Notebooks/02-Pandas-Matplotlib/Pandas and Matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4b4fdfe1-46ad-451b-b813-e48c2b46c057",
   "metadata": {},
   "source": [
    "###### based loosley on the documentation of libraries mentioned "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "266d1d57-6613-437f-a326-18617951772d",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "76ad4406-f1b1-4f53-a195-c2bc2fb1227e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36b468bc-a766-4bb0-a367-daa8015153a8",
   "metadata": {},
   "source": [
    "# Numpy Broadcasting "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6806c777-e493-4814-ba92-e88044f8f2ea",
   "metadata": {},
   "source": [
    "## Scalars and Vectors "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "00b536f7-a6f6-4273-8d1a-2c85808f00a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 40, 60])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([10, 20, 30])\n",
    "b = 2 \n",
    "a * b "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a865fdc6-7a17-429a-b5b7-141ceef4b68a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 40, 60])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.array([2])\n",
    "a * b "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81fb40c1-c9c5-4c8d-ba2c-0cd869ed36cf",
   "metadata": {},
   "source": [
    "## Vectors and Matricies "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "02282f02-4aa0-4667-8371-cf8c049329ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11, 22, 33],\n",
       "       [14, 25, 36]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix = np.array([[1, 2, 3],\n",
    "                   [4, 5, 6]])\n",
    "vector = np.array([10, 20, 30])\n",
    "\n",
    "matrix + vector"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b6109a4-aa7f-4e6c-92d3-5cf948ae5cfb",
   "metadata": {},
   "source": [
    "## Incompatiable shapes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e2337493-cd62-4c76-bbf5-3ed24e0ab36d",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (3,3) (2,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[6], line 6\u001b[0m\n\u001b[1;32m      1\u001b[0m matrix \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([[\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m3\u001b[39m],\n\u001b[1;32m      2\u001b[0m                    [\u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m5\u001b[39m, \u001b[38;5;241m6\u001b[39m],\n\u001b[1;32m      3\u001b[0m                    [\u001b[38;5;241m7\u001b[39m, \u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m9\u001b[39m]])\n\u001b[1;32m      5\u001b[0m vector \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m20\u001b[39m])\n\u001b[0;32m----> 6\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mmatrix\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mvector\u001b[49m\n",
      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (3,3) (2,) "
     ]
    }
   ],
   "source": [
    "matrix = np.array([[1, 2, 3],\n",
    "                   [4, 5, 6],\n",
    "                   [7, 8, 9]])\n",
    "\n",
    "vector = np.array([10, 20])\n",
    "result = matrix + vector"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "928fddb2-f565-47d3-b84a-435588b70250",
   "metadata": {},
   "source": [
    "## Unexpected Behaviour "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "20c963bf-de2c-4a82-9525-d8a3c4d8165c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11, 12, 13],\n",
       "       [24, 25, 26],\n",
       "       [37, 38, 39]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix = np.array([[1, 2, 3],\n",
    "                   [4, 5, 6],\n",
    "                   [7, 8, 9]])\n",
    "\n",
    "column_vector = np.array([[10], [20], [30]])\n",
    "\n",
    "matrix + column_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b3645b53-99e1-47eb-98ae-c6ec8fce8cf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1,2,3]).reshape((3,1)).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8cb6952-436e-4118-a085-a02dc70c7b0d",
   "metadata": {},
   "source": [
    "#### 1. Misaligned Dimensions\n",
    "#### 2. Silent Broadcasting with Different Shapes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a42027bb-3da7-4d27-b214-4f669e66ac5a",
   "metadata": {},
   "source": [
    "## Broadcasting Rules "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "965cd0de-dd11-40ec-b0bd-aa23fda2872d",
   "metadata": {},
   "source": [
    "#### 1.   they are equal\n",
    "#### 2.   one of them is 1\n",
    "#### 3.   One array has fewer dimensions\n",
    "#### 4.  [Documentation link](https://numpy.org/doc/stable/user/basics.broadcasting.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ac19077-9d43-4c4b-89f7-61ed7c575cb1",
   "metadata": {},
   "source": [
    "## Personal Suggestions "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7144f6a2-b4df-4b3c-8fab-07ba9cada971",
   "metadata": {},
   "source": [
    "#### 1. Broadcast when using scalars \n",
    "#### 2. Verify results \n",
    "#### 3. use explicit operations "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8051b4e8-4d7f-4d01-9878-1246952bb267",
   "metadata": {},
   "source": [
    "# Data exploration "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adc04f9a-823f-43fc-b01c-1b9ae91e3d14",
   "metadata": {},
   "source": [
    "## Pandas Data Structures"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd2cf112-e354-4384-ab2f-1bdb4bc64240",
   "metadata": {},
   "source": [
    "### Pandas Series\n",
    "#### one dimentional array with arbitrary data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "676819d8-7df2-4736-bc2d-5ccc8a2a41b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "\n",
    "s = pd.Series([1, 3, 5, np.nan, 6, 8])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8e2d1fa3-1132-42b3-a66f-4f9be8aab314",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14b89867-f416-4943-8787-9e0db1a7564f",
   "metadata": {},
   "source": [
    "### Pandas dataframe  \n",
    "#### two dimentional data structure holding multiple series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "456d2bc3-9b30-466c-9eac-e398026f9a79",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.288143</td>\n",
       "      <td>0.478769</td>\n",
       "      <td>-0.205509</td>\n",
       "      <td>1.465352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.420585</td>\n",
       "      <td>0.024103</td>\n",
       "      <td>-1.311658</td>\n",
       "      <td>0.518876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.283128</td>\n",
       "      <td>0.926661</td>\n",
       "      <td>-0.329657</td>\n",
       "      <td>-0.807062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.931834</td>\n",
       "      <td>1.676377</td>\n",
       "      <td>-1.528005</td>\n",
       "      <td>-2.901596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.275198</td>\n",
       "      <td>-1.678654</td>\n",
       "      <td>0.306297</td>\n",
       "      <td>-0.326512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.693555</td>\n",
       "      <td>-0.301474</td>\n",
       "      <td>1.787025</td>\n",
       "      <td>-0.978977</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0 -0.288143  0.478769 -0.205509  1.465352\n",
       "1  1.420585  0.024103 -1.311658  0.518876\n",
       "2 -0.283128  0.926661 -0.329657 -0.807062\n",
       "3  0.931834  1.676377 -1.528005 -2.901596\n",
       "4  1.275198 -1.678654  0.306297 -0.326512\n",
       "5  0.693555 -0.301474  1.787025 -0.978977"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.random.randn(6, 4)\n",
    "df = pd.DataFrame(data ,  columns=list(\"ABCD\"))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "565f507a-81c4-4dea-8bff-2efd7a1ef77e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              1\n",
       "1              3\n",
       "2              5\n",
       "3            NaN\n",
       "4              6\n",
       "5              8\n",
       "6    some_string\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([1, 3, 5, np.nan, 6, 8 , \"some_string\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7fa1ba8c-52a0-4fd0-aade-5ca0369a6787",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    6.3\n",
       "5    8.4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([1, 3, 5, np.nan, 6.3, 8.4 ])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "585572f6-1d4e-4a38-8ffe-873fcca8070a",
   "metadata": {},
   "source": [
    "#### pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b086d13e-4b0f-4950-acbe-01cb51127bca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A\n",
       "0  1.0\n",
       "1  3.0\n",
       "2  5.0\n",
       "3  NaN\n",
       "4  6.0\n",
       "5  8.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(s, columns=['A'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f939e5ea-761c-4562-8b54-7849330c08a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -0.288143\n",
       "1    1.420585\n",
       "2   -0.283128\n",
       "3    0.931834\n",
       "4    1.275198\n",
       "5    0.693555\n",
       "Name: A, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"A\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "87450073-69cb-4c9a-a2bb-af83975074bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8297b4c6-2081-410d-ad86-3d0ea66bc68b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df[\"A\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7575280a-33cf-4bea-8482-33959019cb19",
   "metadata": {},
   "source": [
    "### Viewing Data "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6e39422-649d-4f8a-806a-c5909ef6df55",
   "metadata": {},
   "source": [
    "#### 1. Head "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6b0fe693-68a5-4999-abab-719172783cf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                  5.1               3.5                1.4               0.2\n",
       "1                  4.9               3.0                1.4               0.2\n",
       "2                  4.7               3.2                1.3               0.2\n",
       "3                  4.6               3.1                1.5               0.2\n",
       "4                  5.0               3.6                1.4               0.2\n",
       "..                 ...               ...                ...               ...\n",
       "145                6.7               3.0                5.2               2.3\n",
       "146                6.3               2.5                5.0               1.9\n",
       "147                6.5               3.0                5.2               2.0\n",
       "148                6.2               3.4                5.4               2.3\n",
       "149                5.9               3.0                5.1               1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "\n",
    "iris = load_iris()\n",
    "\n",
    "iris_df = pd.DataFrame(data=iris.data, columns=iris.feature_names)\n",
    "iris_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "93a0f4b6-507e-4438-baad-321709d392ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
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       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
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       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
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       "    </tr>\n",
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       "      <th>4</th>\n",
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       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cb0d7ac-7e52-4ce6-a7aa-3bf12f077704",
   "metadata": {},
   "source": [
    "#### 2. Tail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b26d4644-7161-4dfc-bd68-ead0f7384cc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
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       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
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       "    <tr>\n",
       "      <th>143</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
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       "      <td>5.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "140                6.7               3.1                5.6               2.4\n",
       "141                6.9               3.1                5.1               2.3\n",
       "142                5.8               2.7                5.1               1.9\n",
       "143                6.8               3.2                5.9               2.3\n",
       "144                6.7               3.3                5.7               2.5\n",
       "145                6.7               3.0                5.2               2.3\n",
       "146                6.3               2.5                5.0               1.9\n",
       "147                6.5               3.0                5.2               2.0\n",
       "148                6.2               3.4                5.4               2.3\n",
       "149                5.9               3.0                5.1               1.8"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8a962ee-025f-4a16-9073-ce555946caed",
   "metadata": {},
   "source": [
    "#### 3. Columns  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1b93f2ef-e88a-444c-a646-0b7b4802452e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n",
       "       'petal width (cm)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "493bda48-3ee3-41eb-b31e-b986dfbd3c6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=150, step=1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "781f549b-12ae-4e77-9a6b-4ca04d5a7aa8",
   "metadata": {},
   "source": [
    "#### 4. numpy backend "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3a9ef1b6-e1d8-42ea-b0eb-f6c258e7d495",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.9, 1.7],\n",
       "       [3.4, 1.4],\n",
       "       [3.4, 1.5],\n",
       "       [2.9, 1.4],\n",
       "       [3.1, 1.5],\n",
       "       [3.7, 1.5],\n",
       "       [3.4, 1.6],\n",
       "       [3. , 1.4],\n",
       "       [3. , 1.1],\n",
       "       [4. , 1.2]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.to_numpy()[5:20][:10][:,1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d8def81-0ff9-4889-9348-0b597e2049ad",
   "metadata": {},
   "source": [
    "#### 5. Describe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ec4e7c2c-de1d-4201-a51a-3277f4c72827",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</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>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</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 (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "count         150.000000        150.000000         150.000000   \n",
       "mean            5.843333          3.057333           3.758000   \n",
       "std             0.828066          0.435866           1.765298   \n",
       "min             4.300000          2.000000           1.000000   \n",
       "25%             5.100000          2.800000           1.600000   \n",
       "50%             5.800000          3.000000           4.350000   \n",
       "75%             6.400000          3.300000           5.100000   \n",
       "max             7.900000          4.400000           6.900000   \n",
       "\n",
       "       petal width (cm)  \n",
       "count        150.000000  \n",
       "mean           1.199333  \n",
       "std            0.762238  \n",
       "min            0.100000  \n",
       "25%            0.300000  \n",
       "50%            1.300000  \n",
       "75%            1.800000  \n",
       "max            2.500000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d54358fa-bcbe-41b5-958f-4502278d2ad7",
   "metadata": {},
   "source": [
    "#### 6. Transpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f00b1c22-2582-41a6-848b-26488d9597d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>sepal length (cm)</th>\n",
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       "      <td>5.9</td>\n",
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       "    <tr>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal length (cm)</th>\n",
       "      <td>1.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>...</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5.7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal width (cm)</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 150 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0    1    2    3    4    5    6    7    8    9    ...  140  \\\n",
       "sepal length (cm)  5.1  4.9  4.7  4.6  5.0  5.4  4.6  5.0  4.4  4.9  ...  6.7   \n",
       "sepal width (cm)   3.5  3.0  3.2  3.1  3.6  3.9  3.4  3.4  2.9  3.1  ...  3.1   \n",
       "petal length (cm)  1.4  1.4  1.3  1.5  1.4  1.7  1.4  1.5  1.4  1.5  ...  5.6   \n",
       "petal width (cm)   0.2  0.2  0.2  0.2  0.2  0.4  0.3  0.2  0.2  0.1  ...  2.4   \n",
       "\n",
       "                   141  142  143  144  145  146  147  148  149  \n",
       "sepal length (cm)  6.9  5.8  6.8  6.7  6.7  6.3  6.5  6.2  5.9  \n",
       "sepal width (cm)   3.1  2.7  3.2  3.3  3.0  2.5  3.0  3.4  3.0  \n",
       "petal length (cm)  5.1  5.1  5.9  5.7  5.2  5.0  5.2  5.4  5.1  \n",
       "petal width (cm)   2.3  1.9  2.3  2.5  2.3  1.9  2.0  2.3  1.8  \n",
       "\n",
       "[4 rows x 150 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b2a8ec5-2fe1-49b3-8b77-d5b6160983c4",
   "metadata": {},
   "source": [
    "#### 7. Info "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "145b5109-1705-4827-a48e-4ed10ae0b8f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 4 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   sepal length (cm)  150 non-null    float64\n",
      " 1   sepal width (cm)   150 non-null    float64\n",
      " 2   petal length (cm)  150 non-null    float64\n",
      " 3   petal width (cm)   150 non-null    float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 4.8 KB\n"
     ]
    }
   ],
   "source": [
    "iris_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07da57d1-0f93-49d1-beb9-393f38b612cf",
   "metadata": {},
   "source": [
    "### Data Access Syntax "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8df8b25a-a048-4dbe-9551-7cf1846a9850",
   "metadata": {},
   "source": [
    "#### 1. Column Access "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7828a081-7708-429c-ac84-b87c29124a0d",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "0   -0.288143\n",
       "1    1.420585\n",
       "2   -0.283128\n",
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       "5    0.693555\n",
       "Name: A, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.A "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "74486dcc-3e5a-4b67-9ddc-7dfaf5e27368",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -0.288143\n",
       "1    1.420585\n",
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       "5    0.693555\n",
       "Name: A, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df[\"A\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d422b0b4-50f5-40ac-9c05-1780e04f587c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      5.1\n",
       "1      4.9\n",
       "2      4.7\n",
       "3      4.6\n",
       "4      5.0\n",
       "      ... \n",
       "145    6.7\n",
       "146    6.3\n",
       "147    6.5\n",
       "148    6.2\n",
       "149    5.9\n",
       "Name: sepal length (cm), Length: 150, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df[\"sepal length (cm)\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5613df45-5211-407f-89da-cd3a6b281649",
   "metadata": {},
   "source": [
    "#### 2. Slicing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0e858495-9383-4059-8c89-5fa6ae814058",
   "metadata": {},
   "outputs": [
    {
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       "          A         B         C         D\n",
       "0 -0.288143  0.478769 -0.205509  1.465352"
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   "source": [
    "df[0:3][:1]"
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   "id": "ba7d52d8-b87d-4dc8-b3e5-43a632f0ccc8",
   "metadata": {},
   "source": [
    "#### 3. Selecting by Labels "
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  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d78275a8-861b-4133-8a35-5a62096497d2",
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   "outputs": [
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       "          A\n",
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   "source": [
    "df.loc[:50,[\"A\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e0759666-8a05-433d-9e0d-d81b9bf73fc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -0.288143\n",
       "1    1.420585\n",
       "2   -0.283128\n",
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     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.loc[:3,\"A\"]"
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  {
   "cell_type": "markdown",
   "id": "ad73d799-9c5b-4f2c-a7ba-0ce729096e1f",
   "metadata": {},
   "source": [
    "#### 4. Selecting by Position "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3a33a1a0-c27c-4dc7-971f-801d79e30e0f",
   "metadata": {},
   "outputs": [
    {
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       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)\n",
       "0                5.1               3.5\n",
       "1                4.9               3.0\n",
       "2                4.7               3.2\n",
       "3                4.6               3.1\n",
       "4                5.0               3.6\n",
       "5                5.4               3.9\n",
       "6                4.6               3.4\n",
       "7                5.0               3.4\n",
       "8                4.4               2.9\n",
       "9                4.9               3.1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.iloc[:10,0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "054e465e-f2db-4b1b-96a3-c0babadec61e",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
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       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2\n",
       "5                5.4               3.9                1.7               0.4\n",
       "6                4.6               3.4                1.4               0.3\n",
       "7                5.0               3.4                1.5               0.2\n",
       "8                4.4               2.9                1.4               0.2\n",
       "9                4.9               3.1                1.5               0.1"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.iloc[:10,[0,1,2,3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6e356190-184f-4331-a800-3d538425e54f",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
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       "      <td>5.0</td>\n",
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       "   sepal length (cm)  sepal width (cm)\n",
       "0                5.1               3.5\n",
       "1                4.9               3.0\n",
       "2                4.7               3.2\n",
       "3                4.6               3.1\n",
       "4                5.0               3.6\n",
       "5                5.4               3.9\n",
       "6                4.6               3.4\n",
       "7                5.0               3.4\n",
       "8                4.4               2.9\n",
       "9                4.9               3.1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "iris_df.iloc[:10,[0,1]]"
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  {
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   "id": "12a1d1b5-944a-4e10-ab34-3a2cc7b7e111",
   "metadata": {},
   "source": [
    "#### 5. Logical Operators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6d033c60-acd7-40e4-a944-ec6658223047",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
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       "      <td>3.5</td>\n",
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       "      <td>5.8</td>\n",
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       "      <td>3.9</td>\n",
       "      <td>1.2</td>\n",
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       "      <th>88</th>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
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       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
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       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
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       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.2</td>\n",
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       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
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       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
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       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
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       "      <td>5.7</td>\n",
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       "      <th>114</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
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       "      <td>5.9</td>\n",
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       "      <td>5.1</td>\n",
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      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "14                 5.8               4.0                1.2               0.2\n",
       "15                 5.7               4.4                1.5               0.4\n",
       "18                 5.7               3.8                1.7               0.3\n",
       "55                 5.7               2.8                4.5               1.3\n",
       "61                 5.9               3.0                4.2               1.5\n",
       "64                 5.6               2.9                3.6               1.3\n",
       "66                 5.6               3.0                4.5               1.5\n",
       "67                 5.8               2.7                4.1               1.0\n",
       "69                 5.6               2.5                3.9               1.1\n",
       "70                 5.9               3.2                4.8               1.8\n",
       "79                 5.7               2.6                3.5               1.0\n",
       "82                 5.8               2.7                3.9               1.2\n",
       "88                 5.6               3.0                4.1               1.3\n",
       "92                 5.8               2.6                4.0               1.2\n",
       "94                 5.6               2.7                4.2               1.3\n",
       "95                 5.7               3.0                4.2               1.2\n",
       "96                 5.7               2.9                4.2               1.3\n",
       "99                 5.7               2.8                4.1               1.3\n",
       "101                5.8               2.7                5.1               1.9\n",
       "113                5.7               2.5                5.0               2.0\n",
       "114                5.8               2.8                5.1               2.4\n",
       "121                5.6               2.8                4.9               2.0\n",
       "142                5.8               2.7                5.1               1.9\n",
       "149                5.9               3.0                5.1               1.8"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = (iris_df.iloc[:,0] >  5.5) & (iris_df.iloc[:,0] < 6.0)\n",
    "# mask = mask & (iris_df.iloc[:,1] >= 4.0)\n",
    "iris_df[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7b40ccf4-fde3-4ffc-818d-d25eed335029",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
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       "      <td>5.1</td>\n",
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       "<p>91 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "14                 5.8               4.0                1.2               0.2\n",
       "15                 5.7               4.4                1.5               0.4\n",
       "18                 5.7               3.8                1.7               0.3\n",
       "50                 7.0               3.2                4.7               1.4\n",
       "51                 6.4               3.2                4.5               1.5\n",
       "..                 ...               ...                ...               ...\n",
       "145                6.7               3.0                5.2               2.3\n",
       "146                6.3               2.5                5.0               1.9\n",
       "147                6.5               3.0                5.2               2.0\n",
       "148                6.2               3.4                5.4               2.3\n",
       "149                5.9               3.0                5.1               1.8\n",
       "\n",
       "[91 rows x 4 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = iris_df[\"sepal length (cm)\"] >  5.5\n",
    "iris_df[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdc55fdb-178a-4ef4-8434-87e60b1e852b",
   "metadata": {},
   "source": [
    "### Missing Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9224de66-cf92-4f59-8397-9df7e13006dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>D</th>\n",
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       "      <th>0</th>\n",
       "      <td>-0.288143</td>\n",
       "      <td>0.478769</td>\n",
       "      <td>-0.205509</td>\n",
       "      <td>1.465352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.420585</td>\n",
       "      <td>0.024103</td>\n",
       "      <td>-1.311658</td>\n",
       "      <td>0.518876</td>\n",
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       "      <th>2</th>\n",
       "      <td>-0.283128</td>\n",
       "      <td>0.926661</td>\n",
       "      <td>-0.329657</td>\n",
       "      <td>-0.807062</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.931834</td>\n",
       "      <td>1.676377</td>\n",
       "      <td>-1.528005</td>\n",
       "      <td>-2.901596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.275198</td>\n",
       "      <td>-1.678654</td>\n",
       "      <td>0.306297</td>\n",
       "      <td>-0.326512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.693555</td>\n",
       "      <td>-0.301474</td>\n",
       "      <td>1.787025</td>\n",
       "      <td>-0.978977</td>\n",
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      "text/plain": [
       "          A         B         C         D\n",
       "0 -0.288143  0.478769 -0.205509  1.465352\n",
       "1  1.420585  0.024103 -1.311658  0.518876\n",
       "2 -0.283128  0.926661 -0.329657 -0.807062\n",
       "3  0.931834  1.676377 -1.528005 -2.901596\n",
       "4  1.275198 -1.678654  0.306297 -0.326512\n",
       "5  0.693555 -0.301474  1.787025 -0.978977"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.copy()\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1fc1d4b8-1375-4bd8-b9aa-a13ca4ee5d8f",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-2.901596</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>NaN</td>\n",
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       "          A         B         C         D   F\n",
       "0 -0.288143  0.478769 -0.205509  1.465352 NaN\n",
       "1  1.420585  0.024103 -1.311658  0.518876 NaN\n",
       "2 -0.283128  0.926661 -0.329657 -0.807062 NaN\n",
       "3  0.931834  1.676377 -1.528005 -2.901596 NaN\n",
       "4  1.275198 -1.678654  0.306297 -0.326512 NaN\n",
       "5  0.693555 -0.301474  1.787025 -0.978977 NaN"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_array = np.full((6), np.nan)\n",
    "df1[\"F\"] = nan_array\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d6167deb-a6e5-4e95-bb39-2e583e33acb0",
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   "outputs": [
    {
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       "          A         B         C         D   E\n",
       "0 -0.288143  0.478769 -0.205509  1.465352 NaN\n",
       "1  1.420585  0.024103 -1.311658  0.518876 NaN\n",
       "2 -0.283128  0.926661 -0.329657 -0.807062 NaN\n",
       "3  0.931834  1.676377 -1.528005 -2.901596 NaN\n",
       "4  1.275198 -1.678654  0.306297 -0.326512 NaN\n",
       "5  0.693555 -0.301474  1.787025 -0.978977 NaN"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "df2[\"E\"] = np.nan\n",
    "df2"
   ]
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   "cell_type": "markdown",
   "id": "f5ddd043-5dcd-4f7b-8e6a-0e4ac44fc711",
   "metadata": {},
   "source": [
    "### Operations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "14a0cd56-32d0-4a97-aa78-1f35f44a00d5",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.863669</td>\n",
       "      <td>3.352754</td>\n",
       "      <td>-3.056009</td>\n",
       "      <td>-5.803192</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hellohello</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.550397</td>\n",
       "      <td>-3.357308</td>\n",
       "      <td>0.612595</td>\n",
       "      <td>-0.653025</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hellohello</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.387110</td>\n",
       "      <td>-0.602948</td>\n",
       "      <td>3.574051</td>\n",
       "      <td>-1.957953</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hellohello</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D   F         AIC\n",
       "0 -0.576287  0.957538 -0.411017  2.930704 NaN  hellohello\n",
       "1  2.841170  0.048206 -2.623316  1.037752 NaN  hellohello\n",
       "2 -0.566256  1.853321 -0.659314 -1.614125 NaN  hellohello\n",
       "3  1.863669  3.352754 -3.056009 -5.803192 NaN  hellohello\n",
       "4  2.550397 -3.357308  0.612595 -0.653025 NaN  hellohello\n",
       "5  1.387110 -0.602948  3.574051 -1.957953 NaN  hellohello"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"AIC\"] = \"hello\"\n",
    "df1 * 2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f5d8687d-7d93-4289-b608-dbb06b30934f",
   "metadata": {},
   "outputs": [
    {
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       "      <td>3.352754</td>\n",
       "      <td>-3.056009</td>\n",
       "      <td>-5.803192</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hellohello</td>\n",
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       "      <td>2.550397</td>\n",
       "      <td>-3.357308</td>\n",
       "      <td>0.612595</td>\n",
       "      <td>-0.653025</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hellohello</td>\n",
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       "      <td>3.574051</td>\n",
       "      <td>-1.957953</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hellohello</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D   F         AIC\n",
       "0 -0.576287  0.957538 -0.411017  2.930704 NaN  hellohello\n",
       "1  2.841170  0.048206 -2.623316  1.037752 NaN  hellohello\n",
       "2 -0.566256  1.853321 -0.659314 -1.614125 NaN  hellohello\n",
       "3  1.863669  3.352754 -3.056009 -5.803192 NaN  hellohello\n",
       "4  2.550397 -3.357308  0.612595 -0.653025 NaN  hellohello\n",
       "5  1.387110 -0.602948  3.574051 -1.957953 NaN  hellohello"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 + df1 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a212a8a9-8d93-467d-87fb-8b74d898c5f9",
   "metadata": {},
   "source": [
    "#### lambda functions and Apply  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "07f76e0d-2424-46c4-bf26-c53f410b267d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "function"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log = lambda x : np.log(x)\n",
    "type(log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4f3d46ce-4c6c-4c47-ba1c-43efaf536cf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4763/832503577.py:1: RuntimeWarning: invalid value encountered in log\n",
      "  log = lambda x : np.log(x)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0         NaN\n",
       "1    0.351069\n",
       "2         NaN\n",
       "3   -0.070600\n",
       "4    0.243102\n",
       "5   -0.365925\n",
       "Name: A, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"A\"].apply(log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "577cd176-827c-4ae4-bbc6-3fff173b91e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    string\n",
       "B    string\n",
       "C    string\n",
       "D    string\n",
       "dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_func = lambda x : \"string\"\n",
    "df.apply(string_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "43066b37-c947-4071-a49e-2cb14c88c5e1",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.093818</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.083027  0.229220  0.042234  2.147257\n",
       "1  2.018062  0.000581  1.720447  0.269232\n",
       "2  0.080161  0.858700  0.108674  0.651350\n",
       "3  0.868315  2.810240  2.334798  8.419258\n",
       "4  1.626131  2.817880  0.093818  0.106610\n",
       "5  0.481019  0.090887  3.193460  0.958395"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda x : x ** 2 )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71c22bc2-074b-4aa2-8abe-a7bc4fa46ec3",
   "metadata": {},
   "source": [
    "#### Deleting Columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4d53288c-691b-4752-ae42-133397e183b7",
   "metadata": {},
   "outputs": [
    {
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       "      <th>B</th>\n",
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       "      <td>0.024103</td>\n",
       "      <td>-1.311658</td>\n",
       "      <td>0.518876</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.926661</td>\n",
       "      <td>-0.329657</td>\n",
       "      <td>-0.807062</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.676377</td>\n",
       "      <td>-1.528005</td>\n",
       "      <td>-2.901596</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.678654</td>\n",
       "      <td>0.306297</td>\n",
       "      <td>-0.326512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.301474</td>\n",
       "      <td>1.787025</td>\n",
       "      <td>-0.978977</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          B         C         D\n",
       "0  0.478769 -0.205509  1.465352\n",
       "1  0.024103 -1.311658  0.518876\n",
       "2  0.926661 -0.329657 -0.807062\n",
       "3  1.676377 -1.528005 -2.901596\n",
       "4 -1.678654  0.306297 -0.326512\n",
       "5 -0.301474  1.787025 -0.978977"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(\"A\",axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd7f4e33-db24-4079-9f4a-49ef9cea506f",
   "metadata": {},
   "source": [
    "#### saving results to file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "feedbc1a-82db-484a-916d-b4b20b5c1747",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.926661</td>\n",
       "      <td>-0.329657</td>\n",
       "      <td>-0.807062</td>\n",
       "      <td>2.0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>-0.326512</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.301474</td>\n",
       "      <td>1.787025</td>\n",
       "      <td>-0.978977</td>\n",
       "      <td>6.0</td>\n",
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      "text/plain": [
       "          B         C         D    E\n",
       "0  0.478769 -0.205509  1.465352  1.0\n",
       "1  0.024103 -1.311658  0.518876  NaN\n",
       "2  0.926661 -0.329657 -0.807062  2.0\n",
       "3  1.676377 -1.528005 -2.901596  NaN\n",
       "4 -1.678654  0.306297 -0.326512  NaN\n",
       "5 -0.301474  1.787025 -0.978977  6.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropped = df.drop(\"A\",axis=1)\n",
    "dropped.to_csv('output.csv', index=False)\n",
    "# dropped.to_csv('output.csv')\n",
    "dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fc5dd950-609d-473b-9a7e-88c9063b57cb",
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>True</td>\n",
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       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       A      B      C      D      E\n",
       "0  False  False  False  False  False\n",
       "1  False  False  False  False   True\n",
       "2  False  False  False  False  False\n",
       "3  False  False  False  False   True\n",
       "4  False  False  False  False   True\n",
       "5  False  False  False  False  False"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"E\"] = np.array([1,np.nan, 2, np.nan , np.nan , 6])\n",
    "df.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "99b3b959-c9ba-452e-8199-6307644ff62c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
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       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
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      "text/plain": [
       "      A     B     C     D      E\n",
       "0  True  True  True  True   True\n",
       "1  True  True  True  True  False\n",
       "2  True  True  True  True   True\n",
       "3  True  True  True  True  False\n",
       "4  True  True  True  True  False\n",
       "5  True  True  True  True   True"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.notna() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0cb3ec5-33c6-486d-82e9-443298e08d60",
   "metadata": {},
   "source": [
    "#### reading data from file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "cbd6e927-0544-4c9c-94f5-1d6403a06aa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>0.478769</td>\n",
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       "      <td>0.024103</td>\n",
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       "      <td>0.518876</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.926661</td>\n",
       "      <td>-0.329657</td>\n",
       "      <td>-0.807062</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.676377</td>\n",
       "      <td>-1.528005</td>\n",
       "      <td>-2.901596</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.678654</td>\n",
       "      <td>0.306297</td>\n",
       "      <td>-0.326512</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.301474</td>\n",
       "      <td>1.787025</td>\n",
       "      <td>-0.978977</td>\n",
       "      <td>6.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          B         C         D    E\n",
       "0  0.478769 -0.205509  1.465352  1.0\n",
       "1  0.024103 -1.311658  0.518876  NaN\n",
       "2  0.926661 -0.329657 -0.807062  2.0\n",
       "3  1.676377 -1.528005 -2.901596  NaN\n",
       "4 -1.678654  0.306297 -0.326512  NaN\n",
       "5 -0.301474  1.787025 -0.978977  6.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"output.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99dbce97-57b5-461f-a5e8-bc98a390a902",
   "metadata": {},
   "source": [
    "# Data Visualization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "956958bc-2c13-4ca4-bea3-3b82721ba049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(range(1,100))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9c610ae0-aa1a-470d-9309-e1d4ea45f1ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = range(1,100) \n",
    "y = log(x)\n",
    "plt.plot(x,y,'go') # you can change shape of your data by adding a third parameter\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "17f07475-5812-4e01-809d-c1cfa9a6a8a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = lambda x : np.array(x) ** 2 +  2\n",
    "x = range(1,100) \n",
    "y = temp(x)\n",
    "plt.plot(x,y,'--') # you can change shape of your data by adding a third parameter\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "5c8aee57-656b-4641-8c98-d635e5c38388",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = iris_df.iloc[:, 0]\n",
    "y = iris_df.iloc[:, 1]\n",
    "plt.plot(x,y, 'y^') # you can change color of your data using a third string parameter\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "d1c77d6c-1754-4e05-9551-0d5927947f73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = iris_df.iloc[:, 0]\n",
    "y = iris_df.iloc[:, 1]\n",
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "2a906e2d-a776-499b-bbb6-4904119abcf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.figure().add_subplot(projection='3d') # project your data into 3d space for visualizing more complex data\n",
    "x = iris_df.iloc[:, 0]\n",
    "y = iris_df.iloc[:, 1]\n",
    "z = iris_df.iloc[:, 3]\n",
    "plt.scatter(x,y,z)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "b1d2f9d4-191a-4e14-9c6f-3dc03486dc06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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 (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</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>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</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 (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "count         150.000000        150.000000         150.000000   \n",
       "mean            5.843333          3.057333           3.758000   \n",
       "std             0.828066          0.435866           1.765298   \n",
       "min             4.300000          2.000000           1.000000   \n",
       "25%             5.100000          2.800000           1.600000   \n",
       "50%             5.800000          3.000000           4.350000   \n",
       "75%             6.400000          3.300000           5.100000   \n",
       "max             7.900000          4.400000           6.900000   \n",
       "\n",
       "       petal width (cm)  \n",
       "count        150.000000  \n",
       "mean           1.199333  \n",
       "std            0.762238  \n",
       "min            0.100000  \n",
       "25%            0.300000  \n",
       "50%            1.300000  \n",
       "75%            1.800000  \n",
       "max            2.500000  "
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "\n",
    "\n",
    "# Create a single figure with all features\n",
    "iris_df.hist(bins=10, figsize=(12, 6), alpha=0.7)\n",
    "plt.suptitle('Histogram of Iris Dataset Features', fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "iris_df.describe()\n"
   ]
  }
 ],
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