eda.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Collections</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Buyers</th>\n",
       "      <th>Txns</th>\n",
       "      <th>Owners</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Axie Infinity</td>\n",
       "      <td>$3,328,148,500</td>\n",
       "      <td>1,079,811</td>\n",
       "      <td>9,755,511</td>\n",
       "      <td>2,656,431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CryptoPunks</td>\n",
       "      <td>$1,664,246,968</td>\n",
       "      <td>4,723</td>\n",
       "      <td>18,961</td>\n",
       "      <td>3,289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Art Blocks</td>\n",
       "      <td>$1,075,223,906</td>\n",
       "      <td>20,934</td>\n",
       "      <td>117,602</td>\n",
       "      <td>25,094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bored Ape Yacht Club</td>\n",
       "      <td>$783,882,186</td>\n",
       "      <td>8,284</td>\n",
       "      <td>22,584</td>\n",
       "      <td>5,862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NBA Top Shot</td>\n",
       "      <td>$781,965,423</td>\n",
       "      <td>374,818</td>\n",
       "      <td>11,790,699</td>\n",
       "      <td>603,928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Evolution</td>\n",
       "      <td>$2,536,468</td>\n",
       "      <td>746</td>\n",
       "      <td>4,961</td>\n",
       "      <td>2,061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>LegionPunks</td>\n",
       "      <td>$2,535,490</td>\n",
       "      <td>1,702</td>\n",
       "      <td>3,794</td>\n",
       "      <td>2,410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>BabyApes</td>\n",
       "      <td>$2,521,714</td>\n",
       "      <td>1,671</td>\n",
       "      <td>3,405</td>\n",
       "      <td>2,785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>Train of the Century</td>\n",
       "      <td>$2,504,080</td>\n",
       "      <td>3,139</td>\n",
       "      <td>39,460</td>\n",
       "      <td>3,160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>Cyber Pharmacist</td>\n",
       "      <td>$2,498,805</td>\n",
       "      <td>3,206</td>\n",
       "      <td>8,452</td>\n",
       "      <td>3,689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Collections           Sales     Buyers        Txns     Owners\n",
       "0           Axie Infinity  $3,328,148,500  1,079,811   9,755,511  2,656,431\n",
       "1             CryptoPunks  $1,664,246,968      4,723      18,961      3,289\n",
       "2              Art Blocks  $1,075,223,906     20,934     117,602     25,094\n",
       "3    Bored Ape Yacht Club    $783,882,186      8,284      22,584      5,862\n",
       "4            NBA Top Shot    $781,965,423    374,818  11,790,699    603,928\n",
       "..                    ...             ...        ...         ...        ...\n",
       "245             Evolution      $2,536,468        746       4,961      2,061\n",
       "246           LegionPunks      $2,535,490      1,702       3,794      2,410\n",
       "247              BabyApes      $2,521,714      1,671       3,405      2,785\n",
       "248  Train of the Century      $2,504,080      3,139      39,460      3,160\n",
       "249      Cyber Pharmacist      $2,498,805      3,206       8,452      3,689\n",
       "\n",
       "[250 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('nft_sales.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Collections</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Buyers</th>\n",
       "      <th>Txns</th>\n",
       "      <th>Owners</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>250</td>\n",
       "      <td>250</td>\n",
       "      <td>250</td>\n",
       "      <td>250</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>250</td>\n",
       "      <td>250</td>\n",
       "      <td>246</td>\n",
       "      <td>249</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Axie Infinity</td>\n",
       "      <td>$3,328,148,500</td>\n",
       "      <td>3,246</td>\n",
       "      <td>4,523</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "          Collections           Sales Buyers   Txns Owners\n",
       "count             250             250    250    250    244\n",
       "unique            250             250    246    249    235\n",
       "top     Axie Infinity  $3,328,148,500  3,246  4,523      1\n",
       "freq                1               1      2      2      5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 250 entries, 0 to 249\n",
      "Data columns (total 5 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   Collections  250 non-null    object\n",
      " 1   Sales        250 non-null    object\n",
      " 2   Buyers       250 non-null    object\n",
      " 3   Txns         250 non-null    object\n",
      " 4   Owners       244 non-null    object\n",
      "dtypes: object(5)\n",
      "memory usage: 9.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hemil Mehta\\AppData\\Roaming\\Python\\Python37\\site-packages\\ipykernel_launcher.py:3: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Collections</th>\n",
       "      <th>Sales in USD</th>\n",
       "      <th>Buyers</th>\n",
       "      <th>Transactions</th>\n",
       "      <th>Owners</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Axie Infinity</td>\n",
       "      <td>3328148500</td>\n",
       "      <td>1079811</td>\n",
       "      <td>9755511</td>\n",
       "      <td>2656431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CryptoPunks</td>\n",
       "      <td>1664246968</td>\n",
       "      <td>4723</td>\n",
       "      <td>18961</td>\n",
       "      <td>3289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Art Blocks</td>\n",
       "      <td>1075223906</td>\n",
       "      <td>20934</td>\n",
       "      <td>117602</td>\n",
       "      <td>25094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bored Ape Yacht Club</td>\n",
       "      <td>783882186</td>\n",
       "      <td>8284</td>\n",
       "      <td>22584</td>\n",
       "      <td>5862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NBA Top Shot</td>\n",
       "      <td>781965423</td>\n",
       "      <td>374818</td>\n",
       "      <td>11790699</td>\n",
       "      <td>603928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Evolution</td>\n",
       "      <td>2536468</td>\n",
       "      <td>746</td>\n",
       "      <td>4961</td>\n",
       "      <td>2061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>LegionPunks</td>\n",
       "      <td>2535490</td>\n",
       "      <td>1702</td>\n",
       "      <td>3794</td>\n",
       "      <td>2410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>BabyApes</td>\n",
       "      <td>2521714</td>\n",
       "      <td>1671</td>\n",
       "      <td>3405</td>\n",
       "      <td>2785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>Train of the Century</td>\n",
       "      <td>2504080</td>\n",
       "      <td>3139</td>\n",
       "      <td>39460</td>\n",
       "      <td>3160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>Cyber Pharmacist</td>\n",
       "      <td>2498805</td>\n",
       "      <td>3206</td>\n",
       "      <td>8452</td>\n",
       "      <td>3689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Collections Sales in USD   Buyers Transactions   Owners\n",
       "0           Axie Infinity   3328148500  1079811      9755511  2656431\n",
       "1             CryptoPunks   1664246968     4723        18961     3289\n",
       "2              Art Blocks   1075223906    20934       117602    25094\n",
       "3    Bored Ape Yacht Club    783882186     8284        22584     5862\n",
       "4            NBA Top Shot    781965423   374818     11790699   603928\n",
       "..                    ...          ...      ...          ...      ...\n",
       "245             Evolution      2536468      746         4961     2061\n",
       "246           LegionPunks      2535490     1702         3794     2410\n",
       "247              BabyApes      2521714     1671         3405     2785\n",
       "248  Train of the Century      2504080     3139        39460     3160\n",
       "249      Cyber Pharmacist      2498805     3206         8452     3689\n",
       "\n",
       "[244 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns = {'Sales':'Sales in USD','Txns':'Transactions'}, inplace = True)\n",
    "df['Sales in USD'] = df['Sales in USD'].str.replace(',', '')\n",
    "df['Sales in USD'] = df['Sales in USD'].str.replace('$', '')\n",
    "df['Buyers'] = df['Buyers'].str.replace(',','')\n",
    "df['Transactions'] = df['Transactions'].str.replace(',','')\n",
    "df['Owners'] = df['Owners'].str.replace(',','')\n",
    "df=df.dropna()\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hemil Mehta\\AppData\\Roaming\\Python\\Python37\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\Hemil Mehta\\AppData\\Roaming\\Python\\Python37\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\Hemil Mehta\\AppData\\Roaming\\Python\\Python37\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n",
      "C:\\Users\\Hemil Mehta\\AppData\\Roaming\\Python\\Python37\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "# CONVERTING STRING NUMERICALS TO INT\n",
    "df['Sales in USD'] = df['Sales in USD'].astype('int64')\n",
    "df['Buyers'] = df['Buyers'].astype('int64')\n",
    "df['Transactions'] = df['Transactions'].astype('int64')\n",
    "df['Owners'] = df['Owners'].astype('int32')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hemil Mehta\\AppData\\Roaming\\Python\\Python37\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# MODIFYING THE DATA SET\n",
    "df['100 mil club'] = np.where(df['Sales in USD']>=100000000, 'Yes', 'No')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sales of the NFTs which are under the 'Bored Ape' Collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Sales of 'Bored Ape' Collection is 982,841,231$\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Collections</th>\n",
       "      <th>Sales in USD</th>\n",
       "      <th>Buyers</th>\n",
       "      <th>Transactions</th>\n",
       "      <th>Owners</th>\n",
       "      <th>100 mil club</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bored Ape Yacht Club</td>\n",
       "      <td>783882186</td>\n",
       "      <td>8284</td>\n",
       "      <td>22584</td>\n",
       "      <td>5862</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Bored Ape Kennel Club</td>\n",
       "      <td>108210267</td>\n",
       "      <td>5622</td>\n",
       "      <td>13715</td>\n",
       "      <td>5013</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Bored Ape Chemistry Club</td>\n",
       "      <td>79456705</td>\n",
       "      <td>1454</td>\n",
       "      <td>2508</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Bored Mummy Waking Up</td>\n",
       "      <td>11292073</td>\n",
       "      <td>6030</td>\n",
       "      <td>14816</td>\n",
       "      <td>3682</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Collections  Sales in USD  Buyers  Transactions  Owners  \\\n",
       "3        Bored Ape Yacht Club     783882186    8284         22584    5862   \n",
       "21      Bored Ape Kennel Club     108210267    5622         13715    5013   \n",
       "31   Bored Ape Chemistry Club      79456705    1454          2508       1   \n",
       "126     Bored Mummy Waking Up      11292073    6030         14816    3682   \n",
       "\n",
       "    100 mil club  \n",
       "3            Yes  \n",
       "21           Yes  \n",
       "31            No  \n",
       "126           No  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bored_collection = df[df['Collections'].str.contains(\"Bored\")]\n",
    "\n",
    "\n",
    "sum_sales = bored_collection['Sales in USD'].sum()\n",
    "\n",
    "print(f\"Total Sales of 'Bored Ape' Collection is {sum_sales:,}$\")\n",
    "bored_collection\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### BELOW GRAPH REPRESENTS TOTAL NUMBER OF OWNERS WHO OWN 'BORED APE' COLLECTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2 = bored_collection.groupby('100 mil club').sum()\n",
    "owners = [df2['Owners'][1],df2['Owners'][0]]\n",
    "con = ['Yes','No']\n",
    "plt.style.use('default')\n",
    "plt.figure(figsize=(5,5))\n",
    "p =plt.bar(con,owners,width=0.45,color='#db4444')\n",
    "plt.ylim(0,14000)\n",
    "plt.title(\"Number of Owners who are a part of 100 mil club('Bored Ape' Collection)\")\n",
    "plt.bar_label(p)\n",
    "plt.xlabel('100 mil club')\n",
    "plt.ylabel('No. of owners')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SALES AS COMPARED TO EACH OTHER\n",
    "collection = bored_collection['Collections']\n",
    "sales = bored_collection['Sales in USD']\n",
    "plt.style.use('ggplot')\n",
    "plt.figure(figsize=(11,4))\n",
    "plt.bar(collection,sales)\n",
    "plt.title('Sales of Bored Ape Collection')\n",
    "plt.xlabel('Collections')\n",
    "plt.ylabel('Sales in $')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LETS FINDOUT AVERAGE SALES OF 100 MILLION CLUB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average sales of 100 million club is 457,926,711$\n"
     ]
    }
   ],
   "source": [
    "df_100 = df.loc[df['100 mil club'] == 'Yes']\n",
    "average_sales_100 =round(df_100['Sales in USD'].mean())\n",
    "df_no100 = df.loc[df['100 mil club'] == 'No']\n",
    "average_sales_no100 = round(df_no100['Sales in USD'].mean())\n",
    "print(f'Average sales of 100 million club is {average_sales_100:,}$')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x= ['100 mil club','Not in 100 mil club']\n",
    "avg = [average_sales_100,average_sales_no100]\n",
    "\n",
    "plt.style.use('seaborn-pastel')\n",
    "plt.figure(figsize=(4,5))\n",
    "\n",
    "plt.bar(x,avg)\n",
    "plt.title('Average sales')\n",
    "plt.xlabel('Collections')\n",
    "plt.ylabel('Sales in USD $')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization of how much percentage of total collections belongs to 100 mil club"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count = df.groupby(['100 mil club']).count()['Collections']\n",
    "labels = [\"Not in 100 mil club\",\"100 mil club\"]\n",
    "plt.style.use('seaborn-pastel')\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.pie(count,labels=labels,autopct='%.2f %%',textprops={'fontsize': 14})\n",
    "plt.title(\"Distribution of Collections\",fontdict={'fontsize': 19})\n",
    "plt.legend(fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### THE COLLECTION MAKING MOST SALES (ALL TIME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The NFT Collection having maxiumum sales is Axie Infinity\n",
      "Total sales of Axie Infinity are 3,328,148,500 USD\n"
     ]
    }
   ],
   "source": [
    "maximum_sale = df['Sales in USD'].max()\n",
    "collection = df[df['Sales in USD'] == maximum_sale ]['Collections'][0]\n",
    "\n",
    "print(f'The NFT Collection having maxiumum sales is {collection}')\n",
    "print(f'Total sales of {collection} are {maximum_sale:,} USD')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DOES MORE NUMBER OF TRANSACTION PROVE MORE SALES? LET'S FIND OUT..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NBA Top Shot has 11790699 transactions (Highest amongst all)\n",
      "CryptoPunks has 18961 transactions (Relatively lower transaction)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "most_transaction  = df['Transactions'].max()\n",
    "most_data = df[df['Transactions']==most_transaction]\n",
    "collection_of_most = most_data['Collections'].iloc[0]\n",
    "sales_of_most = most_data['Sales in USD'].iloc[0]\n",
    "\n",
    "lower_transact = df.iloc[1][3]\n",
    "collection_of_lower = df.iloc[1][0]\n",
    "sales_of_lower = df.iloc[1][1]\n",
    "\n",
    "print(f'{collection_of_most} has {most_transaction} transactions (Highest amongst all)')\n",
    "print(f'{collection_of_lower} has {lower_transact} transactions (Relatively lower transaction)')\n",
    "\n",
    "#LET'S VISUALIZE THIS USING GRAPHS\n",
    "collections = [collection_of_most,collection_of_lower]\n",
    "sales = [sales_of_most,sales_of_lower]\n",
    "transactions = [most_transaction,lower_transact]\n",
    "\n",
    "plt.style.use('Solarize_Light2')\n",
    "fig,ax1 = plt.subplots(figsize=(7,5))\n",
    "ax2=ax1.twinx()\n",
    "p = ax1.bar(collections,sales)\n",
    "ax2.plot(collections,transactions,'r-')\n",
    "ax1.bar_label(p)\n",
    "plt.title('Co-relation between Sales and Transactions')\n",
    "ax1.set_xlabel('NFT Collection')\n",
    "ax1.set_ylabel('Sales in $',color='g')\n",
    "ax2.set_ylabel('Transactions',color='r')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The above graph shows that more number of transactions does not neccessary lead to more number of sales"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "5190bbd88ebfd33625c6a6cce56c7efc2e9866f6c5d9b06001e0ef1b88b50d9a"
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  "kernelspec": {
   "display_name": "Python 3.7.9 64-bit",
   "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": "3.7.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}