lesson_2_describing_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Frequency Table\n",
    "- **Frequency Table** grouping of qualitative data into mutually exclusive classes showing the number of observation in each class\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>hotel</th>\n",
       "      <th>is_canceled</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_year</th>\n",
       "      <th>arrival_date_month</th>\n",
       "      <th>arrival_date_week_number</th>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <th>adults</th>\n",
       "      <th>...</th>\n",
       "      <th>deposit_type</th>\n",
       "      <th>agent</th>\n",
       "      <th>company</th>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>adr</th>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <th>reservation_status</th>\n",
       "      <th>reservation_status_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>342</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>737</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>304.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>240.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          hotel  is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n",
       "0  Resort Hotel            0        342               2015               July   \n",
       "1  Resort Hotel            0        737               2015               July   \n",
       "2  Resort Hotel            0          7               2015               July   \n",
       "3  Resort Hotel            0         13               2015               July   \n",
       "4  Resort Hotel            0         14               2015               July   \n",
       "\n",
       "   arrival_date_week_number  arrival_date_day_of_month  \\\n",
       "0                        27                          1   \n",
       "1                        27                          1   \n",
       "2                        27                          1   \n",
       "3                        27                          1   \n",
       "4                        27                          1   \n",
       "\n",
       "   stays_in_weekend_nights  stays_in_week_nights  adults  ...  deposit_type  \\\n",
       "0                        0                     0       2  ...    No Deposit   \n",
       "1                        0                     0       2  ...    No Deposit   \n",
       "2                        0                     1       1  ...    No Deposit   \n",
       "3                        0                     1       1  ...    No Deposit   \n",
       "4                        0                     2       2  ...    No Deposit   \n",
       "\n",
       "   agent company days_in_waiting_list customer_type   adr  \\\n",
       "0    NaN     NaN                    0     Transient   0.0   \n",
       "1    NaN     NaN                    0     Transient   0.0   \n",
       "2    NaN     NaN                    0     Transient  75.0   \n",
       "3  304.0     NaN                    0     Transient  75.0   \n",
       "4  240.0     NaN                    0     Transient  98.0   \n",
       "\n",
       "   required_car_parking_spaces  total_of_special_requests  reservation_status  \\\n",
       "0                            0                          0           Check-Out   \n",
       "1                            0                          0           Check-Out   \n",
       "2                            0                          0           Check-Out   \n",
       "3                            0                          0           Check-Out   \n",
       "4                            0                          1           Check-Out   \n",
       "\n",
       "  reservation_status_date  \n",
       "0              2015-07-01  \n",
       "1              2015-07-01  \n",
       "2              2015-07-02  \n",
       "3              2015-07-02  \n",
       "4              2015-07-03  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path = \"data/hotel_bookings.csv\"\n",
    "df = pd.read_csv(data_path)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "reservation_status\n",
       "Check-Out    75166\n",
       "Canceled     43017\n",
       "No-Show       1207\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make frequency table\n",
    "df.reservation_status.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Bar Charts\n",
    "- **Bar Chart** is a graphic representation of the frequency distribution\n",
    "- A graph in which the classes are reported on the horizontal axis and the class frequencies on the vertical axis\n",
    "- The class frequencies are proportional to the heights of the bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(6, 6))\n",
    "sns.set_palette('Set1')  # Set color palette\n",
    "reservation_frequency = df.reservation_status.value_counts()\n",
    "sns.barplot(x=reservation_frequency.index, y=reservation_frequency.values, orient='v')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Pie Charts\n",
    "* **Pie Charts** that shows the proportion or percent that each class represents of the total number of requencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "customer_type_freq = df.customer_type.value_counts()\n",
    "sns.set_palette('Set2')  # Set color palette\n",
    "plt.pie(customer_type_freq.values, labels=customer_type_freq.index, autopct='%1.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Relative Class Frequencies\n",
    "* Class frequencies can be converted to relative class frequencies to show the fraction of the total number of observations in each class\n",
    "* A relative frequency captures the relationship between a class total and the total number of observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>reserved_room_type</th>\n",
       "      <th>proportion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0.720278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D</td>\n",
       "      <td>0.160826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E</td>\n",
       "      <td>0.054737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>0.024265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>G</td>\n",
       "      <td>0.017539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B</td>\n",
       "      <td>0.009364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>C</td>\n",
       "      <td>0.007806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>H</td>\n",
       "      <td>0.005034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>P</td>\n",
       "      <td>0.000101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>L</td>\n",
       "      <td>0.000050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  reserved_room_type  proportion\n",
       "0                  A    0.720278\n",
       "1                  D    0.160826\n",
       "2                  E    0.054737\n",
       "3                  F    0.024265\n",
       "4                  G    0.017539\n",
       "5                  B    0.009364\n",
       "6                  C    0.007806\n",
       "7                  H    0.005034\n",
       "8                  P    0.000101\n",
       "9                  L    0.000050"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reserved_room_type_freq = df.reserved_room_type.value_counts()\n",
    "relative_freq = reserved_room_type_freq / len(df)\n",
    "relative_reserved_room_freq = pd.DataFrame(\n",
    "    {'reserved_room_type': reserved_room_type_freq.index,\n",
    "     'proportion': relative_freq.values})\n",
    "relative_reserved_room_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cumulative Frequency Distribution\n",
    "* Starts with the first class and adds the frequency values or the %'s (relative frequency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>reserved_room_type</th>\n",
       "      <th>quantity</th>\n",
       "      <th>proportion</th>\n",
       "      <th>accumulated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>85994</td>\n",
       "      <td>0.720278</td>\n",
       "      <td>0.720278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D</td>\n",
       "      <td>19201</td>\n",
       "      <td>0.160826</td>\n",
       "      <td>0.881104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E</td>\n",
       "      <td>6535</td>\n",
       "      <td>0.054737</td>\n",
       "      <td>0.935841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>2897</td>\n",
       "      <td>0.024265</td>\n",
       "      <td>0.960106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>G</td>\n",
       "      <td>2094</td>\n",
       "      <td>0.017539</td>\n",
       "      <td>0.977645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B</td>\n",
       "      <td>1118</td>\n",
       "      <td>0.009364</td>\n",
       "      <td>0.987009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>C</td>\n",
       "      <td>932</td>\n",
       "      <td>0.007806</td>\n",
       "      <td>0.994815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>H</td>\n",
       "      <td>601</td>\n",
       "      <td>0.005034</td>\n",
       "      <td>0.999849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>P</td>\n",
       "      <td>12</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.999950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>L</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  reserved_room_type  quantity  proportion  accumulated\n",
       "0                  A     85994    0.720278     0.720278\n",
       "1                  D     19201    0.160826     0.881104\n",
       "2                  E      6535    0.054737     0.935841\n",
       "3                  F      2897    0.024265     0.960106\n",
       "4                  G      2094    0.017539     0.977645\n",
       "5                  B      1118    0.009364     0.987009\n",
       "6                  C       932    0.007806     0.994815\n",
       "7                  H       601    0.005034     0.999849\n",
       "8                  P        12    0.000101     0.999950\n",
       "9                  L         6    0.000050     1.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accumulated_freq = relative_freq.cumsum()\n",
    "accumulated_reserved_room_freq = pd.DataFrame(\n",
    "    {'reserved_room_type': reserved_room_type_freq.index,\n",
    "     'quantity': reserved_room_type_freq.values,\n",
    "     'proportion': relative_freq.values,\n",
    "     'accumulated': accumulated_freq.values})\n",
    "accumulated_reserved_room_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Frequency Distribution - Numeric Data\n",
    "* Create from all unique values would be confusing\n",
    "* Class intervals help simplify the frequency distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Step 1: Decide on the number of classes (k)\n",
    "* A useful recipe to determine the number of classes (k) is the \"2 to the k rule\" such that $2^k > n$ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**adr** stands for average daily rate, and it's used to measure the average revenue that a hotel receives for each occupied guest room per day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17.0, True)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find k\n",
    "len_df = len(df)\n",
    "k = np.ceil(np.log2(len_df))\n",
    "#checking\n",
    "k, 2**k > len_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Determine the class interval or width\n",
    "* The formula is: $i >= \\frac{H-L}{k}$, where i is the class interval, H is the highest observed value, L is the lowest observed value and k is the number of classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21, 230, 12)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L, H = df.adr.quantile([0.02, 0.98]).values\n",
    "L, H = int(L), round(H)\n",
    "i = round((H-L)/k)\n",
    "L, H, i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[21, 33],\n",
       " [33, 45],\n",
       " [45, 57],\n",
       " [57, 69],\n",
       " [69, 81],\n",
       " [81, 93],\n",
       " [93, 105],\n",
       " [105, 117],\n",
       " [117, 129],\n",
       " [129, 141],\n",
       " [141, 153],\n",
       " [153, 165],\n",
       " [165, 177],\n",
       " [177, 189],\n",
       " [189, 201],\n",
       " [201, 213],\n",
       " [213, 225],\n",
       " [225, 237]]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls_interval = []\n",
    "ls_checkpoint = list(range(int(L), round(H), i))\n",
    "for index in ls_checkpoint:\n",
    "    ls_interval.append([index, index+i])\n",
    "ls_interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 21.    ,  34.0625,  47.125 ,  60.1875,  73.25  ,  86.3125,\n",
       "        99.375 , 112.4375, 125.5   , 138.5625, 151.625 , 164.6875,\n",
       "       177.75  , 190.8125, 203.875 , 216.9375, 230.    ])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#or \n",
    "np.linspace(L, H, int(k))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 Set the individual class limits and make distribution frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def assign_interval(x):\n",
    "  if x < ls_checkpoint[0]:\n",
    "    return f'->{ls_checkpoint[0]}'\n",
    "  if x > ls_checkpoint[-1]:\n",
    "    return f'{ls_checkpoint[-1]}->'\n",
    "  for lower, upper in ls_interval:\n",
    "    if lower <= x < upper:\n",
    "      return f'{lower}->{upper}'\n",
    "  return ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             ->21\n",
       "1             ->21\n",
       "2           69->81\n",
       "3           69->81\n",
       "4          93->105\n",
       "            ...   \n",
       "119385     93->105\n",
       "119386       225->\n",
       "119387    153->165\n",
       "119388     93->105\n",
       "119389    141->153\n",
       "Name: adr, Length: 119390, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assiged_interval = df.adr.apply(assign_interval)\n",
    "assiged_interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_order = [f'->{ls_checkpoint[0]}']\n",
    "custom_order.extend([f'{ls_checkpoint[i]}->{ls_checkpoint[i+1]}' for i in range(len(ls_checkpoint)-1)])\n",
    "custom_order.append(f'{ls_checkpoint[-1]}->')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "adr\n",
       "->21         2381\n",
       "21->33       1460\n",
       "33->45       4904\n",
       "45->57       6368\n",
       "57->69      14539\n",
       "69->81      14727\n",
       "81->93      13763\n",
       "93->105     12802\n",
       "105->117    11470\n",
       "117->129     8992\n",
       "129->141     7979\n",
       "141->153     4331\n",
       "153->165     3855\n",
       "165->177     3132\n",
       "177->189     1877\n",
       "189->201     1920\n",
       "201->213     1226\n",
       "213->225      920\n",
       "225->        2652\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make distribution frequency\n",
    "adr_freq = assiged_interval.value_counts().reindex(custom_order)\n",
    "adr_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(18, 6))\n",
    "sns.set_palette('Set1')  # Set color palette\n",
    "sns.barplot(x=adr_freq.index, y=adr_freq.values, orient='v')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4 Using Histplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df.adr\n",
    "filtered_data = data[(data >= L) & (data <= H)]\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.xlim(L, H)\n",
    "plt.xlabel('Price')\n",
    "plt.ylabel('Quant')\n",
    "sns.histplot(filtered_data, bins=round(k), kde=False)\n",
    "# sns.kdeplot(filtered_data ,fill=True)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}