vander_plus_3_Pandas_03.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4391ed79",
   "metadata": {},
   "source": [
    "## Операции над данными в библиотеке Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b65d926d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b91d5dfd",
   "metadata": {},
   "source": [
    "### Универсальные ф-ии: сохранение индекса"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bf622a7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.RandomState(42)\n",
    "ser = pd.Series(rng.randint(0, 10, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bb28a552",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6\n",
       "1    3\n",
       "2    7\n",
       "3    4\n",
       "dtype: int32"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "80c0b9c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(rng.randint(0, 10, (3, 4)),\n",
    "                 columns=['A', 'B', 'C', 'D'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5113e7cb",
   "metadata": {},
   "outputs": [
    {
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       "  <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>6</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B  C  D\n",
       "0  6  9  2  6\n",
       "1  7  4  3  7\n",
       "2  7  2  5  4"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "da3aa835",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     403.428793\n",
       "1      20.085537\n",
       "2    1096.633158\n",
       "3      54.598150\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# применение ф-ий NumPy к любому объекту Pandas даст объект Pandas с сохранением индексов\n",
    "np.exp(ser)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6c7f3441",
   "metadata": {},
   "outputs": [
    {
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       "      <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>-1.000000</td>\n",
       "      <td>7.071068e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.707107</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>-7.071068e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.707107</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>1.224647e-16</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "          A             B         C             D\n",
       "0 -1.000000  7.071068e-01  1.000000 -1.000000e+00\n",
       "1 -0.707107  1.224647e-16  0.707107 -7.071068e-01\n",
       "2 -0.707107  1.000000e+00 -0.707107  1.224647e-16"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sin(df * np.pi / 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0b2820e",
   "metadata": {},
   "source": [
    "### Универсальные ф-ии: выравниванеи индексов\n",
    "При бинарных операциях над двумя объектами Series или DataFrame - Pandas будет автоматически выравнивать индексы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b957d55a",
   "metadata": {},
   "outputs": [],
   "source": [
    "area = pd.Series({\n",
    "    'A': 172,\n",
    "    'T': 695,\n",
    "    'C': 423\n",
    "}, name='area')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8159232e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    172\n",
       "T    695\n",
       "C    423\n",
       "Name: area, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b4e88f1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "population = pd.Series({\n",
    "    'C': 3833,\n",
    "    'T': 2644,\n",
    "    'N': 19651\n",
    "}, name='population')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b7627db6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C     3833\n",
       "T     2644\n",
       "N    19651\n",
       "Name: population, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "091a922f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A         NaN\n",
       "C    9.061466\n",
       "N         NaN\n",
       "T    3.804317\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population / area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3600213b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A       NaN\n",
       "C    4256.0\n",
       "N       NaN\n",
       "T    3339.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population + area"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fb876bb",
   "metadata": {},
   "source": [
    "##### Можно изменить поведение по умолчанию, и заменить NaN на иной заполнитель:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f1eedf04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -9999828.0\n",
       "C       4256.0\n",
       "N   -9980349.0\n",
       "T       3339.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population.add(area, fill_value=-10000000)\n",
    "# тоесть когда при выравнивании соответствующее значение партнер не находится, вместо него подставляется заполнитель, \n",
    "# и вычисление продолжается"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5ceb0ac",
   "metadata": {},
   "source": [
    "#### Выравнивание индексов в объектах DataFrame:\n",
    "- При выполнении операций над объектом DataFrame происходит автоматическое выравнивание как столбцов так и индексов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c385d0cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = pd.DataFrame(rng.randint(0, 20, (2,2)),\n",
    "                columns=list('AB'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "73c8aa76",
   "metadata": {},
   "outputs": [
    {
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       "   A   B\n",
       "0  1  11\n",
       "1  5   1"
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   "source": [
    "A"
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  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eae07e61",
   "metadata": {},
   "outputs": [],
   "source": [
    "B = pd.DataFrame(rng.randint(0, 10, (3,3)),\n",
    "                columns=list('BAC'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d903e0b7",
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       "   B  A  C\n",
       "0  4  0  9\n",
       "1  5  8  0\n",
       "2  9  2  6"
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     "execution_count": 24,
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   "source": [
    "B"
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  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c318080b",
   "metadata": {},
   "outputs": [
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       "      A     B   C\n",
       "0   1.0  15.0 NaN\n",
       "1  13.0   6.0 NaN\n",
       "2   NaN   NaN NaN"
      ]
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     "execution_count": 25,
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   "source": [
    "A + B"
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  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "966c8c49",
   "metadata": {},
   "outputs": [],
   "source": [
    "fill = A.stack().mean() # вычислили среднее"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "70e9bcf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.5"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4c52ab2d",
   "metadata": {},
   "outputs": [
    {
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       "      A     B     C\n",
       "0   1.0  15.0  13.5\n",
       "1  13.0   6.0   4.5\n",
       "2   6.5  13.5  10.5"
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     "metadata": {},
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   "source": [
    "A.add(B, fill_value=fill)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9ded1f47",
   "metadata": {},
   "outputs": [
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       "      <td>6.0</td>\n",
       "      <td>-100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-98.0</td>\n",
       "      <td>-91.0</td>\n",
       "      <td>-94.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A     B      C\n",
       "0   1.0  15.0  -91.0\n",
       "1  13.0   6.0 -100.0\n",
       "2 -98.0 -91.0  -94.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.add(B, fill_value=-100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86f1227e",
   "metadata": {},
   "source": [
    "##### Перечень операторов и эквивалентных им методов Pandas:\n",
    "    +    add()\n",
    "    -    sub(), substract()\n",
    "    *    mul(), multiply()\n",
    "    /    truediv(), div(), divide()\n",
    "    //   floordiv()\n",
    "    %    mod()\n",
    "    **   pow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce5effe9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b16fd0e8",
   "metadata": {},
   "source": [
    "### Универсальные ф-ии: выполнение операций м/у объектами DataFrame и Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2b78c163",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = rng.randint(10, size=(3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "34161f94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 8, 2, 4],\n",
       "       [2, 6, 4, 8],\n",
       "       [6, 1, 3, 8]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "da640a52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  0,  0],\n",
       "       [-1, -2,  2,  4],\n",
       "       [ 3, -7,  1,  4]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A - A[0] # в соответствии с правилами транслирования, вычитание строки из массива происходит построчно"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e3e5f856",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(A, columns=list('QRST'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c9a31e58",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   Q  R  S  T\n",
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       "1  2  6  4  8\n",
       "2  6  1  3  8"
      ]
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     "execution_count": 37,
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   "source": [
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f4ac532a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Q    3\n",
       "R    8\n",
       "S    2\n",
       "T    4\n",
       "Name: 0, dtype: int32"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8cddb58d",
   "metadata": {},
   "outputs": [
    {
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       "   Q  R  S  T\n",
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       "1 -1 -2  2  4\n",
       "2  3 -7  1  4"
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     },
     "execution_count": 38,
     "metadata": {},
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   ],
   "source": [
    "df - df.iloc[0] # в Pandas вычитание строки так же происходит построчно, в соответствии с правилами транслирования"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7f82f3f3",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Q  R  S  T\n",
       "0 -5  0 -6 -4\n",
       "1 -4  0 -2  2\n",
       "2  5  0  2  7"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# если же мы хотим выполнить вычитание по столбцам:\n",
    "df.subtract(df['R'], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "58c37834",
   "metadata": {},
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "    Q   R   S   T   0   1   2\n",
       "0 NaN NaN NaN NaN NaN NaN NaN\n",
       "1 NaN NaN NaN NaN NaN NaN NaN\n",
       "2 NaN NaN NaN NaN NaN NaN NaN"
      ]
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     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.subtract(df['R'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "bb7d9b81",
   "metadata": {},
   "outputs": [
    {
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       "    Q   R   S   T   0   1   2\n",
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     "execution_count": 42,
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  {
   "cell_type": "code",
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   "id": "0a111e82",
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    "halfrow = df.iloc[0, ::2]"
   ]
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   "cell_type": "code",
   "execution_count": 46,
   "id": "61187349",
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