Assignment I.ipynb

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  {
   "cell_type": "markdown",
   "id": "9ab18157",
   "metadata": {},
   "source": [
    "Import neccesary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6129cc5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff0f8386",
   "metadata": {},
   "source": [
    "#To read xls file into python enviornment\n",
    "\n",
    "\n",
    "Read the dataset to python environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "24461ffe",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_excel(\"iris.xls\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e733cc69",
   "metadata": {},
   "source": [
    "# To get information about data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "834c3b8e",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SL</th>\n",
       "      <th>SW</th>\n",
       "      <th>PL</th>\n",
       "      <th>PW</th>\n",
       "      <th>Classification</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",
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      ],
      "text/plain": [
       "    SL   SW   PL   PW Classification\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34e036c0",
   "metadata": {},
   "source": [
    "Display the columns in the dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b563d25b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SL', 'SW', 'PL', 'PW', 'Classification'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a220bb5",
   "metadata": {},
   "source": [
    "Calculate the mean of each column of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5fb80837",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Josmy\\AppData\\Local\\Temp/ipykernel_25920/3698961737.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df.mean()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SL    5.843333\n",
       "SW    3.054000\n",
       "PL    3.758667\n",
       "PW    1.198667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b179a00e",
   "metadata": {},
   "source": [
    "Another way of Calculate the mean of each column of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "800b5888",
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       "      <th></th>\n",
       "      <th>SL</th>\n",
       "      <th>SW</th>\n",
       "      <th>PL</th>\n",
       "      <th>PW</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",
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      ],
      "text/plain": [
       "               SL          SW          PL          PW\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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7984e9f4",
   "metadata": {},
   "source": [
    "By default mean() ignores/exclude NaN/null values while calculating mean or average, you can consider these values by using skipna=False param."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2b7b9f80",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Josmy\\AppData\\Local\\Temp/ipykernel_25920/2957203299.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df.mean(axis=0,skipna=False)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SL    5.843333\n",
       "SW    3.054000\n",
       "PL    3.758667\n",
       "PW    1.198667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(axis=0,skipna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32a1a797",
   "metadata": {},
   "source": [
    "Check for the null values present in the dataset.\n",
    "\n",
    "The following function return boolean value,true if there is any null value in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7a44e853",
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       "      <th>147</th>\n",
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       "      <th>148</th>\n",
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       "<p>150 rows × 5 columns</p>\n",
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       "        SL     SW     PL     PW  Classification\n",
       "0    False  False  False  False           False\n",
       "1    False  False  False  False           False\n",
       "2    False  False  False  False           False\n",
       "3    False  False  False  False           False\n",
       "4    False  False  False  False           False\n",
       "..     ...    ...    ...    ...             ...\n",
       "145  False  False  False  False           False\n",
       "146  False  False  False  False           False\n",
       "147  False  False  False  False           False\n",
       "148  False  False  False  False           False\n",
       "149  False  False  False  False           False\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbb9fd46",
   "metadata": {},
   "source": [
    "The following function return boolean value,false if there is any null value in the dataset.\n",
    "Here notnull function is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7bff1778",
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       "<p>150 rows × 5 columns</p>\n",
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      "text/plain": [
       "       SL    SW    PL    PW  Classification\n",
       "0    True  True  True  True            True\n",
       "1    True  True  True  True            True\n",
       "2    True  True  True  True            True\n",
       "3    True  True  True  True            True\n",
       "4    True  True  True  True            True\n",
       "..    ...   ...   ...   ...             ...\n",
       "145  True  True  True  True            True\n",
       "146  True  True  True  True            True\n",
       "147  True  True  True  True            True\n",
       "148  True  True  True  True            True\n",
       "149  True  True  True  True            True\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.notnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87f2a274",
   "metadata": {},
   "source": [
    "#This code will give you total number of null values in each features in the data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d7ab311d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SL                0\n",
       "SW                0\n",
       "PL                0\n",
       "PW                0\n",
       "Classification    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5498f213",
   "metadata": {},
   "source": [
    "#Perform meaningful visualizations using the dataset. Bring at least 3  visualizations.\n",
    "\n",
    "\n",
    "I\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7a4ffdd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "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": [
    "df['Classification'].hist() # Display the histogram of classification column.From this we can understand three groups of Iris present in the dataset,\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cb4314a",
   "metadata": {},
   "source": [
    "Next visualization of dataset using matplotlib.\n",
    "\n",
    "Matplotlib is comprehensive library for creating static,animated and interactive visualizations in python.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "691869ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e02afa42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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czfsGsqTlknYCO4Cdku6XtKzE954l6VvF8rskrZtlGUn6oKQHJO2QNDQPxzMzy0mZy0QfB9ZExNKIWApcTqPCqJMngXMj4mXAqcCrJa1oWuY1wEnFa4JG1VJyVQ22kUKKAUxy2JcUMZQZDKiK7ZTZRlUDF7Wz6sZVaJ0Ov1bduGrGMikGYKri+Bqk4zyHOMso08/g6xFxVqdpHdZxNHAnsDoi7p42/SPA1oi4qfj8PeCciHik1bq6vUxU1WAbKaQYwCSHfUkRQ5nBgKrYTpltVDVwUTtlOnulGICpiuNrkI7zHOKckqLT2TU0biDfRONZRW8Efgp8GiAi7mnz3RFgO/BC4LqI+IOm+bcA74+IO4vPW4A/iDajqHWbDKoabCOFFAOY5LAvKWIoMxhQFdsps42qBi5qp0xnrxQDMFVxfA3ScZ5DnFPm82yiKacW/76vafqZNJJDyz4HEXEIOFXSccBnJb04Ir49Pb7ZvtY8QdIEjctILFnSXV11VYNtpJBiAJMc9iVFDGUGA6piO2W2UdXARfOVYgCmKo6vQTrOc4izrI73DCLilW1epTqfRcTPgK3Aq5tm7QVOmPZ5MfDwLN+fjIjlEbF8bGyszCYPq2qwjRRSDGCSw76kiKHMYEBVbKfMNqoauGi+UgzAVMXxNUjHeQ5xllWmmuiXJX1M0u3F55Ml/W6J740VZwRIejawCvhu02KfBy4pqopWAPvb3S+Yi6oG20ghxQAmOexLihjKDAZUxXbKbKOqgYvaKdPZK8UATFUcX4N0nOcQZ2ntBjso7ifcDvwmcH/xeRTYWeJ7LwXupVGS+m3gymL6ZcBlxXsB1wF/B+wElnda71wGt6lqsI0UUgxgksO+pIihzGBAVWynzDaqGrionZU3rAyu4vBr5Q0rZyyTYgCmKo6vQTrOc4gzYh6D20yR9L8j4tck3RsRLy+m3RcRpybNSiW505mZWfdSPLX0HyX9c4obu1OXcxLFl4261AIPk1xquFPEUdU6UuzLoBimfU2hzJnBacCHgBfTuNwzBrwhInb0PryZenFmkFMtsDXkUsOdIo6q1pFiXwbFMO1rWfPuZ1CsZBR4EY1r/N+LiKfShdidXiSDnGqBrSGXGu4UcVS1jhT7MiiGaV/LmvNlIkm/Jul4gIg4SOOJpeuB/y7pnyWPtI/qVAs8LHKp4U4RR1Xr6GSYjvNh2tdU2t0z+AjwCwBJZwPvpzHK2X5gsvehVadOtcDDIpca7hRxVLWOTobpOB+mfU2lXTIYiYj/W7x/IzAZEZ+OiP9C4/ESA6NWtcBDIpca7hRxVLWOFPsyKIZpX5NpVXNK42bxaPH+u8DZ0+e1q1ft5Wsu/QzKyKUW2J6WSw13ijiqWkeKfRkUw7SvZTDXfgaS1gLnAY8CS4DTIiIkvRC4Ibp4amlK7mdgZta9Od9Ajoj1wDuB/wG8Ip7OGkcBv5cySLPZpBhHoKpa8yrGochlXwepfj+Xfio5KD3sZS58ZjAcUowjUFWteRXjUOSyr4NUv59LP5WqJOlnkBMng+GQYhyBqmrNqxiHIpd9HaT6/Vz6qVQlxeMozCqXYhyBqmrNqxiHIpd9HaT6/Vz6qeTCycCylGIcgapqzasYhyKXfR2k+v1c+qnkwsnAspRiHIGqas2rGIcil30dpPr9XPqpZKNd3WmOr171M7D8pBhHoKpa8yrGochlXwepfj+XfipVYL7jGeTGN5DNzLrnG8g2JznURqeI4ZTrTkHrdPh1ynWn9CWOFNvJ4Wdig8tnBjZDDrXRKWI45bpT+M6j35kx/eRFJ7Pr8l2VxZFiOzn8TKze3M/AupZDbXSKGLROLefF+8od97nU7+fwM7F682Ui61oOtdE5xFBlHFWMZ2DWjpOBzZBDbXQOMVQZRxXjGZi142RgM+RQG50ihpMXndzV9F7FkWI7OfxMbLA5GdgMF7/kYiYvnGR84ThCjC8cr/xGZYoYdl2+a8Yv/m5uHqeKI8V2cviZ2GDzDWQzsyHgG8iWrRR187nU5rsPgLVSl2NjtN8B2HBqrpvfs38PEzc3nrNT9tJHp3Wk2EZV+2KDqU7Hhi8TWV+kqJvPpTbffQCslZyODV8msiylqJvPpTbffQCslTodG04G1hcp6uZzqc13HwBrpU7HhpOB9UWKuvlcavPdB8BaqdOx4WRgfZGibj6X2nz3AbBW6nRs+AaymdkQ6NsNZEknSPqypN2Sdkl6+yzLnCNpv6T7iteVvYrHzMxa6+VlooPAOyPiV4EVwOWSZnsozNci4tTidXUP4xkIVXTUqkqKDmO57EsKa25dw+jVo2idGL16lDW3rqk8hkFqT+tOzzqdRcQjwCPF+8cl7QaeD8wcbcRKqaKjVlVSdBjLZV9SWHPrGjZu23j486E4dPjzhvM3VBLDILWnda+SewaSlgJfBV4cEY9Nm34O8GlgL/Aw8K6IaPsUsWG+Z1BFR62qpOgwlsu+pDB69SiH4tCM6SMa4eCVByuJYZDa02bqdM+g54+jkHQMjV/4V0xPBIV7gPGIeELSecDngJNmWccEMAGwZEl+9blVqaKjVlVSdBjLZV9SmC0RtJveC4PUnta9npaWSlpAIxFsjojPNM+PiMci4oni/W3AAkmLZlluMiKWR8TysbGxXoactSo6alUlRYexXPYlhRGNdDW9FwapPa17vawmEvAxYHdEfKDFMscXyyHp9CKen/QqprqroqNWVVJ0GMtlX1KYWDbR1fReGKT2tDmIiJ68gFcAAewA7ite5wGXAZcVy7wN2AXcD9wFnNlpvcuWLYthtmnHphi/Zjx0lWL8mvHYtGNTX9aRQqc4ysSZy76ksPqW1TGybiS4ihhZNxKrb1ldeQyD1J52JGBbtPnd6k5nZmZDwE8tHTCDVAeeQ129mTV4cJsaGaQ68Bzq6s3saT4zqJG1W9YeTgRTDjx1gLVb1vYpormb3D7Z1XQz6y0ngxoZpDrwHOrqzexpTgY1Mkh14DnU1ZvZ05wMamSQ6sBzqKs3s6c5GdRInQbK6GTD+RtYvXz14TOBEY2wevlq3zw26xP3MzAzGwLuZ5BIner76xJrXeKsitvD+sn9DEqoU31/XWKtS5xVcXtYv/kyUQl1es57XWKtS5xVcXtYr/kyUQJ1qu+vS6x1ibMqbg/rNyeDEupU31+XWOsSZ1XcHtZvTgYl1Km+vy6x1iXOqrg9rN+cDEqoU31/XWKtS5xVcXtYv/kGspnZEPANZLN5SjHugvsQWO7cz8CsjRTjLrgPgdWBzwzM2kgx7sIgjUNhg8vJwKyNFOMuuA+B1YGTgVkbKcZdcB8CqwMnA7M2Uoy74D4EVgdOBmZtpBh3wX0IrA7cz8DMbAi4n4GZmXXkZGBmZk4GZmbmZGBmZjgZmJkZTgZmZoaTgZmZ4WRgZmb0MBlIOkHSlyXtlrRL0ttnWUaSPijpAUk7JJ3Wq3jMzKy1Xp4ZHATeGRG/CqwALpd0ctMyrwFOKl4TwEZs3jyQipl1q2eD20TEI8AjxfvHJe0Gng98Z9pirwVujMYzMe6SdJyk5xXftTnwQCpmNheV3DOQtBR4OXB306znAz+c9nlvMc3myAOpmNlc9DwZSDoG+DRwRUQ81jx7lq/MeHKepAlJ2yRt27dvXy/CHBgeSMXM5qKnyUDSAhqJYHNEfGaWRfYCJ0z7vBh4uHmhiJiMiOURsXxsbKw3wQ4ID6RiZnPRy2oiAR8DdkfEB1os9nngkqKqaAWw3/cL5scDqZjZXPTsBjJwFvDbwE5J9xXT/hBYAhAR1wO3AecBDwAHgEt7GM9QmLpJvHbLWh7a/xBLFi5h/cr1vnlsZm15cBszsyHgwW3MzKwjJwMzM3MyMDMzJwMzM8PJwMzMqGE1kaR9wJ4+hrAIeLSP2+9GXWJ1nGnVJU6oT6yDEOd4RLTstVu7ZNBvkra1K8/KSV1idZxp1SVOqE+swxCnLxOZmZmTgZmZORnMxWS/A+hCXWJ1nGnVJU6oT6wDH6fvGZiZmc8MzMzMyaAtSSOS7pV0yyzzzpG0X9J9xevKPsX4oKSdRQwznuBXPB78g5IekLRD0mn9iLOIpVOsubTpcZI+Jem7knZLOqNpfhZtWiLOXNrzRdNiuE/SY5KuaFqm721aMs5c2vT3Je2S9G1JN0l6VtP87tszIvxq8QLeAfwlcMss886ZbXofYnwQWNRm/nnA7TRGlVsB3J1xrLm06Q3AvyvePwM4Lsc2LRFnFu3ZFNMI8Pc0at6za9MScfa9TWkMDfwD4NnF508Cb51ve/rMoAVJi4HzgY/2O5Z5ei1wYzTcBRwn6Xn9DipXko4FzqYxMBMR8YuI+FnTYn1v05Jx5mgl8HcR0dxxtO9t2qRVnLkYBZ4taRQ4mpkjRHbdnk4GrV0LvBv4pzbLnCHpfkm3SzqlmrBmCOALkrZLmphl/vOBH077vLeY1g+dYoX+t+kLgH3AXxSXCD8q6TlNy+TQpmXihP63Z7M3ATfNMj2HNp2uVZzQ5zaNiB8BfwY8BDxCY4TILzQt1nV7OhnMQtIFwI8jYnubxe6hcQr5MuBDwOeqiG0WZ0XEacBrgMslnd00X7N8p18lZJ1izaFNR4HTgI0R8XLgH4H3NC2TQ5uWiTOH9jxM0jOAi4C/nm32LNP6cpx2iLPvbSrpl2j85X8i8C+B50h6S/Nis3y1bXs6GczuLOAiSQ8CnwDOlbRp+gIR8VhEPFG8vw1YIGlR1YFGxMPFvz8GPguc3rTIXuCEaZ8XM/OUshKdYs2kTfcCeyPi7uLzp2j80m1ept9t2jHOTNpzutcA90TEP8wyL4c2ndIyzkzadBXwg4jYFxFPAZ8Bzmxapuv2dDKYRUS8NyIWR8RSGqeLX4qIIzKvpOMlqXh/Oo22/EmVcUp6jqTnTr0HXgV8u2mxzwOXFNUFK2icUj5SZZxT8XWKNYc2jYi/B34o6UXFpJXAd5oW63ublokzh/Zs8mZaX3rpe5tO0zLOTNr0IWCFpKOLWFYCu5uW6bo9R3sT62CSdBlARFwPvAFYLekg8HPgTVHcxq/QLwOfLY7NUeAvI+JvmuK8jUZlwQPAAeDSimPsJtYc2hTg94DNxeWC/wNcmmmbdoozl/ZE0tHAbwD/Ydq07Nq0RJx9b9OIuFvSp2hcsjoI3AtMzrc93QPZzMx8mcjMzJwMzMwMJwMzM8PJwMzMcDIwMzOcDGzASFpbPM1xhxpPlfw3idd/jlo/xXbG9ITbPU7Smqq2Z8PH/QxsYKjxCOcLgNMi4smiZ+gz+hxWKscBa4ANfY7DBpTPDGyQPA94NCKeBIiIR6cegSFpmaSvFA/J+19TT3CUtFXStZK+ocaz4U8vpp9eTLu3+PdFLbfahqRXSfqmpHsk/bWkY4rpD0paV0zfKelXiuljkr5YTP+IpD1FUns/8K+Ks53/Vqz+GD09nsHmqZ6xZnPhZGCD5AvACZL+VtIGSb8OIGkBjYeKvSEilgEfB9ZP+95zIuJMGn95f7yY9l3g7OIhcFcCf9xtMMUv8f8MrCoe0LeNxhgZUx4tpm8E3lVMex+Nx5+cRuP5TUuK6e+h8UjlUyPiPxXTXg5cAZxM4ymmZ3Ubo9kUXyaygRERT0haBvxb4JXAX0l6D41fwi8Gvlj88TxC49G/U24qvv9VScdKOg54LnCDpJNoPO1xwRxCWkHjF/XXi+0+A/jmtPmfKf7dDry+eP8K4HVFPH8j6adt1v+tiNgLIOk+YClw5xziNHMysMESEYeArcBWSTuB36Hxy3ZXRJzR6muzfP4j4MsR8TpJS4t1dkvAFyPizS3mP1n8e4in/y92c6nnyWnvp6/DrGu+TGQDQ40xbE+aNulUYA/wPWCsuMGMpAU6clCSNxbTX0Hj6Y77gYXAj4r5b51jSHcBZ0l6YbH+oyX96w7fuRP4zWL5VwG/VEx/nMbZillPOBnYIDmGxqWd70jaQeMSzVUR8QsaT5v8r5LuB+7jyOe//1TSN4Drgd8tpv0p8CeSvk7jslIZKyXtnXoBL6SRSG4q4rkL+JUO61gHvErSPTSeq/8I8HhE/ITG5aZvT7uBbJaMn1pqQ03SVuBdEbGt37EASHomcCgiDhZnMhsj4tQ+h2VDwNcYzfKyBPikpKOAXwD/vs/x2JDwmYGZmfmegZmZORmYmRlOBmZmhpOBmZnhZGBmZjgZmJkZ8P8BVlEgZg6tipwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['SL'],df['SW'],color='green')\n",
    "plt.xlabel(\"Sepal Length\")\n",
    "plt.ylabel('Sepal Width')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9908b07a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x18eb63aef40>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 535.25x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(df, hue =\"Classification\",\n",
    "              height = 6).map(plt.scatter,\n",
    "                              'SL',\n",
    "                              'PL').add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89619b68",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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