Data Analysis (Pandas CaseStudy 4).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "91d84f46",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import matplotlib as mp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "155ac91c",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = pd.read_csv(\"IRIS.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ffa76fd",
   "metadata": {},
   "source": [
    "#### get the starting top data , if the parameter any specific no. is mention than only that no. of data will be displayed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1a6f3c2c",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ad182bc",
   "metadata": {},
   "source": [
    "#### get the total no. of unique value present in every attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ca6e5e96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    35\n",
       "sepal_width     23\n",
       "petal_length    43\n",
       "petal_width     22\n",
       "species          3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1e320d8",
   "metadata": {},
   "source": [
    "#### get the no. of rows & columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9c185d9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nuber of rows:  150\n",
      "nuber of columns:  5\n"
     ]
    }
   ],
   "source": [
    "ds.shape\n",
    "print(\"nuber of rows: \",ds.shape[0])\n",
    "print(\"nuber of columns: \",ds.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "223938f0",
   "metadata": {},
   "source": [
    "#### get the data types of all attributes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "36847cde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    float64\n",
       "sepal_width     float64\n",
       "petal_length    float64\n",
       "petal_width     float64\n",
       "species          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eea914ee",
   "metadata": {},
   "source": [
    "#### get all the attributes's unique value which is present in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b35494e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.1, 4.9, 4.7, 4.6, 5. , 5.4, 4.4, 4.8, 4.3, 5.8, 5.7, 5.2, 5.5,\n",
       "       4.5, 5.3, 7. , 6.4, 6.9, 6.5, 6.3, 6.6, 5.9, 6. , 6.1, 5.6, 6.7,\n",
       "       6.2, 6.8, 7.1, 7.6, 7.3, 7.2, 7.7, 7.4, 7.9])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds['sepal_length'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a159814c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.5, 3. , 3.2, 3.1, 3.6, 3.9, 3.4, 2.9, 3.7, 4. , 4.4, 3.8, 3.3,\n",
       "       4.1, 4.2, 2.3, 2.8, 2.4, 2.7, 2. , 2.2, 2.5, 2.6])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds['sepal_width'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "50860a32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.4, 1.3, 1.5, 1.7, 1.6, 1.1, 1.2, 1. , 1.9, 4.7, 4.5, 4.9, 4. ,\n",
       "       4.6, 3.3, 3.9, 3.5, 4.2, 3.6, 4.4, 4.1, 4.8, 4.3, 5. , 3.8, 3.7,\n",
       "       5.1, 3. , 6. , 5.9, 5.6, 5.8, 6.6, 6.3, 6.1, 5.3, 5.5, 6.7, 6.9,\n",
       "       5.7, 6.4, 5.4, 5.2])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds['petal_length'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e2b420c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.2, 0.4, 0.3, 0.1, 0.5, 0.6, 1.4, 1.5, 1.3, 1.6, 1. , 1.1, 1.8,\n",
       "       1.2, 1.7, 2.5, 1.9, 2.1, 2.2, 2. , 2.4, 2.3])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds['petal_width'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7724f2ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds['species'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0a4a1b9",
   "metadata": {},
   "source": [
    "#### get the total count of each species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8747afca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds['species'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "21148794",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91986\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='species', ylabel='count'>"
      ]
     },
     "execution_count": 47,
     "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": [
    "sb.countplot('species',hue='species',data=ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46afa30b",
   "metadata": {},
   "source": [
    "#### get some information about the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0c0344da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   sepal_length  150 non-null    float64\n",
      " 1   sepal_width   150 non-null    float64\n",
      " 2   petal_length  150 non-null    float64\n",
      " 3   petal_width   150 non-null    float64\n",
      " 4   species       150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "ds.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13ad410e",
   "metadata": {},
   "source": [
    "#### get some information based on statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ed21e28b",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.054000      3.758667     1.198667\n",
       "std        0.828066     0.433594      1.764420     0.763161\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e301526",
   "metadata": {},
   "source": [
    "#### check if there are missing values in this dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2661da35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    0\n",
       "sepal_width     0\n",
       "petal_length    0\n",
       "petal_width     0\n",
       "species         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.isnull().sum()  # there is no missing value in this dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bface31e",
   "metadata": {},
   "source": [
    "#### Plotted some scatterplot to get the clear view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e303577b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sepal_length', ylabel='sepal_width'>"
      ]
     },
     "execution_count": 45,
     "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": [
    "sb.scatterplot(x='sepal_length', y='sepal_width', hue='species', data=ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ce8ae9c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='petal_length', ylabel='petal_width'>"
      ]
     },
     "execution_count": 46,
     "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": [
    "sb.scatterplot(x='petal_length',y='petal_width',hue='species', data=ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5a227a9",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}