UmarZein/eda.ipynb

## eda.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "12657923-2416-479c-9ad7-d64542c4de65",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ef857ff1-74fc-44dc-b126-bd6385181a43",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e9875bf6-8474-48ad-b4d8-6b5800446531",
   "metadata": {
    "tags": []
   },
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "raw",
   "id": "94624906-038c-4d3e-91c6-f4f92ba77cb4",
   "metadata": {},
   "source": [
    "categorical = nominal / ordinal\n",
    "numeric = interval / ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "367c2cd5-2755-4193-af1a-e1330d5785bf",
   "metadata": {
    "tags": []
   },
   "source": [
    "\n",
    "- numeric-numeric = scatter plot\n",
    "\n",
    "- categorical-numeric = boxplot\n",
    "\n",
    "- numeric-categorical = boxplot\n",
    "\n",
    "- categorical-categorical = heatmap\n",
    "\n",
    "- numerical = histogram\n",
    "\n",
    "- categorical = bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6cc6f538-c927-4f08-81d4-80aec3fc56f6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x=data['Age']#numeric\n",
    "y=data['Fare']#numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e84bf97f-8b27-4cb8-8528-e4b9d885e7cc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/maru/anaconda3/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/home/maru/anaconda3/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age', ylabel='Fare'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=x,y=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bc086072-b119-40b8-9cec-c8779de7b5b1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x=data['Sex']\n",
    "y=data['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6d319ec8-24fc-493e-b4ba-c887ce28a338",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/maru/anaconda3/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/home/maru/anaconda3/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/home/maru/anaconda3/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sex', ylabel='Age'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=x,y=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "92c6ab4a-590b-4976-be4d-7c2173011ae2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x=data['Pclass']\n",
    "y=data['Sex']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5d21a473-ff26-48df-a5af-799b41c1f656",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "contingency=pd.crosstab(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a1816719-357b-4fbc-b900-e7a879adbc04",
   "metadata": {
    "tags": []
   },
   "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>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>94</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144</td>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex     female  male\n",
       "Pclass              \n",
       "1           94   122\n",
       "2           76   108\n",
       "3          144   347"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contingency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "15438544-6331-4d41-9baa-678e104b446b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sex', ylabel='Pclass'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(contingency,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "876bd23b-0e1b-4a6e-a753-042bf94f6e6e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x=data['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3dff739b-ca04-4d0c-b3d2-d8e91573e78f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1ed926cc-3e2a-4136-ba0a-a6b21643f95e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x=data['Pclass']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ee41da7d-41ee-4831-9847-fd44cc16c94b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pclass\n",
      "3    491\n",
      "1    216\n",
      "2    184\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "y=x.value_counts()\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f60b0036-b12c-4537-bd72-a852069fdff8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Pclass'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "660b095c-6ca9-4538-b9b6-7ccbe7f4baa1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        male\n",
       "1      female\n",
       "2      female\n",
       "3      female\n",
       "4        male\n",
       "        ...  \n",
       "886      male\n",
       "887    female\n",
       "888    female\n",
       "889      male\n",
       "890      male\n",
       "Name: Sex, Length: 891, dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3be7cebc-5e72-400a-8f01-92098c525004",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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