Seminar12/Seminar12.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В этом ноутбуке мы познакомимся с основными фичами pandas. Эта библиотека огромна, поэтому мы сможем лишь немного поиграться с доступной функциональностью. Для детального ознакомления стоит почитать документацию"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 1. Первое знакомство"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  # Стандартный алиас для pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Читаем датасет из csv файла\n",
    "coins = pd.read_csv('coins.csv')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>txCount</th>\n",
       "      <th>txVolume</th>\n",
       "      <th>activeAddresses</th>\n",
       "      <th>symbol</th>\n",
       "      <th>name</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>market</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-04-28</td>\n",
       "      <td>135.300000</td>\n",
       "      <td>41702.0</td>\n",
       "      <td>6.879868e+07</td>\n",
       "      <td>117984.0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>135.300000</td>\n",
       "      <td>135.980000</td>\n",
       "      <td>132.100000</td>\n",
       "      <td>134.210000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.500520e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-04-28</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>9174.0</td>\n",
       "      <td>4.431952e+07</td>\n",
       "      <td>17216.0</td>\n",
       "      <td>LTC</td>\n",
       "      <td>Litecoin</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>4.180000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.377340e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>134.440000</td>\n",
       "      <td>51602.0</td>\n",
       "      <td>1.138128e+08</td>\n",
       "      <td>86925.0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>134.440000</td>\n",
       "      <td>147.490000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>144.540000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.491160e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>4.370000</td>\n",
       "      <td>9275.0</td>\n",
       "      <td>3.647810e+07</td>\n",
       "      <td>18395.0</td>\n",
       "      <td>LTC</td>\n",
       "      <td>Litecoin</td>\n",
       "      <td>4.370000</td>\n",
       "      <td>4.570000</td>\n",
       "      <td>4.230000</td>\n",
       "      <td>4.380000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.495270e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-04-30</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>47450.0</td>\n",
       "      <td>8.426632e+07</td>\n",
       "      <td>76871.0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>146.930000</td>\n",
       "      <td>134.050000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.597780e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37583</th>\n",
       "      <td>2018-06-06</td>\n",
       "      <td>0.293325</td>\n",
       "      <td>4830.0</td>\n",
       "      <td>2.312763e+05</td>\n",
       "      <td>659.0</td>\n",
       "      <td>XLM</td>\n",
       "      <td>Stellar</td>\n",
       "      <td>0.293325</td>\n",
       "      <td>0.299955</td>\n",
       "      <td>0.289500</td>\n",
       "      <td>0.298269</td>\n",
       "      <td>51165000.0</td>\n",
       "      <td>5.450080e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37584</th>\n",
       "      <td>2018-06-06</td>\n",
       "      <td>0.039586</td>\n",
       "      <td>7205.0</td>\n",
       "      <td>4.947760e+06</td>\n",
       "      <td>18228.0</td>\n",
       "      <td>XVG</td>\n",
       "      <td>Verge</td>\n",
       "      <td>0.039586</td>\n",
       "      <td>0.039737</td>\n",
       "      <td>0.037680</td>\n",
       "      <td>0.038797</td>\n",
       "      <td>9307450.0</td>\n",
       "      <td>5.959400e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37585</th>\n",
       "      <td>2018-06-06</td>\n",
       "      <td>239.760000</td>\n",
       "      <td>10687.0</td>\n",
       "      <td>3.986308e+07</td>\n",
       "      <td>96516.0</td>\n",
       "      <td>ZEC</td>\n",
       "      <td>Zcash</td>\n",
       "      <td>239.750000</td>\n",
       "      <td>240.340000</td>\n",
       "      <td>229.210000</td>\n",
       "      <td>236.050000</td>\n",
       "      <td>56887000.0</td>\n",
       "      <td>9.769940e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37586</th>\n",
       "      <td>2018-06-06</td>\n",
       "      <td>0.127555</td>\n",
       "      <td>1313.0</td>\n",
       "      <td>1.596436e+07</td>\n",
       "      <td>733.0</td>\n",
       "      <td>ZIL</td>\n",
       "      <td>Zilliqa</td>\n",
       "      <td>0.127555</td>\n",
       "      <td>0.133254</td>\n",
       "      <td>0.124194</td>\n",
       "      <td>0.131766</td>\n",
       "      <td>54667900.0</td>\n",
       "      <td>9.348810e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37587</th>\n",
       "      <td>2018-06-06</td>\n",
       "      <td>1.270000</td>\n",
       "      <td>795.0</td>\n",
       "      <td>6.094435e+06</td>\n",
       "      <td>599.0</td>\n",
       "      <td>ZRX</td>\n",
       "      <td>0x</td>\n",
       "      <td>1.270000</td>\n",
       "      <td>1.340000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.330000</td>\n",
       "      <td>17778000.0</td>\n",
       "      <td>6.741510e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37588 rows × 13 columns</p>\n",
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       "             date       price  txCount      txVolume  activeAddresses symbol  \\\n",
       "0      2013-04-28  135.300000  41702.0  6.879868e+07         117984.0    BTC   \n",
       "1      2013-04-28    4.300000   9174.0  4.431952e+07          17216.0    LTC   \n",
       "2      2013-04-29  134.440000  51602.0  1.138128e+08          86925.0    BTC   \n",
       "3      2013-04-29    4.370000   9275.0  3.647810e+07          18395.0    LTC   \n",
       "4      2013-04-30  144.000000  47450.0  8.426632e+07          76871.0    BTC   \n",
       "...           ...         ...      ...           ...              ...    ...   \n",
       "37583  2018-06-06    0.293325   4830.0  2.312763e+05            659.0    XLM   \n",
       "37584  2018-06-06    0.039586   7205.0  4.947760e+06          18228.0    XVG   \n",
       "37585  2018-06-06  239.760000  10687.0  3.986308e+07          96516.0    ZEC   \n",
       "37586  2018-06-06    0.127555   1313.0  1.596436e+07            733.0    ZIL   \n",
       "37587  2018-06-06    1.270000    795.0  6.094435e+06            599.0    ZRX   \n",
       "\n",
       "           name        open        high         low       close      volume  \\\n",
       "0       Bitcoin  135.300000  135.980000  132.100000  134.210000         0.0   \n",
       "1      Litecoin    4.300000    4.400000    4.180000    4.350000         0.0   \n",
       "2       Bitcoin  134.440000  147.490000  134.000000  144.540000         0.0   \n",
       "3      Litecoin    4.370000    4.570000    4.230000    4.380000         0.0   \n",
       "4       Bitcoin  144.000000  146.930000  134.050000  139.000000         0.0   \n",
       "...         ...         ...         ...         ...         ...         ...   \n",
       "37583   Stellar    0.293325    0.299955    0.289500    0.298269  51165000.0   \n",
       "37584     Verge    0.039586    0.039737    0.037680    0.038797   9307450.0   \n",
       "37585     Zcash  239.750000  240.340000  229.210000  236.050000  56887000.0   \n",
       "37586   Zilliqa    0.127555    0.133254    0.124194    0.131766  54667900.0   \n",
       "37587        0x    1.270000    1.340000    1.250000    1.330000  17778000.0   \n",
       "\n",
       "             market  \n",
       "0      1.500520e+09  \n",
       "1      7.377340e+07  \n",
       "2      1.491160e+09  \n",
       "3      7.495270e+07  \n",
       "4      1.597780e+09  \n",
       "...             ...  \n",
       "37583  5.450080e+09  \n",
       "37584  5.959400e+08  \n",
       "37585  9.769940e+08  \n",
       "37586  9.348810e+08  \n",
       "37587  6.741510e+08  \n",
       "\n",
       "[37588 rows x 13 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Посмотрим что получилось\n",
    "coins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Поясним значения хранящиеся в колонках\n",
    " - date - дата измерений\n",
    " - name - полное название монеты\n",
    " - symbol - сокращенное название монеты\n",
    " - price - средняя цена монеты за торговый день в USD\n",
    " - txCount - количество транзакций в сети данной монеты\n",
    " - txVolume - объем монет переведенных между адресами в сети данной монеты\n",
    " - activeAddresses - количество адресов совершавших а данный день транзации в сети данной монеты\n",
    " - open - цена монеты в начале торгов данного дня\n",
    " - close - цена монеты в конце торгов данного дня\n",
    " - high - самая высокая цена данной монеты в течение данного торгового дня\n",
    " - low - самая низкая цена данной монеты в течение данного торгового дня\n",
    " - volume - объем торгов данной монетой на биржах в данный день\n",
    " - market - капитализация данной монеты в данный день"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "По выводу можно получить общее представление о таблице. Видно, что в таблице могут храниться как числовые так и категориальные (строковые) данные, количество строк и столбцов. Всю эту информацию и многое другое можно получить и в коде."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для удобства мы далее ограничим размер вывода pandas в jupyter notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(37588, 13)"
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coins.shape  # Размер таблицы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
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       "      <th>0</th>\n",
       "      <td>2013-04-28</td>\n",
       "      <td>135.30</td>\n",
       "      <td>41702.0</td>\n",
       "      <td>6.879868e+07</td>\n",
       "      <td>117984.0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>135.30</td>\n",
       "      <td>135.98</td>\n",
       "      <td>132.10</td>\n",
       "      <td>134.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.500520e+09</td>\n",
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       "      <th>1</th>\n",
       "      <td>2013-04-28</td>\n",
       "      <td>4.30</td>\n",
       "      <td>9174.0</td>\n",
       "      <td>4.431952e+07</td>\n",
       "      <td>17216.0</td>\n",
       "      <td>LTC</td>\n",
       "      <td>Litecoin</td>\n",
       "      <td>4.30</td>\n",
       "      <td>4.40</td>\n",
       "      <td>4.18</td>\n",
       "      <td>4.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.377340e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>134.44</td>\n",
       "      <td>51602.0</td>\n",
       "      <td>1.138128e+08</td>\n",
       "      <td>86925.0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>134.44</td>\n",
       "      <td>147.49</td>\n",
       "      <td>134.00</td>\n",
       "      <td>144.54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.491160e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         date   price  txCount      txVolume  activeAddresses symbol  \\\n",
       "0  2013-04-28  135.30  41702.0  6.879868e+07         117984.0    BTC   \n",
       "1  2013-04-28    4.30   9174.0  4.431952e+07          17216.0    LTC   \n",
       "2  2013-04-29  134.44  51602.0  1.138128e+08          86925.0    BTC   \n",
       "\n",
       "       name    open    high     low   close  volume        market  \n",
       "0   Bitcoin  135.30  135.98  132.10  134.21     0.0  1.500520e+09  \n",
       "1  Litecoin    4.30    4.40    4.18    4.35     0.0  7.377340e+07  \n",
       "2   Bitcoin  134.44  147.49  134.00  144.54     0.0  1.491160e+09  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coins.head(3)  #  Первые 3 строки таблицы. Аналогично tail для последних строк"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
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       "      <th>txVolume</th>\n",
       "      <th>activeAddresses</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>market</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>37261.000000</td>\n",
       "      <td>3.606800e+04</td>\n",
       "      <td>3.575800e+04</td>\n",
       "      <td>3.606800e+04</td>\n",
       "      <td>37588.000000</td>\n",
       "      <td>37588.000000</td>\n",
       "      <td>37588.000000</td>\n",
       "      <td>37588.000000</td>\n",
       "      <td>3.758800e+04</td>\n",
       "      <td>3.758800e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>127.395042</td>\n",
       "      <td>1.960144e+04</td>\n",
       "      <td>4.745076e+53</td>\n",
       "      <td>3.295039e+04</td>\n",
       "      <td>125.652051</td>\n",
       "      <td>131.127449</td>\n",
       "      <td>119.791636</td>\n",
       "      <td>125.771519</td>\n",
       "      <td>1.302249e+08</td>\n",
       "      <td>2.720948e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>859.109058</td>\n",
       "      <td>7.702995e+04</td>\n",
       "      <td>8.972837e+55</td>\n",
       "      <td>1.143196e+05</td>\n",
       "      <td>855.436562</td>\n",
       "      <td>890.527305</td>\n",
       "      <td>813.697042</td>\n",
       "      <td>855.954704</td>\n",
       "      <td>8.042239e+08</td>\n",
       "      <td>1.534546e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.059871</td>\n",
       "      <td>3.410000e+02</td>\n",
       "      <td>2.751161e+05</td>\n",
       "      <td>2.920000e+02</td>\n",
       "      <td>0.053032</td>\n",
       "      <td>0.057809</td>\n",
       "      <td>0.049289</td>\n",
       "      <td>0.053147</td>\n",
       "      <td>8.794400e+04</td>\n",
       "      <td>1.356655e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.020000</td>\n",
       "      <td>1.214500e+03</td>\n",
       "      <td>2.007019e+06</td>\n",
       "      <td>1.388000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.949815e+06</td>\n",
       "      <td>1.323890e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.710000</td>\n",
       "      <td>6.247000e+03</td>\n",
       "      <td>1.425408e+07</td>\n",
       "      <td>1.173400e+04</td>\n",
       "      <td>8.772500</td>\n",
       "      <td>9.480000</td>\n",
       "      <td>8.150000</td>\n",
       "      <td>8.810000</td>\n",
       "      <td>1.770732e+07</td>\n",
       "      <td>5.156410e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19475.800000</td>\n",
       "      <td>1.349890e+06</td>\n",
       "      <td>1.696744e+58</td>\n",
       "      <td>1.283929e+06</td>\n",
       "      <td>19475.800000</td>\n",
       "      <td>20089.000000</td>\n",
       "      <td>18974.100000</td>\n",
       "      <td>19497.400000</td>\n",
       "      <td>2.384090e+10</td>\n",
       "      <td>3.261410e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price       txCount      txVolume  activeAddresses  \\\n",
       "count  37261.000000  3.606800e+04  3.575800e+04     3.606800e+04   \n",
       "mean     127.395042  1.960144e+04  4.745076e+53     3.295039e+04   \n",
       "std      859.109058  7.702995e+04  8.972837e+55     1.143196e+05   \n",
       "min        0.000002  0.000000e+00  0.000000e+00     0.000000e+00   \n",
       "25%        0.059871  3.410000e+02  2.751161e+05     2.920000e+02   \n",
       "50%        1.020000  1.214500e+03  2.007019e+06     1.388000e+03   \n",
       "75%        8.710000  6.247000e+03  1.425408e+07     1.173400e+04   \n",
       "max    19475.800000  1.349890e+06  1.696744e+58     1.283929e+06   \n",
       "\n",
       "               open          high           low         close        volume  \\\n",
       "count  37588.000000  37588.000000  37588.000000  37588.000000  3.758800e+04   \n",
       "mean     125.652051    131.127449    119.791636    125.771519  1.302249e+08   \n",
       "std      855.436562    890.527305    813.697042    855.954704  8.042239e+08   \n",
       "min        0.000002      0.000003      0.000002      0.000002  0.000000e+00   \n",
       "25%        0.053032      0.057809      0.049289      0.053147  8.794400e+04   \n",
       "50%        1.000000      1.050000      1.000000      1.000000  1.949815e+06   \n",
       "75%        8.772500      9.480000      8.150000      8.810000  1.770732e+07   \n",
       "max    19475.800000  20089.000000  18974.100000  19497.400000  2.384090e+10   \n",
       "\n",
       "             market  \n",
       "count  3.758800e+04  \n",
       "mean   2.720948e+09  \n",
       "std    1.534546e+10  \n",
       "min    0.000000e+00  \n",
       "25%    1.356655e+07  \n",
       "50%    1.323890e+08  \n",
       "75%    5.156410e+08  \n",
       "max    3.261410e+11  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coins.describe()  # Простейшие статистики для числовых колонок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        135.300000\n",
       "1          4.300000\n",
       "2        134.440000\n",
       "3          4.370000\n",
       "4        144.000000\n",
       "            ...    \n",
       "37583      0.293325\n",
       "37584      0.039586\n",
       "37585    239.760000\n",
       "37586      0.127555\n",
       "37587      1.270000\n",
       "Name: price, Length: 37588, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coins['price']  # Можно выбрать отдельную колонку"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         3.880000\n",
       "1         0.220000\n",
       "2        13.490000\n",
       "3         0.340000\n",
       "4        12.880000\n",
       "           ...    \n",
       "37583     0.010455\n",
       "37584     0.002057\n",
       "37585    11.130000\n",
       "37586     0.009060\n",
       "37587     0.090000\n",
       "Length: 37588, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coins['high'] - coins['low']  # Над колонками работают поэлементные операции"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "coins['spread'] = coins['high'] - coins['low']  # Можно добавлять новые колонки в таблицу"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "del coins['spread']  # Удаляем добавленную колонку. Можно иначе: coins.pop('spread')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Колонки в таблицах pandas ведут себя как массивы неизменяемой длины. Приятное отличие состоит в том, что колонки имеют много полезных для аналитики методов. Например,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LTC      1866\n",
       "BTC      1866\n",
       "DOGE     1635\n",
       "VTC      1599\n",
       "DGB      1582\n",
       "         ... \n",
       "ZIL       133\n",
       "POLY      125\n",
       "CENNZ      86\n",
       "LOOM       85\n",
       "CTXC       52\n",
       "Name: symbol, Length: 66, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coins['symbol'].value_counts() # Количества записей в таблице для каждой монеты"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19475.8"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Самая высокая цена монеты во всей таблице. \n",
    "coins['price'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6.879868e+07\n",
       "1    4.431952e+07\n",
       "2    1.138128e+08\n",
       "3    3.647810e+07\n",
       "Name: txVolume, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Для колонок работают стандартные методы индексации списков, но есть и другие...\n",
    "coins['txVolume'][:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Для колонок определены бинарные операции и методы которые возвращают специальную колонку из булевских значений. Истина будет храниться только для тех индексов для которых выполнено условие. Лучше объяснит пример"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         True\n",
       "1         True\n",
       "2         True\n",
       "3         True\n",
       "4         True\n",
       "         ...  \n",
       "37583     True\n",
       "37584     True\n",
       "37585     True\n",
       "37586     True\n",
       "37587    False\n",
       "Name: txCount, Length: 37588, dtype: bool"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Строки колонки для которых txCount был больше 1000\n",
    "coins['txCount'] > 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         True\n",
       "1        False\n",
       "2         True\n",
       "3        False\n",
       "4         True\n",
       "         ...  \n",
       "37583    False\n",
       "37584    False\n",
       "37585    False\n",
       "37586    False\n",
       "37587    False\n",
       "Name: symbol, Length: 37588, dtype: bool"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Строки в которых значение попадает в заранее заданный набор\n",
    "coins['symbol'].isin(['BTC', 'ADA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         True\n",
       "1         True\n",
       "2         True\n",
       "3         True\n",
       "4         True\n",
       "         ...  \n",
       "37583    False\n",
       "37584    False\n",
       "37585     True\n",
       "37586    False\n",
       "37587    False\n",
       "Name: symbol, Length: 37588, dtype: bool"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Строки колонки в которых имя монеты содержит букву 'C' \n",
    "coins['symbol'].str.contains('C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         True\n",
       "1        False\n",
       "2         True\n",
       "3        False\n",
       "4         True\n",
       "         ...  \n",
       "37583    False\n",
       "37584    False\n",
       "37585    False\n",
       "37586    False\n",
       "37587    False\n",
       "Length: 37588, dtype: bool"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Для колонок можно составлять сложные логические выражения (скобки обязательны!)\n",
    "((coins['price'] > 18000) | (coins['market'] > 200000)) & (coins['symbol'] == 'BTC')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Основное назначение получающихся булевых колонок в том, что они могут быть использованы как индексирующий объект. Эдакий продвинутый slicing для колонок и таблиц"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>txCount</th>\n",
       "      <th>txVolume</th>\n",
       "      <th>activeAddresses</th>\n",
       "      <th>symbol</th>\n",
       "      <th>name</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>market</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013-05-04</td>\n",
       "      <td>98.10</td>\n",
       "      <td>50102.0</td>\n",
       "      <td>3.896570e+07</td>\n",
       "      <td>69959.0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>98.10</td>\n",
       "      <td>115.00</td>\n",
       "      <td>92.50</td>\n",
       "      <td>112.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.089890e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2013-06-16</td>\n",
       "      <td>99.80</td>\n",
       "      <td>42781.0</td>\n",
       "      <td>4.696943e+07</td>\n",
       "      <td>48785.0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>99.80</td>\n",
       "      <td>101.60</td>\n",
       "      <td>98.95</td>\n",
       "      <td>99.51</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.127120e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2013-06-17</td>\n",
       "      <td>99.90</td>\n",
       "      <td>45798.0</td>\n",
       "      <td>5.455223e+07</td>\n",
       "      <td>56902.0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>99.90</td>\n",
       "      <td>102.21</td>\n",
       "      <td>99.00</td>\n",
       "      <td>101.70</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.128720e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2013-06-29</td>\n",
       "      <td>94.66</td>\n",
       "      <td>36376.0</td>\n",
       "      <td>4.519440e+07</td>\n",
       "      <td>60980.0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>94.66</td>\n",
       "      <td>99.99</td>\n",
       "      <td>93.00</td>\n",
       "      <td>94.99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.074010e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>2013-06-30</td>\n",
       "      <td>95.00</td>\n",
       "      <td>30800.0</td>\n",
       "      <td>3.584930e+07</td>\n",
       "      <td>50485.0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>95.00</td>\n",
       "      <td>98.12</td>\n",
       "      <td>94.22</td>\n",
       "      <td>96.61</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.078260e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>2013-07-25</td>\n",
       "      <td>94.50</td>\n",
       "      <td>42384.0</td>\n",
       "      <td>7.153660e+07</td>\n",
       "      <td>66440.0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>94.50</td>\n",
       "      <td>97.33</td>\n",
       "      <td>93.87</td>\n",
       "      <td>96.90</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.082800e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>2013-07-26</td>\n",
       "      <td>96.95</td>\n",
       "      <td>37209.0</td>\n",
       "      <td>7.056459e+07</td>\n",
       "      <td>62558.0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>96.95</td>\n",
       "      <td>97.47</td>\n",
       "      <td>96.00</td>\n",
       "      <td>96.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.111160e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>2013-07-27</td>\n",
       "      <td>96.02</td>\n",
       "      <td>36711.0</td>\n",
       "      <td>4.158844e+07</td>\n",
       "      <td>52438.0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>96.02</td>\n",
       "      <td>97.00</td>\n",
       "      <td>93.00</td>\n",
       "      <td>94.12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.100900e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>2013-07-28</td>\n",
       "      <td>94.40</td>\n",
       "      <td>37134.0</td>\n",
       "      <td>5.463939e+07</td>\n",
       "      <td>49179.0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>94.40</td>\n",
       "      <td>100.58</td>\n",
       "      <td>94.00</td>\n",
       "      <td>99.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.082750e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>2013-07-29</td>\n",
       "      <td>98.60</td>\n",
       "      <td>41634.0</td>\n",
       "      <td>7.030931e+07</td>\n",
       "      <td>67473.0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>98.60</td>\n",
       "      <td>102.50</td>\n",
       "      <td>98.45</td>\n",
       "      <td>101.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.131340e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>34 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  price  txCount      txVolume  activeAddresses symbol  \\\n",
       "12   2013-05-04  98.10  50102.0  3.896570e+07          69959.0    BTC   \n",
       "98   2013-06-16  99.80  42781.0  4.696943e+07          48785.0    BTC   \n",
       "100  2013-06-17  99.90  45798.0  5.455223e+07          56902.0    BTC   \n",
       "124  2013-06-29  94.66  36376.0  4.519440e+07          60980.0    BTC   \n",
       "126  2013-06-30  95.00  30800.0  3.584930e+07          50485.0    BTC   \n",
       "..          ...    ...      ...           ...              ...    ...   \n",
       "176  2013-07-25  94.50  42384.0  7.153660e+07          66440.0    BTC   \n",
       "178  2013-07-26  96.95  37209.0  7.056459e+07          62558.0    BTC   \n",
       "180  2013-07-27  96.02  36711.0  4.158844e+07          52438.0    BTC   \n",
       "182  2013-07-28  94.40  37134.0  5.463939e+07          49179.0    BTC   \n",
       "184  2013-07-29  98.60  41634.0  7.030931e+07          67473.0    BTC   \n",
       "\n",
       "        name   open    high    low   close  volume        market  \n",
       "12   Bitcoin  98.10  115.00  92.50  112.50     0.0  1.089890e+09  \n",
       "98   Bitcoin  99.80  101.60  98.95   99.51     0.0  1.127120e+09  \n",
       "100  Bitcoin  99.90  102.21  99.00  101.70     0.0  1.128720e+09  \n",
       "124  Bitcoin  94.66   99.99  93.00   94.99     0.0  1.074010e+09  \n",
       "126  Bitcoin  95.00   98.12  94.22   96.61     0.0  1.078260e+09  \n",
       "..       ...    ...     ...    ...     ...     ...           ...  \n",
       "176  Bitcoin  94.50   97.33  93.87   96.90     0.0  1.082800e+09  \n",
       "178  Bitcoin  96.95   97.47  96.00   96.02     0.0  1.111160e+09  \n",
       "180  Bitcoin  96.02   97.00  93.00   94.12     0.0  1.100900e+09  \n",
       "182  Bitcoin  94.40  100.58  94.00   99.76     0.0  1.082750e+09  \n",
       "184  Bitcoin  98.60  102.50  98.45  101.20     0.0  1.131340e+09  \n",
       "\n",
       "[34 rows x 13 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Выбрать из таблицы записи когда цена биткоина была меньше 100 USD\n",
    "coins[(coins['price'] < 100) & (coins['symbol'] == 'BTC')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1         9174.0\n",
       "3         9275.0\n",
       "5         9099.0\n",
       "7         8990.0\n",
       "9         8031.0\n",
       "          ...   \n",
       "37277    22879.0\n",
       "37347    22155.0\n",
       "37417    24326.0\n",
       "37487    25172.0\n",
       "37557    25346.0\n",
       "Name: txCount, Length: 1866, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Количество транзакций лайткойна\n",
    "coins['txCount'][coins['symbol'] == 'LTC']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Задание**: У какой монеты была самая высокая цена?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Для таблиц есть индексирование и по строкам и по колонкам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27257</th>\n",
       "      <td>0.015788</td>\n",
       "      <td>283717000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27322</th>\n",
       "      <td>0.018773</td>\n",
       "      <td>288184000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27387</th>\n",
       "      <td>0.017664</td>\n",
       "      <td>235114000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27452</th>\n",
       "      <td>0.015569</td>\n",
       "      <td>115255000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27517</th>\n",
       "      <td>0.013560</td>\n",
       "      <td>100243000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27582</th>\n",
       "      <td>0.013153</td>\n",
       "      <td>62722000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27647</th>\n",
       "      <td>0.013339</td>\n",
       "      <td>56971300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27712</th>\n",
       "      <td>0.013653</td>\n",
       "      <td>55890600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27777</th>\n",
       "      <td>0.013187</td>\n",
       "      <td>65899000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27842</th>\n",
       "      <td>0.011779</td>\n",
       "      <td>62765500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           high       volume\n",
       "27257  0.015788  283717000.0\n",
       "27322  0.018773  288184000.0\n",
       "27387  0.017664  235114000.0\n",
       "27452  0.015569  115255000.0\n",
       "27517  0.013560  100243000.0\n",
       "27582  0.013153   62722000.0\n",
       "27647  0.013339   56971300.0\n",
       "27712  0.013653   55890600.0\n",
       "27777  0.013187   65899000.0\n",
       "27842  0.011779   62765500.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Выбрать из таблицы максимальную цену и объем торгов за дни когда цена дож была выше 1 цента\n",
    "coins.loc[\n",
    "    (coins['symbol'] == 'DOGE') & (coins['price'] > 0.01),\n",
    "    ['high', 'volume']\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Начиная с этого момента вы уже можете делать простейшие выборки из данных и немного их преобразовывать. Конечно, не забывайте  использовать help() и tab-completion чтобы узнать больше о возможностях pandas. В голове стоит держать такое правило: если вам очень хочется пройтись циклом по таблице и что-то сделать с данными, очень вероятно, что вы делаете что-то неправильно и, скорее всего, в pandas есть инструмент для вашей задачи. Оставшаяся часть ноутбука посвящена перечислению фич, и может показаться скучной, но не зная этих фич вы каждый раз будете переизобретать велосипед."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 2. Глобальные настройки"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Возможно вам захочется сконфигурировать работу pandas. Большинство глобальных настроек касаются отображения таблицы или данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Узнать максимальное кол-во отображаемых в таблице с\n",
    "pd.get_option('display.max_columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "display.max_columns : int\n",
      "    If max_cols is exceeded, switch to truncate view. Depending on\n",
      "    `large_repr`, objects are either centrally truncated or printed as\n",
      "    a summary view. 'None' value means unlimited.\n",
      "\n",
      "    In case python/IPython is running in a terminal and `large_repr`\n",
      "    equals 'truncate' this can be set to 0 and pandas will auto-detect\n",
      "    the width of the terminal and print a truncated object which fits\n",
      "    the screen width. The IPython notebook, IPython qtconsole, or IDLE\n",
      "    do not run in a terminal and hence it is not possible to do\n",
      "    correct auto-detection.\n",
      "    [default: 20] [currently: 20]\n"
     ]
    }
   ],
   "source": [
    "# Узнать больше про опцию. Список всех опций тут: \n",
    "# https://pandas.pydata.org/pandas-docs/version/0.23.4/options.html#available-options\n",
    "pd.describe_option('display.max_columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Назначть максимальное число отображаемых строк\n",
    "pd.set_option('display.max_columns', 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 3. Типы данных в pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В pandas есть два основных типа данных. pandas.Series и pandas.DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### 3.1 pandas.Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Про pandas.Series стоит думать как про одномерный массив фиксированного размера из данных одного типа. В отличие от массивов доступ к элементам может быть по нечисловому индексу. Индекс стоит понимать как имена строк.\n",
    "\n",
    "Объекты типа pandas.Series очень часто используются как возвращаемые значения в pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     BTC\n",
       "1     LTC\n",
       "2    DOGE\n",
       "3    DASH\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pandas.Series хранящий буквы со стандартной индексацией\n",
    "pd.Series(data=['BTC', 'LTC', 'DOGE', 'DASH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first_coin     BTC\n",
       "second_coin    ETH\n",
       "third_coin     XRP\n",
       "dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pandas.Series хранящий числа типа str cо специальным индексом\n",
    "pd.Series(data=['BTC', 'ETH', 'XRP'], \n",
    "          index=['first_coin', 'second_coin', 'third_coin'], dtype=str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first_coin     BTC\n",
       "second_coin    ETH\n",
       "third_coin     XRP\n",
       "dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pandas.Series созданный из словаря\n",
    "pd.Series({'first_coin': 'BTC', 'second_coin': 'ETH', 'third_coin': 'XRP'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Стандартная индексация\n",
    "sequence = pd.Series(data=['!', 'the', 'Moon', 'To'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3      To\n",
       "1     the\n",
       "2    Moon\n",
       "0       !\n",
       "0       !\n",
       "0       !\n",
       "dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence[[3, 1, 2, 0, 0, 0]]  # Доступ к нескольким элементам по их позициям "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'To'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence[3]  # Доступ к элементу по позиции"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Спец индексация\n",
    "sequence = pd.Series(data=['!', 'the', 'Moon', 'To'], \n",
    "                     index=['04-11-2018', '02-11-2018', '03-11-2018', '01-11-2018'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "01-11-2018      To\n",
       "02-11-2018     the\n",
       "03-11-2018    Moon\n",
       "04-11-2018       !\n",
       "dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence[['01-11-2018', '02-11-2018', '03-11-2018', '04-11-2018']]  # Доступ к нескольким элементам по их индексам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'To'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence['01-11-2018']  # Доступ к элементу по индексу"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Чтобы не засорять global namespace\n",
    "del sequence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### 3.2 pandas.DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данные типа pandas.DataFrame это двумерный массив (переменного размера) разнородных данных (но однородных по колонкам). Лучше всего предстаставлять себе pandas.DataFrame как набор колонок, где каждая колонка это pandas.Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BTC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LTC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XRP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0\n",
       "0  BTC\n",
       "1  LTC\n",
       "2  XRP"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Создание простейшей таблицы\n",
    "pd.DataFrame(data=['BTC', 'LTC', 'XRP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BTC</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LTC</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XRP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  symbol  price\n",
       "0    BTC  10000\n",
       "1    LTC    200\n",
       "2    XRP      1"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cоздание таблицы с именами колонок\n",
    "pd.DataFrame(data=[['BTC', 10000],['LTC', 200],['XRP', 1]], \n",
    "             columns=['symbol', 'price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>BTC</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-11</th>\n",
       "      <td>LTC</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-23</th>\n",
       "      <td>XRP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol  price\n",
       "2018-01-11    BTC  10000\n",
       "2018-03-11    LTC    200\n",
       "2018-08-23    XRP      1"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cоздание таблицы с именами колонок и указанием индекса\n",
    "pd.DataFrame(\n",
    "    data=[['BTC', 10000],['LTC', 200],['XRP', 1]],\n",
    "    columns=['symbol', 'price'],\n",
    "    # DatetimeIndex - это специальный тип данных для индексов содержащих время\n",
    "    index=pd.DatetimeIndex(['01-11-2018', '03-11-2018', '23-08-2018'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>BTC</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-11</th>\n",
       "      <td>LTC</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol  price\n",
       "2018-01-11    BTC  10000\n",
       "2018-03-11    LTC    200"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Создание по готовым pandas.Series\n",
    "pd.DataFrame({\n",
    "    'symbol': pd.Series(data=['BTC','LTC'], index=pd.DatetimeIndex(['01-11-2018', '03-11-2018'])),\n",
    "    'price': pd.Series(data=[10000, 200], index=pd.DatetimeIndex(['01-11-2018', '03-11-2018']))\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Некоторые функции в pandas одинаково применимы как к строкам так и к столбцам. В этом случае, в функцию можно передать аргумент axis. Значение 0 идет по умолчанию и ему соответствует работа по строкам, 1 - по колонкам."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 4. Базовые методы pandas.Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на самые базовые методы класса pandas.Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequence = pd.Series(data=['BTC', 'LTC', 'DOGE', 'DASH'], index=['leader', 'alternative', 'joke', 'bouncer'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Index(['leader', 'alternative', 'joke', 'bouncer'], dtype='object')]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Индекс наших данных\n",
    "sequence.axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Тип наших данных\n",
    "sequence.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Проверка на пустоту\n",
    "sequence.empty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Количество размерностей. Для pandas.Series всегда 1\n",
    "sequence.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Количество элементов\n",
    "sequence.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Размеры\n",
    "sequence.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BTC', 'LTC', 'DOGE', 'DASH'], dtype=object)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Получить данные в виде numpy массива. Низкоуровневое представление с точки зрения pandas\n",
    "sequence.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "leader          BTC\n",
       "alternative     LTC\n",
       "joke           DOGE\n",
       "dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Получить первые 3 элемента\n",
    "sequence.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "joke       DOGE\n",
       "bouncer    DASH\n",
       "dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Получить последние 2 элемента\n",
    "sequence.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "del sequence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 5. Базовые методы pandas.DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Класс DataFrame огромен. Мы рассмотрим только самые часто используемые в нем методы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>BTC</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-11</th>\n",
       "      <td>LTC</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-23</th>\n",
       "      <td>XRP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol  price\n",
       "2018-01-11    BTC  10000\n",
       "2018-03-11    LTC    200\n",
       "2018-08-23    XRP      1"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = pd.DataFrame(\n",
    "    data=[['BTC', 10000],['LTC', 200],['XRP', 1]],\n",
    "    columns=['symbol', 'price'],\n",
    "    # DatetimeIndex - это специальный тип данных для индексов содержащих время\n",
    "    index=pd.DatetimeIndex(['01-11-2018', '03-11-2018', '23-08-2018'])\n",
    ")\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2018-01-11</th>\n",
       "      <th>2018-03-11</th>\n",
       "      <th>2018-08-23</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <td>BTC</td>\n",
       "      <td>LTC</td>\n",
       "      <td>XRP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>10000</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       2018-01-11 2018-03-11 2018-08-23\n",
       "symbol        BTC        LTC        XRP\n",
       "price       10000        200          1"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Транспонировать таблицу\n",
    "table.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[DatetimeIndex(['2018-01-11', '2018-03-11', '2018-08-23'], dtype='datetime64[ns]', freq=None),\n",
       " Index(['symbol', 'price'], dtype='object')]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Получить индексы срок и столбцов\n",
    "table.axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symbol    object\n",
       "price      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Получить типы данных в колонках\n",
    "table.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Проверка на пустоту\n",
    "table.empty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Размеры\n",
    "table.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Количество элементов\n",
    "table.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['BTC', 10000],\n",
       "       ['LTC', 200],\n",
       "       ['XRP', 1]], dtype=object)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Низкоуровневое представление таблицы\n",
    "table.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>BTC</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-11</th>\n",
       "      <td>LTC</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-23</th>\n",
       "      <td>XRP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol  price\n",
       "2018-01-11    BTC  10000\n",
       "2018-03-11    LTC    200\n",
       "2018-08-23    XRP      1"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Получить первые 3 строки\n",
    "table.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-11</th>\n",
       "      <td>LTC</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-23</th>\n",
       "      <td>XRP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol  price\n",
       "2018-03-11    LTC    200\n",
       "2018-08-23    XRP      1"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Получить последние 2 строки\n",
    "table.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "del table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 6. Работа со строками и столбцами"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Обращаться к строкам таблицы можно по номеру или по индексу"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>BTC</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-11</th>\n",
       "      <td>LTC</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-23</th>\n",
       "      <td>XRP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-11</th>\n",
       "      <td>BTC</td>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol  price\n",
       "2018-01-11    BTC  10000\n",
       "2018-03-11    LTC    200\n",
       "2018-08-23    XRP      1\n",
       "2018-02-11    BTC   9000"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = pd.DataFrame(\n",
    "    data=[['BTC', 10000],['LTC', 200],['XRP', 1], ['BTC', 9000]],\n",
    "    columns=['symbol', 'price'],\n",
    "    # DatetimeIndex - это специальный тип данных для индексов содержащих время\n",
    "    index=pd.DatetimeIndex(['01-11-2018', '03-11-2018', '23-08-2018', '02-11-2018'])\n",
    ")\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### 6.1. Доступ к элементам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symbol    XRP\n",
       "price       1\n",
       "Name: 2018-08-23 00:00:00, dtype: object"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Обращение по номеру\n",
    "table.iloc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-11</th>\n",
       "      <td>LTC</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-23</th>\n",
       "      <td>XRP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol  price\n",
       "2018-03-11    LTC    200\n",
       "2018-08-23    XRP      1"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Обращение по диапазону номеров\n",
    "table[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symbol      BTC\n",
       "price     10000\n",
       "Name: 2018-01-11 00:00:00, dtype: object"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Обращение по индексу\n",
    "table.loc[pd.Timestamp('01-11-2018')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>BTC</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-11</th>\n",
       "      <td>LTC</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-11</th>\n",
       "      <td>BTC</td>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol  price\n",
       "2018-01-11    BTC  10000\n",
       "2018-03-11    LTC    200\n",
       "2018-02-11    BTC   9000"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Обращение по диапазону индексов\n",
    "table.loc['01-11-2018':'03-11-2018']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### 6.2. Добавление, удаление, переименование колонок и строк"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-11</th>\n",
       "      <td>LTC</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-11</th>\n",
       "      <td>BTC</td>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol  price\n",
       "2018-03-11    LTC    200\n",
       "2018-02-11    BTC   9000"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Удаление строк по их индексам. Можно использовать для удаления колонок, указав axis=1\n",
    "# Посмотрите еще методы drop_duplicates and dropna\n",
    "table.drop([pd.Timestamp('2018-08-23'), pd.Timestamp('2018-01-11')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>price</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>BTC</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.000000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-11</th>\n",
       "      <td>LTC</td>\n",
       "      <td>200</td>\n",
       "      <td>1.000000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-23</th>\n",
       "      <td>XRP</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-11</th>\n",
       "      <td>BTC</td>\n",
       "      <td>9000</td>\n",
       "      <td>1.000000e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol  price        volume\n",
       "2018-01-11    BTC  10000  1.000000e+09\n",
       "2018-03-11    LTC    200  1.000000e+06\n",
       "2018-08-23    XRP      1  1.000000e+04\n",
       "2018-02-11    BTC   9000  1.000000e+09"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Чтобы добавить новую колонку достаточно присвоить ей значение\n",
    "table['volume'] = [1e9, 1e6, 1e4, 1e9]\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Удалять колонки очень просто\n",
    "del table['volume']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>BTC</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-11</th>\n",
       "      <td>LTC</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-23</th>\n",
       "      <td>XRP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-11</th>\n",
       "      <td>BTC</td>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Symbol  Price\n",
       "2018-01-11    BTC  10000\n",
       "2018-03-11    LTC    200\n",
       "2018-08-23    XRP      1\n",
       "2018-02-11    BTC   9000"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Переименовывание колонок\n",
    "table.rename(columns={'symbol': 'Symbol', 'price': 'Price'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### 6.3. Итерирование"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Итерироваться по таблице можно разными способами, но обычно так не стоит делать и лучше написать решение в виде манипуляций над столбцами/колонками "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "symbol\n",
      "price\n"
     ]
    }
   ],
   "source": [
    "# По колонкам\n",
    "for column in table:\n",
    "    print(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "symbol\n",
      "2018-01-11    BTC\n",
      "2018-03-11    LTC\n",
      "2018-08-23    XRP\n",
      "2018-02-11    BTC\n",
      "Name: symbol, dtype: object\n",
      "price\n",
      "2018-01-11    10000\n",
      "2018-03-11      200\n",
      "2018-08-23        1\n",
      "2018-02-11     9000\n",
      "Name: price, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# По колонкам как по словарю. Значения в \"словаре\" это pandas.Series\n",
    "for column, series in table.iteritems():\n",
    "    print(column)\n",
    "    print(series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-01-11 00:00:00\n",
      "symbol      BTC\n",
      "price     10000\n",
      "Name: 2018-01-11 00:00:00, dtype: object\n",
      "2018-03-11 00:00:00\n",
      "symbol    LTC\n",
      "price     200\n",
      "Name: 2018-03-11 00:00:00, dtype: object\n",
      "2018-08-23 00:00:00\n",
      "symbol    XRP\n",
      "price       1\n",
      "Name: 2018-08-23 00:00:00, dtype: object\n",
      "2018-02-11 00:00:00\n",
      "symbol     BTC\n",
      "price     9000\n",
      "Name: 2018-02-11 00:00:00, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# По строкам как по словарю. Значения в \"словаре\" это pandas.Series\n",
    "for index, row in table.iterrows():\n",
    "    print(index)\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### 6.4. Сортировка"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Строки и столбцы таблицы можно сортировать. Результат сортирующих методов возвращает новую таблицу!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-08-23</th>\n",
       "      <td>XRP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-11</th>\n",
       "      <td>LTC</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-11</th>\n",
       "      <td>BTC</td>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>BTC</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol  price\n",
       "2018-08-23    XRP      1\n",
       "2018-03-11    LTC    200\n",
       "2018-02-11    BTC   9000\n",
       "2018-01-11    BTC  10000"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Отсортировать строки по индексу по убыванию\n",
    "table.sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>10000</td>\n",
       "      <td>BTC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-11</th>\n",
       "      <td>200</td>\n",
       "      <td>LTC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-23</th>\n",
       "      <td>1</td>\n",
       "      <td>XRP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-11</th>\n",
       "      <td>9000</td>\n",
       "      <td>BTC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            price symbol\n",
       "2018-01-11  10000    BTC\n",
       "2018-03-11    200    LTC\n",
       "2018-08-23      1    XRP\n",
       "2018-02-11   9000    BTC"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Отсортировать колонки по возрастанию\n",
    "table.sort_index(axis=1, ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-02-11</th>\n",
       "      <td>BTC</td>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>BTC</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-11</th>\n",
       "      <td>LTC</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-23</th>\n",
       "      <td>XRP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol  price\n",
       "2018-02-11    BTC   9000\n",
       "2018-01-11    BTC  10000\n",
       "2018-03-11    LTC    200\n",
       "2018-08-23    XRP      1"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Отсортировать таблицу алгоритмом quicksort по названию, а если значения равны то по цене\n",
    "table.sort_values(by=['symbol', 'price'], kind='quicksort')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "del table, index, row, column, series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 7. Статистические функции"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим как pandas позволяет вычислять простейшие статистики для датасетов. Методы которые мы перечислим ниже имеют аргумет axis, который указывает вдоль какой оси надо вычислять статистику. Обычно, по-умолчанию axis=0, что соответсвует вычислениям по строкам. Если в данных есть пропуски, то они не учитываются при вычислении статистик!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "earnings = pd.DataFrame(\n",
    "    data=[[7629.39, -9357.49, -1661.3, 8597.23],\n",
    "          [560.68, None, 10.46, 3578.5],\n",
    "          [487.38, 7560.38, 1090.87, -5164.93],\n",
    "          [560.68, 7560.38, None, -5164.93]],\n",
    "    columns=['BTC', 'DOGE', 'ADA', 'ETH'],\n",
    "    index=['yesterday', 'today', 'tomorrow','aftertomorrow']\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BTC</th>\n",
       "      <th>DOGE</th>\n",
       "      <th>ADA</th>\n",
       "      <th>ETH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>yesterday</th>\n",
       "      <td>7629.39</td>\n",
       "      <td>-9357.49</td>\n",
       "      <td>-1661.30</td>\n",
       "      <td>8597.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>today</th>\n",
       "      <td>560.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.46</td>\n",
       "      <td>3578.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tomorrow</th>\n",
       "      <td>487.38</td>\n",
       "      <td>7560.38</td>\n",
       "      <td>1090.87</td>\n",
       "      <td>-5164.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aftertomorrow</th>\n",
       "      <td>560.68</td>\n",
       "      <td>7560.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5164.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   BTC     DOGE      ADA      ETH\n",
       "yesterday      7629.39 -9357.49 -1661.30  8597.23\n",
       "today           560.68      NaN    10.46  3578.50\n",
       "tomorrow        487.38  7560.38  1090.87 -5164.93\n",
       "aftertomorrow   560.68  7560.38      NaN -5164.93"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BTC     4\n",
       "DOGE    3\n",
       "ADA     3\n",
       "ETH     4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Количество не None записей для каждой монеты\n",
    "earnings.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yesterday        5207.83\n",
       "today            4149.64\n",
       "tomorrow         3973.70\n",
       "aftertomorrow    2956.13\n",
       "dtype: float64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Заработки по дням\n",
    "earnings.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BTC     2309.532500\n",
       "DOGE    1921.090000\n",
       "ADA     -186.656667\n",
       "ETH      461.467500\n",
       "dtype: float64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Средний заработок по каждой монете\n",
    "earnings.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BTC      560.680\n",
       "DOGE    7560.380\n",
       "ADA       10.460\n",
       "ETH     -793.215\n",
       "dtype: float64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Медианный заработок по каждой монете\n",
    "earnings.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BTC</th>\n",
       "      <th>DOGE</th>\n",
       "      <th>ADA</th>\n",
       "      <th>ETH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>560.68</td>\n",
       "      <td>7560.38</td>\n",
       "      <td>-1661.30</td>\n",
       "      <td>-5164.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.46</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1090.87</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      BTC     DOGE      ADA      ETH\n",
       "0  560.68  7560.38 -1661.30 -5164.93\n",
       "1     NaN      NaN    10.46      NaN\n",
       "2     NaN      NaN  1090.87      NaN"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Моды заработка по каждой монете\n",
    "earnings.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BTC     3546.739991\n",
       "DOGE    9767.536799\n",
       "ADA     1386.633031\n",
       "ETH     6812.224813\n",
       "dtype: float64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \"Разброс\" заработка по каждой монете\n",
    "earnings.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BTC</th>\n",
       "      <th>DOGE</th>\n",
       "      <th>ADA</th>\n",
       "      <th>ETH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>542.3550</td>\n",
       "      <td>-898.555</td>\n",
       "      <td>-825.420</td>\n",
       "      <td>-5164.9300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>560.6800</td>\n",
       "      <td>7560.380</td>\n",
       "      <td>10.460</td>\n",
       "      <td>-793.2150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>2327.8575</td>\n",
       "      <td>7560.380</td>\n",
       "      <td>550.665</td>\n",
       "      <td>4833.1825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            BTC      DOGE      ADA        ETH\n",
       "0.25   542.3550  -898.555 -825.420 -5164.9300\n",
       "0.50   560.6800  7560.380   10.460  -793.2150\n",
       "0.75  2327.8575  7560.380  550.665  4833.1825"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 25, 50 и 75 квантиль заработка по каждой монете\n",
    "earnings.quantile(q=(0.25, 0.5, 0.75))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BTC      487.38\n",
       "DOGE   -9357.49\n",
       "ADA    -1661.30\n",
       "ETH    -5164.93\n",
       "dtype: float64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Минимальный заработок по каждой монете\n",
    "earnings.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BTC     7629.39\n",
       "DOGE    7560.38\n",
       "ADA     1090.87\n",
       "ETH     8597.23\n",
       "dtype: float64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Максимальный заработок по каждой монете\n",
    "earnings.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BTC</th>\n",
       "      <th>DOGE</th>\n",
       "      <th>ADA</th>\n",
       "      <th>ETH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>yesterday</th>\n",
       "      <td>7629.39</td>\n",
       "      <td>9357.49</td>\n",
       "      <td>1661.30</td>\n",
       "      <td>8597.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>today</th>\n",
       "      <td>560.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.46</td>\n",
       "      <td>3578.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tomorrow</th>\n",
       "      <td>487.38</td>\n",
       "      <td>7560.38</td>\n",
       "      <td>1090.87</td>\n",
       "      <td>5164.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aftertomorrow</th>\n",
       "      <td>560.68</td>\n",
       "      <td>7560.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5164.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   BTC     DOGE      ADA      ETH\n",
       "yesterday      7629.39  9357.49  1661.30  8597.23\n",
       "today           560.68      NaN    10.46  3578.50\n",
       "tomorrow        487.38  7560.38  1090.87  5164.93\n",
       "aftertomorrow   560.68  7560.38      NaN  5164.93"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Абсолюты заработков по каждой монете\n",
    "earnings.abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BTC     1.168928e+12\n",
       "DOGE   -5.348680e+11\n",
       "ADA    -1.895626e+07\n",
       "ETH     8.207076e+14\n",
       "dtype: float64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Произвдение заработков по каждой монете. Да я знаю, что это бессмысленно\n",
    "earnings.prod()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BTC</th>\n",
       "      <th>DOGE</th>\n",
       "      <th>ADA</th>\n",
       "      <th>ETH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>yesterday</th>\n",
       "      <td>7.629390e+03</td>\n",
       "      <td>-9.357490e+03</td>\n",
       "      <td>-1.661300e+03</td>\n",
       "      <td>8.597230e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>today</th>\n",
       "      <td>4.277646e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.737720e+04</td>\n",
       "      <td>3.076519e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tomorrow</th>\n",
       "      <td>2.084839e+09</td>\n",
       "      <td>-7.074618e+07</td>\n",
       "      <td>-1.895626e+07</td>\n",
       "      <td>-1.589000e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aftertomorrow</th>\n",
       "      <td>1.168928e+12</td>\n",
       "      <td>-5.348680e+11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.207076e+14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        BTC          DOGE           ADA           ETH\n",
       "yesterday      7.629390e+03 -9.357490e+03 -1.661300e+03  8.597230e+03\n",
       "today          4.277646e+06           NaN -1.737720e+04  3.076519e+07\n",
       "tomorrow       2.084839e+09 -7.074618e+07 -1.895626e+07 -1.589000e+11\n",
       "aftertomorrow  1.168928e+12 -5.348680e+11           NaN  8.207076e+14"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Кумулятивные произведения заработков по каждой монете\n",
    "earnings.cumprod()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BTC</th>\n",
       "      <th>DOGE</th>\n",
       "      <th>ADA</th>\n",
       "      <th>ETH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>yesterday</th>\n",
       "      <td>7629.39</td>\n",
       "      <td>-9357.49</td>\n",
       "      <td>-1661.30</td>\n",
       "      <td>8597.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>today</th>\n",
       "      <td>8190.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1650.84</td>\n",
       "      <td>12175.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tomorrow</th>\n",
       "      <td>8677.45</td>\n",
       "      <td>-1797.11</td>\n",
       "      <td>-559.97</td>\n",
       "      <td>7010.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aftertomorrow</th>\n",
       "      <td>9238.13</td>\n",
       "      <td>5763.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1845.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   BTC     DOGE      ADA       ETH\n",
       "yesterday      7629.39 -9357.49 -1661.30   8597.23\n",
       "today          8190.07      NaN -1650.84  12175.73\n",
       "tomorrow       8677.45 -1797.11  -559.97   7010.80\n",
       "aftertomorrow  9238.13  5763.27      NaN   1845.87"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Кумулятивные суммы заработков по каждой монете\n",
    "earnings.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BTC</th>\n",
       "      <th>DOGE</th>\n",
       "      <th>ADA</th>\n",
       "      <th>ETH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>yesterday</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>today</th>\n",
       "      <td>-0.926511</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.006296</td>\n",
       "      <td>-0.583761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tomorrow</th>\n",
       "      <td>-0.130734</td>\n",
       "      <td>-1.80795</td>\n",
       "      <td>103.289675</td>\n",
       "      <td>-2.443323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aftertomorrow</th>\n",
       "      <td>0.150396</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    BTC     DOGE         ADA       ETH\n",
       "yesterday           NaN      NaN         NaN       NaN\n",
       "today         -0.926511  0.00000   -1.006296 -0.583761\n",
       "tomorrow      -0.130734 -1.80795  103.289675 -2.443323\n",
       "aftertomorrow  0.150396  0.00000    0.000000  0.000000"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Процентное изменение заработка по сравнению с предыдущим значением\n",
    "earnings.pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-40069185.5944"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ковариация между заработком по BTC и по DOGE\n",
    "earnings['BTC'].cov(earnings['DOGE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.9999600937383021"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Корреляция между заработком по BTC и по DOGE\n",
    "earnings['BTC'].corr(earnings['DOGE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Иногда хочется посмотреть все статистики сразу. Для этого есть метод describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>BTC</th>\n",
       "      <th>DOGE</th>\n",
       "      <th>ADA</th>\n",
       "      <th>ETH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2309.532500</td>\n",
       "      <td>1921.090000</td>\n",
       "      <td>-186.656667</td>\n",
       "      <td>461.467500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3546.739991</td>\n",
       "      <td>9767.536799</td>\n",
       "      <td>1386.633031</td>\n",
       "      <td>6812.224813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>487.380000</td>\n",
       "      <td>-9357.490000</td>\n",
       "      <td>-1661.300000</td>\n",
       "      <td>-5164.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>542.355000</td>\n",
       "      <td>-898.555000</td>\n",
       "      <td>-825.420000</td>\n",
       "      <td>-5164.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>560.680000</td>\n",
       "      <td>7560.380000</td>\n",
       "      <td>10.460000</td>\n",
       "      <td>-793.215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2327.857500</td>\n",
       "      <td>7560.380000</td>\n",
       "      <td>550.665000</td>\n",
       "      <td>4833.182500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7629.390000</td>\n",
       "      <td>7560.380000</td>\n",
       "      <td>1090.870000</td>\n",
       "      <td>8597.230000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               BTC         DOGE          ADA          ETH\n",
       "count     4.000000     3.000000     3.000000     4.000000\n",
       "mean   2309.532500  1921.090000  -186.656667   461.467500\n",
       "std    3546.739991  9767.536799  1386.633031  6812.224813\n",
       "min     487.380000 -9357.490000 -1661.300000 -5164.930000\n",
       "25%     542.355000  -898.555000  -825.420000 -5164.930000\n",
       "50%     560.680000  7560.380000    10.460000  -793.215000\n",
       "75%    2327.857500  7560.380000   550.665000  4833.182500\n",
       "max    7629.390000  7560.380000  1090.870000  8597.230000"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earnings.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "del earnings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 8. Применение пользовательских функций"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Иногда хочется применить к датасету или одной его колонке функцию. Применять можно ко всей таблице, поэлементно, поколоночно или построчно"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>BTC</th>\n",
       "      <th>DOGE</th>\n",
       "      <th>ADA</th>\n",
       "      <th>ETH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>yesterday</th>\n",
       "      <td>7629.39</td>\n",
       "      <td>-9357.49</td>\n",
       "      <td>-1661.30</td>\n",
       "      <td>8597.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>today</th>\n",
       "      <td>560.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.46</td>\n",
       "      <td>3578.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tomorrow</th>\n",
       "      <td>487.38</td>\n",
       "      <td>7560.38</td>\n",
       "      <td>1090.87</td>\n",
       "      <td>-5164.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               BTC     DOGE      ADA      ETH\n",
       "yesterday  7629.39 -9357.49 -1661.30  8597.23\n",
       "today       560.68      NaN    10.46  3578.50\n",
       "tomorrow    487.38  7560.38  1090.87 -5164.93"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earnings = pd.DataFrame(\n",
    "    data=[[7629.39, -9357.49, -1661.3, 8597.23],\n",
    "          [560.68, None, 10.46, 3578.5],\n",
    "          [487.38, 7560.38, 1090.87, -5164.93]],\n",
    "    columns=['BTC', 'DOGE', 'ADA', 'ETH'],\n",
    "    index=['yesterday', 'today', 'tomorrow']\n",
    ")\n",
    "earnings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Для  применения построчно/поколоночно используйте функцию apply. Если хочется применить построчно добавьте axis=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BTC     1.683006e+07\n",
       "DOGE    1.431072e+08\n",
       "ADA     1.922751e+06\n",
       "ETH     4.850538e+07\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Посчитать дисперсию заработка для каждой монеты. В лямбду приходит pandas.Series объект каждой колонки\n",
    "earnings.apply(lambda money: money.std() ** 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Для поэлементного применения используйте функцию applymap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BTC</th>\n",
       "      <th>DOGE</th>\n",
       "      <th>ADA</th>\n",
       "      <th>ETH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>yesterday</th>\n",
       "      <td>7629.39</td>\n",
       "      <td>9357.49</td>\n",
       "      <td>1661.30</td>\n",
       "      <td>8597.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>today</th>\n",
       "      <td>560.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.46</td>\n",
       "      <td>3578.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tomorrow</th>\n",
       "      <td>487.38</td>\n",
       "      <td>7560.38</td>\n",
       "      <td>1090.87</td>\n",
       "      <td>5164.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               BTC     DOGE      ADA      ETH\n",
       "yesterday  7629.39  9357.49  1661.30  8597.23\n",
       "today       560.68      NaN    10.46  3578.50\n",
       "tomorrow    487.38  7560.38  1090.87  5164.93"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Сделаем вид, что потери превратились в профит\n",
    "earnings.applymap(abs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BTC</th>\n",
       "      <th>DOGE</th>\n",
       "      <th>ADA</th>\n",
       "      <th>ETH</th>\n",
       "      <th>BTC+100500</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>yesterday</th>\n",
       "      <td>7629.39</td>\n",
       "      <td>-9357.49</td>\n",
       "      <td>-1661.30</td>\n",
       "      <td>8597.23</td>\n",
       "      <td>108129.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>today</th>\n",
       "      <td>560.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.46</td>\n",
       "      <td>3578.50</td>\n",
       "      <td>101060.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tomorrow</th>\n",
       "      <td>487.38</td>\n",
       "      <td>7560.38</td>\n",
       "      <td>1090.87</td>\n",
       "      <td>-5164.93</td>\n",
       "      <td>100987.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               BTC     DOGE      ADA      ETH  BTC+100500\n",
       "yesterday  7629.39 -9357.49 -1661.30  8597.23   108129.39\n",
       "today       560.68      NaN    10.46  3578.50   101060.68\n",
       "tomorrow    487.38  7560.38  1090.87 -5164.93   100987.38"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Применять функцию можно и к отдельной колонке. В лямбду приходит отдельное значение колонки\n",
    "earnings['BTC+100500'] = earnings['BTC'].apply(lambda money: money + 100500)\n",
    "earnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BTC</th>\n",
       "      <th>DOGE</th>\n",
       "      <th>ADA</th>\n",
       "      <th>ETH</th>\n",
       "      <th>BTC+100500</th>\n",
       "      <th>BTC+DOGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>yesterday</th>\n",
       "      <td>7629.39</td>\n",
       "      <td>-9357.49</td>\n",
       "      <td>-1661.30</td>\n",
       "      <td>8597.23</td>\n",
       "      <td>108129.39</td>\n",
       "      <td>-1728.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>today</th>\n",
       "      <td>560.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.46</td>\n",
       "      <td>3578.50</td>\n",
       "      <td>101060.68</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tomorrow</th>\n",
       "      <td>487.38</td>\n",
       "      <td>7560.38</td>\n",
       "      <td>1090.87</td>\n",
       "      <td>-5164.93</td>\n",
       "      <td>100987.38</td>\n",
       "      <td>8047.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               BTC     DOGE      ADA      ETH  BTC+100500  BTC+DOGE\n",
       "yesterday  7629.39 -9357.49 -1661.30  8597.23   108129.39  -1728.10\n",
       "today       560.68      NaN    10.46  3578.50   101060.68       NaN\n",
       "tomorrow    487.38  7560.38  1090.87 -5164.93   100987.38   8047.76"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Если нужно взаимодействие между колонками - применяйте apply row-wise (по строкам) \n",
    "earnings['BTC+DOGE'] = earnings.apply(lambda x: x['BTC'] + x.DOGE, axis = 1)\n",
    "earnings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Не забудьте самостоятельно посмотреть метод pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "help(earnings.pipe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "del earnings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 9. Работа со строковыми колонками"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На данный момент для работы со строковыми колонками нам пришлось бы постоянно использовать метод apply. Это неудобно и поэтому в pandas сделали удобный инструмент для манипуляций со строками"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LTC</td>\n",
       "      <td>Litecoin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ETC</td>\n",
       "      <td>Etherium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>Doge coin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  symbol       name\n",
       "0    BTC    Bitcoin\n",
       "1    LTC   Litecoin\n",
       "2    ETC   Etherium\n",
       "3   DOGE  Doge coin"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxonomy = pd.DataFrame(\n",
    "    data=[['BTC', 'Bitcoin'],['LTC', 'Litecoin'], ['ETC', 'Etherium'], ['DOGE', 'Doge coin']],\n",
    "    columns=['symbol', 'name']\n",
    ")\n",
    "taxonomy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Чтобы применять строковые функции к колонке надо обратиться к свойству .str."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     btc\n",
       "1     ltc\n",
       "2     etc\n",
       "3    doge\n",
       "Name: symbol, dtype: object"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Привести все к нижнему регистру\n",
    "taxonomy['symbol'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      BITCOIN\n",
       "1     LITECOIN\n",
       "2     ETHERIUM\n",
       "3    DOGE COIN\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Привести все к верхнему регистру\n",
    "taxonomy['name'].str.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3\n",
       "1    3\n",
       "2    3\n",
       "3    4\n",
       "Name: symbol, dtype: int64"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Длины строк\n",
    "taxonomy['symbol'].str.len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Bitcoin\n",
       "1     Litecoin\n",
       "2     Etherium\n",
       "3    Doge coin\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Убрать по краям пробельные символы\n",
    "taxonomy['name'].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       [Bitcoin]\n",
       "1      [Litecoin]\n",
       "2      [Etherium]\n",
       "3    [Doge, coin]\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Разбить строки по заданному символу\n",
    "taxonomy['name'].str.split(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'BTC $$$ LTC $$$ ETC $$$ DOGE'"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Склеить все строки в одну. Аналог str.join\n",
    "taxonomy['symbol'].str.cat(sep=' $$$ ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1     True\n",
       "2    False\n",
       "3     True\n",
       "Name: name, dtype: bool"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Найти везде подстроку\n",
    "taxonomy['name'].str.contains('coin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Bitdough\n",
       "1     Litedough\n",
       "2      Etherium\n",
       "3    Doge dough\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Везде заменить строку на другую\n",
    "taxonomy['name'].str.replace('coin', 'dough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        BitcoinBitcoin\n",
       "1      LitecoinLitecoin\n",
       "2      EtheriumEtherium\n",
       "3    Doge coinDoge coin\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Повторить каждую строку указанное число раз\n",
    "taxonomy['name'].str.repeat(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    0\n",
       "3    1\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Посчитать все появления указанной строки\n",
    "taxonomy['name'].str.count('coin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "Name: symbol, dtype: bool"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Проверка наличия префикса\n",
    "taxonomy['symbol'].str.startswith('B')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1     True\n",
       "2     True\n",
       "3    False\n",
       "Name: symbol, dtype: bool"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Проверка наличия суффикса\n",
    "taxonomy['symbol'].str.endswith('C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3\n",
       "1    4\n",
       "2   -1\n",
       "3    5\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Найти место в строках где начинается искомая строка. \n",
    "taxonomy['name'].str.find('coin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    True\n",
       "1    True\n",
       "2    True\n",
       "3    True\n",
       "Name: symbol, dtype: bool"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Проверка верхнего регистра\n",
    "taxonomy['symbol'].str.isupper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "Name: symbol, dtype: bool"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Проверка нижнего регистра\n",
    "taxonomy['symbol'].str.islower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "Name: name, dtype: bool"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Проверка что все символы - цифры\n",
    "taxonomy['name'].str.isnumeric()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Посмотреть все возможные проверки\n",
    "help(taxonomy['name'].str.isnumeric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 10. Агрегации, трансформации и фильтрации"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 10.1. Скользящие окна"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы начнем со специального случая аггрегаций данных - оконных функций. Мы поговорим о методах rolling и emw. Самостоятельно посмотрите что делает expanding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ADA</th>\n",
       "      <th>DOGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>0.547</td>\n",
       "      <td>0.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02</th>\n",
       "      <td>0.500</td>\n",
       "      <td>0.964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03</th>\n",
       "      <td>0.770</td>\n",
       "      <td>0.718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-04</th>\n",
       "      <td>0.135</td>\n",
       "      <td>0.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05</th>\n",
       "      <td>0.530</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-06</th>\n",
       "      <td>0.150</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ADA   DOGE\n",
       "2018-11-01  0.547  0.745\n",
       "2018-11-02  0.500  0.964\n",
       "2018-11-03  0.770  0.718\n",
       "2018-11-04  0.135  0.845\n",
       "2018-11-05  0.530    NaN\n",
       "2018-11-06  0.150  0.795"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price = pd.DataFrame(\n",
    "    data=[\n",
    "        [0.547, 0.745],\n",
    "        [0.5, 0.964],\n",
    "        [0.77, 0.718],\n",
    "        [0.135, 0.845],\n",
    "        [0.53, None],\n",
    "        [0.15, 0.795]],\n",
    "    columns=['ADA', 'DOGE'],\n",
    "    # Создаем специальный индекс из 6 дней начиная с 2018-11-01 \n",
    "    index=pd.date_range('2018-11-01', periods=6)\n",
    ")\n",
    "price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Если вы хотите посчитать какую-нибудь статистическую функци (sum, mean, median, std) в скользящем окне. То есть методы rolling (обычное окно) и ewm (окно с экспоненциальным сглаживанием). Они возвращают специальные объекты которые могут аггрегировать используя стандартную или пользователскую функцию."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ADA</th>\n",
       "      <th>DOGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02</th>\n",
       "      <td>0.5235</td>\n",
       "      <td>0.8545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03</th>\n",
       "      <td>0.6350</td>\n",
       "      <td>0.8410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-04</th>\n",
       "      <td>0.4525</td>\n",
       "      <td>0.7815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05</th>\n",
       "      <td>0.3325</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-06</th>\n",
       "      <td>0.3400</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               ADA    DOGE\n",
       "2018-11-01     NaN     NaN\n",
       "2018-11-02  0.5235  0.8545\n",
       "2018-11-03  0.6350  0.8410\n",
       "2018-11-04  0.4525  0.7815\n",
       "2018-11-05  0.3325     NaN\n",
       "2018-11-06  0.3400     NaN"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Найти скользящее стандартное отклонение\n",
    "price.rolling(window=2).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>ADA</th>\n",
       "      <th>DOGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02</th>\n",
       "      <td>0.549209</td>\n",
       "      <td>1.484321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03</th>\n",
       "      <td>0.842900</td>\n",
       "      <td>1.444820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-04</th>\n",
       "      <td>0.611125</td>\n",
       "      <td>1.229549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05</th>\n",
       "      <td>0.299125</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-06</th>\n",
       "      <td>0.303400</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ADA      DOGE\n",
       "2018-11-01       NaN       NaN\n",
       "2018-11-02  0.549209  1.484321\n",
       "2018-11-03  0.842900  1.444820\n",
       "2018-11-04  0.611125  1.229549\n",
       "2018-11-05  0.299125       NaN\n",
       "2018-11-06  0.303400       NaN"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Сделать агрегацию c пользовательской функцией\n",
    "price.rolling(window=2).agg(lambda series: sum(series ** 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ADA</th>\n",
       "      <th>DOGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02</th>\n",
       "      <td>0.547</td>\n",
       "      <td>0.964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03</th>\n",
       "      <td>0.770</td>\n",
       "      <td>0.964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-04</th>\n",
       "      <td>0.770</td>\n",
       "      <td>0.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05</th>\n",
       "      <td>0.530</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-06</th>\n",
       "      <td>0.530</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "              ADA   DOGE\n",
       "2018-11-01    NaN    NaN\n",
       "2018-11-02  0.547  0.964\n",
       "2018-11-03  0.770  0.964\n",
       "2018-11-04  0.770  0.845\n",
       "2018-11-05  0.530    NaN\n",
       "2018-11-06  0.530    NaN"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Сделать агрегацию по имени функции\n",
    "price.rolling(window=2).agg('max')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">ADA</th>\n",
       "      <th colspan=\"3\" halign=\"left\">DOGE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "      <th>&lt;lambda&gt;</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "      <th>&lt;lambda&gt;</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02</th>\n",
       "      <td>0.5235</td>\n",
       "      <td>1.047</td>\n",
       "      <td>0.299209</td>\n",
       "      <td>0.8545</td>\n",
       "      <td>1.709</td>\n",
       "      <td>0.929296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03</th>\n",
       "      <td>0.6350</td>\n",
       "      <td>1.270</td>\n",
       "      <td>0.592900</td>\n",
       "      <td>0.8410</td>\n",
       "      <td>1.682</td>\n",
       "      <td>0.929296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-04</th>\n",
       "      <td>0.4525</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.592900</td>\n",
       "      <td>0.7815</td>\n",
       "      <td>1.563</td>\n",
       "      <td>0.714025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05</th>\n",
       "      <td>0.3325</td>\n",
       "      <td>0.665</td>\n",
       "      <td>0.280900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-06</th>\n",
       "      <td>0.3400</td>\n",
       "      <td>0.680</td>\n",
       "      <td>0.280900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               ADA                     DOGE                 \n",
       "              mean    sum  <lambda>    mean    sum  <lambda>\n",
       "2018-11-01     NaN    NaN       NaN     NaN    NaN       NaN\n",
       "2018-11-02  0.5235  1.047  0.299209  0.8545  1.709  0.929296\n",
       "2018-11-03  0.6350  1.270  0.592900  0.8410  1.682  0.929296\n",
       "2018-11-04  0.4525  0.905  0.592900  0.7815  1.563  0.714025\n",
       "2018-11-05  0.3325  0.665  0.280900     NaN    NaN       NaN\n",
       "2018-11-06  0.3400  0.680  0.280900     NaN    NaN       NaN"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Сделать несколько агрегаций\n",
    "price.rolling(window=2).agg(['mean', sum, lambda series: max(series ** 2)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>DOGE</th>\n",
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       "    <tr>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "    <tr>\n",
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       "      <td>0.547</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03</th>\n",
       "      <td>1.270</td>\n",
       "      <td>0.770</td>\n",
       "      <td>0.929296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-04</th>\n",
       "      <td>0.905</td>\n",
       "      <td>0.770</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05</th>\n",
       "      <td>0.665</td>\n",
       "      <td>0.530</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-06</th>\n",
       "      <td>0.680</td>\n",
       "      <td>0.530</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ADA             DOGE\n",
       "              sum    max  <lambda>\n",
       "2018-11-01    NaN    NaN       NaN\n",
       "2018-11-02  1.047  0.547  0.929296\n",
       "2018-11-03  1.270  0.770  0.929296\n",
       "2018-11-04  0.905  0.770  0.714025\n",
       "2018-11-05  0.665  0.530       NaN\n",
       "2018-11-06  0.680  0.530       NaN"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Сделать несколько агрегаций (для каждой колонки свои)\n",
    "price.rolling(window=2).agg({'ADA': [sum, max], 'DOGE': lambda series: max(series ** 2)})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Объект ewm работает аналогично rolling с тем лишь отличием, что он назначает веса. Числам $x_0,\\ldots,x_t$ будут назначены веса $(1-\\alpha)^t, (1-\\alpha)^{t-1}, \\ldots, 1$, где $\\alpha$ - параметр сглаживания. Например, экспоненциальное скользящее среднее будет вычисляться по формуле \n",
    "$$\n",
    "ewm_{\\alpha}(x)=\\frac{\\sum_{i=0}^t (1-\\alpha)^{t-i} x_i}{\\sum_{i=0}^t(1-\\alpha)^i}\n",
    "$$\n",
    "\n",
    "\n",
    "Если указать **adjust=False**, подсчёт будет идти рекурсивно: ```mean[i] = (1-alpha)*mean[i-1] + alpha*x[i]```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ADA</th>\n",
       "      <th>DOGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>0.547000</td>\n",
       "      <td>0.745000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02</th>\n",
       "      <td>0.523500</td>\n",
       "      <td>0.854500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03</th>\n",
       "      <td>0.646750</td>\n",
       "      <td>0.786250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-04</th>\n",
       "      <td>0.390875</td>\n",
       "      <td>0.815625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05</th>\n",
       "      <td>0.460438</td>\n",
       "      <td>0.815625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-06</th>\n",
       "      <td>0.305219</td>\n",
       "      <td>0.801875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ADA      DOGE\n",
       "2018-11-01  0.547000  0.745000\n",
       "2018-11-02  0.523500  0.854500\n",
       "2018-11-03  0.646750  0.786250\n",
       "2018-11-04  0.390875  0.815625\n",
       "2018-11-05  0.460438  0.815625\n",
       "2018-11-06  0.305219  0.801875"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Скользящее среднее cо сглаживанием alpha\n",
    "price.ewm(alpha=0.5, adjust=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "del price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### 10.2. Работа с группами"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Иногда хочется разбить данные по группам и посчитать статистики / преобразовать / пофильтровать. Для этого есть метод **groupby**. Он возвращает специальный объект для работы со сгруппированными данными."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>buy</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5900.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>sell</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>BTC</td>\n",
       "      <td>sell</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6200.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-06</th>\n",
       "      <td>ETH</td>\n",
       "      <td>sell</td>\n",
       "      <td>10.0</td>\n",
       "      <td>400.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-07</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>ETH</td>\n",
       "      <td>buy</td>\n",
       "      <td>20.0</td>\n",
       "      <td>350.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-09</th>\n",
       "      <td>ETH</td>\n",
       "      <td>sell</td>\n",
       "      <td>10.0</td>\n",
       "      <td>300.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-10</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>sell</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol  type  amount    price\n",
       "2018-01-01   DOGE   buy  5000.0     0.20\n",
       "2018-01-02    BTC   buy    10.0  6000.00\n",
       "2018-01-03    BTC   buy     2.5  5900.00\n",
       "2018-01-04   DOGE  sell  3000.0     0.10\n",
       "2018-01-05    BTC  sell     4.0  6200.00\n",
       "2018-01-06    ETH  sell    10.0   400.00\n",
       "2018-01-07    BTC   buy     1.0  5600.00\n",
       "2018-01-08    ETH   buy    20.0   350.00\n",
       "2018-01-09    ETH  sell    10.0   300.00\n",
       "2018-01-10   DOGE  sell  3000.0     0.15"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "operations = pd.DataFrame(\n",
    "    data=[\n",
    "        ['DOGE', 'buy', 5000, 0.2],\n",
    "        ['BTC', 'buy', 10, 6000],\n",
    "        ['BTC', 'buy', 2.5, 5900],\n",
    "        ['DOGE', 'sell', 3000, 0.1],\n",
    "        ['BTC', 'sell', 4, 6200],\n",
    "        ['ETH', 'sell', 10, 400],\n",
    "        ['BTC', 'buy', 1, 5600],\n",
    "        ['ETH', 'buy', 20, 350],\n",
    "        ['ETH', 'sell', 10, 300],\n",
    "        ['DOGE', 'sell', 3000, 0.15]\n",
    "    ],\n",
    "    columns=['symbol', 'type', 'amount', 'price'],\n",
    "    index=pd.date_range('2018-01-01', periods=10)\n",
    ")\n",
    "operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fc0c9de8730>"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Сгруппировать операции по типу монеты\n",
    "operations.groupby('symbol')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fc0c9de84c0>"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Сгруппировать операции по типу монеты и типу операции\n",
    "operations.groupby(['symbol', 'type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('BTC',\n",
       "  'buy'): DatetimeIndex(['2018-01-02', '2018-01-03', '2018-01-07'], dtype='datetime64[ns]', freq=None),\n",
       " ('BTC',\n",
       "  'sell'): DatetimeIndex(['2018-01-05'], dtype='datetime64[ns]', freq='D'),\n",
       " ('DOGE',\n",
       "  'buy'): DatetimeIndex(['2018-01-01'], dtype='datetime64[ns]', freq='D'),\n",
       " ('DOGE',\n",
       "  'sell'): DatetimeIndex(['2018-01-04', '2018-01-10'], dtype='datetime64[ns]', freq='6D'),\n",
       " ('ETH',\n",
       "  'buy'): DatetimeIndex(['2018-01-08'], dtype='datetime64[ns]', freq='D'),\n",
       " ('ETH',\n",
       "  'sell'): DatetimeIndex(['2018-01-06', '2018-01-09'], dtype='datetime64[ns]', freq='3D')}"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Получить индексы строк каждой группы\n",
    "operations.groupby(['symbol', 'type']).groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('BTC', 'buy')\n",
      "           symbol type  amount   price\n",
      "2018-01-02    BTC  buy    10.0  6000.0\n",
      "2018-01-03    BTC  buy     2.5  5900.0\n",
      "2018-01-07    BTC  buy     1.0  5600.0\n",
      "('BTC', 'sell')\n",
      "           symbol  type  amount   price\n",
      "2018-01-05    BTC  sell     4.0  6200.0\n",
      "('DOGE', 'buy')\n",
      "           symbol type  amount  price\n",
      "2018-01-01   DOGE  buy  5000.0    0.2\n",
      "('DOGE', 'sell')\n",
      "           symbol  type  amount  price\n",
      "2018-01-04   DOGE  sell  3000.0   0.10\n",
      "2018-01-10   DOGE  sell  3000.0   0.15\n",
      "('ETH', 'buy')\n",
      "           symbol type  amount  price\n",
      "2018-01-08    ETH  buy    20.0  350.0\n",
      "('ETH', 'sell')\n",
      "           symbol  type  amount  price\n",
      "2018-01-06    ETH  sell    10.0  400.0\n",
      "2018-01-09    ETH  sell    10.0  300.0\n"
     ]
    }
   ],
   "source": [
    "# Посмотреть на содержимое каждой группы \n",
    "for name, group in operations.groupby(['symbol', 'type']):\n",
    "    print(name)\n",
    "    print(group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-07</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol type  amount   price\n",
       "2018-01-02    BTC  buy    10.0  6000.0\n",
       "2018-01-03    BTC  buy     2.5  5900.0\n",
       "2018-01-07    BTC  buy     1.0  5600.0"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Получить группу\n",
    "operations.groupby(['symbol', 'type']).get_group(('BTC', 'buy'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Теперь посмотрим как агрегировать данные для каждой группы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symbol  type\n",
       "BTC     buy       13.5\n",
       "        sell       4.0\n",
       "DOGE    buy     5000.0\n",
       "        sell    6000.0\n",
       "ETH     buy       20.0\n",
       "        sell      20.0\n",
       "Name: amount, dtype: float64"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Найти обороты для каждой монеты и операции\n",
    "grouper = operations.groupby(['symbol', 'type'])\n",
    "grouper['amount'].agg(sum)\n",
    "\n",
    "# Есть ещё полезный метод operations.groupby(...).transform(func) - предлагается разобрать самостоятельно"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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></th>\n",
       "      <th>sum</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">BTC</th>\n",
       "      <th>buy</th>\n",
       "      <td>13.5</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sell</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">DOGE</th>\n",
       "      <th>buy</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sell</th>\n",
       "      <td>6000.0</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">ETH</th>\n",
       "      <th>buy</th>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sell</th>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                sum     max\n",
       "symbol type                \n",
       "BTC    buy     13.5    10.0\n",
       "       sell     4.0     4.0\n",
       "DOGE   buy   5000.0  5000.0\n",
       "       sell  6000.0  3000.0\n",
       "ETH    buy     20.0    20.0\n",
       "       sell    20.0    10.0"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Найти обороты и самые крупные сделки для каждой монеты и операции\n",
    "grouper = operations.groupby(['symbol', 'type'])\n",
    "grouper['amount'].agg([sum, max])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>amount</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">BTC</th>\n",
       "      <th>buy</th>\n",
       "      <td>13.5</td>\n",
       "      <td>5833.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sell</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">DOGE</th>\n",
       "      <th>buy</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sell</th>\n",
       "      <td>6000.0</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">ETH</th>\n",
       "      <th>buy</th>\n",
       "      <td>20.0</td>\n",
       "      <td>350.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sell</th>\n",
       "      <td>20.0</td>\n",
       "      <td>350.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             amount        price\n",
       "symbol type                     \n",
       "BTC    buy     13.5  5833.333333\n",
       "       sell     4.0  6200.000000\n",
       "DOGE   buy   5000.0     0.200000\n",
       "       sell  6000.0     0.125000\n",
       "ETH    buy     20.0   350.000000\n",
       "       sell    20.0   350.000000"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Найти обороты и среднюю цену для каждой монеты и операции\n",
    "grouper = operations.groupby(['symbol', 'type'])\n",
    "grouper.agg({'amount': sum, 'price': lambda series: series.mean()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symbol  type\n",
       "BTC     buy     3\n",
       "        sell    1\n",
       "DOGE    buy     1\n",
       "        sell    2\n",
       "ETH     buy     1\n",
       "        sell    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Получить размер группы в которой сидит запись\n",
    "operations.groupby(['symbol', 'type']).apply(lambda series: series.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symbol  type\n",
       "BTC     buy     3\n",
       "        sell    1\n",
       "DOGE    buy     1\n",
       "        sell    1\n",
       "ETH     buy     1\n",
       "        sell    1\n",
       "Name: amount, dtype: int64"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Вывести группы по убыванию числа уникальных значений в колонке amount\n",
    "operations.groupby(['symbol', 'type'])['amount'].nunique().nlargest(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Сгруппированные данные можно фильтровать. Вы можете решить какие группы оставить с помощью вашей функции. Она на вход принимает pandas.DataFrame и возвращает bool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>buy</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>sell</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-06</th>\n",
       "      <td>ETH</td>\n",
       "      <td>sell</td>\n",
       "      <td>10.0</td>\n",
       "      <td>400.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>ETH</td>\n",
       "      <td>buy</td>\n",
       "      <td>20.0</td>\n",
       "      <td>350.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-09</th>\n",
       "      <td>ETH</td>\n",
       "      <td>sell</td>\n",
       "      <td>10.0</td>\n",
       "      <td>300.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-10</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>sell</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol  type  amount   price\n",
       "2018-01-01   DOGE   buy  5000.0    0.20\n",
       "2018-01-04   DOGE  sell  3000.0    0.10\n",
       "2018-01-06    ETH  sell    10.0  400.00\n",
       "2018-01-08    ETH   buy    20.0  350.00\n",
       "2018-01-09    ETH  sell    10.0  300.00\n",
       "2018-01-10   DOGE  sell  3000.0    0.15"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Выбрать монеты и операции для которых оборот был больше 20 монет\n",
    "operations.groupby(['symbol', 'type']).filter(lambda table: table['amount'].sum() >= 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "del operations, name, group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### 10.3 Сводные таблицы"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Большинство таблиц в сыром виде похожи на логи где каждая строка это запись. Никакой оргинизации в данных в этих таблицах нет, а хотелось бы. Для этого в pandas существуют сводные таблицы. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>symbol</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>price</th>\n",
       "      <th>user</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>buy</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>Joe Doe</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>Elon Musk</td>\n",
       "      <td>error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5900.00</td>\n",
       "      <td>George Bush</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>sell</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>John Romero</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>BTC</td>\n",
       "      <td>sell</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6200.00</td>\n",
       "      <td>Jack Ma</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-06</th>\n",
       "      <td>ETH</td>\n",
       "      <td>sell</td>\n",
       "      <td>10.0</td>\n",
       "      <td>400.00</td>\n",
       "      <td>Satoshi Nakomoto</td>\n",
       "      <td>error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-07</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5600.00</td>\n",
       "      <td>Vladimir Vladimirovich</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>ETH</td>\n",
       "      <td>buy</td>\n",
       "      <td>20.0</td>\n",
       "      <td>350.00</td>\n",
       "      <td>George Bush</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-09</th>\n",
       "      <td>ETH</td>\n",
       "      <td>sell</td>\n",
       "      <td>10.0</td>\n",
       "      <td>300.00</td>\n",
       "      <td>Jack Ma</td>\n",
       "      <td>error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-10</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>sell</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Joe Doe</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol  type  amount    price                    user   status\n",
       "2018-01-01   DOGE   buy  5000.0     0.20                 Joe Doe  success\n",
       "2018-01-02    BTC   buy    10.0  6000.00               Elon Musk    error\n",
       "2018-01-03    BTC   buy     2.5  5900.00             George Bush  success\n",
       "2018-01-04   DOGE  sell  3000.0     0.10             John Romero  success\n",
       "2018-01-05    BTC  sell     4.0  6200.00                 Jack Ma  success\n",
       "2018-01-06    ETH  sell    10.0   400.00        Satoshi Nakomoto    error\n",
       "2018-01-07    BTC   buy     1.0  5600.00  Vladimir Vladimirovich  success\n",
       "2018-01-08    ETH   buy    20.0   350.00             George Bush  success\n",
       "2018-01-09    ETH  sell    10.0   300.00                 Jack Ma    error\n",
       "2018-01-10   DOGE  sell  3000.0     0.15                 Joe Doe  success"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "operations = pd.DataFrame(\n",
    "    data=[\n",
    "        ['DOGE', 'buy', 5000, 0.2, 'Joe Doe', 'success'],\n",
    "        ['BTC', 'buy', 10, 6000, 'Elon Musk', 'error'],\n",
    "        ['BTC', 'buy', 2.5, 5900, 'George Bush', 'success'],\n",
    "        ['DOGE', 'sell', 3000, 0.1, 'John Romero', 'success'],\n",
    "        ['BTC', 'sell', 4, 6200, 'Jack Ma', 'success'],\n",
    "        ['ETH', 'sell', 10, 400, 'Satoshi Nakomoto', 'error'],\n",
    "        ['BTC', 'buy', 1, 5600, 'Vladimir Vladimirovich', 'success'],\n",
    "        ['ETH', 'buy', 20, 350, 'George Bush', 'success'],\n",
    "        ['ETH', 'sell', 10, 300, 'Jack Ma', 'error'],\n",
    "        ['DOGE', 'sell', 3000, 0.15, 'Joe Doe', 'success']\n",
    "    ],\n",
    "    columns=['symbol', 'type', 'amount', 'price', 'user', 'status'],\n",
    "    index=pd.date_range('2018-01-01', periods=10)\n",
    ")\n",
    "operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Лучше всего понять как они работают это разобрать пример. Найдем сколько суммарно  было куплено и продано каждого типа монет. В результирующей сводной таблице индексом будет название монеты, колонки - типы транзакций (т.е. покупки или продажи). В самих ячейках будет суммарное количество монет."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>type</th>\n",
       "      <th>buy</th>\n",
       "      <th>sell</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BTC</th>\n",
       "      <td>13.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOGE</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>6000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ETH</th>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "type       buy    sell\n",
       "symbol                \n",
       "BTC       13.5     4.0\n",
       "DOGE    5000.0  6000.0\n",
       "ETH       20.0    20.0"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "operations.pivot_table(\n",
    "    values='amount', \n",
    "    index='symbol', \n",
    "    columns='type', \n",
    "    aggfunc='sum'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Если не указывать явно аргумент aggfunc, то агрегация будет через усреднение (а не через суммирование как было в нашем примере). Расмотрим более сложный пример."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"4\" halign=\"left\">sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th colspan=\"2\" halign=\"left\">buy</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sell</th>\n",
       "      <th colspan=\"2\" halign=\"left\">buy</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sell</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>status</th>\n",
       "      <th>error</th>\n",
       "      <th>success</th>\n",
       "      <th>error</th>\n",
       "      <th>success</th>\n",
       "      <th>error</th>\n",
       "      <th>success</th>\n",
       "      <th>error</th>\n",
       "      <th>success</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <th>user</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">BTC</th>\n",
       "      <th>Elon Musk</th>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>George Bush</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jack Ma</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vladimir Vladimirovich</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">DOGE</th>\n",
       "      <th>Joe Doe</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Romero</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">ETH</th>\n",
       "      <th>George Bush</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jack Ma</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Satoshi Nakomoto</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               mean                         sum                \\\n",
       "type                            buy          sell           buy          sell   \n",
       "status                        error success error success error success error   \n",
       "symbol user                                                                     \n",
       "BTC    Elon Musk               10.0     NaN   NaN     NaN  10.0     NaN   NaN   \n",
       "       George Bush              NaN     2.5   NaN     NaN   NaN     2.5   NaN   \n",
       "       Jack Ma                  NaN     NaN   NaN     4.0   NaN     NaN   NaN   \n",
       "       Vladimir Vladimirovich   NaN     1.0   NaN     NaN   NaN     1.0   NaN   \n",
       "DOGE   Joe Doe                  NaN  5000.0   NaN  3000.0   NaN  5000.0   NaN   \n",
       "       John Romero              NaN     NaN   NaN  3000.0   NaN     NaN   NaN   \n",
       "ETH    George Bush              NaN    20.0   NaN     NaN   NaN    20.0   NaN   \n",
       "       Jack Ma                  NaN     NaN  10.0     NaN   NaN     NaN  10.0   \n",
       "       Satoshi Nakomoto         NaN     NaN  10.0     NaN   NaN     NaN  10.0   \n",
       "\n",
       "                                       \n",
       "type                                   \n",
       "status                        success  \n",
       "symbol user                            \n",
       "BTC    Elon Musk                  NaN  \n",
       "       George Bush                NaN  \n",
       "       Jack Ma                    4.0  \n",
       "       Vladimir Vladimirovich     NaN  \n",
       "DOGE   Joe Doe                 3000.0  \n",
       "       John Romero             3000.0  \n",
       "ETH    George Bush                NaN  \n",
       "       Jack Ma                    NaN  \n",
       "       Satoshi Nakomoto           NaN  "
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "operations.pivot_table(\n",
    "    values='amount',\n",
    "    index=['symbol', 'user'],  # Индекс может быть иерархическим и строиться по нескольким колонкам\n",
    "    columns=['type', 'status'],  # Аналогично иерархическими могут быть и колонки\n",
    "    aggfunc={'amount': ['sum', 'mean']}  # Можно вычислять сразу несколько агрегаций\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "И самый монструозный пример, где мы сделаем сводную таблиц для цен и объемов торгов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">amount</th>\n",
       "      <th colspan=\"4\" halign=\"left\">price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"4\" halign=\"left\">sum</th>\n",
       "      <th colspan=\"4\" halign=\"left\">max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th colspan=\"2\" halign=\"left\">buy</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sell</th>\n",
       "      <th colspan=\"2\" halign=\"left\">buy</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sell</th>\n",
       "      <th colspan=\"2\" halign=\"left\">buy</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sell</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>status</th>\n",
       "      <th>error</th>\n",
       "      <th>success</th>\n",
       "      <th>error</th>\n",
       "      <th>success</th>\n",
       "      <th>error</th>\n",
       "      <th>success</th>\n",
       "      <th>error</th>\n",
       "      <th>success</th>\n",
       "      <th>error</th>\n",
       "      <th>success</th>\n",
       "      <th>error</th>\n",
       "      <th>success</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <th>user</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">BTC</th>\n",
       "      <th>Elon Musk</th>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>George Bush</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5900.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jack Ma</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6200.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vladimir Vladimirovich</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">DOGE</th>\n",
       "      <th>Joe Doe</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Romero</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">ETH</th>\n",
       "      <th>George Bush</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jack Ma</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Satoshi Nakomoto</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              amount                                      \\\n",
       "                                mean                         sum           \n",
       "type                             buy          sell           buy           \n",
       "status                         error success error success error success   \n",
       "symbol user                                                                \n",
       "BTC    Elon Musk                10.0     NaN   NaN     NaN  10.0     NaN   \n",
       "       George Bush               NaN     2.5   NaN     NaN   NaN     2.5   \n",
       "       Jack Ma                   NaN     NaN   NaN     4.0   NaN     NaN   \n",
       "       Vladimir Vladimirovich    NaN     1.0   NaN     NaN   NaN     1.0   \n",
       "DOGE   Joe Doe                   NaN  5000.0   NaN  3000.0   NaN  5000.0   \n",
       "       John Romero               NaN     NaN   NaN  3000.0   NaN     NaN   \n",
       "ETH    George Bush               NaN    20.0   NaN     NaN   NaN    20.0   \n",
       "       Jack Ma                   NaN     NaN  10.0     NaN   NaN     NaN   \n",
       "       Satoshi Nakomoto          NaN     NaN  10.0     NaN   NaN     NaN   \n",
       "\n",
       "                                              price                          \n",
       "                                                max                          \n",
       "type                           sell             buy           sell           \n",
       "status                        error success   error success  error  success  \n",
       "symbol user                                                                  \n",
       "BTC    Elon Musk                NaN     NaN  6000.0     NaN    NaN      NaN  \n",
       "       George Bush              NaN     NaN     NaN  5900.0    NaN      NaN  \n",
       "       Jack Ma                  NaN     4.0     NaN     NaN    NaN  6200.00  \n",
       "       Vladimir Vladimirovich   NaN     NaN     NaN  5600.0    NaN      NaN  \n",
       "DOGE   Joe Doe                  NaN  3000.0     NaN     0.2    NaN     0.15  \n",
       "       John Romero              NaN  3000.0     NaN     NaN    NaN     0.10  \n",
       "ETH    George Bush              NaN     NaN     NaN   350.0    NaN      NaN  \n",
       "       Jack Ma                 10.0     NaN     NaN     NaN  300.0      NaN  \n",
       "       Satoshi Nakomoto        10.0     NaN     NaN     NaN  400.0      NaN  "
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "operations.pivot_table(\n",
    "    values=['amount', 'price'],  # Можно находить статистику по разным числовым показателям\n",
    "    index=['symbol', 'user'],\n",
    "    columns=['type', 'status'],\n",
    "    aggfunc={\n",
    "        'amount': ['sum', 'mean'], \n",
    "        'price': 'max'}  # Тогда для каждого показателя надо указать какие агрегации вы хотите\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Для построения сводных таблиц в pandas есть более простая функция. pandas.pivot. Она не делает никаких агрегаций, поэтому при ее использовании вы должны быть уверены, что на каждую ячейку результирующей сводной таблицы придется только одно значение показателя из исходной таблицы. В противном случае будет ошибка. Можете попробовать построить сводную таблицу\n",
    "```\n",
    "operations.pivot(values='amount', index='user', columns='user')\n",
    "```\n",
    "чтобы убедиться."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Задание:** Самостоятельно разберитесь с функциями pandas.stack, pandas.unstack и pandas.melt. Вам поможет [этот tutorial](https://pandas.pydata.org/pandas-docs/stable/reshaping.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 11. Пропуски и ошибки в данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В реальной жизни данные с которыми надо работать содержат пропуски или просто неверные значения. Мы обсудим как это можно исправить"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ADA</th>\n",
       "      <th>DOGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>0.547</td>\n",
       "      <td>0.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02</th>\n",
       "      <td>-2.000</td>\n",
       "      <td>-1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-04</th>\n",
       "      <td>0.135</td>\n",
       "      <td>0.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05</th>\n",
       "      <td>0.530</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ADA   DOGE\n",
       "2018-11-01  0.547  0.745\n",
       "2018-11-02 -2.000 -1.000\n",
       "2018-11-03    NaN  0.718\n",
       "2018-11-04  0.135  0.845\n",
       "2018-11-05  0.530    NaN\n",
       "2018-11-06    NaN -1.000"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price = pd.DataFrame(\n",
    "    data=[\n",
    "        [0.547, 0.745],\n",
    "        [-2, -1],\n",
    "        [None, 0.718],\n",
    "        [0.135, 0.845],\n",
    "        [0.53, None],\n",
    "        [None, -1]],\n",
    "    columns=['ADA', 'DOGE'],\n",
    "    # Создаем специальный индекс из 6 дней начиная с 2018-11-01 \n",
    "    index=pd.date_range('2018-11-01', periods=6)\n",
    ")\n",
    "price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ADA</th>\n",
       "      <th>DOGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-04</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-06</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ADA   DOGE\n",
       "2018-11-01  False  False\n",
       "2018-11-02  False  False\n",
       "2018-11-03   True  False\n",
       "2018-11-04  False  False\n",
       "2018-11-05  False   True\n",
       "2018-11-06   True  False"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Найти места где есть пропуски методом isnull. Аналогично есть метод notnull\n",
    "price.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
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       "      <td>0.135</td>\n",
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       "      <td>0.530</td>\n",
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      "text/plain": [
       "              ADA   DOGE\n",
       "2018-11-01  0.547  0.745\n",
       "2018-11-02 -2.000 -1.000\n",
       "2018-11-03  0.000  0.718\n",
       "2018-11-04  0.135  0.845\n",
       "2018-11-05  0.530  0.000\n",
       "2018-11-06  0.000 -1.000"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Заполнить все пропуски дефолтным занчением\n",
    "price.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
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       "      <td>0.530</td>\n",
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       "      <th>2018-11-06</th>\n",
       "      <td>NaN</td>\n",
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       "              ADA   DOGE\n",
       "2018-11-01  0.547  0.745\n",
       "2018-11-02 -2.000 -1.000\n",
       "2018-11-03  0.135  0.718\n",
       "2018-11-04  0.135  0.845\n",
       "2018-11-05  0.530 -1.000\n",
       "2018-11-06    NaN -1.000"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Заполнить все пропуски занчениями с предыдущей строки\n",
    "price.fillna(method='bfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>2018-11-05</th>\n",
       "      <td>0.530</td>\n",
       "      <td>0.845</td>\n",
       "    </tr>\n",
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       "      <th>2018-11-06</th>\n",
       "      <td>0.530</td>\n",
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      "text/plain": [
       "              ADA   DOGE\n",
       "2018-11-01  0.547  0.745\n",
       "2018-11-02 -2.000 -1.000\n",
       "2018-11-03 -2.000  0.718\n",
       "2018-11-04  0.135  0.845\n",
       "2018-11-05  0.530  0.845\n",
       "2018-11-06  0.530 -1.000"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Заполнить все пропуски занчениями с cледующей строки\n",
    "price.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <td>0.745</td>\n",
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      "text/plain": [
       "              ADA   DOGE\n",
       "2018-11-01  0.547  0.745\n",
       "2018-11-02 -2.000 -1.000\n",
       "2018-11-04  0.135  0.845"
      ]
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     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#  Выкинуть строки содержащие nan'ы\n",
    "price.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
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       "    <tr>\n",
       "      <th>2018-11-02</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-04</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-06</th>\n",
       "    </tr>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [2018-11-01 00:00:00, 2018-11-02 00:00:00, 2018-11-03 00:00:00, 2018-11-04 00:00:00, 2018-11-05 00:00:00, 2018-11-06 00:00:00]"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#  Выкинуть колонки содержащие nan'ы\n",
    "price.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>2018-11-01</th>\n",
       "      <td>0.547</td>\n",
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       "      <td>0.718</td>\n",
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       "    <tr>\n",
       "      <th>2018-11-04</th>\n",
       "      <td>0.135</td>\n",
       "      <td>0.845</td>\n",
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       "    <tr>\n",
       "      <th>2018-11-05</th>\n",
       "      <td>0.530</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2018-11-06</th>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ADA   DOGE\n",
       "2018-11-01  0.547  0.745\n",
       "2018-11-02  0.000  0.000\n",
       "2018-11-03    NaN  0.718\n",
       "2018-11-04  0.135  0.845\n",
       "2018-11-05  0.530    NaN\n",
       "2018-11-06    NaN  0.000"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Подменить значения\n",
    "price.replace({-1:0, -2:0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>2018-11-04</th>\n",
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       "      <th>2018-11-05</th>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2018-11-06</th>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "              ADA   DOGE\n",
       "2018-11-01  0.547  0.745\n",
       "2018-11-02 -2.000 -1.000\n",
       "2018-11-03    NaN  0.718\n",
       "2018-11-04  0.135  0.845\n",
       "2018-11-05  0.530    NaN\n",
       "2018-11-06    NaN -1.000"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "del price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 12. Слияния и конкатенации таблиц"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если у вас есть две таблицы, то информацию в них можно объединить двумя способами: конкатенация (\"прилепить снизу новую таблицу\") и объединение (найти объединение записей таблиц если у них хранятся одни и те же значения в выбранных колонках)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "operations_old = pd.DataFrame(\n",
    "    data=[\n",
    "        ['DOGE', 'buy', 5000, 0.2],\n",
    "        ['BTC', 'buy', 10, 6000],\n",
    "        ['BTC', 'buy', 2.5, 5900],\n",
    "        ['DOGE', 'sell', 3000, 0.1],\n",
    "        ['BTC', 'sell', 4, 6200],\n",
    "    ],\n",
    "    columns=['symbol', 'type', 'amount', 'price']\n",
    ")\n",
    "\n",
    "operations_new = pd.DataFrame(\n",
    "    data=[\n",
    "        ['ETH', 'sell', 10, 400],\n",
    "        ['BTC', 'buy', 1, 5600],\n",
    "        ['ETH', 'buy', 20, 350],\n",
    "        ['ETH', 'sell', 10, 300],\n",
    "        ['DOGE', 'sell', 3000, 0.15]\n",
    "    ],\n",
    "    columns=['symbol', 'type', 'amount', 'price']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>buy</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5900.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>sell</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BTC</td>\n",
       "      <td>sell</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6200.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ETH</td>\n",
       "      <td>sell</td>\n",
       "      <td>10.0</td>\n",
       "      <td>400.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ETH</td>\n",
       "      <td>buy</td>\n",
       "      <td>20.0</td>\n",
       "      <td>350.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ETH</td>\n",
       "      <td>sell</td>\n",
       "      <td>10.0</td>\n",
       "      <td>300.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>sell</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  symbol  type  amount    price\n",
       "0   DOGE   buy  5000.0     0.20\n",
       "1    BTC   buy    10.0  6000.00\n",
       "2    BTC   buy     2.5  5900.00\n",
       "3   DOGE  sell  3000.0     0.10\n",
       "4    BTC  sell     4.0  6200.00\n",
       "0    ETH  sell    10.0   400.00\n",
       "1    BTC   buy     1.0  5600.00\n",
       "2    ETH   buy    20.0   350.00\n",
       "3    ETH  sell    10.0   300.00\n",
       "4   DOGE  sell  3000.0     0.15"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Сконкатенировать таблицы в указанном порядке\n",
    "pd.concat([operations_old, operations_new])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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></th>\n",
       "      <th>symbol</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">old</th>\n",
       "      <th>0</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>buy</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5900.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>sell</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BTC</td>\n",
       "      <td>sell</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6200.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">new</th>\n",
       "      <th>0</th>\n",
       "      <td>ETH</td>\n",
       "      <td>sell</td>\n",
       "      <td>10.0</td>\n",
       "      <td>400.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ETH</td>\n",
       "      <td>buy</td>\n",
       "      <td>20.0</td>\n",
       "      <td>350.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ETH</td>\n",
       "      <td>sell</td>\n",
       "      <td>10.0</td>\n",
       "      <td>300.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>sell</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      symbol  type  amount    price\n",
       "old 0   DOGE   buy  5000.0     0.20\n",
       "    1    BTC   buy    10.0  6000.00\n",
       "    2    BTC   buy     2.5  5900.00\n",
       "    3   DOGE  sell  3000.0     0.10\n",
       "    4    BTC  sell     4.0  6200.00\n",
       "new 0    ETH  sell    10.0   400.00\n",
       "    1    BTC   buy     1.0  5600.00\n",
       "    2    ETH   buy    20.0   350.00\n",
       "    3    ETH  sell    10.0   300.00\n",
       "    4   DOGE  sell  3000.0     0.15"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# При конкатенации таблицам можно назначать теги чтобы понимать, где какая таблица\n",
    "pd.concat([operations_old, operations_new], keys=['old', 'new'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ETH</td>\n",
       "      <td>sell</td>\n",
       "      <td>10.0</td>\n",
       "      <td>400.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ETH</td>\n",
       "      <td>buy</td>\n",
       "      <td>20.0</td>\n",
       "      <td>350.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ETH</td>\n",
       "      <td>sell</td>\n",
       "      <td>10.0</td>\n",
       "      <td>300.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>sell</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>buy</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5900.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>sell</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BTC</td>\n",
       "      <td>sell</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6200.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  symbol  type  amount    price\n",
       "0    ETH  sell    10.0   400.00\n",
       "1    BTC   buy     1.0  5600.00\n",
       "2    ETH   buy    20.0   350.00\n",
       "3    ETH  sell    10.0   300.00\n",
       "4   DOGE  sell  3000.0     0.15\n",
       "5   DOGE   buy  5000.0     0.20\n",
       "6    BTC   buy    10.0  6000.00\n",
       "7    BTC   buy     2.5  5900.00\n",
       "8   DOGE  sell  3000.0     0.10\n",
       "9    BTC  sell     4.0  6200.00"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Как видно при конкатенации индексы старых таблиц копируются. \n",
    "# Если вам не нужно это поведение, используйте ignore_index\n",
    "operations = pd.concat([operations_new, operations_old], ignore_index=True)\n",
    "operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Данные можно собирать вместе и более умным способом. А именно, вы выбираете какие колонки вас интересуют. Далее если есть записи из двух таблиц у котороых в выбранных колонках значения совпадают мы их \"объединяем\". Это очень упрощенное объяснение. В pandas эта операция называется merge в SQL - join. Делать объединение можно разными сопособами, поэтому возникает несколько стратегий: left, right, inner и full. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>symbol</th>\n",
       "      <th>type</th>\n",
       "      <th>user</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>sell</td>\n",
       "      <td>Rockfeller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>buy</td>\n",
       "      <td>J.P. Morgan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>John Doe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ADA</td>\n",
       "      <td>sell</td>\n",
       "      <td>Rick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ETH</td>\n",
       "      <td>buy</td>\n",
       "      <td>Morty</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  symbol  type         user\n",
       "0   DOGE  sell   Rockfeller\n",
       "1   DOGE   buy  J.P. Morgan\n",
       "2    BTC   buy     John Doe\n",
       "3    ADA  sell         Rick\n",
       "4    ETH   buy        Morty"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions = pd.DataFrame(\n",
    "    data=[\n",
    "        ['DOGE', 'sell', 'Rockfeller'],\n",
    "        ['DOGE', 'buy', 'J.P. Morgan'],\n",
    "        ['BTC', 'buy', 'John Doe'],\n",
    "        ['ADA', 'sell', 'Rick'],\n",
    "        ['ETH', 'buy', 'Morty']\n",
    "    ],\n",
    "    columns=['symbol', 'type', 'user']\n",
    ")\n",
    "transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>symbol</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>price</th>\n",
       "      <th>user</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5600.00</td>\n",
       "      <td>John Doe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>John Doe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5900.00</td>\n",
       "      <td>John Doe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ETH</td>\n",
       "      <td>buy</td>\n",
       "      <td>20.0</td>\n",
       "      <td>350.00</td>\n",
       "      <td>Morty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>sell</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Rockfeller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>sell</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>Rockfeller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>buy</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>J.P. Morgan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  symbol  type  amount    price         user\n",
       "0    BTC   buy     1.0  5600.00     John Doe\n",
       "1    BTC   buy    10.0  6000.00     John Doe\n",
       "2    BTC   buy     2.5  5900.00     John Doe\n",
       "3    ETH   buy    20.0   350.00        Morty\n",
       "4   DOGE  sell  3000.0     0.15   Rockfeller\n",
       "5   DOGE  sell  3000.0     0.10   Rockfeller\n",
       "6   DOGE   buy  5000.0     0.20  J.P. Morgan"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Объединение таблиц по типу монеты и операции\n",
    "# По умолчанию производится inner join остаются записи в которых были полные совпадения\n",
    "pd.merge(operations, transactions, on=['symbol', 'type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>price</th>\n",
       "      <th>user</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ETH</td>\n",
       "      <td>sell</td>\n",
       "      <td>10.0</td>\n",
       "      <td>400.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5600.00</td>\n",
       "      <td>John Doe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ETH</td>\n",
       "      <td>buy</td>\n",
       "      <td>20.0</td>\n",
       "      <td>350.00</td>\n",
       "      <td>Morty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ETH</td>\n",
       "      <td>sell</td>\n",
       "      <td>10.0</td>\n",
       "      <td>300.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>sell</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Rockfeller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>buy</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>J.P. Morgan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>John Doe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5900.00</td>\n",
       "      <td>John Doe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>sell</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>Rockfeller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BTC</td>\n",
       "      <td>sell</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6200.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  symbol  type  amount    price         user\n",
       "0    ETH  sell    10.0   400.00          NaN\n",
       "1    BTC   buy     1.0  5600.00     John Doe\n",
       "2    ETH   buy    20.0   350.00        Morty\n",
       "3    ETH  sell    10.0   300.00          NaN\n",
       "4   DOGE  sell  3000.0     0.15   Rockfeller\n",
       "5   DOGE   buy  5000.0     0.20  J.P. Morgan\n",
       "6    BTC   buy    10.0  6000.00     John Doe\n",
       "7    BTC   buy     2.5  5900.00     John Doe\n",
       "8   DOGE  sell  3000.0     0.10   Rockfeller\n",
       "9    BTC  sell     4.0  6200.00          NaN"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Левый join таблиц - все записи из первой таблицы остаются и к ним присоединяются записи из правой.\n",
    "# Аналогично работает правый join\n",
    "pd.merge(operations, transactions, on=['symbol', 'type'], how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>price</th>\n",
       "      <th>user</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ETH</td>\n",
       "      <td>sell</td>\n",
       "      <td>10.0</td>\n",
       "      <td>400.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ETH</td>\n",
       "      <td>sell</td>\n",
       "      <td>10.0</td>\n",
       "      <td>300.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5600.00</td>\n",
       "      <td>John Doe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>John Doe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BTC</td>\n",
       "      <td>buy</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5900.00</td>\n",
       "      <td>John Doe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>sell</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Rockfeller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>sell</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>Rockfeller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>buy</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>J.P. Morgan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BTC</td>\n",
       "      <td>sell</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6200.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ADA</td>\n",
       "      <td>sell</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rick</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symbol  type  amount    price         user\n",
       "0     ETH  sell    10.0   400.00          NaN\n",
       "1     ETH  sell    10.0   300.00          NaN\n",
       "2     BTC   buy     1.0  5600.00     John Doe\n",
       "3     BTC   buy    10.0  6000.00     John Doe\n",
       "4     BTC   buy     2.5  5900.00     John Doe\n",
       "..    ...   ...     ...      ...          ...\n",
       "6    DOGE  sell  3000.0     0.15   Rockfeller\n",
       "7    DOGE  sell  3000.0     0.10   Rockfeller\n",
       "8    DOGE   buy  5000.0     0.20  J.P. Morgan\n",
       "9     BTC  sell     4.0  6200.00          NaN\n",
       "10    ADA  sell     NaN      NaN         Rick\n",
       "\n",
       "[11 rows x 5 columns]"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Outer join - собираем вместе записи из обеих таблиц\n",
    "pd.merge(operations, transactions, on=['symbol', 'type'], how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "del operations_new, operations_old, operations, transactions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 13. Чтение таблиц"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы данные анализировать их надо где-то взять. Для этого в pandas есть множество функций. Все их названия начинаются с префикса 'read_'. Мы разберем функцию read_csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>txCount</th>\n",
       "      <th>txVolume</th>\n",
       "      <th>activeAddresses</th>\n",
       "      <th>symbol</th>\n",
       "      <th>name</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>market</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-04-28</td>\n",
       "      <td>135.300000</td>\n",
       "      <td>41702.0</td>\n",
       "      <td>6.879868e+07</td>\n",
       "      <td>117984.0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>135.300000</td>\n",
       "      <td>135.980000</td>\n",
       "      <td>132.100000</td>\n",
       "      <td>134.210000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.500520e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-04-28</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>9174.0</td>\n",
       "      <td>4.431952e+07</td>\n",
       "      <td>17216.0</td>\n",
       "      <td>LTC</td>\n",
       "      <td>Litecoin</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>4.180000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.377340e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>134.440000</td>\n",
       "      <td>51602.0</td>\n",
       "      <td>1.138128e+08</td>\n",
       "      <td>86925.0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>134.440000</td>\n",
       "      <td>147.490000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>144.540000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.491160e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>4.370000</td>\n",
       "      <td>9275.0</td>\n",
       "      <td>3.647810e+07</td>\n",
       "      <td>18395.0</td>\n",
       "      <td>LTC</td>\n",
       "      <td>Litecoin</td>\n",
       "      <td>4.370000</td>\n",
       "      <td>4.570000</td>\n",
       "      <td>4.230000</td>\n",
       "      <td>4.380000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.495270e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-04-30</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>47450.0</td>\n",
       "      <td>8.426632e+07</td>\n",
       "      <td>76871.0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>146.930000</td>\n",
       "      <td>134.050000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.597780e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37583</th>\n",
       "      <td>2018-06-06</td>\n",
       "      <td>0.293325</td>\n",
       "      <td>4830.0</td>\n",
       "      <td>2.312763e+05</td>\n",
       "      <td>659.0</td>\n",
       "      <td>XLM</td>\n",
       "      <td>Stellar</td>\n",
       "      <td>0.293325</td>\n",
       "      <td>0.299955</td>\n",
       "      <td>0.289500</td>\n",
       "      <td>0.298269</td>\n",
       "      <td>51165000.0</td>\n",
       "      <td>5.450080e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37584</th>\n",
       "      <td>2018-06-06</td>\n",
       "      <td>0.039586</td>\n",
       "      <td>7205.0</td>\n",
       "      <td>4.947760e+06</td>\n",
       "      <td>18228.0</td>\n",
       "      <td>XVG</td>\n",
       "      <td>Verge</td>\n",
       "      <td>0.039586</td>\n",
       "      <td>0.039737</td>\n",
       "      <td>0.037680</td>\n",
       "      <td>0.038797</td>\n",
       "      <td>9307450.0</td>\n",
       "      <td>5.959400e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37585</th>\n",
       "      <td>2018-06-06</td>\n",
       "      <td>239.760000</td>\n",
       "      <td>10687.0</td>\n",
       "      <td>3.986308e+07</td>\n",
       "      <td>96516.0</td>\n",
       "      <td>ZEC</td>\n",
       "      <td>Zcash</td>\n",
       "      <td>239.750000</td>\n",
       "      <td>240.340000</td>\n",
       "      <td>229.210000</td>\n",
       "      <td>236.050000</td>\n",
       "      <td>56887000.0</td>\n",
       "      <td>9.769940e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37586</th>\n",
       "      <td>2018-06-06</td>\n",
       "      <td>0.127555</td>\n",
       "      <td>1313.0</td>\n",
       "      <td>1.596436e+07</td>\n",
       "      <td>733.0</td>\n",
       "      <td>ZIL</td>\n",
       "      <td>Zilliqa</td>\n",
       "      <td>0.127555</td>\n",
       "      <td>0.133254</td>\n",
       "      <td>0.124194</td>\n",
       "      <td>0.131766</td>\n",
       "      <td>54667900.0</td>\n",
       "      <td>9.348810e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37587</th>\n",
       "      <td>2018-06-06</td>\n",
       "      <td>1.270000</td>\n",
       "      <td>795.0</td>\n",
       "      <td>6.094435e+06</td>\n",
       "      <td>599.0</td>\n",
       "      <td>ZRX</td>\n",
       "      <td>0x</td>\n",
       "      <td>1.270000</td>\n",
       "      <td>1.340000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.330000</td>\n",
       "      <td>17778000.0</td>\n",
       "      <td>6.741510e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37588 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date       price  txCount      txVolume  activeAddresses symbol  \\\n",
       "0      2013-04-28  135.300000  41702.0  6.879868e+07         117984.0    BTC   \n",
       "1      2013-04-28    4.300000   9174.0  4.431952e+07          17216.0    LTC   \n",
       "2      2013-04-29  134.440000  51602.0  1.138128e+08          86925.0    BTC   \n",
       "3      2013-04-29    4.370000   9275.0  3.647810e+07          18395.0    LTC   \n",
       "4      2013-04-30  144.000000  47450.0  8.426632e+07          76871.0    BTC   \n",
       "...           ...         ...      ...           ...              ...    ...   \n",
       "37583  2018-06-06    0.293325   4830.0  2.312763e+05            659.0    XLM   \n",
       "37584  2018-06-06    0.039586   7205.0  4.947760e+06          18228.0    XVG   \n",
       "37585  2018-06-06  239.760000  10687.0  3.986308e+07          96516.0    ZEC   \n",
       "37586  2018-06-06    0.127555   1313.0  1.596436e+07            733.0    ZIL   \n",
       "37587  2018-06-06    1.270000    795.0  6.094435e+06            599.0    ZRX   \n",
       "\n",
       "           name        open        high         low       close      volume  \\\n",
       "0       Bitcoin  135.300000  135.980000  132.100000  134.210000         0.0   \n",
       "1      Litecoin    4.300000    4.400000    4.180000    4.350000         0.0   \n",
       "2       Bitcoin  134.440000  147.490000  134.000000  144.540000         0.0   \n",
       "3      Litecoin    4.370000    4.570000    4.230000    4.380000         0.0   \n",
       "4       Bitcoin  144.000000  146.930000  134.050000  139.000000         0.0   \n",
       "...         ...         ...         ...         ...         ...         ...   \n",
       "37583   Stellar    0.293325    0.299955    0.289500    0.298269  51165000.0   \n",
       "37584     Verge    0.039586    0.039737    0.037680    0.038797   9307450.0   \n",
       "37585     Zcash  239.750000  240.340000  229.210000  236.050000  56887000.0   \n",
       "37586   Zilliqa    0.127555    0.133254    0.124194    0.131766  54667900.0   \n",
       "37587        0x    1.270000    1.340000    1.250000    1.330000  17778000.0   \n",
       "\n",
       "             market  \n",
       "0      1.500520e+09  \n",
       "1      7.377340e+07  \n",
       "2      1.491160e+09  \n",
       "3      7.495270e+07  \n",
       "4      1.597780e+09  \n",
       "...             ...  \n",
       "37583  5.450080e+09  \n",
       "37584  5.959400e+08  \n",
       "37585  9.769940e+08  \n",
       "37586  9.348810e+08  \n",
       "37587  6.741510e+08  \n",
       "\n",
       "[37588 rows x 13 columns]"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Прочитать таблицу ни о чем не задумываясь\n",
    "pd.read_csv('coins.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>txCount</th>\n",
       "      <th>txVolume</th>\n",
       "      <th>activeAddresses</th>\n",
       "      <th>symbol</th>\n",
       "      <th>name</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>market</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-04-28</th>\n",
       "      <td>135.300000</td>\n",
       "      <td>41702.0</td>\n",
       "      <td>6.879868e+07</td>\n",
       "      <td>117984.0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>135.300000</td>\n",
       "      <td>135.980000</td>\n",
       "      <td>132.100000</td>\n",
       "      <td>134.210000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.500520e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-28</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>9174.0</td>\n",
       "      <td>4.431952e+07</td>\n",
       "      <td>17216.0</td>\n",
       "      <td>LTC</td>\n",
       "      <td>Litecoin</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>4.180000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.377340e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-29</th>\n",
       "      <td>134.440000</td>\n",
       "      <td>51602.0</td>\n",
       "      <td>1.138128e+08</td>\n",
       "      <td>86925.0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>134.440000</td>\n",
       "      <td>147.490000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>144.540000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.491160e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-29</th>\n",
       "      <td>4.370000</td>\n",
       "      <td>9275.0</td>\n",
       "      <td>3.647810e+07</td>\n",
       "      <td>18395.0</td>\n",
       "      <td>LTC</td>\n",
       "      <td>Litecoin</td>\n",
       "      <td>4.370000</td>\n",
       "      <td>4.570000</td>\n",
       "      <td>4.230000</td>\n",
       "      <td>4.380000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.495270e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-30</th>\n",
       "      <td>144.000000</td>\n",
       "      <td>47450.0</td>\n",
       "      <td>8.426632e+07</td>\n",
       "      <td>76871.0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>146.930000</td>\n",
       "      <td>134.050000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.597780e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-06</th>\n",
       "      <td>0.293325</td>\n",
       "      <td>4830.0</td>\n",
       "      <td>2.312763e+05</td>\n",
       "      <td>659.0</td>\n",
       "      <td>XLM</td>\n",
       "      <td>Stellar</td>\n",
       "      <td>0.293325</td>\n",
       "      <td>0.299955</td>\n",
       "      <td>0.289500</td>\n",
       "      <td>0.298269</td>\n",
       "      <td>51165000.0</td>\n",
       "      <td>5.450080e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-06</th>\n",
       "      <td>0.039586</td>\n",
       "      <td>7205.0</td>\n",
       "      <td>4.947760e+06</td>\n",
       "      <td>18228.0</td>\n",
       "      <td>XVG</td>\n",
       "      <td>Verge</td>\n",
       "      <td>0.039586</td>\n",
       "      <td>0.039737</td>\n",
       "      <td>0.037680</td>\n",
       "      <td>0.038797</td>\n",
       "      <td>9307450.0</td>\n",
       "      <td>5.959400e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-06</th>\n",
       "      <td>239.760000</td>\n",
       "      <td>10687.0</td>\n",
       "      <td>3.986308e+07</td>\n",
       "      <td>96516.0</td>\n",
       "      <td>ZEC</td>\n",
       "      <td>Zcash</td>\n",
       "      <td>239.750000</td>\n",
       "      <td>240.340000</td>\n",
       "      <td>229.210000</td>\n",
       "      <td>236.050000</td>\n",
       "      <td>56887000.0</td>\n",
       "      <td>9.769940e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-06</th>\n",
       "      <td>0.127555</td>\n",
       "      <td>1313.0</td>\n",
       "      <td>1.596436e+07</td>\n",
       "      <td>733.0</td>\n",
       "      <td>ZIL</td>\n",
       "      <td>Zilliqa</td>\n",
       "      <td>0.127555</td>\n",
       "      <td>0.133254</td>\n",
       "      <td>0.124194</td>\n",
       "      <td>0.131766</td>\n",
       "      <td>54667900.0</td>\n",
       "      <td>9.348810e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-06</th>\n",
       "      <td>1.270000</td>\n",
       "      <td>795.0</td>\n",
       "      <td>6.094435e+06</td>\n",
       "      <td>599.0</td>\n",
       "      <td>ZRX</td>\n",
       "      <td>0x</td>\n",
       "      <td>1.270000</td>\n",
       "      <td>1.340000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.330000</td>\n",
       "      <td>17778000.0</td>\n",
       "      <td>6.741510e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37588 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 price  txCount      txVolume  activeAddresses symbol  \\\n",
       "date                                                                    \n",
       "2013-04-28  135.300000  41702.0  6.879868e+07         117984.0    BTC   \n",
       "2013-04-28    4.300000   9174.0  4.431952e+07          17216.0    LTC   \n",
       "2013-04-29  134.440000  51602.0  1.138128e+08          86925.0    BTC   \n",
       "2013-04-29    4.370000   9275.0  3.647810e+07          18395.0    LTC   \n",
       "2013-04-30  144.000000  47450.0  8.426632e+07          76871.0    BTC   \n",
       "...                ...      ...           ...              ...    ...   \n",
       "2018-06-06    0.293325   4830.0  2.312763e+05            659.0    XLM   \n",
       "2018-06-06    0.039586   7205.0  4.947760e+06          18228.0    XVG   \n",
       "2018-06-06  239.760000  10687.0  3.986308e+07          96516.0    ZEC   \n",
       "2018-06-06    0.127555   1313.0  1.596436e+07            733.0    ZIL   \n",
       "2018-06-06    1.270000    795.0  6.094435e+06            599.0    ZRX   \n",
       "\n",
       "                name        open        high         low       close  \\\n",
       "date                                                                   \n",
       "2013-04-28   Bitcoin  135.300000  135.980000  132.100000  134.210000   \n",
       "2013-04-28  Litecoin    4.300000    4.400000    4.180000    4.350000   \n",
       "2013-04-29   Bitcoin  134.440000  147.490000  134.000000  144.540000   \n",
       "2013-04-29  Litecoin    4.370000    4.570000    4.230000    4.380000   \n",
       "2013-04-30   Bitcoin  144.000000  146.930000  134.050000  139.000000   \n",
       "...              ...         ...         ...         ...         ...   \n",
       "2018-06-06   Stellar    0.293325    0.299955    0.289500    0.298269   \n",
       "2018-06-06     Verge    0.039586    0.039737    0.037680    0.038797   \n",
       "2018-06-06     Zcash  239.750000  240.340000  229.210000  236.050000   \n",
       "2018-06-06   Zilliqa    0.127555    0.133254    0.124194    0.131766   \n",
       "2018-06-06        0x    1.270000    1.340000    1.250000    1.330000   \n",
       "\n",
       "                volume        market  \n",
       "date                                  \n",
       "2013-04-28         0.0  1.500520e+09  \n",
       "2013-04-28         0.0  7.377340e+07  \n",
       "2013-04-29         0.0  1.491160e+09  \n",
       "2013-04-29         0.0  7.495270e+07  \n",
       "2013-04-30         0.0  1.597780e+09  \n",
       "...                ...           ...  \n",
       "2018-06-06  51165000.0  5.450080e+09  \n",
       "2018-06-06   9307450.0  5.959400e+08  \n",
       "2018-06-06  56887000.0  9.769940e+08  \n",
       "2018-06-06  54667900.0  9.348810e+08  \n",
       "2018-06-06  17778000.0  6.741510e+08  \n",
       "\n",
       "[37588 rows x 12 columns]"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Прочитать таблицу и использовать колонку date как индекс \n",
    "pd.read_csv('coins.csv', index_col='date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>price</th>\n",
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       "      <th>txVolume</th>\n",
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       "      <th>low</th>\n",
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       "      <th>market</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-04-28</td>\n",
       "      <td>135.300000</td>\n",
       "      <td>41702.0</td>\n",
       "      <td>6.879868e+07</td>\n",
       "      <td>117984.0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>135.300000</td>\n",
       "      <td>135.980000</td>\n",
       "      <td>132.100000</td>\n",
       "      <td>134.210000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.500520e+09</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-04-28</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>9174.0</td>\n",
       "      <td>4.431952e+07</td>\n",
       "      <td>17216.0</td>\n",
       "      <td>LTC</td>\n",
       "      <td>Litecoin</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>4.180000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.377340e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>134.440000</td>\n",
       "      <td>51602.0</td>\n",
       "      <td>1.138128e+08</td>\n",
       "      <td>86925.0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>134.440000</td>\n",
       "      <td>147.490000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>144.540000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.491160e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>4.370000</td>\n",
       "      <td>9275.0</td>\n",
       "      <td>3.647810e+07</td>\n",
       "      <td>18395.0</td>\n",
       "      <td>LTC</td>\n",
       "      <td>Litecoin</td>\n",
       "      <td>4.370000</td>\n",
       "      <td>4.570000</td>\n",
       "      <td>4.230000</td>\n",
       "      <td>4.380000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.495270e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-04-30</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>47450.0</td>\n",
       "      <td>8.426632e+07</td>\n",
       "      <td>76871.0</td>\n",
       "      <td>BTC</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>146.930000</td>\n",
       "      <td>134.050000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.597780e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37583</th>\n",
       "      <td>2018-06-06</td>\n",
       "      <td>0.293325</td>\n",
       "      <td>4830.0</td>\n",
       "      <td>2.312763e+05</td>\n",
       "      <td>659.0</td>\n",
       "      <td>XLM</td>\n",
       "      <td>Stellar</td>\n",
       "      <td>0.293325</td>\n",
       "      <td>0.299955</td>\n",
       "      <td>0.289500</td>\n",
       "      <td>0.298269</td>\n",
       "      <td>51165000.0</td>\n",
       "      <td>5.450080e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37584</th>\n",
       "      <td>2018-06-06</td>\n",
       "      <td>0.039586</td>\n",
       "      <td>7205.0</td>\n",
       "      <td>4.947760e+06</td>\n",
       "      <td>18228.0</td>\n",
       "      <td>XVG</td>\n",
       "      <td>Verge</td>\n",
       "      <td>0.039586</td>\n",
       "      <td>0.039737</td>\n",
       "      <td>0.037680</td>\n",
       "      <td>0.038797</td>\n",
       "      <td>9307450.0</td>\n",
       "      <td>5.959400e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37585</th>\n",
       "      <td>2018-06-06</td>\n",
       "      <td>239.760000</td>\n",
       "      <td>10687.0</td>\n",
       "      <td>3.986308e+07</td>\n",
       "      <td>96516.0</td>\n",
       "      <td>ZEC</td>\n",
       "      <td>Zcash</td>\n",
       "      <td>239.750000</td>\n",
       "      <td>240.340000</td>\n",
       "      <td>229.210000</td>\n",
       "      <td>236.050000</td>\n",
       "      <td>56887000.0</td>\n",
       "      <td>9.769940e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37586</th>\n",
       "      <td>2018-06-06</td>\n",
       "      <td>0.127555</td>\n",
       "      <td>1313.0</td>\n",
       "      <td>1.596436e+07</td>\n",
       "      <td>733.0</td>\n",
       "      <td>ZIL</td>\n",
       "      <td>Zilliqa</td>\n",
       "      <td>0.127555</td>\n",
       "      <td>0.133254</td>\n",
       "      <td>0.124194</td>\n",
       "      <td>0.131766</td>\n",
       "      <td>54667900.0</td>\n",
       "      <td>9.348810e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37587</th>\n",
       "      <td>2018-06-06</td>\n",
       "      <td>1.270000</td>\n",
       "      <td>795.0</td>\n",
       "      <td>6.094435e+06</td>\n",
       "      <td>599.0</td>\n",
       "      <td>ZRX</td>\n",
       "      <td>0x</td>\n",
       "      <td>1.270000</td>\n",
       "      <td>1.340000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.330000</td>\n",
       "      <td>17778000.0</td>\n",
       "      <td>6.741510e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37588 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date       price  txCount      txVolume  activeAddresses symbol  \\\n",
       "0      2013-04-28  135.300000  41702.0  6.879868e+07         117984.0    BTC   \n",
       "1      2013-04-28    4.300000   9174.0  4.431952e+07          17216.0    LTC   \n",
       "2      2013-04-29  134.440000  51602.0  1.138128e+08          86925.0    BTC   \n",
       "3      2013-04-29    4.370000   9275.0  3.647810e+07          18395.0    LTC   \n",
       "4      2013-04-30  144.000000  47450.0  8.426632e+07          76871.0    BTC   \n",
       "...           ...         ...      ...           ...              ...    ...   \n",
       "37583  2018-06-06    0.293325   4830.0  2.312763e+05            659.0    XLM   \n",
       "37584  2018-06-06    0.039586   7205.0  4.947760e+06          18228.0    XVG   \n",
       "37585  2018-06-06  239.760000  10687.0  3.986308e+07          96516.0    ZEC   \n",
       "37586  2018-06-06    0.127555   1313.0  1.596436e+07            733.0    ZIL   \n",
       "37587  2018-06-06    1.270000    795.0  6.094435e+06            599.0    ZRX   \n",
       "\n",
       "           name        open        high         low       close      volume  \\\n",
       "0       Bitcoin  135.300000  135.980000  132.100000  134.210000         0.0   \n",
       "1      Litecoin    4.300000    4.400000    4.180000    4.350000         0.0   \n",
       "2       Bitcoin  134.440000  147.490000  134.000000  144.540000         0.0   \n",
       "3      Litecoin    4.370000    4.570000    4.230000    4.380000         0.0   \n",
       "4       Bitcoin  144.000000  146.930000  134.050000  139.000000         0.0   \n",
       "...         ...         ...         ...         ...         ...         ...   \n",
       "37583   Stellar    0.293325    0.299955    0.289500    0.298269  51165000.0   \n",
       "37584     Verge    0.039586    0.039737    0.037680    0.038797   9307450.0   \n",
       "37585     Zcash  239.750000  240.340000  229.210000  236.050000  56887000.0   \n",
       "37586   Zilliqa    0.127555    0.133254    0.124194    0.131766  54667900.0   \n",
       "37587        0x    1.270000    1.340000    1.250000    1.330000  17778000.0   \n",
       "\n",
       "             market  \n",
       "0      1.500520e+09  \n",
       "1      7.377340e+07  \n",
       "2      1.491160e+09  \n",
       "3      7.495270e+07  \n",
       "4      1.597780e+09  \n",
       "...             ...  \n",
       "37583  5.450080e+09  \n",
       "37584  5.959400e+08  \n",
       "37585  9.769940e+08  \n",
       "37586  9.348810e+08  \n",
       "37587  6.741510e+08  \n",
       "\n",
       "[37588 rows x 13 columns]"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Прочитать таблицу и дать pandas подсказки по поводу типов данных в колонках symbol и name\n",
    "pd.read_csv('coins.csv', dtype={'symbol': str, 'name': str})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Запись таблиц"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "coins = pd.read_csv('coins.csv')\n",
    "\n",
    "coins.to_csv('file_name.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 14. Визуализация"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В pandas есть очень простые средства визуализации. Они подойдут для быстрого анализа, но если хочется сделать красиво и информативно, то вы не по адресу. Любая визуализация делается через аттрибут plot у объекта pandas.Series или pandas.DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Стоит отметить что для отображения графиков нужно использовать магическую команду для jupyter\n",
    "```\n",
    "%matplotlib\n",
    "```\n",
    "При этом график появится в отдельном окне. Там же будет несколько полезных кнопок для редактирования и сохранения графика. Если хочется чтобы графики отображались прямо в ноутбуке то надо передать аргумент inline\n",
    "```\n",
    "%matplotlib inline\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ADA</th>\n",
       "      <th>DOGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>0.547</td>\n",
       "      <td>0.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02</th>\n",
       "      <td>0.500</td>\n",
       "      <td>0.964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03</th>\n",
       "      <td>0.770</td>\n",
       "      <td>0.718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-04</th>\n",
       "      <td>0.135</td>\n",
       "      <td>0.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05</th>\n",
       "      <td>0.530</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-06</th>\n",
       "      <td>0.150</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ADA   DOGE\n",
       "2018-11-01  0.547  0.745\n",
       "2018-11-02  0.500  0.964\n",
       "2018-11-03  0.770  0.718\n",
       "2018-11-04  0.135  0.845\n",
       "2018-11-05  0.530    NaN\n",
       "2018-11-06  0.150  0.795"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price = pd.DataFrame(\n",
    "    data=[\n",
    "        [0.547, 0.745],\n",
    "        [0.5, 0.964],\n",
    "        [0.77, 0.718],\n",
    "        [0.135, 0.845],\n",
    "        [0.53, None],\n",
    "        [0.15, 0.795]],\n",
    "    columns=['ADA', 'DOGE'],\n",
    "    index=pd.date_range('2018-11-01', periods=6)\n",
    ")\n",
    "price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc0c04e7940>"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Нарисовать график цены монеты ADA. \n",
    "# Так как индекс в нашей таблице временный, то pandas догадывается, что его можно использовать\n",
    "# как ось времени на графиках.\n",
    "price['ADA'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc0c0426be0>"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Можно на одном графике нарисовать поведение цены сразу нескольких монет\n",
    "price[['ADA', 'DOGE']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc0bfd4c880>"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Цену можно рисовать и в виде столбцовой диаграммы c различными настройками\n",
    "price.plot.bar(stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc0bfccfa90>"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Горизонтальная столбцовая диаграмма\n",
    "price.plot.barh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc0bfc8ed00>"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Гистограмма\n",
    "price.plot.hist(bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc0bfbc96d0>"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ящики с усами\n",
    "price.plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc0bfb34b80>"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Графики с заливкой\n",
    "price.plot.area()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc0bfa9ff70>"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Если хочется нарисовать пары каких либо значений в виде точек на плоскости, то подойдет scatter plot\n",
    "price.plot.scatter(x='ADA', y='DOGE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fc0bfa7c550>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fc0bfa26400>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x540 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# И на десерт - пирожковая диаграмма\n",
    "price.plot.pie(subplots=True, figsize=(15,7.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "del price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# NB\n",
    "\n",
    "\n",
    "## Другие инструменты работы с данными\n",
    "\n",
    "Если ваш датасет не слишком большой, то pandas это де-факто лучший инструмент в наличии. В противном случае есть альтернативы:\n",
    " - [numpy](http://www.numpy.org/) - библиотека для низкоуровневой работы с многомерными массивами из примитивных типов данных. Подходит для \"векторизованной\" обработки. Numpy - ваш последний шанс все еще писать на обычном питоне, без биндингов на С. Подходит для задач быстрой обработки не слишком больших данных. На самом деле внутри, pandas использует numpy.\n",
    " - [Graphlab](https://turi.com/) - Платная питоновская библиотека для работы как большими данными так и с маленькими датасетами. Данные представляются как SFrame объекты, во многом похожие на DataFrame из pandas.\n",
    " - [Hadoop](https://hadoop.apache.org/) - стек технологии для работы с очень большими данными, в частности есть классический MapReduce.\n",
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Другие инструменты визуализации\n",
    "\n",
    "\n",
    "Для визуализации библиотек намного больше. Вот самые популярные\n",
    " - [Pandas](https://pandas.pydata.org/) - для быстрой и очень простой визуализации. Любая мелкая настройка потребует знания matplotlib\n",
    " - [Seaborn](https://seaborn.pydata.org/) - широкий но фиксированный набор сложных визуализаций с красивыми цветовыми схемами. Библиотека является надстройкой над matplotlib, и, опять же, тонкие настройки потребуют знания matplotlib.\n",
    "   - [Галерея с примерами](https://seaborn.pydata.org/examples/index.html)\n",
    "   - [Руководство](https://seaborn.pydata.org/tutorial.html)\n",
    " - [ggplot](http://ggplot.yhathq.com/) - многообещающая библиотека скопированная из R. Пока на этапе развития\n",
    " - [Bokeh](https://bokeh.pydata.org/en/latest/) - библиотека для визуализации в web.\n",
    " - [Plot.ly](https://plot.ly/) - библиотека для интерактивных и статических визуализаций. Нацелена на работу в облаке, но можно работать и локально.\n",
    "   - [Галерея с примерами](https://plot.ly/python/)\n",
    "   - [Краткое введение](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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