02_barplot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0de949e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import libraries\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# load dataset\n",
    "phool = sns.load_dataset(\"iris\")\n",
    "phool\n",
    "\n",
    "# draw a line plot\n",
    "sns.barplot(x=\"species\", y=\"sepal_width\", data=phool)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "78156711",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</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>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db66538c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import libraries\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# load dataset\n",
    "phool = sns.load_dataset(\"iris\")\n",
    "phool\n",
    "\n",
    "# draw a line plot\n",
    "sns.barplot(x=\"species\", y=\"petal_length\", data=phool)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0f6f4933",
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "0      man        True  NaN  Southampton    no  False  \n",
       "1    woman       False    C    Cherbourg   yes  False  \n",
       "2    woman       False  NaN  Southampton   yes   True  \n",
       "3    woman       False    C  Southampton   yes  False  \n",
       "4      man        True  NaN  Southampton    no   True  \n",
       "..     ...         ...  ...          ...   ...    ...  \n",
       "886    man        True  NaN  Southampton    no   True  \n",
       "887  woman       False    B  Southampton   yes   True  \n",
       "888  woman       False  NaN  Southampton    no  False  \n",
       "889    man        True    C    Cherbourg   yes   True  \n",
       "890    man        True  NaN   Queenstown    no   True  \n",
       "\n",
       "[891 rows x 15 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import libraries\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# load dataset\n",
    "kashti = sns.load_dataset(\"titanic\")\n",
    "kashti\n",
    "\n",
    "## draw a line plot\n",
    "#sns.barplot(x=\"species\", y=\"petal_length\", data=phool)\n",
    "#plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9627b2f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import libraries\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# load dataset\n",
    "kashti = sns.load_dataset(\"titanic\")\n",
    "kashti\n",
    "\n",
    "# draw a line plot\n",
    "sns.barplot(x=\"sex\", y=\"alone\", hue=\"who\",  data=kashti)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "581612b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import libraries\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# load dataset\n",
    "kashti = sns.load_dataset(\"titanic\")\n",
    "kashti\n",
    "\n",
    "# draw a line plot\n",
    "sns.barplot(x=\"sex\", y=\"alone\", hue=\"who\",  data=kashti, order=[\"female\", \"male\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "288136b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import libraries\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# load dataset\n",
    "kashti = sns.load_dataset(\"titanic\")\n",
    "kashti\n",
    "\n",
    "# draw a line plot\n",
    "sns.barplot(x=\"sex\", y=\"alone\", hue=\"who\",  data=kashti, order=[\"female\", \"male\"], color=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f6d0d457",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import libraries\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# load dataset\n",
    "kashti = sns.load_dataset(\"titanic\")\n",
    "kashti\n",
    "\n",
    "# draw a line plot\n",
    "sns.barplot(x=\"sex\", y=\"alone\", hue=\"who\",  data=kashti, order=[\"female\", \"male\"], color=\"green\", ci=None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "532e4662",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import libraries\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# load dataset\n",
    "kashti = sns.load_dataset(\"titanic\")\n",
    "kashti\n",
    "\n",
    "# draw a line plot\n",
    "sns.barplot(x=\"sex\", y=\"alone\", hue=\"who\",  data=kashti, order=[\"female\", \"male\"], color=\"green\", ci=None,palette=\"pastel\", saturation=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3d6a96cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import libraries\n",
    "import seaborn as sns\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# load dataset\n",
    "kashti = sns.load_dataset(\"titanic\")\n",
    "kashti\n",
    "\n",
    "# draw a line plot\n",
    "sns.barplot(x=\"class\", y=\"fare\", hue=\"sex\",  data=kashti,   saturation=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "168e94f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# here making horizontal plot\n",
    "# import libraries\n",
    "import seaborn as sns\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# load dataset\n",
    "kashti = sns.load_dataset(\"titanic\")\n",
    "kashti\n",
    "\n",
    "# draw a line plot\n",
    "sns.barplot(x=\"fare\", y=\"class\", hue=\"sex\",  data=kashti,   saturation=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f8153a85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='class', ylabel='fare'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import libraries\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# read a titanic.csv file\n",
    "# from seaborn library\n",
    "kashti = sns.load_dataset(\"titanic\")\n",
    "\n",
    "sns.barplot(x=\"class\", y=\"fare\", data=kashti, linewidth=2.5, facecolor=(1,1,1,0), errcolor=\".2\", edgecolor=\".2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "64fbb63e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='class', ylabel='fare'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import libraries\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# read a titanic.csv file\n",
    "# from seaborn library\n",
    "kashti = sns.load_dataset(\"titanic\")\n",
    "\n",
    "sns.barplot(x=\"class\", y=\"fare\", data=kashti, linewidth=5, facecolor=(0.12,0.889,0.76768,0.378), errcolor=\".5\", edgecolor=\".1\")\n",
    "# in this plot line width defining the width of black line errco;ot definig to top vertical small line and edge color darkens the line width 1 to 0.1 it is at extreme black during the position 0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75604240",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}