5)Intro_Statistics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Statistics in Python\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two types of data - Numeric and Categorical\n",
    "\n",
    "Numeric data can by either continouos (measured) or discrete (counted)\n",
    "\n",
    "Catgorical can be either nominal(unordered) or ordinal(ordered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.1104055944056"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "food = pd.read_csv(\"databases/food.csv\", index_col=0)\n",
    "\n",
    "np.mean(food['consumption'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.89"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(food['consumption'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Argentina'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mode - the most frequent value\n",
    "\n",
    "food[\"country\"].value_counts()\n",
    "\n",
    "#OR\n",
    "\n",
    "import statistics\n",
    "statistics.mode(food[\"country\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean is much more senstitive to the extreme values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2481.837553789192"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(food[\"consumption\"], ddof=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49.81804445970548"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(np.var(food[\"consumption\"], ddof=1))\n",
    "\n",
    "#OR\n",
    "\n",
    "np.std(food[\"consumption\"], ddof=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.        ,   4.16333333,  18.79333333, 430.76      ])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.quantile(food[\"consumption\"], 0.5)\n",
    "np.quantile(food[\"consumption\"], [0, 0.25, 0.5, 0.75, 1])\n",
    "\n",
    "#We will get the same result if we use:\n",
    "np.quantile(food[\"consumption\"], np.linspace(0, 1, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "\n",
    "plt.boxplot(food[\"consumption\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IQR\n",
    "\n",
    "The distance between 25th and 75th percentile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25.767500000000002\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "25.767500000000002"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.quantile(food[\"consumption\"], 0.75) - np.quantile(food[\"consumption\"], 0.25))\n",
    "\n",
    "# OR \n",
    "from scipy.stats import iqr\n",
    "iqr(food[\"consumption\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the lower and upper cutoffs for outliers\n",
    "\n",
    "lower = q1 - 1.5 * iqr\n",
    "upper = q3 + 1.5 * iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "country\n",
      "Argentina    2172.4\n",
      "Name: co2_emission, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate total co2_emission per country: emissions_by_country\n",
    "emissions_by_country = food.groupby('country')['co2_emission'].sum()\n",
    "\n",
    "# Compute the first and third quantiles and IQR of emissions_by_country\n",
    "q1 = np.quantile(emissions_by_country, 0.25)\n",
    "q3 = np.quantile(emissions_by_country, 0.75)\n",
    "iqr = q3 - q1\n",
    "\n",
    "# Calculate the lower and upper cutoffs for outliers\n",
    "lower = q1 - 1.5 * iqr\n",
    "upper = q3 + 1.5 * iqr\n",
    "\n",
    "# Subset emissions_by_country to find outliers\n",
    "outliers = emissions_by_country[(emissions_by_country < lower) | (emissions_by_country > upper)]\n",
    "print(outliers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Numbers and Probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country\n",
       "0  Brazil"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "\n",
    "brics = pd.read_csv(\"databases/brics.csv\")\n",
    "\n",
    "brics[[\"country\"]].sample() #choses a random element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Russia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country\n",
       "1  Russia"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(20) #by setting the seed we gurantee that we will always get the same result\n",
    "brics[[\"country\"]].sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country\n",
       "3         China\n",
       "4  South Africa"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brics[[\"country\"]].sample(2) #this give us two random elements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Samplings can be either with replacement (if we replace, then we always have the same chances like that the person will be picked for the job) or without replacement"
   ]
  },
  {
   "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>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country\n",
       "2   India\n",
       "0  Brazil\n",
       "0  Brazil"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to allow samplings with replacement we should set replace to True\n",
    "brics[[\"country\"]].sample(3, replace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Events can be either dependant (when the outcome of the first one influences the second one) or independant (when there is no influene between events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2., 3., 4., 5., 6., 7.])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(1, 7, 7) #return a list of 7 numbers between 1 and 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Law of large numbers*** - as the size of your sample increases, the sample mean will approach the expected value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Expected value of the distribution is the mean that is the sample is expected to have at large sample size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Continuous distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uniform distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.58\n",
      "0.42\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.25"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To find out the probability that we will have to wait for the bus 7 minutes\n",
    "#Given that it arrives every 12 minutes and that we have a uniform distribution\n",
    "from scipy.stats import uniform\n",
    "less_than_seven = uniform.cdf(7, 0, 12)\n",
    "print(round(less_than_seven, 2))\n",
    "\n",
    "#Waiting more that 7 minutes\n",
    "more_than_seven = 1 - uniform.cdf(7, 0, 12)\n",
    "print(round(more_than_seven, 2))\n",
    "\n",
    "#Waiting from 4 to 7 minutes\n",
    "greater4_less7 = uniform.cdf(7, 0, 12) - uniform.cdf(4, 0, 12) \n",
    "round(greater4_less7, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.20586664, 4.20787067, 4.08277148, 3.91255706, 3.6763367 ,\n",
       "       2.38693851, 4.33101161, 1.57762107, 4.53266975, 0.00685486])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import uniform\n",
    "uniform.rvs(0, 5, size=10) \n",
    "#generating 10 random values between 0 and 5 according to the uniform distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discrete distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "binomial distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n",
      "[0 0 0 1 1 0 0 0]\n",
      "[3]\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import binom\n",
    "#we throw a coin - 1 is head 0 is tails\n",
    "\n",
    "#Fliping 1 coin with a 50% probability of getting heads and we flip it 1 time only\n",
    "print(binom.rvs(1, 0.5, size=1))\n",
    "\n",
    "#Flipiing 1 coin 8 times - we get an array of values\n",
    "print(binom.rvs(1, 0.5, size=8))\n",
    "\n",
    "#we flip 8 coins one time and as a reult we get a single number, which represents the number of heads we got\n",
    "print(binom.rvs(8, 0.5, size=1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11718749999999996"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculates the prob that we will get EXACTLY 7 heads out of 10 flips with a 0.5 chance of getting a head\n",
    "binom.pmf(7, 10, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9453125"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The prob of getting LESS THAN OR EQUAL TO 7 heads out of ten\n",
    "binom.cdf(7, 10, 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Expected value is equal to n * p.\n",
    "\n",
    "Remember that in binomial distribution all trials must be independent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More Distributions and the Central Limit Theorem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The normal distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the normal distribution has a mean = 0 and std = 1 it is a special distribution called standard normal distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15865525393145707"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#What percent of women are shorter than 154 cm?\n",
    "\n",
    "from scipy.stats import norm\n",
    "norm.cdf(154, 161, 7) #we specify the needed value, mean and std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8413447460685429"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#What percent of women are taller than 154 cm?\n",
    "\n",
    "from scipy.stats import norm\n",
    "1 - norm.cdf(154, 161, 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "169.9708609588122"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To calculate percentiles\n",
    "## What height are 90% of women shorter than?\n",
    "\n",
    "norm.ppf(0.9, 161, 7) #we specify the percentile, mean and std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([158.3210443 , 165.74786657, 166.25483646, 148.50684267,\n",
       "       156.79976555, 166.00483276, 164.55977287, 165.82418208,\n",
       "       155.02204549, 160.14625069])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Generating random numbers\n",
    "\n",
    "norm.rvs(161, 7, size=10) #mean, std, size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The central Limit Theorem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sampling distribution of a statistic becomes closer to the normal distribution as the number of trials increases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#If we work with a DataFrame we should use:\n",
    "df[\"column\"].hist()\n",
    "\n",
    "#if we work with a list or a numpy array:\n",
    "plt.hist(list)\n",
    "\n",
    "#This is because pandas is built on top of matplotlib and has inherited some of its features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The poisson distribution (discrete)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19123606207962535"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#If the average number of adoptions per week is 8, what is the probability it is going to equal 5\n",
    "#equal 5\n",
    "from scipy.stats import poisson\n",
    "\n",
    "poisson.pmf(5, 8)\n",
    "\n",
    "#Less than 5\n",
    "poisson.cdf(5, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6,  7,  6, 13, 11, 13,  8,  4,  9,  7], dtype=int64)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sampling with lambda=8\n",
    "\n",
    "poisson.rvs(8, size=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exponential distribution (continuous)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Probability of time between Poisson events"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lambda (expected value) in the case of Poisson signifies number of events per minute - if ticket is bought every two minutes, the lambda will be equal to 0.5\n",
    "\n",
    "As for the expected value of Exponential distribution it can be calculated by dividing 1 by lambda so in our example it wiil be 1/0.5 = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3934693402873666"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import expon\n",
    "\n",
    "expon.cdf(1, scale=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### t-distribution (continuous)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is similar to a normal distribution. however the tails are thicker (there are more observations there)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Log-normal distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation and Experimental Design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "freedom = pd.read_csv(\"databases/freedom.csv\")\n",
    "sns.scatterplot(x=\"gdp_per_cap\", y=\"life_exp\", data = freedom)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"gdp_per_cap\", y=\"life_exp\", data = freedom, ci=None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7019547642148015"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculating correlation1\n",
    "freedom[\"gdp_per_cap\"].corr(freedom[\"life_exp\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important not to forget to visualize your data as sometimes correlation can't be trusted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Log transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The idea is to make relatioship between variables more linear\n",
    "freedom[\"log_life_exp\"] = np.log(freedom[\"life_exp\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6841582633142026"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freedom[\"gdp_per_cap\"].corr(freedom[\"log_life_exp\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"gdp_per_cap\", y=\"log_life_exp\", data = freedom, ci=None)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}