DM_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = {\n",
    "    \"age\": [21,2,18,224,34],\n",
    "\"agegroup\": [\"adult\",\"child\",\"adult\",\"elderly\",\"adult\"],\n",
    "\"height\": [6.0,3,5.7,5,-7],\n",
    "\"status\": [\"Single\",\"Married\",\"Married\",\"Widowed\",\"Married\"],\n",
    "\"yearsmarried\": [-1,0,20,2,3]}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': [21, 2, 18, 224, 34],\n",
       " 'agegroup': ['adult', 'child', 'adult', 'elderly', 'adult'],\n",
       " 'height': [6.0, 3, 5.7, 5, -7],\n",
       " 'status': ['Single', 'Married', 'Married', 'Widowed', 'Married'],\n",
       " 'yearsmarried': [-1, 0, 20, 2, 3]}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (i)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>agegroup</th>\n",
       "      <th>height</th>\n",
       "      <th>status</th>\n",
       "      <th>yearsmarried</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>adult</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>child</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>adult</td>\n",
       "      <td>5.7</td>\n",
       "      <td>Married</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>224</td>\n",
       "      <td>elderly</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "      <td>adult</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age agegroup  height   status  yearsmarried\n",
       "0   21    adult     6.0   Single            -1\n",
       "1    2    child     3.0  Married             0\n",
       "2   18    adult     5.7  Married            20\n",
       "3  224  elderly     5.0  Widowed             2\n",
       "4   34    adult    -7.0  Married             3"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(df)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('people.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('people.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>agegroup</th>\n",
       "      <th>height</th>\n",
       "      <th>status</th>\n",
       "      <th>yearsmarried</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>adult</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>child</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>adult</td>\n",
       "      <td>5.7</td>\n",
       "      <td>Married</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>224</td>\n",
       "      <td>elderly</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "      <td>adult</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age agegroup  height   status  yearsmarried\n",
       "0   21    adult     6.0   Single            -1\n",
       "1    2    child     3.0  Married             0\n",
       "2   18    adult     5.7  Married            20\n",
       "3  224  elderly     5.0  Widowed             2\n",
       "4   34    adult    -7.0  Married             3"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (ii)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def d1(): \n",
    "        \n",
    "\n",
    "    print(\"Age range:\")\n",
    "    print((df['age']<150) & (df['age']>-1))\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def d2(): \n",
    "    print(\"Years Married:\")\n",
    "    print(df['age'] > df['yearsmarried'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def d3(): \n",
    "    print(\"Status:\")\n",
    "    print((df[\"status\"]==\"Married\") | (df[\"status\"]==\"Single\") | (df[\"status\"]==\"Widowed\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def d4():  \n",
    "    df.loc[df['age']<18, 'agegroup'] = 'child'\n",
    "    df.loc[(df['age']>=18) & (df['age']<65), 'agegroup'] = 'adult'\n",
    "    df.loc[df['age']>=65, 'agegroup'] = 'elderly'\n",
    "    print(\"Age group:\")\n",
    "    print(df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (iv)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no.of valid values: 5\n",
      "no.of valid values: 0\n"
     ]
    }
   ],
   "source": [
    "x=(df[\"status\"]==\"Married\") | (df[\"status\"]==\"Single\") | (df[\"status\"]==\"Widowed\")\n",
    "print(\"no.of valid values:\",x.sum())\n",
    "print(\"no.of valid values:\",len(x)-x.sum())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (iii)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age range:\n",
      "0     True\n",
      "1     True\n",
      "2     True\n",
      "3    False\n",
      "4     True\n",
      "Name: age, dtype: bool\n",
      "Years Married:\n",
      "0     True\n",
      "1     True\n",
      "2    False\n",
      "3     True\n",
      "4     True\n",
      "dtype: bool\n",
      "Status:\n",
      "0    True\n",
      "1    True\n",
      "2    True\n",
      "3    True\n",
      "4    True\n",
      "Name: status, dtype: bool\n",
      "Age group:\n",
      "   age agegroup  height   status  yearsmarried\n",
      "0   21    adult     6.0   Single            -1\n",
      "1    2    child     3.0  Married             0\n",
      "2   18    adult     5.7  Married            20\n",
      "3  224  elderly     5.0  Widowed             2\n",
      "4   34    adult    -7.0  Married             3\n",
      "{None}\n"
     ]
    }
   ],
   "source": [
    "E = {d1(),d2(),d3(),d4()}\n",
    "print(E)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cecc8ad148>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}