seaborn_01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "94f97df9",
   "metadata": {},
   "source": [
    "# -Seaborn plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0fd6bfee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# body mass index (BMI)\n",
    "# input weight\n",
    "# Height\n",
    "# BMI Input (Height-Weight)\n",
    "# Calculate BMI\n",
    "# print BMI (My name is ....., and my BMI is ....)\n",
    "# Weight in KG/Height in m and it's square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bed0147f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def BMI(weight : float, height: float) -> float:\n",
    "    return round((weight / height**2), 2)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ef31d35a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My name is Kamran, and my BMI is 33.69\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    name = \"Kamran\" #input(\"What is your name: \")\n",
    "    height = 1.71  #float(input(\"What is your height in Meters: \"))\n",
    "    weight = 98.5   #float(input(\"What is your weight in KGs: \"))\n",
    "except:\n",
    "    print(\"Input is not proper\")\n",
    "else:\n",
    "    print(\"My name is {}, and my BMI is {}\".format(name, BMI(weight, height)))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "882e722c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "af2a61e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_theme(style=\"ticks\", color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e683f5a4",
   "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",
       "  </tbody>\n",
       "</table>\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",
       "     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  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This is an online Dataset available by default on seaborn\n",
    "titanic = sns.load_dataset(\"titanic\")\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f6a6db52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 436.975x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"sex\", y=\"survived\", hue=\"class\", kind=\"bar\", data=titanic)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9bb2b782",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p1=sns.countplot(x='sex', data=titanic, hue='class')\n",
    "p1.set_title(\"Plot for counting\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ab14c4be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 282.35x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(titanic, row=\"sex\", hue=\"alone\")\n",
    "g = (g.map(plt.scatter, \"age\", \"fare\").add_legend())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fd5bbfcd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     total_bill   tip     sex smoker   day    time  size\n",
      "0         16.99  1.01  Female     No   Sun  Dinner     2\n",
      "1         10.34  1.66    Male     No   Sun  Dinner     3\n",
      "2         21.01  3.50    Male     No   Sun  Dinner     3\n",
      "3         23.68  3.31    Male     No   Sun  Dinner     2\n",
      "4         24.59  3.61  Female     No   Sun  Dinner     4\n",
      "..          ...   ...     ...    ...   ...     ...   ...\n",
      "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
      "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
      "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
      "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
      "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
      "\n",
      "[244 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "tips = sns.load_dataset(\"tips\")\n",
    "print(tips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5935cb75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432.225x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"sex\", y=\"smoker\", hue=\"time\", data=tips)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9563748b",
   "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
}