ds/Pandas_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "80928f6d",
   "metadata": {
    "id": "80928f6d"
   },
   "source": [
    "## Lambda-выражения\n",
    "\n",
    "Под lambda-выражениями будем понимать обработку каждого элемента списка одной операцией. Данный функциональный подход наиболее оптимизирован в Python для обработки большого массива данных и позволяет грамотно и лаконично задать процесс обработки в одну строку"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0bc557da",
   "metadata": {
    "id": "0bc557da"
   },
   "outputs": [],
   "source": [
    "def more3(l):\n",
    "    result = []\n",
    "    for n in l:\n",
    "        if n > 3:\n",
    "            result.append(n)\n",
    "    return result #[x > 3  for x in l]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "vZ_csTYBHhwS",
   "metadata": {
    "id": "vZ_csTYBHhwS"
   },
   "outputs": [],
   "source": [
    "list1 = [1, 2, 5, 4, 6, 3, 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "97883a44",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "97883a44",
    "outputId": "481832c7-c4f1-4ece-f987-67ae80170fe9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 4, 6, 4]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "more3(list1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3f1dabaa",
   "metadata": {
    "id": "3f1dabaa"
   },
   "outputs": [],
   "source": [
    "def isMoreThan3(n):\n",
    "    return n > 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f720a1c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 6]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(filter(isMoreThan3, list1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6158be00",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6158be00",
    "outputId": "869d8cb9-49de-4ff6-c2f5-7d13268533c4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 6]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list4 = filter(lambda x: x > 4, list1)\n",
    "list(list4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ca0be33b",
   "metadata": {
    "id": "ca0be33b",
    "outputId": "a3a945c2-180f-4a1d-b59b-861e540d3fb4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "322 ns ± 4.05 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit more3(list1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "452ea6cb",
   "metadata": {
    "id": "452ea6cb",
    "outputId": "412480ee-0735-4262-a336-da21cddf43e4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "122 ns ± 0.105 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit filter(lambda x: x > 3, list1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d11e17b1",
   "metadata": {
    "id": "d11e17b1",
    "outputId": "db34cb25-8b08-4870-a41b-e4d8f69016e3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "81.3 ns ± 0.285 ns per loop (mean ± std. dev. of 7 runs, 10,000,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit filter(isMoreThan3, list1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8971ecad",
   "metadata": {
    "id": "8971ecad",
    "outputId": "55f4e1f1-7b71-45dc-81ed-359c184aac4b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 4, 10, 8, 12, 6, 8]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list5 = map(lambda x: x * 2, list1)\n",
    "list(list5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f5f72aa2",
   "metadata": {
    "id": "f5f72aa2",
    "outputId": "5403aec7-dbe4-4276-ea0e-48374497f628"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('a', 1), ('b', 2), ('c', 3)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strings = ['a', 'b', 'c']\n",
    "numbers = [1, 2, 3, 4, 5]\n",
    "list(zip(strings, numbers))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c3c7dbe5",
   "metadata": {
    "id": "c3c7dbe5",
    "outputId": "88c1b62f-8d03-4029-e076-fde50f713feb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from functools import reduce\n",
    "list6 = [1, 2, 3, 4, 5]\n",
    "reduce(lambda x, y: x * y, list6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "aac78d48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'id': 1, 'timestamp': 3, 'amount': 2},\n",
       " {'id': 2, 'timestamp': 4, 'amount': 8}]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter(operations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "tobpdSPRHdQV",
   "metadata": {
    "id": "tobpdSPRHdQV"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36396f4c",
   "metadata": {
    "id": "36396f4c"
   },
   "source": [
    "## Датафреймы\n",
    "\n",
    "Для обработки данных в питоне обычно используют датафреймы. В датафреймах удобно работать с различного рода таблицами, проводить над ними операции, использовать некоторые операции из баз данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f06545d5",
   "metadata": {
    "id": "f06545d5"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0d81ef0c",
   "metadata": {
    "id": "0d81ef0c"
   },
   "outputs": [],
   "source": [
    "ser = pd.Series([1, 2, 3], index=[3, 2, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8c8efb29",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8c8efb29",
    "outputId": "6e2ffd5a-1d3f-4503-f5ad-9b93b100fdda"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    1\n",
       "2    2\n",
       "1    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "w4aPRY_nWzxy",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "w4aPRY_nWzxy",
    "outputId": "77f996e1-ffe5-4edc-ad6c-03b06950de8c"
   },
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>0</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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      "text/plain": [
       "   index  0\n",
       "0      3  1\n",
       "1      2  2\n",
       "2      1  3"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "ser.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e0971d1",
   "metadata": {},
   "source": [
    "`DataFrame` - это удобное представление табличных данных в Python. Каждая колонка данных состоит из специального масси `pd.Series`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "512af0ec",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 112
    },
    "id": "512af0ec",
    "outputId": "27e41b75-3389-4d52-8ad4-aa9c77a371b4"
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   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
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       "      <th>2</th>\n",
       "      <td>4</td>\n",
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      "text/plain": [
       "   a  b\n",
       "0  1  2\n",
       "1  3  4\n",
       "2  4  5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'a': [1, 3, 4], 'b': [2, 4, 5]})\n",
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a1ceeeb",
   "metadata": {},
   "source": [
    "Проверим тип с которым создалась переменная df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3229c72a",
   "metadata": {
    "id": "3229c72a",
    "outputId": "102c1951-ba4a-4fac-cc29-c235d6046ad6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88985bb2",
   "metadata": {},
   "source": [
    "В DataFrame так же можно добавлять значения в колонки, как в примере ниже"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "88c4441d",
   "metadata": {
    "id": "88c4441d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_9144/2466297743.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({'a': 5, 'b': 6}, ignore_index=True)\n"
     ]
    }
   ],
   "source": [
    "df = df.append({'a': 5, 'b': 6}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d5551bc8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "id": "d5551bc8",
    "outputId": "894eea7e-ba57-40f3-d975-e338dcb270e2"
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   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .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>a</th>\n",
       "      <th>b</th>\n",
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       "  <tbody>\n",
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       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
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      "text/plain": [
       "   a  b\n",
       "0  1  2\n",
       "1  3  4\n",
       "2  4  5\n",
       "3  5  6"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cda56d0",
   "metadata": {},
   "source": [
    "Так же DataFrame поддерживает загрузки из популярных форматов хранения данных CSV (Comma Seporated Value), формат MS Excel, JSON и тд.\n",
    "\n",
    "> **Запомни!**\n",
    "> \n",
    "> Не стоит загружать большие наборы данных через DataFrame. Почему? Потому что DataFrame загружается сразу весь файл в память, и твоей памяти может просто не хватить. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e75c4c9b",
   "metadata": {
    "id": "e75c4c9b"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/TSLA.csv') # можно считывать разные файлы: csv - read_csv, xlsx - read_excel и тд"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "111812c1",
   "metadata": {},
   "source": [
    "При простом вызове переменной `df` вы увидете первые 5 значние и последние 5 значений"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1d065fc9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 597
    },
    "id": "1d065fc9",
    "outputId": "7bb15e93-2bec-4893-8b80-f0b0840b4ffb"
   },
   "outputs": [
    {
     "data": {
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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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-06-29</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>17.540001</td>\n",
       "      <td>23.889999</td>\n",
       "      <td>23.889999</td>\n",
       "      <td>18766300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-06-30</td>\n",
       "      <td>25.790001</td>\n",
       "      <td>30.420000</td>\n",
       "      <td>23.299999</td>\n",
       "      <td>23.830000</td>\n",
       "      <td>23.830000</td>\n",
       "      <td>17187100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-07-01</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.920000</td>\n",
       "      <td>20.270000</td>\n",
       "      <td>21.959999</td>\n",
       "      <td>21.959999</td>\n",
       "      <td>8218800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-07-02</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>18.709999</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>5139800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-07-06</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>15.830000</td>\n",
       "      <td>16.110001</td>\n",
       "      <td>16.110001</td>\n",
       "      <td>6866900</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2411</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>568.489990</td>\n",
       "      <td>576.809998</td>\n",
       "      <td>558.080017</td>\n",
       "      <td>566.900024</td>\n",
       "      <td>566.900024</td>\n",
       "      <td>11788500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2412</th>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>575.690002</td>\n",
       "      <td>589.799988</td>\n",
       "      <td>567.429993</td>\n",
       "      <td>580.989990</td>\n",
       "      <td>580.989990</td>\n",
       "      <td>17801500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2413</th>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>632.419983</td>\n",
       "      <td>650.880005</td>\n",
       "      <td>618.000000</td>\n",
       "      <td>640.809998</td>\n",
       "      <td>640.809998</td>\n",
       "      <td>29005700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2414</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>640.000000</td>\n",
       "      <td>653.000000</td>\n",
       "      <td>632.520020</td>\n",
       "      <td>650.570007</td>\n",
       "      <td>650.570007</td>\n",
       "      <td>15719300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2415</th>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>673.690002</td>\n",
       "      <td>786.140015</td>\n",
       "      <td>673.520020</td>\n",
       "      <td>780.000000</td>\n",
       "      <td>780.000000</td>\n",
       "      <td>47065000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2416 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date        Open        High         Low       Close   Adj Close  \\\n",
       "0     2010-06-29   19.000000   25.000000   17.540001   23.889999   23.889999   \n",
       "1     2010-06-30   25.790001   30.420000   23.299999   23.830000   23.830000   \n",
       "2     2010-07-01   25.000000   25.920000   20.270000   21.959999   21.959999   \n",
       "3     2010-07-02   23.000000   23.100000   18.709999   19.200001   19.200001   \n",
       "4     2010-07-06   20.000000   20.000000   15.830000   16.110001   16.110001   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "2411  2020-01-28  568.489990  576.809998  558.080017  566.900024  566.900024   \n",
       "2412  2020-01-29  575.690002  589.799988  567.429993  580.989990  580.989990   \n",
       "2413  2020-01-30  632.419983  650.880005  618.000000  640.809998  640.809998   \n",
       "2414  2020-01-31  640.000000  653.000000  632.520020  650.570007  650.570007   \n",
       "2415  2020-02-03  673.690002  786.140015  673.520020  780.000000  780.000000   \n",
       "\n",
       "        Volume  \n",
       "0     18766300  \n",
       "1     17187100  \n",
       "2      8218800  \n",
       "3      5139800  \n",
       "4      6866900  \n",
       "...        ...  \n",
       "2411  11788500  \n",
       "2412  17801500  \n",
       "2413  29005700  \n",
       "2414  15719300  \n",
       "2415  47065000  \n",
       "\n",
       "[2416 rows x 7 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2baf041",
   "metadata": {},
   "source": [
    "Или можем сразу вызвать функцию `head()` которая выведет первые 5 строк, ну или указав из количество `head(3)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "47dbdd67",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "47dbdd67",
    "outputId": "884fbb44-0ac4-4765-b8a3-c585d5814c94"
   },
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-06-29</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.00</td>\n",
       "      <td>17.540001</td>\n",
       "      <td>23.889999</td>\n",
       "      <td>23.889999</td>\n",
       "      <td>18766300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-06-30</td>\n",
       "      <td>25.790001</td>\n",
       "      <td>30.42</td>\n",
       "      <td>23.299999</td>\n",
       "      <td>23.830000</td>\n",
       "      <td>23.830000</td>\n",
       "      <td>17187100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-07-01</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.92</td>\n",
       "      <td>20.270000</td>\n",
       "      <td>21.959999</td>\n",
       "      <td>21.959999</td>\n",
       "      <td>8218800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-07-02</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>23.10</td>\n",
       "      <td>18.709999</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>5139800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-07-06</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.00</td>\n",
       "      <td>15.830000</td>\n",
       "      <td>16.110001</td>\n",
       "      <td>16.110001</td>\n",
       "      <td>6866900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date       Open   High        Low      Close  Adj Close    Volume\n",
       "0  2010-06-29  19.000000  25.00  17.540001  23.889999  23.889999  18766300\n",
       "1  2010-06-30  25.790001  30.42  23.299999  23.830000  23.830000  17187100\n",
       "2  2010-07-01  25.000000  25.92  20.270000  21.959999  21.959999   8218800\n",
       "3  2010-07-02  23.000000  23.10  18.709999  19.200001  19.200001   5139800\n",
       "4  2010-07-06  20.000000  20.00  15.830000  16.110001  16.110001   6866900"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# head(x) дает взглянуть на x первых строк\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec3b0a7b",
   "metadata": {},
   "source": [
    "Тоже самое что и `head` только с конца"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5FivPKCqRFS0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 293
    },
    "id": "5FivPKCqRFS0",
    "outputId": "4e2a4081-82e3-4a2f-9e5b-0d52af764e74"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2411</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>568.489990</td>\n",
       "      <td>576.809998</td>\n",
       "      <td>558.080017</td>\n",
       "      <td>566.900024</td>\n",
       "      <td>566.900024</td>\n",
       "      <td>11788500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2412</th>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>575.690002</td>\n",
       "      <td>589.799988</td>\n",
       "      <td>567.429993</td>\n",
       "      <td>580.989990</td>\n",
       "      <td>580.989990</td>\n",
       "      <td>17801500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2413</th>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>632.419983</td>\n",
       "      <td>650.880005</td>\n",
       "      <td>618.000000</td>\n",
       "      <td>640.809998</td>\n",
       "      <td>640.809998</td>\n",
       "      <td>29005700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2414</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>640.000000</td>\n",
       "      <td>653.000000</td>\n",
       "      <td>632.520020</td>\n",
       "      <td>650.570007</td>\n",
       "      <td>650.570007</td>\n",
       "      <td>15719300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2415</th>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>673.690002</td>\n",
       "      <td>786.140015</td>\n",
       "      <td>673.520020</td>\n",
       "      <td>780.000000</td>\n",
       "      <td>780.000000</td>\n",
       "      <td>47065000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date        Open        High         Low       Close   Adj Close  \\\n",
       "2411  2020-01-28  568.489990  576.809998  558.080017  566.900024  566.900024   \n",
       "2412  2020-01-29  575.690002  589.799988  567.429993  580.989990  580.989990   \n",
       "2413  2020-01-30  632.419983  650.880005  618.000000  640.809998  640.809998   \n",
       "2414  2020-01-31  640.000000  653.000000  632.520020  650.570007  650.570007   \n",
       "2415  2020-02-03  673.690002  786.140015  673.520020  780.000000  780.000000   \n",
       "\n",
       "        Volume  \n",
       "2411  11788500  \n",
       "2412  17801500  \n",
       "2413  29005700  \n",
       "2414  15719300  \n",
       "2415  47065000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1484adf6",
   "metadata": {},
   "source": [
    "При помощи функции `info()` можно взглянуть на общую информацию о наборе данных. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "84283b6e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "84283b6e",
    "outputId": "79128f3e-6354-4eed-d464-5453a9331f7b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2416 entries, 0 to 2415\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Date       2416 non-null   object \n",
      " 1   Open       2416 non-null   float64\n",
      " 2   High       2416 non-null   float64\n",
      " 3   Low        2416 non-null   float64\n",
      " 4   Close      2416 non-null   float64\n",
      " 5   Adj Close  2416 non-null   float64\n",
      " 6   Volume     2416 non-null   int64  \n",
      "dtypes: float64(5), int64(1), object(1)\n",
      "memory usage: 132.2+ KB\n"
     ]
    }
   ],
   "source": [
    "#info() сразу дает информацию о типах данных и количестве NaN значений\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a77e7ab9",
   "metadata": {},
   "source": [
    "Доступ к конкретным колонкам, можно получить просто указав из как в словаре. Вам возратиться объект `Series`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "NXRm7RJeRD__",
   "metadata": {
    "id": "NXRm7RJeRD__"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       18766300\n",
       "1       17187100\n",
       "2        8218800\n",
       "3        5139800\n",
       "4        6866900\n",
       "          ...   \n",
       "2411    11788500\n",
       "2412    17801500\n",
       "2413    29005700\n",
       "2414    15719300\n",
       "2415    47065000\n",
       "Name: Volume, Length: 2416, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Volume\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b9961f99",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b9961f99",
    "outputId": "6470f69d-d031-4c25-c667-2515147ef1c5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2010-06-29\n",
       "1       2010-06-30\n",
       "2       2010-07-01\n",
       "3       2010-07-02\n",
       "4       2010-07-06\n",
       "           ...    \n",
       "2411    2020-01-28\n",
       "2412    2020-01-29\n",
       "2413    2020-01-30\n",
       "2414    2020-01-31\n",
       "2415    2020-02-03\n",
       "Name: Date, Length: 2416, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Чтобы посмотреть на колонку, надо указать ее название в ковычках и квадратных скобках\n",
    "\n",
    "df['Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bNWzmIAM1UAB",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bNWzmIAM1UAB",
    "outputId": "f8573cd7-bd4f-467b-f9b5-6b3d844998d4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2010-06-29\n",
       "1       2010-06-30\n",
       "2       2010-07-01\n",
       "3       2010-07-02\n",
       "4       2010-07-06\n",
       "           ...    \n",
       "2411    2020-01-28\n",
       "2412    2020-01-29\n",
       "2413    2020-01-30\n",
       "2414    2020-01-31\n",
       "2415    2020-02-03\n",
       "Name: Date, Length: 2416, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# либо через точку\n",
    "\n",
    "df.Date"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8e14dd3",
   "metadata": {},
   "source": [
    "Так же если вы хотите получить несколко колонок сразу, то необходимо указать их в двойных квадратных скобках `[[ ]]`. \n",
    "\n",
    "Пример ниже"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f040f045",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "f040f045",
    "outputId": "0ae6d909-1d78-48f1-ae92-c3b412d60c10"
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-07-02</td>\n",
       "      <td>5139800</td>\n",
       "      <td>19.200001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-07-06</td>\n",
       "      <td>6866900</td>\n",
       "      <td>16.110001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2411</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>11788500</td>\n",
       "      <td>566.900024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2412</th>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>17801500</td>\n",
       "      <td>580.989990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2413</th>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>29005700</td>\n",
       "      <td>640.809998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2414</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>15719300</td>\n",
       "      <td>650.570007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2415</th>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>47065000</td>\n",
       "      <td>780.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2416 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date    Volume       Close\n",
       "0     2010-06-29  18766300   23.889999\n",
       "1     2010-06-30  17187100   23.830000\n",
       "2     2010-07-01   8218800   21.959999\n",
       "3     2010-07-02   5139800   19.200001\n",
       "4     2010-07-06   6866900   16.110001\n",
       "...          ...       ...         ...\n",
       "2411  2020-01-28  11788500  566.900024\n",
       "2412  2020-01-29  17801500  580.989990\n",
       "2413  2020-01-30  29005700  640.809998\n",
       "2414  2020-01-31  15719300  650.570007\n",
       "2415  2020-02-03  47065000  780.000000\n",
       "\n",
       "[2416 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Date', 'Volume', \"Close\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb7b5b79",
   "metadata": {},
   "source": [
    "Получить можно не только колонку но и всю строку по последовательности, через экземпляр свойства (property) `loc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c2d78f0a",
   "metadata": {
    "id": "c2d78f0a",
    "outputId": "8925edd7-c89a-467a-fac4-fcc72142e0ac"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         2010-06-30\n",
       "Open          25.790001\n",
       "High              30.42\n",
       "Low           23.299999\n",
       "Close             23.83\n",
       "Adj Close         23.83\n",
       "Volume         17187100\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Строка с указанным номером\n",
    "\n",
    "df.loc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f4e6eefe",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = [100,101,1231, 2199]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dca2f9d",
   "metadata": {},
   "source": [
    "Или же можем получить несколько строк сразу передав массив"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a960e5d9",
   "metadata": {
    "id": "a960e5d9",
    "outputId": "215e5200-10cf-4e42-a131-715280357de5"
   },
   "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",
       "    .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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2010-11-18</td>\n",
       "      <td>30.670000</td>\n",
       "      <td>30.740000</td>\n",
       "      <td>28.920000</td>\n",
       "      <td>29.889999</td>\n",
       "      <td>29.889999</td>\n",
       "      <td>956100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>2010-11-19</td>\n",
       "      <td>30.160000</td>\n",
       "      <td>31.370001</td>\n",
       "      <td>29.700001</td>\n",
       "      <td>30.990000</td>\n",
       "      <td>30.990000</td>\n",
       "      <td>1150500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>2015-05-20</td>\n",
       "      <td>247.130005</td>\n",
       "      <td>247.740005</td>\n",
       "      <td>241.369995</td>\n",
       "      <td>244.350006</td>\n",
       "      <td>244.350006</td>\n",
       "      <td>3755600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2199</th>\n",
       "      <td>2019-03-26</td>\n",
       "      <td>264.440002</td>\n",
       "      <td>270.260010</td>\n",
       "      <td>264.429993</td>\n",
       "      <td>267.769989</td>\n",
       "      <td>267.769989</td>\n",
       "      <td>7350900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date        Open        High         Low       Close   Adj Close  \\\n",
       "100   2010-11-18   30.670000   30.740000   28.920000   29.889999   29.889999   \n",
       "101   2010-11-19   30.160000   31.370001   29.700001   30.990000   30.990000   \n",
       "1231  2015-05-20  247.130005  247.740005  241.369995  244.350006  244.350006   \n",
       "2199  2019-03-26  264.440002  270.260010  264.429993  267.769989  267.769989   \n",
       "\n",
       "       Volume  \n",
       "100    956100  \n",
       "101   1150500  \n",
       "1231  3755600  \n",
       "2199  7350900  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Либо строки\n",
    "\n",
    "df.loc[index]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60f40b43",
   "metadata": {},
   "source": [
    "Получить можно не только колонку но и всю строку по индексу, через экземпляр свойства (property) `iloc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ba7214a",
   "metadata": {
    "id": "9ba7214a",
    "outputId": "f7f2ec65-3feb-4494-c726-c299865ff5a2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         2010-07-07\n",
       "Open              3.280\n",
       "High              3.326\n",
       "Low               2.996\n",
       "Close             3.160\n",
       "Adj Close         3.160\n",
       "Volume         34608500\n",
       "Name: 5, dtype: object"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# iloc - строка с указанным индексом\n",
    "\n",
    "df.iloc[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f2dab2c",
   "metadata": {
    "id": "2f2dab2c",
    "outputId": "e47595bc-265d-45af-96c1-f3da9200d2e0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2010-07-07</td>\n",
       "      <td>3.280</td>\n",
       "      <td>3.326</td>\n",
       "      <td>2.996</td>\n",
       "      <td>3.16</td>\n",
       "      <td>3.16</td>\n",
       "      <td>34608500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2010-07-09</td>\n",
       "      <td>3.516</td>\n",
       "      <td>3.580</td>\n",
       "      <td>3.310</td>\n",
       "      <td>3.48</td>\n",
       "      <td>3.48</td>\n",
       "      <td>20253000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Open   High    Low  Close  Adj Close    Volume\n",
       "5  2010-07-07  3.280  3.326  2.996   3.16       3.16  34608500\n",
       "7  2010-07-09  3.516  3.580  3.310   3.48       3.48  20253000"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[5, 7]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "37831453",
   "metadata": {
    "id": "37831453",
    "outputId": "65569efa-9ae4-427c-eeab-39de79b37c92"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2010-07-28</td>\n",
       "      <td>20.549999</td>\n",
       "      <td>20.900000</td>\n",
       "      <td>20.510000</td>\n",
       "      <td>20.719999</td>\n",
       "      <td>20.719999</td>\n",
       "      <td>467200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2010-07-29</td>\n",
       "      <td>20.770000</td>\n",
       "      <td>20.879999</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.350000</td>\n",
       "      <td>20.350000</td>\n",
       "      <td>616000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2010-07-30</td>\n",
       "      <td>20.200001</td>\n",
       "      <td>20.440001</td>\n",
       "      <td>19.549999</td>\n",
       "      <td>19.940001</td>\n",
       "      <td>19.940001</td>\n",
       "      <td>426900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2010-08-02</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>20.969999</td>\n",
       "      <td>20.330000</td>\n",
       "      <td>20.920000</td>\n",
       "      <td>20.920000</td>\n",
       "      <td>718100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2010-08-03</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>21.950001</td>\n",
       "      <td>20.820000</td>\n",
       "      <td>21.950001</td>\n",
       "      <td>21.950001</td>\n",
       "      <td>1230500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2010-08-04</td>\n",
       "      <td>21.950001</td>\n",
       "      <td>22.180000</td>\n",
       "      <td>20.850000</td>\n",
       "      <td>21.260000</td>\n",
       "      <td>21.260000</td>\n",
       "      <td>913000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2010-08-05</td>\n",
       "      <td>21.540001</td>\n",
       "      <td>21.549999</td>\n",
       "      <td>20.049999</td>\n",
       "      <td>20.450001</td>\n",
       "      <td>20.450001</td>\n",
       "      <td>796200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2010-08-06</td>\n",
       "      <td>20.100000</td>\n",
       "      <td>20.160000</td>\n",
       "      <td>19.520000</td>\n",
       "      <td>19.590000</td>\n",
       "      <td>19.590000</td>\n",
       "      <td>741900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2010-08-09</td>\n",
       "      <td>19.900000</td>\n",
       "      <td>19.980000</td>\n",
       "      <td>19.450001</td>\n",
       "      <td>19.600000</td>\n",
       "      <td>19.600000</td>\n",
       "      <td>812700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2010-08-10</td>\n",
       "      <td>19.650000</td>\n",
       "      <td>19.650000</td>\n",
       "      <td>18.820000</td>\n",
       "      <td>19.030001</td>\n",
       "      <td>19.030001</td>\n",
       "      <td>1281300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2010-08-11</td>\n",
       "      <td>18.690001</td>\n",
       "      <td>18.879999</td>\n",
       "      <td>17.850000</td>\n",
       "      <td>17.900000</td>\n",
       "      <td>17.900000</td>\n",
       "      <td>797600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2010-08-12</td>\n",
       "      <td>17.799999</td>\n",
       "      <td>17.900000</td>\n",
       "      <td>17.389999</td>\n",
       "      <td>17.600000</td>\n",
       "      <td>17.600000</td>\n",
       "      <td>691000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2010-08-13</td>\n",
       "      <td>18.180000</td>\n",
       "      <td>18.450001</td>\n",
       "      <td>17.660000</td>\n",
       "      <td>18.320000</td>\n",
       "      <td>18.320000</td>\n",
       "      <td>634000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2010-08-16</td>\n",
       "      <td>18.450001</td>\n",
       "      <td>18.799999</td>\n",
       "      <td>18.260000</td>\n",
       "      <td>18.780001</td>\n",
       "      <td>18.780001</td>\n",
       "      <td>485800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2010-08-17</td>\n",
       "      <td>18.959999</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>18.780001</td>\n",
       "      <td>19.150000</td>\n",
       "      <td>19.150000</td>\n",
       "      <td>447900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2010-08-18</td>\n",
       "      <td>19.590000</td>\n",
       "      <td>19.590000</td>\n",
       "      <td>18.600000</td>\n",
       "      <td>18.770000</td>\n",
       "      <td>18.770000</td>\n",
       "      <td>601300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2010-08-19</td>\n",
       "      <td>18.540001</td>\n",
       "      <td>19.250000</td>\n",
       "      <td>18.330000</td>\n",
       "      <td>18.790001</td>\n",
       "      <td>18.790001</td>\n",
       "      <td>579100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2010-08-20</td>\n",
       "      <td>18.650000</td>\n",
       "      <td>19.110001</td>\n",
       "      <td>18.510000</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>296000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2010-08-23</td>\n",
       "      <td>19.090000</td>\n",
       "      <td>20.389999</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>20.129999</td>\n",
       "      <td>20.129999</td>\n",
       "      <td>1088100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2010-08-24</td>\n",
       "      <td>19.250000</td>\n",
       "      <td>19.709999</td>\n",
       "      <td>18.950001</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>673100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2010-08-25</td>\n",
       "      <td>19.160000</td>\n",
       "      <td>19.980000</td>\n",
       "      <td>18.559999</td>\n",
       "      <td>19.900000</td>\n",
       "      <td>19.900000</td>\n",
       "      <td>503300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date       Open       High        Low      Close  Adj Close   Volume\n",
       "20  2010-07-28  20.549999  20.900000  20.510000  20.719999  20.719999   467200\n",
       "21  2010-07-29  20.770000  20.879999  20.000000  20.350000  20.350000   616000\n",
       "22  2010-07-30  20.200001  20.440001  19.549999  19.940001  19.940001   426900\n",
       "23  2010-08-02  20.500000  20.969999  20.330000  20.920000  20.920000   718100\n",
       "24  2010-08-03  21.000000  21.950001  20.820000  21.950001  21.950001  1230500\n",
       "25  2010-08-04  21.950001  22.180000  20.850000  21.260000  21.260000   913000\n",
       "26  2010-08-05  21.540001  21.549999  20.049999  20.450001  20.450001   796200\n",
       "27  2010-08-06  20.100000  20.160000  19.520000  19.590000  19.590000   741900\n",
       "28  2010-08-09  19.900000  19.980000  19.450001  19.600000  19.600000   812700\n",
       "29  2010-08-10  19.650000  19.650000  18.820000  19.030001  19.030001  1281300\n",
       "30  2010-08-11  18.690001  18.879999  17.850000  17.900000  17.900000   797600\n",
       "31  2010-08-12  17.799999  17.900000  17.389999  17.600000  17.600000   691000\n",
       "32  2010-08-13  18.180000  18.450001  17.660000  18.320000  18.320000   634000\n",
       "33  2010-08-16  18.450001  18.799999  18.260000  18.780001  18.780001   485800\n",
       "34  2010-08-17  18.959999  19.400000  18.780001  19.150000  19.150000   447900\n",
       "35  2010-08-18  19.590000  19.590000  18.600000  18.770000  18.770000   601300\n",
       "36  2010-08-19  18.540001  19.250000  18.330000  18.790001  18.790001   579100\n",
       "37  2010-08-20  18.650000  19.110001  18.510000  19.100000  19.100000   296000\n",
       "38  2010-08-23  19.090000  20.389999  19.000000  20.129999  20.129999  1088100\n",
       "39  2010-08-24  19.250000  19.709999  18.950001  19.200001  19.200001   673100\n",
       "40  2010-08-25  19.160000  19.980000  18.559999  19.900000  19.900000   503300"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[20:41]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4a95e5a",
   "metadata": {
    "id": "f4a95e5a",
    "outputId": "d1adf574-14f6-465d-b56b-a7d2f399c2b3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2010-09-09</td>\n",
       "      <td>4.200</td>\n",
       "      <td>4.210</td>\n",
       "      <td>4.138</td>\n",
       "      <td>4.142</td>\n",
       "      <td>4.142</td>\n",
       "      <td>1881000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2010-09-10</td>\n",
       "      <td>4.150</td>\n",
       "      <td>4.186</td>\n",
       "      <td>3.952</td>\n",
       "      <td>4.034</td>\n",
       "      <td>4.034</td>\n",
       "      <td>1933000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2010-09-13</td>\n",
       "      <td>4.178</td>\n",
       "      <td>4.180</td>\n",
       "      <td>4.100</td>\n",
       "      <td>4.144</td>\n",
       "      <td>4.144</td>\n",
       "      <td>1804000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2010-09-14</td>\n",
       "      <td>4.108</td>\n",
       "      <td>4.320</td>\n",
       "      <td>4.106</td>\n",
       "      <td>4.224</td>\n",
       "      <td>4.224</td>\n",
       "      <td>3273500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2010-09-15</td>\n",
       "      <td>4.196</td>\n",
       "      <td>4.400</td>\n",
       "      <td>4.158</td>\n",
       "      <td>4.396</td>\n",
       "      <td>4.396</td>\n",
       "      <td>3423000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2010-09-16</td>\n",
       "      <td>4.430</td>\n",
       "      <td>4.632</td>\n",
       "      <td>4.168</td>\n",
       "      <td>4.188</td>\n",
       "      <td>4.188</td>\n",
       "      <td>13422500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2010-09-17</td>\n",
       "      <td>4.204</td>\n",
       "      <td>4.264</td>\n",
       "      <td>3.960</td>\n",
       "      <td>4.046</td>\n",
       "      <td>4.046</td>\n",
       "      <td>5992500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2010-09-20</td>\n",
       "      <td>4.134</td>\n",
       "      <td>4.270</td>\n",
       "      <td>4.032</td>\n",
       "      <td>4.212</td>\n",
       "      <td>4.212</td>\n",
       "      <td>4737500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2010-09-21</td>\n",
       "      <td>4.178</td>\n",
       "      <td>4.310</td>\n",
       "      <td>4.134</td>\n",
       "      <td>4.154</td>\n",
       "      <td>4.154</td>\n",
       "      <td>3980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2010-09-22</td>\n",
       "      <td>4.174</td>\n",
       "      <td>4.190</td>\n",
       "      <td>3.960</td>\n",
       "      <td>3.974</td>\n",
       "      <td>3.974</td>\n",
       "      <td>4814500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2010-09-23</td>\n",
       "      <td>3.978</td>\n",
       "      <td>4.028</td>\n",
       "      <td>3.900</td>\n",
       "      <td>3.912</td>\n",
       "      <td>3.912</td>\n",
       "      <td>3340500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2010-09-24</td>\n",
       "      <td>3.990</td>\n",
       "      <td>4.038</td>\n",
       "      <td>3.930</td>\n",
       "      <td>4.020</td>\n",
       "      <td>4.020</td>\n",
       "      <td>2894500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2010-09-27</td>\n",
       "      <td>4.080</td>\n",
       "      <td>4.162</td>\n",
       "      <td>4.010</td>\n",
       "      <td>4.106</td>\n",
       "      <td>4.106</td>\n",
       "      <td>2093000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2010-09-28</td>\n",
       "      <td>4.208</td>\n",
       "      <td>4.298</td>\n",
       "      <td>4.152</td>\n",
       "      <td>4.280</td>\n",
       "      <td>4.280</td>\n",
       "      <td>6072500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2010-09-29</td>\n",
       "      <td>4.238</td>\n",
       "      <td>4.406</td>\n",
       "      <td>4.226</td>\n",
       "      <td>4.396</td>\n",
       "      <td>4.396</td>\n",
       "      <td>9846500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2010-09-30</td>\n",
       "      <td>4.400</td>\n",
       "      <td>4.430</td>\n",
       "      <td>4.038</td>\n",
       "      <td>4.082</td>\n",
       "      <td>4.082</td>\n",
       "      <td>10979000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2010-10-01</td>\n",
       "      <td>4.138</td>\n",
       "      <td>4.150</td>\n",
       "      <td>4.062</td>\n",
       "      <td>4.120</td>\n",
       "      <td>4.120</td>\n",
       "      <td>2988500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2010-10-04</td>\n",
       "      <td>4.086</td>\n",
       "      <td>4.234</td>\n",
       "      <td>4.060</td>\n",
       "      <td>4.198</td>\n",
       "      <td>4.198</td>\n",
       "      <td>3218000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2010-10-05</td>\n",
       "      <td>4.230</td>\n",
       "      <td>4.256</td>\n",
       "      <td>4.202</td>\n",
       "      <td>4.224</td>\n",
       "      <td>4.224</td>\n",
       "      <td>1660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2010-10-06</td>\n",
       "      <td>4.212</td>\n",
       "      <td>4.252</td>\n",
       "      <td>4.064</td>\n",
       "      <td>4.092</td>\n",
       "      <td>4.092</td>\n",
       "      <td>1567000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2010-10-07</td>\n",
       "      <td>4.114</td>\n",
       "      <td>4.128</td>\n",
       "      <td>4.068</td>\n",
       "      <td>4.086</td>\n",
       "      <td>4.086</td>\n",
       "      <td>705000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2010-10-08</td>\n",
       "      <td>4.086</td>\n",
       "      <td>4.158</td>\n",
       "      <td>4.078</td>\n",
       "      <td>4.086</td>\n",
       "      <td>4.086</td>\n",
       "      <td>1339000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>2010-10-11</td>\n",
       "      <td>4.088</td>\n",
       "      <td>4.140</td>\n",
       "      <td>4.014</td>\n",
       "      <td>4.048</td>\n",
       "      <td>4.048</td>\n",
       "      <td>856000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>2010-10-12</td>\n",
       "      <td>4.040</td>\n",
       "      <td>4.056</td>\n",
       "      <td>4.006</td>\n",
       "      <td>4.048</td>\n",
       "      <td>4.048</td>\n",
       "      <td>1220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>2010-10-13</td>\n",
       "      <td>4.128</td>\n",
       "      <td>4.170</td>\n",
       "      <td>4.072</td>\n",
       "      <td>4.108</td>\n",
       "      <td>4.108</td>\n",
       "      <td>1591000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>2010-10-14</td>\n",
       "      <td>4.200</td>\n",
       "      <td>4.206</td>\n",
       "      <td>4.080</td>\n",
       "      <td>4.150</td>\n",
       "      <td>4.150</td>\n",
       "      <td>1474000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>2010-10-15</td>\n",
       "      <td>4.178</td>\n",
       "      <td>4.180</td>\n",
       "      <td>4.050</td>\n",
       "      <td>4.108</td>\n",
       "      <td>4.108</td>\n",
       "      <td>1423500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>2010-10-18</td>\n",
       "      <td>4.104</td>\n",
       "      <td>4.128</td>\n",
       "      <td>4.044</td>\n",
       "      <td>4.046</td>\n",
       "      <td>4.046</td>\n",
       "      <td>814000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>2010-10-19</td>\n",
       "      <td>4.040</td>\n",
       "      <td>4.082</td>\n",
       "      <td>4.000</td>\n",
       "      <td>4.010</td>\n",
       "      <td>4.010</td>\n",
       "      <td>1226000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>2010-10-20</td>\n",
       "      <td>4.032</td>\n",
       "      <td>4.138</td>\n",
       "      <td>4.008</td>\n",
       "      <td>4.130</td>\n",
       "      <td>4.130</td>\n",
       "      <td>1562500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2010-10-21</td>\n",
       "      <td>4.122</td>\n",
       "      <td>4.190</td>\n",
       "      <td>4.090</td>\n",
       "      <td>4.150</td>\n",
       "      <td>4.150</td>\n",
       "      <td>2085500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2010-10-22</td>\n",
       "      <td>4.136</td>\n",
       "      <td>4.186</td>\n",
       "      <td>4.110</td>\n",
       "      <td>4.144</td>\n",
       "      <td>4.144</td>\n",
       "      <td>805500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2010-10-25</td>\n",
       "      <td>4.188</td>\n",
       "      <td>4.196</td>\n",
       "      <td>4.146</td>\n",
       "      <td>4.170</td>\n",
       "      <td>4.170</td>\n",
       "      <td>592500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>2010-10-26</td>\n",
       "      <td>4.160</td>\n",
       "      <td>4.374</td>\n",
       "      <td>4.102</td>\n",
       "      <td>4.272</td>\n",
       "      <td>4.272</td>\n",
       "      <td>3304500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>2010-10-27</td>\n",
       "      <td>4.250</td>\n",
       "      <td>4.276</td>\n",
       "      <td>4.130</td>\n",
       "      <td>4.200</td>\n",
       "      <td>4.200</td>\n",
       "      <td>1782500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2010-10-28</td>\n",
       "      <td>4.278</td>\n",
       "      <td>4.300</td>\n",
       "      <td>4.192</td>\n",
       "      <td>4.238</td>\n",
       "      <td>4.238</td>\n",
       "      <td>1121000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>2010-10-29</td>\n",
       "      <td>4.228</td>\n",
       "      <td>4.370</td>\n",
       "      <td>4.210</td>\n",
       "      <td>4.368</td>\n",
       "      <td>4.368</td>\n",
       "      <td>1403000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2010-11-01</td>\n",
       "      <td>4.388</td>\n",
       "      <td>4.550</td>\n",
       "      <td>4.262</td>\n",
       "      <td>4.282</td>\n",
       "      <td>4.282</td>\n",
       "      <td>2279000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2010-11-02</td>\n",
       "      <td>4.336</td>\n",
       "      <td>4.376</td>\n",
       "      <td>4.210</td>\n",
       "      <td>4.250</td>\n",
       "      <td>4.250</td>\n",
       "      <td>1612500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2010-11-03</td>\n",
       "      <td>4.256</td>\n",
       "      <td>4.500</td>\n",
       "      <td>4.232</td>\n",
       "      <td>4.354</td>\n",
       "      <td>4.354</td>\n",
       "      <td>1863000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2010-11-04</td>\n",
       "      <td>4.520</td>\n",
       "      <td>5.066</td>\n",
       "      <td>4.430</td>\n",
       "      <td>4.980</td>\n",
       "      <td>4.980</td>\n",
       "      <td>9370000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2010-11-05</td>\n",
       "      <td>4.974</td>\n",
       "      <td>4.994</td>\n",
       "      <td>4.744</td>\n",
       "      <td>4.888</td>\n",
       "      <td>4.888</td>\n",
       "      <td>5055000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2010-11-08</td>\n",
       "      <td>4.900</td>\n",
       "      <td>5.000</td>\n",
       "      <td>4.806</td>\n",
       "      <td>4.996</td>\n",
       "      <td>4.996</td>\n",
       "      <td>2547500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>2010-11-09</td>\n",
       "      <td>5.000</td>\n",
       "      <td>5.138</td>\n",
       "      <td>4.810</td>\n",
       "      <td>4.926</td>\n",
       "      <td>4.926</td>\n",
       "      <td>4782000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2010-11-10</td>\n",
       "      <td>4.896</td>\n",
       "      <td>5.994</td>\n",
       "      <td>4.810</td>\n",
       "      <td>5.872</td>\n",
       "      <td>5.872</td>\n",
       "      <td>15302500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2010-11-11</td>\n",
       "      <td>5.720</td>\n",
       "      <td>5.820</td>\n",
       "      <td>5.466</td>\n",
       "      <td>5.608</td>\n",
       "      <td>5.608</td>\n",
       "      <td>9726500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2010-11-12</td>\n",
       "      <td>5.650</td>\n",
       "      <td>6.100</td>\n",
       "      <td>5.614</td>\n",
       "      <td>5.968</td>\n",
       "      <td>5.968</td>\n",
       "      <td>13645500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>2010-11-15</td>\n",
       "      <td>6.044</td>\n",
       "      <td>6.588</td>\n",
       "      <td>6.044</td>\n",
       "      <td>6.160</td>\n",
       "      <td>6.160</td>\n",
       "      <td>13114500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2010-11-16</td>\n",
       "      <td>6.200</td>\n",
       "      <td>6.280</td>\n",
       "      <td>5.684</td>\n",
       "      <td>5.934</td>\n",
       "      <td>5.934</td>\n",
       "      <td>6738000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2010-11-17</td>\n",
       "      <td>6.040</td>\n",
       "      <td>6.150</td>\n",
       "      <td>5.722</td>\n",
       "      <td>5.898</td>\n",
       "      <td>5.898</td>\n",
       "      <td>3750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date   Open   High    Low  Close  Adj Close    Volume\n",
       "50  2010-09-09  4.200  4.210  4.138  4.142      4.142   1881000\n",
       "51  2010-09-10  4.150  4.186  3.952  4.034      4.034   1933000\n",
       "52  2010-09-13  4.178  4.180  4.100  4.144      4.144   1804000\n",
       "53  2010-09-14  4.108  4.320  4.106  4.224      4.224   3273500\n",
       "54  2010-09-15  4.196  4.400  4.158  4.396      4.396   3423000\n",
       "55  2010-09-16  4.430  4.632  4.168  4.188      4.188  13422500\n",
       "56  2010-09-17  4.204  4.264  3.960  4.046      4.046   5992500\n",
       "57  2010-09-20  4.134  4.270  4.032  4.212      4.212   4737500\n",
       "58  2010-09-21  4.178  4.310  4.134  4.154      4.154   3980000\n",
       "59  2010-09-22  4.174  4.190  3.960  3.974      3.974   4814500\n",
       "60  2010-09-23  3.978  4.028  3.900  3.912      3.912   3340500\n",
       "61  2010-09-24  3.990  4.038  3.930  4.020      4.020   2894500\n",
       "62  2010-09-27  4.080  4.162  4.010  4.106      4.106   2093000\n",
       "63  2010-09-28  4.208  4.298  4.152  4.280      4.280   6072500\n",
       "64  2010-09-29  4.238  4.406  4.226  4.396      4.396   9846500\n",
       "65  2010-09-30  4.400  4.430  4.038  4.082      4.082  10979000\n",
       "66  2010-10-01  4.138  4.150  4.062  4.120      4.120   2988500\n",
       "67  2010-10-04  4.086  4.234  4.060  4.198      4.198   3218000\n",
       "68  2010-10-05  4.230  4.256  4.202  4.224      4.224   1660000\n",
       "69  2010-10-06  4.212  4.252  4.064  4.092      4.092   1567000\n",
       "70  2010-10-07  4.114  4.128  4.068  4.086      4.086    705000\n",
       "71  2010-10-08  4.086  4.158  4.078  4.086      4.086   1339000\n",
       "72  2010-10-11  4.088  4.140  4.014  4.048      4.048    856000\n",
       "73  2010-10-12  4.040  4.056  4.006  4.048      4.048   1220000\n",
       "74  2010-10-13  4.128  4.170  4.072  4.108      4.108   1591000\n",
       "75  2010-10-14  4.200  4.206  4.080  4.150      4.150   1474000\n",
       "76  2010-10-15  4.178  4.180  4.050  4.108      4.108   1423500\n",
       "77  2010-10-18  4.104  4.128  4.044  4.046      4.046    814000\n",
       "78  2010-10-19  4.040  4.082  4.000  4.010      4.010   1226000\n",
       "79  2010-10-20  4.032  4.138  4.008  4.130      4.130   1562500\n",
       "80  2010-10-21  4.122  4.190  4.090  4.150      4.150   2085500\n",
       "81  2010-10-22  4.136  4.186  4.110  4.144      4.144    805500\n",
       "82  2010-10-25  4.188  4.196  4.146  4.170      4.170    592500\n",
       "83  2010-10-26  4.160  4.374  4.102  4.272      4.272   3304500\n",
       "84  2010-10-27  4.250  4.276  4.130  4.200      4.200   1782500\n",
       "85  2010-10-28  4.278  4.300  4.192  4.238      4.238   1121000\n",
       "86  2010-10-29  4.228  4.370  4.210  4.368      4.368   1403000\n",
       "87  2010-11-01  4.388  4.550  4.262  4.282      4.282   2279000\n",
       "88  2010-11-02  4.336  4.376  4.210  4.250      4.250   1612500\n",
       "89  2010-11-03  4.256  4.500  4.232  4.354      4.354   1863000\n",
       "90  2010-11-04  4.520  5.066  4.430  4.980      4.980   9370000\n",
       "91  2010-11-05  4.974  4.994  4.744  4.888      4.888   5055000\n",
       "92  2010-11-08  4.900  5.000  4.806  4.996      4.996   2547500\n",
       "93  2010-11-09  5.000  5.138  4.810  4.926      4.926   4782000\n",
       "94  2010-11-10  4.896  5.994  4.810  5.872      5.872  15302500\n",
       "95  2010-11-11  5.720  5.820  5.466  5.608      5.608   9726500\n",
       "96  2010-11-12  5.650  6.100  5.614  5.968      5.968  13645500\n",
       "97  2010-11-15  6.044  6.588  6.044  6.160      6.160  13114500\n",
       "98  2010-11-16  6.200  6.280  5.684  5.934      5.934   6738000\n",
       "99  2010-11-17  6.040  6.150  5.722  5.898      5.898   3750000"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[50:100]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23c12fbd",
   "metadata": {},
   "source": [
    "Для поиска нужных строк можно использовать lambda-выражения или другие условия"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5bc2bc81",
   "metadata": {
    "id": "5bc2bc81",
    "outputId": "c0df15a1-a1ea-4de1-e9ce-48118892124d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-06-29</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>17.540001</td>\n",
       "      <td>23.889999</td>\n",
       "      <td>23.889999</td>\n",
       "      <td>18766300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2010-07-07</td>\n",
       "      <td>16.400000</td>\n",
       "      <td>16.629999</td>\n",
       "      <td>14.980000</td>\n",
       "      <td>15.800000</td>\n",
       "      <td>15.800000</td>\n",
       "      <td>6921700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2010-07-14</td>\n",
       "      <td>17.940001</td>\n",
       "      <td>20.150000</td>\n",
       "      <td>17.760000</td>\n",
       "      <td>19.840000</td>\n",
       "      <td>19.840000</td>\n",
       "      <td>4195200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2010-07-21</td>\n",
       "      <td>20.660000</td>\n",
       "      <td>20.900000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>20.219999</td>\n",
       "      <td>20.219999</td>\n",
       "      <td>1252500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2010-07-28</td>\n",
       "      <td>20.549999</td>\n",
       "      <td>20.900000</td>\n",
       "      <td>20.510000</td>\n",
       "      <td>20.719999</td>\n",
       "      <td>20.719999</td>\n",
       "      <td>467200</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2395</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>440.500000</td>\n",
       "      <td>454.000000</td>\n",
       "      <td>436.920013</td>\n",
       "      <td>443.010010</td>\n",
       "      <td>443.010010</td>\n",
       "      <td>17778500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2400</th>\n",
       "      <td>2020-01-10</td>\n",
       "      <td>481.790009</td>\n",
       "      <td>484.940002</td>\n",
       "      <td>473.700012</td>\n",
       "      <td>478.149994</td>\n",
       "      <td>478.149994</td>\n",
       "      <td>12959500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2405</th>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>507.609985</td>\n",
       "      <td>515.669983</td>\n",
       "      <td>503.160004</td>\n",
       "      <td>510.500000</td>\n",
       "      <td>510.500000</td>\n",
       "      <td>13629100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2410</th>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>541.989990</td>\n",
       "      <td>564.440002</td>\n",
       "      <td>539.280029</td>\n",
       "      <td>558.020020</td>\n",
       "      <td>558.020020</td>\n",
       "      <td>13608100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2415</th>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>673.690002</td>\n",
       "      <td>786.140015</td>\n",
       "      <td>673.520020</td>\n",
       "      <td>780.000000</td>\n",
       "      <td>780.000000</td>\n",
       "      <td>47065000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>484 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date        Open        High         Low       Close   Adj Close  \\\n",
       "0     2010-06-29   19.000000   25.000000   17.540001   23.889999   23.889999   \n",
       "5     2010-07-07   16.400000   16.629999   14.980000   15.800000   15.800000   \n",
       "10    2010-07-14   17.940001   20.150000   17.760000   19.840000   19.840000   \n",
       "15    2010-07-21   20.660000   20.900000   19.500000   20.219999   20.219999   \n",
       "20    2010-07-28   20.549999   20.900000   20.510000   20.719999   20.719999   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "2395  2020-01-03  440.500000  454.000000  436.920013  443.010010  443.010010   \n",
       "2400  2020-01-10  481.790009  484.940002  473.700012  478.149994  478.149994   \n",
       "2405  2020-01-17  507.609985  515.669983  503.160004  510.500000  510.500000   \n",
       "2410  2020-01-27  541.989990  564.440002  539.280029  558.020020  558.020020   \n",
       "2415  2020-02-03  673.690002  786.140015  673.520020  780.000000  780.000000   \n",
       "\n",
       "        Volume  \n",
       "0     18766300  \n",
       "5      6921700  \n",
       "10     4195200  \n",
       "15     1252500  \n",
       "20      467200  \n",
       "...        ...  \n",
       "2395  17778500  \n",
       "2400  12959500  \n",
       "2405  13629100  \n",
       "2410  13608100  \n",
       "2415  47065000  \n",
       "\n",
       "[484 rows x 7 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[lambda x: x.index % 5 == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81e3dd09",
   "metadata": {},
   "source": [
    "`iloc` так же позволяет вернуть не только строки которые интересны, но и колонки через указания их индексов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac09734c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[[100, 300], [1, 4]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffe2b31d",
   "metadata": {},
   "source": [
    "Вместо индексирование обычными числами можно производить индексацию при помощи дат (вприниципе иных типов данных datetime,str, object и тд.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1gSJ2fUBSlT4",
   "metadata": {
    "id": "1gSJ2fUBSlT4"
   },
   "outputs": [],
   "source": [
    "df.index = df.Date"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dba955c",
   "metadata": {},
   "source": [
    "Посмотрим что же получилось"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "89a4dd93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-06-29</th>\n",
       "      <td>2010-06-29</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.00</td>\n",
       "      <td>17.540001</td>\n",
       "      <td>23.889999</td>\n",
       "      <td>23.889999</td>\n",
       "      <td>18766300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-30</th>\n",
       "      <td>2010-06-30</td>\n",
       "      <td>25.790001</td>\n",
       "      <td>30.42</td>\n",
       "      <td>23.299999</td>\n",
       "      <td>23.830000</td>\n",
       "      <td>23.830000</td>\n",
       "      <td>17187100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>2010-07-01</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.92</td>\n",
       "      <td>20.270000</td>\n",
       "      <td>21.959999</td>\n",
       "      <td>21.959999</td>\n",
       "      <td>8218800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-02</th>\n",
       "      <td>2010-07-02</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>23.10</td>\n",
       "      <td>18.709999</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>5139800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-06</th>\n",
       "      <td>2010-07-06</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.00</td>\n",
       "      <td>15.830000</td>\n",
       "      <td>16.110001</td>\n",
       "      <td>16.110001</td>\n",
       "      <td>6866900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date       Open   High        Low      Close  Adj Close  \\\n",
       "Date                                                                        \n",
       "2010-06-29  2010-06-29  19.000000  25.00  17.540001  23.889999  23.889999   \n",
       "2010-06-30  2010-06-30  25.790001  30.42  23.299999  23.830000  23.830000   \n",
       "2010-07-01  2010-07-01  25.000000  25.92  20.270000  21.959999  21.959999   \n",
       "2010-07-02  2010-07-02  23.000000  23.10  18.709999  19.200001  19.200001   \n",
       "2010-07-06  2010-07-06  20.000000  20.00  15.830000  16.110001  16.110001   \n",
       "\n",
       "              Volume  \n",
       "Date                  \n",
       "2010-06-29  18766300  \n",
       "2010-06-30  17187100  \n",
       "2010-07-01   8218800  \n",
       "2010-07-02   5139800  \n",
       "2010-07-06   6866900  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d643c283",
   "metadata": {},
   "source": [
    "Возьмем объект по `df.index` при помощи `loc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6f75fdb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         2010-06-29\n",
       "Open               19.0\n",
       "High               25.0\n",
       "Low           17.540001\n",
       "Close         23.889999\n",
       "Adj Close     23.889999\n",
       "Volume         18766300\n",
       "Name: 2010-06-29, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2010-06-29']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4558d2e3",
   "metadata": {},
   "source": [
    "А обратно можем вернуть все применим генератор `range`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ooTgPNd_Sx0F",
   "metadata": {
    "id": "ooTgPNd_Sx0F"
   },
   "outputs": [],
   "source": [
    "df.index = range(len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cb7ddd98",
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-06-29</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>17.540001</td>\n",
       "      <td>23.889999</td>\n",
       "      <td>23.889999</td>\n",
       "      <td>18766300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-06-30</td>\n",
       "      <td>25.790001</td>\n",
       "      <td>30.420000</td>\n",
       "      <td>23.299999</td>\n",
       "      <td>23.830000</td>\n",
       "      <td>23.830000</td>\n",
       "      <td>17187100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-07-01</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.920000</td>\n",
       "      <td>20.270000</td>\n",
       "      <td>21.959999</td>\n",
       "      <td>21.959999</td>\n",
       "      <td>8218800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-07-02</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>18.709999</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>5139800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-07-06</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>15.830000</td>\n",
       "      <td>16.110001</td>\n",
       "      <td>16.110001</td>\n",
       "      <td>6866900</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2411</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>568.489990</td>\n",
       "      <td>576.809998</td>\n",
       "      <td>558.080017</td>\n",
       "      <td>566.900024</td>\n",
       "      <td>566.900024</td>\n",
       "      <td>11788500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2412</th>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>575.690002</td>\n",
       "      <td>589.799988</td>\n",
       "      <td>567.429993</td>\n",
       "      <td>580.989990</td>\n",
       "      <td>580.989990</td>\n",
       "      <td>17801500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2413</th>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>632.419983</td>\n",
       "      <td>650.880005</td>\n",
       "      <td>618.000000</td>\n",
       "      <td>640.809998</td>\n",
       "      <td>640.809998</td>\n",
       "      <td>29005700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2414</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>640.000000</td>\n",
       "      <td>653.000000</td>\n",
       "      <td>632.520020</td>\n",
       "      <td>650.570007</td>\n",
       "      <td>650.570007</td>\n",
       "      <td>15719300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2415</th>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>673.690002</td>\n",
       "      <td>786.140015</td>\n",
       "      <td>673.520020</td>\n",
       "      <td>780.000000</td>\n",
       "      <td>780.000000</td>\n",
       "      <td>47065000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2416 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date        Open        High         Low       Close   Adj Close  \\\n",
       "0     2010-06-29   19.000000   25.000000   17.540001   23.889999   23.889999   \n",
       "1     2010-06-30   25.790001   30.420000   23.299999   23.830000   23.830000   \n",
       "2     2010-07-01   25.000000   25.920000   20.270000   21.959999   21.959999   \n",
       "3     2010-07-02   23.000000   23.100000   18.709999   19.200001   19.200001   \n",
       "4     2010-07-06   20.000000   20.000000   15.830000   16.110001   16.110001   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "2411  2020-01-28  568.489990  576.809998  558.080017  566.900024  566.900024   \n",
       "2412  2020-01-29  575.690002  589.799988  567.429993  580.989990  580.989990   \n",
       "2413  2020-01-30  632.419983  650.880005  618.000000  640.809998  640.809998   \n",
       "2414  2020-01-31  640.000000  653.000000  632.520020  650.570007  650.570007   \n",
       "2415  2020-02-03  673.690002  786.140015  673.520020  780.000000  780.000000   \n",
       "\n",
       "        Volume  \n",
       "0     18766300  \n",
       "1     17187100  \n",
       "2      8218800  \n",
       "3      5139800  \n",
       "4      6866900  \n",
       "...        ...  \n",
       "2411  11788500  \n",
       "2412  17801500  \n",
       "2413  29005700  \n",
       "2414  15719300  \n",
       "2415  47065000  \n",
       "\n",
       "[2416 rows x 7 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d128c0a",
   "metadata": {},
   "source": [
    "Объект `Series` имеет встроенные статисчиеские методы "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bcace7eb",
   "metadata": {
    "id": "bcace7eb",
    "outputId": "1b4cf635-193c-4415-c10b-11ae8ad1c2ab"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(186.40365078187088, 780.0, 15.8, 212.960007)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Close'].mean(), df['Close'].max(),df['Close'].min(), df['Close'].median(), "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b25e9190",
   "metadata": {},
   "source": [
    "Бывает иногда что занчение в колонке пропущены и иногда их проще удалить чем восстанавливать или апроксимировать "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f64f3072",
   "metadata": {
    "id": "f64f3072"
   },
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57fb083b",
   "metadata": {},
   "source": [
    "Построение запроса с  условием"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "cQ0loy-yTN47",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cQ0loy-yTN47",
    "outputId": "9d502f81-151f-4aea-d567-b20ca8ea2e93"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        23.889999\n",
       "720      69.400002\n",
       "721      76.760002\n",
       "722      87.800003\n",
       "723      83.239998\n",
       "           ...    \n",
       "2407    569.559998\n",
       "2408    572.200012\n",
       "2412    580.989990\n",
       "2413    640.809998\n",
       "2415    780.000000\n",
       "Name: Close, Length: 92, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Поиск строк по условию\n",
    "\n",
    "\n",
    "df[df['Volume'] > 17187100]['Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "9d23bec6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9d23bec6",
    "outputId": "69ce026b-af7b-4e37-9519-379dbdbf69aa",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1475"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df['Open'] > df['Open'].mean()]) # количество дней, когда цена была выше стредней"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bcf6b11",
   "metadata": {
    "id": "5bcf6b11",
    "outputId": "a2b7d620-6e1f-4221-d1cb-c935bd1a4959"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2813 entries, 0 to 2812\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Date       2813 non-null   object \n",
      " 1   Open       2813 non-null   float64\n",
      " 2   High       2813 non-null   float64\n",
      " 3   Low        2813 non-null   float64\n",
      " 4   Close      2813 non-null   float64\n",
      " 5   Adj Close  2813 non-null   float64\n",
      " 6   Volume     2813 non-null   int64  \n",
      "dtypes: float64(5), int64(1), object(1)\n",
      "memory usage: 175.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "b55fcbf9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b55fcbf9",
    "outputId": "206c7142-bd0f-432d-d05b-a754ee7848c1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      19.000000\n",
       "1      25.790001\n",
       "2      25.000000\n",
       "3      23.000000\n",
       "4      20.000000\n",
       "         ...    \n",
       "741    98.930000\n",
       "742    98.180000\n",
       "743    96.800003\n",
       "744    99.000000\n",
       "751    96.500000\n",
       "Name: Open, Length: 742, dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Open'].where(lambda x: x < 100).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "2cf1f02d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        19.000000\n",
       "1        25.790001\n",
       "2        25.000000\n",
       "3        23.000000\n",
       "4        20.000000\n",
       "           ...    \n",
       "2411    568.489990\n",
       "2412    575.690002\n",
       "2413    632.419983\n",
       "2414    640.000000\n",
       "2415    673.690002\n",
       "Name: Open, Length: 2416, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Open']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11fce40f",
   "metadata": {},
   "source": [
    "Для модицикации всей колонки, допустим увеличение ее в 10 раз, можно изпользовать метод `apply`, который итерируемом достает элемент и передает его в `lambda` выражение"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "24d24807",
   "metadata": {
    "id": "24d24807",
    "outputId": "a0b170cd-d4c3-4917-e967-f2c10100e332"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        190.00000\n",
       "1        257.90001\n",
       "2        250.00000\n",
       "3        230.00000\n",
       "4        200.00000\n",
       "           ...    \n",
       "2411    5684.89990\n",
       "2412    5756.90002\n",
       "2413    6324.19983\n",
       "2414    6400.00000\n",
       "2415    6736.90002\n",
       "Name: Open, Length: 2416, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df['Open'].apply(lambda x: x * 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "07ffa6f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "1bfb3713",
   "metadata": {},
   "outputs": [],
   "source": [
    "def average(item):\n",
    "    if item > np.average(df['Close']):\n",
    "        return item"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c42a23a",
   "metadata": {},
   "source": [
    "Ну или передовать элемент непосредственно в функцию. А вней написать большое количество взаимодейсвтия и элементов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "1513ce50",
   "metadata": {
    "id": "1513ce50",
    "outputId": "bd3e59f9-926a-4296-a448-43aae8954b40"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "817     186.699997\n",
       "818     187.520004\n",
       "819     189.000000\n",
       "820     193.960007\n",
       "821     188.589996\n",
       "           ...    \n",
       "2411    568.489990\n",
       "2412    575.690002\n",
       "2413    632.419983\n",
       "2414    640.000000\n",
       "2415    673.690002\n",
       "Name: Open, Length: 1475, dtype: float64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Open'].apply(lambda x: average(x)).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "twEiCwm4UqEJ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "twEiCwm4UqEJ",
    "outputId": "1ba62434-cedf-48ef-cf7e-f1969338d252"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    }
   ],
   "source": [
    "mean = df['Open'].mean()\n",
    "def is_too_low(x):\n",
    "  if x < mean*0.8:\n",
    "    return True\n",
    "  else:\n",
    "    return False\n",
    "\n",
    "def replace_too_low(x):\n",
    "  if x < mean*0.9:\n",
    "    return  mean*0.9\n",
    "  else:\n",
    "    return x\n",
    "\n",
    "def my_replace(x):\n",
    "  return x*0.95\n",
    "\n",
    "df[df['Open'].apply(is_too_low)]['Open'] = mean*0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "uVFxfvShWNq7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uVFxfvShWNq7",
    "outputId": "434cd05c-aa69-4a75-9ada-4d61ec3f8956"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       167.644032\n",
       "1       167.644032\n",
       "2       167.644032\n",
       "3       167.644032\n",
       "4       167.644032\n",
       "           ...    \n",
       "2411    568.489990\n",
       "2412    575.690002\n",
       "2413    632.419983\n",
       "2414    640.000000\n",
       "2415    673.690002\n",
       "Name: Open, Length: 2416, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Open'].apply(replace_too_low)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49RWrtaQWewv",
   "metadata": {
    "id": "49RWrtaQWewv"
   },
   "outputs": [],
   "source": [
    "df['Open'] = df[df['Open'] < mean*0.95]['Open'].apply(my_replace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "873d7070",
   "metadata": {
    "id": "873d7070",
    "outputId": "fc046def-c3b5-47ce-af2c-c702c11175b6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.8"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean([5, 4.6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "01fd1500",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-06-29</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.00</td>\n",
       "      <td>17.540001</td>\n",
       "      <td>23.889999</td>\n",
       "      <td>23.889999</td>\n",
       "      <td>18766300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-06-30</td>\n",
       "      <td>25.790001</td>\n",
       "      <td>30.42</td>\n",
       "      <td>23.299999</td>\n",
       "      <td>23.830000</td>\n",
       "      <td>23.830000</td>\n",
       "      <td>17187100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-07-01</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.92</td>\n",
       "      <td>20.270000</td>\n",
       "      <td>21.959999</td>\n",
       "      <td>21.959999</td>\n",
       "      <td>8218800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-07-02</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>23.10</td>\n",
       "      <td>18.709999</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>5139800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-07-06</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.00</td>\n",
       "      <td>15.830000</td>\n",
       "      <td>16.110001</td>\n",
       "      <td>16.110001</td>\n",
       "      <td>6866900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date       Open   High        Low      Close  Adj Close    Volume\n",
       "0  2010-06-29  19.000000  25.00  17.540001  23.889999  23.889999  18766300\n",
       "1  2010-06-30  25.790001  30.42  23.299999  23.830000  23.830000  17187100\n",
       "2  2010-07-01  25.000000  25.92  20.270000  21.959999  21.959999   8218800\n",
       "3  2010-07-02  23.000000  23.10  18.709999  19.200001  19.200001   5139800\n",
       "4  2010-07-06  20.000000  20.00  15.830000  16.110001  16.110001   6866900"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "0b602cce",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_60853/1067998117.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  df = df.drop('Adj Close', 1)\n"
     ]
    }
   ],
   "source": [
    "df = df.drop('Adj Close', 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "2fe1eb69",
   "metadata": {},
   "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",
       "        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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-06-29</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>17.540001</td>\n",
       "      <td>23.889999</td>\n",
       "      <td>18766300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-06-30</td>\n",
       "      <td>25.790001</td>\n",
       "      <td>30.420000</td>\n",
       "      <td>23.299999</td>\n",
       "      <td>23.830000</td>\n",
       "      <td>17187100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-07-01</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.920000</td>\n",
       "      <td>20.270000</td>\n",
       "      <td>21.959999</td>\n",
       "      <td>8218800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-07-02</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>18.709999</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>5139800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-07-06</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>15.830000</td>\n",
       "      <td>16.110001</td>\n",
       "      <td>6866900</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2411</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>568.489990</td>\n",
       "      <td>576.809998</td>\n",
       "      <td>558.080017</td>\n",
       "      <td>566.900024</td>\n",
       "      <td>11788500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2412</th>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>575.690002</td>\n",
       "      <td>589.799988</td>\n",
       "      <td>567.429993</td>\n",
       "      <td>580.989990</td>\n",
       "      <td>17801500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2413</th>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>632.419983</td>\n",
       "      <td>650.880005</td>\n",
       "      <td>618.000000</td>\n",
       "      <td>640.809998</td>\n",
       "      <td>29005700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2414</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>640.000000</td>\n",
       "      <td>653.000000</td>\n",
       "      <td>632.520020</td>\n",
       "      <td>650.570007</td>\n",
       "      <td>15719300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2415</th>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>673.690002</td>\n",
       "      <td>786.140015</td>\n",
       "      <td>673.520020</td>\n",
       "      <td>780.000000</td>\n",
       "      <td>47065000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2416 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date        Open        High         Low       Close    Volume\n",
       "0     2010-06-29   19.000000   25.000000   17.540001   23.889999  18766300\n",
       "1     2010-06-30   25.790001   30.420000   23.299999   23.830000  17187100\n",
       "2     2010-07-01   25.000000   25.920000   20.270000   21.959999   8218800\n",
       "3     2010-07-02   23.000000   23.100000   18.709999   19.200001   5139800\n",
       "4     2010-07-06   20.000000   20.000000   15.830000   16.110001   6866900\n",
       "...          ...         ...         ...         ...         ...       ...\n",
       "2411  2020-01-28  568.489990  576.809998  558.080017  566.900024  11788500\n",
       "2412  2020-01-29  575.690002  589.799988  567.429993  580.989990  17801500\n",
       "2413  2020-01-30  632.419983  650.880005  618.000000  640.809998  29005700\n",
       "2414  2020-01-31  640.000000  653.000000  632.520020  650.570007  15719300\n",
       "2415  2020-02-03  673.690002  786.140015  673.520020  780.000000  47065000\n",
       "\n",
       "[2416 rows x 6 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "f1cfdacf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import figure\n",
    "\n",
    "figure(figsize=(15, 3), dpi=120)\n",
    "\n",
    "df['Volume'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "QeQhdd3DXi1_",
   "metadata": {
    "id": "QeQhdd3DXi1_"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "ind = [np.random.randint(0, len(df)) for i in range(50)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "PZF4YiMZXC1C",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PZF4YiMZXC1C",
    "outputId": "139b7b77-b926-46b9-9b92-5113bb746f8d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df['Close'][ind] = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8InxuS9xX-jt",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8InxuS9xX-jt",
    "outputId": "3f46f19b-8433-48fd-96ad-830e2093190f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2416 entries, 0 to 2415\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Date       2416 non-null   object \n",
      " 1   Open       2416 non-null   float64\n",
      " 2   High       2416 non-null   float64\n",
      " 3   Low        2416 non-null   float64\n",
      " 4   Close      2366 non-null   float64\n",
      " 5   Adj Close  2416 non-null   float64\n",
      " 6   Volume     2416 non-null   int64  \n",
      "dtypes: float64(5), int64(1), object(1)\n",
      "memory usage: 132.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eynr0eBeX-rR",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "eynr0eBeX-rR",
    "outputId": "5640bab7-bed8-4ca2-80ec-b32932d3bfba"
   },
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>2010-10-20</td>\n",
       "      <td>20.160000</td>\n",
       "      <td>20.690001</td>\n",
       "      <td>20.040001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.650000</td>\n",
       "      <td>312500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>2011-03-10</td>\n",
       "      <td>24.440001</td>\n",
       "      <td>24.490000</td>\n",
       "      <td>23.730000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.010000</td>\n",
       "      <td>1017000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>2011-06-13</td>\n",
       "      <td>28.070000</td>\n",
       "      <td>28.879999</td>\n",
       "      <td>27.879999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.430000</td>\n",
       "      <td>1713400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2011-06-17</td>\n",
       "      <td>26.870001</td>\n",
       "      <td>27.700001</td>\n",
       "      <td>26.139999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.500000</td>\n",
       "      <td>1714000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>2011-11-11</td>\n",
       "      <td>31.900000</td>\n",
       "      <td>34.500000</td>\n",
       "      <td>30.570000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.639999</td>\n",
       "      <td>3868300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>2012-01-13</td>\n",
       "      <td>28.400000</td>\n",
       "      <td>28.500000</td>\n",
       "      <td>22.639999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.790001</td>\n",
       "      <td>5500400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>2012-03-09</td>\n",
       "      <td>33.200001</td>\n",
       "      <td>35.310001</td>\n",
       "      <td>33.200001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.740002</td>\n",
       "      <td>1553400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>2012-08-03</td>\n",
       "      <td>26.900000</td>\n",
       "      <td>27.549999</td>\n",
       "      <td>26.740000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.270000</td>\n",
       "      <td>1209500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>2012-08-20</td>\n",
       "      <td>30.150000</td>\n",
       "      <td>30.389999</td>\n",
       "      <td>29.100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.510000</td>\n",
       "      <td>1179100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>2012-12-07</td>\n",
       "      <td>34.299999</td>\n",
       "      <td>34.490002</td>\n",
       "      <td>33.849998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.169998</td>\n",
       "      <td>664400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>2013-01-09</td>\n",
       "      <td>34.009998</td>\n",
       "      <td>34.189999</td>\n",
       "      <td>33.400002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.639999</td>\n",
       "      <td>698000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>2013-05-17</td>\n",
       "      <td>92.500000</td>\n",
       "      <td>94.440002</td>\n",
       "      <td>87.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.500000</td>\n",
       "      <td>19002200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>2013-05-20</td>\n",
       "      <td>91.120003</td>\n",
       "      <td>92.500000</td>\n",
       "      <td>88.629997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.940002</td>\n",
       "      <td>8348400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>2013-07-03</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>119.250000</td>\n",
       "      <td>114.269997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>115.239998</td>\n",
       "      <td>4806700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>789</th>\n",
       "      <td>2013-08-16</td>\n",
       "      <td>141.630005</td>\n",
       "      <td>143.910004</td>\n",
       "      <td>140.970001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>7108100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>2014-01-17</td>\n",
       "      <td>170.190002</td>\n",
       "      <td>173.199997</td>\n",
       "      <td>167.949997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170.009995</td>\n",
       "      <td>9206200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>964</th>\n",
       "      <td>2014-04-29</td>\n",
       "      <td>198.210007</td>\n",
       "      <td>207.149994</td>\n",
       "      <td>195.529999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>206.919998</td>\n",
       "      <td>5779100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>2014-06-19</td>\n",
       "      <td>228.880005</td>\n",
       "      <td>235.309998</td>\n",
       "      <td>227.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>227.789993</td>\n",
       "      <td>8793100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1140</th>\n",
       "      <td>2015-01-08</td>\n",
       "      <td>212.809998</td>\n",
       "      <td>213.800003</td>\n",
       "      <td>210.009995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.619995</td>\n",
       "      <td>3442500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>2015-04-13</td>\n",
       "      <td>210.440002</td>\n",
       "      <td>213.000000</td>\n",
       "      <td>209.050003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>209.779999</td>\n",
       "      <td>3758200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1212</th>\n",
       "      <td>2015-04-23</td>\n",
       "      <td>218.270004</td>\n",
       "      <td>221.479996</td>\n",
       "      <td>217.149994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>218.600006</td>\n",
       "      <td>4411200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>2015-07-30</td>\n",
       "      <td>262.690002</td>\n",
       "      <td>266.940002</td>\n",
       "      <td>262.109985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>266.790009</td>\n",
       "      <td>2034600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>2015-08-21</td>\n",
       "      <td>236.000000</td>\n",
       "      <td>243.800003</td>\n",
       "      <td>230.509995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230.770004</td>\n",
       "      <td>6590200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325</th>\n",
       "      <td>2015-10-02</td>\n",
       "      <td>235.600006</td>\n",
       "      <td>247.699997</td>\n",
       "      <td>234.929993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>247.570007</td>\n",
       "      <td>4424000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1368</th>\n",
       "      <td>2015-12-03</td>\n",
       "      <td>235.479996</td>\n",
       "      <td>237.449997</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>232.710007</td>\n",
       "      <td>2939600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1371</th>\n",
       "      <td>2015-12-08</td>\n",
       "      <td>227.520004</td>\n",
       "      <td>228.800003</td>\n",
       "      <td>224.199997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>226.720001</td>\n",
       "      <td>2687600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1397</th>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>198.970001</td>\n",
       "      <td>205.070007</td>\n",
       "      <td>197.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>204.990005</td>\n",
       "      <td>5578600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1566</th>\n",
       "      <td>2016-09-16</td>\n",
       "      <td>200.419998</td>\n",
       "      <td>205.699997</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>205.399994</td>\n",
       "      <td>3107800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1581</th>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.320007</td>\n",
       "      <td>195.800003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>196.610001</td>\n",
       "      <td>3493000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1584</th>\n",
       "      <td>2016-10-12</td>\n",
       "      <td>200.949997</td>\n",
       "      <td>203.880005</td>\n",
       "      <td>200.419998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>201.509995</td>\n",
       "      <td>1970700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598</th>\n",
       "      <td>2016-11-01</td>\n",
       "      <td>198.039993</td>\n",
       "      <td>198.500000</td>\n",
       "      <td>188.110001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>190.789993</td>\n",
       "      <td>7060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1632</th>\n",
       "      <td>2016-12-20</td>\n",
       "      <td>203.050003</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>202.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>208.789993</td>\n",
       "      <td>4689100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1664</th>\n",
       "      <td>2017-02-07</td>\n",
       "      <td>258.190002</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>256.420013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>257.480011</td>\n",
       "      <td>4244800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1713</th>\n",
       "      <td>2017-04-19</td>\n",
       "      <td>302.459991</td>\n",
       "      <td>306.619995</td>\n",
       "      <td>302.109985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>305.519989</td>\n",
       "      <td>3898000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1730</th>\n",
       "      <td>2017-05-12</td>\n",
       "      <td>325.480011</td>\n",
       "      <td>327.000000</td>\n",
       "      <td>321.529999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>324.809998</td>\n",
       "      <td>4121600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1731</th>\n",
       "      <td>2017-05-15</td>\n",
       "      <td>318.380005</td>\n",
       "      <td>320.200012</td>\n",
       "      <td>312.529999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>315.880005</td>\n",
       "      <td>7622000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1752</th>\n",
       "      <td>2017-06-14</td>\n",
       "      <td>381.089996</td>\n",
       "      <td>384.250000</td>\n",
       "      <td>376.309998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>380.660004</td>\n",
       "      <td>12818400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1764</th>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>363.709991</td>\n",
       "      <td>366.769989</td>\n",
       "      <td>359.619995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>361.609985</td>\n",
       "      <td>5848500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1774</th>\n",
       "      <td>2017-07-17</td>\n",
       "      <td>325.540009</td>\n",
       "      <td>327.100006</td>\n",
       "      <td>313.450012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>319.570007</td>\n",
       "      <td>9876900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1796</th>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>363.000000</td>\n",
       "      <td>366.500000</td>\n",
       "      <td>362.519989</td>\n",
       "      <td>NaN</td>\n",
       "      <td>362.910004</td>\n",
       "      <td>3413800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1854</th>\n",
       "      <td>2017-11-07</td>\n",
       "      <td>301.019989</td>\n",
       "      <td>306.500000</td>\n",
       "      <td>300.029999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>306.049988</td>\n",
       "      <td>5294300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td>2018-01-22</td>\n",
       "      <td>349.399994</td>\n",
       "      <td>357.829987</td>\n",
       "      <td>349.200012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>351.559998</td>\n",
       "      <td>6210400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1943</th>\n",
       "      <td>2018-03-19</td>\n",
       "      <td>316.500000</td>\n",
       "      <td>320.750000</td>\n",
       "      <td>309.670013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>313.559998</td>\n",
       "      <td>7484300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1957</th>\n",
       "      <td>2018-04-09</td>\n",
       "      <td>300.369995</td>\n",
       "      <td>309.500000</td>\n",
       "      <td>289.209991</td>\n",
       "      <td>NaN</td>\n",
       "      <td>289.660004</td>\n",
       "      <td>10249800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>2018-06-29</td>\n",
       "      <td>353.329987</td>\n",
       "      <td>353.859985</td>\n",
       "      <td>342.410004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>342.950012</td>\n",
       "      <td>6492400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2098</th>\n",
       "      <td>2018-10-26</td>\n",
       "      <td>308.250000</td>\n",
       "      <td>339.899994</td>\n",
       "      <td>306.649994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330.899994</td>\n",
       "      <td>27425500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2102</th>\n",
       "      <td>2018-11-01</td>\n",
       "      <td>338.260010</td>\n",
       "      <td>347.839996</td>\n",
       "      <td>334.730011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>344.279999</td>\n",
       "      <td>8000100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2124</th>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>356.049988</td>\n",
       "      <td>368.679993</td>\n",
       "      <td>352.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>359.700012</td>\n",
       "      <td>8461900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2273</th>\n",
       "      <td>2019-07-11</td>\n",
       "      <td>238.139999</td>\n",
       "      <td>241.500000</td>\n",
       "      <td>235.800003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>238.600006</td>\n",
       "      <td>7514400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2312</th>\n",
       "      <td>2019-09-05</td>\n",
       "      <td>222.500000</td>\n",
       "      <td>229.800003</td>\n",
       "      <td>220.850006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>229.580002</td>\n",
       "      <td>7395300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date        Open        High  ...  Close   Adj Close    Volume\n",
       "79    2010-10-20   20.160000   20.690001  ...    NaN   20.650000    312500\n",
       "176   2011-03-10   24.440001   24.490000  ...    NaN   24.010000   1017000\n",
       "241   2011-06-13   28.070000   28.879999  ...    NaN   28.430000   1713400\n",
       "245   2011-06-17   26.870001   27.700001  ...    NaN   26.500000   1714000\n",
       "348   2011-11-11   31.900000   34.500000  ...    NaN   33.639999   3868300\n",
       "390   2012-01-13   28.400000   28.500000  ...    NaN   22.790001   5500400\n",
       "428   2012-03-09   33.200001   35.310001  ...    NaN   34.740002   1553400\n",
       "530   2012-08-03   26.900000   27.549999  ...    NaN   27.270000   1209500\n",
       "541   2012-08-20   30.150000   30.389999  ...    NaN   29.510000   1179100\n",
       "616   2012-12-07   34.299999   34.490002  ...    NaN   34.169998    664400\n",
       "637   2013-01-09   34.009998   34.189999  ...    NaN   33.639999    698000\n",
       "726   2013-05-17   92.500000   94.440002  ...    NaN   91.500000  19002200\n",
       "727   2013-05-20   91.120003   92.500000  ...    NaN   89.940002   8348400\n",
       "758   2013-07-03  118.000000  119.250000  ...    NaN  115.239998   4806700\n",
       "789   2013-08-16  141.630005  143.910004  ...    NaN  142.000000   7108100\n",
       "895   2014-01-17  170.190002  173.199997  ...    NaN  170.009995   9206200\n",
       "964   2014-04-29  198.210007  207.149994  ...    NaN  206.919998   5779100\n",
       "1000  2014-06-19  228.880005  235.309998  ...    NaN  227.789993   8793100\n",
       "1140  2015-01-08  212.809998  213.800003  ...    NaN  210.619995   3442500\n",
       "1204  2015-04-13  210.440002  213.000000  ...    NaN  209.779999   3758200\n",
       "1212  2015-04-23  218.270004  221.479996  ...    NaN  218.600006   4411200\n",
       "1280  2015-07-30  262.690002  266.940002  ...    NaN  266.790009   2034600\n",
       "1296  2015-08-21  236.000000  243.800003  ...    NaN  230.770004   6590200\n",
       "1325  2015-10-02  235.600006  247.699997  ...    NaN  247.570007   4424000\n",
       "1368  2015-12-03  235.479996  237.449997  ...    NaN  232.710007   2939600\n",
       "1371  2015-12-08  227.520004  228.800003  ...    NaN  226.720001   2687600\n",
       "1397  2016-01-15  198.970001  205.070007  ...    NaN  204.990005   5578600\n",
       "1566  2016-09-16  200.419998  205.699997  ...    NaN  205.399994   3107800\n",
       "1581  2016-10-07  201.000000  201.320007  ...    NaN  196.610001   3493000\n",
       "1584  2016-10-12  200.949997  203.880005  ...    NaN  201.509995   1970700\n",
       "1598  2016-11-01  198.039993  198.500000  ...    NaN  190.789993   7060000\n",
       "1632  2016-12-20  203.050003  209.000000  ...    NaN  208.789993   4689100\n",
       "1664  2017-02-07  258.190002  260.000000  ...    NaN  257.480011   4244800\n",
       "1713  2017-04-19  302.459991  306.619995  ...    NaN  305.519989   3898000\n",
       "1730  2017-05-12  325.480011  327.000000  ...    NaN  324.809998   4121600\n",
       "1731  2017-05-15  318.380005  320.200012  ...    NaN  315.880005   7622000\n",
       "1752  2017-06-14  381.089996  384.250000  ...    NaN  380.660004  12818400\n",
       "1764  2017-06-30  363.709991  366.769989  ...    NaN  361.609985   5848500\n",
       "1774  2017-07-17  325.540009  327.100006  ...    NaN  319.570007   9876900\n",
       "1796  2017-08-16  363.000000  366.500000  ...    NaN  362.910004   3413800\n",
       "1854  2017-11-07  301.019989  306.500000  ...    NaN  306.049988   5294300\n",
       "1904  2018-01-22  349.399994  357.829987  ...    NaN  351.559998   6210400\n",
       "1943  2018-03-19  316.500000  320.750000  ...    NaN  313.559998   7484300\n",
       "1957  2018-04-09  300.369995  309.500000  ...    NaN  289.660004  10249800\n",
       "2015  2018-06-29  353.329987  353.859985  ...    NaN  342.950012   6492400\n",
       "2098  2018-10-26  308.250000  339.899994  ...    NaN  330.899994  27425500\n",
       "2102  2018-11-01  338.260010  347.839996  ...    NaN  344.279999   8000100\n",
       "2124  2018-12-04  356.049988  368.679993  ...    NaN  359.700012   8461900\n",
       "2273  2019-07-11  238.139999  241.500000  ...    NaN  238.600006   7514400\n",
       "2312  2019-09-05  222.500000  229.800003  ...    NaN  229.580002   7395300\n",
       "\n",
       "[50 rows x 7 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Close.isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "kpmF-PytYNut",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "kpmF-PytYNut",
    "outputId": "d1becfdd-66b0-4f30-d047-55d053a6d4f0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff2786458d0>]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W6lxurvnrimaf52waOlRew5Of7MQY42u6gYYmpcse+Zj3Nx8I+jrBhpY2tybuHa80npdQVeviq//3E1buPMJfP47uHNSjlf5fZjlZaVF9v5bQZh+lVFj+/ulO33ZKUhIvryrmcBidtM7gn3/f+wCcPSwn5PGvf76XaWP7NiovPlLp99gV0AdRXeciIyBp2vIdjXJKUuW4Go92E9DRCv+7nl5d4yf465W/UqrFUpOFYGNWih6Yybu3TubUAT34f9dMAGBFkAD845c+D/naoWYHO+8UAMqr65k1vr/vcbC5BYELp8we35+KmoZhqD27RLfz9VjAlX9aGJ3Z7UWDv1KqWd5hnj+9eBTgGSvvbLn+y3cm8PEdUwAY3a8b//7R+Vw0pg8AbwUZibO91JPnZlTfro32uUOMhAy8Sj9eXYczX3CwuQXr95T5Pc7JSve7W+ke5eB/tNL/C+mjL+Nn7RIN/kqpZp2wnbbeYHmovNYviA7r3ZVBOZl+z/HOvt1Scpw9Rz1NNt0y/Fua//2jxguZ19QHb4qpCgj+9W7j6yiGxs1AwWQGjDpKiuJ03037yhrNc5hxSuPmrFjR4K+UCqmq1sX/vreVW//hGVUTGLy90oPMmnWO7fd2zDpTG4zN6x50TH+o1T+8KSR+8JWTAHi5oJg9R6t8+/eVVfkd7/0SueLMgb6ywCGn7iguNfLA21+w2/ZT/OqrY7loTB/+9K0zovZ+LaUdvkqpkOY9W8DH2w75HvcI0UzSM7Pp5hNvs5Gz6SawPXz13dO47qlVIa/gvSmRu2d43mv+h9v99l//1CqKHpjpe1xZ43mvsf27s3TKhRytrGV1kf86Ay8XFLO55Dg/mTaK4iOVnDuid5Pn0RLe3EcA1583jOvPi49snl4a/JVSITkDPzQO8tefN5SnPi3yBeRAE4flsHLnEa74yzLKa/zb5PeV+ecKys5Mo0eXVD4pPITLbRqNh7//bU8TSqi7D4CKmnqy0j37ve+XlZ7CsN5ZDCOL9QGrjH15oJwvD5Tz2hrPKrTOL4+28tzVRCfBXSRos49SqpHK2nr+FmQMfOCV/y8vH0vRAzN9qZwD/eiiEQCNAn8wSUnCJ4WeL5slW4KP9QcYmN0l5L5D5TUYY6h3uX2jg5zt/O052qZ/z4zmD4qhZoO/iGSIyEoRWScim0Tk17Z8mIisEJFCEfmHiKTZ8nT7uNDuHxrdU1BKRdqjHxRy31tbGpX36OI/Tj1Uzh6vptIX/+lb4xuVefPzO/MI1dS7MMZw8di+nJzXnay00Ff+h8prGPvLdxlx19u+ZiVnHdozo+foft0AeP77Z7fbe7ZEOL+JGuAiY8zpwHjgEhGZBDwIPGyMGQEcBeba4+cCR235w/Y4pVQHsv1gedBy75X/LVNHctOU4c2+TrCvhvdvm8xHt09h9hkDAHjvJ5NZdMsFADwzdyIANz6/hj+9/yXVdS5G3/0ODy3+EpfbkJIkTBiSzVQ7jDRQ6Yla36igvcc8HcCpjhTKzQV/dxgjhsLlchv6dc/gvAj2I0RSs8HfeHj/J6Tafwa4CHjFli8AZtvtWfYxdv9Uae7yQCkVV94LkWLBGzxvu3gUt88Y0+zrOCdeXXZqPwB6d01ncK+GYaGj+nZjbP/uAPTr3tBU8qf3t/ny/zz6QSF1LjfJSUJqchJPXneW3/t4vwy8Q0qhYdx/SpLjyr+ZXPrh5CoKl8tN3OTxCSaseyARSRaRtcBBYDGwHThmjPE25O0BBtjtAUAxgN1fBvQK8przRKRARApKS+Nn4oNSqrHLT8tjy28uafHzvOkMBudk8ti3J1Bw9zR6ZoZOcdC/p397/pvrSwDP1fvH2w75ruYDPXndWXRLT+GP7231lXn7GVKaufL/2hkDfNvN5QpqCZfb3fGDvzHGZYwZDwwEJgLNf+U3/5pPGGPyjTH5ubm5bX05pVQUfGfSYABOG9ijVXnwzxySw2s3nsuHP7sQEaF31/Qmjw8VLL0J0ZxZOOfb9BFeWekpVNc1BO9PbeexMw+F98vlh19paLIa0adhlnFtvZvqOpdfqurWcpn4vvJv0VBPY8wxEVkKnAP0FJEUe3U/ENhrD9sLDAL2iEgK0ANonNxDKRXXzjmpF/fNPpVv5Q/mFNss0xoTIrBg+YHjjYPxxQHJ31JT/APtZ3bN4e2l5b7x+2P6deeNm85jbP/u/OU/nnkCo/t28z3nrN+979tu67BPd5DhqvEknNE+uSLS0253AS4GtgBLgW/aw+YAb9jthfYxdv8HxkRxGp1SDvM/3M5LK3fHuhodmjGeoHXmEE/QPnVgj5BDOWMpJTmJ22eM5k2bIuJgkC8IwNef4HX6oJ5+I4CmntyHy0/Li3j96t1ukuO4uzOcZp88YKmIrAdWAYuNMW8CPwduE5FCPG36T9rjnwR62fLbgDsjX22lGnO7DQ++8wV3vrah0XqzkVbvcnOkopbb/7kuZDt0R2GM4d1N+33pE+pcBpfbkJEaf9OAfjvrFL/HN00ZwbgBnjV5A9vrvfMBxuYFX7P3k59P4eM7piAiUQn+Ljdx+aXp1WyzjzFmPdAoIYUxZgee9v/A8mrgiojUTqkWcP7x/+6tzfx61riovdf8D7fzv4u/BDwjWp64Nj9q7xVta4uP8YNnV3NK/+68dcsFvnw0gbnx29uMU/ry7qYDXHP2YJ5f4bmb+/bZQ8J+vjfvT6iml4HZDSOOuqY3nqFc73KT0sQ8hea43G7fGsPxKP6+2pVqJWc2yGhP5nl0aaFvO7UdJw5FwzGbdnjTPs8avO9u8qRgPmto6AVX2sO8ySex8dcz+N75DTlxmmpDD8zY6RVOAM4LMht3xF1vt+kO0mXi+8q/Y/+vVcrBOdIj2lNLnEFhy77jLFy3L6rvF00ljhw7NfUuyqrqSE9J4vRBPWNYK0hLTqZregrDc7ty3+xx/PvmxumfnUIF+XAC8PDcrvzlO2c2KncOHW0pt52UFq80+KtOw5k/JtRqUJHwmOOqH2DHoQpuebFhZSpjDG0d43Ciuo6CoiNteo1w7TpS4duurXdTVlkXMntne3LevX1n0hBOHRi87d6rrVfZk0c1nokbuEaBU+mJGt86B8F0hg5fpeJe6Ykarnx8me/xU58WUVZVF9YCHy31h3eDXw16Z7MO+8Uihv1iEQePVwc9Lhw3v/A53/zLMo43EVwipcZxx7TrcCX/KChu1xw4obS0Dl1tNs9BOaETvzUl05EzKM8u//iUY93iQGf97n0uf/SToPuq61ws33EkeH6LOBH7T1ipCPj6/E85ErCY+Om/fo+bX1gDeHLHf/3/fdooh3wknf7r96h33HG8sbb1TUHr93hSDwdbmjDSdjsWRr/iL54vUOciKbHS0uD/3NyzueOS0bx76+RWv+el4zwpKLwrjIW6g/SW7zpcGXT//9rmopU72+furTU0+Ku4YYzhofe28sEXodP5hlJ8JHiwenujp/PytpfXsWb3Mc747eI21dHZvBOMM2imt2GopLdj87wHPohq88+Tn+zkgy8O+h4HLpUYSy1tLx/aO4sbLxzhdwX/u6+1bMTXH684nVd+eA69u6YzflBPio9U8fKq4kbH3fDc6iZfZ/mO+A36Xhr8Vdx4c30Jj3xQyPeeLmjR8w6eaGheueOS0UGP2VLiGclyUu+sVtXN7TY8t3yXr2P3lqkjuXRcPwYE5KLxzioFOHSi9SkCnB3W3/zLsiaObL2Kmnp+++ZmoPW/l2g4387GbSoddLimndyyNXOz0lPIt6OcvOml73h1faPj3t/i+cL0Ng8F8i5rmdWKlBjtRYO/ihvvN7GAR1O2HWhIP3zjhSNY8tOvNDrGO6Jle2lFq9rif/LyWu7+10bf4ymjc5n/nTNZfNtkrjl7sK/8f17f4Nt+5AP/juGWCOxwjUYH9kHHl5N3opTXM99rNIWn3Tx2zQSe+d5EXz6f1jhzSDbZman07d76BVVCDR2FhqA+Nq9h9vDnu4/y/QWr2LCnjHq34dQBPdhw74xWv3+06TKOKm44U+9W17nCnmTkXYTbuzjI8NyufvufW77L7/Gmfcfp04KgUFlb79d+f8vUkZxh89VkpqUwpl+3UE9l9a6jvjQJLdG7axqFDa0xHC6vpV+Iq8zWOmC/BO+5fCzfnDDQd1czoGcXJo+KXbLFHl1S2/z+r95wbpvr4RyoY4zxuxvLzkqjoraKJV8cxO02JCUJ3/nbCipqXb67givzB+o4f6XCMbxPQ9PDmHveafLYdzbu5+YX1uByG9+VvzP52JTRDcHDecUO4S0p6OTsEF1/73R+Mm2k3/6mMlWWVfl3MHsXC3lx5W5fp2CgLw+coPBgOReN6cPtMzzNWO9t3t+iOodjnV3PdvrYvvTITGXn/Zdxw4XD+fNVjVfYSkTeIA7+fTnVdS72OVJ6PPiOZ23hwLunUwc0PTQ11jT4q7gR7mzKnYcq+OFzq3lzfQmlJ2ootgt4OK+MH7pyfKOmE2/7bEs7NZd+4Vlv4tUbzqV7RmqjCWSn9Pf/I3/1hnO4/+unAv6jdf7zZSkn/c8iXlq5m1+8toFHQzQLTX/4Iw6V19I1PYWL7CIl3k7MZ5fvYvWuo75jK2rqeXN960YVeRdE96Y5FhF+fskYX5u3arDf0VS4bPthnCOIH/9oB8VHKskOWKfgpIA70HijwV/FjcDgv3zH4aCTpR63qXgBKmrrqa5zkZOVRreMhmCfnZXGT6eP8nveGYM9M1b3l4Xf5r9ix2Hfld3pISYZDe6VyQM22IMnh/2MUzxDBo9V1vHZ9kNc9cQyfvWG5w7kztc2BH2dQFnpyfSy7d7V9gvrnn9t5BvzP/Mdc9MLa7j5hc95Z2NJ2OcEnqYsr3hOOxxLV08c5Nt+0zGD+/qnVzU6ds5TKzlc4d/B39zaBbGmwV/Fjdp6t18n21VPLOeV1XsaHfeSY+jds8t28eLK4kZj/KEhZ43XdecOIz0liW0Hy/nX53t9OWya8quFm3zbTSX5Cmyj7p6R4nv+t/+6guU7jlAUYky4kzM/0bLth8myE5cqa+sbdfoWH6nkw62eu5IfPrfGb45Bc/7yoecLNNRauAru//ppPPgNz5f6gmWefqPdIT7DHaUVFAasezy0d+jZwfFAg7+KG7UuN2kpSbz435N8ZZ/bdulQnv6sKOQ+5xfCmH7dmDgshwtG5rJ5Xxm3/mMtP3i26bHaABOHeZpALrFX8qF0CeicDvZFEWzceuCdzeHyhjrPveAk3+uW17g45/4lvn0VNfVc8Pulfs8dcdfbFB2qIByb7dDX333t1GaOTGxfGdXw5Xiiuo7Jf2j4nT/27Ql+I5KOOi423rn1AtJT4neYJ2jwV3Gktt5NWnKSX9v90Ypav0W5wdN8c+5w/2WhhwUZp37zRSO4YKRnzPhIu1rTSblZbC9tCJBlVXV8VniIyx/92K8pxMs7Q/OPV57eZN3DWeKwPkiqicAZos61Ab47aQhJSUJmWjLrio9xyPHF8FKQiUcALxcEL3eqqKnn/S0HyeuREfERRJ1Nvx4ZTB6Vy+mDerLT8cU6pl83Zp6Wx5p7LvY7vpu9UxvcRE6geKHBX8WN2nrPlX/f7g1tpW9v3M/5Dy7lf17f4BspU1Xr8jWHeC392YWNXq9313SenXs2L/z32b7b96MBzUOFB8v5wbOr2bj3OGN/+S6rd/nPzPxi/wmgIW9MKN5JPd+YMDCMM4Wv2GaiwDQK3lEk/7rpPF9ZZa2L/3xZ6necd3IWwMd3TGHn/ZfRo0sqh8JYe9Y7n6KkBX0fiSwzNZmq2nq/3/lDVwYfEfXJzy+i6IGZfrOM45UGfxU3jlfX0TU9JegfzgsrdrNxXxlLthzwzQFYddc0AH7/jdOafN1zh/f2veaXAe2yu49U+A3R+/07DcMvi+0Qz3DGnIsIm38zgz9e0VCX8UFSIr96w7ncOm0kv/zqWIBGdzXeBcqD3ckAfhPKAFbfPY1BOZmICD0zU3m5wNNHcvMLa7jszx8HfQ1nIjfVvMy0ZCprXawq8oyy+uTnU/yWhuznmDPSIzP22VDDFc4avoNEZKmIbBaRTSLyY1ueIyKLRWSb/Zlty0VEHhGRQhFZLyITon0SqnN4f8vBJtakicUAABK6SURBVK9GZz32KXMXFFB0uJIuqUnkdkun6IGZXHnWoJDPCfTnb/lfsd2/6Au/YXzeZhhjjK9NfUSYQ/Yy01L8hoE6r969zhySza3TRjHENgsEjvz5vc0Y6u0wDnTf7HG+L5WJQ3Po5RhRkmb7GVbvOsqb60vYXHLcd7fktL3U8wUYbCa0aqxLWrJviUtoPIpn8W2eRHI//Mrwdq1XW4Vz5V8P/NQYMxaYBNwkImPxrM27xBgzElhCw1q9lwIj7b95wPyI11p1Ws4mn0DOvlFv+uSWCszPfvBEjV9brveLwPmFEE5TSii/nR08sZizQ9j7+tV1Lt9wV+eXyPxrGq6fRMSX7fLCMf53JN4Vr5xDQR9bWsizy4r8jtu07zin9O/eaCa0Ci4zLZnDjubCwJnn3TJSKXpgJndeOqa9q9Ym4azhWwKU2O0TIrIFGADMAi60hy0APsSzqPss4BnjGcawXER6ikiefR2lgvqs8BAAX9rZuoW/u5QXV+7mnjc2BT0+J6t1Y6jDGdM+9M63fNu9stJ8s2xb47uThnDpuH5kpCaHzFL5ld8vZdNvLmk0VNDr0lPzuGXqSPrbzlnvKKa+3fw7a91B5kR41xmecUo/+nTPoLbezSeFh8Lum1ANk+AAxg3o3sSRHUuLeiVEZCiexdxXAH0dAX0/4E2fNwBwDjnYY8v8gr+IzMNzZ8Dgwf7tmCrxBE6cSUlOYlTfxjlzbp8xmqG9srjs1KaHXobj7pknc99bWwC4ddpIVuw4wrIdh/2OefOW88nr0brFQbxCTfb56un9+fe6fVTUujhwvNo3G/jx7zZeTvC2ixsmrB22dwqBI3WkiZVDNpccZ/GWA3y+2zN0dmB2284pkTg/v2tasIB8vAu7w1dEugKvArcaY44799mr/BYtmWSMecIYk2+Myc/NjV0SKRUdxyprufWlzxt1aIZSY5s7ejo6zJwzdr1G9unKzNPy2rRG72s3nsuTc/IZmN3QBPTjqSODDucMHL8fSY9efYZv+1dvbGLNbk+HYqjOXi9vs0NgOoFvntlwNf/5PRdzsiPj5HVPreKu1zf6Js1dka9X/uHa5rgjCxxl1pGFFfxFJBVP4H/eGPOaLT4gInl2fx7gzYK0F3D2wA20ZSqB/Ht9Cf9au4+nPy0K63hv6oVXfniOr+zkvG7cPfNk/jGvYdJXc4ExHBMGZzP15L6clOt5rQtG9kZEGuXmh8btu5HmbQpKThLf8pC9mkll/PT1E5k1vj8j+/q32aelJLH4J5NZdMsFZGel8ffr8kO2Qzu/+FTTvnbGAN92txAd8R1ROKN9BHgS2GKMecixayEwx27PAd5wlF9rR/1MAsq0vT/xfG6vYg+V1/DlgRPNHn+iup5Lx/VjRJ+Gph4R4fsXnOTX7DKkV+QWHRnVtxvrfjndL3e982ocoh/8500+CYARfRoCec/MpoP/6H7d+PNVZwRd7GRk326+YYh5PboEHYHS0gVOEp3zgqNbgl35nwd8F7hIRNbaf5cBDwAXi8g2YJp9DLAI2AEUAn8Fbox8tVU8M8bw2hrPzd6/1u5j+sMfhVyM5FhlLUPvfIvCg+UhF+9ISW5o4on0wuI9Mv2zdH719P4UPTCT0wb24J7Lx0b0vYLxdiZ7m71OHdAj4onWAod0eu+yVPhG2/6naF8MtKdwRvt8Qug16KcGOd4AN7WxXqoDq6htnDL5nY37yR+a3ajz1DuuHeBYiOGb3qaR9syPvvDm89vlfUSE9JQkPtvuGe30rRbMWQjX8NyuXH/eUHp2SWP17qN8e6IOsGip0f26sfXAicQK/kq11InqxkH8Ry9+Tl6PDJb9wv96wZklsbw6+CIrfbpn8H++diozTumczRU19W7W7ykDaHJVsLb41VdPicrrJor7v34ql5+W59c819Fp8FcR9dePdvD4RzuC7gs2e/eAYzLVQ00kT/v22YlxtTqyT3SCv2qbrPQUpjeT2bWj0dw+KmLKqur43aItvhmrV5zpP5wwWFO2N9PlU9ef5ZeqIJE4O2DDyQ6qVCRo8FcR4x2n7nXXzJNZ7mjmCZaw7UR1PVdPHMyU0Ym7qEhGasOfYaQ7tJUKRZt9VMQUOxY6n3POEN+QxdRkoc5l6NW18Wieytp6uqYn9tWutxOxuQVjlIokDf4qItbvOcanNj/Pyrumkutowvngpxdywe+X+nVm/uHdL/j7J0VU17uiOou2I/Be+TvvAJSKNg3+KiKufmK5b4hnn4CEY4NyMhk3oDt1Lk/7/sJ1+3hsacMi7BkJ3s6dkuQJ+ona56FiQy81VEQEG9vvlJKUxOHyGupcbh56b6vfviDJKBOKN6V0bjcN/qr9aPBXbeYcq98nRABbW3yMdXvK+N7TqygKWLf2SMDSiolmpB07nhYkXYNS0aLNPqrNnIuOzzwtr8ljP952qFFZZTN3DZ3dz2aMJjsrjWsmJcZcBhUf9FJDtdlra/b4tm+8cETQY7ID1jYdnJPJ+nunc+05Q/jJxSOjWr94l5GazE1TRpCekth9H6p9afBXbbJ611H+aXPEXz1xcMh26zX3XEy6HcOemix8dMcUumek8ptZ4xp1ECulok+Dv2q1NbuP+taLFfHkPwlFRHyZK72jfpRSsaPBX7Xavz5vWKMnWN54pVT80uCvWu2ZZbt827ktGKPuXF5QKRUbGvxVq5SUVeFcRve6c4c2+5xXfngOZw7J5m9z8qNXMaVUWDT4qxbZduAExhj2Hav2m5yVFMbqU/lDc3j1hnODrpWrlGpf4azh+3cROSgiGx1lOSKyWES22Z/ZtlxE5BERKRSR9SIyIZqVV+1rxY7DXPzwR7ywcjevrN7T/BOUUnErnCv/p4FLAsruBJYYY0YCS+xjgEuBkfbfPGB+ZKqp4oF3Mtddr2/kxZW7Abhv9rhGa8QqpeJfs8HfGPMRcCSgeBawwG4vAGY7yp8xHsuBniLS9JRP1WFkZzZOyfydSUMYntt5lrZTKlG0ts2/rzGmxG7vB7xLEQ0Aih3H7bFljYjIPBEpEJGC0tLSVlZDtaeaev80DD+9eFSMaqKUaqs2d/gaYwzQ4lk7xpgnjDH5xpj83NzctlZDRZkxhv+z6Avf4xF9uvKjqYmdlkGpjqy1wf+AtznH/jxoy/cCgxzHDbRlqoPbUnKC3Xalrq+fMYDXbzw3xjVSSrVFa4P/QmCO3Z4DvOEov9aO+pkElDmah1QHNueplb7th741nm4ZqU0crZSKd82mdBaRF4ELgd4isgf4FfAA8LKIzAV2AVfawxcBlwGFQCVwfRTqrGLAm2v+6evPinFNlFKR0GzwN8ZcHWLX1CDHGuCmtlZKxZ+Lx/bl6c+KuHB0n1hXRSkVATrDV4Wlsraeft019bJSnYUGfxWWyloXmem62IhSnYUGfxWWyloXmWka/JXqLDT4q7AcrqglM1WXfFaqs9C/ZtWsw+U1rCs+FutqKKUiSK/8VbO+9/SqWFdBKRVhGvxVk3YdrmDdnjIA3r11coxro5SKFA3+KqTSEzX85t+bAfj9N09jdL9uMa6RUipStM1fBbVxbxmXP/oJADlZaVyZP6iZZyilOhIN/nGqzuXmbx/vZPWuIzz+3XySw1gmsbX+82Up1z21EmNg/KCe9O6azodbD/r2//VaXXNXqc4m4YL/yp1HuPLxZdxxyWiuPWco6SlJpCZHv/XLGMP20goefOcL8npkkJqcxN0zT0Ycq6CvKz7GzS+uofhIld9zv/vkCk7O686w3llcPXEwyUnC1v0n+NP7XzJ5VC6/eG0D6++dTndHsrUT1XWkpySTltJwbm634c0NJSz4rIjVu44CMDw3i+2lFb5j1gaM6nnzR+czbkCPiP4ulFKxJ8a0OBV/xOXn55uCgoKov8/OQxVM+eOHfmW3zxjN9y8YRnpK8xOYXG7D8ao6bv3HWq47byjnDu/Fm+tKGNo7iySBMf260yXIRCiX23Dvwk08u3yXX/lzc88mf2g2aclJ/OyVdby2xj/7dWZaMpW1/guohOI89vYZo/nDu1sBuOHC4XwrfxD3v72FdzcdCPrcIb0yeW7u2SzbcZg7XlnP1DF96NcjgxsuHM7A7Myw3l8p1f5EZLUxplW35h06+Btj/K6cmzP0zreA4EH1me9N5KyhOdS53aQmJVHndrPtQDnfmP8ZeT0y6NMt3TfqpSm//q9TuPy0PFzGMO+Z1WzaV0adq+F33DMzlWOVdSGf/6dvjefC0bmICD26pLJhTxnPLCti28HyRlflAGcM7snnu8Mfg5+TlcarN5zL0F6ZHKuso6rORc/MVDLTEu4mUKkOL2GD/4LPivjbJzu4b/apHK2oJTsrjckje1PvNnz0ZSllVXWkpSRx9rBerNl9lB88uxqAnfdfxp6jVUz+w1JCnX5aShK19W7f4+QkweVufPC0k/uwpeSEb3HzUHKy0nj8u2dy1tAcALaXlvP3T3ayfk8ZG/aWMX1sXx65+gwyUkPfgRw4Xs1TnxaRkiT06Z7OdycNQUTYX1bNpPuXcO7wXnz/gmG8umYvFTX1DM/tStGhCpZ8cZBRfbvy5JyzGJSjV/JKdRYJG/yf+nQnv7ZDEcN13+xxfGfSEMDTqepyG95aX8LvFm3hSEUt4An8XxmVy5pdR/naGQM4bVBPzh6WQ9/uGSHvNspr6pn+0H/YV1btK7t75skUHizntotH0UczYiqlIixhg391nYsx97wDwOi+3dh64IRv36Xj+rFhbxl5PTKYPrYfLmOYMDibicNymnzNOpfnar+1ncBut0GEFjVHKaVUa7Ql+Hfoht6M1GTunnky5TX13DptFPUuN8er68nJSmv1a7Z15E9SFIdkKqVUpEQl+IvIJcCfgWTgb8aYB6LxPgDfv+Ak33ZKclKbAr9SSiWKiA9wF5Fk4DHgUmAscLWIjI30+yillGq9aMxumggUGmN2GGNqgZeAWVF4H6WUUq0UjeA/ACh2PN5jy/yIyDwRKRCRgtLS0ihUQymlVCgxy+ppjHnCGJNvjMnPzc2NVTWUUiohRSP47wWcKSAH2jKllFJxIhrBfxUwUkSGiUgacBWwMArvo5RSqpUiPtTTGFMvIjcD7+IZ6vl3Y8ymSL+PUkqp1ovKOH9jzCJgUTReWymlVNvFRXoHESkFdjV7YHC9gUMRrE5HoueemPTcE1Owcx9ijGnViJm4CP5tISIFrc1t0dHpueu5Jxo998iduy7grpRSCUiDv1JKJaDOEPyfiHUFYkjPPTHpuSemiJ57h2/zV0op1XKd4cpfKaVUC2nwV0qpBNShg7+IXCIiW0WkUETujHV9okFEikRkg4isFZECW5YjIotFZJv9mW3LRUQesb+P9SIyIba1bxkR+buIHBSRjY6yFp+riMyxx28TkTmxOJeWCnHu94rIXvvZrxWRyxz7fmHPfauIzHCUd7i/CREZJCJLRWSziGwSkR/b8k792Tdx3u3zuRtjOuQ/PKkjtgMnAWnAOmBsrOsVhfMsAnoHlP0euNNu3wk8aLcvA94GBJgErIh1/Vt4rpOBCcDG1p4rkAPssD+z7XZ2rM+tled+L/CzIMeOtf/f04Fh9u8guaP+TQB5wAS73Q340p5jp/7smzjvdvncO/KVfyIvGjMLWGC3FwCzHeXPGI/lQE8RyYtFBVvDGPMRcCSguKXnOgNYbIw5Yow5CiwGLol+7dsmxLmHMgt4yRhTY4zZCRTi+XvokH8TxpgSY8wau30C2IJnDZBO/dk3cd6hRPRz78jBP6xFYzoBA7wnIqtFZJ4t62uMKbHb+4G+drsz/k5aeq6d7Xdws23a+Lu32YNOfO4iMhQ4A1hBAn32AecN7fC5d+TgnyjON8ZMwLMm8k0iMtm503juBxNivG4inas1HxgOjAdKgP+NbXWiS0S6Aq8Ctxpjjjv3debPPsh5t8vn3pGDf0IsGmOM2Wt/HgRex3OLd8DbnGN/HrSHd8bfSUvPtdP8DowxB4wxLmOMG/grns8eOuG5i0gqngD4vDHmNVvc6T/7YOfdXp97Rw7+nX7RGBHJEpFu3m1gOrARz3l6RzLMAd6w2wuBa+1oiElAmeO2uaNq6bm+C0wXkWx7uzzdlnU4Af01X8Pz2YPn3K8SkXQRGQaMBFbSQf8mRESAJ4EtxpiHHLs69Wcf6rzb7XOPdY93G3vLL8PTQ74duCvW9YnC+Z2Ep+d+HbDJe45AL2AJsA14H8ix5QI8Zn8fG4D8WJ9DC8/3RTy3uXV42i3ntuZcge/h6QwrBK6P9Xm14dyftee23v4x5zmOv8ue+1bgUkd5h/ubAM7H06SzHlhr/13W2T/7Js67XT53Te+glFIJqCM3+yillGolDf5KKZWANPgrpVQC0uCvlFIJSIO/UkolIA3+SimVgDT4K6VUAvr/wr3UAbcFkEwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df[df.Close.isna() == False]['Close'].index, df[df.Close.isna() == False]['Close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "SIEjfDzDY0Ns",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "SIEjfDzDY0Ns",
    "outputId": "577d6617-bd9e-437b-f9f4-a324273f5363"
   },
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</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",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2125</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2272</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2274</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2311</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2313</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Date  Open  High  Low  Close  Adj Close  Volume\n",
       "78     NaN   NaN   NaN  NaN    NaN        NaN     NaN\n",
       "80     NaN   NaN   NaN  NaN    NaN        NaN     NaN\n",
       "175    NaN   NaN   NaN  NaN    NaN        NaN     NaN\n",
       "177    NaN   NaN   NaN  NaN    NaN        NaN     NaN\n",
       "240    NaN   NaN   NaN  NaN    NaN        NaN     NaN\n",
       "...    ...   ...   ...  ...    ...        ...     ...\n",
       "2125   NaN   NaN   NaN  NaN    NaN        NaN     NaN\n",
       "2272   NaN   NaN   NaN  NaN    NaN        NaN     NaN\n",
       "2274   NaN   NaN   NaN  NaN    NaN        NaN     NaN\n",
       "2311   NaN   NaN   NaN  NaN    NaN        NaN     NaN\n",
       "2313   NaN   NaN   NaN  NaN    NaN        NaN     NaN\n",
       "\n",
       "[100 rows x 7 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Заполним пропуски средним значением между вчера и завтра\n",
    "\n",
    "# а как\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "P8L_6VGbX-wd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "P8L_6VGbX-wd",
    "outputId": "55eca9de-acae-4054-d7c1-b89400df4ed7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        23.889999\n",
       "1        23.830000\n",
       "2        21.959999\n",
       "3        19.200001\n",
       "4        16.110001\n",
       "           ...    \n",
       "2411    566.900024\n",
       "2412    580.989990\n",
       "2413    640.809998\n",
       "2414    650.570007\n",
       "2415    780.000000\n",
       "Name: Close, Length: 2416, dtype: float64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Close'].fillna(df['Close'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "gaNBQKkaX-0s",
   "metadata": {
    "id": "gaNBQKkaX-0s"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "36396f4c"
   ],
   "name": "Pandas_1.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}