Assignment 1-Answers

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "McSxJAwcOdZ1"
   },
   "source": [
    "# Basic Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CU48hgo4Owz5"
   },
   "source": [
    "## 1. Split this string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "s07c7JK7Oqt-"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Hi', 'there', 'Sam!']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = \"Hi there Sam!\"\n",
    "x=s.split()\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GH1QBn8HP375"
   },
   "source": [
    "*`italicized text`*## 2. Use .format() to print the following string. \n",
    "\n",
    "## 2. Output should be: The diameter of Earth is 12742 kilometers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "_ZHoml3kPqic"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The diameter of Earth is  12742 kilometers'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet = \"Earth\"\n",
    "diameter = 12742\n",
    "txt1 = \"The diameter of {} is  {} kilometers\".format(planet, diameter)\n",
    "txt1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KE74ZEwkRExZ"
   },
   "source": [
    "## 3. In this nest dictionary grab the word \"hello\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "fcVwbCc1QrQI"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'k1':[1,2,3,{'tricky':['oh','man','inception',{'target':[1,2,3,'hello']}]}]}\n",
    "d['k1'][3]['tricky'][3]['target'][3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bw0vVp-9ddjv"
   },
   "source": [
    "# Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "LLiE_TYrhA1O"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wOg8hinbgx30"
   },
   "source": [
    "## 4.1 Create an array of 10 zeros? \n",
    "## 4.2 Create an array of 10 fives?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "e4005lsTYXxx"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "e4005lsTYXxx"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones(10)*5\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gZHHDUBvrMX4"
   },
   "source": [
    "## 5. Create an array of all the even integers from 20 to 35"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "oAI2tbU2Yag-"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array of all the even integers from 20 to 35\n",
      "[20 22 24 26 28 30 32 34]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "array=np.arange(20,36,2)\n",
    "print(\"Array of all the even integers from 20 to 35\")\n",
    "print(array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NaOM308NsRpZ"
   },
   "source": [
    "## 6. Create a 3x3 matrix with values ranging from 0 to 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "tOlEVH7BYceE"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 2]\n",
      " [3 4 5]\n",
      " [6 7 8]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "x =  np.arange(0, 9).reshape(3,3)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hQ0dnhAQuU_p"
   },
   "source": [
    "## 7. Concatinate a and b \n",
    "## a = np.array([1, 2, 3]), b = np.array([4, 5, 6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "rAPSw97aYfE0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 2, 3])\n",
    "b = np.array([4, 5, 6])\n",
    "arr = np.concatenate((a,b))\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dlPEY9DRwZga"
   },
   "source": [
    "# Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ijoYW51zwr87"
   },
   "source": [
    "## 8. Create a dataframe with 3 rows and 2 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "T5OxJRZ8uvR7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>24000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Salary\n",
       "0   22   10000\n",
       "1   23   24000\n",
       "2   24   20000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "d={'Age':[22,23,24],'Salary':[10000,24000,20000]}\n",
    "data=pd.DataFrame(d)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UXSmdNclyJQD"
   },
   "source": [
    "## 9. Generate the series of dates from 1st Jan, 2023 to 10th Feb, 2023"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "dgyC0JhVYl4F"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-01-01 00:00:00\n",
      "2023-01-02 00:00:00\n",
      "2023-01-03 00:00:00\n",
      "2023-01-04 00:00:00\n",
      "2023-01-05 00:00:00\n",
      "2023-01-06 00:00:00\n",
      "2023-01-07 00:00:00\n",
      "2023-01-08 00:00:00\n",
      "2023-01-09 00:00:00\n",
      "2023-01-10 00:00:00\n",
      "2023-01-11 00:00:00\n",
      "2023-01-12 00:00:00\n",
      "2023-01-13 00:00:00\n",
      "2023-01-14 00:00:00\n",
      "2023-01-15 00:00:00\n",
      "2023-01-16 00:00:00\n",
      "2023-01-17 00:00:00\n",
      "2023-01-18 00:00:00\n",
      "2023-01-19 00:00:00\n",
      "2023-01-20 00:00:00\n",
      "2023-01-21 00:00:00\n",
      "2023-01-22 00:00:00\n",
      "2023-01-23 00:00:00\n",
      "2023-01-24 00:00:00\n",
      "2023-01-25 00:00:00\n",
      "2023-01-26 00:00:00\n",
      "2023-01-27 00:00:00\n",
      "2023-01-28 00:00:00\n",
      "2023-01-29 00:00:00\n",
      "2023-01-30 00:00:00\n",
      "2023-01-31 00:00:00\n",
      "2023-02-01 00:00:00\n",
      "2023-02-02 00:00:00\n",
      "2023-02-03 00:00:00\n",
      "2023-02-04 00:00:00\n",
      "2023-02-05 00:00:00\n",
      "2023-02-06 00:00:00\n",
      "2023-02-07 00:00:00\n",
      "2023-02-08 00:00:00\n",
      "2023-02-09 00:00:00\n",
      "2023-02-10 00:00:00\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "  \n",
    "per1 = pd.date_range(start ='01-01-2023', \n",
    "         end ='02-10-2023', freq ='24H')\n",
    "  \n",
    "for val in per1:\n",
    "    print(val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZizSetD-y5az"
   },
   "source": [
    "## 10. Create 2D list to DataFrame\n",
    "\n",
    "lists = [[1, 'aaa', 22],\n",
    "         [2, 'bbb', 25],\n",
    "         [3, 'ccc', 24]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "_XMC8aEt0llB",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Id Name  Age\n",
      "0   1  aaa   22\n",
      "1   2  bbb   25\n",
      "2   3  ccc   24\n"
     ]
    }
   ],
   "source": [
    "lists = [[1, 'aaa', 22], [2, 'bbb', 25], [3, 'ccc', 24]]\n",
    "df = pd.DataFrame(lists, columns =['Id', 'Name','Age']) \n",
    "print(df )"
   ]
  }
 ],
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