOff+lFlEKoGcn580W4evUqOlSetGQNB6CWbKOc685mnIgGW5uaMVteuqO1r421K+gwWhBWOTodfUlRkz0rk0I9glcOVnogUlJSXH1mZAq7i8tj4FPmi1IeNUHhatekFEJQhJj7SL3Idi8wwZqkI+7YaYNFzT5NOFq4bNBMSNjovSpKDKUxZV1TQ7vQaCyVL5IlXmtoNhjF8fS+zh4qvpxxB2trGwoo3/6a2+U4NU6dOgEgScyMJby66dBBzEjxc8gmgTIDhnXMAITSYSwTP8EEl4WeXt6RCxLev+bue/DUyQi4s2cbY6AQDJpxPGZPCqm991nhbruQPbK929BGWRSoGH7JjJHC3dT/kM+HMWaXUNyNJqLcpuHb1KY2tT1le8JTCggqIqXiXGSACsDsfXKfyXw1hVNOCxTMS5QA7VhiUlqyJRqHjKEdQsY+5UdZpwbi53WSrKALOxBuSNWEtNqLNyfS0hgOgKHwdbolz9EAgYJgTPHTTW9wYF9k6XKfpGLtDLawqixgtkNx6MoqbAQIrUXOovFGw0gNW73X0E+l5xhuNQZOeCjJownK24Hyhxg+1ekcJfxxzqEnIWlqA81BbOBAflVi1NeUO7Ztrlpo2pIxQEwtp/1yfOL+R6NRC+iN42pUl5qh4Lbct62dASBAdS2qDVtbAwWX6eXMy/XcdPhmbG5G3pC24d7ZxuqSdAIeRG+3L6BzWZRwUt+mVQO2UKqEr7ZlPLNnU679luO3yBjH+3z+7AX14BYWpaPwxUvYEME75a1lMEMhbjVD94BGpXjqTA46noRReRWjve18670C2sKGTusFEx3BaPjZ/huZ8l+dFjD1lKY2tantKdsTnhJAoqJVnIIAcK7zorO5ekANnM748pdelDFaJ5WnJUPZFj7njB+80VolemsRF5Hfyoo7IonMlbpaXjgTa8DK4NEXQbZGOkgUqj/TxeJSXDnZaWM4HGrWlTjKcMQVqISV9DQBTa503W4XVlLphJa2BkPMamfWuKqWlPxtGpXitQSTK4Mx8RYVuicR02jCgPyIpml0Ve10+u2xC0FJeJP4UW4mS017TTCIR+NcWpnp8ckWrrSoiVUR6wqpjo890awwtevRWOWLDevPQoDtROB8Szrq8v55m45NcBrOqojcWLbP68RKuT7WofWcgZP9kwqgY+A9hsOILxEPcrZASdxH7pOyrI1BYZO6AZBIjmW3o/unBtfW+oZei53wMgGvuBqfsVwIjl6OKi7YAsZMNBNoKr0Z9Azp7dR1ncZFa0ut0iIma99u5CXFX78CM8b8HWPM48aYx4wxv22M6RljbjXGfN4Y86wx5t9LS++pTW1qU/ua7Ov2lIwxRwD8PIC7QwgDY8zvAPgRAO8H8L+FED5mjPlXAD4M4Fe/hv0JstRecWkhBM3w5wQ9Yhi6H0clgUKrnFMGIGiZAlSDJ2UFdFbnFiagYg2eLL1B6tyGdaP77XIFsgFGVhd6Xa6SbN32Jo4ejfgAxd1feOE57cemWI9kegZjoMuyDDlmV0tdOjAmZlJYujEa1tgexdV4XvqJVZKB6nQKzYpRodM5g5JxP1dsZiuDB+q2p9Q6R8r/yu8Ka9Awq8nxMgFoKFNLbEj+P9RJd0qwHF+HlC3Mu85CVueCOlXSpXY4UG9gZk6kYokT9mZUaGAg7aaqulYPbDCMGA7bErluHxsbF/VaAGB+337sW46YzfrlWFKyJplRYxy68jDO90gJCChFOZJNAhrxckwI6JT68MbtTdDOtUG+Y682ekAAcOHsORkTKpIuJIKnlLHM9rrYWItUFHpRmu3ymRqrvBt1XauK66T8dP5vzw7LIcsQyjaqZFqWuzJt1lrtoJt/xn3//00JKADMmNiyow/gHIDvAPBj8v1HAfwj3GBSMsbAOYemGcPJy18LKCxZWJQoUElIRPZsVcc6KgAwErI4Rn/wShdotR/mDsVYHBgyjW6+pD4rZuQDdsdtMQX84rOn8C2viX23TCeynkfDNfREsmEkBZQDmRhGoyHOSJjXk6aGM90+xsINcnIrGG5UPgAyoQ0lfOCDbV2lfJruNifBAluD+JAeOBBr2GaLeJyLF8+jy5mWE0Ndoye1b7CpngwAxkMDGEq6xsnPBKecHD7kFSflvtUCzG2ZBKzt6iRRSeiik31I7O0K5L9UqfsHmdoMlX1Ax/DYso1v0JFwjduRy9F4j5p1hhLOuLJU7o+vhAowFFb2+kXtWXb33VGWZXtzC88/H0Xy+AL3peloEcboujh2HQm9AhpQF7ZkXZ8+akYF47S4F4nNYeW8h/WWbsM0e9ET7pxcY+GCsoAO3xqpInZ2CRevxrCzT0ldn2r42G0kFag3iQJgE9wBRLB9UuInwCukYdnYMuOesZCbWvgxK8HrlPvVsP6uUJD95ezrDt9CCGcA/C8ATiFORusAHgawFgLnSZwGcOR6vzfG/KQx5iFjzEObQvia2tSmNrVXEr4tA/gAgFsBrAH4PwC87zqbXndaDCF8BMBHAOC2I/uDrxuYkGQNVLpEJRBMynP6TMKE3g1ToLK1NynFXbKmzZusG7iAcwoMGgX7qHpq4GBltR+KEgC7uH7yU3+J97/nbQCAeaZm1y5gWzyf7a040XYcZUm7uHThYmsMytKpagFbbdN6M11F3J0Ay7OzcRWsmxF8LR6JAsoGcwvS7lpqkWZ74n3VXsXLujNx5V3sLWnVf086ZrATb9mxsOKxVhKqNb5E4yXdrzVqkP9P3YgZlhUmhUKm04YVW/36NB4wKnSndWuspwtGPYyxthZPlAAyqckddS4lCbjqz83P4epGDNuuSLvrTl9S/fsO4NzZKNC/sxFD682tHcyJl0Lvg+B5r9fDjEgTqyddA7WMpyF/xKRnWUO6hgCx11rOybR87I0mXvEEyXS4s40diSJYSdDA4JyIwXWFZZ86vAT1TCg3Ym1+bu3at9w0MWEtmpBkXuIgMxxr9HpdkSIThpuUk0nkzDZkcj17JUD3uwG8GEK4FEKoAPxfAN4GYMkYbWdwM4Czr+AYU5va1L7J7JVgSqcAvMUY0wcwAPAuAA8B+CSAHwDwMQAfBPB7X9vupJeWb6fqG00hJjEpBc+MSeUN6llJLGs1qd2a8XVvbvdsTYlTbU8TgpIHa1nh+jNM3de4eCUCoIfvisJu88sHsHktfjY7y/ZLgl+MKljhE8zMJLr/xlbEnhYWY/U407Dj0VC7pB4TadrtEUF9j8HWmmwXV/+usyo4NpB+dztb5+S6HBrxHGZElH91ZQWPfSX2dpudj2OxvBL7ym1cuwqD6InRBfWoNc1byZh3jFADMuJpWnGDrsZOW11RICythUG8wdKZVtMHAErp8D5oac72esTNttbXML8QV35Wxg/EQ5xbXMCMjB37p9V1jcuXpRRDjnnk+K0AYuX+1nqkXYx34v5nujOYoRicPFt9wS9nZmcA9b5ZQxa0gUHCbuT/DOCbdgKnKNLzTLoDy/O99zpmFFXjsHZKhyuSKLn5lohvXtrYxBnxlI4fu0XGjsc2WTnRdbwh3w5kvFexacW6rAeMTZhQa/tQKy1Hu0Nbq6oR2EVRuLFKwNc9KYUQPm+M+V0AXwRQA3gEMRz7AwAfM8b89/LZr91wZ8ZE3k09VsA0cVr4UjiV6SCrOoSg7nmTtSQGAGcKDcd0H8ajVDU9eSBUsdKqZIMCfSFpMPCFYWbk9a9/HT75l58BANx1548CABb3HUBHbkwp4diOTHBzrsQ16SiyJG2sLQxG8iLtkwmhJy2lN9bXtE9ZxYxZIY0YF2Y1o7XGMNF00IjLXsk19iU82RkPUIhA3uxc3Mfm5npqpCjH4eR3ot7BS6dOAgCO3x6bUV64vKmg60I/AvuUVpmdWUAXDA1E8KsotJYuqWOmQuR0XxOHjA8+M2y2JdgXt1sS6Q/4gBm5voWFmG3sy5gMh0M4OVfKvpw9d051rm+TZMW2JAbOnVnHTQfjfrXg2jvN/hU8b5nMxpubyonSgmjvk74gM1VZSFTJc3PslsM6FmfPRXVJp6EU9a+TJjYngSB8t54rsS7h59ulI8wTL57V4mU+xKx+UPAZGeMaQcH7IF1JiD3nyR1laodUvK2wSqB+fD6FkNEdYCXjRDBfs7g+6H1+OXtF2bcQwi8B+KWJj18A8KZXst+pTW1q37y2ZxjdHgYoLEJFToV4Sqyi90bTtvQMS1ekoqhASU5Ja4/HKCcgdu9rBNtP2YgAACAASURBVHJh6A1poZtVFnORhXaphqjNsD148BA+/vE/BgC8+z3fBgC48+b92NqMYc9qX9LIcxF8LoxF7+YYZgTxUHZ2drBfBL6oqTyQVP/xO+/C6dORe7K+Gb0hguzra9dwTbwUDsb2oNL6wd5y9GSW98d9h2vnsbMdw5hmzCp44KjUVXVklb16JWpkG2MwK8z0GeE8rboORqKAsLyyT7YTgNOUqMbiTYwl5ClLDb9swcr+aMPBAE68oVlJEjhXYiRdcMciqdshq98UaAwr3aNHtrCwqLwe9brG0ZsYb+1gU7ymbdlnXddYFD7TUFjbZJUXXac0DXoH1iQBwcR3kyH3XpMQDFnKXvJIxvKMUGomWAfH3mhSqT+qhioXQgiWtW9V1ajXRHCanYE3dwaYm4/3d0FE4Z5+9i8wPz8v48huOFQcyEI0rR5wiW0+pmcFHcukzU0VhjYIn4+5MSYLMRlu1/BMirCej6H4hIjj9Wxa+za1qU1tT9me8JQCYvree58q0DWrKp6TD/CUe2WNF4LOqiobWpGRltLImnY2DsQmUjdVbm60dz1FzEIWSzOVS49pZ2cb7/i22FH2n/+zyA39H//JP8TCvkhcfOmlKG97cxlXc3R66ApZcVNEycbjSjvi9peix7B5SSRwZ+ZQ9KSThWAIoYnndeqFJ1Gqho1gA0UHtZcuGiRdylAMdiqsr0dAnSvkvgMHlfpA4HfzcsQ4umUHq4cjeD+U85tfXMYMe9oJGXJJPLJgSgwH8WA7lyMIuzC3pIkDUg0q8dI63S5WpK7MCcFzdnYWm9ci5rZexeusRZy+LKFYCakZzlrsiBQtBfpZFzdT9NBIUmD9WvQQFxcXMCeAOLWuyrnUrYTjQoyoauoE7pr2axKMVza4MANQWqvPGZ87YoJ+PEJXPJ6TJyNWF2zA7GIcP+MpeSvJndEIlbDObSl1dwTbF1fxfe/9XgDAOemo+8jDD+HYzbfIdgSY5WR9nSWByK6+Thdpej4h1bcVmcfkJ9UESM68Ti2bcVbJnnnSiP9POs7L2dRTmtrUpranbE94SggB8DVCSKsSiXopY2BUo4Xms+yYztiyugXjEUisZGdQW+xKR6qnFVLatpfVD00qMFoptRiPx1gWUf2DB6JX8cef+Ev8+Ad/BAAwkLKXl56KrY2O3n4nNmR1pXzr7EwfZ7eid2IEC7jz1bGr7PbmFhYE12FGaCBYi20aFeVn51jTLTE3vyrXFH9wSbyuTmcGy/ti1qcn2bdiZh6BtVmSgVlejr9vmgZg7Z5oCXX686hFs4eaQEO5jrn5OTjBSnbEiyo7BkNRXqT32hdMx1oLJ+n1WjyawXbAjsj/NlW7Kn91dQHrUkZBguKortGRDBv9Ze0BWNfYliYIcyy7aLyWf3TlmugRVE2Npm6TFL0LmOl1Wvvd3I5eWn9+CauHoke8IRSQ9fVr6FGloG6Xy1hr1V2nR1b0uth/MGJ+Vy+tyXWwJCag76SpwHJ8Bj75mc8CAN7/Ax/E/mMx6/bvfu9X4jXOzihuRyKmEoEzr0RLbrIutdcjbirMyp6IIShWRWJu7mHxeU5qlFk9KdpYVJ3VjL6c7YlJyZh40jY0ym9gndUIIouLJtUuIQPZ2EONBacykFGzm6JkEoKF5JKqlII2pfTo2HbjSe99AlF9WzqjLApcld5fd9x5FwDgj/7o47j1ttsAAPff/1oAQF9CkOdPvITZ2fgyLBOU9DUWZXK5JiCzhqGjEfYJkPm8SJfsCLv62vZIW3IbeRG6M7O45dZ47K6ETZeFQd6b7epD25uRDhuzfVyVcI1cniCJhE6vp6D2TC8C3UVRYkvaUjM0KvoUcSsRQvxsVZpR2qKDy6JjXcrkceRYnLxPnXoJzzzzXDyWLAChbrAmPCK2rF6ciyHt1taO0rUpADesh9i/GoH/hZUYCjJ1/9hDX0g1kB0JM+pGqRUEoGn9mVlNorBDCGytADdT9uy1NzvXxy3HjgMAzsj7d/n8GXSLeE/Ilzp4Uzy/5dUVbFPmWPbfneml+rE6jiuEhtbtdrC9Hb97UQpy3/SOdwMAVg4cxe/87v8DAHjw4S8CAI4ePZyoFXw3AkF0k6XzZZJp8hT/xEKPoJUQHBNngLEu+u1JhscAgFKEDQtbarhNrf18ErvRpDQN36Y2tantKdsTnhJgpGtnUOA5yS2w5iYoYKfC+6HWOqxdHXXroBenYHXwSVqD7NmQpTi5MjKNCYeabcRNEhcDosdEV/zqevQ03vnud+NX/+W/AQD8xI9/EADwjre/GQCwePNtOPlMDOUuXors2+XFJTSyWo4k3Dgrf7tlB1tXhWUs9XRHxRM6eOSYnuPWZgxrXGGwJWzniwIALyxGT6vTn4eXzMEcU/BhrJ0vNjbiKr60ErdHY5QRXEvIubmxHnvdAbDyl2Jpvq60h9yO7HPlwAJuEZIiw7eBdGoZ7QzUU+0Lw9wYg4sXo6d092vvAwAUIW7z8INfRFcGfknS4WXZ1frC/Yfj+YzlukejERYlRKt2xNO2WQt21pyJ51kUFgcOxfB2TcZiY/Myarkmnn8hKg2bG1dx4rmYyNgWcbX9+/djXjzbBfE+Dh05pr8fiVfdl3rEuhrimoSrXiRnjKTxL129iJGoM9z62vsBALfdGdULHv7io/itj/4GAOCe178O8YaY1L1FxflSREAPqZB3yZbJF2EDBJeRLHdLRqf6OTNRayq6Aq3tAZ8qJ+R91sYo8NqJ5+Vs6ilNbWpT21O2RzwlqaRGins1Zc8Mpy20H1gSsEq1aQogZulOYkMqQG+wOwVqsjIB1gjJjF+HpMmkaeGQCdezrkdOZ217E29/57cDAH7r3/02AOCaVHR/+1vux133vREAsHk1ekprVy+jkFW4Kz3nuKJ4X+NZWY1f/0AkyC+tRi+n259DI9c9PilV/OMdvPhC1P9ZWlqWfcR9VXWtq/25zUjI3Nm4hmtrxJxiXntxJQKv1XiIICRO33AVHyNQEExW1TBmOyirRMZnT8Zq+6pqcPxVEbQvynjeVy5Fz2+ws6PjOCqjJ1OFgE5Pet/JmG0I1rWytKT1gtsUyLcOQfCZ0888HcdTahFnyy6C4DolybRVFTWqkNQLKDG7dvWyVvGP2fV41GjqfKYfx2dbNLI2rl5R4Td69IvLS0p9oCTz5dPxeDs7O7hyNWKGKjHrvfZyu3AtYnv7hOx6+2vvw/KR6BWvj+O5/v4ffhIA8PE/+D3cd++98Z4INjOugtIvakkQqZZY9m99H5DA74QHSdIghIxKo2GByiiTfKztsrxpvY/ch36mZVzt9+6r2Z6ZlACZMLTwL35mBHANgA4MrQ3iySR2HSkG7RJhrMospIFkQV0q+tQBD0FbeWunD9YnFckt1iYdISgj+/43x7Dtj//o4wCAxx95GO/6tih1csftRwEAs4eOYWU5uvxzAnhvrsWHfWewhbtlcumIZMmayGrsXDyfOrXISze/sorXSDaQ4QnDMzMYYkvCQoZZna7F6sGDAIAjx2OYwVo139RohvEFXL8i9VmuVl7TWMTRnMinXLq4hf37I4h9SDJK2xtrOPnUk3H/t8b6ucWFWF82GtdYvxbPpxAwvApAX4poH3/8K/EzOf87b7sTd95zDwDgqS/H73auXUEpnWDWL8Qso2X2ypbKCm8yZU+qfFJwjBnb/mwf22txAvSGwnEeVhpyLggrn3WPJuygJ5k5fVBtga31Ldl/nGiDaLHXwaMQ6Y5anq35+WUcEW7aXcuxImB2Pt7v7bHHE89HQcBPf/avAQDPPRcTA/e+7n5dqLUlojGagKHp4osEOZBrZJ1TWGSyrXbwRldZbXCSbUezSIt/ybCQMX/GEdTONKmp97Rt99SmNrVvLNtbnpI16nUo2Carvm9qNASdtbA/zbraWUNbLCeQ3Gg9k9HUMnW+ScUwxqhIFYXNTCbrSe6Jino1DVJVNL0zh0q8J2ofP3BvBCgvX13Dr3zkNwAA9z8QAcpbbzuKozffBAA4cCB6GH1xoxfml3H44C0TIxT3ueBrXXl7EjbVHrggLcPJ5RnLtQ6G49SHTtLzRdeiK/VethvT/iOfwGr2LNsQpvbG+hqWxdPZGcTxGe3Ev5vDGrYTvYLDB6L3tba+gaeeegoA8NKF6P0tC9t9czBWGRZX8xyHOCfeDcXV2B+vPvEiLpPtvBW9s+HmFrbFq1wR5QC2GO+6LhYWFuS8YyKgKAoESg+TcyOew1xZYHGG6gnbsn2p4DGF3xjS9jolhuI19aQ2cP9Nh+AlZDx76kUAwBPPPBvP7+B+vP273h+PJXwxV/Q0fDyzEc/r8RdeAgB84cEv4tmnY0i6uhKv4757IsWkbgzGrLtjvVoIcKwVRVt21xqj7OoEYJdJ9WKig4y1VqkQ6m1ZBzfBBUs93q7v9ejnhEn0fNzUU5ra1Kb2jWV7wlMyxqAoOhiOB2Aps3bIJckRQfuZQ4BW45zW+lA3jKuPr5skp8qVsWk0LaozfcH9N5rGTIS+Cq4j58EKa5v2RS/KdShBapIwlpzPjngtC4vLeOvbI3bAWq0vPvIkvvDgV2T7+IND4jEdOrAf+/ZHhvXiQsQeVqW7xsLCHPoCTndHqYPw1jCe5IwQK/sio+tDjX37YmV/I73Sdna2UAqrGkVMn/cFU7pyaQvwglXti4DryuESpdRvVVJU50Usp9ObwWgU/02G9qHbX4OVW2JjBWpGGemKe9c9q7o9719dezSN9KtzbWGzajTGmtAPbhIcrNProS8dhF93/+sBJBD8+WeexZHbIx3h9YejZ/LMU0/j4ukIwh+UurtTZ6JneXFjAx3xygZCcnRFJyVDdtqeA5yFlwduKFSRrfEYffHOVm+PAP/+u6J305mbAxbjfb2wGb2i06dP4KWT8fjPn4rirC88H+vier0+7rxdmlIIBsq6OKbwgawJgQ0qV0sLqlNVpwp9zy5AJYqJnoha+2eDRicExo03yvwmObPRztR+F95kAsAmCiAhWc+rwcTmu2zqKU1talPbU7YnPKUgcqqFsajZAkgWBK4GIetxzt5teX9zo8StoN8xQ6BqlIWDFclY6tTUxKCQMKgZqQ8DUomB19WbEqmpREUzgD4omdOx3Y/UkI18rbE9tW8WFxeV5s/6ITYeePzpF7D18KPxXMUznJuL+IUrC/RFobIvafR+v4/ZPj0lacsjB+zM9NT767iE0RFHoGZS6qk2gpXr7sr+y14PdR09PK0Zk9XThWvqIe0IQZJlLfH8ExkViPiXlwwYO/02dUjnKOn/noydmTVYWpT7wBrElR56ck8usxuEKGLO3nQUl2WsG8nxj+aXsHpH9AhZ7nN0IWa7gk3HZn1cMKnrL5+7omRK2GAk106B4mHdYKOWEiHB76jScOGF87j02UcAANeEtnDh3GVUQpxdmo/jf9vNMStbFAUGgmvWkxQWpO7FpI/U3mujByUdM3PmXOY1pVKdVO3fVr/It1ML6V3j/rX7tE/ETZWTzgRDtewrkzr+xqh9QxxEHxoVKkt8IgGaq3oX78gGoDHtz8gR4UQHQHvJVcEnoSv+gxIMTaPtqzV8y/gWpASwVbQJGZ+JBZiwWgTMybRSxqxTl1a3r5vU30smyXkJAZZXVhTR54TlKfc6HmFTwoxNCQULY1HVMYXOB4Hxj3MOVy/Fl4EPzMq+fcrOHW4L61nGa3V1dVeb5XHtU9oY7MLBsbA6obkyCX0xHFOxMD68ZVdBZlqnKGFM3jcs9ceD8RrK5c0Neawd6TVHideZXkeBaKbxFxcX0ZUXqyehirbJdlFOJe5DQlrXqMibjqeYR6MiatvCGB+MxxjLJDOgbIoI/u3s7KCU854TeseRm4/pSwx5HoZV3FfdNAgsivVtwBg+hUuG0IXroK6YiJHFUxdKKCcgcf+8Tmg6QbCyAS7ry0YKgdHJq53oIS2HI5NNftTrlpA95B2IbmDT8G1qU5vanrI94SmRUGWtVUAP2iFBVsaQRKoI3JVFoSl9ekyGroA1Wq9GgTYUhXaO4KrPNK4zWXeJutZtlPHNTp9ZOjMnqAEx/NNeEOxaIYeu63GiDhCVd1Z/W7NFsnh69WiYPD3ZnGHQfL+EtUybi9RL06Cq+jyR1nkVpsChxQgQM51sytSLrVhs9x9rgtfV0qoURoVOGVd5SmB4SOdb20EjQmVOl7mmvVojkXvjPWUYkLVRT0oZAICBkCddkfqONVJMNR4Pk8SseBybVapzo+yqk52NxusYD4WGIF4pPcWmaXSsWTUQjFeWM0XwjAr8+xSySEhaWKeUErZDnxOW/kFJUMQxS8esCUNw0MRDHzVj+IpdXigHLZ43jIrP8Ti+bhJQrc+nPGsw6uUXcn6j8W46S95tRPseav+MBoHHIliudYRGx5rvG4xD8G04he9D1TQ39JamntLUpja1PWV7wlMKCPBoYJyDkdXDqiypCJEZoz3CCIwYa1WEn62THGfrUOlKjQw/Ymqf6WniIoCHFWEtWxLIHaHyXKnEk2HtmwEaejfUXKpGu0hj6W/SgxrXvCanpDffZNgToooBQfWG3hOxNJd0p6jPY4JHU3Alb5fLAEmwXWud7LhFjpPhkX0lUqpWDpQGthRSKaV4JStc11twnXZ5Tx08ajkPk4HeQExKaL80EmKbWrfrisdQ1KR+2IQnEoqaTRpIQcmErN8yCtTnAvd1FTGqwrLEotHfJ4grYVbEfmtl9GZ1X/Tq9Bmzu+o2mfQYD3dSyyPxMBBs6sdWMzqIfzooAPH+WNZEh6MDq14TPZmiLLXMhDLSNiuvaXQMxHtHo6VXKtAmY9/4seKOxKxsKNXjpKaUlux4j1RKJ94sgmKlJnXeSJd4HQnd3Kae0tSmNrU9ZXvCU4ppToemGelnxGa4UlgTMiJlynrRK2ApyWSmBMjibGtQG5K+KI4e/7f2DSA4zb7VSLib7S5goSMNHSHNBDKNmYatdFpSpLIa8byYRQlp9cgLeOnNNApmxD9FAmfUg/C6oKa0sHqPgLajUgpEk+RJEy7CFc6kkkrb9jiMMSp6r9koAH7I1Z5lGnJvmo52dFUF0LpWr4n3iKS8GgnDUZKsteqRNDVxDsncZM0LkxJoloViutnnHhlXaunm2+nAy34bPS/IeIUsTS2/8ymrxOYOJNrWDfQmpqaakUaQ7QK+5jUWaSzoONiUth/6tscN2KzlEcm+lOuts8y0vA82OeS1unyJPMksI2WGXaer50PaA+9N4TqqQJG8TGgrMm2KICVJhfF6f7jP0jrFRmHK1r4miZbXsz0xKcHEwbfBZKno3e43IzPtXOocxpRLIEgtKfgIUl9nH/L8UuJEpW/rRkHURx6K/KCV3gzuEGG1c9JpoxAWdFlYdNiGuiuutk0vsSWqm958fdA4iRk41AwXJrr51ja9sNRu1rSttRr2OYLajYeTSVXnYKTtx5R4ocRF8AmsJAjJh7FI41JImOtcVycN7ZJayAPtbfZyysvQyUBRMoRZpyjhev6Zcw4V4wDtMMsJImjftzwlPbmPSVmaeCLps65rJ0/Yn62xQCljETQsSyEGO8f49ACmQRazgMIK45qTmExESBN5WhR8i5Edt0sTrsrihPbkx+8hIxnHAiBwTbG2XNJ2UgkAIZvQ0Q6lIkwix+E+4TIekzyvJgHrpfC3OGkDie9Fu+69eRm7YfhmjPm3xpiLxpjHss9WjDF/Yox5Vv4uy+fGGPPPjDHPGWMeNca84YZnMLWpTW1qmX0tntJvAPgXAH4z++wXAfxZCOGfGmN+Uf7/FwC8D8Cd8t+bAfyq/P3qFuLM7pxDI8Db9fRhLBllLIFzDrZh2l9CiYLM63y+FRAQTgE+1lwZYQNbHzBbxpXnv/wbsaLbVEE9qnGIekGaNq/HqduurHijaqzdHtiBZCigdtMk8awhBdS8V8CX3i7pCDvNSIXZuq4r28dtKt8oYVDDz9EYo6EwdqmLRP2jUZ2Y8dmKpSxd2bASl7wOQEeejAIM34yulpQUrgy7oWSentyi2YV5DSM74j1ui1zt3OICOtJRlmTXpmkwI0xrhkEMTEvrUIsLw2sLAeiI/hKPw++cK9SR4fV2S6fdaZPoX/JCmqbtOVe1V8oBvVIlEBa7K90L66KrDKA3QUwMIcBKwkZlZ7POsk6eQfUMS5t18RHxQu3onL7jRVokhnbqHsJNsh5Abkaue4RyQtBfibEhAei8Xu+RaCaq1EHgG/oMF+qhG93fZMIhGGQJqOvbDT2lEMKnAFyd+PgDAD4q//4ogO/LPv/NEO1zAJaMMYdvdIypTW1qU6N9vZjSwRDCOQAIIZwzxhyQz48AeCnb7rR8dm5yB8aYnwTwkwCwb2kOwfhYj8WYnrotivil1UnDfR+SjpIuZgn3mJTKjb/lfuOMX4puULW9jcWZuIp933veGn+X4To+nUb8G7x6IjmIye66ICmQMrrW6DXp+WS4BUXq87jcT2A4VMFsMkxAmyg0lY6VV2H2pIEzWbtUB6/jw89Y+tE0TUrB16G1TX4+44bp6qTBwyV6nHl67ABLfsFwnL7TbqxMlQMYEevRUgYHL+U9Iy2n6GT9xgSHE8WEqmrQMFEi12gDtOyFNWe0jY21XfLIRXAKAo+kHpFdcZ1zmpjgOTSVx2iiz13ZlR57RaGqCIV4jUVR4vIVafrg255Sf25GlSTmRZF0JIV0IWS93TJ8La8DBYBF6V5cVZV67wMpJzp41MD++N+CHFT2RdUAr2l/k4H/fkzPrZ0QCCGgEQ+7VIGn1DhAlWSzyIXe3MvZf2qg+3p+2XXPIITwEQAfAYDbjh4IrBkjt4OcHHW1bQofHNzk7jR7kB9U67AYZhmPWl5YduZwcgOeevRLKLpygwQQLTtOX5pSXH9yOOosvCxtxm61zFzI9ZA9bJzWtyVhupGK0lG0zWWFi3nWMP5xOhbpwl32T5n0MtE5ADDWKTDZUMyrtPqgMMNjNVwp9GEtWlEwJ8K2kJiBUyY3wzFTpP5ewbcfC+NsenFZf3ade5pqHC0CJPwRMD/eiHYdFs8r/4zcIedMepYYumddNRS8zx7XRE/aDdKGPI0m3002ocxfB63NVOkRq/tgcXKSB2nSWGsZGp8Fp89e1aTKA9j2oqM1o+OqFWoBwNitwe1idCP7285OGt8ORXOLfeLanznnlJM3WXz7nwTofhm7wLBM/l6Uz08DOJptdzOAs1/nMaY2tal9E9rX6yn9PoAPAvin8vf3ss9/1hjzMUSAe51h3lczgxhe2CKoi60MU87uVQ0ErvYp3ameDBmwlAyp61adGhBnbc+VU3hKZMCGcYPtHeFxiPexk6kEeC/iX5ZApd/FiSqKQtdZra3TSvlCXWyuYt5U6iHlnSCAuFrw/I1yedrAbtw+eVN0u4NNtIj4VbErDLUWykeZbLecS1dwpTPIQjQeO7serVEkgIq840UbmDXGqLdHtQZjkieTpJBZV2bpkMCR5R5SKJQK6dO+SqUlsy4rsdTJ6FauTtYJWRn2vlFpmsm6MmNMku7IOuWQYb6rDs0nhnkncwMS5YMXx30migXr+mh1yOsxCz2eQgLKP0peG4/DJI93B2EkegjqZVrdplBuVPL4VA7a7faAmDSynTQmNgvv4likfVr71b2lG05KxpjfBvBOAPuMMacB/BLiZPQ7xpgPAzgF4Adl8z8E8H4AzwHYAfChG+1/alOb2tRyu+GkFEL40Zf56l3X2TYA+Jmv50QMHGo/hteeXG2iW2kNRsQJiD85pzF9wgug3xnG13IMb4zG1So+pSQ1i9nO3MQFJeIZu4rmdU1MXae6JrebUS71dA5OMSHP3yFVaxe6SqZVRFcnEtVkRfXGKz5Fa+qAsuC1iFwt8XRjFAQ3SZQqVcnXbQ/I2gxjoaRw5iklEmQC4i1X/bF4gSGgUr9xApSH1/tKXCTus41zsM7QwaBWlmuGA9HDKNtpaht2p7qL0u7yxPJEAj0kZcMbKC6SvExREsjY6oprea/Me3ryPF7piqwmMk/EUK+ojSeGkLzGyfPKNb4U28s826REYXZ9loikpWJ47ELcLRPp0qdCQDmOg3UpCRKNzO4maSeJJ2+t3UVcTvuyN/SUprVvU5va1PaU7Y0yE3DlCCnDxuYAknWpkDJs9ExaqL/Mvj1plWOtRRiJp0RcxBuw7sPqaskVqcjS7LuzRqmkgeebregZEU33wRVL9XCMEgBJZwtI8fVkbZoPBk7IgQlvIpZm9HxI4CydQ2Pa6oDEmJB5PvTuYm1Swkha1x2aTNKUJS7ZyksIJMMLNBOn3k0ag7RasmwhZf6cSx7AZKZGMRAPlJZY23WydNTNIqk2y8Qyu2ngdqlp0uPOxe9Lk6/2PO92/Vm36GYZ0UKHh54nSajpJKyOJw8ZQlBpX+KnyeOxKDrMLAoVgJlU42AnmgQYtxuPa7T2zKT8N70vX7YUPOOxk4c/mWkzCOrlmywqkI3RFW0um3mgZuLe5+e3S253wvbGpGRiRrMoCnhJmVIWl6JvPjTqVgafBlS7hTJNKtrGPhjYsh3ieJOAVa8pYO4gTTheX+A6pYj5EGL3wKc6Pa8vPWuXqkCpXKNyu5yIQoAC75zYPNLE2yiPiQ9YEtHSoZN91qZBY9qTEakNPhh4efA1xZyuXIHT2qTjpIep0vNPYyDXS+lfEz/NzzWE1Bw6b6Uex6tSVr6+RMEjML2uNKtCjx0m6tx4DCBN8hTBA4JSAXRasV7va+LaMBlxvZfIZFAAqRaFXq+m3hkiB4AqI6Vtg+DOlkluhPsHtC22pV559uKqBIky6uXZCXYXRGBtohckwDqTQ9FFDTJOdjdPx/I+BP231w7QIVEOZF8UqAtVpWOdd5ZOcMpEciQLP1/OpuHb1KY2tT1le8NTCrtnVK02z1aPpoleUCEsbGszxi67kwhb1yNkK6NY5mIr8KvLoU0p9ZxktNyzjAAAHbVJREFUx3CHgLrsipK8QCIrNsgpBHJI9RLCru6/cf8MJyfIcllIRMCVK7xBCi0ouNY0XrtWQK8teQ7aKEHdI6fHVHE3MiCzsFXBae93eVawWlKf0R3k2pp6F0tamwVYp2OtoUKT0sjqvGaUBiY06C0757ReUB0rZQ+HXWFDZGfzHk6oF8BoPR/NGN8KZ+NlpmfATIQ4AVBPVsNz1ghWlRJ5eW0hPvSyHVrbx3Ay0RXyfTZoEGzbUwrG6H6bCekSZLIsKXxN4HSYqBrwxqhXx9C0cAXG4zbQrTSSstzFJo9gNmN8XlJ6jm7UzWTqKU1talPbU7YnPKUAoKqDVK5z9ZA4VdK942qUaO+6Aodd4KWu2CE5BewT5zpFwhq0PY32ssm8NVlZbNAe6shAvHh+vlUSAkRsQCVa9diyKreaYSXCHb0a9VZkaYl6BvHDYmIliitZGwPxsChD+xxpReHQSG1dDmImJ7FdVuCDT14LITU4PTd1yLQG0SSMQgc9eYGaxc9wD67GXldll/6dETZ5rooD0dNtMszGtTEcE2zmHidawqT3xLIO6xJ8nnvsk947PXVbOMUHFYdssgKViWQHbEiytvTOrAHYjAL5Z9ELqyfKRRLQtpvS4E2V8DcZYz7zcf/0cpgJSNdUyO9IP4Exig3VUgoUTEg0FrkNKk4YrJYW2ZyEO+GBBU0oOX0HX872xKRUliUO33Qzzpw+pUJjaSAT45S8jLz/mwpk6SDQVS1hJ7MsPiiQrACr8qEMMOF2h2A0a5IGlwWhNlMD5MSVvQvk5qTex7syJFFgi85qW3IlhOTet+LPdJat73zw6eF27YfW+pBAWjJ5WwtAO1SOwGn7hYyTVMoOAek+GJvkNPjyOZM0q00mCBYPmD2U2YuoQK8uPun80pjx6r1mrXjv8+xVgtmTFRP1kSk8DprFVDazQdb0NB7H5RM1wWOkMUsLo9n1VxcrTij1eFc2Wd/4YGFT0Zt8lLZJOUF+5vVZ4WIbbLpHeiuVB1Vr/WgzkfW1QKZAKrt3QXmDzAYyW+dDUrZkosRfB+hWalJo8pY317Vp+Da1qU1tT9me8JSMtSh7Xew/cAjPP/c0gMQ10XolV6KRlYueJpBW9FI1ouP2wyagI327DOuHXJE0kpv2qtZAy4aU8OJDk8IXlScRJnKolcHa4mDoRcmuCMyaFFKk9LZVsJbH5gpUFIWucNa2PROYPAEA3UFNRjRd+czrUq9JW80aZc/nNIF4jUlgjaoNTcjDH/k2tT/R5Y3KBMGnRAM7XyT+V0AK+BIlQOulMu8vXqNJHkkOCvPfyi+QMbFGd5LRq7SLDFf25JGls1Fvy2X1gtepEWSwph5lk1wStp7XmrOM6c8xKEw3efdak8dOJ8mjZZjITrMwRp9dZWgHozQZdUl4Wk2DQt4DsvMtvD6LZuI9C8HvUkXw3muoHtLN0TFUnXWTM8wnOHP6rJjr6ujnNvWUpja1qe0p2xOeEhDT0XOLC7jt1jsBAKdOvhi/EPJeU6fYld03jXGaaizku0pWq6dOn8dCP1IHuNpbU8BIOpUs1Lw6v8NKa5/Snk4r+qGfAUBhOggCWBfdJLPajCnmJSQ2oS84GBXbYnfbAA9TtFcl14n7H4XEbp8kHxqTpHVz4X1l/4bMY0D0LhxF7OU6Kt/Aaoqbq2VH/j+mxIGEIkVMvu2RKM5hQobnUBbXaP0VcT7WyhlXpnMkAGp316YlCkJiATc5zqZeE2kC7JWWEgHqgjYehaTlqzrhgnFXBtZRQlhdgsxT4zgl4FqZ3BnupJSEou2J+SbATigmxC6A6bmMp5iOnZj9cvrs5GxLOK1xpMdkdtEQ+Hy4skgMD2WCG1VA0Hq7Ssa+LNRbTx2OC702Z+UZMUlRg56bdgt2SZBRGePExgB97l7Opp7S1KY2tT1le8NTMga2KDDaGWFmNlbqH7/1dgDAiRPPAAB8GMOyYt2QPGlVsrQ7K1pDUmby/PlLqCWFS+sUXfgQJUHZAqkrq5oNFo4p+Aw7MBOZkVxa93pelGZ4qOukJMECPektX4jXYl0m2asp/pQ6NS6dG68XiORA/q4jnhh82CUG32EThew6UgkNVGmAWAYxnbqu9RxpJoSsvEQ+6yYtKFUMle1D8En5cqJ1kjFJy4nj1Cs7unr7HAhCTFs3YFfhhIFoZodeDnEeJM+N2KR1ha7oCfeS1dw5PbfGsm4taLkI3cUx0/reoBAP15IU6b1idHr6nudfgsRKn3kJSc6W+AzHNyiGpo4hGw44aCuq4JJHzx5z3AedIu8SPsX7bFxHdb6IaTqJHDxCahzA58h1MJRXybGJRbMZ/9a1eruKCXqjXvGkxRyxue53tD0xKRkY2FCg2wkYDmOXDspR3HJbnJxeev55bK9H3eL+4qJsYmHEnUQhE1VgeFXC9aJOMdsWewDBzMfPZMzkaNEVdok6AESQUHHhgsJaikirCFmeCqbWduo4IfygUMDKnSVQ3IRab0+S+E08qCSbIi8dQXGk73RCCVZTrdrJIiRwdSha0gyBASTBLl62Z3FvkVLYrULKttxrT15a50oN5RIr2eiLyImBEsTW7q7fipNMmy1ss+8mF4UoWzMh/VpnALMy5BNfRvW3VTY30TwIBnPSq+vRLvG7vGYr8cMYfkJbwmvXlyZNmnwRjRYBu1Sn5tv67E3waVLlRMIzDo0em9drUKLXi4XoyqPLFjdNciAuyIUpVGalYtt6Tiw+pfOpc+7cFiqhBGyM42S0MBePtzUeouzKMbui+W5rnfgJZyThPoNwg/hsGr5NbWpT21O2JzwlgKGJQ78fw7fRKHaGCNKP6+itt+G5Z54FAFTSfthU29i5HJun7Jtt9xYzJiTAUY7R+AAnHgDdXU2RFxYF2iQ/D5O2E9ddndIGuvLSxzHwmbMV91uKB9dxvcS2ZUrdJS+FnOIUkuwmNVLOpWmqXXVlzhSZfIicg7ZbMeiUEyS8zFJ/tuTteIrgyRg2CEqHcPxMjhO7gygsLX8snMjVsgnEYCAdTHydeSGhfa7Z+StI2jTKFs7FziYJei7bpttrSwfn4V4SO0u/57VoxxkXWuMX959qyLjfJBkbkkwzpWwlXPd1o9+lRgkGtXgRfqLK3hqXsw3lvDJ6xITETpNJ/lAWmi3Vgd0tya8nd0zQPAepwwR4DqTuP7252KMPjYWVlvY2tBUN8v3rPq25QfA29ZSmNrWp7THbM55SLj4PADMzjJHjalN2Z3DXvfGzehBn5quXL+LsyecBAEv9ONPPyOrchDpR4gUMbxC0N9hM2ZP9x98Nt0aoiaNoNX/Wi6xug+bep3i/ZBq/rlOlPX8nK2llC609Yuq3KIoWmJhbMEbLKNhbrNeTcw4h1W2pWFfR8gr4GSAr3QQu4hFaoD2QQGoTkvCYArRVBTZuIGBckyDaeBRKkBScrUm1UFryIGUIhelm3iLJgRmJc8ITiOMh46T0Doe8rESvE0KZII5SJo9SFSUmyJzeByWcKvZhM2xFGzIkj4GdmZks8CbdV/WYxCcoYbUnWkgsRHR74uGxGr9ItITUUTbhm0CbrMjjFNZoQ4VUc6aFiTrGyjgwPiUAJuWkbdDxTK3AMhxLhmDMdwQFSgG/nUwnDkmITrFGcPsb2x6ZlFJDOxpBQ77kZeGw3OvLv+N3715+Nx540wMAAGs4GckeQ9DGkAqqNsBoFD9TUTgpOmSjQSA9HHXttcdW0nyG/j5JU7BRYpq4lPtDsbqq0QaYyfX3OmFOFlmOqjHG46Ecu98aG2OMTkpDuUZjPExoh4A+YxbXnj3C4j6ciX3ngMQh4YRSNzV8JQ9kJllhNC3Dl4L62g6QCY6/895jyFlL7iGbQIYQRPkyM2d1UppUfAwhwBteSwrLdimF8v9Dehn4Xe0blBMTv/K6kLricAysCxnbfmLSc/n9StvknWLatruW0DmjiQgnQm6lSxUCWiA7ETpaa1X4cDJszf+dF44rgM6JzaUESeJZkU+02/LFQQH4Ok5E22vr2NneklNlIqRJ6T+Gob59/V/NpuHb1KY2tT1le8ZTQhBRMILBWTV4NIeG0qx1nEtnFhfQX4r0gLpup1B9qNXbYp+v8XisHUpVXaBJa4O2DA8ZmEqPbRKItlZDm7zdta4kBHIZAjReQwRandUZcZlNIUOS4tXedhmzmzrQ9PRidXeb+U1PwNoiVfHThW9SeKshGkvafAZu2hQ+NBXBWtZSDeRvChUJUg8G2ymEIp2CHCOfGNocf++9MsYnz9W4AkE4Z3n7cS7g9FRTiJOJ4Dnod74mj0bGQBnmGZscZFcPgUAeVgpjAMBYnzoBZ6EOw+wwUfcVjM3qC7nLkOgHgzYTP8p7JN5Q3JdcBwJCYJiUgqHJ0N037JDSSZLRIY1Jo7ym9nMdQhIETJSUrNuxOHA96SZdjBscvekgAGB+Xu5NPVZRRKXLZI/TpNc4aVNPaWpTm9qesj3iKUkK31it19LP6Tnl8qrihQzHtfYNU/KXEMRywTgFRH3IvBr5HUmIxrbARCCm5Sm2lfACie2bHFCWud0FWEqicsWy9JwATIhbdZyF1evj/ik16xQLoHeQA7lGhfEz1vaEYH0upjW5D2sCJntzcSW1yGqX2MvMV7r6GalIN4WQ5cJuTzJkaX/ia0kGzWYs4AyvoJC/khDpvVh149pqB6ynaqsWmJydl7koKgA4UVEf0GTnL8dGnUTtJoCWvPNHLmWbPIAJrMuka0kEz2IXjjjZVRbI+uJpAiWAcDFrIpt6txh/TuidJHrC+ExHrA2CO5OL/pEhnxFrSWnIsLrOTEzALK7GqGU4HOh2jTbSSGoBUzncqU1tat9Qtoc8pYmUtQqmR7OFTfKo1EJCwIzU7DQVSzi4AhgNxFXhz4aoJYNUxa+WtdnRcwpAKZ1xvWkvl4UplNCm6fZgtFFA+k6OEzJ6oaZ5K3j1dCY8jaaB0bZRqSI7bpNStMHH63Yo4C1xl/Z1WAs0aH+XYxzNRKI24gqhdd2xD5qQUYnJ1LlX2B4fY0xKhU6oQHqk80em3pmUDyYxh7Q9K+prnyrvVZlAy2uyY014uEA7YxaP5xTL1HtkiyzbKNtnw5Sez7SPpNgwgQ/65OWH9ERnypP8JL0DKls08dyh2Z0N9Pn1TehmNU2l2U+j45R5jyohkLAfjg/pDiE0+sCoggCpH7BaNzcmvmmSF0hMlSPpw409pRtOSsaYfwvgewBcDCHcI5/9zwC+F8AYwPMAPhRCWJPv/gGADyM+iT8fQvjjr+EYbU4NEos5PYwmSblm7m2t4Vv7uyZcp/DPZA8sXwoVdNvtAgfv9SllwWkKT3xKc6Z5J9WyqWAXJSdsYlrTvYdJqVM9duZWy3edoj02rihQe4LYdKddJhSXTcx6Pu3xzE988iHIJxl2aoGN9Yn57zxYe5UZ675Cmmj1K9bipUAiC0uSHK7Kgmhs1IATD3/nCpeFg6yxkxApm5S0M4crUntytt9muGdDSrAo6xvQyXQCpY7b5OFU5GApQ7zO7i84nnwuOUFfR3I4kw32ExM5zWRSuaRkBJu1+Z5gbRsbYEjN0GfNZxM3w7cUJrK6wNR8F2vlmPksBAeAwvVgZdFkbSOATM6kPa5RvPCVA92/AeC7Jj77EwD3hBDuA/AMgH8AAMaYuwH8CIDXym/+pZnsvzK1qU1tal/FbugphRA+ZYw5PvHZJ7L//RyAH5B/fwDAx0IIo/+vvbOLtaOq4vhvzZx7q6CmYEWrbWwxhAQfFEIMqDFEVD4eML5BSMSoMRof/IhRmiYaH3hAjSEmRiSiMQZRRNSmiSEEedRqUYEiNJSAUgQpxqJRH+49s3zYe+21Z59Tbu29Z+5E9z+5uefMzJlZs2bP3ut7AU+IyBHgLcAvX+wagrA0WQ4zaNGFNc2w0qDW5TUZ57oZ43QSeyGtiKlAFZLysNwV77O2t0nLAwb7QZN5gJ6V9/CsailW2nAn4Rg/h4k0stQm12y5Oqiql5/IWnMDdG2mJppkKbmL2OjJJImyu4Q0Ls1Jf1/ZPSWwxMtRzDOqpkoDVrisbVALroz7PItevY9ZyhfTVPwrtUjHfjeBJVNX/dpFdZVE1yQr4m9dPpqmQZZsTNl4cNWoTSezyOXZMMJ03yL4EzP1zcvJmNqXeqRMSQ0oprkGYBu7fl+5oL6Z+kOPZrr8HfFgy5lKBpnaV3ZxCc6FMgre24/7OxH5KctJCi9VTWiSg8XG20QaGitBLRZGkQXCrtG2eyMM3R8Efh4/vw54Ktt3NG6bgYh8REQOisjBv73wjw0go6Ki4n8B6zJ0i8heQsrUbbZpzmFzFUhVvQW4BeCN5+5WM5aZu99yhfJs+NIFH4yvfX25n6/Udxnr1NsQURS/V8klADeyu4Gyf346pahSy+p0mla0ZD+KofcNHkBnKRCdSrp+WxghV3XVO5YVxdpDBYEu3xR4URSq8TZDWR5XVhTea/YUqyziLnI8KE+zLqdho9sNZuwEbZNoTLtSoKpmUpBf06STVL44azlkBl8rF9x1XQpSLPvviQqTiR8X0FEWyc+L5affJhsRqedfblC2GyqdFqEZgu/PzxUs8PE5m5ODDq8u3A93yLs1JyQHziTRm0oWa5ekTMuoclusZp9t3GmydSbjd7qO27KSfUrVw0FsXKeS0aRUKgvfkaZNb72HdThd5fMqccqTkohcRzCAX6o+Io8CO7PDdgB/XutcTdMkI1kqopbqP9tENIFCfRARZ5a96PGcExpPBly1yGV1A2tRpkRVZ2bPppnMTIS5d7BUY5aSJ8mRXibaTFSO12xa7xxhKo6JucBq4YWSOflD+TlLMd254bzI43zyyHWjMXzPb8CN+Z6U6SpXoEuyZxK2rXTTmVy2PMGz5/myS1l+XlLz4u86fwFLHubb0n1k9b6n2f020udBDm9SGr4vN+3M801lU5iN5emdq5OZbe6YiAbibOxqXLiWLJ4rMxsYcpNFGc+kTJMKZSnkfbr699s04h5Iigmia2a8e5NM1ZymkjZx6miyZ58lNTfJA+kdWuycZdJ6iVNS30TkcuBzwFWq+q9s1z7gahHZIiK7gXOAX5/KNSoqKv4/cTIhAbcDlwDbROQo8AWCt20LcE+cJX+lqh9V1YdF5A7gDwS17uO6Vo9eglv/+PHjNPism8pFZJLKTAQ1+Wxuvd18XxvLCXSrXoEgL8uZ/56mSTlaksrcOunJkWsLy7yantJlMUi2qnrwzEzvNVbTeWzlNQ1H2iZF7CZaex1ti1W/05SqldTWLGbLSvdaad0pXkM7rZo6K33kkcF2y8uFy75pshiyVIplVkTPJaaygFq3msUuJZUo0tIL5XBJySWFvsoc+uP2PR+55JD3srN9uZHc7tvLhxSGfTS7ljGsySTIUlLqMle634ZJptZZ1iSgvFQIhcTUdV2qAz/FeVEilyjzTIBwPz52LU4p5evp7DnC7Tlt4KptMNgXRvMmd7oUUp1qOv5EOBnv2zVzNt/6IsffANyw1nkrKioq5mEUEd3TlVX+euw52nwFLdzaTdOk2dkkjZXpNK0kFqiX21WawmesU6Vp+27hSaxhI+L93mclmr5Ob9/L3vFt6/lzJvmk7iGauayz4mrltVIkroqvwkXel2TlUi3grhGBpWQaj/+jBJHlsqXVTzo3OBYZ3fPoyTPp87pCEFz2VucoQZtktG+LyOhOnddWqaCdw+tUGyjLKyvpir+Idx0NvzkZmfTnGfTGg/hrkWRIS+xRt1M2hdTXZJKVS5mwpZAmkltfvC2ES19mJ01m0ySlLGuXpBl3rMyW9W0zASk9E6PfeuB1nbvzLVA4k9rs0fRK+UbpyUo7T/GI7pQJMbXnMPEgTvuBdu7TKC21Ip7tcALU3LeKiopRYRSS0tLyMjt37kRUkx2nzGBXVZpIrs30q13XKxbfg2gWhGcSgTKT8p3pxslTk+nx3i7nRUxjZqPoVpJrNrlTTYqinVn9pIHWvBpxtVnN+nH9OzZP8DY1Lg1aBQHLxl5qJt5/Hl+N/T76NiI6bzmUfHVWpnfJpSKTxLrchlNkw7dIKvU7z75h9HSZ1Jt65WVRJKVnzSTK6XTaC4JM57AVeo5HbkayFR/qubRovytTW5rWx00SrHKvb5GLF7jRbxFlEmg4vxmhzIYDtH0PcJOVOM4lvBxt49K31afq9Ru0Z2+VNCdLTGPFT0n1oHL7bEYPYSh7Q4XMztb2bZiTJbtHSS2rpibqTTVJx9PGc+oMawVPjmJSQoSuCcqZl3uIE0+KEm3dKJyM1W0a3KmX19TygbSXeAgg0xZZsmjtvtoUDHbxwBTL0/QGFpQu9f5Aaxo8RiXFP1nHE+/zZSppeEl9ksivI42momF2LpuAuq7zRpVJjXEXf1m7msbV1qS6dJL2+/j3SPY2RVfPGkytprSVZelW/XifcF3l6orYmZ4ROZ3To5i9yFsMp1AfB17kbQVSgnDhXMhy/nJnRZ5bFrgTHQKNlxRJZVZ0BZ0WDTDtnes6512mVhrX06RqycOrq9lYjPcmpFy8nGf2vUyc7hmaTR0zJ08zySbH/nPTQHCkJ+5rZWbSNuoa8X02KefdW8pk2vDe9CdQUfHn1MSJsHM+rdWMsqpvFRUVo4KslbE7CBEix4B/As9vNi3ANsZBB4yHlkpHH2OhA8ZDy6nQ8XpVfVW5cRSTEoCIHFTVCysdjrHQUukYJx0wHlo2ko6qvlVUVIwKdVKqqKgYFcY0Kd2y2QREjIUOGA8tlY4+xkIHjIeWDaNjNDalioqKChiXpFRRUVFRJ6WKiopxYRSTkohcLiKHReSIiFw/4HV3ish9IvKIiDwsIp+I288UkXtE5LH4/4yB6GlF5Hcisj9+3y0iByIdPxSR5QFo2Coid4rIo5EvF28GP0TkU/GZHBKR20XkJUPxQ0S+LSLPicihbNtcHkjA1+LYfVBELlgwHV+Oz+ZBEfmJiGzN9u2JdBwWkcsWSUe27zMioiKyLX5fPz+ss+dm/RF65zwOnA0sAw8A5w107e3ABfHzywmdWc4DvgRcH7dfD9w4ED2fBr4P7I/f7wCujp9vBj42AA3fBT4cPy8DW4fmB6Gu+xPASzM+fGAofgDvAC4ADmXb5vIAuJJQo16Ai4ADC6bjPcAkfr4xo+O8+O5sAXbHd6pdFB1x+07gbuCPwLaN4sdCB/hJ3vDFwN3Z9z3Ank2i5WfAu4HDwPa4bTtweIBr7wDuBd4J7I8P9flsAPb4tCAaXhEnAym2D8oPvAHFmYT8zP3AZUPyA9hVTAZzeQB8E7hm3nGLoKPY9z7gtvi5997EyeLiRdIB3Am8CXgym5TWzY8xqG8n3QFlkRCRXcD5wAHg1ar6DED8f9YAJNwEfBYvY/BK4Lhan6Jh+HI2cAz4TlQjvyUipzMwP1T1aeArwJ+AZ4AXgPsZnh85TsSDzRy/p9RJaCMgIlcBT6vqA8WuddMxhklpXsrwoHEKIvIy4MfAJ1X170NeO17fOhDfn2+ec+ii+TIhiOnfUNXzCfmIg9n4DNFe816CGvJa4HTgijmHjiGeZVPGr6yjk9AGXPs0YC/w+Xm710vHGCalU+qAslEQkSXChHSbqt4VN/9FRLbH/duB5xZMxtuAq0TkSeAHBBXuJmCrSKpJMQRfjgJHVfVA/H4nYZIamh/vAp5Q1WOqugLcBbyV4fmR40Q8GHz8incSulajjjQwHW8gLBgPxDG7A/itiLxmI+gYw6T0G+Cc6FlZJrT93jfEhSUUrbkVeERVv5rt2gdcFz9fR7A1LQyqukdVd6jqLsL9/0JVrwXuw7sPD0HHs8BTInJu3HQpoQnEoPwgqG0Xichp8RkZHYPyo8CJeLAPeH/0Ol0EvGBq3iIgI+gkpKoPqepZqrorjtmjBIfRs2wEPxZlKPwvjWhXEjxfjwN7B7zu2wmi5YPA7+PflQR7zr3AY/H/mQPSdAnufTubMLCOAD8Ctgxw/TcDByNPfgqcsRn8AL4IPAocAr5H8CoNwg/gdoItayW+cB86EQ8I6srX49h9CLhwwXQcIdhsbLzenB2/N9JxGLhikXQU+5/EDd3r5kdNM6moqBgVxqC+VVRUVCTUSamiomJUqJNSRUXFqFAnpYqKilGhTkoVFRWjQp2UKioqRoU6KVVUVIwK/wEvzNX/P91DSwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "img = mpimg.imread('wallhanging.jpg')\n",
    "imgplot = plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(img) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ghc-7PWgdYg9"
   },
   "source": [
    "# Pandas \n",
    "### pandas is the python library for easy working with tabular data. For example the data in database tables, spreadsheets. It provides two data structures - dataframe and series and many functions to explore, clean and process your data.\n",
    "\n",
    "\n",
    "## Installation\n",
    "\n",
    "- conda install pandas\n",
    "- pip install pandas\n",
    "\n",
    "## Features\n",
    "\n",
    "1. pandas is the python library for easy working with tabular data. For example the data in database tables, spreadsheets. \n",
    "\n",
    "2. supports many file formats as data sources  like csv, excel, sql, json, parquet, HDF5.\n",
    "\n",
    "3. efficient slicing, selecting, and extracting of the data\n",
    "\n",
    "4. builtin plotting functionality using Matplotlib. \n",
    "\n",
    "5. Vectorization of operations.\n",
    "\n",
    "6. built in functions for basic statistics (mean, median, min, max, counts. . . ), data aggregation and transformation\n",
    "\n",
    "7. alows change in shape of data tables using melt and pivot.  melt() your data table from wide to long form or pivot() from long to wide format. \n",
    "\n",
    "8. supports database-like join/merge operations to concatenate multiple tables both column wise and row wise.\n",
    "\n",
    "9. great support for time series data, dates, times, and time-indexed data.\n",
    "\n",
    "10. works well with text data as well\n",
    "11. pandas works well with\n",
    "\n",
    "    - Tabular data \n",
    "    - Ordered and unordered  time series data.\n",
    "    - Arbitrary matrix data \n",
    "    - observational / statistical data \n",
    "    - labelled or unlabelled data\n",
    "      \n",
    "12. built on top of NumPy \n",
    "13. The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional)\n",
    "14. easy conversion of data in other python data structures into dataframe objects\n",
    "15. intelligent data alignment, slicing, indexing, subsetting. Multilevel and hierarchical labeling of axes. \n",
    "16. pandas is fast, many low-level algorithmic functions are in Cython code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sam Desilva</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joseph Allen</td>\n",
       "      <td>35</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Liza James</td>\n",
       "      <td>19</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name  Age Sex\n",
       "0   Sam Desilva   25   M\n",
       "1  Joseph Allen   35   M\n",
       "2    Liza James   19   F"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pandas sample code - Data items from titanic.csv dataset\n",
    "import pandas as pd\n",
    "df = pd.DataFrame( \n",
    "    { \"Name\": [ \"Sam Desilva\", \"Joseph Allen\", \"Liza James\",],\n",
    "      \"Age\": [25, 35, 19], \n",
    "      \"Sex\": [\"M\", \"M\", \"F\"],\n",
    "     } )\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary = pd.Series([22000, 35000, 58000], name=\"Salary\")\n",
    "salary.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sam Desilva</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>22000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joseph Allen</td>\n",
       "      <td>35</td>\n",
       "      <td>M</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Liza James</td>\n",
       "      <td>19</td>\n",
       "      <td>F</td>\n",
       "      <td>58000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name  Age Sex  Salary\n",
       "0   Sam Desilva   25   M   22000\n",
       "1  Joseph Allen   35   M   35000\n",
       "2    Liza James   19   F   58000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Salary'] = salary\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Titanic Passengers data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df = pd.read_csv('titanic.csv')\n",
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let me know the Name, age and survival status of passengers above the age of 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Wheadon, Mr. Edward H</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Ostby, Mr. Engelhart Cornelius</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>Hays, Mrs. Charles Melville (Clara Jennings Gr...</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
       "      <td>62.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>Svensson, Mr. Johan</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>857</th>\n",
       "      <td>Daly, Mr. Peter Denis</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Name   Age  Survived\n",
       "6                              McCarthy, Mr. Timothy J  54.0         0\n",
       "11                            Bonnell, Miss. Elizabeth  58.0         1\n",
       "15                    Hewlett, Mrs. (Mary D Kingcome)   55.0         1\n",
       "33                               Wheadon, Mr. Edward H  66.0         0\n",
       "54                      Ostby, Mr. Engelhart Cornelius  65.0         0\n",
       "..                                                 ...   ...       ...\n",
       "820  Hays, Mrs. Charles Melville (Clara Jennings Gr...  52.0         1\n",
       "829          Stone, Mrs. George Nelson (Martha Evelyn)  62.0         1\n",
       "851                                Svensson, Mr. Johan  74.0         0\n",
       "857                             Daly, Mr. Peter Denis   51.0         1\n",
       "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  56.0         1\n",
       "\n",
       "[64 rows x 3 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "passengers_above_50 = titanic_df[titanic_df['Age'] > 50]\n",
    "print(type(passengers_above_50))\n",
    "passengers_above_50[['Name', 'Age', 'Survived']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Passengers whose age is not available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>Razi, Mr. Raihed</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>van Melkebeke, Mr. Philemon</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>Laleff, Mr. Kristo</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Name  Age\n",
       "5                            Moran, Mr. James  NaN\n",
       "17               Williams, Mr. Charles Eugene  NaN\n",
       "19                    Masselmani, Mrs. Fatima  NaN\n",
       "26                    Emir, Mr. Farred Chehab  NaN\n",
       "28              O'Dwyer, Miss. Ellen \"Nellie\"  NaN\n",
       "..                                        ...  ...\n",
       "859                          Razi, Mr. Raihed  NaN\n",
       "863         Sage, Miss. Dorothy Edith \"Dolly\"  NaN\n",
       "868               van Melkebeke, Mr. Philemon  NaN\n",
       "878                        Laleff, Mr. Kristo  NaN\n",
       "888  Johnston, Miss. Catherine Helen \"Carrie\"  NaN\n",
       "\n",
       "[177 rows x 2 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "passengers_age_na = titanic_df[~titanic_df['Age'].notna()]\n",
    "passengers_age_na[['Name', 'Age']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# All Passengers above 60 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Wheadon, Mr. Edward H</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Ostby, Mr. Engelhart Cornelius</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Goldschmidt, Mr. George B</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Connors, Mr. Patrick</td>\n",
       "      <td>70.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Van der hoef, Mr. Wyckoff</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>Stead, Mr. William Thomas</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>Andrews, Miss. Kornelia Theodosia</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>Duane, Mr. Frank</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>Nysveen, Mr. Johan Hansen</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>Fortune, Mr. Mark</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Millet, Mr. Francis Davis</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>Turkula, Mrs. (Hedwig)</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>Artagaveytia, Mr. Ramon</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>Nicholson, Mr. Arthur Ernest</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>Wright, Mr. George</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>Harris, Mr. George</td>\n",
       "      <td>62.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>Sutton, Mr. Frederick</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>Mitchell, Mr. Henry Michael</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>Crosby, Capt. Edward Gifford</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
       "      <td>62.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>Svensson, Mr. Johan</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Name   Age  Survived\n",
       "33                       Wheadon, Mr. Edward H  66.0         0\n",
       "54              Ostby, Mr. Engelhart Cornelius  65.0         0\n",
       "96                   Goldschmidt, Mr. George B  71.0         0\n",
       "116                       Connors, Mr. Patrick  70.5         0\n",
       "170                  Van der hoef, Mr. Wyckoff  61.0         0\n",
       "252                  Stead, Mr. William Thomas  62.0         0\n",
       "275          Andrews, Miss. Kornelia Theodosia  63.0         1\n",
       "280                           Duane, Mr. Frank  65.0         0\n",
       "326                  Nysveen, Mr. Johan Hansen  61.0         0\n",
       "438                          Fortune, Mr. Mark  64.0         0\n",
       "456                  Millet, Mr. Francis Davis  65.0         0\n",
       "483                     Turkula, Mrs. (Hedwig)  63.0         1\n",
       "493                    Artagaveytia, Mr. Ramon  71.0         0\n",
       "545               Nicholson, Mr. Arthur Ernest  64.0         0\n",
       "555                         Wright, Mr. George  62.0         0\n",
       "570                         Harris, Mr. George  62.0         1\n",
       "625                      Sutton, Mr. Frederick  61.0         0\n",
       "630       Barkworth, Mr. Algernon Henry Wilson  80.0         1\n",
       "672                Mitchell, Mr. Henry Michael  70.0         0\n",
       "745               Crosby, Capt. Edward Gifford  70.0         0\n",
       "829  Stone, Mrs. George Nelson (Martha Evelyn)  62.0         1\n",
       "851                        Svensson, Mr. Johan  74.0         0"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "above_75_names = titanic_df.loc[titanic_df[\"Age\"] > 60, [\"Name\",\"Age\",\"Survived\"]]\n",
    "above_75_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Patients above 75 who survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Name   Age  Survived\n",
       "630  Barkworth, Mr. Algernon Henry Wilson  80.0         1"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "above_75_names_survived = titanic_df.loc[(np.logical_and(titanic_df[\"Age\"] > 75, titanic_df[\"Survived\"] ==1)), [\"Name\",\"Age\",\"Survived\"]]\n",
    "above_75_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataframe object has a plot method for quick and easy plotting of data in a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['area',\n",
       " 'bar',\n",
       " 'barh',\n",
       " 'box',\n",
       " 'density',\n",
       " 'hexbin',\n",
       " 'hist',\n",
       " 'kde',\n",
       " 'line',\n",
       " 'pie',\n",
       " 'scatter']"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Printing the names of different plot types in plot() function of dataframe object\n",
    "[\n",
    "    method_name for method_name in dir(titanic_df.plot) if not method_name.startswith(\"_\")\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of missing values in each column of the titanic dataset\n",
    "titanic_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We can delete all rows or columns having null values using dropna() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df=pd.read_csv('titanic.csv')\n",
    "titanic_df.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(183, 12)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Delete the rows having null values\n",
    "titanic_df_nona = titanic_df.dropna()\n",
    "titanic_df_nona.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df_nona.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Delete the columns having null values\n",
    "titanic_df_nona_columns = titanic_df.dropna(axis='columns' , how = 'all')\n",
    "titanic_df_nona_columns.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "female    82\n",
       "male      41\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of survivals in each sex\n",
    "titanic_df_nona[titanic_df_nona.Survived == 1].Sex.value_counts(normalize=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Number of men who survivd and did not survive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    54\n",
       "1    41\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many males survived\n",
    "titanic_df_nona[titanic_df_nona.Sex == \"male\"].Survived.value_counts(normalize=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Groupby() - Split, apply, transform "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    0.931818\n",
       "male      0.431579\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the rows by value of Sex column,\n",
    "# compute the number of passesengers that survived in each group, \n",
    "# find the mean number of survived people in each group\n",
    "\n",
    "titanic_df_nona.groupby('Sex').Survived.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "1    0.670886\n",
       "2    0.800000\n",
       "3    0.500000\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df_nona.groupby('Pclass').Survived.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15974264388>"
      ]
     },
     "execution_count": 33,
     "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": [
    "titanic_df_nona.groupby('Sex').Survived.mean().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>88.0</td>\n",
       "      <td>32.676136</td>\n",
       "      <td>14.240552</td>\n",
       "      <td>2.00</td>\n",
       "      <td>22.75</td>\n",
       "      <td>32.25</td>\n",
       "      <td>41.5</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>95.0</td>\n",
       "      <td>38.451789</td>\n",
       "      <td>16.429746</td>\n",
       "      <td>0.92</td>\n",
       "      <td>28.00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>49.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count       mean        std   min    25%    50%   75%   max\n",
       "Sex                                                                \n",
       "female   88.0  32.676136  14.240552  2.00  22.75  32.25  41.5  63.0\n",
       "male     95.0  38.451789  16.429746  0.92  28.00  37.00  49.0  80.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df_nona.groupby('Sex').Age.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15974c13688>"
      ]
     },
     "execution_count": 46,
     "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": [
    "## histograms for age distribution in data\n",
    "titanic_df_nona.Age.plot(kind = 'hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15974cb9a48>"
      ]
     },
     "execution_count": 47,
     "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": [
    "titanic_df_nona.Age.value_counts().sort_index().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000015974DFC148>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000015975FAA388>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000015975F82C48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001597600EE48>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_df_nona.hist('Age', by = 'Pclass' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(183, 2)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Subsetting only two columns from titanic data\n",
    "age_sex = titanic_df_nona[['Sex', 'Age']]\n",
    "age_sex.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of passengers of all sexes :  Sex\n",
      "female    88\n",
      "male      95\n",
      "Name: Sex, dtype: int64\n",
      "\n",
      "Number of groups as per sexes :  2\n",
      "\n",
      " Female and Male Rows Numbers in dataframe:  {'female': Int64Index([  1,   3,  10,  11,  52,  66,  88, 123, 136, 151, 177, 194, 195,\n",
      "            205, 215, 218, 230, 251, 257, 268, 269, 275, 291, 297, 299, 307,\n",
      "            309, 310, 311, 318, 319, 325, 327, 329, 337, 341, 345, 356, 366,\n",
      "            369, 393, 394, 412, 435, 473, 486, 496, 498, 504, 516, 520, 523,\n",
      "            539, 540, 556, 558, 571, 577, 581, 585, 591, 609, 618, 627, 641,\n",
      "            689, 700, 710, 716, 717, 730, 742, 759, 763, 765, 772, 779, 781,\n",
      "            796, 809, 820, 823, 835, 853, 862, 871, 879, 887],\n",
      "           dtype='int64'), 'male': Int64Index([  6,  21,  23,  27,  54,  62,  75,  92,  96,  97, 102, 110, 118,\n",
      "            124, 137, 139, 148, 170, 174, 183, 193, 209, 224, 245, 248, 252,\n",
      "            262, 263, 273, 292, 305, 331, 332, 336, 339, 340, 370, 377, 390,\n",
      "            429, 430, 434, 438, 445, 449, 452, 453, 456, 460, 462, 484, 487,\n",
      "            492, 505, 512, 515, 536, 544, 550, 572, 583, 587, 599, 621, 625,\n",
      "            630, 632, 645, 647, 659, 662, 671, 679, 681, 690, 698, 699, 701,\n",
      "            707, 712, 715, 724, 737, 741, 745, 748, 751, 782, 789, 802, 806,\n",
      "            857, 867, 872, 889],\n",
      "           dtype='int64')}\n",
      "\n",
      " Size of each group :  Sex\n",
      "female    88\n",
      "male      95\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "age_sex_groups = age_sex.groupby(['Sex'])\n",
    "print(\"Count of passengers of all sexes : \", age_sex_groups['Sex'].count())\n",
    "print(\"\\nNumber of groups as per sexes : \", age_sex_groups.ngroups)\n",
    "print(\"\\n Female and Male Rows Numbers in dataframe: \" , age_sex_groups.groups)\n",
    "print(\"\\n Size of each group : \", age_sex_groups.size())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2740d8dc7c8>"
      ]
     },
     "execution_count": 127,
     "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": [
    "# Plotting the mean age of both sexes in the group\n",
    "age_sex_groups['Age'].mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2740ede77c8>"
      ]
     },
     "execution_count": 128,
     "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": [
    "## Creating a subset of 3 columns\n",
    "sex_age_fare = titanic_df_nona.loc[:, ['Sex', 'Age', 'Fare']]\n",
    "# Grouping by multiple categories\n",
    "sex_age_fare.groupby(['Sex', 'Fare']).mean()\n",
    "sex_age_fare.groupby('Sex').mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Preview First Entry of age_sex_group :\n",
      "           Age\n",
      "Sex         \n",
      "female  38.0\n",
      "male    54.0\n",
      "\n",
      " Preview Last Entry age_sex_group: \n",
      "          Age\n",
      "Sex         \n",
      "female  19.0\n",
      "male    26.0\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n Preview First Entry of age_sex_group :\\n \", age_sex_groups.first())\n",
    "print(\"\\n Preview Last Entry age_sex_group: \\n\", age_sex_groups.last())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Female Group : \n",
      "       Age\n",
      "1    38.0\n",
      "2    26.0\n",
      "3    35.0\n",
      "8    27.0\n",
      "9    14.0\n",
      "..    ...\n",
      "879  56.0\n",
      "880  25.0\n",
      "882  22.0\n",
      "885  39.0\n",
      "887  19.0\n",
      "\n",
      "[261 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n Ages of Females in the Group : \\n\", age_sex_groups.get_group('female'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The “group by” process: split-apply-combine -group by” is referring to a process involving one or more of the following steps:\n",
    "\n",
    "1. Splitting the data into groups. \n",
    "2. Applying a function to each group independently, \n",
    "3. Combining the results into a data structure.\n",
    "\n",
    "groupby() is widely used for the split step.\n",
    "\n",
    "In the apply step, we might wish to do one of the following:\n",
    " - Aggregation: compute a summary statistic for each group. for example, sum, mean, or count.\n",
    " - Transformation: perform some group-specific computations and return a like-indexed object. For example, standardize data within a group or replacing missing values within groups.\n",
    " - Filtration: discard some groups, according to a group-wise computation that evaluates True or False. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum ages of females and males :  Sex\n",
      "female    63.0\n",
      "male      80.0\n",
      "Name: Age, dtype: float64\n"
     ]
    },
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>63.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>88</td>\n",
       "      <td>32.676136</td>\n",
       "      <td>32.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>80.0</td>\n",
       "      <td>0.92</td>\n",
       "      <td>95</td>\n",
       "      <td>38.451789</td>\n",
       "      <td>37.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Age                              \n",
       "         max   min count       mean median\n",
       "Sex                                       \n",
       "female  63.0  2.00    88  32.676136  32.25\n",
       "male    80.0  0.92    95  38.451789  37.00"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Source Credit: https://towardsdatascience.com/all-pandas-groupby-you-should-know-for-grouping-data-and-performing-operations-2a8ec1327b5\n",
    "print(\"Maximum ages of females and males : \", age_sex_groups.Age.max())\n",
    "age_sex_groups.agg(['max','min','count','mean','median'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>age_categories</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>32.676136</td>\n",
       "      <td>Young</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>38.451789</td>\n",
       "      <td>Middle Age</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age               \n",
       "             mean age_categories\n",
       "Sex                             \n",
       "female  32.676136          Young\n",
       "male    38.451789     Middle Age"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Source Credit : https://github.com/BindiChen/machine-learning/blob/master/data-analysis/032-pandas-groupby/pandas-groupby.ipynb\n",
    "# Apply a function to all groups\n",
    "def age_categories(x):\n",
    "    meanx=x.mean()\n",
    "    return \"Middle Age\" if meanx > 35 else \"Young\"\n",
    "\n",
    "age_sex_groups.agg(['mean', age_categories])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['agg',\n",
       " 'aggregate',\n",
       " 'all',\n",
       " 'any',\n",
       " 'apply',\n",
       " 'backfill',\n",
       " 'bfill',\n",
       " 'boxplot',\n",
       " 'corr',\n",
       " 'corrwith',\n",
       " 'count',\n",
       " 'cov',\n",
       " 'cumcount',\n",
       " 'cummax',\n",
       " 'cummin',\n",
       " 'cumprod',\n",
       " 'cumsum',\n",
       " 'describe',\n",
       " 'diff',\n",
       " 'expanding',\n",
       " 'ffill',\n",
       " 'fillna',\n",
       " 'filter',\n",
       " 'first',\n",
       " 'get_group',\n",
       " 'head',\n",
       " 'hist',\n",
       " 'idxmax',\n",
       " 'idxmin',\n",
       " 'last',\n",
       " 'mad',\n",
       " 'max',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'min',\n",
       " 'ngroup',\n",
       " 'nth',\n",
       " 'nunique',\n",
       " 'ohlc',\n",
       " 'pad',\n",
       " 'pct_change',\n",
       " 'pipe',\n",
       " 'plot',\n",
       " 'prod',\n",
       " 'quantile',\n",
       " 'rank',\n",
       " 'resample',\n",
       " 'rolling',\n",
       " 'sem',\n",
       " 'shift',\n",
       " 'size',\n",
       " 'skew',\n",
       " 'std',\n",
       " 'sum',\n",
       " 'tail',\n",
       " 'take',\n",
       " 'transform',\n",
       " 'tshift',\n",
       " 'var']"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "methods = [method_name for method_name in dir(age_sex_groups) \n",
    "           if callable(getattr(age_sex_groups, method_name)) & ~method_name.startswith('_')]\n",
    "methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      0.373852\n",
       "3      0.163186\n",
       "6      0.946345\n",
       "10    -2.013696\n",
       "11     1.778292\n",
       "         ...   \n",
       "871    1.005850\n",
       "872   -0.331824\n",
       "879    1.637848\n",
       "887   -0.960366\n",
       "889   -0.757881\n",
       "Name: Age, Length: 183, dtype: float64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transforming data\n",
    "standardization = lambda x: (x - x.mean()) / x.std()\n",
    "titanic_df_nona.groupby('Sex').Age.transform(standardization)\n",
    "#titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      0.373852\n",
       "3      0.163186\n",
       "6      0.946345\n",
       "10    -2.013696\n",
       "11     1.778292\n",
       "         ...   \n",
       "871    1.005850\n",
       "872   -0.331824\n",
       "879    1.637848\n",
       "887   -0.960366\n",
       "889   -0.757881\n",
       "Name: Age, Length: 183, dtype: float64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df_nona.groupby('Sex').Age.apply(standardization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cabin\n",
       "A10    1\n",
       "A16    1\n",
       "A20    1\n",
       "A23    1\n",
       "A24    1\n",
       "      ..\n",
       "F2     3\n",
       "F33    3\n",
       "F4     2\n",
       "G6     4\n",
       "T      1\n",
       "Length: 133, dtype: int64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering\n",
    "titanic_df_nona.groupby('Cabin').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>19.00</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nye, Mrs. (Elizabeth Ramell)</td>\n",
       "      <td>female</td>\n",
       "      <td>29.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 29395</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>F33</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Miss. Mabel Helen</td>\n",
       "      <td>female</td>\n",
       "      <td>23.00</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>149</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Navratil, Mr. Michel (\"Louis M Hoffman\")</td>\n",
       "      <td>male</td>\n",
       "      <td>36.50</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>230080</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>F2</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>194</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Navratil, Master. Michel M</td>\n",
       "      <td>male</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>230080</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>F2</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>206</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Strom, Miss. Telma Matilda</td>\n",
       "      <td>female</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>347054</td>\n",
       "      <td>10.4625</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>252</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Strom, Mrs. Wilhelm (Elna Matilda Persson)</td>\n",
       "      <td>female</td>\n",
       "      <td>29.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>347054</td>\n",
       "      <td>10.4625</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>293</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Levy, Mr. Rene Jacques</td>\n",
       "      <td>male</td>\n",
       "      <td>36.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/Paris 2163</td>\n",
       "      <td>12.8750</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>306</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>328</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Ball, Mrs. (Ada E Hall)</td>\n",
       "      <td>female</td>\n",
       "      <td>36.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28551</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>D</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>341</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Navratil, Master. Edmond Roger</td>\n",
       "      <td>male</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>230080</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>F2</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>342</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Miss. Alice Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>24.00</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Brown, Miss. Amelia \"Mildred\"</td>\n",
       "      <td>female</td>\n",
       "      <td>24.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248733</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>F33</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>391</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Carter, Mr. William Ernest</td>\n",
       "      <td>male</td>\n",
       "      <td>36.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113760</td>\n",
       "      <td>120.0000</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>395</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengt...</td>\n",
       "      <td>female</td>\n",
       "      <td>24.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>436</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Carter, Miss. Lucile Polk</td>\n",
       "      <td>female</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113760</td>\n",
       "      <td>120.0000</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>439</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Mark</td>\n",
       "      <td>male</td>\n",
       "      <td>64.00</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>474</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Jerwan, Mrs. Amin S (Marie Marthe Thuillard)</td>\n",
       "      <td>female</td>\n",
       "      <td>23.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/AH Basle 541</td>\n",
       "      <td>13.7917</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>499</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>517</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Lemore, Mrs. (Amelia Milley)</td>\n",
       "      <td>female</td>\n",
       "      <td>34.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 34260</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>F33</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>764</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Carter, Mrs. William Ernest (Lucile Polk)</td>\n",
       "      <td>female</td>\n",
       "      <td>36.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113760</td>\n",
       "      <td>120.0000</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>803</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Carter, Master. William Thornton II</td>\n",
       "      <td>male</td>\n",
       "      <td>11.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113760</td>\n",
       "      <td>120.0000</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "10            11         1       3   \n",
       "27            28         0       1   \n",
       "66            67         1       2   \n",
       "88            89         1       1   \n",
       "148          149         0       2   \n",
       "193          194         1       2   \n",
       "205          206         0       3   \n",
       "251          252         0       3   \n",
       "292          293         0       2   \n",
       "297          298         0       1   \n",
       "305          306         1       1   \n",
       "327          328         1       2   \n",
       "340          341         1       2   \n",
       "341          342         1       1   \n",
       "345          346         1       2   \n",
       "390          391         1       1   \n",
       "394          395         1       3   \n",
       "435          436         1       1   \n",
       "438          439         0       1   \n",
       "473          474         1       2   \n",
       "498          499         0       1   \n",
       "516          517         1       2   \n",
       "763          764         1       1   \n",
       "802          803         1       1   \n",
       "\n",
       "                                                  Name     Sex    Age  SibSp  \\\n",
       "10                     Sandstrom, Miss. Marguerite Rut  female   4.00      1   \n",
       "27                      Fortune, Mr. Charles Alexander    male  19.00      3   \n",
       "66                        Nye, Mrs. (Elizabeth Ramell)  female  29.00      0   \n",
       "88                          Fortune, Miss. Mabel Helen  female  23.00      3   \n",
       "148           Navratil, Mr. Michel (\"Louis M Hoffman\")    male  36.50      0   \n",
       "193                         Navratil, Master. Michel M    male   3.00      1   \n",
       "205                         Strom, Miss. Telma Matilda  female   2.00      0   \n",
       "251         Strom, Mrs. Wilhelm (Elna Matilda Persson)  female  29.00      1   \n",
       "292                             Levy, Mr. Rene Jacques    male  36.00      0   \n",
       "297                       Allison, Miss. Helen Loraine  female   2.00      1   \n",
       "305                     Allison, Master. Hudson Trevor    male   0.92      1   \n",
       "327                            Ball, Mrs. (Ada E Hall)  female  36.00      0   \n",
       "340                     Navratil, Master. Edmond Roger    male   2.00      1   \n",
       "341                     Fortune, Miss. Alice Elizabeth  female  24.00      3   \n",
       "345                      Brown, Miss. Amelia \"Mildred\"  female  24.00      0   \n",
       "390                         Carter, Mr. William Ernest    male  36.00      1   \n",
       "394  Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengt...  female  24.00      0   \n",
       "435                          Carter, Miss. Lucile Polk  female  14.00      1   \n",
       "438                                  Fortune, Mr. Mark    male  64.00      1   \n",
       "473       Jerwan, Mrs. Amin S (Marie Marthe Thuillard)  female  23.00      0   \n",
       "498    Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female  25.00      1   \n",
       "516                       Lemore, Mrs. (Amelia Milley)  female  34.00      0   \n",
       "763          Carter, Mrs. William Ernest (Lucile Polk)  female  36.00      1   \n",
       "802                Carter, Master. William Thornton II    male  11.00      1   \n",
       "\n",
       "     Parch           Ticket      Fare        Cabin Embarked  \n",
       "10       1          PP 9549   16.7000           G6        S  \n",
       "27       2            19950  263.0000  C23 C25 C27        S  \n",
       "66       0       C.A. 29395   10.5000          F33        S  \n",
       "88       2            19950  263.0000  C23 C25 C27        S  \n",
       "148      2           230080   26.0000           F2        S  \n",
       "193      1           230080   26.0000           F2        S  \n",
       "205      1           347054   10.4625           G6        S  \n",
       "251      1           347054   10.4625           G6        S  \n",
       "292      0    SC/Paris 2163   12.8750            D        C  \n",
       "297      2           113781  151.5500      C22 C26        S  \n",
       "305      2           113781  151.5500      C22 C26        S  \n",
       "327      0            28551   13.0000            D        S  \n",
       "340      1           230080   26.0000           F2        S  \n",
       "341      2            19950  263.0000  C23 C25 C27        S  \n",
       "345      0           248733   13.0000          F33        S  \n",
       "390      2           113760  120.0000      B96 B98        S  \n",
       "394      2          PP 9549   16.7000           G6        S  \n",
       "435      2           113760  120.0000      B96 B98        S  \n",
       "438      4            19950  263.0000  C23 C25 C27        S  \n",
       "473      0  SC/AH Basle 541   13.7917            D        C  \n",
       "498      2           113781  151.5500      C22 C26        S  \n",
       "516      0       C.A. 34260   10.5000          F33        S  \n",
       "763      2           113760  120.0000      B96 B98        S  \n",
       "802      2           113760  120.0000      B96 B98        S  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter data to return all passengers that lived in a cabin has ≥ 4 people.\n",
    "titanic_df_nona.groupby('Cabin').filter(lambda x: len(x) >= 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CAyUWip5MNAL"
   },
   "source": [
    "# Case Study\n",
    "###  This case study is heavily inspired by the data analysis case study from NVIDIA Deep Learning Institute's Course \"Fundamentals of Accelerated Data Science with Rapids\". \n",
    "\n",
    "## https://www.nvidia.com/en-in/training/instructor-led-workshops/#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "pop_df = pd.read_csv(\"population.csv\")\n",
    "hospitals_df = pd.read_csv(\"hospitals.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Age', 'Gender', 'City', 'Latitude', 'Longitude'], dtype='object')"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2.85 ms\n",
      "The number of Bangalore Residents is :  193\n"
     ]
    }
   ],
   "source": [
    "# Find the number of residents of Bangalore\n",
    "%time pop_bangalore = pop_df.loc[pop_df['City'] == 'Bangalore']\n",
    "print(\"The number of Bangalore Residents is : \",  f'{pop_bangalore.shape[0]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abid</td>\n",
       "      <td>70.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aabida</td>\n",
       "      <td>54.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Achal</td>\n",
       "      <td>65.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aadesh</td>\n",
       "      <td>76.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>aadil</td>\n",
       "      <td>34.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name   Age Gender   City   Latitude  Longitude\n",
       "0    abid  70.0      F  Delhi  28.679079   77.06971\n",
       "1  Aabida  54.0      M  Delhi  28.679079   77.06971\n",
       "2   Achal  65.0      M  Delhi  28.679079   77.06971\n",
       "3  aadesh  76.0      M  Delhi  28.679079   77.06971\n",
       "4   aadil  34.0      M  Delhi  28.679079   77.06971"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change the datatype of age column from int to float\n",
    "pop_df['Age'] = pop_df['Age'].astype('float32')\n",
    "pop_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48.4954948425293"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the average Age\n",
    "pop_df['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abid</td>\n",
       "      <td>70.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aabida</td>\n",
       "      <td>54.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Achal</td>\n",
       "      <td>65.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aadesh</td>\n",
       "      <td>76.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aadil</td>\n",
       "      <td>34.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name   Age Gender   City   Latitude  Longitude\n",
       "0    Abid  70.0      F  Delhi  28.679079   77.06971\n",
       "1  Aabida  54.0      M  Delhi  28.679079   77.06971\n",
       "2   Achal  65.0      M  Delhi  28.679079   77.06971\n",
       "3  Aadesh  76.0      M  Delhi  28.679079   77.06971\n",
       "4   Aadil  34.0      M  Delhi  28.679079   77.06971"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert all names to title case\n",
    "pop_df['Name'] = pop_df['Name'].str.title()\n",
    "pop_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Achal</td>\n",
       "      <td>65.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aadesh</td>\n",
       "      <td>76.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aadil</td>\n",
       "      <td>34.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Aadish</td>\n",
       "      <td>23.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name   Age Gender   City   Latitude  Longitude\n",
       "2   Achal  65.0      M  Delhi  28.679079   77.06971\n",
       "3  Aadesh  76.0      M  Delhi  28.679079   77.06971\n",
       "4   Aadil  34.0      M  Delhi  28.679079   77.06971\n",
       "5  Aadish  23.0      M  Delhi  28.679079   77.06971"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select specific rows from dataframe\n",
    "pop_df.loc[2:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Achal</td>\n",
       "      <td>65.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aadesh</td>\n",
       "      <td>76.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aadil</td>\n",
       "      <td>34.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name   Age Gender   City   Latitude  Longitude\n",
       "2   Achal  65.0      M  Delhi  28.679079   77.06971\n",
       "3  Aadesh  76.0      M  Delhi  28.679079   77.06971\n",
       "4   Aadil  34.0      M  Delhi  28.679079   77.06971"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select specific rows from dataframe\n",
    "pop_df.iloc[2:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>Yadram</td>\n",
       "      <td>69.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Nasik</td>\n",
       "      <td>19.9975</td>\n",
       "      <td>73.7898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>Yadvir</td>\n",
       "      <td>65.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Nasik</td>\n",
       "      <td>19.9975</td>\n",
       "      <td>73.7898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>Yaksh</td>\n",
       "      <td>26.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Nasik</td>\n",
       "      <td>19.9975</td>\n",
       "      <td>73.7898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951</th>\n",
       "      <td>Yaman</td>\n",
       "      <td>15.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Nasik</td>\n",
       "      <td>19.9975</td>\n",
       "      <td>73.7898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>952</th>\n",
       "      <td>Yameen</td>\n",
       "      <td>18.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Nasik</td>\n",
       "      <td>19.9975</td>\n",
       "      <td>73.7898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>Yamin</td>\n",
       "      <td>90.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Nasik</td>\n",
       "      <td>19.9975</td>\n",
       "      <td>73.7898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>954</th>\n",
       "      <td>Yamini</td>\n",
       "      <td>24.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>23.0225</td>\n",
       "      <td>72.5714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>Yamuna</td>\n",
       "      <td>98.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>23.0225</td>\n",
       "      <td>72.5714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>Yaqoob</td>\n",
       "      <td>83.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>23.0225</td>\n",
       "      <td>72.5714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>Yaqub</td>\n",
       "      <td>48.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>23.0225</td>\n",
       "      <td>72.5714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>Yasar</td>\n",
       "      <td>58.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>23.0225</td>\n",
       "      <td>72.5714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>959</th>\n",
       "      <td>Yash</td>\n",
       "      <td>86.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>23.0225</td>\n",
       "      <td>72.5714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>Yashbir</td>\n",
       "      <td>8.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>23.0225</td>\n",
       "      <td>72.5714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>961</th>\n",
       "      <td>Yashdhra</td>\n",
       "      <td>57.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>23.0225</td>\n",
       "      <td>72.5714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>962</th>\n",
       "      <td>Yashika</td>\n",
       "      <td>72.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>23.0225</td>\n",
       "      <td>72.5714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>963</th>\n",
       "      <td>Yashin</td>\n",
       "      <td>81.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>23.0225</td>\n",
       "      <td>72.5714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>964</th>\n",
       "      <td>Yashmin</td>\n",
       "      <td>6.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>965</th>\n",
       "      <td>Yashoda</td>\n",
       "      <td>97.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966</th>\n",
       "      <td>Yashodha</td>\n",
       "      <td>47.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>967</th>\n",
       "      <td>Yashpal</td>\n",
       "      <td>89.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>968</th>\n",
       "      <td>Yashsvi</td>\n",
       "      <td>68.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>Yashwant</td>\n",
       "      <td>80.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>Yasodha</td>\n",
       "      <td>12.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>Yaswani</td>\n",
       "      <td>50.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>Yatharth</td>\n",
       "      <td>57.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>Yatin</td>\n",
       "      <td>14.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>Yesh</td>\n",
       "      <td>39.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>Yogendra</td>\n",
       "      <td>19.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>Yogesh</td>\n",
       "      <td>24.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>Yogeshwar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>Yogeshwari</td>\n",
       "      <td>84.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>Yoginder</td>\n",
       "      <td>31.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>Yogita</td>\n",
       "      <td>29.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>Yognder</td>\n",
       "      <td>40.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>Yograj</td>\n",
       "      <td>31.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>Yoshoda</td>\n",
       "      <td>45.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>Youraj</td>\n",
       "      <td>49.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>Yudhbir</td>\n",
       "      <td>78.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>Yukilal</td>\n",
       "      <td>38.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>Yuveraj</td>\n",
       "      <td>76.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>Yuvraj</td>\n",
       "      <td>88.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.3850</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name   Age Gender       City  Latitude  Longitude\n",
       "948      Yadram  69.0      F      Nasik   19.9975    73.7898\n",
       "949      Yadvir  65.0      M      Nasik   19.9975    73.7898\n",
       "950       Yaksh  26.0      M      Nasik   19.9975    73.7898\n",
       "951       Yaman  15.0      M      Nasik   19.9975    73.7898\n",
       "952      Yameen  18.0      M      Nasik   19.9975    73.7898\n",
       "953       Yamin  90.0      M      Nasik   19.9975    73.7898\n",
       "954      Yamini  24.0      M  Ahmedabad   23.0225    72.5714\n",
       "955      Yamuna  98.0      F  Ahmedabad   23.0225    72.5714\n",
       "956      Yaqoob  83.0      M  Ahmedabad   23.0225    72.5714\n",
       "957       Yaqub  48.0      M  Ahmedabad   23.0225    72.5714\n",
       "958       Yasar  58.0      F  Ahmedabad   23.0225    72.5714\n",
       "959        Yash  86.0      M  Ahmedabad   23.0225    72.5714\n",
       "960     Yashbir   8.0      M  Ahmedabad   23.0225    72.5714\n",
       "961    Yashdhra  57.0      M  Ahmedabad   23.0225    72.5714\n",
       "962     Yashika  72.0      F  Ahmedabad   23.0225    72.5714\n",
       "963      Yashin  81.0      M  Ahmedabad   23.0225    72.5714\n",
       "964     Yashmin   6.0      M  Hyderabad   17.3850    78.4867\n",
       "965     Yashoda  97.0      F  Hyderabad   17.3850    78.4867\n",
       "966    Yashodha  47.0      M  Hyderabad   17.3850    78.4867\n",
       "967     Yashpal  89.0      M  Hyderabad   17.3850    78.4867\n",
       "968     Yashsvi  68.0      M  Hyderabad   17.3850    78.4867\n",
       "969    Yashwant  80.0      F  Hyderabad   17.3850    78.4867\n",
       "970     Yasodha  12.0      F  Hyderabad   17.3850    78.4867\n",
       "971     Yaswani  50.0      M  Hyderabad   17.3850    78.4867\n",
       "972    Yatharth  57.0      M  Hyderabad   17.3850    78.4867\n",
       "973       Yatin  14.0      F  Hyderabad   17.3850    78.4867\n",
       "974        Yesh  39.0      M  Hyderabad   17.3850    78.4867\n",
       "975    Yogendra  19.0      F  Hyderabad   17.3850    78.4867\n",
       "976      Yogesh  24.0      F  Hyderabad   17.3850    78.4867\n",
       "977   Yogeshwar  52.0      F  Hyderabad   17.3850    78.4867\n",
       "978  Yogeshwari  84.0      M  Hyderabad   17.3850    78.4867\n",
       "979    Yoginder  31.0      M  Hyderabad   17.3850    78.4867\n",
       "980      Yogita  29.0      F  Hyderabad   17.3850    78.4867\n",
       "981     Yognder  40.0      M  Hyderabad   17.3850    78.4867\n",
       "982      Yograj  31.0      F  Hyderabad   17.3850    78.4867\n",
       "983     Yoshoda  45.0      F  Hyderabad   17.3850    78.4867\n",
       "984      Youraj  49.0      M  Hyderabad   17.3850    78.4867\n",
       "985     Yudhbir  78.0      M  Hyderabad   17.3850    78.4867\n",
       "986     Yukilal  38.0      M  Hyderabad   17.3850    78.4867\n",
       "987     Yuveraj  76.0      M  Hyderabad   17.3850    78.4867\n",
       "988      Yuvraj  88.0      M  Hyderabad   17.3850    78.4867"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the rows such that name starts with 'Y'\n",
    "pop_df.loc[pop_df['Name'].str.startswith('Y')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>Yograj</td>\n",
       "      <td>31.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.385</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>Youraj</td>\n",
       "      <td>49.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.385</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>Yuveraj</td>\n",
       "      <td>76.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.385</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>Yuvraj</td>\n",
       "      <td>88.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>17.385</td>\n",
       "      <td>78.4867</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name   Age Gender       City  Latitude  Longitude\n",
       "982   Yograj  31.0      F  Hyderabad    17.385    78.4867\n",
       "984   Youraj  49.0      M  Hyderabad    17.385    78.4867\n",
       "987  Yuveraj  76.0      M  Hyderabad    17.385    78.4867\n",
       "988   Yuvraj  88.0      M  Hyderabad    17.385    78.4867"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the rows such that name starts with Y and ends with j\n",
    "pop_df.loc[np.logical_and(pop_df['Name'].str.startswith('Y'), pop_df['Name'].str.endswith('j'))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.9716"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the latitude of Bangalore\n",
    "bangalore_residents = pop_df.loc[pop_df['City']=='Bangalore']\n",
    "bangalore_latitude = bangalore_residents['Latitude'].max()\n",
    "bangalore_latitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Delhi', 'Kolkatta', 'Chennai', 'Kanpur', 'Noida', 'Palampur',\n",
       "       'Guahati', 'Lucknow', 'Dehradun', 'Jammu', 'Udaipur', 'Surat',\n",
       "       'Ujjain', 'Pune', 'Cuttack', 'Kargil', 'Ladhakh', 'Srinagar',\n",
       "       'Raipur', 'Gurgaon', 'Jaipur', 'Amritsar', 'Ahmedabad', 'Ranchi',\n",
       "       'Nasik', 'Hyderabad'], dtype=object)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the cities that are to north of Bangalore\n",
    "northofbangalore = pop_df.loc[pop_df['Latitude']>bangalore_latitude]\n",
    "northofbangalore['City'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Coimbatore', 'Kochi', 'Mysore', 'Madurai'], dtype=object)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the cities that are to south of Bangalore\n",
    "southofbangalore = pop_df.loc[pop_df['Latitude']<bangalore_latitude]\n",
    "southofbangalore['City'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "998     Zanmi\n",
       "997    Zamile\n",
       "996    Zameer\n",
       "995     Zakir\n",
       "994    Zainab\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the names in reverse typographic order\n",
    "names_df = pop_df['Name'].sort_values(ascending=False)\n",
    "names_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "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>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ahmedabad</th>\n",
       "      <td>53.058823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amritsar</th>\n",
       "      <td>49.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bangalore</th>\n",
       "      <td>51.160622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>52.051949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coimbatore</th>\n",
       "      <td>45.333332</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Age\n",
       "City                 \n",
       "Ahmedabad   53.058823\n",
       "Amritsar    49.636364\n",
       "Bangalore   51.160622\n",
       "Chennai     52.051949\n",
       "Coimbatore  45.333332"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the mean age group of the residents of each city\n",
    "city_age_df = pop_df[['City' , 'Age']].groupby(['City'])\n",
    "average_ages = city_age_df.mean()\n",
    "average_ages[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Name   Age       City\n",
      "913    Uganta  22.0  Ahmedabad\n",
      "914      Ugma  23.0  Ahmedabad\n",
      "915     Ujama   3.0  Ahmedabad\n",
      "916    Ujamma  94.0  Ahmedabad\n",
      "917      Ujir  42.0  Ahmedabad\n",
      "918    Ujjair  56.0  Ahmedabad\n",
      "919   Ujjawal  47.0  Ahmedabad\n",
      "954    Yamini  24.0  Ahmedabad\n",
      "955    Yamuna  98.0  Ahmedabad\n",
      "956    Yaqoob  83.0  Ahmedabad\n",
      "957     Yaqub  48.0  Ahmedabad\n",
      "958     Yasar  58.0  Ahmedabad\n",
      "959      Yash  86.0  Ahmedabad\n",
      "960   Yashbir   8.0  Ahmedabad\n",
      "961  Yashdhra  57.0  Ahmedabad\n",
      "962   Yashika  72.0  Ahmedabad\n",
      "963    Yashin  81.0  Ahmedabad\n",
      "         Name   Age      City\n",
      "902      Udai  79.0  Amritsar\n",
      "903  Udaiveer   2.0  Amritsar\n",
      "904     Udaiy  96.0  Amritsar\n",
      "905      Udal  12.0  Amritsar\n",
      "906      Uday  55.0  Amritsar\n",
      "907     Udesh  13.0  Amritsar\n",
      "908     Udham  13.0  Amritsar\n",
      "909       Udi  84.0  Amritsar\n",
      "910      Udit  88.0  Amritsar\n",
      "911     Udita  59.0  Amritsar\n",
      "912     Udmai  45.0  Amritsar\n",
      "         Name   Age       City\n",
      "386     Genda  74.0  Bangalore\n",
      "387     Gendi  26.0  Bangalore\n",
      "388     Gendu  12.0  Bangalore\n",
      "389  Gensingh  39.0  Bangalore\n",
      "390      Ghan   8.0  Bangalore\n",
      "..        ...   ...        ...\n",
      "750     Rafal  99.0  Bangalore\n",
      "751     Rafat  86.0  Bangalore\n",
      "752  Rafeddin  73.0  Bangalore\n",
      "753      Rafi  37.0  Bangalore\n",
      "754     Rafiq  32.0  Bangalore\n",
      "\n",
      "[193 rows x 3 columns]\n",
      "            Name   Age     City\n",
      "80        Balbir  32.0  Chennai\n",
      "81       Balbiri  20.0  Chennai\n",
      "82      Balchand  35.0  Chennai\n",
      "83        Baldev  47.0  Chennai\n",
      "84     Baleshwar  59.0  Chennai\n",
      "..           ...   ...      ...\n",
      "152    Chhedilal  25.0  Chennai\n",
      "153       Chhidu  98.0  Chennai\n",
      "154  Chhinderpal  66.0  Chennai\n",
      "155      Chhitar  87.0  Chennai\n",
      "156    Chhotelal  90.0  Chennai\n",
      "\n",
      "[77 rows x 3 columns]\n",
      "          Name   Age        City\n",
      "463      Indal  51.0  Coimbatore\n",
      "464     Indara  54.0  Coimbatore\n",
      "465      Inder  57.0  Coimbatore\n",
      "466  Inderjeet  85.0  Coimbatore\n",
      "467   Inderjit   2.0  Coimbatore\n",
      "468  Indernath  26.0  Coimbatore\n",
      "469  Inderpaal  13.0  Coimbatore\n",
      "470   Inderpal   6.0  Coimbatore\n",
      "471  Indervesh  47.0  Coimbatore\n",
      "472     Indira  17.0  Coimbatore\n",
      "473      Indra  49.0  Coimbatore\n",
      "474     Indraj  33.0  Coimbatore\n",
      "475    Indresh  80.0  Coimbatore\n",
      "476   Indrvati  16.0  Coimbatore\n",
      "477      Indta  19.0  Coimbatore\n",
      "478       Indu  40.0  Coimbatore\n",
      "479     Injmam  16.0  Coimbatore\n",
      "830     Sanjit  71.0  Coimbatore\n",
      "831     Sanjiv  33.0  Coimbatore\n",
      "832     Sanjna  88.0  Coimbatore\n",
      "833   Sanjogta  25.0  Coimbatore\n",
      "834     Sanjoh  38.0  Coimbatore\n",
      "890      Tunda  80.0  Coimbatore\n",
      "891   Tundiram  77.0  Coimbatore\n",
      "892      Tunni  71.0  Coimbatore\n",
      "893    Turveny  36.0  Coimbatore\n",
      "894     Tushal  94.0  Coimbatore\n",
      "            Name    Age     City\n",
      "610      Laukush   71.0  Cuttack\n",
      "611          Lav   55.0  Cuttack\n",
      "612       Lavali   68.0  Cuttack\n",
      "613         Lave   30.0  Cuttack\n",
      "614         Lavi   22.0  Cuttack\n",
      "615       Lavish   35.0  Cuttack\n",
      "616      Lavkush   16.0  Cuttack\n",
      "617        Lavli   31.0  Cuttack\n",
      "618     Lavpreet   48.0  Cuttack\n",
      "619       Lavtar   81.0  Cuttack\n",
      "620        Lawli   34.0  Cuttack\n",
      "621     Lawrence   85.0  Cuttack\n",
      "622       Laxami   81.0  Cuttack\n",
      "623       Laxita   20.0  Cuttack\n",
      "624       Laxman   93.0  Cuttack\n",
      "625    Laxmanram   58.0  Cuttack\n",
      "626     Laxmeena    4.0  Cuttack\n",
      "627        Laxmi   72.0  Cuttack\n",
      "628    Laxmikant   25.0  Cuttack\n",
      "629  Laxminarain   90.0  Cuttack\n",
      "630        Layba  100.0  Cuttack\n",
      "631       Layman   68.0  Cuttack\n",
      "632        Leela   31.0  Cuttack\n",
      "633    Leelawati   19.0  Cuttack\n",
      "634        Leelu   75.0  Cuttack\n",
      "635        Leena   30.0  Cuttack\n",
      "636        Lehru   42.0  Cuttack\n",
      "          Name   Age      City\n",
      "516  Jaijairam  54.0  Dehradun\n",
      "517      Jakir  66.0  Dehradun\n",
      "518   Jalsingh  21.0  Dehradun\n",
      "519     Jalwad  70.0  Dehradun\n",
      "520     Jamaal   4.0  Dehradun\n",
      "521      Jamad  33.0  Dehradun\n",
      "522    Jamadar  62.0  Dehradun\n",
      "523      Jamal  39.0  Dehradun\n",
      "524      Jamee  93.0  Dehradun\n",
      "525    Jameela  17.0  Dehradun\n",
      "526     Jameer  14.0  Dehradun\n",
      "527      Jamil  95.0  Dehradun\n",
      "528     Jamila  57.0  Dehradun\n",
      "529      Jamir  42.0  Dehradun\n",
      "530      Jamna  85.0  Dehradun\n",
      "531     Jamnaa  13.0  Dehradun\n",
      "           Name   Age   City\n",
      "0          Abid  70.0  Delhi\n",
      "1        Aabida  54.0  Delhi\n",
      "2         Achal  65.0  Delhi\n",
      "3        Aadesh  76.0  Delhi\n",
      "4         Aadil  34.0  Delhi\n",
      "..          ...   ...    ...\n",
      "877  Trlokchand  48.0  Delhi\n",
      "878        Tuba  64.0  Delhi\n",
      "879      Tufail  24.0  Delhi\n",
      "880       Tufel   8.0  Delhi\n",
      "881        Tuka  97.0  Delhi\n",
      "\n",
      "[112 rows x 3 columns]\n",
      "          Name   Age     City\n",
      "288     Farhin  75.0  Guahati\n",
      "289      Farid  33.0  Guahati\n",
      "290     Farida  87.0  Guahati\n",
      "291     Farina  11.0  Guahati\n",
      "292    Farjana   6.0  Guahati\n",
      "293    Farjand  28.0  Guahati\n",
      "294   Farjanul  19.0  Guahati\n",
      "295      Farku  87.0  Guahati\n",
      "296    Farmaan  42.0  Guahati\n",
      "297     Farman  42.0  Guahati\n",
      "298   Farmeena  69.0  Guahati\n",
      "299      Faroj  10.0  Guahati\n",
      "300     Farukh  14.0  Guahati\n",
      "301    Farzana  28.0  Guahati\n",
      "302     Fasrun  90.0  Guahati\n",
      "303      Fateh   3.0  Guahati\n",
      "304     Father  32.0  Guahati\n",
      "305     Fatima  25.0  Guahati\n",
      "306      Fatma  77.0  Guahati\n",
      "307     Fauina  60.0  Guahati\n",
      "308    Faujdar  28.0  Guahati\n",
      "309      Fauji  13.0  Guahati\n",
      "310    Fazaley  49.0  Guahati\n",
      "311      Fazaz  83.0  Guahati\n",
      "312      Fazia  88.0  Guahati\n",
      "313      Fazin  87.0  Guahati\n",
      "314    Fazulla  50.0  Guahati\n",
      "315   Feflibai  15.0  Guahati\n",
      "316    Felicia   2.0  Guahati\n",
      "317       Feru  38.0  Guahati\n",
      "318   Fhaishal  73.0  Guahati\n",
      "319    Fharman   1.0  Guahati\n",
      "320       Fida  53.0  Guahati\n",
      "321       Fija  40.0  Guahati\n",
      "322    Firakat  74.0  Guahati\n",
      "323    Firasat  28.0  Guahati\n",
      "324    Firdosh  77.0  Guahati\n",
      "325    Firdous  41.0  Guahati\n",
      "326     Firida  59.0  Guahati\n",
      "327      Firoj  95.0  Guahati\n",
      "328     Firoja  20.0  Guahati\n",
      "329      Firoz  15.0  Guahati\n",
      "330     Firoza  18.0  Guahati\n",
      "331      Firtu  76.0  Guahati\n",
      "332     Fitrat  18.0  Guahati\n",
      "333      Fiyaz  21.0  Guahati\n",
      "334       Fiza  22.0  Guahati\n",
      "335       Fool  64.0  Guahati\n",
      "336      Foola  38.0  Guahati\n",
      "337  Fooljhnah  60.0  Guahati\n",
      "338   Foolmaya  29.0  Guahati\n",
      "339   Foolwati  51.0  Guahati\n",
      "340        For  84.0  Guahati\n",
      "          Name   Age     City\n",
      "819     Saniya  38.0  Gurgaon\n",
      "820     Sanjai  37.0  Gurgaon\n",
      "821     Sanjan  55.0  Gurgaon\n",
      "822    Sanjana  19.0  Gurgaon\n",
      "823     Sanjay  49.0  Gurgaon\n",
      "824    Sanjeet   6.0  Gurgaon\n",
      "825   Sanjeeta  87.0  Gurgaon\n",
      "826    Sanjeev  27.0  Gurgaon\n",
      "827  Sanjeevan  80.0  Gurgaon\n",
      "828     Sanjey  21.0  Gurgaon\n",
      "829    Sanjida  20.0  Gurgaon\n",
      "           Name   Age       City\n",
      "964     Yashmin   6.0  Hyderabad\n",
      "965     Yashoda  97.0  Hyderabad\n",
      "966    Yashodha  47.0  Hyderabad\n",
      "967     Yashpal  89.0  Hyderabad\n",
      "968     Yashsvi  68.0  Hyderabad\n",
      "969    Yashwant  80.0  Hyderabad\n",
      "970     Yasodha  12.0  Hyderabad\n",
      "971     Yaswani  50.0  Hyderabad\n",
      "972    Yatharth  57.0  Hyderabad\n",
      "973       Yatin  14.0  Hyderabad\n",
      "974        Yesh  39.0  Hyderabad\n",
      "975    Yogendra  19.0  Hyderabad\n",
      "976      Yogesh  24.0  Hyderabad\n",
      "977   Yogeshwar  52.0  Hyderabad\n",
      "978  Yogeshwari  84.0  Hyderabad\n",
      "979    Yoginder  31.0  Hyderabad\n",
      "980      Yogita  29.0  Hyderabad\n",
      "981     Yognder  40.0  Hyderabad\n",
      "982      Yograj  31.0  Hyderabad\n",
      "983     Yoshoda  45.0  Hyderabad\n",
      "984      Youraj  49.0  Hyderabad\n",
      "985     Yudhbir  78.0  Hyderabad\n",
      "986     Yukilal  38.0  Hyderabad\n",
      "987     Yuveraj  76.0  Hyderabad\n",
      "988      Yuvraj  88.0  Hyderabad\n",
      "989       Zafar  18.0  Hyderabad\n",
      "990      Zaheer  24.0  Hyderabad\n",
      "991       Zahid  33.0  Hyderabad\n",
      "992       Zahir   8.0  Hyderabad\n",
      "993      Zahira  98.0  Hyderabad\n",
      "994      Zainab  98.0  Hyderabad\n",
      "995       Zakir  89.0  Hyderabad\n",
      "996      Zameer  42.0  Hyderabad\n",
      "997      Zamile  70.0  Hyderabad\n",
      "998       Zanmi  70.0  Hyderabad\n",
      "          Name   Age    City\n",
      "835      Sanju  31.0  Jaipur\n",
      "836     Sanjya  15.0  Jaipur\n",
      "837     Sankar  23.0  Jaipur\n",
      "838  Sankarlal  55.0  Jaipur\n",
      "839      Sanna  20.0  Jaipur\n",
      "840      Sanni  73.0  Jaipur\n",
      "841      Sanno  17.0  Jaipur\n",
      "842      Sanny  82.0  Jaipur\n",
      "          Name   Age   City\n",
      "532  Jamrujaha  68.0  Jammu\n",
      "533    Jamshed  10.0  Jammu\n",
      "534     Jamuna  52.0  Jammu\n",
      "535      Janak  78.0  Jammu\n",
      "536     Janaki  86.0  Jammu\n",
      "537      Janam  71.0  Jammu\n",
      "538   Janardam   7.0  Jammu\n",
      "539     Janesh  21.0  Jammu\n",
      "540  Janeshwar   3.0  Jammu\n",
      "541       Jang  94.0  Jammu\n",
      "542       Jani  37.0  Jammu\n",
      "543      Janid  56.0  Jammu\n",
      "           Name   Age    City\n",
      "157      Chhoti  28.0  Kanpur\n",
      "158      Chhotu  64.0  Kanpur\n",
      "159  Chhotudevi  55.0  Kanpur\n",
      "160      Chiddu  46.0  Kanpur\n",
      "161      Chinki  63.0  Kanpur\n",
      "162      Chinku  24.0  Kanpur\n",
      "163      Chinta  90.0  Kanpur\n",
      "164     Chintan  17.0  Kanpur\n",
      "165      Chintu  67.0  Kanpur\n",
      "166       Chinu  87.0  Kanpur\n",
      "167      Chirag  93.0  Kanpur\n",
      "168       Deepa  95.0  Kanpur\n",
      "169     Deepaak  34.0  Kanpur\n",
      "170      Deepak   9.0  Kanpur\n",
      "171     Deepali  40.0  Kanpur\n",
      "172      Deepan  20.0  Kanpur\n",
      "173   Deepanker  61.0  Kanpur\n",
      "174   Deepanshi   3.0  Kanpur\n",
      "175   Deepanshu  26.0  Kanpur\n",
      "176    Deepansu  35.0  Kanpur\n",
      "177    Deepanti  32.0  Kanpur\n",
      "178   Deepchand  32.0  Kanpur\n",
      "          Name   Age    City\n",
      "637    Lekhraj  24.0  Kargil\n",
      "638    Mahadev  46.0  Kargil\n",
      "639   Mahadevi  54.0  Kargil\n",
      "640      Mahak   5.0  Kargil\n",
      "641    Mahammd  86.0  Kargil\n",
      "642  Mahasingh  25.0  Kargil\n",
      "643   Mahaveer  21.0  Kargil\n",
      "644    Mahavir  82.0  Kargil\n",
      "645     Mahbir  13.0  Kargil\n",
      "646    Mahboob  16.0  Kargil\n",
      "647     Mahbub  80.0  Kargil\n",
      "648   Mahendar  18.0  Kargil\n",
      "649   Mahender  15.0  Kargil\n",
      "650   Mahendra  27.0  Kargil\n",
      "651     Mahesh  24.0  Kargil\n",
      "652   Maheshar  96.0  Kargil\n",
      "653    Mahfooj  29.0  Kargil\n",
      "           Name   Age   City\n",
      "480       Injum  27.0  Kochi\n",
      "481       Jagan  29.0  Kochi\n",
      "482       Jagat  83.0  Kochi\n",
      "483      Jagbir  47.0  Kochi\n",
      "484    Jagdamba  43.0  Kochi\n",
      "485  Jagdambika  55.0  Kochi\n",
      "486     Jagdeep  98.0  Kochi\n",
      "487    Jagdeesh  18.0  Kochi\n",
      "488     Jagdesh  46.0  Kochi\n",
      "489    Jagdiesh  20.0  Kochi\n",
      "         Name    Age      City\n",
      "44      Aarif   58.0  Kolkatta\n",
      "45     Aarifa   78.0  Kolkatta\n",
      "46    Aarifun   21.0  Kolkatta\n",
      "47      Aarju    6.0  Kolkatta\n",
      "48      Aarti    3.0  Kolkatta\n",
      "49    Baburam    6.0  Kolkatta\n",
      "50  Badrinath  100.0  Kolkatta\n",
      "51  Badrealam   81.0  Kolkatta\n",
      "52      Badri   61.0  Kolkatta\n",
      "53      Badru   86.0  Kolkatta\n",
      "54     Bagwan   96.0  Kolkatta\n",
      "55    Bahadur   71.0  Kolkatta\n",
      "56    Bahalen   88.0  Kolkatta\n",
      "57     Bahart  100.0  Kolkatta\n",
      "58      Bahnu   54.0  Kolkatta\n",
      "59     Bahrat   51.0  Kolkatta\n",
      "60    Bahwana   65.0  Kolkatta\n",
      "61    Baichan   33.0  Kolkatta\n",
      "62   Baidnath    1.0  Kolkatta\n",
      "63   Baijnath   66.0  Kolkatta\n",
      "64      Baita   34.0  Kolkatta\n",
      "65   Bajinder   52.0  Kolkatta\n",
      "66    Bajrang   22.0  Kolkatta\n",
      "67   Bajrangi   95.0  Kolkatta\n",
      "68    Bajulal   43.0  Kolkatta\n",
      "69       Bake   41.0  Kolkatta\n",
      "70  Bakhtvaar   76.0  Kolkatta\n",
      "71     Bakila   60.0  Kolkatta\n",
      "72      Baksi   63.0  Kolkatta\n",
      "73        Bal   15.0  Kolkatta\n",
      "74       Bala   59.0  Kolkatta\n",
      "75      Balak   98.0  Kolkatta\n",
      "76    Balalia   14.0  Kolkatta\n",
      "77      Balam   13.0  Kolkatta\n",
      "78    Balaram   31.0  Kolkatta\n",
      "79    Balbeer   24.0  Kolkatta\n",
      "          Name   Age     City\n",
      "654    Mahfooz   9.0  Ladhakh\n",
      "655     Mahfuj  52.0  Ladhakh\n",
      "656       Mahi  14.0  Ladhakh\n",
      "657     Mahima  51.0  Ladhakh\n",
      "658   Mahinder  19.0  Ladhakh\n",
      "659   Mahindra  60.0  Ladhakh\n",
      "660  Mahinudin  57.0  Ladhakh\n",
      "661    Mahipal  35.0  Ladhakh\n",
      "662    Mahjabi  60.0  Ladhakh\n",
      "663     Mahkar  49.0  Ladhakh\n",
      "664   Mahlikka  33.0  Ladhakh\n",
      "665     Mahmun  38.0  Ladhakh\n",
      "666   Mahnatha   9.0  Ladhakh\n",
      "667      Najar   8.0  Ladhakh\n",
      "668     Najara  54.0  Ladhakh\n",
      "669   Najarana  80.0  Ladhakh\n",
      "670      Najni  64.0  Ladhakh\n",
      "671       Najo  80.0  Ladhakh\n",
      "672     Omwati  16.0  Ladhakh\n",
      "673        One  80.0  Ladhakh\n",
      "674    Onginye   9.0  Ladhakh\n",
      "675    Opendar  78.0  Ladhakh\n",
      "676      Osier  27.0  Ladhakh\n",
      "677     Ovaish  47.0  Ladhakh\n",
      "678    Pabitra  58.0  Ladhakh\n",
      "           Name   Age     City\n",
      "341     Foranta  68.0  Lucknow\n",
      "342     Foranti  28.0  Lucknow\n",
      "343      Fornta  60.0  Lucknow\n",
      "344     Forshan  38.0  Lucknow\n",
      "345       Fozia  65.0  Lucknow\n",
      "346      Gandhi   6.0  Lucknow\n",
      "347      Ganesh  75.0  Lucknow\n",
      "348     Ganeshi  76.0  Lucknow\n",
      "349      Ganesi  12.0  Lucknow\n",
      "350       Ganga   9.0  Lucknow\n",
      "351   Gangadhar  52.0  Lucknow\n",
      "352   Gangajali  53.0  Lucknow\n",
      "353    Gangaram  66.0  Lucknow\n",
      "354       Gango  72.0  Lucknow\n",
      "355      Ganita  83.0  Lucknow\n",
      "356      Ganpat  49.0  Lucknow\n",
      "357     Ganpati   3.0  Lucknow\n",
      "358   Ganpatram  84.0  Lucknow\n",
      "359   Gantantra  77.0  Lucknow\n",
      "360       Gappu   8.0  Lucknow\n",
      "361       Gargi  83.0  Lucknow\n",
      "362       Garib  45.0  Lucknow\n",
      "363      Garima  44.0  Lucknow\n",
      "364       Garsh  29.0  Lucknow\n",
      "365     Garupal  32.0  Lucknow\n",
      "366        Garv   9.0  Lucknow\n",
      "367       Gattu  94.0  Lucknow\n",
      "368       Gaura   1.0  Lucknow\n",
      "369      Gaurav  41.0  Lucknow\n",
      "370   Gauravgil  47.0  Lucknow\n",
      "371       Gauri  57.0  Lucknow\n",
      "372      Gautam  99.0  Lucknow\n",
      "373      Gawali  83.0  Lucknow\n",
      "374       Gawri  77.0  Lucknow\n",
      "375     Gayatri  54.0  Lucknow\n",
      "376     Gaytree  85.0  Lucknow\n",
      "377      Gaytri   2.0  Lucknow\n",
      "378    Geernish  66.0  Lucknow\n",
      "379       Geesa  83.0  Lucknow\n",
      "380        Geet  51.0  Lucknow\n",
      "381       Geeta  19.0  Lucknow\n",
      "382  Geetanjali  59.0  Lucknow\n",
      "383     Geetika  16.0  Lucknow\n",
      "384       Geetu  58.0  Lucknow\n",
      "385         Gen  67.0  Lucknow\n",
      "        Name   Age     City\n",
      "784   Rajbir  69.0  Madurai\n",
      "785  Rajbiri  48.0  Madurai\n",
      "786   Rajdev  41.0  Madurai\n",
      "787   Rajdut   4.0  Madurai\n",
      "788  Rajeena  36.0  Madurai\n",
      "789   Rajeet   3.0  Madurai\n",
      "790   Rajeev  99.0  Madurai\n",
      "        Name   Age    City\n",
      "590  Khushbu  55.0  Mysore\n",
      "591   Khushi  30.0  Mysore\n",
      "592   Laliya  93.0  Mysore\n",
      "593    Lallu  69.0  Mysore\n",
      "594  Lalmuni  50.0  Mysore\n",
      "595   Laltes  70.0  Mysore\n",
      "596   Laltha  75.0  Mysore\n",
      "597   Laltia   7.0  Mysore\n",
      "598     Lalu  44.0  Mysore\n",
      "599  Lalutha  47.0  Mysore\n",
      "600   Lamani  78.0  Mysore\n",
      "601   Lambhu  63.0  Mysore\n",
      "                Name   Age   City\n",
      "940  Warnakulasuriya  76.0  Nasik\n",
      "941           Waseem  69.0  Nasik\n",
      "942            Wasim  11.0  Nasik\n",
      "943       Wasimuddin   1.0  Nasik\n",
      "944            Wazid  61.0  Nasik\n",
      "945            Wazir  42.0  Nasik\n",
      "946          Wiliyam  57.0  Nasik\n",
      "947          Wilkish  96.0  Nasik\n",
      "948           Yadram  69.0  Nasik\n",
      "949           Yadvir  65.0  Nasik\n",
      "950            Yaksh  26.0  Nasik\n",
      "951            Yaman  15.0  Nasik\n",
      "952           Yameen  18.0  Nasik\n",
      "953            Yamin  90.0  Nasik\n",
      "            Name   Age   City\n",
      "179    Deepender  85.0  Noida\n",
      "180        Deepi  49.0  Noida\n",
      "181      Deepika  76.0  Noida\n",
      "182       Deepka  64.0  Noida\n",
      "183     Deepmala  15.0  Noida\n",
      "184  Deepnarayan  25.0  Noida\n",
      "185   Deepshikha  14.0  Noida\n",
      "186       Deepti   7.0  Noida\n",
      "187        Deepu  39.0  Noida\n",
      "188    Deepvhand  76.0  Noida\n",
      "189       Deeraj   8.0  Noida\n",
      "190    Deerendra   3.0  Noida\n",
      "191       Deewan   9.0  Noida\n",
      "192        Deeya  14.0  Noida\n",
      "193       Denesh   4.0  Noida\n",
      "194         Deni   1.0  Noida\n",
      "195         Deou  37.0  Noida\n",
      "196       Depali  96.0  Noida\n",
      "197     Depander  24.0  Noida\n",
      "198         Desh  65.0  Noida\n",
      "199      Deshraj  19.0  Noida\n",
      "200          Dev  92.0  Noida\n",
      "201         Deva   1.0  Noida\n",
      "202     Devanand  55.0  Noida\n",
      "203      Devanti  91.0  Noida\n",
      "204    Devasheeh  67.0  Noida\n",
      "205    Devashish  97.0  Noida\n",
      "206     Devasish  75.0  Noida\n",
      "207     Devdhari   4.0  Noida\n",
      "208     Devender  20.0  Noida\n",
      "209      Devendr  35.0  Noida\n",
      "210     Devendra  87.0  Noida\n",
      "211     Devendri  30.0  Noida\n",
      "212       Devesh  63.0  Noida\n",
      "213         Devi   7.0  Noida\n",
      "214        Devid  66.0  Noida\n",
      "215     Devideen  23.0  Noida\n",
      "216        Devik   2.0  Noida\n",
      "217       Devika  33.0  Noida\n",
      "218      Devilal  30.0  Noida\n",
      "219     Devinder  66.0  Noida\n",
      "220      Devkali  18.0  Noida\n",
      "221        Devki  11.0  Noida\n",
      "222      Devkran  41.0  Noida\n",
      "223        Devli  25.0  Noida\n",
      "224       Devpal  21.0  Noida\n",
      "225       Devraj  62.0  Noida\n",
      "226        Devya  21.0  Noida\n",
      "           Name   Age      City\n",
      "227     Devyani  98.0  Palampur\n",
      "228       Deyji  22.0  Palampur\n",
      "229  Dhakhadevi  51.0  Palampur\n",
      "230   Dhalchand  27.0  Palampur\n",
      "231       Edris  73.0  Palampur\n",
      "..          ...   ...       ...\n",
      "283     Farhana  83.0  Palampur\n",
      "284   Farhanaaz  14.0  Palampur\n",
      "285    Farhanaz  52.0  Palampur\n",
      "286     Farheen  44.0  Palampur\n",
      "287      Farhib  23.0  Palampur\n",
      "\n",
      "[61 rows x 3 columns]\n",
      "          Name   Age  City\n",
      "602      Lasar  69.0  Pune\n",
      "603     Lashma  33.0  Pune\n",
      "604  Laskshita  67.0  Pune\n",
      "605       Lata  84.0  Pune\n",
      "606     Lateef  33.0  Pune\n",
      "607     Latesh  60.0  Pune\n",
      "608     Latoor  51.0  Pune\n",
      "609      Latur  51.0  Pune\n",
      "         Name   Age    City\n",
      "805   Sandiya  84.0  Raipur\n",
      "806    Sandna  34.0  Raipur\n",
      "807    Sandya  75.0  Raipur\n",
      "808    Saneha  31.0  Raipur\n",
      "809  Sangeena  11.0  Raipur\n",
      "810   Sangeet  24.0  Raipur\n",
      "811  Sangeeta  41.0  Raipur\n",
      "812   Sangeta  37.0  Raipur\n",
      "813    Sangit  28.0  Raipur\n",
      "814   Sangita   8.0  Raipur\n",
      "815   Sangram  67.0  Raipur\n",
      "816     Sanha  91.0  Raipur\n",
      "817     Sania   2.0  Raipur\n",
      "818    Sanita  42.0  Raipur\n",
      "           Name    Age    City\n",
      "920      Ujjwal    6.0  Ranchi\n",
      "921         Uma   51.0  Ranchi\n",
      "922     Umakant   72.0  Ranchi\n",
      "923     Umanath    5.0  Ranchi\n",
      "924       Umang   10.0  Ranchi\n",
      "925        Umar   81.0  Ranchi\n",
      "926   Umardaraz   13.0  Ranchi\n",
      "927    Umardeen  100.0  Ranchi\n",
      "928   Umasankar   65.0  Ranchi\n",
      "929  Umashankar   38.0  Ranchi\n",
      "930  Umashanker   35.0  Ranchi\n",
      "931        Umda   20.0  Ranchi\n",
      "932       Umedi    2.0  Ranchi\n",
      "933       Umesh   48.0  Ranchi\n",
      "934      Umesha   57.0  Ranchi\n",
      "935     Vaibhav   46.0  Ranchi\n",
      "936        Vaid   21.0  Ranchi\n",
      "937    Vaijanti   17.0  Ranchi\n",
      "938    Vaishali   54.0  Ranchi\n",
      "939   Vaishanvi   77.0  Ranchi\n",
      "          Name   Age      City\n",
      "791     Rajend  12.0  Srinagar\n",
      "792   Rajendar  28.0  Srinagar\n",
      "793   Rajender  37.0  Srinagar\n",
      "794  Rajenderi  65.0  Srinagar\n",
      "795   Rajendra  31.0  Srinagar\n",
      "796     Rajesh  39.0  Srinagar\n",
      "797   Rajeshvr  52.0  Srinagar\n",
      "798  Rajeshwar  90.0  Srinagar\n",
      "799    Sandeep  38.0  Srinagar\n",
      "800   Sandeepa  74.0  Srinagar\n",
      "801     Sandha  46.0  Srinagar\n",
      "802   Sandhaya  23.0  Srinagar\n",
      "803    Sandhya   6.0  Srinagar\n",
      "804     Sandip  57.0  Srinagar\n",
      "          Name   Age   City\n",
      "553      Janwi   7.0  Surat\n",
      "554    Japneet  27.0  Surat\n",
      "555     Jaquir  92.0  Surat\n",
      "556      Jyoti  34.0  Surat\n",
      "557   Jyotsana  61.0  Surat\n",
      "558      Jyoty  18.0  Surat\n",
      "559    Kaamini  56.0  Surat\n",
      "560      Kaari  98.0  Surat\n",
      "561     Kaarti  59.0  Surat\n",
      "562      Kabal  86.0  Surat\n",
      "563      Kabid  38.0  Surat\n",
      "882  Tulcharam  12.0  Surat\n",
      "883      Tulki  16.0  Surat\n",
      "884      Tulsa  74.0  Surat\n",
      "885     Tulsha  77.0  Surat\n",
      "886     Tulshi  84.0  Surat\n",
      "887      Tulsi   1.0  Surat\n",
      "888   Tulsiram  11.0  Surat\n",
      "889      Tumpa  51.0  Surat\n",
      "895     Tushar  87.0  Surat\n",
      "896    Twenkle  84.0  Surat\n",
      "897    Twinkal  70.0  Surat\n",
      "898    Twinkil  40.0  Surat\n",
      "899    Twinkle  47.0  Surat\n",
      "900      Tyara  72.0  Surat\n",
      "901        Uda  51.0  Surat\n",
      "          Name   Age     City\n",
      "544      Janki  34.0  Udaipur\n",
      "545   Jankidas  81.0  Udaipur\n",
      "546    Jankraj  67.0  Udaipur\n",
      "547    Janmesh  64.0  Udaipur\n",
      "548     Jannat  86.0  Udaipur\n",
      "549      Janta  40.0  Udaipur\n",
      "550  Jantarpal  97.0  Udaipur\n",
      "551     Januka  55.0  Udaipur\n",
      "552      Janvi   1.0  Udaipur\n",
      "           Name   Age    City\n",
      "564       Kabir  53.0  Ujjain\n",
      "565       Kabul  98.0  Ujjain\n",
      "566      Kachan  75.0  Ujjain\n",
      "567      Kachri  23.0  Ujjain\n",
      "568       Kadir  29.0  Ujjain\n",
      "569        Kadu  90.0  Ujjain\n",
      "570       Kafia  80.0  Ujjain\n",
      "571     Kailesh  29.0  Ujjain\n",
      "572     Kaishav  34.0  Ujjain\n",
      "573       Kajal  66.0  Ujjain\n",
      "574  Kajamuddih  26.0  Ujjain\n",
      "575       Kajar  28.0  Ujjain\n"
     ]
    }
   ],
   "source": [
    "# Find the Youngest Names in Each City\n",
    "name_age_city_df = pop_df[['Name' , 'Age' , 'City']].groupby(['City'])\n",
    "for key, item in name_age_city_df:\n",
    "    print(name_age_city_df.get_group(key))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ahmedabad</th>\n",
       "      <td>17.0</td>\n",
       "      <td>53.058823</td>\n",
       "      <td>29.894547</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.00</td>\n",
       "      <td>56.0</td>\n",
       "      <td>81.00</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amritsar</th>\n",
       "      <td>11.0</td>\n",
       "      <td>49.636364</td>\n",
       "      <td>34.889175</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>55.0</td>\n",
       "      <td>81.50</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bangalore</th>\n",
       "      <td>193.0</td>\n",
       "      <td>51.160622</td>\n",
       "      <td>29.086258</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>77.00</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>77.0</td>\n",
       "      <td>52.051949</td>\n",
       "      <td>25.457855</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.00</td>\n",
       "      <td>49.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coimbatore</th>\n",
       "      <td>27.0</td>\n",
       "      <td>45.333332</td>\n",
       "      <td>27.522018</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>40.0</td>\n",
       "      <td>71.00</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cuttack</th>\n",
       "      <td>27.0</td>\n",
       "      <td>51.259258</td>\n",
       "      <td>27.702530</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>48.0</td>\n",
       "      <td>73.50</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dehradun</th>\n",
       "      <td>16.0</td>\n",
       "      <td>47.812500</td>\n",
       "      <td>29.521109</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>48.0</td>\n",
       "      <td>67.00</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>112.0</td>\n",
       "      <td>49.464287</td>\n",
       "      <td>28.119093</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.75</td>\n",
       "      <td>49.5</td>\n",
       "      <td>70.00</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guahati</th>\n",
       "      <td>53.0</td>\n",
       "      <td>44.301888</td>\n",
       "      <td>28.163387</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>40.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gurgaon</th>\n",
       "      <td>11.0</td>\n",
       "      <td>39.909092</td>\n",
       "      <td>25.797110</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.50</td>\n",
       "      <td>37.0</td>\n",
       "      <td>52.00</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hyderabad</th>\n",
       "      <td>35.0</td>\n",
       "      <td>51.228573</td>\n",
       "      <td>28.510149</td>\n",
       "      <td>6.0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>47.0</td>\n",
       "      <td>77.00</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jaipur</th>\n",
       "      <td>8.0</td>\n",
       "      <td>39.500000</td>\n",
       "      <td>26.726124</td>\n",
       "      <td>15.0</td>\n",
       "      <td>19.25</td>\n",
       "      <td>27.0</td>\n",
       "      <td>59.50</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jammu</th>\n",
       "      <td>12.0</td>\n",
       "      <td>48.583332</td>\n",
       "      <td>32.284130</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18.25</td>\n",
       "      <td>54.0</td>\n",
       "      <td>72.75</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kanpur</th>\n",
       "      <td>22.0</td>\n",
       "      <td>46.409092</td>\n",
       "      <td>27.791159</td>\n",
       "      <td>3.0</td>\n",
       "      <td>26.50</td>\n",
       "      <td>37.5</td>\n",
       "      <td>63.75</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kargil</th>\n",
       "      <td>17.0</td>\n",
       "      <td>38.882355</td>\n",
       "      <td>29.425505</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>25.0</td>\n",
       "      <td>54.00</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kochi</th>\n",
       "      <td>10.0</td>\n",
       "      <td>46.599998</td>\n",
       "      <td>26.395287</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.50</td>\n",
       "      <td>44.5</td>\n",
       "      <td>53.00</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kolkatta</th>\n",
       "      <td>36.0</td>\n",
       "      <td>51.805557</td>\n",
       "      <td>31.018721</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.50</td>\n",
       "      <td>56.0</td>\n",
       "      <td>76.50</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ladhakh</th>\n",
       "      <td>25.0</td>\n",
       "      <td>43.480000</td>\n",
       "      <td>24.467869</td>\n",
       "      <td>8.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>49.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lucknow</th>\n",
       "      <td>45.0</td>\n",
       "      <td>50.777779</td>\n",
       "      <td>28.222240</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.00</td>\n",
       "      <td>54.0</td>\n",
       "      <td>75.00</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madurai</th>\n",
       "      <td>7.0</td>\n",
       "      <td>42.857143</td>\n",
       "      <td>34.182980</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>41.0</td>\n",
       "      <td>58.50</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mysore</th>\n",
       "      <td>12.0</td>\n",
       "      <td>56.750000</td>\n",
       "      <td>23.277475</td>\n",
       "      <td>7.0</td>\n",
       "      <td>46.25</td>\n",
       "      <td>59.0</td>\n",
       "      <td>71.25</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nasik</th>\n",
       "      <td>14.0</td>\n",
       "      <td>49.714287</td>\n",
       "      <td>30.783184</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>59.0</td>\n",
       "      <td>69.00</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Noida</th>\n",
       "      <td>48.0</td>\n",
       "      <td>39.020832</td>\n",
       "      <td>30.265135</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.00</td>\n",
       "      <td>30.0</td>\n",
       "      <td>65.25</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Palampur</th>\n",
       "      <td>61.0</td>\n",
       "      <td>46.590164</td>\n",
       "      <td>29.187538</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>49.0</td>\n",
       "      <td>64.00</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pune</th>\n",
       "      <td>8.0</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>17.720045</td>\n",
       "      <td>33.0</td>\n",
       "      <td>46.50</td>\n",
       "      <td>55.5</td>\n",
       "      <td>67.50</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Raipur</th>\n",
       "      <td>14.0</td>\n",
       "      <td>41.071430</td>\n",
       "      <td>28.168352</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>35.5</td>\n",
       "      <td>60.75</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ranchi</th>\n",
       "      <td>20.0</td>\n",
       "      <td>40.900002</td>\n",
       "      <td>28.802229</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>42.0</td>\n",
       "      <td>59.00</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Srinagar</th>\n",
       "      <td>14.0</td>\n",
       "      <td>42.714287</td>\n",
       "      <td>23.394375</td>\n",
       "      <td>6.0</td>\n",
       "      <td>28.75</td>\n",
       "      <td>38.5</td>\n",
       "      <td>55.75</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Surat</th>\n",
       "      <td>26.0</td>\n",
       "      <td>52.038460</td>\n",
       "      <td>29.528942</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.75</td>\n",
       "      <td>53.5</td>\n",
       "      <td>76.25</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Udaipur</th>\n",
       "      <td>9.0</td>\n",
       "      <td>58.333332</td>\n",
       "      <td>29.807716</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.00</td>\n",
       "      <td>64.0</td>\n",
       "      <td>81.00</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ujjain</th>\n",
       "      <td>12.0</td>\n",
       "      <td>52.583332</td>\n",
       "      <td>27.864799</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28.75</td>\n",
       "      <td>43.5</td>\n",
       "      <td>76.25</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age                                                       \n",
       "            count       mean        std   min    25%   50%    75%    max\n",
       "City                                                                    \n",
       "Ahmedabad    17.0  53.058823  29.894547   3.0  24.00  56.0  81.00   98.0\n",
       "Amritsar     11.0  49.636364  34.889175   2.0  13.00  55.0  81.50   96.0\n",
       "Bangalore   193.0  51.160622  29.086258   3.0  24.00  51.0  77.00  100.0\n",
       "Chennai      77.0  52.051949  25.457855   1.0  32.00  49.0  70.00  100.0\n",
       "Coimbatore   27.0  45.333332  27.522018   2.0  22.00  40.0  71.00   94.0\n",
       "Cuttack      27.0  51.259258  27.702530   4.0  30.00  48.0  73.50  100.0\n",
       "Dehradun     16.0  47.812500  29.521109   4.0  20.00  48.0  67.00   95.0\n",
       "Delhi       112.0  49.464287  28.119093   1.0  23.75  49.5  70.00  100.0\n",
       "Guahati      53.0  44.301888  28.163387   1.0  20.00  40.0  73.00   95.0\n",
       "Gurgaon      11.0  39.909092  25.797110   6.0  20.50  37.0  52.00   87.0\n",
       "Hyderabad    35.0  51.228573  28.510149   6.0  30.00  47.0  77.00   98.0\n",
       "Jaipur        8.0  39.500000  26.726124  15.0  19.25  27.0  59.50   82.0\n",
       "Jammu        12.0  48.583332  32.284130   3.0  18.25  54.0  72.75   94.0\n",
       "Kanpur       22.0  46.409092  27.791159   3.0  26.50  37.5  63.75   95.0\n",
       "Kargil       17.0  38.882355  29.425505   5.0  18.00  25.0  54.00   96.0\n",
       "Kochi        10.0  46.599998  26.395287  18.0  27.50  44.5  53.00   98.0\n",
       "Kolkatta     36.0  51.805557  31.018721   1.0  23.50  56.0  76.50  100.0\n",
       "Ladhakh      25.0  43.480000  24.467869   8.0  19.00  49.0  60.00   80.0\n",
       "Lucknow      45.0  50.777779  28.222240   1.0  29.00  54.0  75.00   99.0\n",
       "Madurai       7.0  42.857143  34.182980   3.0  20.00  41.0  58.50   99.0\n",
       "Mysore       12.0  56.750000  23.277475   7.0  46.25  59.0  71.25   93.0\n",
       "Nasik        14.0  49.714287  30.783184   1.0  20.00  59.0  69.00   96.0\n",
       "Noida        48.0  39.020832  30.265135   1.0  14.00  30.0  65.25   97.0\n",
       "Palampur     61.0  46.590164  29.187538   1.0  22.00  49.0  64.00   98.0\n",
       "Pune          8.0  56.000000  17.720045  33.0  46.50  55.5  67.50   84.0\n",
       "Raipur       14.0  41.071430  28.168352   2.0  25.00  35.5  60.75   91.0\n",
       "Ranchi       20.0  40.900002  28.802229   2.0  16.00  42.0  59.00  100.0\n",
       "Srinagar     14.0  42.714287  23.394375   6.0  28.75  38.5  55.75   90.0\n",
       "Surat        26.0  52.038460  29.528942   1.0  28.75  53.5  76.25   98.0\n",
       "Udaipur       9.0  58.333332  29.807716   1.0  40.00  64.0  81.00   97.0\n",
       "Ujjain       12.0  52.583332  27.864799  23.0  28.75  43.5  76.25   98.0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_age_city_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "City Wise Mean Ages in Ascending Order:  \n",
      "\n",
      "             mean_age  count\n",
      "City                        \n",
      "Kargil      38.882355     17\n",
      "Noida       39.020832     48\n",
      "Jaipur      39.500000      8\n",
      "Gurgaon     39.909092     11\n",
      "Ranchi      40.900002     20\n",
      "Raipur      41.071430     14\n",
      "Srinagar    42.714287     14\n",
      "Madurai     42.857143      7\n",
      "Ladhakh     43.480000     25\n",
      "Guahati     44.301888     53\n",
      "Coimbatore  45.333332     27\n",
      "Kanpur      46.409092     22\n",
      "Palampur    46.590164     61\n",
      "Kochi       46.599998     10\n",
      "Dehradun    47.812500     16\n",
      "Jammu       48.583332     12\n",
      "Delhi       49.464287    112\n",
      "Amritsar    49.636364     11\n",
      "Nasik       49.714287     14\n",
      "Lucknow     50.777779     45\n",
      "Bangalore   51.160622    193\n",
      "Hyderabad   51.228573     35\n",
      "Cuttack     51.259258     27\n",
      "Kolkatta    51.805557     36\n",
      "Surat       52.038460     26\n",
      "Chennai     52.051949     77\n",
      "Ujjain      52.583332     12\n",
      "Ahmedabad   53.058823     17\n",
      "Pune        56.000000      8\n",
      "Mysore      56.750000     12\n",
      "Udaipur     58.333332      9\n",
      "\n",
      "\n",
      "Top 3 Cities with Youngest Population:  \n",
      "\n",
      "         mean_age  count\n",
      "City                    \n",
      "Kargil  38.882355     17\n",
      "Noida   39.020832     48\n",
      "Jaipur  39.500000      8\n"
     ]
    }
   ],
   "source": [
    "name_ages = name_age_city_df['Age'].mean()\n",
    "name_counts = name_age_city_df['Age'].count()\n",
    "\n",
    "\n",
    "ages_counts = pd.DataFrame()\n",
    "ages_counts['mean_age'] = name_ages\n",
    "ages_counts['count'] = name_counts\n",
    "\n",
    "ages_counts = ages_counts.sort_values('mean_age')\n",
    "print(\"\\n\\nCity Wise Mean Ages in Ascending Order:  \\n\")\n",
    "print(ages_counts)\n",
    "\n",
    "print(\"\\n\\nTop 3 Cities with Youngest Population:  \\n\")\n",
    "print(ages_counts[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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>nodeid</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23A</td>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34F</td>\n",
       "      <td>13.082700</td>\n",
       "      <td>80.27070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45FG</td>\n",
       "      <td>13.082700</td>\n",
       "      <td>80.27070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65D</td>\n",
       "      <td>22.570000</td>\n",
       "      <td>88.36390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36GF</td>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  nodeid        lat      long\n",
       "0    23A  28.679079  77.06971\n",
       "1    34F  13.082700  80.27070\n",
       "2   45FG  13.082700  80.27070\n",
       "3    65D  22.570000  88.36390\n",
       "4   36GF  28.679079  77.06971"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "road_nodes = pd.read_csv(\"roadnodes.csv\")\n",
    "road_nodes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "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>sourcenode</th>\n",
       "      <th>dstnode</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23A</td>\n",
       "      <td>34F</td>\n",
       "      <td>12000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45FG</td>\n",
       "      <td>65D</td>\n",
       "      <td>2333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65D</td>\n",
       "      <td>36GF</td>\n",
       "      <td>23525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>90FD</td>\n",
       "      <td>55GF</td>\n",
       "      <td>4565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>89UY</td>\n",
       "      <td>55GF</td>\n",
       "      <td>45678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sourcenode dstnode  length\n",
       "0        23A     34F   12000\n",
       "1       45FG     65D    2333\n",
       "2        65D    36GF   23525\n",
       "3       90FD    55GF    4565\n",
       "4       89UY    55GF   45678"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "road_edges = pd.read_csv(\"roadedges.csv\")\n",
    "road_edges.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 edges, 10 nodes\n"
     ]
    }
   ],
   "source": [
    "print(f'{road_edges.shape[0]} edges, {road_nodes.shape[0]} nodes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " Number of nodes in the graph : \n",
      " 10\n",
      "\n",
      "\n",
      " Number of edges in the graph : \n",
      " 7\n",
      "\n",
      "\n",
      " Degree of the Graph : \n",
      " [('23A', 1), ('34F', 1), ('45FG', 1), ('65D', 3), ('36GF', 1), ('90FD', 1), ('55GF', 2), ('89UY', 2), ('33IK', 1), ('78HJ', 1)]\n"
     ]
    }
   ],
   "source": [
    "road_graph = nx.convert_matrix.from_pandas_edgelist(road_edges, source='sourcenode', target='dstnode', edge_attr='length')\n",
    "print(\"\\n\\n Number of nodes in the graph : \\n\", road_graph.number_of_nodes())\n",
    "print(\"\\n\\n Number of edges in the graph : \\n\", road_graph.number_of_edges())\n",
    "print(\"\\n\\n Degree of the Graph : \\n\", road_graph.degree())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "nx.draw(road_graph, with_labels = True)\n",
    "plt.savefig(\"road_graph.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n"
     ]
    },
    {
     "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>lat</th>\n",
       "      <th>long</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nodeid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23A</th>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33IK</th>\n",
       "      <td>28.535500</td>\n",
       "      <td>77.39100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34F</th>\n",
       "      <td>13.082700</td>\n",
       "      <td>80.27070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36GF</th>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45FG</th>\n",
       "      <td>13.082700</td>\n",
       "      <td>80.27070</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              lat      long\n",
       "nodeid                     \n",
       "23A     28.679079  77.06971\n",
       "33IK    28.535500  77.39100\n",
       "34F     13.082700  80.27070\n",
       "36GF    28.679079  77.06971\n",
       "45FG    13.082700  80.27070"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "road_nodes = road_nodes.set_index('nodeid', drop=True)\n",
    "%time road_nodes = road_nodes.sort_index()\n",
    "road_nodes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "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>Hospital Name</th>\n",
       "      <th>Hospital ID</th>\n",
       "      <th>City</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABC</td>\n",
       "      <td>7132</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DEF</td>\n",
       "      <td>7574</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GHI</td>\n",
       "      <td>8471</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JKL</td>\n",
       "      <td>4877</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>28.679079</td>\n",
       "      <td>77.06971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MNO</td>\n",
       "      <td>4454</td>\n",
       "      <td>Kolkatta</td>\n",
       "      <td>22.570000</td>\n",
       "      <td>88.36390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Hospital Name  Hospital ID      City   Latitude  Longitude\n",
       "0           ABC         7132     Delhi  28.679079   77.06971\n",
       "1           DEF         7574     Delhi  28.679079   77.06971\n",
       "2           GHI         8471     Delhi  28.679079   77.06971\n",
       "3           JKL         4877     Delhi  28.679079   77.06971\n",
       "4           MNO         4454  Kolkatta  22.570000   88.36390"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hospitals_df = pd.read_csv(\"hospitals.csv\")\n",
    "hospitals_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Count of Hospitals per City is : \n",
      " City\n",
      "Ahmedabad     2\n",
      "Amritsar      2\n",
      "Bangalore     3\n",
      "Chennai       5\n",
      "Coimbatore    7\n",
      "Cuttack       4\n",
      "Dehradun      2\n",
      "Delhi         4\n",
      "Guahati       5\n",
      "Gurgaon       2\n",
      "Hyderabad     2\n",
      "Jaipur        2\n",
      "Jammu         3\n",
      "Kanpur        5\n",
      "Kargil        5\n",
      "Kochi         4\n",
      "Kolkatta      3\n",
      "Ladhakh       4\n",
      "Lucknow       3\n",
      "Mysore        2\n",
      "Nasik         2\n",
      "Noida         4\n",
      "Palampur      1\n",
      "Pune          3\n",
      "Raipur        3\n",
      "Ranchi        2\n",
      "Srinagar      3\n",
      "Surat         5\n",
      "Udaipur       4\n",
      "Ujjain        3\n",
      "Name: Hospital Name, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Find the number of hospitals per city\n",
    "hospitals_per_city = hospitals_df[['Hospital Name','Hospital ID','City']].groupby(['City'])\n",
    "hospitals_per_city_count = hospitals_per_city['Hospital Name'].count()\n",
    "print(\"\\n\\nCount of Hospitals per City is : \\n\", hospitals_per_city_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Hospital Name</th>\n",
       "      <th>Hospital ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abid</td>\n",
       "      <td>70.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>ABC</td>\n",
       "      <td>7132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abid</td>\n",
       "      <td>70.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>DEF</td>\n",
       "      <td>7574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abid</td>\n",
       "      <td>70.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>GHI</td>\n",
       "      <td>8471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abid</td>\n",
       "      <td>70.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>JKL</td>\n",
       "      <td>4877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aabida</td>\n",
       "      <td>54.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>ABC</td>\n",
       "      <td>7132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3434</th>\n",
       "      <td>Zameer</td>\n",
       "      <td>42.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>DWS</td>\n",
       "      <td>8944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3435</th>\n",
       "      <td>Zamile</td>\n",
       "      <td>70.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>FGV</td>\n",
       "      <td>5232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3436</th>\n",
       "      <td>Zamile</td>\n",
       "      <td>70.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>DWS</td>\n",
       "      <td>8944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3437</th>\n",
       "      <td>Zanmi</td>\n",
       "      <td>70.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>FGV</td>\n",
       "      <td>5232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3438</th>\n",
       "      <td>Zanmi</td>\n",
       "      <td>70.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>DWS</td>\n",
       "      <td>8944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3439 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name   Age Gender       City Hospital Name  Hospital ID\n",
       "0       Abid  70.0      F      Delhi           ABC         7132\n",
       "1       Abid  70.0      F      Delhi           DEF         7574\n",
       "2       Abid  70.0      F      Delhi           GHI         8471\n",
       "3       Abid  70.0      F      Delhi           JKL         4877\n",
       "4     Aabida  54.0      M      Delhi           ABC         7132\n",
       "...      ...   ...    ...        ...           ...          ...\n",
       "3434  Zameer  42.0      F  Hyderabad           DWS         8944\n",
       "3435  Zamile  70.0      M  Hyderabad           FGV         5232\n",
       "3436  Zamile  70.0      M  Hyderabad           DWS         8944\n",
       "3437   Zanmi  70.0      M  Hyderabad           FGV         5232\n",
       "3438   Zanmi  70.0      M  Hyderabad           DWS         8944\n",
       "\n",
       "[3439 rows x 6 columns]"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MErge the population data and hospitals data\n",
    "hospitals_population_df = pd.merge(pop_df[['Name', 'Age', 'Gender', 'City']], hospitals_df[['Hospital Name', 'Hospital ID', 'City']], how = \"inner\", on = 'City',suffixes=(\"_pop\", \"_hospitals\"))\n",
    "hospitals_population_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "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>Name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hospital Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AB</th>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABA</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABB</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABC</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABCD</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WXYZ</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XAB</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YZ</th>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YZAB</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZAB</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name\n",
       "Hospital Name      \n",
       "AB               77\n",
       "ABA              10\n",
       "ABB              16\n",
       "ABC             112\n",
       "ABCD             53\n",
       "...             ...\n",
       "WXYZ             27\n",
       "XAB              17\n",
       "YZ              138\n",
       "YZAB             45\n",
       "ZAB              17\n",
       "\n",
       "[98 rows x 1 columns]"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Patients per hospital\n",
    "hospitals_by_city_df = hospitals_population_df[['Name', 'Hospital Name','City']].groupby(['Hospital Name'])\n",
    "hospitals_by_city_df[['Name']].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for key, item in hospitals_by_city_df:\n",
    "    print(hospitals_by_city_df.get_group(key))"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Data Analysis with Pandas and Numpy.ipynb",
   "provenance": []
  },
  "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}