D-20_Univariate-Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "183cbe7a",
   "metadata": {},
   "source": [
    "# Analysing individual Cols independently"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e508e3f",
   "metadata": {},
   "source": [
    "## Types of Data\n",
    "<br>\n",
    "<h3>1. Numerical</h3>\n",
    "<h3>2. Categorical</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d9942608",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a8c7220d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('Datasets/titanic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4dbb675a",
   "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>Unnamed: 0</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>0</td>\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>1</td>\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>2</td>\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>3</td>\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>4</td>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  PassengerId  Survived  Pclass  \\\n",
       "0           0            1         0       3   \n",
       "1           1            2         1       1   \n",
       "2           2            3         1       3   \n",
       "3           3            4         1       1   \n",
       "4           4            5         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",
       "   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  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bb3822b",
   "metadata": {},
   "source": [
    "<h1>1. Categorical Data</h1>\n",
    "<h2>a. CountPlot</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5bbad93b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 6,
     "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": [
    "# Survived\n",
    "# sns.countplot(df['Survived'])\n",
    "df['Survived'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bdbae9af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3    491\n",
      "1    216\n",
      "2    184\n",
      "Name: Pclass, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 13,
     "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": [
    "# Pclass\n",
    "print(df['Pclass'].value_counts())\n",
    "df['Pclass'].value_counts().plot(kind='bar') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f3c1ba96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32.204207968574636\n"
     ]
    }
   ],
   "source": [
    "df1=df.groupby('Pclass')['Fare'].mean()\n",
    "print(df['Fare'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "62e6e7ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=df['Pclass']==1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "83d1207a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1       True\n",
       "2      False\n",
       "3       True\n",
       "4      False\n",
       "       ...  \n",
       "886    False\n",
       "887     True\n",
       "888    False\n",
       "889     True\n",
       "890    False\n",
       "Name: Pclass, Length: 891, dtype: bool"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2eaaef67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 24,
     "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": [
    "# Embarked\n",
    "df['Embarked'].value_counts().plot(kind='bar')\n",
    "df['Embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "362b1763",
   "metadata": {},
   "source": [
    "<h3>b. Piechart</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5d2fbb6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Sex'>"
      ]
     },
     "execution_count": 25,
     "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": [
    "df['Sex'].value_counts().plot(kind='pie',autopct='%.2f')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72f67e07",
   "metadata": {},
   "source": [
    "<h1>2. Numerical Data</h1>\n",
    "<h3>a. Histogram</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b477f45f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "37b78ce4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 54.,  46., 177., 169., 118.,  70.,  45.,  24.,   9.,   2.]),\n",
       " array([ 0.42 ,  8.378, 16.336, 24.294, 32.252, 40.21 , 48.168, 56.126,\n",
       "        64.084, 72.042, 80.   ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 30,
     "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": [
    "# Age\n",
    "plt.hist(df['Age'],bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "826123ac",
   "metadata": {},
   "source": [
    "<h3>b. Distplot</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b00f710d",
   "metadata": {},
   "source": [
    "## Probability Distribution Function \n",
    "## Gives probability of x-axis values by a curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9bf80b44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.displot(df['Age'],bins=10,kde=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77c66f71",
   "metadata": {},
   "source": [
    "## c. BoxPlot\n",
    "### min=Q1-1.5*IQR\n",
    "### max=Q3+1.5*IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "069ea4bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Age'>"
      ]
     },
     "execution_count": 5,
     "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(df['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dfe577aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Fare'>"
      ]
     },
     "execution_count": 6,
     "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(df['Fare'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e43866b9",
   "metadata": {},
   "source": [
    "## Generel purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2106bded",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "512.3292\n",
      "49.6934285971809\n",
      "4.787316519674893\n"
     ]
    }
   ],
   "source": [
    "print(df['Fare'].min())\n",
    "print(df['Fare'].max())\n",
    "print(df['Fare'].std())\n",
    "print(df['Fare'].skew()) # If skew==0 the data is perfectly balanced!(Normal Distribution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03e15c43",
   "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": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}