3_Histogram_Seaborn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Histogram in Seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Mahzaib Khalid\\anaconda3\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.1\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n",
       "       'species'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data= sns.load_dataset(\"iris\")\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data[\"sepal_length\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Parameters of Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bins parameter is used to keep data in fix range. It is Optional parameter contains integer \n",
    "# or sequence or strings\n",
    "\n",
    "\n",
    "sns.displot(data[\"sepal_length\"],bins=[4.5,5.0,5.5,6.0,6.5,7.0,7.5,8.0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# kde Parameter gives a line graph. It stands for kernel density estimator\n",
    "\n",
    "from weakref import KeyedRef\n",
    "\n",
    "\n",
    "\n",
    "sns.displot(data[\"sepal_length\"],bins=[4.5,5.0,5.5,6.0,6.5,7.0,7.5,8.0], kde=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Color Parameter is used to give color to graph\n",
    "\n",
    "sns.displot(data[\"sepal_length\"],bins=[4.5,5.0,5.5,6.0,6.5,7.0,7.5,8.0], color=\"Red\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Color Parameter is used to give color to graph\n",
    "\n",
    "sns.displot(data[\"sepal_length\"],color=\"Red\",log=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "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"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "8de2fd29472d8520918489d2f7698684b73d156be72bb6a0af57d9dbbf4a217c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}