Cryptocurrency - EDA and Visualization Fresco.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b83ebdbd",
   "metadata": {},
   "source": [
    "# Mini Project on EDA and Visualization\n",
    "\n",
    "Welcome to the Use Case Challenge, In this Scenario,You are supposed to perform a EDA and Visualization on Top100 list of Cryptocurrencies dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3b91850a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b48a1072",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Rank</th>\n",
       "      <th>Currency_Name</th>\n",
       "      <th>Market_Capital</th>\n",
       "      <th>Price</th>\n",
       "      <th>Circulating_Supply</th>\n",
       "      <th>Volume(24h)</th>\n",
       "      <th>Change(24h)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>6.021918e+10</td>\n",
       "      <td>3631.72</td>\n",
       "      <td>16581450.0</td>\n",
       "      <td>1.226800e+09</td>\n",
       "      <td>-0.0084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>2.506128e+10</td>\n",
       "      <td>264.44</td>\n",
       "      <td>94772198.0</td>\n",
       "      <td>4.625760e+08</td>\n",
       "      <td>0.0132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Bitcoin Cash</td>\n",
       "      <td>6.815418e+09</td>\n",
       "      <td>410.47</td>\n",
       "      <td>16603775.0</td>\n",
       "      <td>2.608570e+08</td>\n",
       "      <td>-0.0322</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank Currency_Name  Market_Capital    Price  Circulating_Supply  \\\n",
       "0     1       Bitcoin    6.021918e+10  3631.72          16581450.0   \n",
       "1     2      Ethereum    2.506128e+10   264.44          94772198.0   \n",
       "2     3  Bitcoin Cash    6.815418e+09   410.47          16603775.0   \n",
       "\n",
       "    Volume(24h)  Change(24h)  \n",
       "0  1.226800e+09      -0.0084  \n",
       "1  4.625760e+08       0.0132  \n",
       "2  2.608570e+08      -0.0322  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./datasets/Crypto Data.csv\")\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7501eafb",
   "metadata": {},
   "source": [
    "## Question 1\n",
    "\n",
    "### Extract the first 3 rows of your dataframe then convert it into dictionary with the column names as the key values and their values as list into a dictionary then assign to A1 variable.\n",
    "\n",
    "\n",
    "**Note:** The output must be in the following format\n",
    "\n",
    "        {Column_Name : List}\n",
    "        \n",
    "**Example**\n",
    "\n",
    "        { 'S.no' : [1,2,3,4],\n",
    "          'Name' : ['GridCoin','Golos','BlackCion','Groestlcoin']\n",
    "           ........}\n",
    "           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "06ca60d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Rank': [1, 2, 3],\n",
       " 'Currency_Name': ['Bitcoin', 'Ethereum', 'Bitcoin Cash'],\n",
       " 'Market_Capital': [60219183594.0, 25061275598.0, 6815417939.0],\n",
       " 'Price': [3631.72, 264.44, 410.47],\n",
       " 'Circulating_Supply': [16581450.0, 94772198.0, 16603775.0],\n",
       " 'Volume(24h)': [1226800000.0, 462576000.0, 260857000.0],\n",
       " 'Change(24h)': [-0.0084, 0.0132, -0.0322]}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A1 = {column: list(df.loc[:2, column]) for column in df.columns}\n",
    "A1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceda05ed",
   "metadata": {},
   "source": [
    "## Question 2\n",
    "\n",
    "### Return the Curreny_Name which has highest value in Price column and assign to A2 varaible.\n",
    "\n",
    "**Note:** The Output must be String"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4cc9ed61",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Currency_Name</th>\n",
       "      <th>Market_Capital</th>\n",
       "      <th>Price</th>\n",
       "      <th>Circulating_Supply</th>\n",
       "      <th>Volume(24h)</th>\n",
       "      <th>Change(24h)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>6.021918e+10</td>\n",
       "      <td>3631.72</td>\n",
       "      <td>16581450.0</td>\n",
       "      <td>1.226800e+09</td>\n",
       "      <td>-0.0084</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank Currency_Name  Market_Capital    Price  Circulating_Supply  \\\n",
       "0     1       Bitcoin    6.021918e+10  3631.72          16581450.0   \n",
       "\n",
       "    Volume(24h)  Change(24h)  \n",
       "0  1.226800e+09      -0.0084  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nlargest(1, \"Price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1c008844",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Bitcoin'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A2 = df.nlargest(1, \"Price\").loc[0, \"Currency_Name\"]\n",
    "A2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "871fbe97",
   "metadata": {},
   "source": [
    "## Question 3\n",
    "\n",
    "### Return the mean value of the Change(24h) column and assign to A3 variable.\n",
    "\n",
    "**Note:** The Output must be Float value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d2d1225c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.013364000000000003"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A3 = df[\"Change(24h)\"].mean()\n",
    "A3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9dccfb3",
   "metadata": {},
   "source": [
    "## Question 4\n",
    "\n",
    "### Return the sorted (Ascending order) list of currency names which are having Market_Capital greater than '2000000000' and Price greater than or equal to '200'. Assign to A4 variable.\n",
    "\n",
    "**Note:** The Output must be list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2f93f8cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>Rank</th>\n",
       "      <th>Currency_Name</th>\n",
       "      <th>Market_Capital</th>\n",
       "      <th>Price</th>\n",
       "      <th>Circulating_Supply</th>\n",
       "      <th>Volume(24h)</th>\n",
       "      <th>Change(24h)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>6.021918e+10</td>\n",
       "      <td>3631.72</td>\n",
       "      <td>16581450.0</td>\n",
       "      <td>1.226800e+09</td>\n",
       "      <td>-0.0084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>2.506128e+10</td>\n",
       "      <td>264.44</td>\n",
       "      <td>94772198.0</td>\n",
       "      <td>4.625760e+08</td>\n",
       "      <td>0.0132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Bitcoin Cash</td>\n",
       "      <td>6.815418e+09</td>\n",
       "      <td>410.47</td>\n",
       "      <td>16603775.0</td>\n",
       "      <td>2.608570e+08</td>\n",
       "      <td>-0.0322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Dash</td>\n",
       "      <td>2.607961e+09</td>\n",
       "      <td>344.36</td>\n",
       "      <td>7573378.0</td>\n",
       "      <td>9.714230e+07</td>\n",
       "      <td>0.0362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank Currency_Name  Market_Capital    Price  Circulating_Supply  \\\n",
       "0     1       Bitcoin    6.021918e+10  3631.72          16581450.0   \n",
       "1     2      Ethereum    2.506128e+10   264.44          94772198.0   \n",
       "2     3  Bitcoin Cash    6.815418e+09   410.47          16603775.0   \n",
       "4     5          Dash    2.607961e+09   344.36           7573378.0   \n",
       "\n",
       "    Volume(24h)  Change(24h)  \n",
       "0  1.226800e+09      -0.0084  \n",
       "1  4.625760e+08       0.0132  \n",
       "2  2.608570e+08      -0.0322  \n",
       "4  9.714230e+07       0.0362  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"Market_Capital\"] > 2000000000) & (df[\"Price\"] >= 200)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "421de3cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Bitcoin', 'Bitcoin Cash', 'Dash', 'Ethereum']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A4 = df[(df[\"Market_Capital\"] > 2000000000) & (df[\"Price\"] >= 200)].sort_values(by = \"Currency_Name\").loc[:, \"Currency_Name\"].to_list()\n",
    "A4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5ce1aab",
   "metadata": {},
   "source": [
    "## Question 5\n",
    "\n",
    "### Return the lowest value in Volume(24h) column and Assign to A5 variable.\n",
    "\n",
    "**Note:** Output must be Float value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4babbd2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4460.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A5 = df[\"Volume(24h)\"].nsmallest(1).iloc[0]\n",
    "A5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf16617c",
   "metadata": {},
   "source": [
    "## Question 6\n",
    "\n",
    "### Draw the Heatmap with correlation between Rank, Marcket Captial, Price, Circulating_Supply, Volume(24h) and Change(24h). Assign to A6 variable.\n",
    "\n",
    "**Instructions:**\n",
    "- Use seaborn\n",
    "- Create a figure with figsize(15,8)\n",
    "- xlabel,ylabel,title must be present\n",
    "- Plot the heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b5263402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "# Plot the Heatmap with axes (ax) and assign the returned object(path collection) to q6\n",
    "q6 = sns.heatmap(df[['Rank', 'Market_Capital', 'Price', 'Circulating_Supply', 'Volume(24h)', 'Change(24h)']].corr(),\n",
    "                 ax = ax, annot = True)\n",
    "ax.set(title = \"Heatmap Correlation\", xlabel = \"X-axis\", ylabel = \"Y-axis\")\n",
    "\n",
    "A6 = q6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b40c643",
   "metadata": {},
   "source": [
    "## Question 7\n",
    "\n",
    "### Draw the barplot containing Top 10 currency names with highest Market Capital And Assign to A7 variable.\n",
    "\n",
    "**Instructions:**\n",
    "* Use Seaborn\n",
    "* Order the cryptocurrencies is must be in Descending order (i.e,bar corresponding to cryptocurrency with highest market value must be on top of bar plot).\n",
    "* xlabel,ylabel,title must be present\n",
    "* Plot the barplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1a057f92",
   "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>Currency_Name</th>\n",
       "      <th>Market_Capital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>6.021918e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ethereum</td>\n",
       "      <td>2.506128e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bitcoin Cash</td>\n",
       "      <td>6.815418e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ripple</td>\n",
       "      <td>6.646330e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dash</td>\n",
       "      <td>2.607961e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Litecoin</td>\n",
       "      <td>2.562272e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NEM</td>\n",
       "      <td>1.880775e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>IOTA</td>\n",
       "      <td>1.400024e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Monero</td>\n",
       "      <td>1.318572e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ethereum Classic</td>\n",
       "      <td>9.807523e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Currency_Name  Market_Capital\n",
       "0           Bitcoin    6.021918e+10\n",
       "1          Ethereum    2.506128e+10\n",
       "2      Bitcoin Cash    6.815418e+09\n",
       "3            Ripple    6.646330e+09\n",
       "4              Dash    2.607961e+09\n",
       "5          Litecoin    2.562272e+09\n",
       "6               NEM    1.880775e+09\n",
       "7              IOTA    1.400024e+09\n",
       "8            Monero    1.318572e+09\n",
       "9  Ethereum Classic    9.807523e+08"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top10 = df[[\"Currency_Name\", \"Market_Capital\"]].sort_values(by = \"Market_Capital\", ascending = False).iloc[:10, :]\n",
    "top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "910fc206",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig7, ax7 = plt.subplots(figsize=(8,8))\n",
    "\n",
    "# Plot the countplot with axes (ax7) and assign the returned object(path collection) to q7\n",
    "q7 = sns.barplot(x = top10.loc[:, \"Market_Capital\"], y = top10.loc[:, \"Currency_Name\"], ax = ax7)\n",
    "ax7.set(title = \"Barplot containing Top10 Curreny names with highest market capital\")\n",
    "\n",
    "A7 = [q7,ax7]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "840755c1",
   "metadata": {},
   "source": [
    "## Question 8\n",
    "\n",
    "### Draw the violinplot that represents the graph of Change(24h). And Assign the variable to A8.\n",
    "\n",
    "**Instructions:**\n",
    "* Use seaborn\n",
    "* Change(24h) should be in the y-axis\n",
    "* xlabel,ylabel,title must be present\n",
    "* Plot the violinplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "949cd5b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig8, ax8 = plt.subplots(figsize = (8, 8))\n",
    "\n",
    "# Plot the violinplot with axes (ax8) and assign the returned object(path collection) to q8\n",
    "q8 = sns.violinplot(y = df[\"Change(24h)\"], ax = ax8)\n",
    "ax8.set(title = \"violinplot for Change(24h)\",\n",
    "       xlabel = \"X_axis\", ylabel = \"Y_axis\")\n",
    "\n",
    "A8 = q8"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0390e751",
   "metadata": {},
   "source": [
    "## Question 9\n",
    "\n",
    "### Draw a scatterplot that represents the relation between Price and Market Capital. And Assign to A9 variable.\n",
    "\n",
    "**Instructions:**\n",
    "* use seaborn\n",
    "* Consider data as 50 Cryptocurrencies, with higest Market Value\n",
    "* xlabel,ylabel,title must be present\n",
    "* Plot the Scatterplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "06693d82",
   "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>Market_Capital</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.021918e+10</td>\n",
       "      <td>3631.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.506128e+10</td>\n",
       "      <td>264.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.815418e+09</td>\n",
       "      <td>410.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Market_Capital    Price\n",
       "0    6.021918e+10  3631.72\n",
       "1    2.506128e+10   264.44\n",
       "2    6.815418e+09   410.47"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top50 = df[[\"Market_Capital\", \"Price\"]].sort_values(by = \"Market_Capital\", ascending=False).iloc[:50, ]\n",
    "top50.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a104b872",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig9, ax9 = plt.subplots(figsize=(8,8))\n",
    "\n",
    "# Plot the scatterplot with axes (ax9) and assign the returned object(path collection) to q9\n",
    "q9 = sns.scatterplot(x = top50[\"Market_Capital\"],y = top50[\"Price\"], ax = ax9)\n",
    "ax9.set_title(\"Scatterplot between Price and Market Capital\")\n",
    "\n",
    "A9 = q9"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ebde26b",
   "metadata": {},
   "source": [
    "## Question 10\n",
    "\n",
    "### Draw the barplot containing Top 10 currency names with Change(24h) in descending order And Assign to A10 variable.\n",
    "\n",
    "**Instructions:**\n",
    "* Use Seaborn\n",
    "* xlabel,ylabel,title must be present\n",
    "* Plot the barplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ccb50a15",
   "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>Currency_Name</th>\n",
       "      <th>Change(24h)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Sprouts</td>\n",
       "      <td>0.7752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Mooncoin</td>\n",
       "      <td>0.6474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Compcoin</td>\n",
       "      <td>0.2386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Metaverse ETP</td>\n",
       "      <td>0.1397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Neblio</td>\n",
       "      <td>0.1341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Zcash</td>\n",
       "      <td>0.1284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>ZCoin</td>\n",
       "      <td>0.1227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>GXShares</td>\n",
       "      <td>0.1078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Namecoin</td>\n",
       "      <td>0.1074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NEO</td>\n",
       "      <td>0.1014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Currency_Name  Change(24h)\n",
       "65        Sprouts       0.7752\n",
       "76       Mooncoin       0.6474\n",
       "88       Compcoin       0.2386\n",
       "48  Metaverse ETP       0.1397\n",
       "86         Neblio       0.1341\n",
       "13          Zcash       0.1284\n",
       "50          ZCoin       0.1227\n",
       "32       GXShares       0.1078\n",
       "67       Namecoin       0.1074\n",
       "10            NEO       0.1014"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top10_ = df[[\"Currency_Name\", \"Change(24h)\"]].sort_values(by = \"Change(24h)\", ascending = False).iloc[:10, :]\n",
    "top10_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "291844e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig10, ax10 = plt.subplots(figsize=(8,8))\n",
    "\n",
    "# Plot the barplot with axes (ax10) and assign the returned object(path collection) to q10\n",
    "q10 = sns.barplot(x = top10_.loc[:, \"Change(24h)\"], y = top10_.loc[:, \"Currency_Name\"], ax = ax10)\n",
    "ax10.set(title = \"Barplot containing Top10 Curreny names with Change(24h)\")\n",
    "\n",
    "A10 = [q10,ax10]"
   ]
  }
 ],
 "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": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}