240217-AIML-lecture_groupproject.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 240217: In Class Group Work \n",
    "- Import relevant python libraries.\n",
    "- Import the CSV file – NSMES1988new.csv into a dataframe and enforce the dtypes recommended in step 9 of Week 1.\n",
    "- Perform the following operations on age and income columns. Multiply Age by 10 and income by 10000.\n",
    "- Perform basic statistical analysis on the new dataframe and generate a brief report on the outcome.  \n",
    "Aggregations:\n",
    "- total visits by gender\n",
    "- average income by health\n",
    "- average visit by region\n",
    "- Invoke describe command on the dataframe and compare that with the basic statistical analysis done in the previous step, and report.\n",
    "- build a scatterplot for income vs age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import relevant python libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chand\\AppData\\Local\\Temp\\ipykernel_45472\\545415830.py:1: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the CSV file – NSMES1988new.csv into a dataframe and enforce the dtypes recommended in step 9 of Week 1.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('NSMES1988.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Unnamed: 0', 'visits', 'nvisits', 'ovisits', 'novisits', 'emergency',\n",
      "       'hospital', 'chronic', 'school'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "numerical_cols = df.select_dtypes('int').columns\n",
    "print(numerical_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform the following operations on age and income columns. Multiply Age by 10 and income by 10000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['age10'] = df['age']*10\n",
    "df['income10'] = df['income']*10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4406 entries, 0 to 4405\n",
      "Data columns (total 22 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  4406 non-null   int16  \n",
      " 1   visits      4406 non-null   int16  \n",
      " 2   nvisits     4406 non-null   int16  \n",
      " 3   ovisits     4406 non-null   int16  \n",
      " 4   novisits    4406 non-null   int16  \n",
      " 5   emergency   4406 non-null   int16  \n",
      " 6   hospital    4406 non-null   int16  \n",
      " 7   health      4406 non-null   object \n",
      " 8   chronic     4406 non-null   int16  \n",
      " 9   adl         4406 non-null   object \n",
      " 10  region      4406 non-null   object \n",
      " 11  age         4406 non-null   float64\n",
      " 12  afam        4406 non-null   object \n",
      " 13  gender      4406 non-null   object \n",
      " 14  married     4406 non-null   object \n",
      " 15  school      4406 non-null   int16  \n",
      " 16  income      4406 non-null   float64\n",
      " 17  employed    4406 non-null   object \n",
      " 18  insurance   4406 non-null   object \n",
      " 19  medicaid    4406 non-null   object \n",
      " 20  age10       4406 non-null   float64\n",
      " 21  income10    4406 non-null   float64\n",
      "dtypes: float64(4), int16(9), object(9)\n",
      "memory usage: 525.1+ KB\n"
     ]
    }
   ],
   "source": [
    "#dtypes \n",
    "df[numerical_cols] = df[numerical_cols].astype('int16')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform basic statistical analysis on the new dataframe and generate a brief report on the outcome.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aggregations:\n",
    "- total visits by gender\n",
    "- average income by health\n",
    "- average visit by region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>visits</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>15805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>9637</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        visits\n",
       "gender        \n",
       "female   15805\n",
       "male      9637"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('gender').agg({'visits':'sum'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>income10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>health</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>average</th>\n",
       "      <td>25519.896449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>excellent</th>\n",
       "      <td>32774.114577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poor</th>\n",
       "      <td>19051.624621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "               income10\n",
       "health                 \n",
       "average    25519.896449\n",
       "excellent  32774.114577\n",
       "poor       19051.624621"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('health').agg({'income10':'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>visits</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>midwest</th>\n",
       "      <td>5.408816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>northeast</th>\n",
       "      <td>6.091995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>5.578067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>west</th>\n",
       "      <td>6.368421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             visits\n",
       "region             \n",
       "midwest    5.408816\n",
       "northeast  6.091995\n",
       "other      5.578067\n",
       "west       6.368421"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('region').agg({'visits':'mean'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Invoke describe command on the dataframe and compare that with the basic statistical analysis done in the previous step, and report.\n",
    "- Average visits by region \n",
    "    - Average visits 5.78 in data set \n",
    "    - West and northeast has higher average of visits than the average \n",
    "- Average incom\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>visits</th>\n",
       "      <th>nvisits</th>\n",
       "      <th>ovisits</th>\n",
       "      <th>novisits</th>\n",
       "      <th>emergency</th>\n",
       "      <th>hospital</th>\n",
       "      <th>chronic</th>\n",
       "      <th>age</th>\n",
       "      <th>school</th>\n",
       "      <th>income</th>\n",
       "      <th>age10</th>\n",
       "      <th>income10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4406.000000</td>\n",
       "      <td>4406.000000</td>\n",
       "      <td>4406.000000</td>\n",
       "      <td>4406.000000</td>\n",
       "      <td>4406.000000</td>\n",
       "      <td>4406.000000</td>\n",
       "      <td>4406.000000</td>\n",
       "      <td>4406.000000</td>\n",
       "      <td>4406.000000</td>\n",
       "      <td>4406.000000</td>\n",
       "      <td>4406.000000</td>\n",
       "      <td>4406.000000</td>\n",
       "      <td>4406.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2203.500000</td>\n",
       "      <td>5.774399</td>\n",
       "      <td>1.618021</td>\n",
       "      <td>0.750794</td>\n",
       "      <td>0.536087</td>\n",
       "      <td>0.263504</td>\n",
       "      <td>0.295960</td>\n",
       "      <td>1.541988</td>\n",
       "      <td>7.402406</td>\n",
       "      <td>10.290286</td>\n",
       "      <td>2.527132</td>\n",
       "      <td>74.024058</td>\n",
       "      <td>25271.320468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1272.046972</td>\n",
       "      <td>6.759225</td>\n",
       "      <td>5.317056</td>\n",
       "      <td>3.652759</td>\n",
       "      <td>3.879506</td>\n",
       "      <td>0.703659</td>\n",
       "      <td>0.746398</td>\n",
       "      <td>1.349632</td>\n",
       "      <td>0.633405</td>\n",
       "      <td>3.738736</td>\n",
       "      <td>2.924648</td>\n",
       "      <td>6.334050</td>\n",
       "      <td>29246.476178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.012500</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>-10125.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1102.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.912150</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>9121.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2203.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.698150</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>16981.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3304.750000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.172850</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>31728.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4406.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>10.900000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>54.835100</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>548351.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0       visits      nvisits      ovisits     novisits  \\\n",
       "count  4406.000000  4406.000000  4406.000000  4406.000000  4406.000000   \n",
       "mean   2203.500000     5.774399     1.618021     0.750794     0.536087   \n",
       "std    1272.046972     6.759225     5.317056     3.652759     3.879506   \n",
       "min       1.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%    1102.250000     1.000000     0.000000     0.000000     0.000000   \n",
       "50%    2203.500000     4.000000     0.000000     0.000000     0.000000   \n",
       "75%    3304.750000     8.000000     1.000000     0.000000     0.000000   \n",
       "max    4406.000000    89.000000   104.000000   141.000000   155.000000   \n",
       "\n",
       "         emergency     hospital      chronic          age       school  \\\n",
       "count  4406.000000  4406.000000  4406.000000  4406.000000  4406.000000   \n",
       "mean      0.263504     0.295960     1.541988     7.402406    10.290286   \n",
       "std       0.703659     0.746398     1.349632     0.633405     3.738736   \n",
       "min       0.000000     0.000000     0.000000     6.600000     0.000000   \n",
       "25%       0.000000     0.000000     1.000000     6.900000     8.000000   \n",
       "50%       0.000000     0.000000     1.000000     7.300000    11.000000   \n",
       "75%       0.000000     0.000000     2.000000     7.800000    12.000000   \n",
       "max      12.000000     8.000000     8.000000    10.900000    18.000000   \n",
       "\n",
       "            income        age10       income10  \n",
       "count  4406.000000  4406.000000    4406.000000  \n",
       "mean      2.527132    74.024058   25271.320468  \n",
       "std       2.924648     6.334050   29246.476178  \n",
       "min      -1.012500    66.000000  -10125.000000  \n",
       "25%       0.912150    69.000000    9121.500000  \n",
       "50%       1.698150    73.000000   16981.500000  \n",
       "75%       3.172850    78.000000   31728.500000  \n",
       "max      54.835100   109.000000  548351.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "build a scatterplot for income vs age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='age10', ylabel='income10'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=df['age10'],y=df['income10'],alpha=0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>income10</th>\n",
       "      <th>age10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>income10</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>-0.07313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age10</th>\n",
       "      <td>-0.07313</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          income10    age10\n",
       "income10   1.00000 -0.07313\n",
       "age10     -0.07313  1.00000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['income10','age10']].corr()\n",
    "#the correlation is -0.07, which is very weakly correlated. Run a statistical test to see if this is significantly different from no correlation "
   ]
  }
 ],
 "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": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}