assignment1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d7dc37fe-8f25-4d98-8b7d-c7e422c527b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sns.load_dataset('planets')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print/ Display the first 5 rows of dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f57c2abf-fa5a-4b72-953f-3e63422dcdde",
   "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",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>number</th>\n",
       "      <th>orbital_period</th>\n",
       "      <th>mass</th>\n",
       "      <th>distance</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>269.300</td>\n",
       "      <td>7.10</td>\n",
       "      <td>77.40</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>874.774</td>\n",
       "      <td>2.21</td>\n",
       "      <td>56.95</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>763.000</td>\n",
       "      <td>2.60</td>\n",
       "      <td>19.84</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>326.030</td>\n",
       "      <td>19.40</td>\n",
       "      <td>110.62</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>516.220</td>\n",
       "      <td>10.50</td>\n",
       "      <td>119.47</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            method  number  orbital_period   mass  distance  year\n",
       "0  Radial Velocity       1         269.300   7.10     77.40  2006\n",
       "1  Radial Velocity       1         874.774   2.21     56.95  2008\n",
       "2  Radial Velocity       1         763.000   2.60     19.84  2011\n",
       "3  Radial Velocity       1         326.030  19.40    110.62  2007\n",
       "4  Radial Velocity       1         516.220  10.50    119.47  2009"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code here\n",
    "df.head()   #It's default value is 5.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print/ Display the last 5 rows of dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4c27adad-ccd9-4871-8d81-dcc11c742dae",
   "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",
       "    }\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>method</th>\n",
       "      <th>number</th>\n",
       "      <th>orbital_period</th>\n",
       "      <th>mass</th>\n",
       "      <th>distance</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1030</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>3.941507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>172.0</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>2.615864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>3.191524</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.0</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>4.125083</td>\n",
       "      <td>NaN</td>\n",
       "      <td>293.0</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1034</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>4.187757</td>\n",
       "      <td>NaN</td>\n",
       "      <td>260.0</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       method  number  orbital_period  mass  distance  year\n",
       "1030  Transit       1        3.941507   NaN     172.0  2006\n",
       "1031  Transit       1        2.615864   NaN     148.0  2007\n",
       "1032  Transit       1        3.191524   NaN     174.0  2007\n",
       "1033  Transit       1        4.125083   NaN     293.0  2008\n",
       "1034  Transit       1        4.187757   NaN     260.0  2008"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code here\n",
    "df.tail()       #Default value is 5, like 'head'."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "252d274f",
   "metadata": {},
   "source": [
    "### How many unique methods are there?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2887c6e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "df.groupby('method').size().count()     #displays amount of unique methods\n",
    "\n",
    "#df.groupby('method').size()             #displays these methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the number of data collected in each year?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1989      1\n",
       "1992      2\n",
       "1994      1\n",
       "1995      1\n",
       "1996      6\n",
       "1997      1\n",
       "1998      5\n",
       "1999     15\n",
       "2000     16\n",
       "2001     12\n",
       "2002     32\n",
       "2003     25\n",
       "2004     26\n",
       "2005     39\n",
       "2006     31\n",
       "2007     53\n",
       "2008     74\n",
       "2009     98\n",
       "2010    102\n",
       "2011    185\n",
       "2012    140\n",
       "2013    118\n",
       "2014     52\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code here\n",
    "df.groupby('year').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which columns have missing values and how many?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "method              0\n",
       "number              0\n",
       "orbital_period     43\n",
       "mass              522\n",
       "distance          227\n",
       "year                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code here\n",
    "df.isnull().sum()\n",
    "\n",
    "#df.info()          #info can also be used to examine \"Non-Null Count\" column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write docstring for the function below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def do_something(x, y, z):\n",
    "    \"\"\"Takes in 3 numbers, returns sum of 1 over every variable.\"\"\"   \n",
    "\n",
    "    result = 1 / x + 1 / y + 1 / z\n",
    "\n",
    "    return 1 / result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get (print) the docstring of the function defined above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Takes in 3 numbers, returns sum of 1 over every variable.'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code here\n",
    "do_something.__doc__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Call do_something function for the (11,13,15) values and save it as result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.26441351888668"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code here\n",
    "value = do_something(11, 13, 15)\n",
    "value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In the variable 's' containing a sentence, extract the word using result defined above as index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'and'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = \"I love Machine Learning and Deep Learning\"\n",
    "\n",
    "# use int() to convert the result to integer\n",
    "# your code here\n",
    "\n",
    "ind = int(value)\n",
    "words = s.split()\n",
    "words[ind]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sort the given array below, do not use built in functions. All sorting methods are allowed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9, 28, 37, 37, 54, 55, 61, 63, 76, 82]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "arr = [random.randint(1, 100) for i in range(10)]\n",
    "\n",
    "# your code here\n",
    "\n",
    "def compute_lenght(arr):\n",
    "    i = 0\n",
    "    while( arr[i:] ):\n",
    "        i+=1\n",
    "    return i\n",
    "\n",
    "\n",
    "\n",
    "def selection_sort(arr, size):\n",
    "    for i in range(size):\n",
    "        max_index = i\n",
    "        \n",
    "        for j in range(max_index+1, size):\n",
    "            if( arr[max_index] > arr[j] ):\n",
    "                max_index = j\n",
    "        \n",
    "        (arr[max_index], arr[i]) = (arr[i], arr[max_index])\n",
    "\n",
    "\n",
    "size = compute_lenght(arr)\n",
    "selection_sort(arr, size)\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### define a recursive function to calculate input numbers factorial named factorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "720"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code here\n",
    "\n",
    "def rec_factorial(numb):\n",
    "    if( numb == 1 or numb == 0 ):\n",
    "        return 1\n",
    "    return numb*rec_factorial(numb-1)\n",
    "\n",
    "rec_factorial(6)        #Working example"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3640d41e",
   "metadata": {},
   "source": [
    "### define a function to merge the given arrays and calculate the median named twoMedian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 19, 20, 24, 24, 28, 31, 32, 33, 34, 42, 44, 65, 71, 74, 88, 91, 92, 94, 99]\n"
     ]
    }
   ],
   "source": [
    "# your code here\n",
    "\n",
    "def merge_arr(arr1, arr2):      #this function assumes that the lists are sorted.\n",
    "    merged = []\n",
    "    i, j = 0, 0\n",
    "    while( i<len(arr1) and j<len(arr2) ):\n",
    "        if( arr1[i] < arr2[j] ):\n",
    "            merged.append(arr1[i])\n",
    "            i+=1\n",
    "        else:\n",
    "            merged.append(arr2[j])\n",
    "            j+=1\n",
    "            \n",
    "    while( i<len(arr1) ):\n",
    "        merged.append(arr1[i])      #Append remainig elements of arr1 \n",
    "        i+=1\n",
    "    while( j<len(arr2) ):\n",
    "        merged.append(arr2[j])      #Append remainig elements of arr2\n",
    "        j+=1\n",
    "    \n",
    "    return merged\n",
    "\n",
    "arr1 = [random.randint(1, 100) for i in range(10)]\n",
    "arr2 = [random.randint(1, 100) for i in range(10)]\n",
    "\n",
    "selection_sort(arr1, compute_lenght(arr1))          #Sort the arrays because merge_arr function expects them to be sorted.\n",
    "selection_sort(arr2, compute_lenght(arr2))\n",
    "\n",
    "print(merge_arr(arr1, arr2))"
   ]
  }
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