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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What is Numpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "NumPy is a python library used for working with arrays.\n",
+    "\n",
+    "It also has functions for working in domain of linear algebra, fourier transform, and matrices.\n",
+    "\n",
+    "NumPy was created in 2005 by Travis Oliphant. It is an open source project and you can use it freely.\n",
+    "\n",
+    "NumPy stands for Numerical Python.\n",
+    "\n",
+    "Numpy is also incredibly fast, as it has bindings to C libraries. For more info on why you would want to use Arrays instead of lists, check out this great"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation Instructions\n",
+    "\n",
+    "**It is highly recommended you install Python using the Anaconda distribution to make sure all underlying dependencies (such as Linear Algebra libraries) all sync up with the use of a conda install. If you have Anaconda, install NumPy by going to your terminal or command prompt and typing:**\n",
+    "    \n",
+    "    conda install numpy\n",
+    "    \n",
+    "**If you do not have Anaconda and can not install it, please refer to [Numpy's official documentation on various installation instructions.](http://docs.scipy.org/doc/numpy-1.10.1/user/install.html)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[9, 8, 7]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_list= [9,8,7]\n",
+    "my_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([9, 8, 7])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "np.array(my_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4, 5])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list1=[1,2,3,4,5]\n",
+    "np.array(list1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1, 2, 3], [4, 5, 6], [6, 7, 7]]\n",
+      "\n",
+      "\n",
+      "[[1 2 3]\n",
+      " [4 5 6]\n",
+      " [6 7 7]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "matrix=[[1,2,3],[4,5,6],[6,7,7]]\n",
+    "print(matrix)\n",
+    "print ('\\n')\n",
+    "\n",
+    "print (np.array(matrix))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Built-in Methods\n",
+    "\n",
+    "There are lots of built-in ways to generate Arrays"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### NumPy is used to work with arrays. The array object in NumPy is called ndarray."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3 4 5]\n",
+      "<class 'numpy.ndarray'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = np.array([1, 2, 3, 4, 5])\n",
+    "\n",
+    "print(arr)\n",
+    "\n",
+    "print(type(arr))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
+       "       17, 18, 19])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.arange(0,20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  3,  6,  9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45, 48])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.arange(0,50,3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### linspace\n",
+    "Return evenly spaced numbers over a specified interval."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0., 10., 20., 30., 40., 50.])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linspace (0,50,6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.        ,  0.25641026,  0.51282051,  0.76923077,  1.02564103,\n",
+       "        1.28205128,  1.53846154,  1.79487179,  2.05128205,  2.30769231,\n",
+       "        2.56410256,  2.82051282,  3.07692308,  3.33333333,  3.58974359,\n",
+       "        3.84615385,  4.1025641 ,  4.35897436,  4.61538462,  4.87179487,\n",
+       "        5.12820513,  5.38461538,  5.64102564,  5.8974359 ,  6.15384615,\n",
+       "        6.41025641,  6.66666667,  6.92307692,  7.17948718,  7.43589744,\n",
+       "        7.69230769,  7.94871795,  8.20512821,  8.46153846,  8.71794872,\n",
+       "        8.97435897,  9.23076923,  9.48717949,  9.74358974, 10.        ])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linspace (0,10,40)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### zeros and ones\n",
+    "\n",
+    "Generate arrays of zeros or ones"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0., 0., 0., 0., 0., 0., 0., 0., 0.])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.zeros(9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.zeros((3,4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1., 1., 1., 1.])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.ones(4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1.]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.ones((3,4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## eye\n",
+    "\n",
+    "Creates an identity matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 0., 0., 0., 0.],\n",
+       "       [0., 1., 0., 0., 0.],\n",
+       "       [0., 0., 1., 0., 0.],\n",
+       "       [0., 0., 0., 1., 0.],\n",
+       "       [0., 0., 0., 0., 1.]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.eye(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 0., 0., 0., 0.],\n",
+       "       [0., 1., 0., 0., 0.],\n",
+       "       [0., 0., 1., 0., 0.],\n",
+       "       [0., 0., 0., 1., 0.]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.eye(4,5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Random\n",
+    "### What is a Random Number?\n",
+    "Random number does NOT mean a different number every time. Random means something that can not be predicted logically."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### rand\n",
+    "Create an array of the given shape and populate it with\n",
+    "random samples from a uniform distribution\n",
+    "over ``[0, 1)``."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.7489597 , 0.78917258, 0.70866872])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.random.rand(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.54892249, 0.34257905, 0.0378045 , 0.94850873],\n",
+       "       [0.64952372, 0.60916155, 0.02955791, 0.22780147],\n",
+       "       [0.23216998, 0.41829629, 0.53139821, 0.33340072],\n",
+       "       [0.51921418, 0.72563794, 0.38172859, 0.32186366]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.random.rand(4,4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### randn\n",
+    "\n",
+    "Return a sample (or samples) from the \"standard normal\" distribution. Unlike rand which is uniform:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.38705145, -0.00165486])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.random.randn(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.28120086,  0.68961856, -1.08746915],\n",
+       "       [-0.8188998 , -0.94987156,  0.39145483]])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.random.randn(2,3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### randint\n",
+    "Return random integers from `low` (inclusive) to `high` (exclusive)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.random.randint(1,7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([53,  1,  5, 95, 28, 22, 76, 12, 45, 61])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.random.randint(1,100,10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.random.randint (1,111)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Some of the Array Attributes and methods\n",
+    "\n",
+    "Have a look some of the main attributes and methods or an array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arr = np.arange(50)\n",
+    "ranarr=np.random.randint (0,100,10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
+       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
+       "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([60, 44, 98, 38, 27, 97, 75, 53, 10, 40])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ranarr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reshape \n",
+    "Covert existing array into new shape with same data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],\n",
+       "       [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
+       "       [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],\n",
+       "       [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],\n",
+       "       [40, 41, 42, 43, 44, 45, 46, 47, 48, 49]])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr.reshape(5,10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15,\n",
+       "        16, 17, 18, 19, 20, 21, 22, 23, 24],\n",
+       "       [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,\n",
+       "        41, 42, 43, 44, 45, 46, 47, 48, 49]])"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr.reshape (2,25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arr1= np.arange(24)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
+       "       17, 18, 19, 20, 21, 22, 23])"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[ 0,  1,  2,  3],\n",
+       "        [ 4,  5,  6,  7]],\n",
+       "\n",
+       "       [[ 8,  9, 10, 11],\n",
+       "        [12, 13, 14, 15]],\n",
+       "\n",
+       "       [[16, 17, 18, 19],\n",
+       "        [20, 21, 22, 23]]])"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr1.reshape(3,2,4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### max,min,argmax,argmin\n",
+    "\n",
+    "These are useful methods for finding max or min values. Or to find their index locations using argmin or argmax"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
+       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
+       "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "arr.min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "49"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([60, 44, 98, 38, 27, 97, 75, 53, 10, 40])"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ranarr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "98"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ranarr.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ranarr.argmax()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ranarr.argmin()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Shape\n",
+    "The shape property is usually used to get the current shape of an array, but may also be used to reshape the array in-place by assigning a tuple of array dimensions to it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(50,)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Shape is an attribute that array have \n",
+    "# Vector\n",
+    "arr.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3,  4],\n",
+       "       [ 5,  6,  7,  8,  9],\n",
+       "       [10, 11, 12, 13, 14],\n",
+       "       [15, 16, 17, 18, 19],\n",
+       "       [20, 21, 22, 23, 24],\n",
+       "       [25, 26, 27, 28, 29],\n",
+       "       [30, 31, 32, 33, 34],\n",
+       "       [35, 36, 37, 38, 39],\n",
+       "       [40, 41, 42, 43, 44],\n",
+       "       [45, 46, 47, 48, 49]])"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Notice the the two set of array\n",
+    "arr.reshape(10,5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15,\n",
+       "        16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,\n",
+       "        32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,\n",
+       "        48, 49]])"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr.reshape (1,50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(50, 1)"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr.reshape(50,1).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('int32')"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# You Can find the datatype of object in the array \n",
+    "arr.dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A data type object (an instance of numpy.dtype class) describes how the bytes in the fixed-size block of memory corresponding to an array item should be interpreted. It describes the following aspects of the data:\n",
+    "\n",
+    "Type of the data (integer, float, Python object, etc.)\n",
+    "Size of the data (how many bytes is in e.g. the integer)\n",
+    "Byte order of the data (little-endian or big-endian)\n",
+    "If the data type is structured, an aggregate of other data types, (e.g., describing an array item consisting of an integer and a float),\n",
+    "what are the names of the “fields” of the structure, by which they can be accessed,\n",
+    "what is the data-type of each field, and\n",
+    "which part of the memory block each field takes.\n",
+    "If the data type is a sub-array, what is its shape and data type.\n",
+    "\n",
+    "https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.dtypes.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Numpy Indexing and Selection\n",
+    "We will explore how we are going to grab a prticular elements of an array "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
+       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
+       "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bracket Indexing and Selection\n",
+    "The simplest way to pick one or some elements of an array looks very similar to python lists:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "24"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the value at the index 24\n",
+    "arr[24]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]\n",
+      "\n",
+      "\n",
+      "[0 1 2 3 4 5 6 7 8 9]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get the value in the range \n",
+    "print (arr[4:24])\n",
+    "print ('\\n')\n",
+    "print (arr[0:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Broadcasting\n",
+    "\n",
+    "Here Numpy is different from normal List in python since it is having ability to broadcast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 10, 11, 12, 13, 14, 15, 16,\n",
+       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
+       "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Setting the value with index range (Broadcasting)\n",
+    "arr[0:10]=88\n",
+    "# Show \n",
+    "arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
+       "       17, 18, 19, 20])"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr=np.arange (0,21)\n",
+    "arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15])"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Slicing is Very important \n",
+    "slice_of_arr=arr[0:16]\n",
+    "#Show slice \n",
+    "slice_of_arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50])"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Change the value of Slice \n",
+    "slice_of_arr[:]=50\n",
+    "slice_of_arr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Note : Now the change is also there in original array "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 16,\n",
+       "       17, 18, 19, 20])"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Data is not copied, it's a view of the original array! This avoids memory problems!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 16,\n",
+       "       17, 18, 19, 20])"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# To get a copy , need to be explicit \n",
+    "arr_copy=arr.copy ()\n",
+    "arr_copy "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Indexing & Selection for 2D Array(Matrix)\n",
+    "Two-dimensional (2D) arrays are indexed by two subscripts, one for the row and one for the column. Each element in the 2D array must by the same type, either a primitive type or object type.\n",
+    "\n",
+    "\n",
+    "The general format is arr_2d[row][col] or arr_2d[row,col]. I recommend usually using the comma notation for clarity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3],\n",
+       "       [4, 5, 6],\n",
+       "       [7, 8, 9]])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "matrix= np.array(([1,2,3],[4,5,6],[7,8,9]))\n",
+    "# Show the value \n",
+    "matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([7, 8, 9])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Indexing Rows \n",
+    "# Grab 3nd row\n",
+    "matrix[2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2],\n",
+       "       [5],\n",
+       "       [8]])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#row[1][2]    #row[1,2]\n",
+    "# Indexing Column \n",
+    "# Grab 2nd column \n",
+    "matrix[0:,1:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Format is arr_2d[row][col] or arr_2d[row,col]\n",
+    "\n",
+    "# Getting individual element value\n",
+    "matrix[1][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "matrix[1][2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Getting individual element value\n",
+    "matrix[1,0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3],\n",
+       "       [4, 5, 6],\n",
+       "       [7, 8, 9]])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2],\n",
+       "       [4, 5]])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Matrix slicing \n",
+    "# Shape (2,2) from top left corner \n",
+    "matrix[0:2,0:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2, 3],\n",
+       "       [5, 6]])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Matrix slicing \n",
+    "# Shape (2,2) from top right corner \n",
+    "matrix[0:2,1:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[5, 6],\n",
+       "       [8, 9]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Matrix slicing \n",
+    "# Shape (2,2) from bottom right corner \n",
+    "matrix[1:,1:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arr=np.arange(25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b=arr.reshape(5,5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3,  4],\n",
+       "       [ 5,  6,  7,  8,  9],\n",
+       "       [10, 11, 12, 13, 14],\n",
+       "       [15, 16, 17, 18, 19],\n",
+       "       [20, 21, 22, 23, 24]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[18, 19],\n",
+       "       [23, 24]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b[3:,3:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 3],\n",
+       "       [ 8],\n",
+       "       [13],\n",
+       "       [18],\n",
+       "       [23]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Grab all the elements in 4th column\n",
+    "b[0:,3:4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fancing Indexing \n",
+    "Fancy indexing is conceptually simple: it means passing an array of indices to access multiple array elements at once.\n",
+    "\n",
+    "lets see an example "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up matrix \n",
+    "matrix1 = np.zeros ((11,11))\n",
+    "# Length of matrix\n",
+    "matrix1_length = matrix1.shape[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
+       "       [ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.],\n",
+       "       [ 4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.],\n",
+       "       [ 6.,  6.,  6.,  6.,  6.,  6.,  6.,  6.,  6.,  6.,  6.],\n",
+       "       [ 8.,  8.,  8.,  8.,  8.,  8.,  8.,  8.,  8.,  8.,  8.],\n",
+       "       [10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10.],\n",
+       "       [12., 12., 12., 12., 12., 12., 12., 12., 12., 12., 12.],\n",
+       "       [14., 14., 14., 14., 14., 14., 14., 14., 14., 14., 14.],\n",
+       "       [16., 16., 16., 16., 16., 16., 16., 16., 16., 16., 16.],\n",
+       "       [18., 18., 18., 18., 18., 18., 18., 18., 18., 18., 18.],\n",
+       "       [20., 20., 20., 20., 20., 20., 20., 20., 20., 20., 20.]])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for i in range (matrix1_length):\n",
+    "    matrix1[i]+=i\n",
+    "matrix1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.],\n",
+       "       [ 6.,  6.,  6.,  6.,  6.,  6.,  6.,  6.,  6.,  6.,  6.],\n",
+       "       [ 8.,  8.,  8.,  8.,  8.,  8.,  8.,  8.,  8.,  8.,  8.],\n",
+       "       [10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10.]])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fancy Indexing allows The following \n",
+    "matrix1[[2,3,4,5]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[10., 10., 10., 10., 10., 10., 10., 10., 10., 10., 10.],\n",
+       "       [12., 12., 12., 12., 12., 12., 12., 12., 12., 12., 12.],\n",
+       "       [14., 14., 14., 14., 14., 14., 14., 14., 14., 14., 14.],\n",
+       "       [16., 16., 16., 16., 16., 16., 16., 16., 16., 16., 16.]])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "matrix1[[5,6,7,8]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## More Indexing Help\n",
+    "Indexing a 2d matrix can be a bit confusing at first, especially when you start to add in step size. Try google image searching NumPy indexing to fins useful images, like this one:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "## Selection\n",
+    "\n",
+    "Let's briefly go over how to use brackets for selection based off of comparison operators."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr = np.arange(1,22)\n",
+    "arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False, False, False,  True,  True,  True,  True,  True,\n",
+       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "        True,  True,  True])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr>4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bool_arr= arr>5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False, False, False, False,  True,  True,  True,  True,\n",
+       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "        True,  True,  True])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool_arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr[bool_arr]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,\n",
+       "       21])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr[arr>3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x=6\n",
+    "arr[arr>x]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## NumPy Operations\n",
+    "\n",
+    "### Arithmetic \n",
+    "Arithmetic Operations. Input arrays for performing arithmetic operations such as add(), subtract(), multiply(), and divide() must be either of the same shape or should conform to array broadcasting rules.\n",
+    "\n",
+    "You Can easily perform array with array arithematic , or scalar with array arithematic \n",
+    "\n",
+    "lets see some examples \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arr= np.arange (0,25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
+       "       17, 18, 19, 20, 21, 22, 23, 24])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32,\n",
+       "       34, 36, 38, 40, 42, 44, 46, 48])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr+arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  0,   1,   4,   9,  16,  25,  36,  49,  64,  81, 100, 121, 144,\n",
+       "       169, 196, 225, 256, 289, 324, 361, 400, 441, 484, 529, 576])"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr*arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0])"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr-arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\pskj0\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([nan,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
+       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.])"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Warning on division by zero, but not an error!\n",
+    "# Just replaced with nan\n",
+    "arr/arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\pskj0\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: RuntimeWarning: divide by zero encountered in true_divide\n",
+      "  \n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([       inf, 1.        , 0.5       , 0.33333333, 0.25      ,\n",
+       "       0.2       , 0.16666667, 0.14285714, 0.125     , 0.11111111,\n",
+       "       0.1       , 0.09090909, 0.08333333, 0.07692308, 0.07142857,\n",
+       "       0.06666667, 0.0625    , 0.05882353, 0.05555556, 0.05263158,\n",
+       "       0.05      , 0.04761905, 0.04545455, 0.04347826, 0.04166667])"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Also warning, but not an error instead infinity\n",
+    "1/arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([    0,     1,     8,    27,    64,   125,   216,   343,   512,\n",
+       "         729,  1000,  1331,  1728,  2197,  2744,  3375,  4096,  4913,\n",
+       "        5832,  6859,  8000,  9261, 10648, 12167, 13824], dtype=int32)"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr**3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Universal Array Functions\n",
+    "\n",
+    "Numpy comes with many [universal array functions](http://docs.scipy.org/doc/numpy/reference/ufuncs.html), which are essentially just mathematical operations you can use to perform the operation across the array. Let's show some common ones:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.        , 1.        , 1.41421356, 1.73205081, 2.        ,\n",
+       "       2.23606798, 2.44948974, 2.64575131, 2.82842712, 3.        ,\n",
+       "       3.16227766, 3.31662479, 3.46410162, 3.60555128, 3.74165739,\n",
+       "       3.87298335, 4.        , 4.12310563, 4.24264069, 4.35889894,\n",
+       "       4.47213595, 4.58257569, 4.69041576, 4.79583152, 4.89897949])"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Taking square Roots \n",
+    "np.sqrt (arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "24"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.max(arr)   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1.00000000e+00, 2.71828183e+00, 7.38905610e+00, 2.00855369e+01,\n",
+       "       5.45981500e+01, 1.48413159e+02, 4.03428793e+02, 1.09663316e+03,\n",
+       "       2.98095799e+03, 8.10308393e+03, 2.20264658e+04, 5.98741417e+04,\n",
+       "       1.62754791e+05, 4.42413392e+05, 1.20260428e+06, 3.26901737e+06,\n",
+       "       8.88611052e+06, 2.41549528e+07, 6.56599691e+07, 1.78482301e+08,\n",
+       "       4.85165195e+08, 1.31881573e+09, 3.58491285e+09, 9.74480345e+09,\n",
+       "       2.64891221e+10])"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calculating exponential (e^)\n",
+    "np.exp(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "24"
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.max(arr) #same as arr.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.        ,  0.84147098,  0.90929743,  0.14112001, -0.7568025 ,\n",
+       "       -0.95892427, -0.2794155 ,  0.6569866 ,  0.98935825,  0.41211849,\n",
+       "       -0.54402111, -0.99999021, -0.53657292,  0.42016704,  0.99060736,\n",
+       "        0.65028784, -0.28790332, -0.96139749, -0.75098725,  0.14987721,\n",
+       "        0.91294525,  0.83665564, -0.00885131, -0.8462204 , -0.90557836])"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sin(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\pskj0\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning: divide by zero encountered in log\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([      -inf, 0.        , 0.69314718, 1.09861229, 1.38629436,\n",
+       "       1.60943791, 1.79175947, 1.94591015, 2.07944154, 2.19722458,\n",
+       "       2.30258509, 2.39789527, 2.48490665, 2.56494936, 2.63905733,\n",
+       "       2.7080502 , 2.77258872, 2.83321334, 2.89037176, 2.94443898,\n",
+       "       2.99573227, 3.04452244, 3.09104245, 3.13549422, 3.17805383])"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.log(arr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Some Valueble insight of numpy lot more to explore  \n",
+    "https://numpy.org/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pandas/03.14 Pandas For Data Analysis.ipynb b/pandas/03.14 Pandas For Data Analysis.ipynb
new file mode 100644
index 00000000..ca8dca15
--- /dev/null
+++ b/pandas/03.14 Pandas For Data Analysis.ipynb	
@@ -0,0 +1,6589 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pandas Introduction \n",
+    "\n",
+    "Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.\n",
+    "\n",
+    "You can say pandas is extremely powerful version of Excel\n",
+    "\n",
+    "In this section we are going to talk about \n",
+    "\n",
+    "* Introduction To pandas \n",
+    "* Seies \n",
+    "* DataFrames \n",
+    "* Missing Data\n",
+    "* Merging , Joining , And Concatenating \n",
+    "* Operations \n",
+    "* Data Input and Output "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Series "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fristly we are going to talk about Series DataType .\n",
+    "\n",
+    "A Series is very similar to numpy array , it is built on top of NumPy Array..\n",
+    "But Series can have axis labels , meaning it can be indexed by labels instead of just number location \n",
+    "\n",
+    "Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). The axis labels are collectively called index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Lets import numpy and pandas \n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We can convert a list , numpy array , or dict to Series\n",
+    "\n",
+    "labels = ['Shivendra','Ragavendra','Narendra']\n",
+    "my_list= [21,25,30]\n",
+    "arr=np.array([10,20,30])\n",
+    "d={'Shivendra':21,'Raghavendra':25,'Narendra':30}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    21\n",
+       "1    25\n",
+       "2    30\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Using List \n",
+    "pd.Series(data=my_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Shivendra     21\n",
+       "Ragavendra    25\n",
+       "Narendra      30\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series (data=my_list,index=labels )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Shivendra     21\n",
+       "Ragavendra    25\n",
+       "Narendra      30\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(my_list,labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    10\n",
+       "1    20\n",
+       "2    30\n",
+       "dtype: int32"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# NumPy Array\n",
+    "pd.Series(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Shivendra     10\n",
+       "Ragavendra    20\n",
+       "Narendra      30\n",
+       "dtype: int32"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series (data=arr,index=labels )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Shivendra      21\n",
+       "Raghavendra    25\n",
+       "Narendra       30\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Dictonary\n",
+    "pd.Series (d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data In A Series \n",
+    "\n",
+    "A Pandas Series can hold a variety of Objects "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     Shivendra\n",
+       "1    Ragavendra\n",
+       "2      Narendra\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series (data=labels )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using an Index\n",
+    "\n",
+    "The key to using a Series is understanding its index. Pandas makes use of these index names or numbers by allowing for fast look ups of information (works like a hash table or dictionary).\n",
+    "\n",
+    "Let's see some examples of how to grab information from a Series. Let us create two sereis, ser1 and ser2:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ser1= pd.Series ([1,2,3,4], index =['Chennai','Bihar','West Bengal','Rajasthan'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Chennai        1\n",
+       "Bihar          2\n",
+       "West Bengal    3\n",
+       "Rajasthan      4\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ser1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ser2=pd.Series ([1,2,5,4],index=['Chennai','Bihar','Assam','Rajasthan'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Chennai      1\n",
+       "Bihar        2\n",
+       "Assam        5\n",
+       "Rajasthan    4\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ser2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ser1['Chennai']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Assam          NaN\n",
+       "Bihar          4.0\n",
+       "Chennai        2.0\n",
+       "Rajasthan      8.0\n",
+       "West Bengal    NaN\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Operations are then also done based off of index:\n",
+    "ser1+ser2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## DataFrames\n",
+    "Pandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. Pandas DataFrame consists of three principal components, the data, rows, and columns.\n",
+    "\n",
+    "DataFrames are the workhorse of pandas and are directly inspired by the R programming language. We can think of a DataFrame as a bunch of Series objects put together to share the same index. Let's use pandas to explore this topic!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from numpy.random import randn\n",
+    "np.random.seed(101)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=pd.DataFrame (randn(5,5),index='Chennai Bihar UtterPredesh Delhi Mumbai'.split(),columns ='SRM NIT_PATNA BHU IIT_DELHI IIT_Bombay'.split())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "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>SRM</th>\n",
+       "      <th>NIT_PATNA</th>\n",
+       "      <th>BHU</th>\n",
+       "      <th>IIT_DELHI</th>\n",
+       "      <th>IIT_Bombay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Chennai</th>\n",
+       "      <td>2.706850</td>\n",
+       "      <td>0.628133</td>\n",
+       "      <td>0.907969</td>\n",
+       "      <td>0.503826</td>\n",
+       "      <td>0.651118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bihar</th>\n",
+       "      <td>-0.319318</td>\n",
+       "      <td>-0.848077</td>\n",
+       "      <td>0.605965</td>\n",
+       "      <td>-2.018168</td>\n",
+       "      <td>0.740122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UtterPredesh</th>\n",
+       "      <td>0.528813</td>\n",
+       "      <td>-0.589001</td>\n",
+       "      <td>0.188695</td>\n",
+       "      <td>-0.758872</td>\n",
+       "      <td>-0.933237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Delhi</th>\n",
+       "      <td>0.955057</td>\n",
+       "      <td>0.190794</td>\n",
+       "      <td>1.978757</td>\n",
+       "      <td>2.605967</td>\n",
+       "      <td>0.683509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mumbai</th>\n",
+       "      <td>0.302665</td>\n",
+       "      <td>1.693723</td>\n",
+       "      <td>-1.706086</td>\n",
+       "      <td>-1.159119</td>\n",
+       "      <td>-0.134841</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay\n",
+       "Chennai       2.706850   0.628133  0.907969   0.503826    0.651118\n",
+       "Bihar        -0.319318  -0.848077  0.605965  -2.018168    0.740122\n",
+       "UtterPredesh  0.528813  -0.589001  0.188695  -0.758872   -0.933237\n",
+       "Delhi         0.955057   0.190794  1.978757   2.605967    0.683509\n",
+       "Mumbai        0.302665   1.693723 -1.706086  -1.159119   -0.134841"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selection and Indexing\n",
+    "\n",
+    "Let's learn the various methods to grab data from a DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Chennai         2.706850\n",
+       "Bihar          -0.319318\n",
+       "UtterPredesh    0.528813\n",
+       "Delhi           0.955057\n",
+       "Mumbai          0.302665\n",
+       "Name: SRM, dtype: float64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['SRM']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "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>SRM</th>\n",
+       "      <th>BHU</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Chennai</th>\n",
+       "      <td>2.706850</td>\n",
+       "      <td>0.907969</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bihar</th>\n",
+       "      <td>-0.319318</td>\n",
+       "      <td>0.605965</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UtterPredesh</th>\n",
+       "      <td>0.528813</td>\n",
+       "      <td>0.188695</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Delhi</th>\n",
+       "      <td>0.955057</td>\n",
+       "      <td>1.978757</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mumbai</th>\n",
+       "      <td>0.302665</td>\n",
+       "      <td>-1.706086</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   SRM       BHU\n",
+       "Chennai       2.706850  0.907969\n",
+       "Bihar        -0.319318  0.605965\n",
+       "UtterPredesh  0.528813  0.188695\n",
+       "Delhi         0.955057  1.978757\n",
+       "Mumbai        0.302665 -1.706086"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We can pass a list of columns names \n",
+    "df[['SRM' , 'BHU']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Chennai         2.706850\n",
+       "Bihar          -0.319318\n",
+       "UtterPredesh    0.528813\n",
+       "Delhi           0.955057\n",
+       "Mumbai          0.302665\n",
+       "Name: SRM, dtype: float64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.SRM # SQL syntax"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dataframe  Columns are just Series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.series.Series"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(df['SRM'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Creating a new columns \n",
+    "df['UPES']=df['SRM']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Harshita']=df['SRM'] + df['BHU']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "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>SRM</th>\n",
+       "      <th>NIT_PATNA</th>\n",
+       "      <th>BHU</th>\n",
+       "      <th>IIT_DELHI</th>\n",
+       "      <th>IIT_Bombay</th>\n",
+       "      <th>UPES</th>\n",
+       "      <th>Harshita</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Chennai</th>\n",
+       "      <td>2.706850</td>\n",
+       "      <td>0.628133</td>\n",
+       "      <td>0.907969</td>\n",
+       "      <td>0.503826</td>\n",
+       "      <td>0.651118</td>\n",
+       "      <td>2.706850</td>\n",
+       "      <td>3.614819</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bihar</th>\n",
+       "      <td>-0.319318</td>\n",
+       "      <td>-0.848077</td>\n",
+       "      <td>0.605965</td>\n",
+       "      <td>-2.018168</td>\n",
+       "      <td>0.740122</td>\n",
+       "      <td>-0.319318</td>\n",
+       "      <td>0.286647</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UtterPredesh</th>\n",
+       "      <td>0.528813</td>\n",
+       "      <td>-0.589001</td>\n",
+       "      <td>0.188695</td>\n",
+       "      <td>-0.758872</td>\n",
+       "      <td>-0.933237</td>\n",
+       "      <td>0.528813</td>\n",
+       "      <td>0.717509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Delhi</th>\n",
+       "      <td>0.955057</td>\n",
+       "      <td>0.190794</td>\n",
+       "      <td>1.978757</td>\n",
+       "      <td>2.605967</td>\n",
+       "      <td>0.683509</td>\n",
+       "      <td>0.955057</td>\n",
+       "      <td>2.933814</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mumbai</th>\n",
+       "      <td>0.302665</td>\n",
+       "      <td>1.693723</td>\n",
+       "      <td>-1.706086</td>\n",
+       "      <td>-1.159119</td>\n",
+       "      <td>-0.134841</td>\n",
+       "      <td>0.302665</td>\n",
+       "      <td>-1.403420</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay      UPES  \\\n",
+       "Chennai       2.706850   0.628133  0.907969   0.503826    0.651118  2.706850   \n",
+       "Bihar        -0.319318  -0.848077  0.605965  -2.018168    0.740122 -0.319318   \n",
+       "UtterPredesh  0.528813  -0.589001  0.188695  -0.758872   -0.933237  0.528813   \n",
+       "Delhi         0.955057   0.190794  1.978757   2.605967    0.683509  0.955057   \n",
+       "Mumbai        0.302665   1.693723 -1.706086  -1.159119   -0.134841  0.302665   \n",
+       "\n",
+       "              Harshita  \n",
+       "Chennai       3.614819  \n",
+       "Bihar         0.286647  \n",
+       "UtterPredesh  0.717509  \n",
+       "Delhi         2.933814  \n",
+       "Mumbai       -1.403420  "
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "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>SRM</th>\n",
+       "      <th>NIT_PATNA</th>\n",
+       "      <th>BHU</th>\n",
+       "      <th>IIT_DELHI</th>\n",
+       "      <th>IIT_Bombay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Chennai</th>\n",
+       "      <td>2.706850</td>\n",
+       "      <td>0.628133</td>\n",
+       "      <td>0.907969</td>\n",
+       "      <td>0.503826</td>\n",
+       "      <td>0.651118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bihar</th>\n",
+       "      <td>-0.319318</td>\n",
+       "      <td>-0.848077</td>\n",
+       "      <td>0.605965</td>\n",
+       "      <td>-2.018168</td>\n",
+       "      <td>0.740122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UtterPredesh</th>\n",
+       "      <td>0.528813</td>\n",
+       "      <td>-0.589001</td>\n",
+       "      <td>0.188695</td>\n",
+       "      <td>-0.758872</td>\n",
+       "      <td>-0.933237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Delhi</th>\n",
+       "      <td>0.955057</td>\n",
+       "      <td>0.190794</td>\n",
+       "      <td>1.978757</td>\n",
+       "      <td>2.605967</td>\n",
+       "      <td>0.683509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mumbai</th>\n",
+       "      <td>0.302665</td>\n",
+       "      <td>1.693723</td>\n",
+       "      <td>-1.706086</td>\n",
+       "      <td>-1.159119</td>\n",
+       "      <td>-0.134841</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay\n",
+       "Chennai       2.706850   0.628133  0.907969   0.503826    0.651118\n",
+       "Bihar        -0.319318  -0.848077  0.605965  -2.018168    0.740122\n",
+       "UtterPredesh  0.528813  -0.589001  0.188695  -0.758872   -0.933237\n",
+       "Delhi         0.955057   0.190794  1.978757   2.605967    0.683509\n",
+       "Mumbai        0.302665   1.693723 -1.706086  -1.159119   -0.134841"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.drop('UPES',axis=1) # Axis = 1 for column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SRM</th>\n",
+       "      <th>NIT_PATNA</th>\n",
+       "      <th>BHU</th>\n",
+       "      <th>IIT_DELHI</th>\n",
+       "      <th>IIT_Bombay</th>\n",
+       "      <th>UPES</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Chennai</th>\n",
+       "      <td>2.706850</td>\n",
+       "      <td>0.628133</td>\n",
+       "      <td>0.907969</td>\n",
+       "      <td>0.503826</td>\n",
+       "      <td>0.651118</td>\n",
+       "      <td>2.706850</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bihar</th>\n",
+       "      <td>-0.319318</td>\n",
+       "      <td>-0.848077</td>\n",
+       "      <td>0.605965</td>\n",
+       "      <td>-2.018168</td>\n",
+       "      <td>0.740122</td>\n",
+       "      <td>-0.319318</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UtterPredesh</th>\n",
+       "      <td>0.528813</td>\n",
+       "      <td>-0.589001</td>\n",
+       "      <td>0.188695</td>\n",
+       "      <td>-0.758872</td>\n",
+       "      <td>-0.933237</td>\n",
+       "      <td>0.528813</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Delhi</th>\n",
+       "      <td>0.955057</td>\n",
+       "      <td>0.190794</td>\n",
+       "      <td>1.978757</td>\n",
+       "      <td>2.605967</td>\n",
+       "      <td>0.683509</td>\n",
+       "      <td>0.955057</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mumbai</th>\n",
+       "      <td>0.302665</td>\n",
+       "      <td>1.693723</td>\n",
+       "      <td>-1.706086</td>\n",
+       "      <td>-1.159119</td>\n",
+       "      <td>-0.134841</td>\n",
+       "      <td>0.302665</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay      UPES\n",
+       "Chennai       2.706850   0.628133  0.907969   0.503826    0.651118  2.706850\n",
+       "Bihar        -0.319318  -0.848077  0.605965  -2.018168    0.740122 -0.319318\n",
+       "UtterPredesh  0.528813  -0.589001  0.188695  -0.758872   -0.933237  0.528813\n",
+       "Delhi         0.955057   0.190794  1.978757   2.605967    0.683509  0.955057\n",
+       "Mumbai        0.302665   1.693723 -1.706086  -1.159119   -0.134841  0.302665"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df # But again it will be appeared we need to use inplace to remove it parmanently"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop('UPES',axis=1,inplace =True )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop('Harshita',axis=1,inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "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>SRM</th>\n",
+       "      <th>NIT_PATNA</th>\n",
+       "      <th>BHU</th>\n",
+       "      <th>IIT_DELHI</th>\n",
+       "      <th>IIT_Bombay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Chennai</th>\n",
+       "      <td>2.706850</td>\n",
+       "      <td>0.628133</td>\n",
+       "      <td>0.907969</td>\n",
+       "      <td>0.503826</td>\n",
+       "      <td>0.651118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bihar</th>\n",
+       "      <td>-0.319318</td>\n",
+       "      <td>-0.848077</td>\n",
+       "      <td>0.605965</td>\n",
+       "      <td>-2.018168</td>\n",
+       "      <td>0.740122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UtterPredesh</th>\n",
+       "      <td>0.528813</td>\n",
+       "      <td>-0.589001</td>\n",
+       "      <td>0.188695</td>\n",
+       "      <td>-0.758872</td>\n",
+       "      <td>-0.933237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Delhi</th>\n",
+       "      <td>0.955057</td>\n",
+       "      <td>0.190794</td>\n",
+       "      <td>1.978757</td>\n",
+       "      <td>2.605967</td>\n",
+       "      <td>0.683509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mumbai</th>\n",
+       "      <td>0.302665</td>\n",
+       "      <td>1.693723</td>\n",
+       "      <td>-1.706086</td>\n",
+       "      <td>-1.159119</td>\n",
+       "      <td>-0.134841</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay\n",
+       "Chennai       2.706850   0.628133  0.907969   0.503826    0.651118\n",
+       "Bihar        -0.319318  -0.848077  0.605965  -2.018168    0.740122\n",
+       "UtterPredesh  0.528813  -0.589001  0.188695  -0.758872   -0.933237\n",
+       "Delhi         0.955057   0.190794  1.978757   2.605967    0.683509\n",
+       "Mumbai        0.302665   1.693723 -1.706086  -1.159119   -0.134841"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "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>SRM</th>\n",
+       "      <th>NIT_PATNA</th>\n",
+       "      <th>BHU</th>\n",
+       "      <th>IIT_DELHI</th>\n",
+       "      <th>IIT_Bombay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Chennai</th>\n",
+       "      <td>2.706850</td>\n",
+       "      <td>0.628133</td>\n",
+       "      <td>0.907969</td>\n",
+       "      <td>0.503826</td>\n",
+       "      <td>0.651118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bihar</th>\n",
+       "      <td>-0.319318</td>\n",
+       "      <td>-0.848077</td>\n",
+       "      <td>0.605965</td>\n",
+       "      <td>-2.018168</td>\n",
+       "      <td>0.740122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UtterPredesh</th>\n",
+       "      <td>0.528813</td>\n",
+       "      <td>-0.589001</td>\n",
+       "      <td>0.188695</td>\n",
+       "      <td>-0.758872</td>\n",
+       "      <td>-0.933237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mumbai</th>\n",
+       "      <td>0.302665</td>\n",
+       "      <td>1.693723</td>\n",
+       "      <td>-1.706086</td>\n",
+       "      <td>-1.159119</td>\n",
+       "      <td>-0.134841</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay\n",
+       "Chennai       2.706850   0.628133  0.907969   0.503826    0.651118\n",
+       "Bihar        -0.319318  -0.848077  0.605965  -2.018168    0.740122\n",
+       "UtterPredesh  0.528813  -0.589001  0.188695  -0.758872   -0.933237\n",
+       "Mumbai        0.302665   1.693723 -1.706086  -1.159119   -0.134841"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.drop('Delhi',axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SRM           2.706850\n",
+       "NIT_PATNA     0.628133\n",
+       "BHU           0.907969\n",
+       "IIT_DELHI     0.503826\n",
+       "IIT_Bombay    0.651118\n",
+       "Name: Chennai, dtype: float64"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc['Chennai']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SRM          -0.319318\n",
+       "NIT_PATNA    -0.848077\n",
+       "BHU           0.605965\n",
+       "IIT_DELHI    -2.018168\n",
+       "IIT_Bombay    0.740122\n",
+       "Name: Bihar, dtype: float64"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We can select based on indexing \n",
+    "df.iloc[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.8480769834036315"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc['Bihar','NIT_PATNA']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "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>NIT_PATNA</th>\n",
+       "      <th>IIT_DELHI</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Bihar</th>\n",
+       "      <td>-0.848077</td>\n",
+       "      <td>-2.018168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mumbai</th>\n",
+       "      <td>1.693723</td>\n",
+       "      <td>-1.159119</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        NIT_PATNA  IIT_DELHI\n",
+       "Bihar   -0.848077  -2.018168\n",
+       "Mumbai   1.693723  -1.159119"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[['Bihar','Mumbai'],['NIT_PATNA','IIT_DELHI']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "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>SRM</th>\n",
+       "      <th>NIT_PATNA</th>\n",
+       "      <th>BHU</th>\n",
+       "      <th>IIT_DELHI</th>\n",
+       "      <th>IIT_Bombay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Chennai</th>\n",
+       "      <td>2.706850</td>\n",
+       "      <td>0.628133</td>\n",
+       "      <td>0.907969</td>\n",
+       "      <td>0.503826</td>\n",
+       "      <td>0.651118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bihar</th>\n",
+       "      <td>-0.319318</td>\n",
+       "      <td>-0.848077</td>\n",
+       "      <td>0.605965</td>\n",
+       "      <td>-2.018168</td>\n",
+       "      <td>0.740122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UtterPredesh</th>\n",
+       "      <td>0.528813</td>\n",
+       "      <td>-0.589001</td>\n",
+       "      <td>0.188695</td>\n",
+       "      <td>-0.758872</td>\n",
+       "      <td>-0.933237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Delhi</th>\n",
+       "      <td>0.955057</td>\n",
+       "      <td>0.190794</td>\n",
+       "      <td>1.978757</td>\n",
+       "      <td>2.605967</td>\n",
+       "      <td>0.683509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mumbai</th>\n",
+       "      <td>0.302665</td>\n",
+       "      <td>1.693723</td>\n",
+       "      <td>-1.706086</td>\n",
+       "      <td>-1.159119</td>\n",
+       "      <td>-0.134841</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay\n",
+       "Chennai       2.706850   0.628133  0.907969   0.503826    0.651118\n",
+       "Bihar        -0.319318  -0.848077  0.605965  -2.018168    0.740122\n",
+       "UtterPredesh  0.528813  -0.589001  0.188695  -0.758872   -0.933237\n",
+       "Delhi         0.955057   0.190794  1.978757   2.605967    0.683509\n",
+       "Mumbai        0.302665   1.693723 -1.706086  -1.159119   -0.134841"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "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>SRM</th>\n",
+       "      <th>NIT_PATNA</th>\n",
+       "      <th>BHU</th>\n",
+       "      <th>IIT_DELHI</th>\n",
+       "      <th>IIT_Bombay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Chennai</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bihar</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UtterPredesh</th>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Delhi</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mumbai</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                SRM  NIT_PATNA    BHU  IIT_DELHI  IIT_Bombay\n",
+       "Chennai        True       True   True       True        True\n",
+       "Bihar         False      False   True      False        True\n",
+       "UtterPredesh   True      False   True      False       False\n",
+       "Delhi          True       True   True       True        True\n",
+       "Mumbai         True       True  False      False       False"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df>0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "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>SRM</th>\n",
+       "      <th>NIT_PATNA</th>\n",
+       "      <th>BHU</th>\n",
+       "      <th>IIT_DELHI</th>\n",
+       "      <th>IIT_Bombay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Chennai</th>\n",
+       "      <td>2.706850</td>\n",
+       "      <td>0.628133</td>\n",
+       "      <td>0.907969</td>\n",
+       "      <td>0.503826</td>\n",
+       "      <td>0.651118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bihar</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.605965</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.740122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UtterPredesh</th>\n",
+       "      <td>0.528813</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.188695</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Delhi</th>\n",
+       "      <td>0.955057</td>\n",
+       "      <td>0.190794</td>\n",
+       "      <td>1.978757</td>\n",
+       "      <td>2.605967</td>\n",
+       "      <td>0.683509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mumbai</th>\n",
+       "      <td>0.302665</td>\n",
+       "      <td>1.693723</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay\n",
+       "Chennai       2.706850   0.628133  0.907969   0.503826    0.651118\n",
+       "Bihar              NaN        NaN  0.605965        NaN    0.740122\n",
+       "UtterPredesh  0.528813        NaN  0.188695        NaN         NaN\n",
+       "Delhi         0.955057   0.190794  1.978757   2.605967    0.683509\n",
+       "Mumbai        0.302665   1.693723       NaN        NaN         NaN"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df>0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "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>SRM</th>\n",
+       "      <th>NIT_PATNA</th>\n",
+       "      <th>BHU</th>\n",
+       "      <th>IIT_DELHI</th>\n",
+       "      <th>IIT_Bombay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Chennai</th>\n",
+       "      <td>2.706850</td>\n",
+       "      <td>0.628133</td>\n",
+       "      <td>0.907969</td>\n",
+       "      <td>0.503826</td>\n",
+       "      <td>0.651118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UtterPredesh</th>\n",
+       "      <td>0.528813</td>\n",
+       "      <td>-0.589001</td>\n",
+       "      <td>0.188695</td>\n",
+       "      <td>-0.758872</td>\n",
+       "      <td>-0.933237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Delhi</th>\n",
+       "      <td>0.955057</td>\n",
+       "      <td>0.190794</td>\n",
+       "      <td>1.978757</td>\n",
+       "      <td>2.605967</td>\n",
+       "      <td>0.683509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mumbai</th>\n",
+       "      <td>0.302665</td>\n",
+       "      <td>1.693723</td>\n",
+       "      <td>-1.706086</td>\n",
+       "      <td>-1.159119</td>\n",
+       "      <td>-0.134841</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay\n",
+       "Chennai       2.706850   0.628133  0.907969   0.503826    0.651118\n",
+       "UtterPredesh  0.528813  -0.589001  0.188695  -0.758872   -0.933237\n",
+       "Delhi         0.955057   0.190794  1.978757   2.605967    0.683509\n",
+       "Mumbai        0.302665   1.693723 -1.706086  -1.159119   -0.134841"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df [df['SRM']>0] # It will not print Bihar Cz Bihar is having negetive number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Chennai         0.907969\n",
+       "UtterPredesh    0.188695\n",
+       "Delhi           1.978757\n",
+       "Mumbai         -1.706086\n",
+       "Name: BHU, dtype: float64"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df['SRM']>0]['BHU'] # It will not print Bihar data since it is having negetive number "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Chennai         2.706850\n",
+       "Bihar          -0.319318\n",
+       "UtterPredesh    0.528813\n",
+       "Delhi           0.955057\n",
+       "Name: SRM, dtype: float64"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df['BHU']>0]['SRM'] # It will not print mumbai's data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "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>BHU</th>\n",
+       "      <th>IIT_DELHI</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Chennai</th>\n",
+       "      <td>0.907969</td>\n",
+       "      <td>0.503826</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UtterPredesh</th>\n",
+       "      <td>0.188695</td>\n",
+       "      <td>-0.758872</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Delhi</th>\n",
+       "      <td>1.978757</td>\n",
+       "      <td>2.605967</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mumbai</th>\n",
+       "      <td>-1.706086</td>\n",
+       "      <td>-1.159119</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   BHU  IIT_DELHI\n",
+       "Chennai       0.907969   0.503826\n",
+       "UtterPredesh  0.188695  -0.758872\n",
+       "Delhi         1.978757   2.605967\n",
+       "Mumbai       -1.706086  -1.159119"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df['SRM']>0][['BHU','IIT_DELHI']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "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>SRM</th>\n",
+       "      <th>NIT_PATNA</th>\n",
+       "      <th>BHU</th>\n",
+       "      <th>IIT_DELHI</th>\n",
+       "      <th>IIT_Bombay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Chennai</th>\n",
+       "      <td>2.706850</td>\n",
+       "      <td>0.628133</td>\n",
+       "      <td>0.907969</td>\n",
+       "      <td>0.503826</td>\n",
+       "      <td>0.651118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Delhi</th>\n",
+       "      <td>0.955057</td>\n",
+       "      <td>0.190794</td>\n",
+       "      <td>1.978757</td>\n",
+       "      <td>2.605967</td>\n",
+       "      <td>0.683509</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay\n",
+       "Chennai  2.706850   0.628133  0.907969   0.503826    0.651118\n",
+       "Delhi    0.955057   0.190794  1.978757   2.605967    0.683509"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[(df['SRM']>0.955)& df['BHU']>0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## More Index Details\n",
+    "\n",
+    "Let's discuss some more features of indexing, including resetting the index or setting it something else. We'll also talk about index hierarchy!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "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>SRM</th>\n",
+       "      <th>NIT_PATNA</th>\n",
+       "      <th>BHU</th>\n",
+       "      <th>IIT_DELHI</th>\n",
+       "      <th>IIT_Bombay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Chennai</th>\n",
+       "      <td>2.706850</td>\n",
+       "      <td>0.628133</td>\n",
+       "      <td>0.907969</td>\n",
+       "      <td>0.503826</td>\n",
+       "      <td>0.651118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bihar</th>\n",
+       "      <td>-0.319318</td>\n",
+       "      <td>-0.848077</td>\n",
+       "      <td>0.605965</td>\n",
+       "      <td>-2.018168</td>\n",
+       "      <td>0.740122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UtterPredesh</th>\n",
+       "      <td>0.528813</td>\n",
+       "      <td>-0.589001</td>\n",
+       "      <td>0.188695</td>\n",
+       "      <td>-0.758872</td>\n",
+       "      <td>-0.933237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Delhi</th>\n",
+       "      <td>0.955057</td>\n",
+       "      <td>0.190794</td>\n",
+       "      <td>1.978757</td>\n",
+       "      <td>2.605967</td>\n",
+       "      <td>0.683509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mumbai</th>\n",
+       "      <td>0.302665</td>\n",
+       "      <td>1.693723</td>\n",
+       "      <td>-1.706086</td>\n",
+       "      <td>-1.159119</td>\n",
+       "      <td>-0.134841</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay\n",
+       "Chennai       2.706850   0.628133  0.907969   0.503826    0.651118\n",
+       "Bihar        -0.319318  -0.848077  0.605965  -2.018168    0.740122\n",
+       "UtterPredesh  0.528813  -0.589001  0.188695  -0.758872   -0.933237\n",
+       "Delhi         0.955057   0.190794  1.978757   2.605967    0.683509\n",
+       "Mumbai        0.302665   1.693723 -1.706086  -1.159119   -0.134841"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\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>index</th>\n",
+       "      <th>SRM</th>\n",
+       "      <th>NIT_PATNA</th>\n",
+       "      <th>BHU</th>\n",
+       "      <th>IIT_DELHI</th>\n",
+       "      <th>IIT_Bombay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Chennai</td>\n",
+       "      <td>2.706850</td>\n",
+       "      <td>0.628133</td>\n",
+       "      <td>0.907969</td>\n",
+       "      <td>0.503826</td>\n",
+       "      <td>0.651118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bihar</td>\n",
+       "      <td>-0.319318</td>\n",
+       "      <td>-0.848077</td>\n",
+       "      <td>0.605965</td>\n",
+       "      <td>-2.018168</td>\n",
+       "      <td>0.740122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>UtterPredesh</td>\n",
+       "      <td>0.528813</td>\n",
+       "      <td>-0.589001</td>\n",
+       "      <td>0.188695</td>\n",
+       "      <td>-0.758872</td>\n",
+       "      <td>-0.933237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Delhi</td>\n",
+       "      <td>0.955057</td>\n",
+       "      <td>0.190794</td>\n",
+       "      <td>1.978757</td>\n",
+       "      <td>2.605967</td>\n",
+       "      <td>0.683509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Mumbai</td>\n",
+       "      <td>0.302665</td>\n",
+       "      <td>1.693723</td>\n",
+       "      <td>-1.706086</td>\n",
+       "      <td>-1.159119</td>\n",
+       "      <td>-0.134841</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          index       SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay\n",
+       "0       Chennai  2.706850   0.628133  0.907969   0.503826    0.651118\n",
+       "1         Bihar -0.319318  -0.848077  0.605965  -2.018168    0.740122\n",
+       "2  UtterPredesh  0.528813  -0.589001  0.188695  -0.758872   -0.933237\n",
+       "3         Delhi  0.955057   0.190794  1.978757   2.605967    0.683509\n",
+       "4        Mumbai  0.302665   1.693723 -1.706086  -1.159119   -0.134841"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "newind ='Tamil_Nadu BIHAR UP Delhi Maharastra'.split()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['States']=newind"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
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+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SRM</th>\n",
+       "      <th>NIT_PATNA</th>\n",
+       "      <th>BHU</th>\n",
+       "      <th>IIT_DELHI</th>\n",
+       "      <th>IIT_Bombay</th>\n",
+       "      <th>States</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Chennai</th>\n",
+       "      <td>2.706850</td>\n",
+       "      <td>0.628133</td>\n",
+       "      <td>0.907969</td>\n",
+       "      <td>0.503826</td>\n",
+       "      <td>0.651118</td>\n",
+       "      <td>Tamil_Nadu</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bihar</th>\n",
+       "      <td>-0.319318</td>\n",
+       "      <td>-0.848077</td>\n",
+       "      <td>0.605965</td>\n",
+       "      <td>-2.018168</td>\n",
+       "      <td>0.740122</td>\n",
+       "      <td>BIHAR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UtterPredesh</th>\n",
+       "      <td>0.528813</td>\n",
+       "      <td>-0.589001</td>\n",
+       "      <td>0.188695</td>\n",
+       "      <td>-0.758872</td>\n",
+       "      <td>-0.933237</td>\n",
+       "      <td>UP</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Delhi</th>\n",
+       "      <td>0.955057</td>\n",
+       "      <td>0.190794</td>\n",
+       "      <td>1.978757</td>\n",
+       "      <td>2.605967</td>\n",
+       "      <td>0.683509</td>\n",
+       "      <td>Delhi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mumbai</th>\n",
+       "      <td>0.302665</td>\n",
+       "      <td>1.693723</td>\n",
+       "      <td>-1.706086</td>\n",
+       "      <td>-1.159119</td>\n",
+       "      <td>-0.134841</td>\n",
+       "      <td>Maharastra</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay      States\n",
+       "Chennai       2.706850   0.628133  0.907969   0.503826    0.651118  Tamil_Nadu\n",
+       "Bihar        -0.319318  -0.848077  0.605965  -2.018168    0.740122       BIHAR\n",
+       "UtterPredesh  0.528813  -0.589001  0.188695  -0.758872   -0.933237          UP\n",
+       "Delhi         0.955057   0.190794  1.978757   2.605967    0.683509       Delhi\n",
+       "Mumbai        0.302665   1.693723 -1.706086  -1.159119   -0.134841  Maharastra"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
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+       "        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>SRM</th>\n",
+       "      <th>NIT_PATNA</th>\n",
+       "      <th>BHU</th>\n",
+       "      <th>IIT_DELHI</th>\n",
+       "      <th>IIT_Bombay</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>States</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>Tamil_Nadu</th>\n",
+       "      <td>2.706850</td>\n",
+       "      <td>0.628133</td>\n",
+       "      <td>0.907969</td>\n",
+       "      <td>0.503826</td>\n",
+       "      <td>0.651118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BIHAR</th>\n",
+       "      <td>-0.319318</td>\n",
+       "      <td>-0.848077</td>\n",
+       "      <td>0.605965</td>\n",
+       "      <td>-2.018168</td>\n",
+       "      <td>0.740122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UP</th>\n",
+       "      <td>0.528813</td>\n",
+       "      <td>-0.589001</td>\n",
+       "      <td>0.188695</td>\n",
+       "      <td>-0.758872</td>\n",
+       "      <td>-0.933237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Delhi</th>\n",
+       "      <td>0.955057</td>\n",
+       "      <td>0.190794</td>\n",
+       "      <td>1.978757</td>\n",
+       "      <td>2.605967</td>\n",
+       "      <td>0.683509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Maharastra</th>\n",
+       "      <td>0.302665</td>\n",
+       "      <td>1.693723</td>\n",
+       "      <td>-1.706086</td>\n",
+       "      <td>-1.159119</td>\n",
+       "      <td>-0.134841</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay\n",
+       "States                                                          \n",
+       "Tamil_Nadu  2.706850   0.628133  0.907969   0.503826    0.651118\n",
+       "BIHAR      -0.319318  -0.848077  0.605965  -2.018168    0.740122\n",
+       "UP          0.528813  -0.589001  0.188695  -0.758872   -0.933237\n",
+       "Delhi       0.955057   0.190794  1.978757   2.605967    0.683509\n",
+       "Maharastra  0.302665   1.693723 -1.706086  -1.159119   -0.134841"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.set_index('States')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\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>SRM</th>\n",
+       "      <th>NIT_PATNA</th>\n",
+       "      <th>BHU</th>\n",
+       "      <th>IIT_DELHI</th>\n",
+       "      <th>IIT_Bombay</th>\n",
+       "      <th>States</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Chennai</th>\n",
+       "      <td>2.706850</td>\n",
+       "      <td>0.628133</td>\n",
+       "      <td>0.907969</td>\n",
+       "      <td>0.503826</td>\n",
+       "      <td>0.651118</td>\n",
+       "      <td>Tamil_Nadu</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bihar</th>\n",
+       "      <td>-0.319318</td>\n",
+       "      <td>-0.848077</td>\n",
+       "      <td>0.605965</td>\n",
+       "      <td>-2.018168</td>\n",
+       "      <td>0.740122</td>\n",
+       "      <td>BIHAR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UtterPredesh</th>\n",
+       "      <td>0.528813</td>\n",
+       "      <td>-0.589001</td>\n",
+       "      <td>0.188695</td>\n",
+       "      <td>-0.758872</td>\n",
+       "      <td>-0.933237</td>\n",
+       "      <td>UP</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Delhi</th>\n",
+       "      <td>0.955057</td>\n",
+       "      <td>0.190794</td>\n",
+       "      <td>1.978757</td>\n",
+       "      <td>2.605967</td>\n",
+       "      <td>0.683509</td>\n",
+       "      <td>Delhi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mumbai</th>\n",
+       "      <td>0.302665</td>\n",
+       "      <td>1.693723</td>\n",
+       "      <td>-1.706086</td>\n",
+       "      <td>-1.159119</td>\n",
+       "      <td>-0.134841</td>\n",
+       "      <td>Maharastra</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay      States\n",
+       "Chennai       2.706850   0.628133  0.907969   0.503826    0.651118  Tamil_Nadu\n",
+       "Bihar        -0.319318  -0.848077  0.605965  -2.018168    0.740122       BIHAR\n",
+       "UtterPredesh  0.528813  -0.589001  0.188695  -0.758872   -0.933237          UP\n",
+       "Delhi         0.955057   0.190794  1.978757   2.605967    0.683509       Delhi\n",
+       "Mumbai        0.302665   1.693723 -1.706086  -1.159119   -0.134841  Maharastra"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.set_index('States',inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SRM</th>\n",
+       "      <th>NIT_PATNA</th>\n",
+       "      <th>BHU</th>\n",
+       "      <th>IIT_DELHI</th>\n",
+       "      <th>IIT_Bombay</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>States</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>Tamil_Nadu</th>\n",
+       "      <td>2.706850</td>\n",
+       "      <td>0.628133</td>\n",
+       "      <td>0.907969</td>\n",
+       "      <td>0.503826</td>\n",
+       "      <td>0.651118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BIHAR</th>\n",
+       "      <td>-0.319318</td>\n",
+       "      <td>-0.848077</td>\n",
+       "      <td>0.605965</td>\n",
+       "      <td>-2.018168</td>\n",
+       "      <td>0.740122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UP</th>\n",
+       "      <td>0.528813</td>\n",
+       "      <td>-0.589001</td>\n",
+       "      <td>0.188695</td>\n",
+       "      <td>-0.758872</td>\n",
+       "      <td>-0.933237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Delhi</th>\n",
+       "      <td>0.955057</td>\n",
+       "      <td>0.190794</td>\n",
+       "      <td>1.978757</td>\n",
+       "      <td>2.605967</td>\n",
+       "      <td>0.683509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Maharastra</th>\n",
+       "      <td>0.302665</td>\n",
+       "      <td>1.693723</td>\n",
+       "      <td>-1.706086</td>\n",
+       "      <td>-1.159119</td>\n",
+       "      <td>-0.134841</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay\n",
+       "States                                                          \n",
+       "Tamil_Nadu  2.706850   0.628133  0.907969   0.503826    0.651118\n",
+       "BIHAR      -0.319318  -0.848077  0.605965  -2.018168    0.740122\n",
+       "UP          0.528813  -0.589001  0.188695  -0.758872   -0.933237\n",
+       "Delhi       0.955057   0.190794  1.978757   2.605967    0.683509\n",
+       "Maharastra  0.302665   1.693723 -1.706086  -1.159119   -0.134841"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multi-Index and Index Hierarchy\n",
+    "\n",
+    "Let us go over how to work with Multi-Index, first we'll create a quick example of what a Multi-Indexed DataFrame would look like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# index Levels \n",
+    "outside =['Big_data','Big_data','Big_data','AI','AI','AI']\n",
+    "inside =[1,2,3,1,2,3]\n",
+    "hier_index=list(zip(outside,inside))\n",
+    "hier_index=pd.MultiIndex.from_tuples(hier_index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MultiIndex([('Big_data', 1),\n",
+       "            ('Big_data', 2),\n",
+       "            ('Big_data', 3),\n",
+       "            (      'AI', 1),\n",
+       "            (      'AI', 2),\n",
+       "            (      'AI', 3)],\n",
+       "           )"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hier_index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=pd.DataFrame(np.random.rand (6,2),index=hier_index,columns=['Core','volunteers'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "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></th>\n",
+       "      <th>Core</th>\n",
+       "      <th>volunteers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Big_data</th>\n",
+       "      <th>1</th>\n",
+       "      <td>0.701371</td>\n",
+       "      <td>0.487635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.680678</td>\n",
+       "      <td>0.521548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.043397</td>\n",
+       "      <td>0.223937</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">AI</th>\n",
+       "      <th>1</th>\n",
+       "      <td>0.575205</td>\n",
+       "      <td>0.120434</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.500117</td>\n",
+       "      <td>0.138010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.052808</td>\n",
+       "      <td>0.178277</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Core  volunteers\n",
+       "Big_data 1  0.701371    0.487635\n",
+       "         2  0.680678    0.521548\n",
+       "         3  0.043397    0.223937\n",
+       "AI       1  0.575205    0.120434\n",
+       "         2  0.500117    0.138010\n",
+       "         3  0.052808    0.178277"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's show how to index this! For index hierarchy we use df.loc[], if this was on the columns axis, you would just use normal bracket notation df[]. Calling one level of the index returns the sub-dataframe:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "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>Core</th>\n",
+       "      <th>volunteers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.701371</td>\n",
+       "      <td>0.487635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.680678</td>\n",
+       "      <td>0.521548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.043397</td>\n",
+       "      <td>0.223937</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Core  volunteers\n",
+       "1  0.701371    0.487635\n",
+       "2  0.680678    0.521548\n",
+       "3  0.043397    0.223937"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc['Big_data']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Core          0.701371\n",
+       "volunteers    0.487635\n",
+       "Name: 1, dtype: float64"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc['Big_data'].loc[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FrozenList([None, None])"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.index.names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.index.names=['Domain','S.NO']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "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></th>\n",
+       "      <th>Core</th>\n",
+       "      <th>volunteers</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Domain</th>\n",
+       "      <th>S.NO</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Big_data</th>\n",
+       "      <th>1</th>\n",
+       "      <td>0.701371</td>\n",
+       "      <td>0.487635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.680678</td>\n",
+       "      <td>0.521548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.043397</td>\n",
+       "      <td>0.223937</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">AI</th>\n",
+       "      <th>1</th>\n",
+       "      <td>0.575205</td>\n",
+       "      <td>0.120434</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.500117</td>\n",
+       "      <td>0.138010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.052808</td>\n",
+       "      <td>0.178277</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Core  volunteers\n",
+       "Domain   S.NO                      \n",
+       "Big_data 1     0.701371    0.487635\n",
+       "         2     0.680678    0.521548\n",
+       "         3     0.043397    0.223937\n",
+       "AI       1     0.575205    0.120434\n",
+       "         2     0.500117    0.138010\n",
+       "         3     0.052808    0.178277"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "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>Core</th>\n",
+       "      <th>volunteers</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>S.NO</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.701371</td>\n",
+       "      <td>0.487635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.680678</td>\n",
+       "      <td>0.521548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.043397</td>\n",
+       "      <td>0.223937</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Core  volunteers\n",
+       "S.NO                      \n",
+       "1     0.701371    0.487635\n",
+       "2     0.680678    0.521548\n",
+       "3     0.043397    0.223937"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.xs('Big_data')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather_data = {\n",
+    "    'day': ['1/1/2017','1/2/2017','1/3/2017','1/4/2017','1/5/2017','1/6/2017'],\n",
+    "    'temperature': [32,35,28,24,32,31],\n",
+    "    'windspeed': [6,7,2,7,4,2],\n",
+    "    'event': ['Rain', 'Sunny', 'Snow','Snow','Rain', 'Sunny']\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=pd.DataFrame(weather_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "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>day</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>windspeed</th>\n",
+       "      <th>event</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1/1/2017</td>\n",
+       "      <td>32</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Rain</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1/2/2017</td>\n",
+       "      <td>35</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Sunny</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1/3/2017</td>\n",
+       "      <td>28</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Snow</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1/4/2017</td>\n",
+       "      <td>24</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Snow</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1/5/2017</td>\n",
+       "      <td>32</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Rain</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1/6/2017</td>\n",
+       "      <td>31</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Sunny</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        day  temperature  windspeed  event\n",
+       "0  1/1/2017           32          6   Rain\n",
+       "1  1/2/2017           35          7  Sunny\n",
+       "2  1/3/2017           28          2   Snow\n",
+       "3  1/4/2017           24          7   Snow\n",
+       "4  1/5/2017           32          4   Rain\n",
+       "5  1/6/2017           31          2  Sunny"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(6, 4)"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape # rows, columns = df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "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>day</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>windspeed</th>\n",
+       "      <th>event</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1/1/2017</td>\n",
+       "      <td>32</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Rain</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1/2/2017</td>\n",
+       "      <td>35</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Sunny</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1/3/2017</td>\n",
+       "      <td>28</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Snow</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1/4/2017</td>\n",
+       "      <td>24</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Snow</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1/5/2017</td>\n",
+       "      <td>32</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Rain</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        day  temperature  windspeed  event\n",
+       "0  1/1/2017           32          6   Rain\n",
+       "1  1/2/2017           35          7  Sunny\n",
+       "2  1/3/2017           28          2   Snow\n",
+       "3  1/4/2017           24          7   Snow\n",
+       "4  1/5/2017           32          4   Rain"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head() # df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "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>day</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>windspeed</th>\n",
+       "      <th>event</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1/2/2017</td>\n",
+       "      <td>35</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Sunny</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1/3/2017</td>\n",
+       "      <td>28</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Snow</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1/4/2017</td>\n",
+       "      <td>24</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Snow</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1/5/2017</td>\n",
+       "      <td>32</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Rain</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1/6/2017</td>\n",
+       "      <td>31</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Sunny</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        day  temperature  windspeed  event\n",
+       "1  1/2/2017           35          7  Sunny\n",
+       "2  1/3/2017           28          2   Snow\n",
+       "3  1/4/2017           24          7   Snow\n",
+       "4  1/5/2017           32          4   Rain\n",
+       "5  1/6/2017           31          2  Sunny"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.tail() # df.tail(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "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>day</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>windspeed</th>\n",
+       "      <th>event</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1/2/2017</td>\n",
+       "      <td>35</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Sunny</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1/3/2017</td>\n",
+       "      <td>28</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Snow</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        day  temperature  windspeed  event\n",
+       "1  1/2/2017           35          7  Sunny\n",
+       "2  1/3/2017           28          2   Snow"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[1:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## <font color='blue'>Columns</font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['day', 'temperature', 'windspeed', 'event'], dtype='object')"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    1/1/2017\n",
+       "1    1/2/2017\n",
+       "2    1/3/2017\n",
+       "3    1/4/2017\n",
+       "4    1/5/2017\n",
+       "5    1/6/2017\n",
+       "Name: day, dtype: object"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['day']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.series.Series"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(df['day'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "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>day</th>\n",
+       "      <th>temperature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1/1/2017</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1/2/2017</td>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1/3/2017</td>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1/4/2017</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1/5/2017</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1/6/2017</td>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        day  temperature\n",
+       "0  1/1/2017           32\n",
+       "1  1/2/2017           35\n",
+       "2  1/3/2017           28\n",
+       "3  1/4/2017           24\n",
+       "4  1/5/2017           32\n",
+       "5  1/6/2017           31"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[['day','temperature']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## <font color='blue'>Operations On DataFrame</font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "35"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['temperature'].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "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>day</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>windspeed</th>\n",
+       "      <th>event</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1/2/2017</td>\n",
+       "      <td>35</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Sunny</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        day  temperature  windspeed  event\n",
+       "1  1/2/2017           35          7  Sunny"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df['temperature']>32]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    1/2/2017\n",
+       "Name: day, dtype: object"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['day'][df['temperature'] == df['temperature'].max()] # Kinda doing SQL in pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.8297084310253524"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['temperature'].std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Sunny'"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['event'].max() # But mean() won't work since data type is string"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "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>temperature</th>\n",
+       "      <th>windspeed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>30.333333</td>\n",
+       "      <td>4.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.829708</td>\n",
+       "      <td>2.338090</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>28.750000</td>\n",
+       "      <td>2.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>31.500000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>32.000000</td>\n",
+       "      <td>6.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>35.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       temperature  windspeed\n",
+       "count     6.000000   6.000000\n",
+       "mean     30.333333   4.666667\n",
+       "std       3.829708   2.338090\n",
+       "min      24.000000   2.000000\n",
+       "25%      28.750000   2.500000\n",
+       "50%      31.500000   5.000000\n",
+       "75%      32.000000   6.750000\n",
+       "max      35.000000   7.000000"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Missing Data\n",
+    "\n",
+    "Let's show a few convenient methods to deal with Missing Data in pandas:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame({'A':[1,2,np.nan],\n",
+    "                  'B':[5,np.nan,np.nan],\n",
+    "                  'C':[1,2,3]})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "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>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A    B  C\n",
+       "0  1.0  5.0  1\n",
+       "1  2.0  NaN  2\n",
+       "2  NaN  NaN  3"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "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>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A    B  C\n",
+       "0  1.0  5.0  1"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "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>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   C\n",
+       "0  1\n",
+       "1  2\n",
+       "2  3"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dropna(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "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>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A    B  C\n",
+       "0  1.0  5.0  1\n",
+       "1  2.0  NaN  2"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dropna(thresh=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "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>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>shivendra</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>shivendra</td>\n",
+       "      <td>shivendra</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           A          B  C\n",
+       "0          1          5  1\n",
+       "1          2  shivendra  2\n",
+       "2  shivendra  shivendra  3"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.fillna(value='shivendra')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    1.0\n",
+       "1    2.0\n",
+       "2    1.5\n",
+       "Name: A, dtype: float64"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['A'].fillna(value=df['A'].mean())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Groupby\n",
+    "\n",
+    "The groupby method allows you to group rows of data together and call aggregate functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "# Create dataframe\n",
+    "data = {'Company':['GOOG','GOOG','MSFT','MSFT','FB','FB'],\n",
+    "       'Person':['Shivendra','Abhishek','Sowjanya','Manish','Mini','Satya'],\n",
+    "       'Sales':[200,120,340,124,243,350]}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "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>Company</th>\n",
+       "      <th>Person</th>\n",
+       "      <th>Sales</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>GOOG</td>\n",
+       "      <td>Shivendra</td>\n",
+       "      <td>200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>GOOG</td>\n",
+       "      <td>Abhishek</td>\n",
+       "      <td>120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>MSFT</td>\n",
+       "      <td>Sowjanya</td>\n",
+       "      <td>340</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>MSFT</td>\n",
+       "      <td>Manish</td>\n",
+       "      <td>124</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>FB</td>\n",
+       "      <td>Mini</td>\n",
+       "      <td>243</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>FB</td>\n",
+       "      <td>Satya</td>\n",
+       "      <td>350</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Company     Person  Sales\n",
+       "0    GOOG  Shivendra    200\n",
+       "1    GOOG   Abhishek    120\n",
+       "2    MSFT   Sowjanya    340\n",
+       "3    MSFT     Manish    124\n",
+       "4      FB       Mini    243\n",
+       "5      FB      Satya    350"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Now you can use the .groupby() method to group rows together based off of a column name. For instance let's group based off of Company. This will create a DataFrameGroupBy object:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001F12523CB08>"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('Company')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "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>Sales</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Company</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>FB</th>\n",
+       "      <td>296.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GOOG</th>\n",
+       "      <td>160.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MSFT</th>\n",
+       "      <td>232.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Sales\n",
+       "Company       \n",
+       "FB       296.5\n",
+       "GOOG     160.0\n",
+       "MSFT     232.0"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#You can save this object as a new variable:\n",
+    "by_comp = df.groupby(\"Company\")\n",
+    "#And then call aggregate methods off the object:\n",
+    "by_comp.mean()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "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>Sales</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Company</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>FB</th>\n",
+       "      <td>296.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GOOG</th>\n",
+       "      <td>160.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MSFT</th>\n",
+       "      <td>232.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Sales\n",
+       "Company       \n",
+       "FB       296.5\n",
+       "GOOG     160.0\n",
+       "MSFT     232.0"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('Company').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "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>Sales</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Company</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>FB</th>\n",
+       "      <td>75.660426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GOOG</th>\n",
+       "      <td>56.568542</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MSFT</th>\n",
+       "      <td>152.735065</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Sales\n",
+       "Company            \n",
+       "FB        75.660426\n",
+       "GOOG      56.568542\n",
+       "MSFT     152.735065"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#More examples of aggregate methods:\n",
+    "by_comp.std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "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>Person</th>\n",
+       "      <th>Sales</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Company</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>FB</th>\n",
+       "      <td>Satya</td>\n",
+       "      <td>350</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GOOG</th>\n",
+       "      <td>Shivendra</td>\n",
+       "      <td>200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MSFT</th>\n",
+       "      <td>Sowjanya</td>\n",
+       "      <td>340</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Person  Sales\n",
+       "Company                  \n",
+       "FB           Satya    350\n",
+       "GOOG     Shivendra    200\n",
+       "MSFT      Sowjanya    340"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "by_comp.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "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>Person</th>\n",
+       "      <th>Sales</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Company</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>FB</th>\n",
+       "      <td>Mini</td>\n",
+       "      <td>243</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GOOG</th>\n",
+       "      <td>Abhishek</td>\n",
+       "      <td>120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MSFT</th>\n",
+       "      <td>Manish</td>\n",
+       "      <td>124</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Person  Sales\n",
+       "Company                 \n",
+       "FB           Mini    243\n",
+       "GOOG     Abhishek    120\n",
+       "MSFT       Manish    124"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "by_comp.min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "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\">Sales</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>Company</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>FB</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>296.5</td>\n",
+       "      <td>75.660426</td>\n",
+       "      <td>243.0</td>\n",
+       "      <td>269.75</td>\n",
+       "      <td>296.5</td>\n",
+       "      <td>323.25</td>\n",
+       "      <td>350.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GOOG</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>56.568542</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>140.00</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>180.00</td>\n",
+       "      <td>200.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MSFT</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>232.0</td>\n",
+       "      <td>152.735065</td>\n",
+       "      <td>124.0</td>\n",
+       "      <td>178.00</td>\n",
+       "      <td>232.0</td>\n",
+       "      <td>286.00</td>\n",
+       "      <td>340.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Sales                                                        \n",
+       "        count   mean         std    min     25%    50%     75%    max\n",
+       "Company                                                              \n",
+       "FB        2.0  296.5   75.660426  243.0  269.75  296.5  323.25  350.0\n",
+       "GOOG      2.0  160.0   56.568542  120.0  140.00  160.0  180.00  200.0\n",
+       "MSFT      2.0  232.0  152.735065  124.0  178.00  232.0  286.00  340.0"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "by_comp.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "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>Company</th>\n",
+       "      <th>FB</th>\n",
+       "      <th>GOOG</th>\n",
+       "      <th>MSFT</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"8\" valign=\"top\">Sales</th>\n",
+       "      <th>count</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>296.500000</td>\n",
+       "      <td>160.000000</td>\n",
+       "      <td>232.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>75.660426</td>\n",
+       "      <td>56.568542</td>\n",
+       "      <td>152.735065</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>243.000000</td>\n",
+       "      <td>120.000000</td>\n",
+       "      <td>124.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>269.750000</td>\n",
+       "      <td>140.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>296.500000</td>\n",
+       "      <td>160.000000</td>\n",
+       "      <td>232.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>323.250000</td>\n",
+       "      <td>180.000000</td>\n",
+       "      <td>286.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>350.000000</td>\n",
+       "      <td>200.000000</td>\n",
+       "      <td>340.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Company              FB        GOOG        MSFT\n",
+       "Sales count    2.000000    2.000000    2.000000\n",
+       "      mean   296.500000  160.000000  232.000000\n",
+       "      std     75.660426   56.568542  152.735065\n",
+       "      min    243.000000  120.000000  124.000000\n",
+       "      25%    269.750000  140.000000  178.000000\n",
+       "      50%    296.500000  160.000000  232.000000\n",
+       "      75%    323.250000  180.000000  286.000000\n",
+       "      max    350.000000  200.000000  340.000000"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "by_comp.describe().transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Sales  count      2.000000\n",
+       "       mean     160.000000\n",
+       "       std       56.568542\n",
+       "       min      120.000000\n",
+       "       25%      140.000000\n",
+       "       50%      160.000000\n",
+       "       75%      180.000000\n",
+       "       max      200.000000\n",
+       "Name: GOOG, dtype: float64"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "by_comp.describe().transpose()['GOOG']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Merging, Joining, and Concatenating\n",
+    "\n",
+    "There are 3 main ways of combining DataFrames together: Merging, Joining and Concatenating. In this we will discuss these 3 methods with examples.\n",
+    "\n",
+    "____"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],\n",
+    "                        'B': ['B0', 'B1', 'B2', 'B3'],\n",
+    "                        'C': ['C0', 'C1', 'C2', 'C3'],\n",
+    "                        'D': ['D0', 'D1', 'D2', 'D3']},\n",
+    "                        index=[0, 1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],\n",
+    "                        'B': ['B4', 'B5', 'B6', 'B7'],\n",
+    "                        'C': ['C4', 'C5', 'C6', 'C7'],\n",
+    "                        'D': ['D4', 'D5', 'D6', 'D7']},\n",
+    "                         index=[4, 5, 6, 7]) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'],\n",
+    "                        'B': ['B8', 'B9', 'B10', 'B11'],\n",
+    "                        'C': ['C8', 'C9', 'C10', 'C11'],\n",
+    "                        'D': ['D8', 'D9', 'D10', 'D11']},\n",
+    "                        index=[8, 9, 10, 11])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "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>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A0</td>\n",
+       "      <td>B0</td>\n",
+       "      <td>C0</td>\n",
+       "      <td>D0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A1</td>\n",
+       "      <td>B1</td>\n",
+       "      <td>C1</td>\n",
+       "      <td>D1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A2</td>\n",
+       "      <td>B2</td>\n",
+       "      <td>C2</td>\n",
+       "      <td>D2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>A3</td>\n",
+       "      <td>B3</td>\n",
+       "      <td>C3</td>\n",
+       "      <td>D3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    A   B   C   D\n",
+       "0  A0  B0  C0  D0\n",
+       "1  A1  B1  C1  D1\n",
+       "2  A2  B2  C2  D2\n",
+       "3  A3  B3  C3  D3"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "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>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>A4</td>\n",
+       "      <td>B4</td>\n",
+       "      <td>C4</td>\n",
+       "      <td>D4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>A5</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>C5</td>\n",
+       "      <td>D5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>A6</td>\n",
+       "      <td>B6</td>\n",
+       "      <td>C6</td>\n",
+       "      <td>D6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>A7</td>\n",
+       "      <td>B7</td>\n",
+       "      <td>C7</td>\n",
+       "      <td>D7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    A   B   C   D\n",
+       "4  A4  B4  C4  D4\n",
+       "5  A5  B5  C5  D5\n",
+       "6  A6  B6  C6  D6\n",
+       "7  A7  B7  C7  D7"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "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>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>A8</td>\n",
+       "      <td>B8</td>\n",
+       "      <td>C8</td>\n",
+       "      <td>D8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>A9</td>\n",
+       "      <td>B9</td>\n",
+       "      <td>C9</td>\n",
+       "      <td>D9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>A10</td>\n",
+       "      <td>B10</td>\n",
+       "      <td>C10</td>\n",
+       "      <td>D10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>A11</td>\n",
+       "      <td>B11</td>\n",
+       "      <td>C11</td>\n",
+       "      <td>D11</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      A    B    C    D\n",
+       "8    A8   B8   C8   D8\n",
+       "9    A9   B9   C9   D9\n",
+       "10  A10  B10  C10  D10\n",
+       "11  A11  B11  C11  D11"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Concatenation\n",
+    "\n",
+    "Concatenation basically glues together DataFrames. Keep in mind that dimensions should match along the axis you are concatenating on. You can use **pd.concat** and pass in a list of DataFrames to concatenate together:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "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>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A0</td>\n",
+       "      <td>B0</td>\n",
+       "      <td>C0</td>\n",
+       "      <td>D0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A1</td>\n",
+       "      <td>B1</td>\n",
+       "      <td>C1</td>\n",
+       "      <td>D1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A2</td>\n",
+       "      <td>B2</td>\n",
+       "      <td>C2</td>\n",
+       "      <td>D2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>A3</td>\n",
+       "      <td>B3</td>\n",
+       "      <td>C3</td>\n",
+       "      <td>D3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>A4</td>\n",
+       "      <td>B4</td>\n",
+       "      <td>C4</td>\n",
+       "      <td>D4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>A5</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>C5</td>\n",
+       "      <td>D5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>A6</td>\n",
+       "      <td>B6</td>\n",
+       "      <td>C6</td>\n",
+       "      <td>D6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>A7</td>\n",
+       "      <td>B7</td>\n",
+       "      <td>C7</td>\n",
+       "      <td>D7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>A8</td>\n",
+       "      <td>B8</td>\n",
+       "      <td>C8</td>\n",
+       "      <td>D8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>A9</td>\n",
+       "      <td>B9</td>\n",
+       "      <td>C9</td>\n",
+       "      <td>D9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>A10</td>\n",
+       "      <td>B10</td>\n",
+       "      <td>C10</td>\n",
+       "      <td>D10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>A11</td>\n",
+       "      <td>B11</td>\n",
+       "      <td>C11</td>\n",
+       "      <td>D11</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      A    B    C    D\n",
+       "0    A0   B0   C0   D0\n",
+       "1    A1   B1   C1   D1\n",
+       "2    A2   B2   C2   D2\n",
+       "3    A3   B3   C3   D3\n",
+       "4    A4   B4   C4   D4\n",
+       "5    A5   B5   C5   D5\n",
+       "6    A6   B6   C6   D6\n",
+       "7    A7   B7   C7   D7\n",
+       "8    A8   B8   C8   D8\n",
+       "9    A9   B9   C9   D9\n",
+       "10  A10  B10  C10  D10\n",
+       "11  A11  B11  C11  D11"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([df1,df2,df3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "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>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A0</td>\n",
+       "      <td>B0</td>\n",
+       "      <td>C0</td>\n",
+       "      <td>D0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A1</td>\n",
+       "      <td>B1</td>\n",
+       "      <td>C1</td>\n",
+       "      <td>D1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A2</td>\n",
+       "      <td>B2</td>\n",
+       "      <td>C2</td>\n",
+       "      <td>D2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>A3</td>\n",
+       "      <td>B3</td>\n",
+       "      <td>C3</td>\n",
+       "      <td>D3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A4</td>\n",
+       "      <td>B4</td>\n",
+       "      <td>C4</td>\n",
+       "      <td>D4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A5</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>C5</td>\n",
+       "      <td>D5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A6</td>\n",
+       "      <td>B6</td>\n",
+       "      <td>C6</td>\n",
+       "      <td>D6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A7</td>\n",
+       "      <td>B7</td>\n",
+       "      <td>C7</td>\n",
+       "      <td>D7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A8</td>\n",
+       "      <td>B8</td>\n",
+       "      <td>C8</td>\n",
+       "      <td>D8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A9</td>\n",
+       "      <td>B9</td>\n",
+       "      <td>C9</td>\n",
+       "      <td>D9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A10</td>\n",
+       "      <td>B10</td>\n",
+       "      <td>C10</td>\n",
+       "      <td>D10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A11</td>\n",
+       "      <td>B11</td>\n",
+       "      <td>C11</td>\n",
+       "      <td>D11</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      A    B    C    D    A    B    C    D    A    B    C    D\n",
+       "0    A0   B0   C0   D0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
+       "1    A1   B1   C1   D1  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
+       "2    A2   B2   C2   D2  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
+       "3    A3   B3   C3   D3  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
+       "4   NaN  NaN  NaN  NaN   A4   B4   C4   D4  NaN  NaN  NaN  NaN\n",
+       "5   NaN  NaN  NaN  NaN   A5   B5   C5   D5  NaN  NaN  NaN  NaN\n",
+       "6   NaN  NaN  NaN  NaN   A6   B6   C6   D6  NaN  NaN  NaN  NaN\n",
+       "7   NaN  NaN  NaN  NaN   A7   B7   C7   D7  NaN  NaN  NaN  NaN\n",
+       "8   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   A8   B8   C8   D8\n",
+       "9   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   A9   B9   C9   D9\n",
+       "10  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  A10  B10  C10  D10\n",
+       "11  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  A11  B11  C11  D11"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([df1,df2,df3],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Operations\n",
+    "\n",
+    "There are lots of operations with pandas that will be really useful to you, but don't fall into any distinct category. Let's show them here in this lecture:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "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>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>444</td>\n",
+       "      <td>abc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>555</td>\n",
+       "      <td>def</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>666</td>\n",
+       "      <td>ghi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>444</td>\n",
+       "      <td>xyz</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2 col3\n",
+       "0     1   444  abc\n",
+       "1     2   555  def\n",
+       "2     3   666  ghi\n",
+       "3     4   444  xyz"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame({'col1':[1,2,3,4],'col2':[444,555,666,444],'col3':['abc','def','ghi','xyz']})\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([444, 555, 666], dtype=int64)"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['col2'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['col2'].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "444    2\n",
+       "555    1\n",
+       "666    1\n",
+       "Name: col2, dtype: int64"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['col2'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Select from DataFrame using criteria from multiple columns\n",
+    "newdf = df[(df['col1']>2) & (df['col2']==444)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "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>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>444</td>\n",
+       "      <td>xyz</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2 col3\n",
+       "3     4   444  xyz"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "newdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Applying Functions\n",
+    "def times2(x):\n",
+    "    return x*2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    2\n",
+       "1    4\n",
+       "2    6\n",
+       "3    8\n",
+       "Name: col1, dtype: int64"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['col1'].apply(times2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    3\n",
+       "1    3\n",
+       "2    3\n",
+       "3    3\n",
+       "Name: col3, dtype: int64"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['col3'].apply(len)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['col1'].sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Permanently Removing a Column**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "del df['col1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "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>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>444</td>\n",
+       "      <td>abc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>555</td>\n",
+       "      <td>def</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>666</td>\n",
+       "      <td>ghi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>444</td>\n",
+       "      <td>xyz</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col2 col3\n",
+       "0   444  abc\n",
+       "1   555  def\n",
+       "2   666  ghi\n",
+       "3   444  xyz"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['col2', 'col3'], dtype='object')"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# get columns and index names \n",
+    "df.columns "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RangeIndex(start=0, stop=4, step=1)"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "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>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>444</td>\n",
+       "      <td>abc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>555</td>\n",
+       "      <td>def</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>666</td>\n",
+       "      <td>ghi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>444</td>\n",
+       "      <td>xyz</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col2 col3\n",
+       "0   444  abc\n",
+       "1   555  def\n",
+       "2   666  ghi\n",
+       "3   444  xyz"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "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>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>444</td>\n",
+       "      <td>abc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>444</td>\n",
+       "      <td>xyz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>555</td>\n",
+       "      <td>def</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>666</td>\n",
+       "      <td>ghi</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col2 col3\n",
+       "0   444  abc\n",
+       "3   444  xyz\n",
+       "1   555  def\n",
+       "2   666  ghi"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.sort_values(by='col2') #inplace=False by default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "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>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    col2   col3\n",
+       "0  False  False\n",
+       "1  False  False\n",
+       "2  False  False\n",
+       "3  False  False"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check is there any null value or not \n",
+    "df.isnull()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "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>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>444</td>\n",
+       "      <td>abc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>555</td>\n",
+       "      <td>def</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>666</td>\n",
+       "      <td>ghi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>444</td>\n",
+       "      <td>xyz</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col2 col3\n",
+       "0   444  abc\n",
+       "1   555  def\n",
+       "2   666  ghi\n",
+       "3   444  xyz"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Drop rows with NaN Values\n",
+    "df.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "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>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>abc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>555.0</td>\n",
+       "      <td>def</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>666.0</td>\n",
+       "      <td>ghi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>444.0</td>\n",
+       "      <td>xyz</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1   col2 col3\n",
+       "0   1.0    NaN  abc\n",
+       "1   2.0  555.0  def\n",
+       "2   3.0  666.0  ghi\n",
+       "3   NaN  444.0  xyz"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame({'col1':[1,2,3,np.nan],\n",
+    "                   'col2':[np.nan,555,666,444],\n",
+    "                   'col3':['abc','def','ghi','xyz']})\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "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>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>FILL</td>\n",
+       "      <td>abc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>555</td>\n",
+       "      <td>def</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>666</td>\n",
+       "      <td>ghi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>FILL</td>\n",
+       "      <td>444</td>\n",
+       "      <td>xyz</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2 col3\n",
+       "0     1  FILL  abc\n",
+       "1     2   555  def\n",
+       "2     3   666  ghi\n",
+       "3  FILL   444  xyz"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.fillna('FILL')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = {'A':['foo','foo','foo','bar','bar','bar'],\n",
+    "     'B':['one','one','two','two','one','one'],\n",
+    "       'C':['x','y','x','y','x','y'],\n",
+    "       'D':[1,3,2,5,4,1]}\n",
+    "\n",
+    "df = pd.DataFrame(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "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>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>foo</td>\n",
+       "      <td>one</td>\n",
+       "      <td>x</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>foo</td>\n",
+       "      <td>one</td>\n",
+       "      <td>y</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>foo</td>\n",
+       "      <td>two</td>\n",
+       "      <td>x</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>bar</td>\n",
+       "      <td>two</td>\n",
+       "      <td>y</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>bar</td>\n",
+       "      <td>one</td>\n",
+       "      <td>x</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>bar</td>\n",
+       "      <td>one</td>\n",
+       "      <td>y</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     A    B  C  D\n",
+       "0  foo  one  x  1\n",
+       "1  foo  one  y  3\n",
+       "2  foo  two  x  2\n",
+       "3  bar  two  y  5\n",
+       "4  bar  one  x  4\n",
+       "5  bar  one  y  1"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Great"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
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+}