11  boxplot custimizing plot in python.ipynb

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       "  <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",
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       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
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       "      <th>embark_town</th>\n",
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       "      <th>alone</th>\n",
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       "      <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",
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       "      <td>NaN</td>\n",
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       "      <td>no</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
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       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
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       "      <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",
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       "      <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",
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       "      <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",
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      "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#libreris import\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "kashti=sns.load_dataset(\"titanic\")\n",
    "kashti.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
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       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
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       "      <td>False</td>\n",
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       "      <td>8.0500</td>\n",
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       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
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       "      <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",
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       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>E</td>\n",
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       "      <td>True</td>\n",
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      "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",
       "5         0       3    male   NaN      0      0   8.4583        Q  Third   \n",
       "6         0       1    male  54.0      0      0  51.8625        S  First   \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",
       "5    man        True  NaN   Queenstown    no   True  \n",
       "6    man        True    E  Southampton    no   True  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#libreris import\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "kashti=sns.load_dataset(\"titanic\")\n",
    "kashti.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <th>survived</th>\n",
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       "      <th>0</th>\n",
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       "      <td>22.0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>1</td>\n",
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       "      <td>C</td>\n",
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       "      <td>False</td>\n",
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      "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",
       "\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  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#libreris import\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "kashti=sns.load_dataset(\"titanic\")\n",
    "kashti.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a780e7bec8>"
      ]
     },
     "execution_count": 12,
     "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": [
    "sns.boxplot(x=\"sex\",\n",
    "           y=\"fare\",\n",
    "           data=kashti,\n",
    "           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-1eb00ff78cf2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a780981a08>"
      ]
     },
     "execution_count": 15,
     "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": [
    "sns.boxplot(x=\"survived\",\n",
    "           y=\"age\",\n",
    "           data=kashti,\n",
    "           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#enterquaratile range??median?maximum outlawers/minimum..outlayers?/\n",
    "#q1?q3?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a780ec0148>"
      ]
     },
     "execution_count": 19,
     "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": [
    "#show mean\n",
    "\n",
    "sns.boxplot(x=\"survived\",\n",
    "           y=\"age\",showmeans=True,\n",
    "           data=kashti)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a780c8ed88>"
      ]
     },
     "execution_count": 28,
     "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": [
    "#abb hum apni marzi ka symbol lgaay gy\n",
    "\n",
    "sns.boxplot(x=\"survived\",\n",
    "           y=\"age\",showmeans=True,\n",
    "            meanprops={\"marker\":\"*\",\n",
    "                      \"markersize\":\"12\",\n",
    "                      \"markeredgecolor\":\"red\"},\n",
    "           data=kashti)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a781b7fec8>"
      ]
     },
     "execution_count": 29,
     "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": [
    "#abb hum apni marzi ka symbol lgaay gy\n",
    "\n",
    "sns.boxplot(x=\"survived\",\n",
    "           y=\"age\",showmeans=True,\n",
    "            meanprops={\"marker\":\"d\",\n",
    "                      \"markersize\":\"12\",\n",
    "                      \"markeredgecolor\":\"red\"},\n",
    "           data=kashti)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a781cc8748>"
      ]
     },
     "execution_count": 35,
     "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": [
    "#abb hum apni marzi ka symbol lgaay gy\n",
    "\n",
    "sns.boxplot(x=\"survived\",\n",
    "           y=\"age\",showmeans=True,\n",
    "            meanprops={\"marker\":\"^\",\n",
    "                      \"markersize\":\"12\",\n",
    "                      \"markeredgecolor\":\"red\"},\n",
    "           data=kashti)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'box plot mn kitny dobay kitny mry')"
      ]
     },
     "execution_count": 38,
     "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": [
    "#set label\n",
    "#label set krny k liyay phly matpotlib,pylot wali library import krni\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.boxplot(x=\"survived\",\n",
    "           y=\"age\",showmeans=True,\n",
    "            meanprops={\"marker\":\"^\",\n",
    "                      \"markersize\":\"12\",\n",
    "                      \"markeredgecolor\":\"red\"},\n",
    "           data=kashti)\n",
    "plt.xlabel(\"how many survived\"),\n",
    "plt.ylabel(\"age(years)\"),\n",
    "plt.title(\"box plot mn kitny dobay kitny mry\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": [
    "# title k size change\n",
    "\n",
    "#set label\n",
    "#label set krny k liyay phly matpotlib,pylot wali library import krni\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.boxplot(x=\"survived\",\n",
    "           y=\"age\",showmeans=True,\n",
    "            meanprops={\"marker\":\"^\",\n",
    "                      \"markersize\":\"12\",\n",
    "                      \"markeredgecolor\":\"red\"},\n",
    "           data=kashti)\n",
    "plt.xlabel(\"how many survived\" ,size=11),\n",
    "plt.ylabel(\"age(years)\"),\n",
    "plt.title(\"box plot mn kitny dobay kitny mry\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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": [
    "# title k size change\n",
    "#isko bold bi krty hn\n",
    "\n",
    "#set label\n",
    "#label set krny k liyay phly matpotlib,pylot wali library import krni\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.boxplot(x=\"survived\",\n",
    "           y=\"age\",showmeans=True,\n",
    "            meanprops={\"marker\":\"^\",\n",
    "                      \"markersize\":\"12\",\n",
    "                      \"markeredgecolor\":\"red\"},\n",
    "           data=kashti)\n",
    "plt.xlabel(\"how many survived\" ,size=11,weight='bold'),\n",
    "plt.ylabel(\"age(years)\"),\n",
    "plt.title(\"box plot mn kitny dobay kitny mry\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### faced wrap and faced grid?? #assignment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- in box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}