t_company_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import math \n",
    "from scipy import stats as st\n",
    "from sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Загрузка датасетов из репозитория github"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [],
   "source": [
    "cardio_alco = pd.read_csv('https://raw.githubusercontent.com/voleka-ocrv/turing_data_analyst/main/cardio_alco.csv', sep=';')\n",
    "cardio_base = pd.read_csv('https://raw.githubusercontent.com/voleka-ocrv/turing_data_analyst/main/cardio_base.csv')\n",
    "\n",
    "covid = pd.read_csv('https://raw.githubusercontent.com/voleka-ocrv/turing_data_analyst/main/covid_data.csv', parse_dates=['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: In the original file Age is in days, we should calculate with age in years rounded down.\n",
    "cardio_base['age'] = cardio_base['age'].transform(lambda x: int(math.floor(x/365)), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1:\n",
    "Which country has the 3rd death rate? Death rate: total number of death per million inhabitants.\\n\",\n",
    "- [ ] Italy\n",
    "- [x] **Andorra**\n",
    "- [ ] USA\n",
    "- [ ] Belgium\n",
    "- [ ] Spain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>total_death</th>\n",
       "      <th>inhabitants</th>\n",
       "      <th>death_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>San Marino</th>\n",
       "      <td>42</td>\n",
       "      <td>33938.0</td>\n",
       "      <td>1237.550828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>9619</td>\n",
       "      <td>11589616.0</td>\n",
       "      <td>829.967102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>51</td>\n",
       "      <td>77265.0</td>\n",
       "      <td>660.066007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            total_death  inhabitants   death_rate\n",
       "location                                         \n",
       "San Marino           42      33938.0  1237.550828\n",
       "Belgium            9619   11589616.0   829.967102\n",
       "Andorra              51      77265.0   660.066007"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_death_rate = covid.groupby('location').agg(\n",
    "    total_death=pd.NamedAgg('new_deaths', 'sum'), \n",
    "    inhabitants=pd.NamedAgg('population', 'max'),\n",
    "    )\n",
    "\n",
    "covid_death_rate['death_rate'] = covid_death_rate['total_death']/(covid_death_rate['inhabitants']/1e6)\n",
    "\n",
    "covid_death_rate.sort_values('death_rate', ascending=False).head(3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer: \n",
    "Andorra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Question 2\n",
    "\n",
    "How tall are the tallest 1% of people?\n",
    "- [ ] They are taller than 186 cm\n",
    "- [ ] They are taller than 175 cm\n",
    "- [ ] They are taller than 147 cm\n",
    "- [x] **They are taller than 184 cm**\n",
    "- [ ] They are taller than 191 cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "184.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cardio_base['height'].quantile(0.99)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer: \n",
    "They are taller than 184 cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Question 3\n",
    "\n",
    "Which two features have the highest spearman rank correlation?\n",
    "- [ ] Age and weight\n",
    "- [ ] Age and blood preasure\n",
    "- [x] **Blood preasure levels (ap_hi, ap_low)**\n",
    "- [x] **Gender and height**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spearman_corr = cardio_base.corr(method='spearman')\n",
    "mask = pd.DataFrame(np.triu(np.ones_like(spearman_corr)), index=spearman_corr.index, columns=spearman_corr.columns).replace({0: False, 1: True})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "ax.set_title(\"Spearman rank correlation\")\n",
    "sns.heatmap(spearman_corr, annot=True, fmt='.2f', cmap=\"Reds\", mask=mask);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer: \n",
    "- Blood preasure levels (ap_hi, ap_low) \n",
    "- Gender and height"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Question 4\n",
    "Do people over 50 have higher cholesterol levels than the rest?\n",
    "- [ ] Yes, their cholesterol level is 29% higher on average\n",
    "- [x] **No, it's about the same**\n",
    "- [ ] No, people over 50 have 7% lower cholesterol level\n",
    "- [ ] Yes, their cholesterol level is 14% higher on average\n",
    "- [ ] Yes, their cholesterol level is 19% higher on average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04718033578232861"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cardio_base['cholesterol'].where(cardio_base['age'] > 50).mean() / cardio_base['cholesterol'].mean() - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer: \n",
    "No, it's about the same"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Question 5\n",
    "How much heavier the age group with the highest average weight than the age group with the lowest average weight?\n",
    "- [ ] 45% havier\n",
    "- [x] **28% havier**\n",
    "- [ ] 13% havier\n",
    "- [ ] 87% havier\n",
    "- [ ] 9% havier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2859952671226089"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heavier_age_group = cardio_base.groupby('age')['weight'].mean().agg(['min', 'max'])\n",
    "heavier_age_group['max'] / heavier_age_group['min'] - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer: \n",
    "28% havier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Question 6\n",
    "Which of the following statements is true with 95% confidence?\n",
    "- [ ] Smokers have higher blood preassure than non-smokers\n",
    "- [x] **Smokers have higher cholisterol level than non-smokers**\n",
    "- [ ] Smokers weight less than non-smokers\n",
    "- [x] **Men have higher blood preassure than women**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = .05"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Smokers have higher blood preassure than non-smokers\n",
    "False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H0: Smokers and non-smokers have the same blood pressure\n",
      "H1: Smokers have higher blood preassure than non-smokers\n",
      "p-значение:  0.461244784929987\n",
      "p-значение / 2:  0.2306223924649935\n",
      "Smokers: 128.361\n",
      "Non-smokers: 128.861\n",
      "Do not reject null hypothesis H0\n"
     ]
    }
   ],
   "source": [
    "print('''H0: Smokers and non-smokers have the same blood pressure\\nH1: Smokers have higher blood preassure than non-smokers''')\n",
    "sample_1 = cardio_base[cardio_base['smoke'] == 1]['ap_hi'] # smokeres\n",
    "sample_2 = cardio_base[cardio_base['smoke'] == 0]['ap_hi'] # non-smokeres\n",
    "\n",
    "results = st.ttest_ind(sample_1, sample_2, equal_var = False)\n",
    "\n",
    "print('p-значение: ', results.pvalue)\n",
    "print('p-значение / 2: ', results.pvalue / 2)\n",
    "print(f\"Smokers: {sample_1.mean():.3f}\\nNon-smokers: {sample_2.mean():.3f}\")\n",
    "\n",
    "\n",
    "if results.pvalue < alpha / 2 and sample_1 and sample_1.mean() > sample_2.mean():\n",
    "    print('Reject null hypothesis H0')\n",
    "else:\n",
    "    print('Do not reject null hypothesis H0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H0: Smokers and non-smokers have the same blood pressure\n",
      "H1: Smokers have higher blood preassure than non-smokers\n",
      "p-значение:  0.13044186949533218\n",
      "p-значение / 2:  0.06522093474766609\n",
      "Smokers: 99.775\n",
      "Non-smokers: 96.327\n",
      "Do not reject null hypothesis H0\n"
     ]
    }
   ],
   "source": [
    "print('''H0: Smokers and non-smokers have the same blood pressure\\nH1: Smokers have higher blood preassure than non-smokers''')\n",
    "sample_1 = cardio_base[cardio_base['smoke'] == 1]['ap_lo'] # smokeres\n",
    "sample_2 = cardio_base[cardio_base['smoke'] == 0]['ap_lo'] # non-smokeres\n",
    "\n",
    "results = st.ttest_ind(sample_1, sample_2, equal_var = False)\n",
    "\n",
    "print('p-значение: ', results.pvalue)\n",
    "print('p-значение / 2: ', results.pvalue / 2)\n",
    "print(f\"Smokers: {sample_1.mean():.3f}\\nNon-smokers: {sample_2.mean():.3f}\")\n",
    "\n",
    "\n",
    "if results.pvalue < alpha / 2 and sample_1 and sample_1.mean() > sample_2.mean():\n",
    "    print('Reject null hypothesis H0')\n",
    "else:\n",
    "    print('Do not reject null hypothesis H0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Smokers have higher cholisterol level than non-smokers\n",
    "True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H0: Smokers and non-smokers have the same cholisterol level\n",
      "H1: Smokers have higher cholisterol level than non-smokers\n",
      "p-значение:  0.006222972569800228\n",
      "p-значение / 2:  0.003111486284900114\n",
      "Smokers: 1.390\n",
      "Non-smokers: 1.365\n",
      "Reject null hypothesis H0\n"
     ]
    }
   ],
   "source": [
    "print('''H0: Smokers and non-smokers have the same cholisterol level\\nH1: Smokers have higher cholisterol level than non-smokers''')\n",
    "sample_1 = cardio_base[cardio_base['smoke'] == 1]['cholesterol'] # smokeres\n",
    "sample_2 = cardio_base[cardio_base['smoke'] == 0]['cholesterol'] # non-smokeres\n",
    "results = st.ttest_ind(sample_1, sample_2, equal_var = False)\n",
    "\n",
    "print('p-значение: ', results.pvalue)\n",
    "print('p-значение / 2: ', results.pvalue / 2)\n",
    "print(f\"Smokers: {sample_1.mean():.3f}\\nNon-smokers: {sample_2.mean():.3f}\")\n",
    "\n",
    "if results.pvalue < alpha / 2 and sample_1.mean() > sample_2.mean():\n",
    "    print('Reject null hypothesis H0')\n",
    "else:\n",
    "    print('Do not reject null hypothesis H0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Smokers weight less than non-smokers\n",
    "False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H0: Smokers and non-smokers have the same weight\n",
      "H1: Smokers weight less than non-smokers\n",
      "p-значение:  1.6785734402177582e-65\n",
      "p-значение / 2:  8.392867201088791e-66\n",
      "Smokers: 77.344\n",
      "Non-smokers: 73.902\n",
      "Do not reject null hypothesis H0\n"
     ]
    }
   ],
   "source": [
    "print('''H0: Smokers and non-smokers have the same weight\\nH1: Smokers weight less than non-smokers''')\n",
    "sample_1 = cardio_base[cardio_base['smoke'] == 1]['weight'] # smokeres\n",
    "sample_2 = cardio_base[cardio_base['smoke'] == 0]['weight'] # non-smokeres\n",
    "results = st.ttest_ind(sample_1, sample_2, equal_var = False)\n",
    "\n",
    "print('p-значение: ', results.pvalue)\n",
    "print('p-значение / 2: ', results.pvalue / 2)\n",
    "print(f\"Smokers: {sample_1.mean():.3f}\\nNon-smokers: {sample_2.mean():.3f}\")\n",
    "\n",
    "if results.pvalue < alpha / 2 and sample_1.mean() < sample_2.mean():\n",
    "    print('Reject null hypothesis H0')\n",
    "else:\n",
    "    print('Do not reject null hypothesis H0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Men have higher blood preassure than women\n",
    "True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H0: Men and women have the same blood pressure\n",
      "H1: Men have higher blood pressure than women\n",
      "p-значение:  0.1038392595181402\n",
      "p-значение / 2:  0.0519196297590701\n",
      "Smokers: 128.139\n",
      "Non-smokers: 130.079\n",
      "Do not reject null hypothesis H0\n"
     ]
    }
   ],
   "source": [
    "print('''H0: Men and women have the same blood pressure\\nH1: Men have higher blood pressure than women''')\n",
    "sample_1 = cardio_base[cardio_base['gender'] == 1]['ap_hi'] # women\n",
    "sample_2 = cardio_base[cardio_base['gender'] == 2]['ap_hi'] # men\n",
    "results = st.ttest_ind(sample_1, sample_2, equal_var = False)\n",
    "\n",
    "print('p-значение: ', results.pvalue)\n",
    "print('p-значение / 2: ', results.pvalue / 2)\n",
    "print(f\"Smokers: {sample_1.mean():.3f}\\nNon-smokers: {sample_2.mean():.3f}\")\n",
    "\n",
    "if results.pvalue < alpha / 2 and sample_1.mean() < sample_2.mean():\n",
    "    print('Reject null hypothesis H0')\n",
    "else:\n",
    "    print('Do not reject null hypothesis H0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Too small distinguish between p-value and alpha. So I'd reject H0 here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H0: Men and women have the same blood pressure\n",
      "H1: Men have higher blood pressure than women\n",
      "p-значение:  0.00013735011856314677\n",
      "p-значение / 2:  6.867505928157339e-05\n",
      "Smokers: 94.523\n",
      "Non-smokers: 100.552\n",
      "Reject null hypothesis H0\n"
     ]
    }
   ],
   "source": [
    "print('''H0: Men and women have the same blood pressure\\nH1: Men have higher blood pressure than women''')\n",
    "sample_1 = cardio_base[cardio_base['gender'] == 1]['ap_lo'] # women\n",
    "sample_2 = cardio_base[cardio_base['gender'] == 2]['ap_lo'] # men\n",
    "results = st.ttest_ind(sample_1, sample_2, equal_var = False)\n",
    "\n",
    "print('p-значение: ', results.pvalue)\n",
    "print('p-значение / 2: ', results.pvalue / 2)\n",
    "print(f\"Smokers: {sample_1.mean():.3f}\\nNon-smokers: {sample_2.mean():.3f}\")\n",
    "\n",
    "if results.pvalue < alpha / 2 and sample_1.mean() < sample_2.mean():\n",
    "    print('Reject null hypothesis H0')\n",
    "else:\n",
    "    print('Do not reject null hypothesis H0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer: \n",
    "The fallowing statements are True:\n",
    "- Smokers have higher cholisterol level than non-smokers\n",
    "- Men have higher blood preassure than women"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Question 7\n",
    "What is the probabilyty that a country has GDP over $10 000, if we know that they have at least 5 hospital beds per 1000 inhabitants?\n",
    "- [ ] 92%\n",
    "- [x] **88%**\n",
    "- [ ] 71%\n",
    "- [ ] 54%\n",
    "- [ ] 39%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_beds = covid.copy()\n",
    "\n",
    "# Remove rows are relating to World at all (not to a specific country).\n",
    "# and countries where we do not know about hospital beds number\n",
    "covid_beds = covid_beds[~covid_beds['location'].isin(['World'])]\n",
    "covid_beds = covid_beds[covid_beds['hospital_beds_per_thousand'].notna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [],
   "source": [
    "cond = covid_beds['gdp_per_capita'] > 10000\n",
    "covid_beds['gdp_A'] = '<= 10K'\n",
    "covid_beds.loc[:, 'gdp_A'] = covid_beds['gdp_A'].mask(cond, '> 10K')\n",
    "\n",
    "cond = covid_beds['hospital_beds_per_thousand'] >= 5\n",
    "covid_beds['beds_B'] = '< 5'\n",
    "covid_beds.loc[:, 'beds_B'] = covid_beds['beds_B'].mask(cond, '>= 5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we use Bayes' Theorem\n",
    "$$P(A|B) = \\dfrac{P(B|A)*P(A)}{P(B)}$$\n",
    "- A, B\t-\tevents\n",
    "- P(A|B)\t-\tprobability of A given B is true\n",
    "- P(B|A)\t-\tprobability of B given A is true\n",
    "- P(A), P(B)\t-\tthe independent probabilities of A and B\n",
    "\n",
    "What is the probabilyty that a country has GDP over $10 000, if we know that they have at least 5 hospital beds per 1000 inhabitants?\n",
    "- A = {a country has GDP over $10 000}\n",
    "- B = {a country has at least 5 hospital beds per 1000}\n",
    "- P(A|B) = (posterior probability) the probability that a country has GDP over $10 000, if we know that they have at least 5 hospital beds per 1000 inhabitants\n",
    "- P(A) = (prior probability)the probability that a country has GDP over $10 000\n",
    "- P(B|A) = (likelihood) the probability that a country has at least 5 hospital beds per 1000 inhabitants given that it has had GDP over $10 000\n",
    "- P(B) = (evidence) the probability that a country has at least 5 hospital beds per 100 (regardless its GDP) \n",
    "$$P(B) = \\sum_{i=1}^n{P(B|A_i)*P(A_i)}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {},
   "outputs": [],
   "source": [
    "# P(A)\n",
    "P_A = covid_beds['gdp_A'].value_counts(normalize=True)\n",
    "# P(B)\n",
    "P_B =covid_beds['beds_B'].value_counts(normalize=True)\n",
    "# P(B|A)\n",
    "P_B_and_A = covid_beds.groupby('gdp_A')['beds_B'].value_counts(normalize=True).sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would like to know what is the probability that a country has GDP over $10 000, if we know that they have at least 5 hospital beds per 1000 inhabitants.\n",
    "\n",
    "So, what have we already known?\n",
    "\n",
    "If we did not know anything about hospital beds in the country we would say that the probability that it has GDP over $10 000 is **P(A)** - a prior probability.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(A)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "> 10K     0.665297\n",
       "<= 10K    0.334703\n",
       "Name: gdp_A, dtype: float64"
      ]
     },
     "execution_count": 460,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('P(A)')\n",
    "P_A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But we just have known that the country has at least 5 hospital beds per 1000 inhabitants.\n",
    "\n",
    "**P(A|B) = P(B|A) * P(A) / P(B)**\n",
    "\n",
    "If a country has at least 5 hospital beds per 1000 inhabitants **P(B)** there are two posibilities **P(B|A)**: \n",
    "- either it has GDP over $10 000 - **P('>= 5'|'> 10K')**,\n",
    "- or its GDP is equal or less than $10 000 - **P('>= 5'|'<= 10K')**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(B|A)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "gdp_A   beds_B\n",
       "> 10K   >= 5      0.237330\n",
       "        < 5       0.762670\n",
       "<= 10K  >= 5      0.079018\n",
       "        < 5       0.920982\n",
       "Name: beds_B, dtype: float64"
      ]
     },
     "execution_count": 461,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('P(B|A)')\n",
    "P_B_and_A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "P('>= 5'|'> 10K') = 0.237330 \n",
      "P('>= 5'|'<= 10K') = 0.079018\n"
     ]
    }
   ],
   "source": [
    "print(f'''\n",
    "P('>= 5'|'> 10K') = {P_B_and_A.loc[('> 10K', '>= 5')]:.6f} \n",
    "P('>= 5'|'<= 10K') = {P_B_and_A.loc[('<= 10K', '>= 5')]:.6f}''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "P(B) = sum(P(B|A) * P(A))\n",
      "P('>= 5') = P('>= 5'|'> 10K') * P('> 10K') + P('>= 5'|'<= 10K') * P('<= 10K')\n",
      "P('>= 5') = 0.237330 * 0.665297 + 0.079018 * 0.334703 = 0.184342\n"
     ]
    }
   ],
   "source": [
    "print(f'''\n",
    "P(B) = sum(P(B|A) * P(A))\n",
    "P('>= 5') = P('>= 5'|'> 10K') * P('> 10K') + P('>= 5'|'<= 10K') * P('<= 10K')\n",
    "P('>= 5') = {P_B_and_A.loc[('> 10K', '>= 5')]:.6f} * {P_A['> 10K']:.6f} + {P_B_and_A.loc[('<= 10K', '>= 5')]:.6f} * {P_A['<= 10K']:.6f} = {P_B_and_A.loc[('> 10K', '>= 5')] * P_A['> 10K'] + P_B_and_A.loc[('<= 10K', '>= 5')] * P_A['<= 10K']:.6f}''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(B)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "< 5     0.815658\n",
       ">= 5    0.184342\n",
       "Name: beds_B, dtype: float64"
      ]
     },
     "execution_count": 464,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('P(B)')\n",
    "P_B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thus, the probatility, that the country has GPR over 10K given that it has at least 5 hospital beds per 1000 inhabitants is:\n",
      "P(A|B) = P(B|A) * P(A) / P(B)\n",
      "P('> 10K'|'> 5') = P('>= 5'|'> 10K) * P('> 10K') / P('>= 5')\n",
      "P(B|A) * P(A) = P('>= 5'|'> 10K) * P('> 10K') = 0.16\n",
      "P(B) = P('>= 5') = 0.18\n",
      "P(A|B) = 0.888888888888889\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f'''Thus, the probatility, that the country has GPR over 10K given that it has at least 5 hospital beds per 1000 inhabitants is:\n",
    "P(A|B) = P(B|A) * P(A) / P(B)\n",
    "P('> 10K'|'> 5') = P('>= 5'|'> 10K) * P('> 10K') / P('>= 5')\n",
    "P(B|A) * P(A) = P('>= 5'|'> 10K) * P('> 10K') = {round(P_B_and_A.loc[('> 10K', '>= 5')] * P_A['> 10K'], 2)}\n",
    "P(B) = P('>= 5') = {round(P_B_and_A.loc[('> 10K', '>= 5')] * P_A['> 10K'] + P_B_and_A.loc[('<= 10K', '>= 5')] * P_A['<= 10K'], 2)}\n",
    "P(A|B) = {round(P_B_and_A.loc[('> 10K', '>= 5')] * P_A['> 10K'], 2) / round(P_B_and_A.loc[('> 10K', '>= 5')] * P_A['> 10K'] + P_B_and_A.loc[('<= 10K', '>= 5')] * P_A['<= 10K'], 2)}\n",
    "''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same conclusions we can make with confusion_matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_5de76_row1_col1, #T_5de76_row1_col2 {\n",
       "  background-color: #e6ffe6;\n",
       "  color: green;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_5de76\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >gdp_A</th>\n",
       "      <th id=\"T_5de76_level0_col0\" class=\"col_heading level0 col0\" ><= 10K</th>\n",
       "      <th id=\"T_5de76_level0_col1\" class=\"col_heading level0 col1\" >> 10K</th>\n",
       "      <th id=\"T_5de76_level0_col2\" class=\"col_heading level0 col2\" >sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >beds_B</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_5de76_level0_row0\" class=\"row_heading level0 row0\" >< 5</th>\n",
       "      <td id=\"T_5de76_row0_col0\" class=\"data row0 col0\" >0.31</td>\n",
       "      <td id=\"T_5de76_row0_col1\" class=\"data row0 col1\" >0.51</td>\n",
       "      <td id=\"T_5de76_row0_col2\" class=\"data row0 col2\" >0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de76_level0_row1\" class=\"row_heading level0 row1\" >>= 5</th>\n",
       "      <td id=\"T_5de76_row1_col0\" class=\"data row1 col0\" >0.03</td>\n",
       "      <td id=\"T_5de76_row1_col1\" class=\"data row1 col1\" >0.16</td>\n",
       "      <td id=\"T_5de76_row1_col2\" class=\"data row1 col2\" >0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5de76_level0_row2\" class=\"row_heading level0 row2\" >sum</th>\n",
       "      <td id=\"T_5de76_row2_col0\" class=\"data row2 col0\" >0.33</td>\n",
       "      <td id=\"T_5de76_row2_col1\" class=\"data row2 col1\" >0.67</td>\n",
       "      <td id=\"T_5de76_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fee5f2cf160>"
      ]
     },
     "execution_count": 466,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix = pd.crosstab(covid_beds['beds_B'], covid_beds['gdp_A'], normalize=True)\n",
    "confusion_matrix.loc['sum', :] = confusion_matrix.sum(axis=0)\n",
    "confusion_matrix.loc[:, 'sum'] = confusion_matrix.sum(axis=1)\n",
    "confusion_matrix.style.applymap(lambda x: f\"background-color: #e6ffe6; color: green;\", subset=('>= 5', ['> 10K', 'sum'])).format('{:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer: \n",
    "88%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Question 8\n",
    "What is the F1 score of the following statement: \n",
    "\n",
    "Countries, where more than 20% population is over 65 years old, have death rates over 50 per million inhabitants. Ignore countries, where any of the necessary information is missing!\n",
    "- [ ] 0.15\n",
    "- [ ] 0.73\n",
    "- [ ] 0.87\n",
    "- [x] **0.18**\n",
    "- [ ] 0.21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_fscore = covid.groupby('location').agg(\n",
    "    aged_65_older_percent=pd.NamedAgg('aged_65_older_percent', 'max'), \n",
    ").join(covid_death_rate['death_rate'])\n",
    "covid_fscore = covid_fscore[covid_fscore['aged_65_older_percent'].notna()]\n",
    "covid_fscore['aged_65_older_percent'] = covid_fscore['aged_65_older_percent'].astype('float')\n",
    "\n",
    "cond = covid_fscore['aged_65_older_percent'] > 20\n",
    "covid_fscore.loc[:, 'aged_65_older_percent'] = covid_fscore['aged_65_older_percent'].mask(cond, 1)\n",
    "covid_fscore.loc[:, 'aged_65_older_percent'] = covid_fscore['aged_65_older_percent'].where(cond, 0)\n",
    "\n",
    "cond = covid_fscore['death_rate'] > 50\n",
    "covid_fscore.loc[:, 'death_rate'] = covid_fscore['death_rate'].mask(cond, 1)\n",
    "covid_fscore.loc[:, 'death_rate'] = covid_fscore['death_rate'].where(cond, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# сountries, where more than 20% population is over 65 years old\n",
    "y_pred = covid_fscore['aged_65_older_percent']\n",
    "# have death rates over 50 per million inhabitants\n",
    "y_true = covid_fscore['death_rate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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",
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       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">have death rates over 50 per million inhabitants</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">сountries, where more than 20% population is over 65 years old</th>\n",
       "      <th>True</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>33</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         have death rates over 50 per million inhabitants  \\\n",
       "                                                                                                    True    \n",
       "сountries, where more than 20% population is ov... True                                                 4   \n",
       "                                                   False                                               33   \n",
       "\n",
       "                                                                \n",
       "                                                         False  \n",
       "сountries, where more than 20% population is ov... True      3  \n",
       "                                                   False   144  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confusion_matrix returns tn, fp, fn, tp\n",
    "cf = pd.DataFrame(\n",
    "    confusion_matrix(y_true, y_pred), \n",
    "    columns=pd.MultiIndex.from_tuples([(i, j) for j in [False, True] for i in ['сountries, where more than 20% population is over 65 years old']]),\n",
    "    index=pd.MultiIndex.from_tuples([(i, j) for j in [False, True] for i in ['have death rates over 50 per million inhabitants']]),\n",
    "    )\n",
    "cf = cf.reindex(sorted(cf.index, reverse=True), axis=0).reindex(sorted(cf.columns, reverse=True), axis=1).T\n",
    "cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances\n",
      "Precision = tp / (tp + fp)\n",
      "Precision = 0.57\n",
      "\n",
      "Recall (also known as sensitivity) is the fraction of relevant instances that were retrieved. \n",
      "Recall = tp / (tp + fn)\n",
      "Recall = 0.11\n",
      "\n",
      "f1-score is the harmonic mean of a precision and recall values. \n",
      "f1-score = 2 x (Precision x Recall) / (Precision + Recall)\n",
      "f1-score = 0.18\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances\n",
    "# Precision = tp / (tp + fp)\n",
    "print(f\"\"\"Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances\n",
    "Precision = tp / (tp + fp)\n",
    "Precision = {precision_score(y_true, y_pred, average='binary'):.2f}\n",
    "\n",
    "Recall (also known as sensitivity) is the fraction of relevant instances that were retrieved. \n",
    "Recall = tp / (tp + fn)\n",
    "Recall = {recall_score(y_true, y_pred, average='binary'):.2f}\n",
    "\n",
    "f1-score is the harmonic mean of a precision and recall values. \n",
    "f1-score = 2 x (Precision x Recall) / (Precision + Recall)\n",
    "f1-score = {f1_score(y_true, y_pred, average='binary'):.2f}\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer: \n",
    "0.18"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Question 9: \n",
    "Are men are more likely to be smoker than women? The data contains information to identify gender IDs!\n",
    "- [x] **Yes, men are 12x more likely to be smokers**\n",
    "- [ ] Yes, men are 7x more likely to be smokers\n",
    "- [ ] Similar portion of men and women are smokers\n",
    "- [ ] No, women are 12x more likely to be smokers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identifying gender\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see marks 1 and 2 in column \"gender\" but we don't know if mark 1 represents men or women. \n",
    "\n",
    "We also know that men, on average, are about 15 percent to 20 percent larger than women.\n",
    "\n",
    "So we can estimate average height for gender 1 and 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = cardio_base\n",
    "ax = sns.boxplot(x=df['gender'], y=cardio_base['height'])\n",
    "ax.set_title('Height by gender');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, we can conclude that mark 1 is used for female, 2 - for male."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Probability calculation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say we take a random person whose gender is male. What is the probability that he is a smoker?\n",
    "\n",
    "Here we use Bayes' Theorem\n",
    "$$P(A|B) = \\dfrac{P(B|A)*P(A)}{P(B)}$$\n",
    "- P(A|B) = P(smoker|men)\n",
    "- P(A) = P(smoker)\n",
    "- P(B) = P(men)\n",
    "- P(B|A) = P(men|smoker)\n",
    "$$P(smoker|men) = \\frac{P(men|smoker)*P(smoker)}{P(men)}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "P_A = cardio_base['smoke'].value_counts(normalize=True)\n",
    "P_B = cardio_base['gender'].value_counts(normalize=True)\n",
    "P_BA = cardio_base[cardio_base['smoke'] == 1]['gender'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If we don't know the gender we would say that the probability that a person is a smoker equals P(A)\n",
      "P(A) = P(smoker) - the probability to be a smoker regardless to gender\n",
      "0    0.911871\n",
      "1    0.088129\n",
      "Name: smoke, dtype: float64\n",
      "P(smoker) = 0.088129\n",
      "\n",
      "Then we find out that the person is a MAN.\n",
      "P(B) = P(men) - the probability to choose a man from the whole group\n",
      "1    0.650429\n",
      "2    0.349571\n",
      "Name: gender, dtype: float64\n",
      "P(men) = 0.349571\n",
      "\n",
      "Distributon of men and women among smokers\n",
      "2    0.868212\n",
      "1    0.131788\n",
      "Name: gender, dtype: float64\n",
      "P(B|A) = P(men|smoker) the probability that a random smoker is a man\n",
      "P(men|smoker) = 0.868212\n",
      "P(men|smoker) * P(smoker) = 0.868212 * 0.088129 = 0.076514\n",
      "\n",
      "The probability that a random man is a smoker P(A|B) = P(smoker|men)\n",
      "P(smoker|men) = 0.868212 * 0.088129 / 0.349571 = 0.076514 / 0.349571 = 0.218880\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"\"\"If we don't know the gender we would say that the probability that a person is a smoker equals P(A)\n",
    "P(A) = P(smoker) - the probability to be a smoker regardless to gender\n",
    "{P_A}\n",
    "P(smoker) = {P_A.loc[1]:.6f}\n",
    "\n",
    "Then we find out that the person is a MAN.\n",
    "P(B) = P(men) - the probability of choosing a man from the whole group\n",
    "{P_B}\n",
    "P(men) = {P_B.loc[2]:.6f}\n",
    "\n",
    "Distributon of men and women among smokers\n",
    "{P_BA}\n",
    "P(B|A) = P(men|smoker) the probability that a random smoker is a man\n",
    "P(men|smoker) = {P_BA.loc[2]:.6f}\n",
    "P(men|smoker) * P(smoker) = {P_BA.loc[2]:.6f} * {P_A.loc[1]:.6f} = {P_BA.loc[2] * P_A.loc[1]:.6f}\n",
    "\n",
    "The probability that a random man is a smoker P(A|B) = P(smoker|men)\n",
    "P(smoker|men) = {P_BA.loc[2]:.6f} * {P_A.loc[1]:.6f} / {P_B.loc[2]:.6f} = {P_BA.loc[2]*P_A.loc[1]:.6f} / {P_B.loc[2]:.6f} = {P_BA.loc[2] * P_A.loc[1] / P_B.loc[2]:.6f}\n",
    "\"\"\")\n",
    "P_smoker_men = P_BA.loc[2] * P_A.loc[1] / P_B.loc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If we did't know the gender we would say that the probability that a person is a smoker equals P(A)\n",
      "P(A) = P(smoker) - the probability to be a smoker regardless to gender\n",
      "0    0.911871\n",
      "1    0.088129\n",
      "Name: smoke, dtype: float64\n",
      "P(smoker) = 0.088129\n",
      "\n",
      "Then we find out that the person is a WOMAN.\n",
      "P(B) = P(women) - the probability to choose a woman from the whole group\n",
      "1    0.650429\n",
      "2    0.349571\n",
      "Name: gender, dtype: float64\n",
      "P(women) = 0.650429\n",
      "\n",
      "Distributon of men and women among smokers\n",
      "2    0.868212\n",
      "1    0.131788\n",
      "Name: gender, dtype: float64\n",
      "P(B|A) = P(women|smoker) the probability that a random smoker is a woman\n",
      "P(women|smoker) = 0.131788\n",
      "P(women|smoker) * P(smoker) = 0.131788 * 0.088129 = 0.011614\n",
      "\n",
      "The probability that a random man is a smoker P(A|B) = P(smoker|men)\n",
      "P(smoker|women) = 0.131788 * 0.088129 / 0.650429 = 0.011614 / 0.650429 = 0.017856\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"\"\"If we don't know the gender we would say that the probability that a person is a smoker equals P(A)\n",
    "P(A) = P(smoker) - the probability to be a smoker regardless to gender\n",
    "{P_A}\n",
    "P(smoker) = {P_A.loc[1]:.6f}\n",
    "\n",
    "Then we find out that the person is a WOMAN.\n",
    "P(B) = P(women) - the probability of choosing a woman from the whole group\n",
    "{P_B}\n",
    "P(women) = {P_B.loc[1]:.6f}\n",
    "\n",
    "Distributon of men and women among smokers\n",
    "{P_BA}\n",
    "P(B|A) = P(women|smoker) the probability that a random smoker is a woman\n",
    "P(women|smoker) = {P_BA.loc[1]:.6f}\n",
    "P(women|smoker) * P(smoker) = {P_BA.loc[1]:.6f} * {P_A.loc[1]:.6f} = {P_BA.loc[1] * P_A.loc[1]:.6f}\n",
    "\n",
    "The probability that a random man is a smoker P(A|B) = P(smoker|men)\n",
    "P(smoker|women) = {P_BA.loc[1]:.6f} * {P_A.loc[1]:.6f} / {P_B.loc[1]:.6f} = {P_BA.loc[1]*P_A.loc[1]:.6f} / {P_B.loc[1]:.6f} = {P_BA.loc[1] * P_A.loc[1] / P_B.loc[1]:.6f}\n",
    "\"\"\")\n",
    "P_smoker_women = P_BA.loc[1] * P_A.loc[1] / P_B.loc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The proportion is 12\n"
     ]
    }
   ],
   "source": [
    "print(f\"The proportion is {P_smoker_men / P_smoker_women:.0f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer: \n",
    "Yes, men are 12x more likely to be smokers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Question 10: \n",
    "Look at the cumulative number of confirmed cases in Italy between 2020-02-28 and 2020-03-20. \n",
    "Fit an exponential function (y = Ae^(Bx)) to this set to express cummulative cases as function of days passed, by minimizing squared loss. \n",
    "What is the difference between the exponential curve and the total number of real cases on 2020-03-20?\n",
    "- [ ] 11756\n",
    "- [ ] 37414\n",
    "- [ ] 187963\n",
    "- [ ] ~0\n",
    "- [ ] 9785"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_italy = covid.copy()\n",
    "covid_italy = covid_italy[(covid_italy['location'] == 'Italy') & (covid_italy['date'] >= '2020-02-28') & (covid_italy['date'] <= '2020-03-20')]\n",
    "covid_italy['cum_cases'] = covid_italy['new_cases'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.22133005, 6.36005586])"
      ]
     },
     "execution_count": 489,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(0, covid_italy.shape[0])\n",
    "y = covid_italy['cum_cases']\n",
    "p = np.polyfit(x, np.log(y), 1)\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a = 578.2786581652114\n",
      "b = 0.22133004576497883\n"
     ]
    }
   ],
   "source": [
    "# Convert the polynomial back into an exponential\n",
    "a = np.exp(p[1])\n",
    "b = p[0]\n",
    "print(f\"a = {a}\\nb = {b}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_italy['fitted_cases'] = [a * np.exp(b * i) for i in x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "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>location</th>\n",
       "      <th>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>population</th>\n",
       "      <th>aged_65_older_percent</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>cum_cases</th>\n",
       "      <th>fitted_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10764</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>2823</td>\n",
       "      <td>370</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>23.021</td>\n",
       "      <td>35220.084</td>\n",
       "      <td>3.18</td>\n",
       "      <td>23580</td>\n",
       "      <td>24901.084558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10765</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>4000</td>\n",
       "      <td>347</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>23.021</td>\n",
       "      <td>35220.084</td>\n",
       "      <td>3.18</td>\n",
       "      <td>27580</td>\n",
       "      <td>31069.959032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10766</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>3526</td>\n",
       "      <td>347</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>23.021</td>\n",
       "      <td>35220.084</td>\n",
       "      <td>3.18</td>\n",
       "      <td>31106</td>\n",
       "      <td>38767.080687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10767</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>4207</td>\n",
       "      <td>473</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>23.021</td>\n",
       "      <td>35220.084</td>\n",
       "      <td>3.18</td>\n",
       "      <td>35313</td>\n",
       "      <td>48371.050101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10768</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>5322</td>\n",
       "      <td>429</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>23.021</td>\n",
       "      <td>35220.084</td>\n",
       "      <td>3.18</td>\n",
       "      <td>40635</td>\n",
       "      <td>60354.260534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      location       date  new_cases  new_deaths  population  \\\n",
       "10764    Italy 2020-03-16       2823         370  60461828.0   \n",
       "10765    Italy 2020-03-17       4000         347  60461828.0   \n",
       "10766    Italy 2020-03-18       3526         347  60461828.0   \n",
       "10767    Italy 2020-03-19       4207         473  60461828.0   \n",
       "10768    Italy 2020-03-20       5322         429  60461828.0   \n",
       "\n",
       "       aged_65_older_percent  gdp_per_capita  hospital_beds_per_thousand  \\\n",
       "10764                 23.021       35220.084                        3.18   \n",
       "10765                 23.021       35220.084                        3.18   \n",
       "10766                 23.021       35220.084                        3.18   \n",
       "10767                 23.021       35220.084                        3.18   \n",
       "10768                 23.021       35220.084                        3.18   \n",
       "\n",
       "       cum_cases  fitted_cases  \n",
       "10764      23580  24901.084558  \n",
       "10765      27580  31069.959032  \n",
       "10766      31106  38767.080687  \n",
       "10767      35313  48371.050101  \n",
       "10768      40635  60354.260534  "
      ]
     },
     "execution_count": 492,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_italy.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10768   -19719.260534\n",
       "dtype: float64"
      ]
     },
     "execution_count": 493,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_italy[covid_italy['date'] == '2020-03-20']['cum_cases'] - covid_italy[covid_italy['date'] == '2020-03-20']['fitted_cases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes()\n",
    "ax.plot(covid_italy['date'], covid_italy['cum_cases'], color='r', label='Number of cases')\n",
    "ax.plot(covid_italy['date'], covid_italy['fitted_cases'], 'b:', label='Fitted curve')\n",
    "ax.set(\n",
    "    title='Cumulative number of confirmed cases in Italy between 2020-02-28 and 2020-03-20', \n",
    "    xlabel='date', \n",
    "    ylabel='number of cases'\n",
    "    )\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer: \n",
    "19719"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Question 11: \n",
    "When did the difference in the total number of confirmed cases between Italy and Germaty become more than 10 000?\n",
    "\n",
    "Second dataset COVID-19 cases. This dataset contains daily COVID-19 cases for all countries in the world. Each row represents a calendar day. The rows also contains some simple information about the countries, like population, percentage of the population over 65, GDP, and hospital beds per 1000 inhabitants. \n",
    "- [ ] 2020-02-28\n",
    "- [ ] 2020-02-29\n",
    "- [ ] 2020-03-16\n",
    "- [x] **2020-03-12**\n",
    "- [ ] 2020-03-01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_italy_germany = covid.copy()\n",
    "covid_italy_germany = covid_italy_germany[covid_italy_germany['location'].isin(['Italy', 'Germany'])]\n",
    "covid_italy_germany = pd.pivot_table(covid_italy_germany, values='new_cases', columns='location', index='date').cumsum()\n",
    "covid_italy_germany['difference'] = abs(covid_italy_germany['Germany'] - covid_italy_germany['Italy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "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>location</th>\n",
       "      <th>Germany</th>\n",
       "      <th>Italy</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-12</th>\n",
       "      <td>1567</td>\n",
       "      <td>12462</td>\n",
       "      <td>10895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "location    Germany  Italy  difference\n",
       "date                                  \n",
       "2020-03-12     1567  12462       10895"
      ]
     },
     "execution_count": 536,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_italy_germany[covid_italy_germany['difference'] > 10e3].head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(15, 5),)\n",
    "\n",
    "ax[0].plot(covid_italy_germany.index, covid_italy_germany['difference'], label='Difference')\n",
    "ax[0].plot(covid_italy_germany.index, [10000]*covid_italy_germany.shape[0], color='r', label='10K')\n",
    "ax[0].set(\n",
    "    title='Difference between number of cases in Germany and Italy', \n",
    "    xlabel='date', \n",
    "    ylabel='number of cases'\n",
    "    )\n",
    "ax[0].legend()\n",
    "\n",
    "ax[1].plot(covid_italy_germany.index, covid_italy_germany['Germany'], color='brown', label='Germany')\n",
    "ax[1].plot(covid_italy_germany.index, covid_italy_germany['Italy'], color='green', label='Italy')\n",
    "ax[1].set(\n",
    "    title='Cumulative number of confirmed cases', \n",
    "    xlabel='date', \n",
    "    ylabel='number of cases'\n",
    "    )\n",
    "ax[1].legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer: \n",
    "2020-03-12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Question 12: \n",
    "What percentage of people are more than 2 standard deviations far from the average height?\n",
    "- [ ] 5%\n",
    "- [x] 3%\n",
    "- [ ] 2%\n",
    "- [ ] 7%\n",
    "- [ ] 11%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.033357142857142856"
      ]
     },
     "execution_count": 545,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cardio_base[\n",
    "    (cardio_base['height'] < cardio_base['height'].mean() - 2 * cardio_base['height'].std()) | \n",
    "    (cardio_base['height'] > cardio_base['height'].mean() + 2 * cardio_base['height'].std())\n",
    "    ].shape[0] / cardio_base['height'].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer: \n",
    "3%"
   ]
  }
 ],
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  "interpreter": {
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  },
  "kernelspec": {
   "display_name": "Python 3.10.8 ('env_3_10_4')",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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}