titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b8c381ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a60138f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function seaborn.utils.get_dataset_names()>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.get_dataset_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b320f723",
   "metadata": {},
   "outputs": [
    {
     "ename": "IncompleteRead",
     "evalue": "IncompleteRead(327680 bytes read)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIncompleteRead\u001b[0m                            Traceback (most recent call last)",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\lib\\http\\client.py:591\u001b[0m, in \u001b[0;36mHTTPResponse._read_chunked\u001b[1;34m(self, amt)\u001b[0m\n\u001b[0;32m    589\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m--> 591\u001b[0m value\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_safe_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchunk_left\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m    592\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m amt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\lib\\http\\client.py:632\u001b[0m, in \u001b[0;36mHTTPResponse._safe_read\u001b[1;34m(self, amt)\u001b[0m\n\u001b[0;32m    631\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(data) \u001b[38;5;241m<\u001b[39m amt:\n\u001b[1;32m--> 632\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m IncompleteRead(data, amt\u001b[38;5;241m-\u001b[39m\u001b[38;5;28mlen\u001b[39m(data))\n\u001b[0;32m    633\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data\n",
      "\u001b[1;31mIncompleteRead\u001b[0m: IncompleteRead(1364 bytes read, 27458 more expected)",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mIncompleteRead\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[3], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_dataset_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\lib\\site-packages\\seaborn\\utils.py:513\u001b[0m, in \u001b[0;36mget_dataset_names\u001b[1;34m()\u001b[0m\n\u001b[0;32m    511\u001b[0m url \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://github.com/mwaskom/seaborn-data\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    512\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m urlopen(url) \u001b[38;5;28;01mas\u001b[39;00m resp:\n\u001b[1;32m--> 513\u001b[0m     html \u001b[38;5;241m=\u001b[39m \u001b[43mresp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    515\u001b[0m pat \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/mwaskom/seaborn-data/blob/master/(\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw*).csv\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    516\u001b[0m datasets \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39mfindall(pat, html\u001b[38;5;241m.\u001b[39mdecode())\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\lib\\http\\client.py:459\u001b[0m, in \u001b[0;36mHTTPResponse.read\u001b[1;34m(self, amt)\u001b[0m\n\u001b[0;32m    456\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    458\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mchunked:\n\u001b[1;32m--> 459\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_read_chunked\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    461\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m amt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    462\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m amt \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength:\n\u001b[0;32m    463\u001b[0m         \u001b[38;5;66;03m# clip the read to the \"end of response\"\u001b[39;00m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\lib\\http\\client.py:597\u001b[0m, in \u001b[0;36mHTTPResponse._read_chunked\u001b[1;34m(self, amt)\u001b[0m\n\u001b[0;32m    595\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(value)\n\u001b[0;32m    596\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m IncompleteRead:\n\u001b[1;32m--> 597\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m IncompleteRead(\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(value))\n",
      "\u001b[1;31mIncompleteRead\u001b[0m: IncompleteRead(327680 bytes read)"
     ]
    }
   ],
   "source": [
    "sns.get_dataset_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3a78d4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic = sns.load_dataset('titanic')\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7708b4e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "40453ea6",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'titanic' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[7], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m freq \u001b[38;5;241m=\u001b[39m \u001b[43mtitanic\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msex\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalue_counts()\n",
      "\u001b[1;31mNameError\u001b[0m: name 'titanic' is not defined"
     ]
    }
   ],
   "source": [
    "freq = titanic['sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f0af7e2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experience_coding</th>\n",
       "      <th>python_user</th>\n",
       "      <th>r_user</th>\n",
       "      <th>sql_user</th>\n",
       "      <th>most_used</th>\n",
       "      <th>compensation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Scikit-learn</td>\n",
       "      <td>124267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.3</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Scikit-learn</td>\n",
       "      <td>236889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>74321</td>\n",
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       "      <th>3</th>\n",
       "      <td>2.7</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>62593</td>\n",
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       "      <th>4</th>\n",
       "      <td>1.2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Scikit-learn</td>\n",
       "      <td>36288</td>\n",
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      ],
      "text/plain": [
       "   experience_coding  python_user  r_user  sql_user     most_used  \\\n",
       "0                6.1         True   False      True  Scikit-learn   \n",
       "1               12.3         True    True      True  Scikit-learn   \n",
       "2                2.2         True   False     False          None   \n",
       "3                2.7        False   False      True          None   \n",
       "4                1.2         True   False     False  Scikit-learn   \n",
       "\n",
       "   compensation  \n",
       "0        124267  \n",
       "1        236889  \n",
       "2         74321  \n",
       "3         62593  \n",
       "4         36288  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kaggle = pd.read_csv(r'C:\\Users\\j\\DATA SCIENTIST\\3 - DATA_ANALYTICS\\DATA SETS\\kaggle2021-short.csv')\n",
    "kaggle.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "880b3f44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kaggle['compensation'].plot.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8870f392",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}