Day 1- Essentials of NumPy, Pandas, and Matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ce56e923",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "lst = [1, 2, 3, 4, 5, 6]\n",
    "\n",
    "# Creating an array from list\n",
    "arr = np.array(lst)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3cf1edde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2., 3., 4., 5., 6.])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst = [1, 2, 3, 4, 5, 6]\n",
    "\n",
    "# Creating an array of floats from list \n",
    "arr = np.array(lst, dtype = float)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "78597619",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Jon', 'Mary', 'Paul'],\n",
       "       ['Peter', 'Ben', 'Saul']], dtype='<U5')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = [[\"Jon\", \"Mary\", \"Paul\"], [\"Peter\",\"Ben\", \"Saul\"]]\n",
    "    \n",
    "arr1 = np.array(names)\n",
    "arr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "76d8e61c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for number of dimensions in the array\n",
    "arr1.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "093a6918",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  5, 10, 15, 20, 25, 30, 35, 40, 45])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2 = np.arange(0, 50, 5)\n",
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d0ca1acb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3 = np.zeros((2, 3), dtype = float)\n",
    "arr3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "15a20be7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3 = np.ones((2, 3), dtype = float)\n",
    "arr3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "adc0582b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 8, 4, 0],\n",
       "       [2, 3, 6, 8]], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a random number generator\n",
    "rng = np.random.default_rng()\n",
    "\n",
    "# Generate random integers between 0 and 9\n",
    "rng.integers(low=0, high=10, size=(2, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "96c35125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.33026884, 0.40517732, 0.57473782, 0.50639977],\n",
       "       [0.56421251, 0.56968731, 0.87411653, 0.08643046]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a random number generator\n",
    "rng = np.random.default_rng(seed = 24)\n",
    "\n",
    "# Generate random floats between 0 and 9\n",
    "rng.random((2, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f27170df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Jon', 'Mary', 'Paul'],\n",
       "       ['Peter', 'Ben', 'Saul']], dtype='<U5')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = [[\"Jon\", \"Mary\", \"Paul\"], [\"Peter\",\"Ben\", \"Saul\"]]\n",
    "    \n",
    "arr1 = np.array(names)\n",
    "arr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d8d64def",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mary', 'Paul'], dtype='<U5')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select_mary_paul = arr1[0,1:]\n",
    "select_mary_paul"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1a3e40cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Peter', 'Paul'], dtype='<U5')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating an elements indices\n",
    "select_peter = np.array([1, 0])\n",
    "select_paul = np.array([0, 2])\n",
    "\n",
    "# Using fancy indexing to select\n",
    "select_peter_paul = arr1[select_peter, select_paul]\n",
    "select_peter_paul"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2314fd6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5, 7, 9])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n",
    "\n",
    "# create a boolean mask based on the condition\n",
    "filter_array = arr % 2 != 0\n",
    "\n",
    "# select the elements from the original array based on the filter\n",
    "arr[filter_array]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3cafd8c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating an array\n",
    "arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n",
    "\n",
    "# Flatten the array using reshape\n",
    "np.reshape(arr, 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0f81d8f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Jon', 'Mary', 'Paul'],\n",
       "       ['Peter', 'Ben', 'Saul']], dtype='<U5')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = [[\"Jon\", \"Mary\", \"Paul\"], [\"Peter\",\"Ben\", \"Saul\"]]\n",
    "    \n",
    "arr1 = np.array(names)\n",
    "arr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a27615f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Jon', 'Mary'],\n",
       "       ['Paul', 'Peter'],\n",
       "       ['Ben', 'Saul']], dtype='<U5')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reshaping array to a (3, 2) shape\n",
    "new_array = np.reshape(arr1, (3, 2))\n",
    "new_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a7a4588c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 10,  20,  30],\n",
       "       [ 40,  50,  60],\n",
       "       [ 70,  80,  90],\n",
       "       [100, 110, 120],\n",
       "       [130, 140, 150]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 = np.array([[10, 20, 30], [40, 50, 60], [70, 80, 90]])\n",
    "arr2 = np.array([[100, 110, 120], [130, 140, 150]])\n",
    "\n",
    "# Concatenate on axis-0\n",
    "arr3 = np.concatenate((arr1, arr2), axis=0)\n",
    "arr3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ed583d96",
   "metadata": {},
   "outputs": [],
   "source": [
    "names = [[\"Jon\", \"Mary\", \"Paul\"], [\"Peter\",\"Ben\", \"Saul\"]]\n",
    " \n",
    "# Creating array    \n",
    "arr1 = np.array(names)\n",
    "\n",
    "# using split to split array into two parts\n",
    "split_one, split_two = np.split(arr1, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "03bba650",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Jon', 'Mary', 'Paul']], dtype='<U5')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split_one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9bd9199b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Peter', 'Ben', 'Saul']], dtype='<U5')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split_two"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4af30737",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr1 = np.array([[10, 20, 30], [40, 50, 60], [70, 80, 90]])\n",
    "arr2 = np.array([[100, 110, 120], [130, 140, 150]])\n",
    "\n",
    "# Perfoming a dot operation\n",
    "#np.dot(arr1, arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7a5b685b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6800,  8600],\n",
       "       [16700, 21200],\n",
       "       [26600, 33800]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 = np.array([[10, 20, 30], [40, 50, 60], [70, 80, 90]])\n",
    "arr2 = np.array([[100, 110, 120], [130, 140, 150]])\n",
    "\n",
    "# Perfoming a dot operation and transposing arr2\n",
    "np.dot(arr1, arr2.transpose())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "11bc699f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  5, 10],\n",
       "       [10, 12, 15]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 = np.array([[1, 2, 4], [6, 7, 8]])\n",
    "arr2 = np.array([[3, 3, 6], [4, 5, 7]])\n",
    "\n",
    "# adding two arrays\n",
    "np.add(arr1, arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "48a75c63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2, -1, -2],\n",
       "       [ 2,  2,  1]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Subtracting two arrays\n",
    "np.subtract(arr1, arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "aa7db441",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3,  6, 24],\n",
       "       [24, 35, 56]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 = np.array([[1, 2, 4], [6, 7, 8]])\n",
    "arr2 = np.array([[3, 3, 6], [4, 5, 7]])\n",
    "\n",
    "# multiplying two arrays\n",
    "np.multiply(arr1, arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6fb1761f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.33333333, 0.66666667, 0.66666667],\n",
       "       [1.5       , 1.4       , 1.14285714]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dividing two arrays\n",
    "np.divide(arr1, arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3880b100",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9,  9, 36],\n",
       "       [16, 25, 49]], dtype=int32)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2 = np.array([[3, 3, 6], [4, 5, 7]])\n",
    "\n",
    "# Raising array to power 2\n",
    "np.power(arr2, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "69b499e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.73205081, 1.73205081, 2.44948974],\n",
       "       [2.        , 2.23606798, 2.64575131]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2 = np.array([[3, 3, 6], [4, 5, 7]])\n",
    "\n",
    "# Calculating the square-root of array\n",
    "np.sqrt(arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e46ff38d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.        , 5.33333333])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2 = np.array([[3, 3, 6], [4, 5, 7]])\n",
    "\n",
    "# Calculating the mean of each row\n",
    "np.mean(arr2, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d786186a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3., 5.])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2 = np.array([[3, 3, 6], [4, 5, 7]])\n",
    "\n",
    "# Calculating the median of each row\n",
    "np.median(arr2, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "48a0321f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5, 1. , 0.5])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2 = np.array([[3, 3, 6], [4, 5, 7]])\n",
    "\n",
    "# Calculating the std of each column\n",
    "np.std(arr2, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0c3f16fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.25, 1.  , 0.25])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2 = np.array([[3, 3, 6], [4, 5, 7]])\n",
    "\n",
    "# Calculating the var of each column\n",
    "np.var(arr2, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "44c556f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 3, 6])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2 = np.array([[3, 3, 6], [4, 5, 7]])\n",
    "\n",
    "# Calculating the min of each column\n",
    "np.min(arr2, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "eb1e961a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2 = np.array([[3, 3, 6], [4, 5, 7]])\n",
    "\n",
    "# Calculating the max of the whole array\n",
    "np.max(arr2, axis=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58d81c16",
   "metadata": {},
   "source": [
    "## Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a09504b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Orange\n",
       "1     Apple\n",
       "2     Mango\n",
       "Name: fruits, dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Creating a Series\n",
    "fruits = pd.Series([\"Orange\", \"Apple\", \"Mango\"], \n",
    "                           name=\"fruits\")\n",
    "fruits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e1b0c2be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    Orange\n",
       "b     Apple\n",
       "c     Mango\n",
       "Name: fruits, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruits = pd.Series([\"Orange\", \"Apple\", \"Mango\"], \n",
    "                           name=\"fruits\",\n",
    "                  index = ['a', 'b', 'c'])\n",
    "\n",
    "fruits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3e6894ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    20\n",
       "2    30\n",
       "Name: numbers, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int_numbers = pd.Series([10, 20, 30], name=\"numbers\")\n",
    "int_numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fe153989",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        10\n",
       "1        20\n",
       "2        30\n",
       "3    Orange\n",
       "Name: mixed, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int_str = pd.Series([10, 20, 30, 'Orange'], name=\"mixed\")\n",
    "int_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4e9618c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    20\n",
       "2    30\n",
       "Name: mixed, dtype: int8"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int_str = pd.Series([10, 20, 30], name=\"mixed\", dtype=\"int8\")\n",
    "int_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f1a98d2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    20\n",
       "2    30\n",
       "3    40\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_series = pd.Series([10, 20, 30, 40])\n",
    "num_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d9042d96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10.0\n",
       "1    20.0\n",
       "2    30.0\n",
       "3    40.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Changing data type to float\n",
    "num_series = num_series.astype(\"float64\")\n",
    "num_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ae6df079",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>32</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>18</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>47</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age  Salary\n",
       "0    Alice   25   50000\n",
       "1      Bob   32   80000\n",
       "2  Charlie   18   20000\n",
       "3    David   47  120000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],\n",
    "        'Age': [25, 32, 18, 47],\n",
    "        'Salary': [50000, 80000, 20000, 120000]}\n",
    "\n",
    "df = pd.DataFrame(data, columns=['Name', 'Age', 'Salary'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7aa43e4e",
   "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>Names</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>32</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>18</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>47</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Names  Age  Salary\n",
       "0    Alice   25   50000\n",
       "1      Bob   32   80000\n",
       "2  Charlie   18   20000\n",
       "3    David   47  120000"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = ['Alice', 'Bob', 'Charlie', 'David']\n",
    "age = [25, 32, 18, 47]\n",
    "salary = [50000, 80000, 20000, 120000]\n",
    "\n",
    "df = pd.DataFrame({\"Names\": names, \"Age\":age, \"Salary\": salary})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "551bdb4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File not found\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    df = pd.read_csv(\"Data_csv\")\n",
    "except FileNotFoundError:\n",
    "    print('File not found')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f48a74e9",
   "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>Names</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25.0</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>47.0</td>\n",
       "      <td>120000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Names   Age    Salary\n",
       "0    Alice  25.0   50000.0\n",
       "1      Bob   NaN   80000.0\n",
       "2  Charlie  18.0       NaN\n",
       "3    David  47.0  120000.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = ['Alice', 'Bob', 'Charlie', 'David']\n",
    "age = [25, None, 18, 47]\n",
    "salary = [50000, 80000, None, 120000]\n",
    "\n",
    "df = pd.DataFrame({\"Names\": names, \"Age\":age, \"Salary\": salary})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4b24d9e6",
   "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>Names</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25.0</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>47.0</td>\n",
       "      <td>120000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Names   Age    Salary\n",
       "0  Alice  25.0   50000.0\n",
       "3  David  47.0  120000.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4d3e21d8",
   "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>Names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Names\n",
       "0    Alice\n",
       "1      Bob\n",
       "2  Charlie\n",
       "3    David"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b7803452",
   "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>Names</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>John</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Names  Age\n",
       "0    Alice   25\n",
       "1      Bob   29\n",
       "2  Charlie   33\n",
       "3    David   21\n",
       "4     John   57"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = ['Alice', 'Bob', 'Charlie', 'David', 'John', \n",
    "         'Mpho', 'Steve', 'Ben']\n",
    "age = [25, 29, 33, 21, 57, 66, 50, 30]\n",
    "\n",
    "# Creating a DataFrame\n",
    "df = pd.DataFrame({\"Names\": names, \"Age\":age})\n",
    "# Viewing the first five rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4745d153",
   "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>Names</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Names  Age\n",
       "0  Alice   25\n",
       "1    Bob   29"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a DataFrame\n",
    "df = pd.DataFrame({\"Names\": names, \"Age\":age})\n",
    "# Viewing the first 2 rows\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fe28f3ed",
   "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>Names</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>John</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mpho</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Steve</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ben</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Names  Age\n",
       "3  David   21\n",
       "4   John   57\n",
       "5   Mpho   66\n",
       "6  Steve   50\n",
       "7    Ben   30"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a DataFrame\n",
    "df = pd.DataFrame({\"Names\": names, \"Age\":age})\n",
    "# Viewing the last 5 rows\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "47f79539",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8 entries, 0 to 7\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   Names   8 non-null      object\n",
      " 1   Age     8 non-null      int64 \n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 256.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "# Creating a DataFrame\n",
    "df = pd.DataFrame({\"Names\": names, \"Age\":age})\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f1d33215",
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.522171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>51.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Age\n",
       "count   8.000000\n",
       "mean   38.875000\n",
       "std    16.522171\n",
       "min    21.000000\n",
       "25%    28.000000\n",
       "50%    31.500000\n",
       "75%    51.750000\n",
       "max    66.000000"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4f9dde88",
   "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",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>Products</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Computers</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Phones</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Products  Sales\n",
       "0  Computers   2500\n",
       "1     Phones   3000"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Products = ['Computers', 'Phones', 'Shoes', 'Computers', 'Phones']\n",
    "Sales = [2500, 3000, 1400, 2100, 2800]\n",
    "\n",
    "# Creating a DataFrame\n",
    "df = pd.DataFrame({\"Products\": Products, \"Sales\":Sales})\n",
    "# viewing the first 2 rows\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "04303c79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Products\n",
       "Computers    4600\n",
       "Phones       5800\n",
       "Shoes        1400\n",
       "Name: Sales, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grouping products by the \"Products\" column and summing the sales\n",
    "groupy_products = df.groupby(\"Products\")[\"Sales\"].sum()\n",
    "groupy_products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c890b029",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.SeriesGroupBy object at 0x000001F5513B24D0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupy_products = df.groupby(\"Products\")[\"Sales\"]\n",
    "groupy_products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "32c33897",
   "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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       "\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>Products</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Computers</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Phones</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shoes</td>\n",
       "      <td>1400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Products  Sales\n",
       "0  Computers   2500\n",
       "1     Phones   3000\n",
       "2      Shoes   1400"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Products = ['Computers', 'Phones', 'Shoes']\n",
    "Sales = [2500, 3000, 1400]\n",
    "\n",
    "# Creating a DataFrame\n",
    "df_sales = pd.DataFrame({\"Products\": Products, \"Sales\":Sales})\n",
    "# viewing the rows\n",
    "df_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f0391fbe",
   "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",
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       "    }\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>Products</th>\n",
       "      <th>Costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Computers</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Phones</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shoes</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Products  Costs\n",
       "0  Computers   1800\n",
       "1     Phones   2300\n",
       "2      Shoes   1000"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Products = ['Computers', 'Phones', 'Shoes']\n",
    "costs = [1800, 2300, 1000]\n",
    "\n",
    "# Creating a DataFrame\n",
    "df_costs = pd.DataFrame({\"Products\": Products, \"Costs\":costs})\n",
    "df_costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b825bcac",
   "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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       "\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>Products</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Computers</td>\n",
       "      <td>2500</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Phones</td>\n",
       "      <td>3000</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shoes</td>\n",
       "      <td>1400</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Products  Sales  Costs\n",
       "0  Computers   2500   1800\n",
       "1     Phones   3000   2300\n",
       "2      Shoes   1400   1000"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging the two DataFrames\n",
    "merged_df = df_sales.merge(df_costs, how = 'left', \n",
    "                           on = \"Products\")\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "671aef39",
   "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",
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       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>32</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>18</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>47</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Names  Age  Salary\n",
       "0    Alice   25   50000\n",
       "1      Bob   32   80000\n",
       "2  Charlie   18   20000\n",
       "3    David   47  120000"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = ['Alice', 'Bob', 'Charlie', 'David']\n",
    "age = [25, 32, 18, 47]\n",
    "salary = [50000, 80000, 20000, 120000]\n",
    "\n",
    "df = pd.DataFrame({\"Names\": names, \"Age\":age, \"Salary\": salary})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "54ebf982",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>32</td>\n",
       "      <td>80000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>18</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>47</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Names  Age  Salary\n",
       "1      Bob   32   80000\n",
       "2  Charlie   18   20000\n",
       "3    David   47  120000"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select rows by label\n",
    "first_three_rows = df.loc[1:3]\n",
    "first_three_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b21b2a4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>50000</td>\n",
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       "      <th>3</th>\n",
       "      <td>David</td>\n",
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      ],
      "text/plain": [
       "   Names  Salary\n",
       "0  Alice   50000\n",
       "3  David  120000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alice_and_david = df.loc[[0,3],['Names', 'Salary']]\n",
    "alice_and_david"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c82f595b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>32</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>47</td>\n",
       "      <td>120000</td>\n",
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       "</table>\n",
       "</div>"
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      "text/plain": [
       "   Names  Age  Salary\n",
       "1    Bob   32   80000\n",
       "3  David   47  120000"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "over_30 = df.loc[df[\"Age\"] > 30]\n",
    "over_30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c20282da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25</td>\n",
       "      <td>50000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>32</td>\n",
       "      <td>80000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Names  Age  Salary\n",
       "0  Alice   25   50000\n",
       "1    Bob   32   80000"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alice_and_bob = df.iloc[0:2]\n",
    "alice_and_bob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1df56000",
   "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>Names</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Names  Salary\n",
       "0  Alice   50000\n",
       "1    Bob   80000"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting specific rows and columns\n",
    "alice_bob_salaries = df.iloc[:2,[0,2]]\n",
    "alice_bob_salaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7d159dba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Merging the two DataFrames\n",
    "merged_df = df_sales.merge(df_costs, how = 'left', \n",
    "                           on = \"Products\")\n",
    "\n",
    "# Ploting a line plot\n",
    "merged_df.plot(x = 'Products', y = [\"Sales\", \"Costs\"], )\n",
    "plt.xlabel(\"Products\")\n",
    "plt.ylabel(\"Sales\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6864ad15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ploting a bar plot\n",
    "merged_df.plot(kind = \"bar\",\n",
    "               x = 'Products',\n",
    "               y = [\"Sales\", \"Costs\"], )\n",
    "plt.xlabel(\"Products\")\n",
    "plt.ylabel(\"Sales\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "821629c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_df.plot(kind = \"box\",\n",
    "               x = \"Products\", \n",
    "               y = [\"Sales\", \"Costs\"])\n",
    "plt.ylabel(\"Sales\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a5b881e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ploting a Scatter plot\n",
    "merged_df.plot(kind = \"scatter\", x = \"Costs\", y = \"Sales\")\n",
    "plt.title(\"Sales vs Costs\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "27048346",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting a hist plot\n",
    "merged_df[\"Sales\"].plot(kind = \"hist\", bins = 5)\n",
    "plt.title(\"Sales Distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "fde943f6",
   "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>Products</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shoes</td>\n",
       "      <td>1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Computers</td>\n",
       "      <td>2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Computers</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Phones</td>\n",
       "      <td>2800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Phones</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Products  Sales\n",
       "2      Shoes   1400\n",
       "3  Computers   2100\n",
       "0  Computers   2500\n",
       "4     Phones   2800\n",
       "1     Phones   3000"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Products = ['Computers', 'Phones', 'Shoes', 'Computers', 'Phones']\n",
    "Sales = [2500, 3000, 1400, 2100, 2800]\n",
    "\n",
    "# Creating a DataFrame\n",
    "df = pd.DataFrame({\"Products\": Products, \"Sales\":Sales})\n",
    "\n",
    "# Sorting the DataFrame by the sales column\n",
    "df_sorted = df.sort_values(by='Sales')\n",
    "df_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "70d7e425",
   "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>Products</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Phones</td>\n",
       "      <td>2800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Computers</td>\n",
       "      <td>2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shoes</td>\n",
       "      <td>1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Phones</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Computers</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Products  Sales\n",
       "4     Phones   2800\n",
       "3  Computers   2100\n",
       "2      Shoes   1400\n",
       "1     Phones   3000\n",
       "0  Computers   2500"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Products = ['Computers', 'Phones', 'Shoes', 'Computers', 'Phones']\n",
    "Sales = [2500, 3000, 1400, 2100, 2800]\n",
    "\n",
    "# Creating a DataFrame\n",
    "df = pd.DataFrame({\"Products\": Products, \"Sales\":Sales})\n",
    "\n",
    "# Sorting the DataFrame by the index\n",
    "df_sorted_by_index = df.sort_index(ascending=False, inplace=False)\n",
    "df_sorted_by_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4ce1a2c7",
   "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>Products</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shoes</td>\n",
       "      <td>1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Computers</td>\n",
       "      <td>2100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Products  Sales\n",
       "2      Shoes   1400\n",
       "3  Computers   2100"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a DataFrame\n",
    "df = pd.DataFrame({\"Products\": Products, \"Sales\":Sales})\n",
    "\n",
    "# Getting two products with the least sales\n",
    "df.nsmallest(2, \"Sales\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "875065e7",
   "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>Products</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Phones</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Phones</td>\n",
       "      <td>2800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Products  Sales\n",
       "1   Phones   3000\n",
       "4   Phones   2800"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a DataFrame\n",
    "df = pd.DataFrame({\"Products\": Products, \"Sales\":Sales})\n",
    "\n",
    "# Getting two products with the least sales\n",
    "df.nlargest(2, \"Sales\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcf5617e",
   "metadata": {},
   "source": [
    "## Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "e8b0bda9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating data using NumPy\n",
    "rng = np.random.default_rng(seed = 24)\n",
    "x = rng.random(100)\n",
    "y = rng.random(100)\n",
    "colors = rng.random(100)\n",
    "\n",
    "# Plotting data\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.scatter(x, y, c = colors, alpha = 1.0)\n",
    "plt.title('Random Scatter Plot', fontsize = 20)\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "419827dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "products = ['Cars', 'Beds', 'Phones', 'Shoes', 'Electrical Appliances']\n",
    "sales = [2000, 1500, 1000, 1020, 3000]\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.bar(products, sales, color = \"green\")\n",
    "plt.title('Sales Chart', fontsize= 20)\n",
    "plt.xlabel('Products')\n",
    "plt.ylabel('Sales')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ad28b27e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generating data using NumPy\n",
    "rng = np.random.default_rng(seed = 24)\n",
    "x = rng.standard_normal(1000)\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.hist(x, bins=20)\n",
    "plt.title('Histogram', fontsize = 20)\n",
    "plt.xlabel('Values')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "b338491b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng = np.random.default_rng(seed = 24)\n",
    "img = rng.random((100, 100))\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.imshow(img, cmap='gray')\n",
    "plt.title('Random Image')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "19dbb497",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 4*np.pi, 100)\n",
    "y = np.sin(x)\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.plot(x, y)\n",
    "plt.title('Sine Wave', fontsize=20)\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}