Python Final Assessment_ Monica Grant (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "957298ac",
   "metadata": {},
   "source": [
    "## Python Assessment by Monica Grant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eabb45f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Welcome to your Final Assessment for Python!\n",
    "\n",
    "# At the top of your notebook, you will need to import numpy, pandas, matplotlib, and seaborn. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4e43f173",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5506e5b0",
   "metadata": {},
   "source": [
    "### 1. Create a variable 'squares' and assign it to a list with values 4, 9, 25, 36, 49, and 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "771b592d",
   "metadata": {},
   "outputs": [],
   "source": [
    "squares = [4, 9, 25, 36, 49, 64]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d304b3ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4, 9, 25, 36, 49, 64]\n"
     ]
    }
   ],
   "source": [
    "print(squares)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74be6522",
   "metadata": {},
   "source": [
    "#### Question 1. ANSWER: squares = [4, 9, 25, 36, 49, 64]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a8a7b18",
   "metadata": {},
   "source": [
    "### 2. Write a for loop to iterate over your list and calculate the square root of the members of the list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "34c1bea3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d82dabcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0\n",
      "3.0\n",
      "5.0\n",
      "6.0\n",
      "7.0\n",
      "8.0\n"
     ]
    }
   ],
   "source": [
    "squares = [4, 9, 25, 36, 49, 64]\n",
    "for s in squares:\n",
    "    sq = math.sqrt(s)\n",
    "    print(sq)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a90836c2",
   "metadata": {},
   "source": [
    "#### Question 2. ANSWER \n",
    "squares = [4, 9, 25, 36, 49, 64]\n",
    "for s in squares:\n",
    "    sq = math.sqrt(s)\n",
    "    print(sq)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc19b6f4",
   "metadata": {},
   "source": [
    "### 3. Create a dictionary with make : [Chevy, Ford, Honda] and model:  [Camaro, Mustang, Accord]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4e98a3b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "cars={'Make':['Chevy', 'Ford', 'Honda'], 'Model':['Camaro', 'Mustang', 'Accord']}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e776d851",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Make': ['Chevy', 'Ford', 'Honda'], 'Model': ['Camaro', 'Mustang', 'Accord']}\n",
      "['Chevy', 'Ford', 'Honda']\n",
      "['Camaro', 'Mustang', 'Accord']\n"
     ]
    }
   ],
   "source": [
    "print(cars)\n",
    "print(cars ['Make'])\n",
    "print(cars['Model'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8a585e2",
   "metadata": {},
   "source": [
    "#### Question 3. ANSWER  cars={'Make':['Chevy', 'Ford', 'Honda'], 'Model':['Camaro', 'Mustang', 'Accord']}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05f06474",
   "metadata": {},
   "source": [
    "### 4. Create a 1 dimensional NumPy array with numbers from 0 to 100 incremented by 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4b2cd1e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0.          11.11111111  22.22222222  33.33333333  44.44444444\n",
      "  55.55555556  66.66666667  77.77777778  88.88888889 100.        ]\n"
     ]
    }
   ],
   "source": [
    "array1 = np.linspace(0,100,10) #How many steps you want to take\n",
    "print(array1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95b16e24",
   "metadata": {},
   "source": [
    "#### Question 4. ANSWER  array1 = np.linspace(0,100,10) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "629fef6d",
   "metadata": {},
   "source": [
    "### 5. Import seaborn and use the load_dataset function to create a dataframe from the 'healthexp' dataset.  Create a lineplot showing years on the x-axis, life_expectancy on the y-axis, and country represented by color (hue). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c5259834",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anagrams',\n",
       " 'anscombe',\n",
       " 'attention',\n",
       " 'brain_networks',\n",
       " 'car_crashes',\n",
       " 'diamonds',\n",
       " 'dots',\n",
       " 'dowjones',\n",
       " 'exercise',\n",
       " 'flights',\n",
       " 'fmri',\n",
       " 'geyser',\n",
       " 'glue',\n",
       " 'healthexp',\n",
       " 'iris',\n",
       " 'mpg',\n",
       " 'penguins',\n",
       " 'planets',\n",
       " 'seaice',\n",
       " 'taxis',\n",
       " 'tips',\n",
       " 'titanic']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.get_dataset_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2b517337",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=sns.load_dataset('healthexp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e6377efc",
   "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>Year</th>\n",
       "      <th>Country</th>\n",
       "      <th>Spending_USD</th>\n",
       "      <th>Life_Expectancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1970</td>\n",
       "      <td>Germany</td>\n",
       "      <td>252.311</td>\n",
       "      <td>70.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1970</td>\n",
       "      <td>France</td>\n",
       "      <td>192.143</td>\n",
       "      <td>72.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1970</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>123.993</td>\n",
       "      <td>71.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1970</td>\n",
       "      <td>Japan</td>\n",
       "      <td>150.437</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1970</td>\n",
       "      <td>USA</td>\n",
       "      <td>326.961</td>\n",
       "      <td>70.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>2020</td>\n",
       "      <td>Germany</td>\n",
       "      <td>6938.983</td>\n",
       "      <td>81.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>2020</td>\n",
       "      <td>France</td>\n",
       "      <td>5468.418</td>\n",
       "      <td>82.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>2020</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>5018.700</td>\n",
       "      <td>80.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>2020</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4665.641</td>\n",
       "      <td>84.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>2020</td>\n",
       "      <td>USA</td>\n",
       "      <td>11859.179</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>274 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year        Country  Spending_USD  Life_Expectancy\n",
       "0    1970        Germany       252.311             70.6\n",
       "1    1970         France       192.143             72.2\n",
       "2    1970  Great Britain       123.993             71.9\n",
       "3    1970          Japan       150.437             72.0\n",
       "4    1970            USA       326.961             70.9\n",
       "..    ...            ...           ...              ...\n",
       "269  2020        Germany      6938.983             81.1\n",
       "270  2020         France      5468.418             82.3\n",
       "271  2020  Great Britain      5018.700             80.4\n",
       "272  2020          Japan      4665.641             84.7\n",
       "273  2020            USA     11859.179             77.0\n",
       "\n",
       "[274 rows x 4 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e68022c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 'Year'\n",
    "y = 'Life_Expectancy'\n",
    "hue = 'Country'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ca87082b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question 5. ANSWER\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#life = sns.lineplot(x , y , data = df) \n",
    "\n",
    "life = sns.lineplot(x = df['Year'] , y = 'Life_Expectancy', hue = 'Country',  data = df) \n",
    "\n",
    "print ('Question 5. ANSWER')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42daf8fc",
   "metadata": {},
   "source": [
    "### 6.  Use the load_dataset function to create a dataframe from the 'iris' data.  Use a dataframe method to summarize the data in terms of descriptive statistics.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b91e2fa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=sns.load_dataset('iris')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8a75f252",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fc5545f0",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.057333      3.758000     1.199333\n",
       "std        0.828066     0.435866      1.765298     0.762238\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b6b7ff57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length          7.9\n",
       "sepal_width           4.4\n",
       "petal_length          6.9\n",
       "petal_width           2.5\n",
       "species         virginica\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c5e2ba96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length       4.3\n",
       "sepal_width        2.0\n",
       "petal_length       1.0\n",
       "petal_width        0.1\n",
       "species         setosa\n",
       "dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "69f737bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    float64\n",
       "sepal_width     float64\n",
       "petal_length    float64\n",
       "petal_width     float64\n",
       "species          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "55a88c84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0c320ad1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame._add_numeric_operations.<locals>.mean of      sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.mean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80b4f80e",
   "metadata": {},
   "source": [
    "#### Question 6. ANSWER:  df2.describe() is best for descriptive statistics "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef3bf508",
   "metadata": {},
   "source": [
    "### 7.  Create a new data frame that is a pivot table of the iris dataframe with an index of species, and average sepal_width for the values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ffb16013",
   "metadata": {},
   "outputs": [],
   "source": [
    "#iris = pd.pivot_table(data = df2, values = \"sepal_width.mean()\", index = \"species\") \n",
    "    \n",
    "iris = pd.pivot_table(data=df2, values=\"sepal_width\", index=\"species\", aggfunc=\"mean\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "175465d3",
   "metadata": {},
   "source": [
    "#### Question 7 ANSWER:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6794afea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            sepal_width\n",
      "species                \n",
      "setosa            3.428\n",
      "versicolor        2.770\n",
      "virginica         2.974\n"
     ]
    }
   ],
   "source": [
    "print(iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69eb0489",
   "metadata": {},
   "source": [
    "### 8. Create a scatter plot  with sepal_length in x-axis and petal_length on the y-axis.  Distinguish the different 'species' on the plot using color (hue). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "606b4b95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scatter plot syntax \n",
    "# x = sepal_length in cm\n",
    "# y = petal_ length in cm\n",
    "# distinguish 'species' with 'color' = 'hue'\n",
    "iris = sns.load_dataset(\"iris\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "2dfcd6b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question 8. Answer\n"
     ]
    }
   ],
   "source": [
    "sns.scatterplot(data=iris, x=\"sepal_length\", y=\"petal_length\", hue=\"species\")\n",
    "plt.xlabel(\"Sepal length (cm)\")\n",
    "plt.ylabel(\"Petal length (cm)\")\n",
    "plt.title(\"Iris Flower Species: Petal vs Sepal\")\n",
    "plt.show()\n",
    "print ( \"Question 8. Answer\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68299863",
   "metadata": {},
   "source": [
    "### 9. Create a box plot to show variation in petal_width by species. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "1ff60cf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question 9. Answer\n"
     ]
    }
   ],
   "source": [
    "## example ax = sns.boxplot(x = 'cut', y = 'price', data = df, color = 'green')\n",
    "\n",
    "irisbox = sns.boxplot(x = 'species', y = 'petal_width', data = iris, color = 'green')\n",
    "plt.title(\"Iris Box Plot: Petal Width by Species\")\n",
    "plt.show()\n",
    "print (\"Question 9. Answer\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0d57860",
   "metadata": {},
   "source": [
    "### 10. Create a correlation matrix for the numeric fields in the dataset.  Visualize the correlation matrix as a heatmap.  Choose a color palette and annotate the heatmap cells with the correlation values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "e57a7701",
   "metadata": {},
   "outputs": [],
   "source": [
    "#corr_dataframe = df[['measure1', 'measure2',...]].corr\n",
    "corr_iris=iris[['sepal_length', 'sepal_width', 'petal_length','petal_width' ]].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "b6dfa57f",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.117570</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>-0.117570</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.428440</td>\n",
       "      <td>-0.366126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.428440</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>0.817941</td>\n",
       "      <td>-0.366126</td>\n",
       "      <td>0.962865</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sepal_length  sepal_width  petal_length  petal_width\n",
       "sepal_length      1.000000    -0.117570      0.871754     0.817941\n",
       "sepal_width      -0.117570     1.000000     -0.428440    -0.366126\n",
       "petal_length      0.871754    -0.428440      1.000000     0.962865\n",
       "petal_width       0.817941    -0.366126      0.962865     1.000000"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_iris"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dcf41dc",
   "metadata": {},
   "source": [
    "#### Question 10:  ANSWER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "9248ffab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# hm = sns.heatmap(corr_dataframe, annot =True)\n",
    "\n",
    "irishm= sns.heatmap(corr_iris, annot=True, cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ad60a03",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}