1 NotebookIntroduction_Iris.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 NotebookIntroduction_Iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('IrisMissingData.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 5)\n"
     ]
    },
    {
     "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>class</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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</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        class\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\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src= iris-machinelearning.png style= width:650px>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "Iris-setosa        50\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing Values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    2\n",
       "sepal_width     3\n",
       "petal_length    4\n",
       "petal_width     2\n",
       "class           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna(df.mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['sepal_length'].fillna(df['sepal_length'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df2=df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    0\n",
       "sepal_width     0\n",
       "petal_length    0\n",
       "petal_width     0\n",
       "class           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adity\\anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "C:\\Users\\adity\\anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Sepal Length ka Histogram')"
      ]
     },
     "execution_count": 43,
     "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": [
    "plt.hist(df['sepal_length'],bins=10)\n",
    "plt.xlabel(\"sepal_length\")\n",
    "plt.ylabel(\"counts\")\n",
    "plt.title(\"Sepal Length ka Histogram\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'petal Length ScatterPlot')"
      ]
     },
     "execution_count": 14,
     "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": [
    "plt.scatter(df.index, df.petal_length)\n",
    "plt.xlabel(\"Sampels\")\n",
    "plt.ylabel(\"petal length\")\n",
    "plt.title(\"petal Length ScatterPlot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "fil_s=df['class']=='Iris-setosa'\n",
    "fil_vrs=df['class']=='Iris-versicolor'\n",
    "fil_vig=df['class']=='Iris-virginica'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b488bd29c8>"
      ]
     },
     "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": [
    "plt.scatter(df[fil_s].index, df[fil_s].petal_length, label= 'setosa')\n",
    "plt.scatter(df[fil_vrs].index, df[fil_vrs].petal_length, label= 'versicolor')\n",
    "plt.scatter(df[fil_vig].index, df[fil_vig].petal_length, label= 'virginica')\n",
    "plt.xlabel('Sample') \n",
    "plt.ylabel('Petal Length')\n",
    "plt.title('Petal Length Scatter Plot')\n",
    "plt.legend()\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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
}