5_Lecture Notebook - Intro to Analytical Thinking & Plotting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "73dh_Ip9n9h_"
   },
   "source": [
    "# Introduction into Analytical Thinking & Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m_m0HawYJT3J"
   },
   "source": [
    "## Analysis on the `tips` survey dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6AN3T0LBRU4Q"
   },
   "source": [
    "We will start this analysis trying to understand:\n",
    "> **What are predictors for the amount of cash a dining table will tip in a restaurant?**\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hUXs2SICdlA5"
   },
   "source": [
    "For this we will be using a very famous dataset: the `tips` survey. The Tips dataset is a small dataset so the entire csv file can be read into python in one go without causing any problems. \n",
    "\n",
    "The dataset has the following fields.\n",
    "- `total_bill` which is the total amount spent at the restaurant\n",
    "- `tip` which is the tip left on top of the bill\n",
    "- `sex` of the customer\n",
    "- `smoker` if the customers were smokers (Yes), or not (No)\n",
    "- `day` of the week when the meal took place\n",
    "- `time` of the day it happened\n",
    "- `size` which is the number of people at the table\n",
    "\n",
    "Source: https://rdrr.io/cran/reshape2/man/tips.html -> available at: https://github.com/mwaskom/seaborn-data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 3753,
     "status": "ok",
     "timestamp": 1682359949145,
     "user": {
      "displayName": "Ka Ba",
      "userId": "08277395367003334094"
     },
     "user_tz": -120
    },
    "id": "3TIAGeoJLxez"
   },
   "outputs": [],
   "source": [
    "# import the data\n",
    "import pandas as pd               # https://pandas.pydata.org/ (data analysis & data manipulation)\n",
    "import numpy as np                # https://numpy.org/ (mathematical functions)\n",
    "import matplotlib.pyplot as plt   # https://matplotlib.org/ (creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy)\n",
    "import seaborn as sns             # https://seaborn.pydata.org/index.html (provides a high-level interface for drawing attractive and informative statistical graphics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 698,
     "status": "ok",
     "timestamp": 1682359985944,
     "user": {
      "displayName": "Ka Ba",
      "userId": "08277395367003334094"
     },
     "user_tz": -120
    },
    "id": "TPYve6PwLI5U",
    "outputId": "9ddaefcd-8b6f-407e-b2b3-df550e11af2c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first five rows in the dataset:  \n",
      "    total_bill   tip     sex smoker  day    time  size\n",
      "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
      "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
      "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
      "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
      "4       24.59  3.61  Female     No  Sun  Dinner     4 \n",
      "\n",
      "\n",
      "The last five rows in the dataset:  \n",
      "      total_bill   tip     sex smoker   day    time  size\n",
      "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
      "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
      "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
      "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
      "243       18.78  3.00  Female     No  Thur  Dinner     2\n"
     ]
    }
   ],
   "source": [
    "# load tips dataset\n",
    "tips = sns.load_dataset(\"tips\")\n",
    "# check if the dataframe looks ok\n",
    "print(\"The first five rows in the dataset:  \\n\", tips.head(), \"\\n\\n\")\n",
    "print(\"The last five rows in the dataset:  \\n\", tips.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 227,
     "status": "ok",
     "timestamp": 1682360049997,
     "user": {
      "displayName": "Ka Ba",
      "userId": "08277395367003334094"
     },
     "user_tz": -120
    },
    "id": "2390Ct2qP4bJ",
    "outputId": "d9314d5b-ff94-463d-8293-de918ee15e89"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 244 entries, 0 to 243\n",
      "Data columns (total 7 columns):\n",
      " #   Column      Non-Null Count  Dtype   \n",
      "---  ------      --------------  -----   \n",
      " 0   total_bill  244 non-null    float64 \n",
      " 1   tip         244 non-null    float64 \n",
      " 2   sex         244 non-null    category\n",
      " 3   smoker      244 non-null    category\n",
      " 4   day         244 non-null    category\n",
      " 5   time        244 non-null    category\n",
      " 6   size        244 non-null    int64   \n",
      "dtypes: category(4), float64(2), int64(1)\n",
      "memory usage: 7.4 KB\n"
     ]
    }
   ],
   "source": [
    "# the tips dataset has 244 rows and 7 columns\n",
    "tips.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 222,
     "status": "ok",
     "timestamp": 1682360133242,
     "user": {
      "displayName": "Ka Ba",
      "userId": "08277395367003334094"
     },
     "user_tz": -120
    },
    "id": "v31xfjtIQMUw",
    "outputId": "4ca22a34-01ad-413b-de68-f1b787fbe27f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total_bill     float64\n",
      "tip            float64\n",
      "sex           category\n",
      "smoker        category\n",
      "day           category\n",
      "time          category\n",
      "size             int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# the data type attributes for each column in the tips dataset\n",
    "print(tips.dtypes) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NxBAD27CQzY5"
   },
   "source": [
    "### Exploring the `tips` dataset visually"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 252,
     "status": "ok",
     "timestamp": 1682360224563,
     "user": {
      "displayName": "Ka Ba",
      "userId": "08277395367003334094"
     },
     "user_tz": -120
    },
    "id": "v_roLXC8MPCx"
   },
   "outputs": [],
   "source": [
    "# There are five preset seaborn themes: darkgrid, whitegrid, dark, white, and ticks. They are each suited to different applications and personal preferences. The default theme is darkgrid.\n",
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 518
    },
    "executionInfo": {
     "elapsed": 1046,
     "status": "ok",
     "timestamp": 1682360376460,
     "user": {
      "displayName": "Ka Ba",
      "userId": "08277395367003334094"
     },
     "user_tz": -120
    },
    "id": "WaPvQ6w9kXT3",
    "outputId": "7ea3fcee-f4f6-42ca-adef-27d087fbf5a8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f24b10d4a00>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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96zaVlZWS1ObYckc4HI5271aIdS1l50hMUGJCYtjjJTqazyOIxAM30WEmY8tYkvmcLpcrbh47kcZcmsE8mtHZ8xhyIS9ZskQ7d+7UrFmzNHLkyDbHdU358MMP5fV61adPHyUlJamgoED//d//rdtuu611m/Lycg0YMIATugAAMSvkQn777bd122236YEHHjAWpqSkRFdffbVycnKUnJysjz76SL/4xS+Uk5OjCRMmSJK+//3va/bs2frRj36koqIi7dmzR6+++qp+9rOfGcsBAEBnC7mQk5OT1bt3b5NZNHToUJWXl2vt2rUKBALq3bu3iouLdeeddyopKUmSNHLkSK1atUpPPPGEXnzxRV111VVatmyZioqKjGYBAKAzhVzIU6dO1Ztvvql/+Id/MBZmzpw5mjNnzkW3Gz9+fETebgUAQLSEXMgTJ07Uvn37dOedd2rGjBm68sorlZjY9uSbvLy8sAICABAPQi7klg8IkaR33nmnzXpTZ1kDABAPQi7k5cuXm8wBAEBcC7mQp0+fbjIHAABxre2VIQAAQKcL+RXygw8+eNFtHA6HfvKTn4R6FwAAxI2QC3nPnj1tlvn9ftXU1Mjn86lnz558lioAAO1k/GpPXq9XmzZt0rPPPqt169aFHAyR0/JZ1gAAexg/hux0OvW9731P3/zmN/XYY4+ZHj4u+f1mrqDUIjm5+apH1DIA2CPsyy9+lcGDB2vr1q2RGj6uJCQ4tOmNQ6o5Y+a6wAMzu+umUdlGxgIAmBGxQn7nnXc4hmxQzZlGnfyswchYPdO7GhkHAGBOyIW8evXqCy6vr6/Xvn379Mc//rFdn0sNAAAiUMjdu3dXZmamli5dqu985zshBwMAIJ6EXMgfffSRyRwAAMQ1PqkLAAALhH1S1969e/XWW2/p5MmTkqSrrrpKY8aM0XXXXRd2OAAA4kXIhezxePQv//IvevPNNxUIBJSWliZJqqur09NPP60bbrhBjz/+uJxOp7GwAABcqkLeZV1aWqo33nhD//iP/6jf/OY32rt3r/bu3au3335bd9xxh15//XWVlpaazAoAwCUr5EL+1a9+penTp+uBBx7QX/3VX7Uuv/zyy/Wv//qvuvnmm/XKK68YCQkAwKUu5EKuqanR0KFDv3L90KFDVVNTE+rwAADElZAL+corr9TevXu/cv2+fft05ZVXhjo8AABxJeRCvvnmm/Xaa69pyZIlqqyslM/nk9/vV2VlpX74wx+qoqJC06dPN5kVAIBLVshnWc+dO1fV1dV64YUXtHnzZiUkNHe73+9XIBDQ9OnTNXfuXGNBAQC4lIVcyImJiVqxYoVuv/12/frXv9aJEyckSb1799bo0aM1ePBgYyEBALjUdaiQz507px//+Mf667/+a82aNUtS82UW/7J8169fr+eff14PP/ww70MGAKAdOnQMedOmTXr55Zc1ZsyYr91uzJgxeumll7R58+ZwsgEAEDc6VMivvfaabrzxRmVmZn7tdn379tVNN92kbdu2hRUOAIB40aFCPnTokEaMGNGubYcPH66DBw+GFAoAgHjToUL2er3tPibsdDrl8XhCCgUAQLzpUCFfccUVOnz4cLu2PXz4sK644oqQQgEAEG86VMjf+MY3tHXrVp0+ffprtzt9+rS2bt2qb3zjG2GFQ2zoluKU3x+IdgwAiGkdetvT3XffrVdeeUW33XabfvzjH+uaa65ps82HH36oRx55ROfOndNdd91lLCjs5UrqooQEhza9cUg1ZxqNjDmobw/dWNjPyFgAEAs6VMiZmZl64okndP/992vmzJnKzMzUoEGDlJqaqoaGBh0+fFiffPKJkpOT9dOf/lR9+/aNVG5YqOZMo05+1mBkrIx0l5FxACBWdPiTusaMGaNXXnlFZWVleuutt/Tmm2+2rrviiitUXFysu++++6JvjQIAAH8W0kdn9unTR0uXLpUknT17Vg0NDUpNTVW3bt2MhgMAIF6E/FnWLbp160YRAwAQppAvvwgAAMyhkAEAsACFDACABShkAAAsQCEDAGABChkAAAtQyAAAWIBCBgDAAhQyAAAWoJABALAAhQwAgAUoZAAALEAhAwBgAQoZAAALUMgAAFiAQgYAwAIUMgAAFqCQAQCwAIUMAIAFKGQAACxAIQMAYAEKGQAAC1DIAABYgEIGwuB0OuVwOKIdA8AloEu0AwCxyuFwKDcvT10SE42O6/cHlJBAyQPxxqpCfu211/TKK6/oD3/4g+rq6tSvXz/NmjVLt9xyS9CrkM2bN+upp57SyZMnlZ2drQULFmjs2LFRTI541SUxURtf/0inv2gyMl5GjxTNuGGQkbEAxBarCvmZZ55R7969tWjRIvXo0UPvvPOOFi9erE8//VQlJSWSpG3btmnx4sWaO3euCgsLVV5erpKSEj333HMaNmxYdH8AxKX/O9OoU6fd0Y4BIMZZVchr1qxRz549W2+PGjVKX3zxhZ5++mn90z/9kxISErRy5UpNnjxZ8+fPlyQVFhbq0KFDKi0tVVlZWZSSAwAQHqtO6vpyGbcYMmSIzp49q8bGRlVXV6uqqkpFRUVB20yaNEm7d++Wx+PprKgAABhlVSFfyHvvvadevXqpW7duqqyslCRlZ2cHbTNgwAB5vV5VV1dHIyIAAGGzapf1X3r33XdVXl6uH/zgB5Kk2tpaSVJaWlrQdi23W9aHIhAIqLGxMeTvjxSHwyGXyyVfwC+f32dkzIAv0PzVb2ZMn/ySJL/BjBEZM9A8ptvtViAQCHs8j8cjl8ulgM/ejLHC7XYHfUVomEczTM9jSkpKu7aztpA//fRTLViwQAUFBZo9e3bE78/r9erAgQMRv5+Ocrlcys3NVZO7SY0NZp4weL1eSZLH6zUypqep+VDBuXPnjGWMxJhN3Zof7seOHTPyH83lcik9PV3nPB5rM8aaqqqqaEe4JDCPZpiaxxEjRrRrOysLua6uTnfffbfS09O1atUqJSQ071nv3r27JKm+vl4ZGRlB2395fSicTqcGDhwYRurIaHm7V7IrWSmp542M6XQ6JUlJTqdSUtv3zO3rJCUnSZK6du1qZLxIjZnsSpbUfMjD1CtkSeqalGRtxljhdrtVVVWlrKwsuVyuaMeJWcyjGdGaR+sKuampSffcc4/q6+u1adMmXXbZZa3r+vfvL0mqrKxs/XvLbafTqczMzJDv1+FwtHu3QjQkOhKUmGDmAygcic0l70gwM2bin05FSDCYMSJjOprHNPUfrOXJkiPR3oyxxuVyWf3/MFYwj2Z09jxadVLX+fPnNX/+fFVWVuqpp55Sr169gtZnZmYqKytLFRUVQcvLy8s1atQoJSUldWZcAACMseoV8tKlS7Vz504tWrRIZ8+e1QcffNC6Ljc3V0lJSZo3b54WLlyovn37qqCgQOXl5dq/f782bNgQveAAAITJqkJ+++23JUkrVqxos2779u3q06ePpkyZIrfbrbKyMq1du1bZ2dlavXq1hg8f3tlxAQAwxqpC3rFjR7u2Ky4uVnFxcYTTAF+PqzwBMMmqY8hAJHVLccrvN3fmcnJy8xnR1DIAE6x6hQxEkiupixISHNr0xiHVnAn/fcMDM7vrplHZF98QANqBQkbcqTnTqJOfNYQ9Ts/0rgbSAEAzdlkDAGABChkAAAtQyAAAWIBCBgDAAhQyAAAWoJABALAAhQwAgAUoZAAALEAhAwBgAQoZAAALUMgAAFiAQgYAwAIUcgSYvMQfACA+cLWnCDB5ib9BfXvoxsJ+BlIBAGxGIUeIqUv8ZaS7DKQBANiOXdYAAFiAQgYAwAIUMgAAFqCQAQCwAIUMAIAFKGTgEheJ98XzXnvAPN72BFziTL4vXpIyeqRoxg2DjIwF4M8oZCAOmHpfPIDIYZc1AAAWoJABALAAhQwAgAUoZAAALEAhAwBgAQoZAAALUMgAAFiAQgYAwAIUMgAAFqCQAQCwAIUMAIAFKGQAACxAIQMAYAEKGQAAC1DIAABYgEIGAMACFDIAABagkAEAsACFDACABShkAAAsQCEDAGABChkAAAtQyAAAWIBCBgDAAhQyAAAWoJABALAAhQwAgAUoZAAALEAhAwBgAQoZAAALUMgAAFiAQgYAwAIUMgAAFqCQAQCwAIUMAIAFrCrkjz/+WEuWLNG0adOUm5urKVOmXHC7zZs3a+LEicrPz9fUqVO1c+fOTk4KAIBZVhXy4cOHtWvXLvXr108DBgy44Dbbtm3T4sWLVVRUpLKyMg0bNkwlJSX64IMPOjcsAAAGdYl2gC8bN26cJkyYIElatGiR/vd//7fNNitXrtTkyZM1f/58SVJhYaEOHTqk0tJSlZWVdWZcAACMseoVckLC18eprq5WVVWVioqKgpZPmjRJu3fvlsfjiWQ8AAAixqpCvpjKykpJUnZ2dtDyAQMGyOv1qrq6OhqxAAAIm1W7rC+mtrZWkpSWlha0vOV2y/pQBAIBNTY2hh7uTxwOh1wul3wBv3x+X9jj+eSXJPkNjSdJAV+g+avf3oyxMKbpeZQkX6A5o9vtViAQCHs8049HyXzGlrG+/BWhYR7NMD2PKSkp7doupgo5krxerw4cOBD2OC6XS7m5uWpyN6mxIfyC9zQ174Y/d+6ckfGk5p9Vkjxer7UZY2FM0/MoSU3dmv9LHjt2zMgvA9OPR8l8xi+rqqoyOl68Yh7NMDWPI0aMaNd2MVXI3bt3lyTV19crIyOjdXldXV3Q+lA4nU4NHDgwvIBqfkUiScmuZKWkng97vKTkJElS165dlZLavmdZF+N0OpvHdjqNjBmJjLEwpul5lJofN1LzYRlTr5BbxjXxeGwZSzKXUWp+JVJVVaWsrCy5XC4jY8Yj5tGMaM1jTBVy//79JTUfS275e8ttp9OpzMzMkMd2OBzt3q3QHomOBCUmJIY/zp8O8ycYGk+SHInNv6QdCfZmjIUxTc+j1Py4kWT8l4Cpx2PLWJL5jC1jmvx/GK+YRzM6ex5j6qSuzMxMZWVlqaKiImh5eXm5Ro0apaSkpCglAwAgPFa9Qna73dq1a5ck6cSJEzp79mxr+V533XXq2bOn5s2bp4ULF6pv374qKChQeXm59u/frw0bNkQzOgAAYbGqkE+fPq377rsvaFnL7fXr16ugoEBTpkyR2+1WWVmZ1q5dq+zsbK1evVrDhw+PRmQAAIywqpD79OmjgwcPXnS74uJiFRcXd0IiAAA6R0wdQwYA4FJFIQMAYAEKGQAAC1DIAABYgEIGAMACFDIAABagkAEAsACFDACABShkAAAsQCEDAGABChkAAAtQyAAAWIBCBgDAAhQyAAAWoJABALAAhQwAgAUoZAAALEAhAwBgAQoZAAALUMgAAFiAQgYAwAIUMgAAFqCQAQCwAIUMAIAFKGQAACxAIQMAYAEKGQAAC1DIAABYgEIGAMACFDIAABagkAEAsACFDACABShkAAAsQCEDAGABChkAAAtQyAAAWIBCBgDAAhQygKhzOBxyOp3RjgFEFYUMWKRbilN+fyDaMb5WJDK6XC7l5uXJ4XAYGzMS82j7vw1iW5doBwDwZ66kLkpIcGjTG4dUc6Yx7PEG9e2hGwv7GUj2Z6YzStLl6cn67o2D5TUyWjPTGTN6pGjGDYOMjAVcCIUMWKjmTKNOftYQ9jgZ6S4DaS7MVEZJ8gX8Rsb5SyYzApHGLmsAACxAIQMAYAEKGQAAC1DIAABYgEIGAMACFDIAABagkAEAsACFDACABShkAAAsQCEDAGABChkAAAtQyAAAWIBCBgDAAhQyAAAWoJABALAAhQwAgAUoZAAALEAhAwBgAQoZgDUcDke0I8Q0h8Mhp9MZ7RgxL1rzSCEDiLrLUpzy+wNKTk6OdpRO5fcHjI7ncrmUm5dn/RMb0z+36TGjNY9dOvXeDDl69KiWLVum3/3ud0pNTdW0adM0f/58JSUlRTsagBAkd+2ihASHnn/9I332RVPY4w3q20M3FvYzkCyyEhIc2vTGIdWcaTQy3uXpyfrujYPlNTJa5Jj+uVv+vU2NGa15jLlCrq2t1W233aasrCytWrVKp06d0ooVK9TU1KQlS5ZEOx6AMPzfmUZ9etod9jgZ6S4DaTpHzZlGnfyswchYvoDfyDidweTP3fLvbWrMaM1jzBXy888/r4aGBq1evVrp6emSJJ/Pp6VLl+qee+5Rr169ohsQAIAQxNwx5F//+tcaNWpUaxlLUlFRkfx+v95+++3oBQMAIAwxV8iVlZXq379/0LK0tDRlZGSosrIySqkAAAiPIxAImD/dLYLy8vJ03333ac6cOUHLp0yZouHDh+uxxx7r8Jjvv/++AoGAsdPcHQ6Hzrq9Rs76c3ZJkKtrFzW4vfIZOouwZcxYyGjzmKbn8ctjms5o8zx+eUybH5MJCQ51czll8lemyd8VUmQyRoLpn9v0v3fLPPr9fiNnWnft2lU5OTkX3S7mjiFHQsuEmzzFvZvL7HvYUg2PJ8VGxlgY0/Q8SuYzxsI8SrHxmDT9VphIPH5sf9uTFBv/bxISOncncswVclpamurr69ssr62tVffu3UMac/jw4eHGAgAgLDF3DLl///5tjhXX19erpqamzbFlAABiRcwV8ujRo/XOO++orq6udVlFRYUSEhL0zW9+M4rJAAAIXcyd1FVbW6vJkycrOztb99xzT+sHg/zt3/4tHwwCAIhZMVfIUvNHZz722GNBH525YMECPjoTABCzYrKQAQC41MTcMWQAAC5FFDIAABagkAEAsACFDACABShkAAAsQCEDAGABCjlOvPbaa/r+97+v0aNHa9iwYZo2bZpefPFF668KY7uGhgaNHj1aOTk5+v3vfx/tODHp5Zdf1s0336z8/HwVFBTorrvuUlNTU7RjxZTt27eruLhYw4cP17e+9S3dd999qq6ujnYs63388cdasmSJpk2bptzcXE2ZMuWC223evFkTJ05Ufn6+pk6dqp07d0YkD4UcJ5555hm5XC4tWrRIa9as0ejRo7V48WKVlpZGO1pMe/LJJ+Xz+aIdI2atWbNGjz32mCZNmqRf/OIXevTRR9WnTx/mtAP27NmjkpISDRw4UKWlpXrooYf00Ucf6Y477uCJzUUcPnxYu3btUr9+/TRgwIALbrNt2zYtXrxYRUVFKisr07Bhw1RSUqIPPvjAfKAA4sLp06fbLHvkkUcC1157bcDn80UhUew7cuRIYNiwYYGNGzcGBg0aFNi/f3+0I8WUo0ePBnJzcwNvvfVWtKPEtMWLFwfGjRsX8Pv9rct2794dGDRoUGDfvn1RTGa/L//u+8EPfhCYPHlym21uvPHGwP333x+0bMaMGYG77rrLeB5eIceJnj17tlk2ZMgQnT17Vo2NjVFIFPuWLVummTNnKjs7O9pRYtKWLVvUp08fffvb3452lJh2/vx5paamBl0D+bLLLpMkDkldxMWud1xdXa2qqioVFRUFLZ80aZJ2794tj8djNo/R0RBT3nvvPfXq1UvdunWLdpSYU1FRoUOHDunee++NdpSY9eGHH2rQoEF68sknNWrUKF199dWaOXOmPvzww2hHiyl/93d/p6NHj+q5555TfX29qqur9dOf/lS5ubm69tprox0vprVc6vcvn3QPGDBAXq/X+HF6CjlOvfvuuyovL9cdd9wR7Sgxx+12a8WKFVqwYAFPZsJQU1Oj3/zmN9q6dat++MMfqrS0VA6HQ3fccYdOnz4d7XgxY+TIkVq9erUef/xxjRw5UhMmTNDp06dVVlamxMTEaMeLabW1tZKktLS0oOUtt1vWm0Ihx6FPP/1UCxYsUEFBgWbPnh3tODFnzZo1uvzyy3XLLbdEO0pMCwQCamxs1M9//nPddNNN+va3v601a9YoEAhow4YN0Y4XM95//3098MAD+s53vqNnn31WP//5z+X3+zVnzhxO6ooxXaIdAJ2rrq5Od999t9LT07Vq1aqLHkNBsBMnTmjdunUqLS1VfX29JLUeg29sbFRDQ4NSU1OjGTFmpKWlKT09XYMHD25dlp6ertzcXB05ciSKyWLLsmXLVFhYqEWLFrUuGzZsmMaMGaOtW7dqxowZUUwX27p37y5Jqq+vV0ZGRuvyurq6oPWmUMhxpKmpSffcc4/q6+u1adOm1hM/0H7Hjx+X1+vVnDlz2qybPXu2rrnmGr3wwgtRSBZ7Bg4cqE8++eSC686dO9fJaWLX0aNHNX78+KBlV155pXr06PGV84v26d+/v6TmY8ktf2+57XQ6lZmZafT+KOQ4cf78ec2fP1+VlZV67rnn1KtXr2hHiklDhgzR+vXrg5YdOHBAy5cv19KlS5Wfnx+lZLFn7Nix2rJliw4cOKAhQ4ZIks6cOaM//OEPuv3226MbLoZcddVV+uMf/xi07MSJEzpz5ox69+4dpVSXhszMTGVlZamiokITJkxoXV5eXq5Ro0YpKSnJ6P1RyHFi6dKl2rlzpxYtWqSzZ88Gvak9NzfX+APrUpWWlqaCgoILrsvLy1NeXl4nJ4pdEyZMUH5+vv75n/9ZCxYsUNeuXbV27VolJSXp1ltvjXa8mDFz5kz95Cc/0bJlyzRu3Dh98cUXrec5/OXbdRDM7XZr165dkpqfxJw9e1YVFRWSpOuuu049e/bUvHnztHDhQvXt21cFBQUqLy/X/v37I3KegyPAG9Xiwrhx43TixIkLrtu+fbv69OnTyYkuHXv27NHs2bP14osv8gq5gz7//HMtX75cO3fulNfr1ciRI/Xggw9q4MCB0Y4WMwKBgJ5//nlt3LhR1dXVSk1N1bBhw7RgwYKv/PQpNDt+/Hib3f0t1q9f3/rke/PmzSorK9PJkyeVnZ2t+++/X2PHjjWeh0IGAMACnGILAIAFKGQAACxAIQMAYAEKGQAAC1DIAABYgEIGAMACFDIAABagkAEAsACFDKBdxo0bF3RFIQBmUcgAgrz//vtatWpV6yXmAHQOLi4BIMjvfvc7rV69WtOnT1daWlrr8oqKCjkcjigmAy5tFDKAduGKYEBkscsaQKtVq1bp3/7t3yRJ48ePV05OjnJycnT8+PE2x5C3bNminJwc7du3T0uWLFFBQYGuvfZaPfDAA6qtrY3WjwDELF4hA2h1ww03qKqqSq+++qoefPBB9ejRQ5LUs2fPr/yeRx99VGlpaSopKdGxY8e0ceNGnTx5Uv/1X//FLm6gAyhkAK0GDx6s3Nxcvfrqq5owYUK7rpPtdDr1zDPPyOl0SpKuuuoq/fu//7t27NjxldeaBdAWu6wBhGXGjBmtZSxJ3/3ud9WlSxft2rUriqmA2EMhAwhLv379gm6npqYqIyNDJ06ciFIiIDZRyAAAWIBCBhCkoydiffzxx0G3GxoaVFNTo969e5uMBVzyKGQAQVwulySpvr6+Xdtv2rRJXq+39fbGjRt1/vx5jR49OiL5gEsVZ1kDCJKXlydJ+tnPfqZJkybJ6XRq7NixX7m91+vV7bffrqKiIh07dky//OUvNWLECM6wBjqIQgYQZOjQobrvvvv0/PPP63/+53/k9/u1ffv2r9x+yZIl+tWvfqWVK1fK6/Vq8uTJeuSRR3gPMtBBjkAgEIh2CACxZ8uWLXrwwQf14osvKj8/P9pxgJjHMWQAACxAIQMAYAEKGQAAC3AMGQAAC/AKGQAAC1DIAABYgEIGAMACFDIAABagkAEAsACFDACABShkAAAsQCEDAGABChkAAAv8P2oQCHUqRuq2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution of the tip amount and its frquency\n",
    "# -> around twenty tables have tipped a bit more than 4$\n",
    "sns.displot(x=tips.tip)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_ClP2M8sjVGa"
   },
   "source": [
    "### Plotting the correlation between total_bill and tip: Do we see any pattern in the data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 475
    },
    "executionInfo": {
     "elapsed": 788,
     "status": "ok",
     "timestamp": 1682262274133,
     "user": {
      "displayName": "Ka Ba",
      "userId": "08277395367003334094"
     },
     "user_tz": -120
    },
    "id": "tblkHmyigqfs",
    "outputId": "0102ca54-2560-4a8c-d7f2-7dd60dcfd585"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='total_bill', ylabel='tip'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(\n",
    "    x=tips.total_bill,\n",
    "    y=tips.tip\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VSZYiCrRjp63"
   },
   "source": [
    "**YES!** There seems to be a positive linear correlation between the amount people spent at the restaurant and the amunt they tip! \n",
    "\n",
    "-> There is a positive linear correlation when the variable on the x -axis increases as the variable on the y -axis increases. This is shown by an upwards sloping straight regression line."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "57nTP2jWeTNM"
   },
   "source": [
    "## Recall from the lecture...what plots are available for what problem?\n",
    "\n",
    "Think about what you want to *model*\n",
    "\n",
    "* Change over time\n",
    "* Comparison\n",
    "* Part of a whole\n",
    "* A Correlation\n",
    "* Ranking\n",
    "* Distribution\n",
    "* Flows and relationships\n",
    "* Geospatial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6TXw6FJ2oez2"
   },
   "source": [
    "###**Analytical thinking exercise:**\n",
    "- What are  questions that would be interessting to answer on the tips dataset?\n",
    "- How would you model this question?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jg4Ccc8SqTHe"
   },
   "source": [
    "#### Examplary solutions: \n",
    "* Do smokers tip more than non smokers? > Comparison\n",
    "* Is the size of the group impacting the tipping? > correlation\n",
    "* Is there more tips on Sunday VS Saturday ? > Comparison, change over time if we had the true\n",
    "* Distribution of proportion of tip VS the total amount > distribution\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7bTGL5R2gLhL"
   },
   "source": [
    "## Comparison and Correlation plots with Seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hOqzYg_OgkDM"
   },
   "source": [
    "We could compare how much money was spent given the day of the week or the time of the time for example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ucrsLHlNhsXf"
   },
   "source": [
    "### **Comparison example: Barplot with Seaborn**\n",
    "A bar plot represents an estimate of central tendency for a numeric variable with the height of each rectangle and provides some indication of the uncertainty around that estimate using error bars.\n",
    "\n",
    "https://seaborn.pydata.org/generated/seaborn.barplot.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 475
    },
    "executionInfo": {
     "elapsed": 1084,
     "status": "ok",
     "timestamp": 1682361172335,
     "user": {
      "displayName": "Ka Ba",
      "userId": "08277395367003334094"
     },
     "user_tz": -120
    },
    "id": "azXfZZHvtvf_",
    "outputId": "5cae48ba-b49f-42f1-b1d8-4f87a07c54b2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='day', ylabel='tip'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='day', y='tip', data=tips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 222,
     "status": "ok",
     "timestamp": 1682361228690,
     "user": {
      "displayName": "Ka Ba",
      "userId": "08277395367003334094"
     },
     "user_tz": -120
    },
    "id": "tSwa4WBetA0D",
    "outputId": "64263c3d-3d64-40dc-b8b4-3a3239be58b7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day   sex   \n",
       "Thur  Male      18.714667\n",
       "      Female    16.715312\n",
       "Fri   Male      19.857000\n",
       "      Female    14.145556\n",
       "Sat   Male      20.802542\n",
       "      Female    19.680357\n",
       "Sun   Male      21.887241\n",
       "      Female    19.872222\n",
       "Name: total_bill, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No visualization, but using pandas groupby function: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html\n",
    "tips.groupby(['day','sex'])['total_bill'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gd3et3eYijZ6"
   },
   "source": [
    "### **Correlation example: Scatter plots with Seaborn**\n",
    "https://seaborn.pydata.org/generated/seaborn.scatterplot.html?highlight=scatterplot#seaborn.scatterplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EcEoil9Vi37Z"
   },
   "source": [
    "We could ask ourselves is there a correlation between the price of the meal and the tip?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "executionInfo": {
     "elapsed": 672,
     "status": "ok",
     "timestamp": 1682262716935,
     "user": {
      "displayName": "Ka Ba",
      "userId": "08277395367003334094"
     },
     "user_tz": -120
    },
    "id": "ttY9MhBWigl-",
    "outputId": "cf1e2e68-ac40-4a9d-d38d-fa5997e350f3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6dNyLOUBElEzM4SGimGQaw39tRDoOMAeIiJKPIzxEFBOTXouq8oKgx6rKC2DSa8PenzlARCQHBjxEFBNBFLFgVkVA0OMdoYm0A30sOUDd014imtsdaHd44BABMCAiojhwSotIIqIgwOZ0w2p3IdOog0mvhSCKIa9PJVpRRN2sirheRzQ5QJlZhqDTXpWlBbh1ahku7WeEJsXeMyKSFwMeIgkEO1lXj+yPubWjsFYluSuCKCJTp0FmlqH7iijbH00OUKhpr/1Hui9PHFOIqqH5KRcoEpF8OKVFlGChTtZFl+Vg9SvMXYkmByjctNf+I83Isxi5/J2IYsKAhyjBQp2shxXl+kYoekun+jXR5ABFmvZyOD1RL39PN1zuTxQcp7SIEizUidjh9ES8n296SOUi5QBFmvYy6DVRLX9PN1zuTxQaR3iIEizUidigD/9xS7cTuDcHKD/LgEydxi8fJ9y0V2VpAVra7BGXv6cbLvcnCo8BD1GChTpZH2xqRWVp/PVr0kmoaS/vKq0xTFgOwC0/iMJLr5+UREngPVmv2dqAfYcunoCa/nke991cgbW9ro+2fk26uTjt5UGnzQljhhYZei0MGiHqFWHpJNrl/kTpigEPkQTC5ajEW78mHXVPewnIzO5xouZ7FVQitvwgUjNOaRFJJFSOSrjcFaJ49XXLDyK1Y8BDRKQCfd3yg0jtOMZJRKQSfdnyg0jtGPAQEalIvFt+EKkdp7RkxIqoysB+ICJSP47wyIQVUZWB/SAfNewaT0SpgwGPDCJVRK1jgmFSsB/kw0CTiJKNU1oyYEVUZWA/yENtWyCIgoA2uxuHmlrQ3uVOufYTpQuO8MiAFVGVgf0gj2gCzUxdavwW40gVUepIjW8VlWFFVGVgP8gjmkAzFahtpIpI7RjwyIAVUZWB/SAPtQSanBIlSi0MeGTAiqjKEK4f5t0wGnYX8zGkoJZAUy0jVUTpIjV+SqkQK6IqQ+9+cLg8aDh6Foue2gm7w522+RhSLhkPtZt8qgX8ahmpIkoX/ETKiBVRlUEQRZj0WmzY/hmXqCM5ibhqCPi9I1U9gzYv30hVCr0eIrXjlBYRmI/hlcxE3FTfNZ5T00SphSM8ROASda9kLhlXQ6XliyNVHtgdLhgNOpj0qRe8EaUDBjxEYD6GV7ICPzXVrxFEERajFkWXWdDa2gmXyyN3k4goiKi/xU+fPh3XExQWFsZ1P6JkYj5Gt2QEftzSg4jkEPW31+TJkyHEMX9/4MCBmO9DlGxqWTnUV8kI/NRUaZmIUkfUAc8jjzwSV8BDlCrUsHKor5IR+DFfiojkEHXAM2vWLCnbQaQILBUgfeDHfCkikgPHjSki7gadfqRcMi53pWVREGB1eXC2wwGry8O/Z6I0EfVPqVWrVsX84IIg4L777ov69jt37sT69etx9OhRdHR0oH///pg6dSrq6uqQnZ3tu93bb7+Np59+Gl988QUKCwsxb9483HTTTTG3jyJT02oaUgY586X490yUvgRRjO5TPmzYsNgfXBBiSlretm0bDh06hMrKSvTr1w9HjhzBypUrMXLkSDz33HMAgL1792LOnDm4+eabMX36dLz//vv4r//6Lzz99NP47ne/G3MbvdxuD1paOuO+vxqJgoB12z5FUWEOhhXlwuH0wKDX4GBTK5r+eR7zakelfH5LqtaC0ek0yM01p/Qy6GS/96IgYFWQ1WFAd9DTl9VhaugPtWGfKItU/ZGXZ4ZWG91kVdQjPAcPHoy7QdGaOXOm3+Xq6moYDAYsW7YMZ86cQf/+/bF27VpUVFTgoYceAgCMGzcOJ06cwDPPPNOngIcC2V1ufGfcYGx/rxGb3zzsu76ytAC1E4thd7lhivIPTYn4a19evfOlRABWl0eyAIirw4jSm+I/3f369QMAOJ1OOBwO7NmzJyCwmT59Oo4dO4aTJ0/K0EL1EkUB299rxP4j/ieJ/Ueasf29Rohi6uY+JHMLBbmkUq6K+8LoS90T72L5ut3Y9fFp/LPVhuYEtp27mxOlN0Uuh3C73XC5XDh69ChWr16NyZMnY+DAgTh69CicTieKi4v9bl9SUgIAaGxsxMCBA+Vosip5RDEg2PHaf6QZHlEEoNyTaDhq/7WfSqNXPYNPo0GLJbePDRhVTETbuTqMKL3FVHhQo9HgjTfegF6vj6oQoSAIePPNN2Nu1KRJk3DmzBkAwMSJE/Hkk08CAM6fPw8AsFgsfrf3XvYej5cuhU9wUrB3OMIf73Khn8mYpNYkljXCa7PaXbD0U+5r885ZB5u7dovAmi37Q45e/fjmSmgVFKe22S8Gn7U1JUFHFRPRdrNWE7aoojlDF/djh+sPkgf7RFmU0B9RBzzf+ta3IAgCNBqN32UprFu3DjabDUePHsXatWsxf/58/O53v5Pkubw0GgG5uWZJnyPVdDrDJ5Zlmw0p+56p5bVZLKaA605+1R529Mru8mDgpdlBj8vhq6YW37+HFeX6jez0lIi2L7ylCis37wtYHfbjW6qQ3y/wvYxVsP4gebFPlEXO/og64HnsscfCXk4k74qwqqoqjB49GjNnzsT//M//YOjQoQCA9vZ2v9u3tbUBAHJycuJ+To9HRFubNe77q5FRF/4XsVGnQWtraq5sS/XXptVqYLGY0NZmg9vtH7y1d4YfvWrvdCjqtRkNF7+GHBEC0b62XQfgxzdXorPLDavdiUyjHuYMLbSip0+PG64/SB7sE2WRqj8sFlPiV2nJpby8HHq9Hv/4xz8wefJk6PV6NDY2YuLEib7bNDY2AkBAbk+suHQxULh6KaLbg1RO81TDa3O7PQF/t9Hkqijpb92kvxh8GvThv7gS1fZMnXBxdVgC+zpYf5C82CfKImd/9CngcTgc2Lx5M3bu3IlTp04BAC6//HJcddVVmD17NjIyMvrcwP3798PpdGLgwIEwGAyorq7Gf//3f+OHP/yh7zb19fUoKSlhwrIELm4z4IHd4YLRoINJn9jKu8nSu+5Lpl6ryr2zUm3n956FCA82taKytCBosrxS2h6sfhARKV/UhQd7+/LLL/GjH/0IX3zxBQoKClBUVAQAaGpqQnNzMwYPHoznn38eAwYMiPox6+rqMGrUKJSXl8NoNOLgwYPYsGED8vLy8Morr8BgMPgKD95yyy2YNm0a9uzZgzVr1uCpp57CtGnT4nkpAFh4MJJUL+KVSquWohGpP9yCEHL0SqmvVxQE2F1uaAQN1m37RJFtD/d3NCA/K2U/H2qU6t9ZaqOEwoNxBzz33nsv/u///g+//vWvA+rivPHGG1i6dCm+/e1vY+3atVE/5rp161BfX49//OMfEEURl19+Oa655hrMnTsXWVlZvtu99dZbAVtL3HzzzfG8DB8GPOGl8peHlBV25RJNf6RqFWlAmW2P9He05PaxcHU5U+7zoVap/J2lRkoIeOKe0nr//fdxxx13BK1uPG3aNHz++efYtGlTTI85b948zJs3L+LtpkyZgilTpsT02JQYblHaarhSUHvNnVBSeed3JbY90t/R+Y4umCPkIBGRfOIOeMxmM/Ly8kIez8/Ph9ms/GW9FL3mczas7FXfRYqphkT/uo+mwq7vxEoUQqS/o06bE2Z93/MWiUgacQc8s2bNwmuvvYZbbrkFJpP/uvrOzk5s3bqVO5iriFsEVm7ZF7KYXaKmhaTItWGFXUqESH8nZpM+SS0honhE/U3/17/+1e/y8OHD8e6772LatGm44YYbfEnLx48fx7Zt25CTk4Py8vLEtpZk09nlDrrqB0jctFCk/a3iDapMei2qR/ZH0WXBd31XwsofUr5Iq99ysjLg6nJK8txKzGkiSjVRJy0PGzYMgiDAe/Oe/w754IKAAwcO9L2VScCk5fDOdjjws1V/C3n88boJyO/jtJDV5UHdE++GPL5q8dVxB1UuQcDqVxr8ljtXlhbgvpsroEvBEwcTMuURcvXbTRUYcIk0q7TUtsIwWfgZUZaUSlreuHFj3A2i1JdpDD9cn4hpIalybURBwNpXG4Lu+r42gdNxpH4X61L5j7boNdJssyPVqCdROoppL62+6OrqwhtvvIEJEyYgPz+/T49FyWfOkL6YnVS5Num6SoukEXwFmTQBD/92iRInaZ+U9vZ2/OIXv8CRI0eS9ZSUQFqhe+PFqvICv+u9Q+uJ+JXpzZEIxhdUxSGakSMiJeLfLlHiJHV5Spw1DkkhCvqZLmy86JIkebLnFgPBquzG+zxcpUWpin+7RInDTwvFRCtA0oJwoXIk+hJUpdreUpGIgoA2uxtfNbXAlKGDUZeae5tRZGr72yWSEwMeiijZJ9hEV9mVauRIDlyxk17U9LdLJDcGPBSWWk6wUowcJZvaVuywtkx01PC3S6QEDHgoJLWdYEOOHAkCHB4RXU437F1umE16mPTKmyayOT0RVux4kKmTZrVQoqklkE4WJe4tRpRqGPBQSOmwJNYjCPjqnB0vv3nYr06PEk++nbbwVXw7bU5kZit/TzC1BdJElBpS+2xFklL7klhREPDx0bMBwQ5w8eQrCuFHTERBgNXlwdkOB6wuT8Tb94UxI/yy/EjHlSKaQJqIKNGSNsKTk5ODjRs3Yvjw4cl6yrQgZR6E2pfE2pxu5FmMAcGOV6RRrGRPy2TotagsLQja3srSAmTEWaco2bh7PRHJIeoz1p/+9Ke4nuCGG24AAOj1+j5XayZ/Up9w1b4k1mp3weEMv6dLqJOvHNMyBo2AW6eWAUDAnmC3Ti2DQSOkRH+oPZAmImWK+ptl6dKlMT+4IAi+gIcSKxknXLUvic006tARIS8m1MlXlvwmUcSl/YyYOKYQM2uKfbu+t7TZcWk/Y0oEO4D6A2kiUqaoA5633npLynZQjJJ1wr24JNYDu8MFo0GnyBVM8TDptWhps4ecJgp38pVrWkYjiqgamu/XH0WXZqVUf6g9kCYiZYo64Ln88sulbAfFKJknXEEUYTFqUXSZBa2tnXC5wk8DpQpBFDFmaD4K87MAIOgqrVAnXzmnZdTQH9HUlmGdHiJKJE6WpyjmQSSGRhQxoJ8R984aHVMdHjmnZdSytUS42jKs00NEidans2JzczNeeeUVfP7552hvb4fH4/9LUxAEvPDCC31qIAXHPIgEEkUYBMBg0CLboPVdF45c0zKJCgSUPHrCOj1EJIW4A56DBw9izpw5sNvtGDJkCA4fPoyhQ4eira0NZ86cwRVXXIEBAwYksq3UA/Mg5Jfskv+JCgSUPnqSDgUviSj54g54nnzySWRmZuJPf/oTjEYjvv3tb+OXv/wlxo8fjzfeeAP/9m//hieeeCKRbaVeuMeO/JJZ8j8RgYAUoyfxjhaFuh/r9BCRFOIOeP7+97/jrrvuQmFhIc6dOwcAEC98yU2bNg0fffQRHn/8cWzatCkhDaXguMdO+khEIOD0iLh+YjGuHTcYBr0GB5tasX3XMdgd7rhGT+IdLQp3P+anEZEU4h4X9ng8yM/PBwBYLBZotVpf4AMA5eXl+Oyzz/rcQCK1i3Z7ir4GAm5BwG9f+wQPbdiDxzZ+iIc27MGhplYsuX0sjBdyl2LZLiTSaFGo1xHpfkaDDlXlBUHv68tPIyKKUdw/lQYOHIiTJ08CADQaDQYOHIjdu3dj+vTpALpHgLKzsxPTSiKVimWEpC+J6qGCDO9S/NqaEmx+83BMoyfxTrFFul+X08X8NCJKuLgDngkTJmDHjh1YtGgRAOB73/seHnvsMZw4cQKiKOKDDz7Aj370o4Q1lEhtYs2n6UuierggY/+RZsysKY55dV+8U2yR7tdpc8GUpVFUfpqSV7URUXTiDnjmz5+P6667Dk6nE3q9Hj/84Q9htVrx17/+FRqNBgsWLMA999yTyLaSTNRS90Vq4U6KwY7FM0ISb+XrSEGGCMQ8ehLvFFu091NKfprSV7URUXTiDnhycnKQk5PjuywIAhYsWIAFCxYkpGGkDPyyj06490kAsDrIsduuKQ/7mKFGSOKptBwpyOifmxlzf8Y7xZZKNaRYE4hIPeJOWp4zZw52794d8vj777+POXPmxPvwpAA9v+yNBi1umVqG5XOrcW31YHx1zgaPhrVQgMgnxY+Png16zBkhUEnkaiRvkBFMd5ARe196p9h6P26kKbZ47yeHaEbhiCg1xP2N+sEHH2D27Nkhj7e0tODDDz+M9+FJAbxf9kaDFktuH4vt7zVi85uHfcc50tMt0knx+gnFQY81HD2btJEOqQpVxlsLKlVqSLEmEJF69OknpBBi2SkANDU1wWw29+XhSWZWuwtGgxaLbx+LDIMW08YPxsyaYl/tFqmG9ROVIJqsRNNIJ0WdVoPlc6vhcHr8at9s33UMT91/FdZt+yQpq5GkCjLizbVRSo5OOKwJRKQeMX1aX3vtNbz22mu+y2vXrsXmzZsDbtfe3o5Dhw6hpqam7y0k2ZhNOiy5fSz+/LdGfNxjBKOytABLbh+LFZv2JrzUf6JyhpKZexTppKfRCHho3R7f5Z7vn0f0JHWkIxWCDCVJpXwjIgovprOUzWZDa2srWltbAQCdnZ2+yz3/MxgMuO222/Af//EfkjSakiNDr8P29/yDHaB7GfP29xpRW1MCILZideHEW8hOqscJ9rjBCgSGy4+pLC1Aw9Gzftd537+7Zo6CUaf1BSH5WQZkcgWcoqRSvhERhRfTCM/3v/99fP/73wcATJ48Gf/v//0/TJkyRZKGkfzsDpevMF1v3totQOKG9RO1aaQUm09GGjEKlR8z48pirNi0N+Dx9h9pxl0zR/GEmQJSJd+IiMKL+0z19ttvJ7IdpECRRm4cTk9Ch/UTlSCa6ETTaJYmBzspAgIWP7MLdkfwlTw2uxNmJrymBE4FEqW+Pv80/+CDD/Duu+/i9OnTAIDCwkJcffXV+Na3vtXnxpG8Io3cZGXqEzqsn6gE0UQnmkY7YtT7pGh1eUIGO/G0gxKPFZSJ0kfc37gOhwM//elP8eabb0IURVgsFgBAW1sbfve73+Gaa67Bk08+Cb1en7DGUnJFStgckJcJjSdy0btEPV+0I0mJTjSNd8RIqoRXVr5OjFQqqsnAjKjvBFGM71Pz1FNP4be//S3uvPNO3Hnnnb6d07/++ms899xz2LBhA+bPn4/7778/ke2VjNvtQUtLp9zNUBy3IISs3SLFSSFRz5fIdltdHtQ98W7I46sWXx0yJyjR718qnaSVTBQErAoyTQl0v5+xllrQ6TTIzTVHXfk6Fuzz+EjZJxQ7qfojL88MrTa6nMy4A57Jkyejuroajz76aNDjS5cuxQcffJAyuT4MeELr/nUZfu+mRP4CVVodHlEQsKpX0OIVzckxoe1I4Ek6nfUliA1Gqi9z9nn8GPAoixICnrintJqbm1FRURHyeEVFBV5//fV4H54UJNLeTYn+BZqoBNHejyOi+0QXS+DhDVZuu6YcN08uxf4jZ7F91zHYHe6olyYn6vVIsfosXaVKBWX2OVHixB3wDBgwAB988AG+973vBT3+4YcfYsCAAXE3jJQjXM5IqmyuGE9QFuo+T91/FTyix1dDJ1lS5SSdClKlgjL7nChx4v5U33DDDVi5ciWys7Nxxx13oKioCIIg4Pjx43jhhRewY8cOLFy4MJFtJRlEChRS4RdoPEFZuPus2/ZJyPvYnB502pwwZuiQodfAoBEStoQ5VU7SqSBVKiizz4kSJ+5Py/z583HixAls3rwZW7ZsgebCztkejweiKOLGG2/E/PnzE9ZQSr5oAoVU+AUaT1AW632CBYaVpQW4dWoZLu1nhCYBJ89UOUmnAqk2U0009jlR4sQd8Jw5cwa/+tWvcMcdd2DXrl04deoUAODyyy9HTU0NioqK8OWXX6KwsDBhjaXkiuqkr8BfoL2ThAEBRoM2ZE2cYEFZLIFcqMDQW6V64phCVA3NT8gmnalwko6W3EutU6GCstr6nEhOcZ+NpkyZghUrVmDGjBkYNmxYwPH6+nr89Kc/xYEDB/rUQJJPNCf9S7IzFPULNNQUnHezzmBBT7CgLJZALlxg6N2CI1FTexdP0uFXzSmdUpZap0IF5VQIzIhSQdzfwKIoItyKdqfT6ZvmotQUzUlfSZsrhpuC67nZaU++oKyXcBuC9r5PNFtwJGqDVeDiqrnyojxYjKl34pNqc1c14wazRH0X0whPR0cH2trafJfPnTvn21Kip7a2NtTX16OgIPgJg1JDtPkDSvkFGmmkZfaUUmx+87DvunBBWSxTCZECQ4Neo5jkUrmnkQAutSYiecT0Lfz8889j9erVAABBEPDII4/gkUceCXpbURRTpsoyBRfLSV8JUwORRlEMOg1WLb466pN9tIFcuMCwsrQALW12FF2aFfd7EixIiYdSppFSIdGdiNQnpoDnyiuvRGZmJkRRxIoVK3Dddddh5MiRfrcRBAEmkwkjR47E6NGjE9pYSr5UyhmJZgou1qAsmkDOGxg+u/1TFF2Wg2FFuXA4PcjO1CPbbIDZEP8oSrggJRZKqpekxER3IlK/mL5ZqqqqUFVVBQCw2Wz4zne+g7KyMkkaRsoRqdKyUsi5hFcriphbOwprX20IOm0Wz5hMpCBlye1jo34sJU0jcak1Eckh7m+4uro6BjukKBETqNG9tcTZDgesLk9Ck2NFQcDaBCfiRgpSznd0Rf1Y0UwjJYuSEt2JKH1w7JhUJVTeDQD8/ehZ5FmMcDg96LA50dJmx5ih+QkpCijFCEqkIKTT5oRZnxHVYyltGkkpie5ElD4Y8JDqBOTdAPjynB3vfXzaVwwQ6E4oLszPwoB+xj5PoVjtLhgNWtTWlPhyeAx6DQ42tWL7rmNxJeJGCkLMJj2A6FZeKXEaSQmJ7kSUPhjwUMLFsvQ5GcukHR4RL7952C/YAS5WQr531mgY+ji7ZTbpseT2sdj+XqNfDk9laXfRQ29wEotIQUpOVgY6rY6oVl4psWKvEpbIE1H6YMBDCRXL0udkLZPucroDgh2v/Uea0eV0w2CIPq042InaoBOw/b3GkEHVfTdXxDyCETZIual7lVYsK6+UNI2klCXyRJQ+GPBQwsSy9DmZy6TtXe6w0032Ljeyowx4Qp2o77x+ZNigytblivo5euodpJhNOmTodbA73Ghpa8f3rh2GH0wfjrPn7NBpBd9rCpU3pIRpJCUtkSei9MGAhxImlsTdRCT59hxpuRgIuAJGL7Iyw083ZWUGTjcFnW5B6BGVrybYwrY1lqCqt55BilsQsPqV/QG7stdOLMbjv9+L8qI8375hSi3gp6Ql8kSUPhQV8LzxxhvYvn07PvvsM7S1taGoqAg/+MEPcNNNN0Hosax3y5YtePbZZ3H69GkMGTIEixYtwqRJk2Rsufq5ReDkV+1o73SEnAqJpYJuX6vt9hxpMRq0voCm5yiLd4rEqNeEnW5aOLsS8HiCPnbPx7rnxgocON4StD2RUoDiyeHpLdKu7LU1Jb6ArramRLEF/FhpmYjkoKifUc8//zxMJhOWLl2KtWvXoqamBsuWLfNtZwEAr7/+OpYtW4Zp06Zh/fr1GDNmDOrq6vDxxx/L13CVcwsCntmyH/f++m38bNXfUPfEu1i1tQHuXrVlYln63Jdl0r1P/LU1JUEDGu8Uic0RPofH7nD5HtfqEvGPrzpw/cRi3DK1DMYLozL7DjXjt681BN2AFAAONrVG2Gy07x+1SHuFDSvK9f27Ymh+3FtQSE1pS+SJKD0o6ptl7dq1yMvL810eP348zp07h9/97ndYsGABNBoNnnnmGVx33XW+fbrGjRuHw4cPY/Xq1Vi/fr1MLVevWPItYln63Jdl0r1P/MOKcv2mqnq3s3OqM+xrtNpdyMjOCHid3imvFZv2wu5wY9+hZlw/oTjoY2zfdQxPP3A1fvuadKugotmV3cug4B21lbhEnojUT1EBT89gx2v48OHYvHkzrFYrWltbcfz4cSxZssTvNtOnT8fjjz8Oh8MBg4FD4Ylkd7lRekUurp9YHJDs2zvfItbNRuNdJt37xN/zRB+MMSP8SEemUR/1VFGoVg0fkgeDAElXQUWzK7uX2aRT7LJvJS6RJyL1U1TAE8xHH32E/v37IysrCx999BEAYMiQIX63KSkpgdPpxIkTJ1BSEnzKIRo6JkoG0HiAQ02tQZN9vYmxln5G3zEdgB/fXInOLjesdicyjXqYM7TQCkDvTJdYbttTptE/H8YQYbooQ68LO6Kg02rCThXNrLk4qnNpbmbAY3mXies03W3Wa3Ww+AUnidnCwqzVhN2V/WBTKwCgemR/GHRarAqx7DtDm7gtNeIVb98rlVar8fs/yY99oixK6A9FBzx79+5FfX09fv7znwMAzp8/DwCwWCx+t/Ne9h6Ph0YjIDfXHPf91ajd6sC6zXtDJvvW1pQg22wI+r7lx/A8sdwWAHRWh9+J/2BTKypLC4Lm6VSVF8CSZcCCmyqxptfqpqqyAiy4qRJtneH3pPKOIFWVF+CSHCOW3D4W5zu6urd2MOmRk5WB7MyLI4vtVkfY432x8JYqrNy8zy/o8a7SWrFpL6rKCzDvxgqs2vJx2E1HE9Wevoq175XOYjHJ3QTqhX2iLHL2h2IDni+//BKLFi1CdXU15syZI/nzeTwi2tqskj9PKmmzh0+SnT2lFEadBq2tnUluGfymRLbvOubbOTxgldZNFeiyO/H1ORu+XVHoNzX39Xk7vj5nQ7Y5/MnfoNf4HsvV1Z0PZNZrfPtYubqcaL1wfZdbDFlQLxEjK96REavDDVuXC0aDDjqtBu3WLjzx4xqYM7TotLuCjgIB3UFPy3m773VQYmi1GlgsJrS12eB2h59ipeRgnyiLVP1hsZiiHjVSZMDT1taGu+++G/369cPKlSuh0XS/mJycHABAe3s7CgoK/G7f83i8XC5+KHqy2sOfFA06DUS3B8nbZ/siLRBQkG/h7MrAOjweEV2iiJf+J3BrCaB7dOS+mytCT3mVFaBfdgbmXj8KWo8Ilyd0fkkyC+plZ2hxxQALWls74XJ5cMmFoE10eyL2m9XuRKYu9aaNUoHb7eH3iMKwT5RFzv5Q3OSm3W7HPffcg/b2djz77LPIzs72HSsu7s6laGxs9LtPY2Mj9Ho9Bg0alNS2KkH3UmoPznY4YHV5IAqJO5EpffmwtyBffpYBJq0GGo/HdzmzxyqlSFtL2LpcuOfGClSW+i8rrywtwIwJxfjlmv9F6HTli6IpqJcMSu83IiI5KOqbz+Vy4f7770djYyNefPFF9O/f3+/4oEGDMHjwYOzYsQNTp071XV9fX4/x48en3QotqfcjUsvyYXtX+EDD3uVGdoYOE8cUYmaN/2q0FZv2YviQvKheq1IK6qml34iIEklRAc+DDz6Id955B0uXLkVHR4dfMcERI0bAYDBg4cKFWLx4Ma644gpUV1ejvr4eDQ0N2LRpk3wNl0Eypk/UsnzYbNKH3UvLbNIDoojKofl9eq3JGlkRBQFtdje+amqBKUMHY6+aO2rpNyKiRBJEUTnffpMnT8apU6eCHnvrrbcwcOBAAN1bS6xfv963tcQDDzzQ560l3G4PWlqSn3wbjWD1VOwuN+5b8W7I+6xafHXC9iPqfn4P7I7uJFmTPraidrLXgxEEfHnOjpff9M/jqSwtwK1TyzCgn9E34tGXtoqCgFW9ggyvqvKChAShsYzqyf6+pxGdToPcXLMvp4rkxz5RFqn6Iy/PHHXSsqICHjkpNeAJdYKbN3M0Fj29E3ZH8Omax+smID+B0yfx/rFKPe0WDVEQAmrS9GxLIpOJ3YIQcmSlr683ma+DYsOTq/KwT5RFCQGPoqa0yF+4aat12z7xqwDcmxISU2OZdpNyNCKZu3NrRTHmasvRvnbuMk5EFD/5z4oUUqQT3E2TSoMGPEpJTI32BC31KFBfkoljDcRivb1HEPDx0bPIsxjhcHrQYXOipc2OMUPzoenDbvREROSPAY+CRTrBGXSBWw0oKTE1mhO0KcimnUBik69NEUa7Qh2PNRCLOXATBHx1zo73Pj4dkFtUmJ/ll1sEBG6p0Vuk40RE6YwBj4JFs+pHys0qvSKtCgrXvkjHkzFNo9dqUFVWEDL3BeiuZdTzvYt1FVw8q+YcHjEgkRq4WC363lmjYehRVkmnFUJuoVFZWgCtNvB1EBFRNwY8ChZNPRVv8T3fVEYUJ7pYpl36Mt0UTfu/bg+/j1UipmncHhGzp5TBIyJgJGXGlcVY8swu2B1uv9cVayAWT+AWqSBil9MNg+HiTu/tVgdqJxb7jvd8HbUTi3HiTAceef6DpCeFExGlAgY8CiZFPZVYlzX3ZbopVPsrSwswe3IZREQeBTKbdH1OaHZ7RDy04X3U1pRgZk0xdFoNNBoBDUfPYsWmvb6Vbj1fV6z5MvHk10RVELFHwGPK0GHZb3f7XkfvAok/+8HYgNfBkR4iom4MeBQunlU/ocQawCRiukkL4MqKQlw/wf8E/dCG9zF8SB7uu6nSNwrUuzhgTpYeep02YCl2rCMY9i7/YESn1cDuCB6g+F5XhEDMcWEbD+/7FU/RQbMpfM5N7+MmvRbDh+QFTVSvLC3AwaZW32Wu2iIi8seAJwXEM20VTKwBTCJWBVmdbqzasj/kc3Y5XVgwqwLPbv8U36kejO3vNfpO6LdMLcOhpla/6RujQYvSQbk402qDQaeJKgA0m3RYcvtYv8cGuoOEJbeP9Rvl8b6uS7IzQk7HVZYWoOHoWfQbU+h7v+LZzsGkD0w697+Pxu8+4UbMaicWY8WmvX6PwVVbREQX8edfGokmgOkpEVslRHrOTpsLWlHE3TNH4y9/a/QLboYV5QYEO0tuH4svTp1Hw9Gz6LA5cfKrTnx5zga3JvSfcoZeh+3vNQZNDt7+XiNqa0oCXpcgipg3c3TQDUVrJxZj+65jfq9NADB7clnQ28+eXIZgW7p6A5juxOmLwk1Zekf8Vi2ehF/XTcDyudUoL8oNCNq8r4OIiLrxG1FCSivtH2sAk4hNKKN9TrvDFTD65HD6V+OsrSnBjt3H8d3xg4OO1tx3cwV0Qdpjd7jCJgfPrCkO+ro8ogflRblB82XsDv9pL6vT45cn1Hv67okf1yBTFxj2xDNlKYgiLEYt8nKMWLFpLzcJJSKKAgMeiShhS4XeYg1gEpE0He1zBhsJMuj9R22GFeUCQMjRmrUhEnUjjTJ5A6ver8uo0+LIidaoijt22pywO9whK1932pzIzA4+vRTvlGV2poGbhBIRRYkBjwSSsZN5POIJYC6OQMS3eWi0zxlsJOhgU6tf3RmH04NhRbkhg4pQibqRCvINuCQTy+dWo6XN7jf1FMv7ZczQIpxIx2PRuy7SfTdVosvpQqdNGSOJRERKxIBHAkre86gvUyhFl1ni2vgtmucMNhK0fdcxLLm9e6n1/iPNMOg1AdNcvQVL1I1UsM8jinhowx4AgZtwhmt7zylLi9kQ9jky9DoAfQ9Cem9FYdBrfFtRmLIu/E0x2CEiCsCARwJK3/Mo1imUeCste+/bM1i4JDuj+769qhTbnG7cdk05bp5civ1HzmL7rmOwO9z4657jqJtdiS6HC4CAcx3hCxUGGymKVLDv7Dm777qeAWm4tveesvz+teW4dWpZ0Oe4dWoZBCEBQUiMW1EQEdFFDHgkkIjVTUrRl1ykaO7rFgQ8u+1TFBXm+OrvVJbmY+rYQXB53MjQaiF4PL4RsYy8zJAjKSGXf0dZsM/L1uVChj5wjy9f24GAY1vfOYrSQbmYUFno9xxfn7ejy+lGW6cDl5j9g9xYk9pj3YoiGKUl0hMRJUvqnHlTSCJWNymBeCEYKb0iF9dP9A8Unt3+KebVjgp5sowmjwkAnt32Kb777SE4e87mu02Xw439R89iTGl+wONrPR7cd3MF1saQhxRLwT6gOxk4XNvvuWF0wDG7w41fb/wQtTUlGDqoH858bQUANJ+zYcP2T/HEjyf63T6eQDLWrSh6U2IiPRFRsjDgkYAUW0LIwe7y4Dvjgi8Br51YDLvLA5M2cEhBFAR0OiLnMQFAyaB+yNBr8bf9gdM0lxcEn6bRxZiHFEvBvqryArjcYti2dzmDbwnhXaVVXJiDxzZ+6PeYPYPceJPaY92KoielJtITESULAx6JJHJLCLmIYugl4ABw98xRAffxjiJcO25w2Mf25jmNHd4fL7x+IOZpmljzkLSiiPtuqoS1y4VOmxNZJj06bA78euPFgn3egLQ1woamkQKPnsvpgwW58Sa1x7oVRU9KTqQnIkoGBjwSStSWEHLxiGLYKRS3R4Rb0PimQ3qOIlw/sTjo/by8eUwOV/jniDRNEy23IGDNK/sDpnMeq5uI9s4uZBp1yNDrYHe4kBHh+cwmfdgpy8J8Mx6vmxAyyI03qT3WrSgS8ZxERGrBn3QpThQEWF0enO1wwHphQ8tEsXWFP0nau1xYs7XB95w9RxG8NXSC8U7xmPTaC6uvwj1H+NGUaPTMRVo+txpL53wTy+dWo3RQLn7350+Rl5UBg06L1a/sR90T7+J/G/4Zoe2asFtCGAQgP8uAzBCr2eJNao9nK4q+PicRkVrwWy6FSZ2Earww0tF7F3Nv4rLJqPWbDuk5itC7hk7v9nlPzlmZ4UcVIk3jRMPucgckRgNAQT8Thg/Og93lwbPbPvW9j9G0XQvEPWXZl6T2eAtBqiWRnogoXgx4UlSkJNT5N46GVqOB3eGKO4fIoNfim8P7h9y7asrYQTAatL7pEGPGxT8nu8ONFZv2+i0FLywww2zwb0NmH6ZpoieETIz21s7p+T72brvZqIfZFPj+xTtl2dek9ngKQaolkZ6IKF6CKPKbDgDcbg9aWjrlbkbUrC4P6p54N+Txp+6/Cs+//nnQEYpoR3+cALqcHmzY/lnIujelg3JRM6awe4THJWL9tk9DVhu+e+aooBtougUh5Ik4ESNVDhFYu/WTkO26d9ZozP/12yHv/3jdBORLkN/Sl5o4Op0GubnmmCtfsw6PNOLtD5IO+0RZpOqPvDwztNrosnM4wpOiIiWhtrTZA07wsS5B1mk0sIuha7/sO9SMmyaVQqftrkqs0XjCVjTurjacmB3DYxFN/ZpwpMpvkSOpPdUT6YmI4sWAJ0VFOgmHyl2OdgmyWxDw7J8+wcyrhoa9nUYQcP9TOzF8SB7unVWBdz76B8qLcgMqGv91z3HMqx0V8gQr5YnYFkX9Gua3EBGpG1dppShvEmowVWWB1YN7ijQ65M0P2vP5GdgjraJyuGB3uHHgixY0HD2LW68ZhvKiXABA4+nzePz3e3HkRCvuClOVWWrRrFAKtfpp/o0VaOnoSvgKOCIiSi6O8KSocNWD75o5Cj/9za6Q940UAARbXh4q/+VgUyuMBi2W3D4W299rxKot+33Hq8oL8PQDV3cXDpRxhMSg06CqrCBo4b2qsgLodZqAaTWTUY9DTS34yX++G1CYkNswEBGlHo7wpDDvSXrV4klYNrcay+dWo7woF//bcBrlRXlB7+Obogmj5wjQzr+fwN03jAoY/fDm5WzfdQy1NSVBKzLvO9SM377WALnDA7dHxC1TyzCmzP81jCkrwC1Ty2BzuCAKgm9a7ZLsDDy3/VOs2rLfF+wAF3OgONJDRJR6OMKT4rpP0gKuuDTLN9rjHXEBwtfACcU7AtQvy4Blc8fhhb98jtJBubh+QndeTlamHvn9TPjVuv+D3eHGsKLcoBtzAkrZtkCE3eHGlRWFqJ3ov5O53eFGl8MNvVbja6PSt2EQBQFtdje+amqBKUMHY4gCh0REdBEDHpXoPSVjNumwcHZlXHV4vPlB111ZjPV/6l5m/uGBM363GVNagLtvqMDDz+2Bwxl+iWE02xZIuVzaqNNix+4vUHRZDi7JMfqubz5nwweffYkhl+dg7LBLfW1U8jYM3PGciCg+nNJSEe+UTH6WASatBhqPx3c51DYHoR5nwawK5PczhlzO/fGRZuRfCB56bpYZTKScIY8gYN/Rs/iq1YaWti581WrDvqNn4UnQ1JEgivjR9aNwqKkVD23Yg8c2foiHNuzBoabW7qKKu475tVGp2zBEKjbJqTYiotA4wkNBaUURXRGWc3c53Hi8bgIsZkP8y7oFAV+ds+O9jwOrIBfmZ2FAP2NCEp4NAnDbNaW4Y8YI2OwuZGXqoREE/PPrTiybWw2jQQd4ukeqlLoNg9Kn2oiIlIwBD4VkijCSYTLqkHVhdCfebQscHhEvv3k4YCTJe/neWaO7V3n1kRvA5jeP+AUM3sTrf9+wB8OH5PmmhZS6DYOSp9qIiJSOAQ+FpBWEsMu5tT2mUOKtlhxNFWSDIfyqskhCTQV5n7e2pgSb3zzsV4Va6urP8VDqVBsRUSrgNySFpNNpcOvUMny7ohCX5Bj9VjcNujQLOp0G6LHoPJ5qyfYoqiBn9zHgCTcVtP9IM2bWdG+H0XtaSGnbMCh1qo2IKBUw4FGZoKudAFjjGKlwut2wdrnwvw2n8XGPgGFMWfdUUJa7ezl3X5hN+pDHjAYtcrIyYHX1bZQl0lRQz1VmfZkWknpjTqVOtRERpQIGPCoSasny7MlleGjD+zFXDBY9wLZdgQUFPz7cDFEE7po5qs9tNuk1qB7ZH0WX5WBYUa5vFOnIyXOoLMnHb1/zfz3VI/vjrpmj0XVhub0pQwdBECAIIoy64AFGpKmenqvM4p0WStZy8YtTbR7YHS4YDTqY9KzDQ0QUCQMelQi3ZNnjuZin4r0uml3TRSBsfo0oBt/9PBaCKGJu7SisfqXBr3hhVVkBRhfn48DxFt91RoMW36kejFVb9gfdjf2ve47jrtpRAQFGuKkg7/YYQPzTQpGWi0e7O320BFGExahF0WUWtLZ2wuUKXweJiIgY8CSdVNMe0eapeEWzjNnW5YLRoEVtTYnf6MvBplZs33UM9i4XsvR9WxUkCgLWvtoQuC3F4WZ4RP9ALdQWFt7L5UW5QQOMcPuO1U4sxopNe/s0LcTl4kREyseAJ4mknPaIJU+l533C5auYTXrfpqC9R1/+494rYcwInkwcS1AXS6AWbgsL7203v3k4aIARuOpKD51WQLvVgSd+PLFPgSeXixMRKR8DHgn5n/j1ONj0td8UDZC4aY9Y8lSivU+GXht8U9ALoy91syt9xfq8Yg3qYgnUIm1h4T0eKsAIWHUF4BJz31dgcbk4EZHycZxdIm5BwKpXG1D3xLv42aq/oe6Jd/Dex6ex5PaxMPZaZu2d9oiHKAiwujwAhIAdzb165ql4RbNruq3LFTaH50yL1W87g3i2PoglUIu0hYX3eLIDDG+OUDDRvM9ERCQ9BjwSCFfobvt7jaitKQm4T6SRjmB6BlWLn9mFGVcWo7LU/8RbVV6AW6eWYfuuY37XLZhV0b1c3eXB2Q4HrC5PQEDSaXOGff4Oq9MvUIsml6U3o0EX0GavytICmHpMmx1sag1724NNrbIEGN4cod5BD5eLExEpB8faJRBrAjEQ+6hE76DK7nBjxaa9qK0pwewppTDoNH51eJ748US/nBoRwMoIU0/hauQA3SMq/tNHApbPrQ5Mbr6wHD7YVFOX043aicW+98bLm1CcYdBh1eJJsNqdMJt0mPrNQVgbIvn4r3uOyxZgKLEyMxERXcSARwKxJhDHsxw6WFBld7ix+c3D2PzmYaxafHV34u6Fx+yZuyICWB3FMuqMKJZz14wpBNA92rRh+ycBe1UtuX0sVmzaC7vDHTSo67A6fYHazJpiv2Bpxaa9uP+2b2DgpWbk96h23DOwMGbooLlQh2de7ShZAwylVWYmIqKLGPBIIJa8lHinPfqyMijaZdRaQcA9N1YELBvvOaJi+uYgiEDEvaqOnGgNGtQZM7S+QC2YwFGk4MnHgMAAg4iIQmLAI4FIex4V5pvxeN2EPk17ZBrDb8lgMYfekiGaYMmUnYEvW61YuXkfls8dh9b2LnRYnb7Rlx27j+Oumd0jKlaXJ+wU3uwppbjmm4OCvs4MvTbkBqW9R5GIiIjixYBHApH2PNKKot8UTTx0WgHfHN4fQy6PbkuGns8dzTJqm9ONDqsTZ1ps+OlvdvkVHxxWlAsAaOvswiVmQ8QAyqDTwOFyo9MWGHwZNALumDESnj9/FnYUSYmjN1LvnUVERInDgEciUiexdtgc+OGMEVj/p08jbskA+OfnRLPr9tftXb6pt1BTTt6Rl0gBlMPlwc9WvOv3HL7kaFFEVoYWc2tHoq3T4TeK5N0qQolBRLL2ziIiosTgsnQJeXNN8rMMyNQldoPHLJMB6//0adCigC+/eTjo0ndvfk40y6gzjbqwy8B7Lv8OV4emsrQADUfPBrSjZ10eQRRhztAhP8eIAfmZMBv1mDjmcswLsi+WEsRTb4iIiOTFEZ4U5XKLYYsCBlv6DlzMz3G43Jh7/Sh4ZoiwdwWOQJn0WpxubsfdN4wKCKwqSwtwb49E63BTeDOu7N6rqreeydFuQcCaV/YHHy2J/a2RHPfOIiJKPQx4UpTVHr4oYKhtGExGPVaFmIrpveHmD68biXWvfYLyotyAJeMbtn/qtww82BQeIGDxM7t8dXgCX0N38JXMncYTgXtnERGlHgY8KSrcKi0g+DYMVeUFONTUEnVwYXe48eGBM/jwwJmgz/GDacP9RjJ6Lxe3ujwhg53u16BLydES7p1FRJR6+M2cogx6DR6e/22/JF9vVeOqsgK0tNn9bl9VXoD5N1bgJ//5btDH6x1ciIKAr1o7w7Yh0khGtMnRfXkOOUTzupS4qoyIKJ0x4ElBbkHA2l7TQN6qxjt2H8e0bw9G/7xMrFp8td8KsZaOrrAjLj2DC5vTDQHdNX16LknvGVxFGsmItDxfiHKJvNJE87qIiEhZlHc2SWPR1HUJtzGpRgCun1iMoyfPIb+fKSAZ2ZQRfXBhtbtw9OQ5LJ87Di9f2K7Cq7K0AMvnjkNmFCMZkZbnp+poCffOIiJKLQx4FCLaui5hc14ON2PO9BH4rLEFf/jvQ36Pc8+NFRAEIergItOogwjgTzuPBk1a3rbraHfSchSvLdweU6k8WsK9s4iIUofiAp6mpiZs2LAB+/fvx5EjR1BcXIy//OUvAbfbsmULnn32WZw+fRpDhgzBokWLMGnSJBla3HeR6rr0TCaOtEKopc0eWJvnUDPWvtqAEcV5mHFlMTwe/53JgwUXJr0WFSWXYOjl/bD9vcaAEZ7aicWwu9wwafueUBxytATdic8cQSEior5SXMBz5MgR7Ny5E5WVlfB4PBCDnOBef/11LFu2DPPnz8e4ceNQX1+Puro6vPjiixgzZkzyG91HNqcbB4634JapZUFzZXomE0fMmwkx5OKtzfP477t3Jp89pRQA0C87A0a9FnaHKyCwyM7MwNZ3Pg86wrNj93H88LoRCXsPeo+WuBG4ISkrGRMRUbwEMVhEISOPxwONpvvkvnTpUnz66acBIzzXXnstRo0ahSeffNJ33W233Ybs7GysX78+rud1uz1oaQm/KkkqX3c60NbpxPb3GoPuJ2Ux63GJuTsQEAUBq3pN/3hVlRWg9IrckDuPL53zTTy28UPf5X+7axwMei22vHU4aGBhd7rxVYstZLsuzTPBLMGScVEQAmoF9WybEmvzJJtOp0FurhmtrZ1wuYLXXKLkYX8oD/tEWaTqj7w8M7RRzjQoq8AJ4At2Qjlx4gSOHz+OadOm+V0/ffp07N69Gw6HQ8rmSSI70xAQVADdozLb32tEdubFZdmhtoWoLC3AXTNHYfuuYyGfx6DX4JapZVg+txpL53wTeRYjTjV3hNx3S4AQtl1CVBk8sYumNg8REVEsFDelFUljYyMAYMiQIX7Xl5SUwOl04sSJEygpCdxHSskibRPhcHngEuCbavLmvFidHnzVaoUA4GBTK/634TTKi/KCPlZlaQH6ZWfgUFNrQD7OktvHYsWmvX5L1vcdaoZnRvh2eUQRkCDoYSXj8ERBQJvdja+aWmDK0MGY4H3aiIjUKOUCnvPnzwMALBaL3/Xey97j8dDJVNHX2mEPe/yfZzvx6Asf+qaadBoB1i437F1ODCzIQpfTjWKnB0aDFv8yrD821vdKSi4rwO3ThuOl/z4UdLQGAGprSgKmwmxd4QMPe5cL/UzGWF5qVCJVkc406mXrK7l1ucWQuU0ZWm5aKhfvkHq0Q+skPfaJsiihP1Iu4JGKRiMgN9csy3N3htj3ykt/4eTunWq6sqIQq7bs9x1fNrfal5vjLRTYM8m4X3YG2jocIbeICLXZaKQE6WyzQZL3TGd1hF0+n5dj9JvmSxftVgee2rQ35Gq+JbePTcv3RUksFpPcTaBe2CfKImd/pFzAk5OTAwBob29HQcHFPJa2tja/47HyeES0tVn73sA4GHWakCf4ytICHGxq9V3ed6gZ10/wD056/q63O9wBIzUrF0+CMULRwd6bjVaVFyAzQxc28DDqNGiNsP1EvELW5rmpAq4uJ1q7wm+eqkZtdnfQvgC6/y5aztvhSsP3RQm0Wg0sFhPa2mxwu5kgqwTsE2WRqj8sFlPUo0YpF/AUF3ef7BsbG33/9l7W6/UYNGhQ3I8tZyZ/sBO8dzXUik17/W7bOzg52NSKytKCoPk2VeUFyNRrIETo6p6bjfrq8rg9YYsCim4Pwk96xU8LBK/N4xHh8qRnvorVHj6YsdqdyNRxWktObreHK4IUhn2iLHL2R8oFPIMGDcLgwYOxY8cOTJ061Xd9fX09xo8fD4MhNYf0exbf67S50Gl34mBTa0AyMRC4E/r2Xcew5Pax0GgQslpxpC0cCvPNeLxuQkCBv4vt8sDucMFo0MGkT06SLCsZ+0vFfceIiJRCcd+QNpsNO3fuBACcOnUKHR0d2LFjBwDgW9/6FvLy8rBw4UIsXrwYV1xxBaqrq1FfX4+GhgZs2rRJzqb3mfcEb7Jk4I9vHopqigvonsZasWkvnl50FVzu4JWJI23hoBVF5EcRWIQqbEjSS9V9x4iIlEBxhQdPnjyJKVOmBD22ceNGVFdXA+jeWmL9+vW+rSUeeOCBPm0tIWfhwWDcghA0OJk9uQwPbXg/YNQn2oJ80WxQGtAOVjxWjFB/F+wPebHInfKwT5RFCYUHFRfwyEUJAU/vYMRo0KHL6UKn7WJwIgJYvbUBB75oQW1NCYYV5UIE0D83M2FTTT3b4XR7sP/IWWzfdcwvyGLFY/l090/ypxgpNJ5clYd9oixKCHgUN6WVrsKNpJiyLnSmKEIAsHBWBRwi8LvtnwIAhhXl4uRXHcg263Fpbia0nvj/mIK1I1hxQm/F48w0rYcjJ0EUYTFqUXSZhV/mRERRYsCjALHslg4AIrqDne+MGxx0J/P7bq6ALo5f/KHaEao4YbpXPCYiotTBn+cKEOveUTanG0WFOSH3uVq7tQFiHNnF4dqx/0gzhhXl+l3HVUFERJQqGPAoQDR7R/W+PKwoN+Q+V/FusBmpHT3r//hWBREREaUABjwKEGt9lUyjLqD4YG+Rgpd42uGt/9Ozvg8REVEq4JyEAsRaX8Vo0GHAJZlYOuebMOg1ONjUGrCKKp7ppkjt6J+XiVWLJ3FVEBERpRyO8CiAtyhgVXmB3/XBRlLcgoDVr+zH/U/txGMbP8RDG/bg8D9a8bMfjIXRoPXdL57ppnDtmH9jBewOFwsPEhFRSmIdnguUWIend1FAURCwKsgqKgCoKivAsCF5OHi8pc9F6Hq2w2TU41BTC57d9qlvBImF7uTHGiPKwv5QHvaJsiihDg9HeCQkCgKsLg/OdjhgdXkirpzybi2Rn2VApi5w2sjm9IRezXW4Gd8efRnqEhCIeNtxSXYGntv+KVZt2e83XeZdLh/PSjCiaMX6+SEiCoc5PBKRYkuGTluE3bJtLpgTWAgwmuXyLDxIUuCWJkSUaDxbSSBSIcF4f6kaM8Ln5UQ6HqtYl8sTJYJUnx8iSm8MeCQQayHBaGXotagsLQh6rLK0ABkJrosT63J5okSQ6vNDROmNAY8EpBoZMWgE3Dq1LCDoqSwtwK1Ty2DQJPaXr3eZejAsPEhS4cgiEUmBP9ElINnIiCji0n5GTBxTiJk1xXA4PTDoNWhps+PSfka/Wj2J4F2mvmZrg19tHhYeJClxZJGIpMBvDgnEWkgwFhpRRNXQfL/l60WXZkkWfGhFEXWzKmBzemB3uGA06Fh4kCQl5eeHiNIXp7QkEEshwXgfP9zy9UQTRBEWoxblRXmwGLUMdkhSUn9+iCg9cYRHIhdHRkIXEiSi4Pj5IaJEY8AjIe9ITGaWofsKflkTRY2fHyJKJE5pERERkeox4JEQS+MTEREpA6e0JMLS+ERERMrBER4JsDQ+ERGRsjDgkQBL4xMRESkLAx4JsDQ+ERGRsjDgkQBL4xMRESkLAx4JcNNNIiIiZWHAIwGWxiciIlIWzq1IhKXxiYiIlIMBj4RYGp+IiEgZOKVFREREqseAh4iIiFSPAQ8RERGpHgMeIiIiUj0GPERERKR6DHiIiIhI9RjwEBERkeox4CEiIiLVY8AjIVEQYHV5cLbDAavLA1EQ5G4SERFRWmKlZYm4BQFrXm3AvsPNvuu8e2lpWXGZiIgoqTjCIwExSLADAPsONWPN1gaO9BARESUZAx4J2JzugGDHa9+hZtic7iS3iIiIKL0x4JGA1e7q03EiIiJKLAY8Esg0hk+NinSciIiIEosBjwRMei2qyguCHqsqL4BJr01yi4iIiNIbAx4JCKKIBbMqAoIe7yotgau0iIiIkopzKxLRiiLqZlXA5nTDanch06iDSa9lsENERCQDBjwSEkQRmToNMrMM3Vcw2CEiIpIFp7SIiIhI9RjwEBERkeox4CEiIiLVY8BDREREqseAh4iIiFSPAQ8RERGpHgMeIiIiUj0GPERERKR6DHiIiIhI9RjwEBERkeoJosj9DgBAFEV4PHwrwtFqNXC7PXI3gy5gfygL+0N52CfKIkV/aDQCBEGI6rYMeIiIiEj1OKVFREREqseAh4iIiFSPAQ8RERGpHgMeIiIiUj0GPERERKR6DHiIiIhI9RjwEBERkeox4CEiIiLVY8BDREREqseAh4iIiFSPAQ8RERGpHgMeIiIiUj0GPERERKR6DHgopDfeeAP33nsvampqMGbMGMycOROvvPIKRFGUu2kEoLOzEzU1NSgvL8cnn3wid3PS2muvvYYbbrgBo0ePRnV1Ne666y7Y7Xa5m5WW3nrrLcyePRtVVVWYMGECfvKTn+DEiRNyNystNDU1Yfny5Zg5cyZGjBiBGTNmBL3dli1bcO2112L06NGora3FO++8k5T2MeChkJ5//nmYTCYsXboUa9euRU1NDZYtW4bVq1fL3TQCsGbNGrjdbrmbkfbWrl2Lf//3f8f06dOxYcMGPPTQQxg4cCD7RgZ79uxBXV0dhg4ditWrV+OXv/wlDh48iDvvvJMBaBIcOXIEO3fuRFFREUpKSoLe5vXXX8eyZcswbdo0rF+/HmPGjEFdXR0+/vhj6RsoEoXw9ddfB1z3r//6r+I3vvEN0e12y9Ai8jp69Kg4ZswY8aWXXhLLysrEhoYGuZuUlo4dOyaOGDFCfPfdd+VuComiuGzZMnHy5Mmix+PxXbd7926xrKxM/PDDD2VsWXroeV74+c9/Ll533XUBt/nOd74jPvDAA37X3XrrreJdd90lefs4wkMh5eXlBVw3fPhwdHR0wGq1ytAi8nr44Ydx2223YciQIXI3Ja1t3boVAwcOxFVXXSV3UwiAy+WC2WyGIAi+67KzswGAU/FJoNGEDylOnDiB48ePY9q0aX7XT58+Hbt374bD4ZCyeZzSoth89NFH6N+/P7KysuRuStrasWMHDh8+jPvuu0/upqS9/fv3o6ysDGvWrMH48eMxatQo3Hbbbdi/f7/cTUtLs2bNwrFjx/Diiy+ivb0dJ06cwH/+539ixIgR+MY3viF389JeY2MjAAT8UCspKYHT6ZQ814oBD0Vt7969qK+vx5133il3U9KWzWbDY489hkWLFjHoVIDm5mb87W9/w7Zt2/CrX/0Kq1evhiAIuPPOO/H111/L3by0M3bsWKxatQpPPvkkxo4di6lTp+Lrr7/G+vXrodVq5W5e2jt//jwAwGKx+F3vvew9LhUGPBSVL7/8EosWLUJ1dTXmzJkjd3PS1tq1a3HJJZfgpptukrsphO5pEqvVit/85jf47ne/i6uuugpr166FKIrYtGmT3M1LO3//+9/xs5/9DLfccgteeOEF/OY3v4HH48G8efOYtEzQyd0AUr62tjbcfffd6NevH1auXBlxnpakcerUKTz33HNYvXo12tvbAcCXS2W1WtHZ2Qmz2SxnE9OOxWJBv379MGzYMN91/fr1w4gRI3D06FEZW5aeHn74YYwbNw5Lly71XTdmzBhcffXV2LZtG2699VYZW0c5OTkAgPb2dhQUFPiub2tr8zsuFQY8FJbdbsc999yD9vZ2vPzyy74EQEq+kydPwul0Yt68eQHH5syZg8rKSmzevFmGlqWvoUOH4h//+EfQY11dXUluDR07dgxTpkzxu27AgAHIzc0N2U+UPMXFxQC6c3m8//Ze1uv1GDRokKTPz4CHQnK5XLj//vvR2NiIF198Ef3795e7SWlt+PDh2Lhxo991Bw4cwKOPPooHH3wQo0ePlqll6WvSpEnYunUrDhw4gOHDhwMAWltb8dlnn+GOO+6Qt3FpqLCwEJ9//rnfdadOnUJraysuv/xymVpFXoMGDcLgwYOxY8cOTJ061Xd9fX09xo8fD4PBIOnzM+ChkB588EG88847WLp0KTo6OvwKQ40YMULyP07yZ7FYUF1dHfTYyJEjMXLkyCS3iKZOnYrRo0fjxz/+MRYtWoSMjAysW7cOBoMB3//+9+VuXtq57bbb8Mgjj+Dhhx/G5MmTce7cOV/eW++l0JR4NpsNO3fuBNAdaHZ0dGDHjh0AgG9961vIy8vDwoULsXjxYlxxxRWorq5GfX09GhoakpLzJogsTkAhTJ48GadOnQp67K233sLAgQOT3CLqbc+ePZgzZw5eeeUVjvDIpKWlBY8++ijeeecdOJ1OjB07Fr/4xS8wdOhQuZuWdkRRxB//+Ee89NJLOHHiBMxmM8aMGYNFixaFrPxLiXPy5MmAKUWvjRs3+n6wbdmyBevXr8fp06cxZMgQPPDAA5g0aZLk7WPAQ0RERKrH5TZERESkegx4iIiISPUY8BAREZHqMeAhIiIi1WPAQ0RERKrHgIeIiIhUjwEPERERqR4DHiJSpcmTJ/ttIklE6Y0BDxGltL///e9YuXKlb8dlIqJguJcWEaW0ffv2YdWqVbjxxhthsVh81+/YsQOCIMjYMiJSEgY8RKRK3NyWiHrilBYRpayVK1fi8ccfBwBMmTIF5eXlKC8vx8mTJwNyeLZu3Yry8nJ8+OGHWL58Oaqrq/GNb3wDP/vZz3D+/Hm5XgIRJQlHeIgoZV1zzTU4fvw4/vKXv+AXv/gFcnNzAQB5eXkh7/PQQw/BYrGgrq4OX3zxBV566SWcPn0av//97zkFRqRiDHiIKGUNGzYMI0aMwF/+8hdMnToVAwcOjHgfvV6P559/Hnq9HgBQWFiIFStW4O2338aUKVOkbjIRyYRTWkSUVm699VZfsAMA3/ve96DT6bBz504ZW0VEUmPAQ0RppaioyO+y2WxGQUEBTp06JVOLiCgZGPAQERGR6jHgIaKUFmuicVNTk9/lzs5ONDc34/LLL09ks4hIYRjwEFFKM5lMAID29vaobv/yyy/D6XT6Lr/00ktwuVyoqamRpH1EpAxcpUVEKW3kyJEAgKeeegrTp0+HXq/HpEmTQt7e6XTijjvuwLRp0/DFF1/gD3/4A/7lX/6FK7SIVI4BDxGltIqKCvzkJz/BH//4R7z33nvweDx46623Qt5++fLl+POf/4xnnnkGTqcT1113Hf71X/+VNXiIVE4QRVGUuxFERFLbunUrfvGLX+CVV17B6NGj5W4OESUZc3iIiIhI9RjwEBERkeox4CEiIiLVYw4PERERqR5HeIiIiEj1GPAQERGR6jHgISIiItVjwENERESqx4CHiIiIVI8BDxEREakeAx4iIiJSPQY8REREpHoMeIiIiEj1/j+/11XKeeXeggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='tip', \n",
    "                y='total_bill', \n",
    "                data=tips);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 475
    },
    "executionInfo": {
     "elapsed": 388,
     "status": "ok",
     "timestamp": 1682262727619,
     "user": {
      "displayName": "Ka Ba",
      "userId": "08277395367003334094"
     },
     "user_tz": -120
    },
    "id": "wyXMPyh3igmE",
    "outputId": "3f0bfb1a-ca31-40d8-f3f9-17f22155c91c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='tip', ylabel='total_bill'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='tip', \n",
    "                y='total_bill', \n",
    "                hue='smoker', # Here the females and males will be plotted in different colors\n",
    "                data=tips)\n",
    "\n",
    "# hue > Grouping variable that will produce points with different colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1682361471611,
     "user": {
      "displayName": "Ka Ba",
      "userId": "08277395367003334094"
     },
     "user_tz": -120
    },
    "id": "BoMsHBJiinEm",
    "outputId": "571870e9-3d26-44b4-a538-71a1a6c726ba"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-99bb45af-c354-43ec-ae7b-a8720c742553\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>tipping_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.059447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.160542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.166587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.139780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.146808</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-99bb45af-c354-43ec-ae7b-a8720c742553')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-99bb45af-c354-43ec-ae7b-a8720c742553 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-99bb45af-c354-43ec-ae7b-a8720c742553');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size  tipping_rate\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2      0.059447\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3      0.160542\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3      0.166587\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2      0.139780\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4      0.146808"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can store the average as a new column in our dataframe\n",
    "tips['tipping_rate'] = tips.tip / tips.total_bill\n",
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 475
    },
    "executionInfo": {
     "elapsed": 599,
     "status": "ok",
     "timestamp": 1682361491999,
     "user": {
      "displayName": "Ka Ba",
      "userId": "08277395367003334094"
     },
     "user_tz": -120
    },
    "id": "D51-EVVKi5Cp",
    "outputId": "a5e04c20-1f18-44b3-fbab-17c69b13d626"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='tipping_rate', ylabel='smoker'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='tipping_rate',\n",
    "                y='smoker', # Here the females and males will be plotted in different colors\n",
    "                data=tips)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XukjwNdii_Ey"
   },
   "source": [
    "### **Exercise**\n",
    "\n",
    "*   Plot a graph comparing the `tip` given the `time` of the day. What do you understand from this graph?\n",
    "*   Plot a relationship graph between the number of person of the table `size` and the `total_bill`\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 475
    },
    "executionInfo": {
     "elapsed": 654,
     "status": "ok",
     "timestamp": 1682263259690,
     "user": {
      "displayName": "Ka Ba",
      "userId": "08277395367003334094"
     },
     "user_tz": -120
    },
    "id": "UZQAO_nhyvng",
    "outputId": "2231807d-bd98-467e-99c6-51f372d647e3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='time', ylabel='tip'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='time',\n",
    "            y='tip',\n",
    "            data=tips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 475
    },
    "executionInfo": {
     "elapsed": 586,
     "status": "ok",
     "timestamp": 1682361964874,
     "user": {
      "displayName": "Ka Ba",
      "userId": "08277395367003334094"
     },
     "user_tz": -120
    },
    "id": "tRFb4nM2zDr3",
    "outputId": "0e208482-1db6-4ba0-e0d0-0a679658f6d6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='size', ylabel='total_bill'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LvgEEDgoPEOCOlJep+PihFt/v2bpaj2k7MgBAY7VY4QkPD9eaNWsUExPTUrsEAACQ1ITC88c//tGrHcycOVOS5HQ6m/20ZgAAAG80uvCkpaU1eeMOh6O+8AAAANil0YVn8+bNlzIHAADAJdPowtOnT59LmQMAAOCS4Tk8AADAeM26S+vYsWN644039OWXX6qysrLBw78cDodeffXVZgUEAABoLq8Lz1dffaXk5GSdOnVKUVFR2rt3rwYPHqyKigqVlZWpX79+6tmzpy+zAgAAeMXrU1rLly9XaGio8vLytHr1almWpUceeUT5+fn67W9/q/Lyci1cuNCXWQEAALzideH59NNP9Ytf/EK9e/dWUND3m7EsS5KUmJio6667Ts8884xvUgIAADSD14Wnrq5OXbt2lSSFhYWpTZs2OnnyZP3y6Oho7dmzp9kBAQAAmsvrwtO3b18dOvT9d+cEBQWpb9++2r59e/3yTz/9VJ06dWp+QgAAgGby+qLlq666Snl5eVqwYIEk6eabb9ayZctUWloqy7K0c+dO3XHHHT4LCgAA4C2vC09KSoqmTZsmt9stp9Op2267TTU1Ndq0aZOCgoJ0//33a+7cub7MCgAA4BWvC094eLjCw8PrXzscDt1///26//77fRIMAADAV7y+hic5Odnjmp1/9tFHHyk5OdnbzQMAAPiM14Vn586d+vvf/37B5SdOnNDHH3/s7eYBAAB8plnfpeVwOC64rLi4WB06dGjO5gEAAHyiSdfwrF+/XuvXr69/nZ2drXXr1jVYr7KyUl9//bUmTJjQpDAbN27U22+/rT179qiiokL9+/fXnDlzNGvWLI9y9frrr+vll1/WkSNHFBUVpQULFuiaa65p0r4AAEDr0aTC43K59N1339W/rq6urn/K8g+FhoZq9uzZeuCBB5oU5pVXXlGfPn2Ulpamyy67TB9++KEWL16so0ePKjU1VZK0YcMGLV68WCkpKRozZoxyc3OVmpqq3/3udxo5cmST9gcAAFqHJhWeW265RbfccoskadKkSXr00Uc1efJkn4XJzs5W586d61+PHTtWJ0+e1OrVq3X//fcrKChIL7zwgqZNm6b58+dLksaMGaO9e/cqMzNTOTk5PssC4OKc4e1UW+2unwYAf+b1NTxbtmzxadmR5FF2zomJiVFVVZVqampUWlqqgwcPKjEx0WOdqVOnavv27Tpz5oxP8wC4sIif9FL7Xh3VvldHRfykl91xAOCivH4Ozzk7d+7U+++/ryNHjkiSevfurauvvlqjR49udjhJ+uSTT9SjRw917NhRn3zyiSQpKirKY51BgwbJ7XartLRUgwYN8sl+AVycM7yduk0eYHcMAGgUrwvPmTNn9NBDD+m9996TZVkKCwuTJFVUVGj16tX66U9/quXLl8vpdHodrqCgQLm5uVq0aJEkqby8XJLq93XOudfnlnvDsizV1NR4/X6gpTkcDoWEhNgdw2+4XC5ZluXVexlLT96OJePoic+k71xoLC3Luugd4z/kdeHJzMzUu+++qzvvvFN33nln/TenHz9+XKtWrdLKlSuVmZlZf61NUx09elQLFixQQkJCizzA0O12q7Cw8JLvB/CVkJAQxcbG2h3Db3zzzTdyuVxevZex9OTtWDKOnvhM+s7FxjI4OLhR2/C68Pzf//2fbrjhBv3Hf/yHx/wuXbro3//933X8+HG9/fbbXhWeiooK3XPPPYqIiFB6enr9nWDnvsqisrJS3bp181j/h8u94XQ6NXjwYK/fD7S0xv6tprWIiopq1t+m8Q/ejiXj6InPpO9caCz379/f6G14XXiOHTum4cOHX3D58OHDtWHDhiZv99SpU5o7d64qKyv12muvqVOnTvXLBg4cKEkqKiqqnz732ul0KjIyssn7O8fhcCg0NNTr9wOwF4f/fYex9A3G0XcuNJZNKYZe36XVs2dP7dy584LLP/74Y/Xs2bNJ2zx79qzmz5+voqIivfzyy+rRo4fH8sjISA0YMEB5eXke83NzczV27NhGH9YCAACti9dHeGbOnKn09HR16tRJt99+u/r37y+Hw6GDBw/q1VdfVV5enubNm9ekbS5ZskRbt25VWlqaqqqqtGvXrvplsbGxCg4O1rx587Rw4UL169dPCQkJys3N1eeff661a9d6+6MAAADDeV14UlJSVFpaqnXr1un111+vv86mrq5OlmXphhtuUEpKSpO2uW3bNknSsmXLGizbvHmz+vbtq+nTp8vlciknJ0crVqxQVFSUMjIyNGrUKG9/FAAAYDivC09ZWZkef/xx3X777frzn/+sw4cPS5L69OmjCRMmqH///jp69Kh69+7d6G1u2bKlUeslJSUpKSnJq9wAAKD18brwTJ48Wc8++6ymT5+uoUOHNliem5urhx56iFu9AQCA7by+aNmyrIvebud2u8/7xaIAAAAtrUlHeKqqquqfeSNJJ0+erP9KiR+qqKhQbm6ux7NyAAAA7NKkwvPKK68oMzNT0vf3vj/99NN6+umnz7uuZVleP2UZAADAl5pUeMaNG6fQ0FBZlqVnn31W06ZNU1xcnMc6577/Iy4uTvHx8T4NCwAA4I0mFZ5Ro0bV3/7tcrn0b//2bxoyZMglCQYAAOArXt+llZqa6sscAAAAlwy3UQEAAONReAAAgPEoPAAAwHgUHgAAYDwKDwAAMJ7Xd2kBgai0tFQZGRmSvr/TMDIy0uZEAICWwBEetCqZmZkqKChQQUGBsrKy7I4DAGghFB60KiUlJfXTxcXFNiYBALQkCg8AADAehQcAABiPwgMAAIxH4QEAAMaj8AAAAONReAAAgPEoPGhxVl2d3RH8AuMAAC2HJy2jxTmCgvTNn3LkOv63Ft+3u+qkx/SXrz7Z4hkkKaRLL0VNv8eWfQNAa0ThgS1cx/8mV1nJj6/oY1Ztrce0HRkAAC2PU1oAAMB4FB4AAGA8Cg8AADAehQcAABiPwgMAAIxH4UGr0r2j87zTAACzcVs6WpXrhnaWVXiifhoA0DpQeNCqdO8QrLuv6Gl3DABAC+OUFgAAMB6FBwAAGI/CAwAAjEfhAQAAxqPwAAAA41F4AACA8Sg8AADAeBQeAABgPAoPAAAwHoUHAAAYj8IDAACMR+EBAADGo/AAAADjUXgAAIDxKDwAAMB4FB4AAGA8Cg8AADAehQcAABiPwgMAAIxH4QEAAMaj8AAAAONReAAAgPEoPAAAwHgUHgAAYDwKDwAAMB6FBwAAGI/CAwAAjEfhAQAAxqPwAAAA41F4AACA8Sg8AADAeBQeAABgPAoPAAAwHoUHAAAYj8IDAACMR+EBAADG87vCU1xcrMcee0wzZsxQbGyspk+fft71Xn/9dV177bWKj4/X9ddfr61bt7ZwUgAAECj8rvDs27dP+fn56t+/vwYNGnTedTZs2KDFixcrMTFROTk5GjlypFJTU7Vr166WDQsAAAJCW7sD/LNJkyZpypQpkqS0tDT99a9/bbDOCy+8oGnTpmn+/PmSpDFjxmjv3r3KzMxUTk5OS8YFAAABwO+O8AQFXTxSaWmpDh48qMTERI/5U6dO1fbt23XmzJlLGQ8AAAQgvzvC82OKiookSVFRUR7zBw0aJLfbrdLS0gueCrsYy7JUU1Pjk4y4MIfDoZCQELtj+A2XyyXLsrx6L2PpibH0HW/HknH0xGfSdy40lpZlyeFwNGobAVd4ysvLJUlhYWEe88+9Pre8qdxutwoLC5sXDj8qJCREsbGxdsfwG998841cLpdX72UsPTGWvuPtWDKOnvhM+s7FxjI4OLhR2wi4wnOpOJ1ODR482O4YxmtsE28toqKimvU3QPwDY+k73o4l4+iJz6TvXGgs9+/f3+htBFzhCQ8PlyRVVlaqW7du9fMrKio8ljeVw+FQaGho8wMCTcAha99hLH2HsfQNxtF3LjSWTSmGfnfR8o8ZOHCgpH9cy3NOUVGRnE6nIiMj7YgFAAD8WMAVnsjISA0YMEB5eXke83NzczV27NhGn8sLNKWlpVq0aJEWLVqk0tJSu+MAABBQ/O6UlsvlUn5+viTp8OHDqqqqqi83o0ePVufOnTVv3jwtXLhQ/fr1U0JCgnJzc/X5559r7dq1dka/pDIzM1VQUCBJysrK0tKlS21OBABA4PC7wnP8+HH96le/8ph37vWaNWuUkJCg6dOny+VyKScnRytWrFBUVJQyMjI0atQoOyK3iJKSkvrp4uJiG5MAABB4/K7w9O3bV19//fWPrpeUlKSkpKQWSAQAAAJdwF3DAwAA0FQUHgAAYDwKDwAAMB6FBwAAGI/CAwAAjEfhAQAAxqPwAAAA41F4AACA8Sg8AADAeBQeAABgPAoPAAAwHoUHAAAYj8IDAACMR+EBAADGo/AAAADjUXiaoK7OsjuCX2AcAACBpq3dAQJJUJBDmb/fpsPflrf4vr+rcHlMP/J8botnkKQ+3cP1wM3jbNk3AADeovA00eFvy3Xw8Hctvt+ztXUe03ZkAAAgUHFKCwAAGI/CAwAAjEfhAQAAxqPwAAAA41F4AACA8Sg8AADAeBQeAABgPAoPAAAwHoUHAAAYj8IDAACMR+EBAADGo/AAAADjUXgAAIDxKDwAAMB4FB4AAGA8Cg8AADAehQcAABiPwgMAAIxH4QEAAMaj8ASItu3DfzAdYV8QAAACEIUnQHSKHKPgsD4KDuujTpEJdscBACCgtLU7ABqnbftwXfYv19odAwCAgMQRHgAAYDwKDwAAMB6FBwAAGI/CAwAAjEfhAQAAxqPwAAAA41F4AACA8Sg8AADAeBQeAABgPAoPAAAwHoUHAAAYj8IDAACMR+EBAADGo/AAAADjUXgAAIDxKDwAAMB4FB4AAGA8Cg8AADAehQcAABiPwgMAAIxH4QEAAMaj8AAAAONReAAAgPEoPAAAwHgUHgAAYDwKDwAAMB6FBwAAGI/CAwAAjBewhefAgQO64447NHLkSI0bN07PPPOMzpw5Y3csAADgh9raHcAb5eXluu222zRgwAClp6errKxMy5Yt06lTp/TYY4/ZHQ8AAPiZgCw8//u//6vq6mplZGQoIiJCklRbW6slS5Zo7ty56tGjh70BAQCAXwnIU1p//vOfNXbs2PqyI0mJiYmqq6vTtm3b7AsGAAD8ksOyLMvuEE01duxYzZo1SwsXLvSYP378eM2YMaPB/B/z6aefyrIsOZ3Oi67ncDhUUXVKtXV1Tc5sijZBQQrr2F7N+dg4HA6dramUVVfrw2SBxRHURm1DOzVrHKXvx7LyVJXOtuKxbBvURp3ad/TJWJ6pqFRd7VkfJQs8QW3aKjiseZ9Lh8OhmurTqq1txX9OtglSaId2PvlM1lRXqq629f7/HdSmjUI7XPgz6Xa75XA4dPnll//otgLylFZFRYXCwsIazA8PD1d5eXmTt+dwODz+fTFhHds3efsmasxYXUzb0E4+ShLYmjuOktSpfUcfJAl8vhjL4DA+l1LzxzK0QzsfJQlsvvhMhnbgMyldeCwdDkejxzkgC4+vjRo1yu4IAADgEgrIa3jCwsJUWVnZYH55ebnCw8NtSAQAAPxZQBaegQMHqqioyGNeZWWljh07poEDB9qUCgAA+KuALDwTJkzQhx9+qIqKivp5eXl5CgoK0rhx42xMBgAA/FFA3qVVXl6uadOmKSoqSnPnzq1/8OB1113HgwcBAEADAVl4pO+/WuI3v/mNPvvsM3Xo0EEzZszQggULFBwcbHc0AADgZwK28AAAADRWQF7DAwAA0BQUHgAAYDwKDwAAMB6FBwAAGI/CAwAAjEfhAQAAxqPwBIDi4mI99thjmjFjhmJjYzV9+nS7IwWkjRs36r777tOECRM0cuRIzZgxQ2+88YZ4MkPT5Ofn69Zbb9WYMWM0bNgwTZ48WUuXLj3v99uhaaqrqzVhwgRFR0friy++sDtOQHnzzTcVHR3d4J///M//tDtaQFq/fr1mzpyp+Ph4JSQk6O6779apU6fsjtUsfFt6ANi3b5/y8/M1YsQI1dXV8QvaS6+88or69OmjtLQ0XXbZZfrwww+1ePFiHT16VKmpqXbHCxgnT57U8OHDNWfOHEVERGjfvn1KT0/Xvn37tGrVKrvjBbSsrCzV1tbaHSOgvfzyy+rUqVP96x49etiYJjBlZ2crJydHKSkpGjlypL777jtt37498D+bFvxebW1t/fSiRYusadOm2ZgmcB0/frzBvF//+tfW5Zdf7jHGaLrXXnvNGjJkiHX06FG7owSs/fv3WyNHjrR+//vfW0OGDLE+//xzuyMFlD/84Q/WkCFDzvv/ORrvwIEDVmxsrPX+++/bHcXnOKUVAIKC+M/kC507d24wLyYmRlVVVaqpqbEhkTkiIiIkSW63294gAeypp57S7NmzFRUVZXcUtGJvvvmm+vbtq4kTJ9odxef4TYpW7ZNPPlGPHj3UsWNHu6MEnNraWp0+fVp79uxRZmamJk2apL59+9odKyDl5eVp7969euCBB+yOEvCmT5+umJgYTZ48WS+99FLgn4ZpYbt379aQIUOUlZWlsWPHatiwYZo9e7Z2795td7Rm4xoetFoFBQXKzc3VokWL7I4SkK655hqVlZVJksaPH6/ly5fbnCgwuVwuLVu2TAsWLKB4N0O3bt00b948jRgxQg6HQ1u2bNFzzz2nsrIyPfbYY3bHCxjHjh3TX//6V+3du1ePP/64QkJC9OKLL+rOO+/Upk2b1KVLF7sjeo3Cg1bp6NGjWrBggRISEpScnGx3nIC0YsUKuVwu7d+/X9nZ2UpJSdHq1avVpk0bu6MFlOzsbHXp0kWzZs2yO0pAGz9+vMaPH1//+qqrrlK7du306quvKiUlRd27d7cxXeCwLEs1NTV6/vnnNXToUEnSiBEjNGnSJK1du1a/+tWvbE7oPU5podWpqKjQPffco4iICKWnp3ONlJeGDh2qUaNGKSkpSVlZWdqxY4feffddu2MFlMOHD2vVqlV68MEHVVlZqYqKivrryWpqalRdXW1zwsCWmJio2tpaFRYW2h0lYISFhSkiIqK+7EjfX6MXGxur/fv325is+TjCg1bl1KlTmjt3riorK/Xaa6953L4K70VHR8vpdKqkpMTuKAHl0KFDcrvduvfeexssS05O1ogRI7Ru3TobkqG1Gjx48AX/Pz59+nQLp/EtCg9ajbNnz2r+/PkqKirS7373O57P4UO7d++W2+3mouUmiomJ0Zo1azzmFRYWaunSpVqyZIni4+NtSmaG3NxctWnTRrGxsXZHCRjXXHON3nzzTRUWFiomJkaS9N1332nPnj26/fbb7Q3XTBSeAOByuZSfny/p+0PgVVVVysvLkySNHj36vLdbo6ElS5Zo69atSktLU1VVlXbt2lW/LDY2VsHBwfaFCyCpqakaNmyYoqOj1b59e3311VdauXKloqOjNWXKFLvjBZSwsDAlJCScd1lcXJzi4uJaOFHguuuuu5SQkKDo6GhJ0ubNm7Vu3TolJyerW7duNqcLHFOmTFF8fLwefPBBLViwQO3atdOKFSsUHBysW265xe54zeKwLB7b6+8OHTqkyZMnn3fZmjVrLvgHJjxNmjRJhw8fPu+yzZs3c3SikVasWKHc3FyVlJTIsiz16dNHP/3pT3XXXXdxl5EP7NixQ8nJyXrjjTc4wtMETz31lD744AMdPXpUdXV1GjBggJKSkjRnzhw5HA674wWUEydOaOnSpdq6davcbreuuOIKPfzwwxo8eLDd0ZqFwgMAAIzH7SkAAMB4FB4AAGA8Cg8AADAehQcAABiPwgMAAIxH4QEAAMaj8AAAAONReAAYIy0tTZMmTbI7BgA/ROEBAADG40nLAIzhdrtlWRbfiwagAQoPAAAwHt+WDiBgVFVV6fnnn9fmzZv17bffqlOnTho6dKgWLlyouLg4paWlaefOndqyZYskac6cOdq5c+d5t7V06VLdeOONkqSKigqlp6dr06ZNOn78uHr16qWkpCTdfffdCgrizD9gAgoPgIDx+OOP65133tGtt96qQYMG6eTJk/rkk0904MABxcXFNVg/JSVFN910k8e8t99+W3/5y1/UpUsXSZLL5dKtt96qsrIyzZ49W7169dJnn32m//qv/9KxY8f06KOPtsjPBuDSovAACBj5+fn6+c9/rrS0tPp599xzzwXXHzdunMfrTz/9VDt27NCsWbM0ceJESdLq1atVWlqq9evXa8CAAZKk2bNnq3v37lq5cqXuvPNO9erVy/c/DIAWxbFaAAEjLCxMu3fvVllZWZPfe+zYMT344IMaOnSonnjiifr5eXl5+slPfqKwsDCdOHGi/p8rr7xStbW1+vjjj334EwCwC0d4AASMhQsXKi0tTVdffbXi4uI0ceJEzZw5U5GRkRd939mzZzV//nzV1dUpIyPD4y6u4uJiff311xo7dux533vixAmf/gwA7EHhARAwpk6dqiuuuELvvvuutm3bppUrVyonJ0fp6en1p6jO55lnntGuXbu0evVq9ezZ02NZXV2dxo0bp7vvvvu87z13mgtAYKPwAAgo3bt31y9/+Uv98pe/1PHjx3XDDTfoxRdfvGDh2bBhg1599VU98sgjGj16dIPl/fr1U01Nja688spLHR2AjbiGB0BAqK2tVWVlpce8Ll26qHv37jpz5sx537N37179+te/1vXXX6/bbrvtvOskJibqs88+0wcffNBgWUVFhc6ePdv88ABsxxEeAAGhurpaEydO1LXXXquhQ4cqNDRUH374ob744guPu7Z+6OGHH5Yk/eu//qveeustj2WXX365IiMjddddd2nLli1KSUnRDTfcoLi4OLlcLu3du1fvvPOONm/erM6dO1/ynw/ApUXhARAQ2rdvr5tvvlnbtm3Tpk2bZFmW+vXrp8cff1y33HLLed/z3XffqaamRosXL26wbOnSpYqMjFRISIj++7//Wy+99JLy8vL0xz/+UR07dtSAAQM0b948derU6VL/aABaAF8tAQAAjMc1PAAAwHgUHgAAYDwKDwAAMB6FBwAAGI/CAwAAjEfhAQAAxqPwAAAA41F4AACA8Sg8AADAeBQeAABgPAoPAAAwHoUHAAAYj8IDAACM9/8ANEuhNCW6hyUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='size',\n",
    "            y='total_bill',\n",
    "            data=tips)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SlhH9c0jkQco"
   },
   "source": [
    "### **Distribution example: Boxplot with Seaborn**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vgnIds0mlPHv"
   },
   "source": [
    "https://seaborn.pydata.org/generated/seaborn.boxplot.html?highlight=boxplot#seaborn.boxplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nnxm5fxtzZMI"
   },
   "source": [
    "![Image of Yaktocat](https://cdn-images-1.medium.com/max/1600/1*8noddM5HrclQmqUU9U_dUw.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B_uzJLSbzcE_"
   },
   "source": [
    "#### Median\n",
    "The **median** is the value that separates the higher half of a data from the lower half. It’s calculated by the following steps: order your values, and find the middle one. \n",
    "For example, if we have the numbers 1, 3, 4, **7**, 8, 8, 9, the median will be 7.\n",
    "\n",
    "\n",
    "#### First quartile\n",
    "The first quartile is the median of the data values to the left of the median in our ordered values.\n",
    "Ex: For the numbers 1, **3**, 4, 7, 8, 8, 9, the first quartile will be ?\n",
    "\n",
    "#### Third quartile\n",
    "The third quartile is the median of the data values to the right of the median in our ordered values.\n",
    "Ex: For the numbers 1, 3, 4, 7, 8, 8, 9, the Third quartile will be ?\n",
    "\n",
    "#### Interquartile Range(IQR)\n",
    "The IQR approximates the amount of spread in the middle 50% of the data. The formula is the third quartile - the first quartile.\n",
    "Ex: For the numbers 1, 3, 4, 7, 8, 8, 9, the IQR will be ?\n",
    "\n",
    "\n",
    "#### Outlier\n",
    "An outlier is a data value that lies outside the overall pattern. A commonly used rule says that a value is an outlier if it’s less than the first quartile - 1.5 * IQR or high than the third quartile + 1.5 * IQR.\n",
    "\n",
    "#### Maximum and Minimum\n",
    "The minimum and the maximum are just the min and max values from our data. (outliers are not included)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 475
    },
    "executionInfo": {
     "elapsed": 578,
     "status": "ok",
     "timestamp": 1682362312281,
     "user": {
      "displayName": "Ka Ba",
      "userId": "08277395367003334094"
     },
     "user_tz": -120
    },
    "id": "WCqgCSAgkVcA",
    "outputId": "d0084b93-5962-45d3-c378-c1e537d9d6b3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='tip'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lS6O5uTlmzZoV69atq+1wBf+zN95444Bl1113XTFu3Lji7bffLmEi9nnxxReL5ubmYvny5cXJJ59cPPvss2WPlM5LL71UNDU1FX/605/KHoWiKObNm1dMnjy56Ojo6Fz22GOPFSeffHLx5JNPljhZHvs/L1x99dXFueeee8A6n/nMZ4qrrrqqy7IZM2YUl112WU1n88rAYRg2bNgBy0aPHh07duyIXbt2lTAR+9x4441x0UUXxYknnlj2KGmtXLkyRowYEWeddVbZoxARe/fujYEDB0alUulcdvTRR0dEeGvzCGloePen3I0bN8arr74a55xzTpflU6dOjcceeyz27NlTu9lq9pmTeuqpp+LYY4+N97///WWPktaqVavi73//e3z7298ue5TUnnnmmTj55JNj8eLFceaZZ8bHPvaxuOiii+KZZ54pe7SUvvjFL8ZLL70Ud999d7S2tsbGjRvjZz/7WTQ1NcW4cePKHo+IePnllyMiDvgm5qSTTor29vaaXt8hBqpozZo18fDDD8fMmTPLHiWtt956K370ox/F7NmzBVnJXn/99fjLX/4SDz74YPzgBz+IRYsWRaVSiZkzZ8Ybb7xR9njpjB8/Pm677bb46U9/GuPHj48pU6bEG2+8EUuXLo33ve99ZY9HRGzfvj0iIgYNGtRl+b7b++6vBTFQJf/85z9j9uzZMWHChLjkkkvKHietJUuWxAc+8IH40pe+VPYo6RVFEbt27YpbbrklPve5z8VZZ50VS5YsiaIoYtmyZWWPl87TTz8dc+bMiQsvvDDuuOOOuOWWW6KjoyMuv/xyFxASfcoeoDd488034+tf/3oMGTIkbr311vd8X4ja2Lx5c/zmN7+JRYsWRWtra0RE57Ubu3btip07d8bAgQPLHDGVQYMGxZAhQ+KUU07pXDZkyJBoamqKF198scTJcrrxxhvjE5/4RFxzzTWdy5qbm+NTn/pUPPjggzFjxowSpyMiYvDgwRER0draGsccc0zn8jfffLPL/bUgBg5TW1tbfOMb34jW1ta49957Oy/I4cjbtGlTtLe3x+WXX37AfZdcckmcdtppsWLFihImy2nkyJHxj3/846D37d69+whPw0svvRRnn312l2Uf/OAHY+jQoYc8ThxZH/3oRyPi39cO7Pt43+2+ffvG8ccfX7Nti4HDsHfv3vje974XL7/8ctx9991x7LHHlj1SaqNHj44777yzy7INGzbETTfdFNdff32MGTOmpMlymjRpUqxcuTI2bNgQo0ePjoiIbdu2xV//+te49NJLyx0uoeHDh8f69eu7LNu8eXNs27YtjjvuuJKmYn/HH398fOQjH4lVq1bFlClTOpc//PDDceaZZ0a/fv1qtm0xcBiuv/76eOSRR+Kaa66JHTt2dPmlEE1NTTU9cBxo0KBBMWHChIPed+qpp8app556hCfKbcqUKTFmzJj47ne/G7Nnz47+/fvHL3/5y+jXr1985StfKXu8dC666KJYsGBB3HjjjTF58uT417/+1XmNzTt/lI3aeOutt+LRRx+NiH+H2I4dO2LVqlUREXHGGWfEsGHD4jvf+U58//vfjw9/+MMxYcKEePjhh+PZZ5+t+XU2lcIPmP7PJk+eHJs3bz7ofatXr44RI0Yc4Yl4p8cffzwuueSSaGlp8cpACbZu3Ro33XRTPPLII9He3h7jx4+Pa6+9NkaOHFn2aOkURRH33HNPLF++PDZu3BgDBw6M5ubmmD179iF/Gx7VtWnTpgPeqtnnzjvv7Pxm5r777oulS5fGli1b4sQTT4yrrroqJk2aVNPZxAAAJOeydwBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQBJTZ48ucsfrQHyEgPQyz399NNx6623dv7lM4B38rcJoJdbu3Zt3HbbbXH++efHoEGDOpevWrUqKpVKiZMB9UIMQFL+kBawj7cJoBe79dZbY+HChRERcfbZZ8eoUaNi1KhRsWnTpgOuGVi5cmWMGjUqnnzyyZg/f35MmDAhxo0bF3PmzInt27eX9RCAI8ArA9CLffrTn45XX301Hnroobj22mtj6NChERExbNiwQ/43N9xwQwwaNChmzZoVr7zySixfvjy2bNkSd911l7cVoJcSA9CLnXLKKdHU1BQPPfRQTJkypVt/Vrtv375x++23R9++fSMiYvjw4fGTn/wk/vjHPx7yz68CPZu3CYAuZsyY0RkCERFf/vKXo0+fPvHoo4+WOBVQS2IA6OKEE07ocnvgwIFxzDHHxObNm0uaCKg1MQAAyYkB6OX+24v+XnvttS63d+7cGa+//nocd9xx1RwLqCNiAHq5o446KiIiWltbu7X+vffeG+3t7Z23ly9fHnv37o2JEyfWZD6gfH6aAHq5U089NSIibr755pg6dWr07ds3Jk2adMj129vb49JLL41zzjknXnnllfjtb38bp59+up8kgF5MDEAv9/GPfzyuvPLKuOeee+LPf/5zdHR0xOrVqw+5/vz58+N3v/td/PznP4/29vY499xz47rrrvM7BqAXqxRFUZQ9BFC+lStXxrXXXhstLS0xZsyYsscBjiDXDABAcmIAAJITAwCQnGsGACA5rwwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcv8Pm8zWxeptU5IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='tip',\n",
    "            data = tips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 475
    },
    "executionInfo": {
     "elapsed": 606,
     "status": "ok",
     "timestamp": 1682362357933,
     "user": {
      "displayName": "Ka Ba",
      "userId": "08277395367003334094"
     },
     "user_tz": -120
    },
    "id": "YwjkqHjKkoJw",
    "outputId": "3f2074d7-bbd7-48b2-bb2b-03c4eb54f3a6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='tip', ylabel='sex'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='tip',\n",
    "            y='sex', # Adding y here works as hue\n",
    "            #hue='smoker', # You can add also another hue to have an even more granular comparison\n",
    "            data = tips)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "55F8pIDQkfhk"
   },
   "source": [
    "### **Distribution example: Histogram with Seaborn**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nq1ueiR-lLlw"
   },
   "source": [
    "https://seaborn.pydata.org/tutorial/distributions.html\n",
    "\n",
    "It is similar to a bar chart, but a histogram groups numbers into ranges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "executionInfo": {
     "elapsed": 936,
     "status": "ok",
     "timestamp": 1682362413996,
     "user": {
      "displayName": "Ka Ba",
      "userId": "08277395367003334094"
     },
     "user_tz": -120
    },
    "id": "JvBr8_NsklQC",
    "outputId": "001a4e03-85c1-4ec1-cbc9-890745c90277"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(tips['tip']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "executionInfo": {
     "elapsed": 723,
     "status": "ok",
     "timestamp": 1682362506792,
     "user": {
      "displayName": "Ka Ba",
      "userId": "08277395367003334094"
     },
     "user_tz": -120
    },
    "id": "nVQB2cLrmnFG",
    "outputId": "65099068-8236-4c75-a0e2-c58c50fdfa28"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(tips['tip'], \n",
    "             bins=20,\n",
    "             kde = False\n",
    "             );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iVk6ZvzfNkmD"
   },
   "source": [
    "## Additional sources\n",
    "- https://www.angela1c.com/projects/tips-project-files/part1/"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "gd3et3eYijZ6",
    "XukjwNdii_Ey"
   ],
   "provenance": [
    {
     "file_id": "https://github.com/ReDI-School/nrw-data-analytics/blob/main/5_Lecture_%E2%80%94_Introduction_to_analytical_thinking.ipynb",
     "timestamp": 1682261599688
    }
   ]
  },
  "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": 1
}