hw1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Машинное обучение на матфаке, 2019/2020 уч. год.\n",
    "\n",
    "## Домашнее задание 1 -- numpy, pandas, matplotlib.\n",
    "\n",
    "__Выполнил__: Глеб Красилич\n",
    "\n",
    "__Для проверяющего__: Если что-то плохо отображается в notebook, то можно открыть этот файл на моём форке в Github: [https://github.com/GlebChili/math-ml-hse-2019/blob/master/hw1.ipynb](https://github.com/GlebChili/math-ml-hse-2019/blob/master/hw1.ipynb). Там, вроде, всё отображается также, как и у меня на машине. При решении использовался интерпретатор Python 3.8.1. \n",
    "\n",
    "В этом домашнем задании вам предстоит закрепить навыки работы с важнейшими для data science библиотеками -- numpy, pandas и matplotlib.\n",
    "\n",
    "Правила оценивания на вики-странице. Часть заданий необходимо сдать в [Яндекс.контест](https://contest.yandex.ru/contest/16930/problems/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 1. numpy\n",
    "\n",
    "Задания 1-10 оцениваются в 0.4 баллов."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Задание 1__. Создайте несколько одномерных массивов размера $10^n$ для n от 1 до 6. из случайных чисел при помощи модуля `np.random`. Заполните их независимыми случайными величинами из нормального распределения с матожиданием 5 и дисперсией 10. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_sample_1 = np.random.normal(5, 10, 10)\n",
    "random_sample_2 = np.random.normal(5, 10, 10 ** 2)\n",
    "random_sample_3 = np.random.normal(5, 10, 10 ** 3)\n",
    "random_sample_4 = np.random.normal(5, 10, 10 ** 4)\n",
    "random_sample_5 = np.random.normal(5, 10, 10 ** 5)\n",
    "random_sample_6 = np.random.normal(5, 10, 10 ** 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(random_sample_6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000000,)\n"
     ]
    }
   ],
   "source": [
    "print(random_sample_6.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(random_sample_6, bins=100, density=True, label=\"Histogram of random_sample_6\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В заданиях 2-10 запрещается использование циклов `for` и `while` в сдаваемом вами коде. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Задание 2__. Сгенерируйте случайный вектор (одномерный массив) из нормального распределения со средним в нуле и произвольной дисперсией. С помощью `np.where` замените в созданном массиве все отрицательные элементы на нули."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_sample_problem_2 = np.random.normal(0, 10, 10 ** 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(random_sample_problem_2, bins=20, density=True, label=\"Unmodified random sample\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "modified_random_sample_problem_2 = np.where(random_sample_problem_2 < 0, np.zeros(10 ** 6), random_sample_problem_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(modified_random_sample_problem_2, bins= 10, density=True, label=\"Modified random sample\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.xlim(left=-60, right=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Задание 3__. Дан массив 10х3, который будет использован как массив с цветами RGB (каждый канал обозначается целым числом от 0 до 255), однако вследствие порчи данных при передаче значения массива вышли за границы заданного диапазона. Напишите функцию clip_values, которая вышедшие за границу диапазона значения превращает в ближашие границы диапазона.\n",
    "\n",
    "__Хинт__: воспользуйтесь np.clip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clip_values(image: np.ndarray) -> np.ndarray:\n",
    "    return np.clip(image, 0, 255)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Задание 4__. Сгенерируйте случайный вектор из нормального распределения со средним в нуле и произвольной дисперсией. С помощью `np.insert` в встаьте в него на четные индексы нули."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_sample_problem_4 = np.random.normal(0, 10, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15.84417593, -0.11489952, 12.07526119,  0.77219942, 11.03086938])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_sample_problem_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "modified_random_sample_problem_4 = np.insert(random_sample_problem_4, range(0, 5), 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        , 15.84417593,  0.        , -0.11489952,  0.        ,\n",
       "       12.07526119,  0.        ,  0.77219942,  0.        , 11.03086938])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modified_random_sample_problem_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10,)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modified_random_sample_problem_4.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Задание 5__. Создайте случайный масиив 4х4 из нормального распределения со средним в нуле и произвольной дисперсией и верните индексы положительных элементов (`np.argwhere`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_matrix_problem_5 = np.random.normal(0, 10, (4, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  5.94083665,  12.16526184,  -4.27551582,  14.93767895],\n",
       "       [ 12.76886439,  19.98235241,  16.62245912,  -0.64173237],\n",
       "       [  1.4952312 ,   7.53249097,  -3.25489191, -26.49635365],\n",
       "       [  1.04363729,   7.5696044 ,  12.54936359,  -6.62817476]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_matrix_problem_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "nonzero_values_indices_of_random_matrix_problem_5 = np.argwhere(np.where(random_matrix_problem_5 > 0, random_matrix_problem_5, np.zeros((4, 4))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0],\n",
       "       [0, 1],\n",
       "       [0, 3],\n",
       "       [1, 0],\n",
       "       [1, 1],\n",
       "       [1, 2],\n",
       "       [2, 0],\n",
       "       [2, 1],\n",
       "       [3, 0],\n",
       "       [3, 1],\n",
       "       [3, 2]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nonzero_values_indices_of_random_matrix_problem_5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Задание 6__. Создайте матрицу случайных целых чисел от в интервале от 1 до 10 размера 5х5 и посчитайте следующее (сабмодуль `np.linalg`):\n",
    "\n",
    "- определитель матрицы\n",
    "- обратную матрицу\n",
    "- произведение матрицы на саму себя\n",
    "- собственные вектора\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_matrix_problem_6 = np.random.randint(1, 11, (5, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  1,  4, 10,  6],\n",
       "       [ 4,  5,  7,  7,  4],\n",
       "       [ 6,  8,  2,  5, 10],\n",
       "       [ 5,  7,  3,  8,  6],\n",
       "       [ 3,  5,  8,  6,  5]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_matrix_problem_6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Determinant of matrix:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-1852.9999999999998"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Determinant of matrix:\")\n",
    "np.linalg.det(random_matrix_problem_6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inverse of the matrix:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.00215866,  1.06152186,  0.27792768, -0.49487318, -0.81381543],\n",
       "       [-0.14732866, -0.6988667 , -0.21856449,  0.52509444,  0.5429034 ],\n",
       "       [-0.01349164,  0.1154884 ,  0.01295197, -0.15704263,  0.08634647],\n",
       "       [ 0.06907717, -0.03130059, -0.10631409,  0.16405828, -0.0420939 ],\n",
       "       [ 0.08472747, -0.08526713,  0.15866163, -0.17377226,  0.0577442 ]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Inverse of the matrix:\")\n",
    "np.linalg.inv(random_matrix_problem_6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random_matrix_problem_6^2:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 97, 138,  97, 153, 140],\n",
       "       [113, 154, 118, 190, 176],\n",
       "       [105, 147, 179, 226, 168],\n",
       "       [109, 150, 147, 214, 166],\n",
       "       [116, 159, 121, 183, 179]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"random_matrix_problem_6^2:\")\n",
    "np.matmul(random_matrix_problem_6, random_matrix_problem_6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eigenvectors of the matrix:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.36974356+0.j        ,  0.34063236+0.j        ,\n",
       "         0.8373179 +0.j        ,  0.8373179 -0.j        ,\n",
       "        -0.31968516+0.j        ],\n",
       "       [-0.44496258+0.j        ,  0.32798281+0.j        ,\n",
       "        -0.43432411-0.20882288j, -0.43432411+0.20882288j,\n",
       "        -0.04927914+0.j        ],\n",
       "       [-0.49434265+0.j        , -0.74830823+0.j        ,\n",
       "         0.0859004 -0.06266131j,  0.0859004 +0.06266131j,\n",
       "         0.45819653+0.j        ],\n",
       "       [-0.46853516+0.j        , -0.25989289+0.j        ,\n",
       "        -0.07615722+0.06030761j, -0.07615722-0.06030761j,\n",
       "        -0.59257021+0.j        ],\n",
       "       [-0.44877407+0.j        ,  0.38585923+0.j        ,\n",
       "        -0.13383662+0.16733149j, -0.13383662-0.16733149j,\n",
       "         0.5781777 +0.j        ]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Eigenvectors of the matrix:\")\n",
    "_, eigenvectors_of_random_matrix_problem_6 = np.linalg.eig(random_matrix_problem_6)\n",
    "eigenvectors_of_random_matrix_problem_6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Задания 7-10 необходимо сдать в [Яндекс.контест](https://contest.yandex.ru/contest/16930/problems/) и прикрепить ссылку на решение в формате `https://contest.yandex.ru/contest/xxxxx/run-report/xxxxx/`. Сдавайте задания строго в соответствии с написанной сигнатурой и возвращаемыми типами. Для сдачи необходимо отправить в соответствующую задачу файл, содержащий функции и необходимые импорты. Обратите внимание, что вам доступен только `numpy`. При сдаче выберите компилятор make. __При отсутствии ссылки на решение задание засчитано не будет!__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Задание 7__. Напишите функцию, которая находит сумму квадратов положительных элементов одномерного массива.\n",
    "\n",
    "__Ссылка на посылку в контесте__: [https://contest.yandex.ru/contest/16930/run-report/30592708/](https://contest.yandex.ru/contest/16930/run-report/30592708/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_squares(a: np.ndarray) -> float:\n",
    "    a_without_negatives = np.where(a > 0, a, np.zeros(a.shape))\n",
    "    a_squares = a_without_negatives * a_without_negatives\n",
    "    return a_squares.sum()\n",
    "\n",
    "assert get_squares(np.array([1,-2,3])) == 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Задание 8__. Коля -- мастер покера и умеет выигрывать только целые суммы денег от 0 до n - 1. Коля решил сыграть на турнире. На вход подаётся массив `p` размера $n$, где `p[i]` -- вероятность, что Коля выиграет на турнире $i$ рублей ($\\sum_{i=1}^{n}p_i =1$, $p_i \\geqslant 0, \\forall i$). Посчитайте математическое ожидание выигрыша Коли на турнире.\n",
    "\n",
    "\n",
    "__Ссылка на посылку в контесте__: [https://contest.yandex.ru/contest/16930/run-report/30593096/](https://contest.yandex.ru/contest/16930/run-report/30593096/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def expected_reward(p: np.ndarray) -> float:\n",
    "    values = np.array(range(0, p.shape[0]))\n",
    "    tmp = p * values\n",
    "    return tmp.sum()\n",
    "\n",
    "assert expected_reward(np.array([0.4, 0.5, 0.1])) == 0.7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Задание 9__. Дана квадратная матрица `A`. Посчитайте произведение диагональных элементов матрицы, __строго__ превосходящих среднее значение элементов всей матрицы.\n",
    "\n",
    "__Хинт__: взять диагональ матрицы -- np.diag(), произведение элементов массива -- np.prod(), среднее значение в массиве -- np.mean().\n",
    "\n",
    "__Ссылка на посылку в контесте__: [https://contest.yandex.ru/contest/16930/run-report/30593420/](https://contest.yandex.ru/contest/16930/run-report/30593420/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prod_upper_avg_diagonal(a: np.ndarray) -> float:\n",
    "    mean = np.mean(a)\n",
    "    diag = np.diag(a)\n",
    "    if np.any(diag > mean):\n",
    "        modified_diag = np.where(diag > mean, diag, np.ones(diag.shape))\n",
    "        return modified_diag.prod()\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "assert prod_upper_avg_diagonal(np.array([[ 0,  1,  2,  3],\n",
    "                                         [ 4,  5,  6,  7],\n",
    "                                         [ 8,  9, 10, 11],\n",
    "                                         [12, 13, 14, 15]])) == 150"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Задание 10__. Дано число $n$. Создайте матрицу $A$ размера $n \\times n$ такую, что $A[i][j] = i \\cdot j$. Нумерация строк и столбцов начинается с нуля.\n",
    "\n",
    "__Ссылка на посылку в контесте:__ [https://contest.yandex.ru/contest/16930/run-report/30594201/](https://contest.yandex.ru/contest/16930/run-report/30594201/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_ij(n: int) -> np.ndarray:\n",
    "    pre_matrix = np.repeat(np.array(range(0, n), dtype=int), n)\n",
    "    pre_matrix = np.reshape(pre_matrix, (n, n))\n",
    "    transposed_pre_matrix = pre_matrix.T\n",
    "    return ((np.matmul(pre_matrix, transposed_pre_matrix)) / n).astype(int)\n",
    "\n",
    "assert np.all(create_ij(5) == np.array([[ 0,  0,  0,  0,  0],\n",
    "                                        [ 0,  1,  2,  3,  4],\n",
    "                                        [ 0,  2,  4,  6,  8],\n",
    "                                        [ 0,  3,  6,  9, 12],\n",
    "                                        [ 0,  4,  8, 12, 16]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2. Numpy vs pure python\n",
    "\n",
    "\n",
    "__Задание 11__. (1 балл) Выберите любые 5 функций из заданий 2-10 и имплементируйте их, но без использования сторонних библиотек (считайте, что вам доступны только стандартные библиотеки языка Питон и на вход вместо массивов подаются питоновские списки)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_sum(a) -> float:\n",
    "    tmp = 0\n",
    "    for i in a:\n",
    "        tmp += i\n",
    "    return tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TEST\n",
    "\n",
    "for i in range(1000):\n",
    "    sample = np.random.randint(0, 20, size=20)\n",
    "    first = sample.sum()\n",
    "    second = custom_sum(sample)\n",
    "    assert first == second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_prod(a) -> float:\n",
    "    tmp = 1\n",
    "    for i in a:\n",
    "        tmp = tmp * i\n",
    "    return tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TEST\n",
    "\n",
    "for i in range(1000):\n",
    "    sample = np.random.randint(0, 10, size=5)\n",
    "    first = sample.prod()\n",
    "    second = custom_prod(sample)\n",
    "    assert first == second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_where(bools, first_list, second_list):\n",
    "    tmp = []\n",
    "    for i in range(len(bools)):\n",
    "        if bools[i]:\n",
    "            tmp.append(first_list[i])\n",
    "        else:\n",
    "            tmp.append(second_list[i])\n",
    "    return tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TEST\n",
    "\n",
    "for i in range(1000):\n",
    "    sample = np.random.randint(0, 50, 1000)\n",
    "    ones = np.ones(1000)\n",
    "    zeros = np.zeros(1000)\n",
    "    first = np.where(sample < 25, ones, zeros).tolist()\n",
    "    second = custom_where(sample < 25, ones, zeros)\n",
    "    assert first == second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_mean(a) -> float:\n",
    "    s = custom_sum(a)\n",
    "    return s/len(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TEST\n",
    "\n",
    "for i in range(1000):\n",
    "    sample = np.random.randint(0, 10, 1000)\n",
    "    first = sample.mean()\n",
    "    second = custom_mean(sample)\n",
    "    assert second == first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_argwhere(a):\n",
    "    tmp =[]\n",
    "    for i in range(len(a)):\n",
    "        if a[i]:\n",
    "            tmp.append([i])\n",
    "    return tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TEST\n",
    "\n",
    "for i in range(1000):\n",
    "    sample = np.random.randint(0, 50, 1000)\n",
    "    first = np.argwhere(sample < 25).tolist()\n",
    "    second = custom_argwhere(sample < 25)\n",
    "    assert first == second"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Задание 12__. (3 балла) Сравните время работы векторизованных функций с обычными. Для этого постройте графики зависимости времени исполнения функции от размера входного массива. Перебирайте значения от 1 до $10^6$ по логарифмической шкале. Сделайте выводы.\n",
    "\n",
    "__Графики должны быть опрятными!__. Подписывайте оси, и единицы измерения, указывайте легенду и название графика. Оценка за задание с неопрятными графиками может быть снижена! Сравнение плохого и хорошего графиков:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Хинт__: воспользуйтесь библиотекой `time` или мэджиком `%timeit`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time as time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = range(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "np_where_time =[]\n",
    "custom_where_time = []\n",
    "for i in range(len(x)):\n",
    "    sample = np.random.randint(1, 100, 10 ** x[i])\n",
    "    t1 = time.perf_counter()\n",
    "    np.where(sample < 50, np.ones(10 ** x[i]), np.zeros(10 ** x[i]))\n",
    "    t2 = time.perf_counter()\n",
    "    np_where_time.append((t2 - t1)*1000)\n",
    "    t3 = time.perf_counter()\n",
    "    custom_where(sample < 50, np.ones(10 ** x[i]), np.zeros(10 ** x[i]))\n",
    "    t4 = time.perf_counter()\n",
    "    custom_where_time.append((t4 -t3)*1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np_where_time, label=\"numpy where\")\n",
    "plt.plot(x, custom_where_time, label=\"самодельный where\")\n",
    "\n",
    "plt.xlabel(\"Размер массива $10^x$\")\n",
    "plt.ylabel(\"Длительность выполнения функции (мс)\")\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [10, 8]\n",
    "plt.legend(loc=\"best\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-34-92b02d9305cb>:4: RuntimeWarning: overflow encountered in long_scalars\n",
      "  tmp = tmp * i\n"
     ]
    }
   ],
   "source": [
    "np_prod_time =[]\n",
    "custom_prod_time = []\n",
    "for i in range(len(x)):\n",
    "    sample = np.random.randint(1, 100, 10 ** x[i])\n",
    "    t1 = time.perf_counter()\n",
    "    np.prod(sample)\n",
    "    t2 = time.perf_counter()\n",
    "    np_prod_time.append((t2 - t1)*1000)\n",
    "    t3 = time.perf_counter()\n",
    "    custom_prod(sample)\n",
    "    t4 = time.perf_counter()\n",
    "    custom_prod_time.append((t4 -t3)*1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np_prod_time, label=\"numpy prod\")\n",
    "plt.plot(x, custom_prod_time, label=\"самодельный prod\")\n",
    "\n",
    "plt.xlabel(\"Размер массива $10^x$\")\n",
    "plt.ylabel(\"Длительность выполнения функции (мс)\")\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [10, 8]\n",
    "plt.legend(loc=\"best\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "np_sum_time =[]\n",
    "custom_sum_time = []\n",
    "for i in range(len(x)):\n",
    "    sample = np.random.randint(1, 100, 10 ** x[i])\n",
    "    t1 = time.perf_counter()\n",
    "    np.sum(sample)\n",
    "    t2 = time.perf_counter()\n",
    "    np_sum_time.append((t2 - t1)*1000)\n",
    "    t3 = time.perf_counter()\n",
    "    custom_sum(sample)\n",
    "    t4 = time.perf_counter()\n",
    "    custom_sum_time.append((t4 -t3)*1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np_sum_time, label=\"numpy sum\")\n",
    "plt.plot(x, custom_sum_time, label=\"самодельный sum\")\n",
    "\n",
    "plt.xlabel(\"Размер массива $10^x$\")\n",
    "plt.ylabel(\"Длительность выполнения функции (мс)\")\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [10, 8]\n",
    "plt.legend(loc=\"best\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "np_mean_time =[]\n",
    "custom_mean_time = []\n",
    "for i in range(len(x)):\n",
    "    sample = np.random.randint(1, 100, 10 ** x[i])\n",
    "    t1 = time.perf_counter()\n",
    "    np.mean(sample)\n",
    "    t2 = time.perf_counter()\n",
    "    np_mean_time.append((t2 - t1)*1000)\n",
    "    t3 = time.perf_counter()\n",
    "    custom_mean(sample)\n",
    "    t4 = time.perf_counter()\n",
    "    custom_mean_time.append((t4 -t3)*1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np_mean_time, label=\"numpy mean\")\n",
    "plt.plot(x, custom_mean_time, label=\"самодельный mean\")\n",
    "\n",
    "plt.xlabel(\"Размер массива $10^x$\")\n",
    "plt.ylabel(\"Длительность выполнения функции (мс)\")\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [10, 8]\n",
    "plt.legend(loc=\"best\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "np_argwhere_time =[]\n",
    "custom_argwhere_time = []\n",
    "for i in range(len(x)):\n",
    "    sample = np.random.randint(1, 100, 10 ** x[i])\n",
    "    t1 = time.perf_counter()\n",
    "    np.argwhere(sample < 50)\n",
    "    t2 = time.perf_counter()\n",
    "    np_argwhere_time.append((t2 - t1)*1000)\n",
    "    t3 = time.perf_counter()\n",
    "    custom_argwhere(sample < 50)\n",
    "    t4 = time.perf_counter()\n",
    "    custom_argwhere_time.append((t4 -t3)*1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np_argwhere_time, label=\"numpy argwhere\")\n",
    "plt.plot(x, custom_argwhere_time, label=\"самодельный argwhere\")\n",
    "\n",
    "plt.xlabel(\"Размер массива $10^x$\")\n",
    "plt.ylabel(\"Длительность выполнения функции (мс)\")\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [10, 8]\n",
    "plt.legend(loc=\"best\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Вывод__: Пользуйтесь, дети, функциями из NumPy. Они сильно быстрее всех самодельных функций, особенно на больших входных данных, так как написаны на C и используют SIMD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 3. Pandas.\n",
    "\n",
    "Задания 13-22 оцениваются в 0.4 баллов.  В заданиях 13-22 запрещается пользоваться циклами `for` и `while` в сдаваемом вами коде. Задания 13-15 также сдаются в контест с компилятором python3.6+numpy+pandas. В конце сдаваемого вами файла должны быть строки\n",
    "```\n",
    "import sys\n",
    "exec(sys.stdin.read())\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Задание 13__, Дан датафрейм, в котором существует колонка 'email' (строка, длиной не более 255). Напишите функцию, которая в заданном датафрейме заменяет все невалидные email'ы на \"unknown@unknown.com\". Валидным сичтается емэйл, выглядящий как `[name]@[subdomain].[domain]`, где `[name]` -- строка, содержащая латинские буквы, цифры, точки и знаки `-` и `_`, `[subdomain]` и `[domain]` -- строки, содержащие только латинские буквы и цифры. Длина строки `[domain]` -- не больше 8 символов.\n",
    "\n",
    "__Хинт__. Вопспользуйтесь методом .apply(), в который передайте функцию для обрабоки одного email'a.\n",
    "\n",
    "\n",
    "__Ссылка на посылку в контесте:__ [https://contest.yandex.ru/contest/16930/run-report/30709352/](https://contest.yandex.ru/contest/16930/run-report/30709352/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def email_checker(s):\n",
    "    tmp = s\n",
    "    if not re.match(\"^[a-zA-Z.\\-_0-9]+\\@[a-zA-Z0-9]+\\.[a-zA-Z0-9]{1,8}$\", tmp[\"email\"]):\n",
    "        tmp[\"email\"] = \"unknown@unknown.com\"\n",
    "    return tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reset_emails(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    return df.apply(email_checker, axis=1)\n",
    "\n",
    "import sys\n",
    "exec(sys.stdin.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Задание 14__. Дан датафрейм, в котором существуют колонки 'age' (возраст в годах, целое число) и 'income' (дробное число). Напишите функцию, которая вернёт средний заработок людей младше 18 лет.\n",
    "\n",
    "__Ссылка на посылку в контесте:__ [https://contest.yandex.ru/contest/16930/run-report/30710293/](https://contest.yandex.ru/contest/16930/run-report/30710293/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mean_income(df: pd.DataFrame) -> float:\n",
    "    filtered_df = df.loc[df[\"age\"] < 18]\n",
    "    inc_array = filtered_df[\"income\"].to_numpy()\n",
    "    return inc_array.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Задание 15__. Дан датафрейм, в котором существуют колонки 'id' (идентификатор пользователя, целое положительное число), 'age' (возвраст в годах, целое число), 'sex' (пол пользователя, строка, равная либо \"Male\", либо \"Female\", либо \"Other\"), \"favorite_color\" (любимый цвет пользователя, строка длиной не более 255). Найдите число различных любимых цветов у женщин младше 18 лет.\n",
    "\n",
    "__Ссылка на посылку в контесте__: [https://contest.yandex.ru/contest/16930/run-report/30711963/](https://contest.yandex.ru/contest/16930/run-report/30711963/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_fav_color(df: pd.DataFrame) -> int:\n",
    "    filtered_df = df[(df[\"age\"] < 18) & (df[\"sex\"] == \"Female\")]\n",
    "    colors = filtered_df[\"favorite_color\"]\n",
    "    unique_colors = np.unique(colors)\n",
    "    return unique_colors.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 4. EDA with pandas.\n",
    "\n",
    "В этом задании предлагается сделать разведочный анализ данных в датафрейме. \n",
    "\n",
    "Мы будем работать с датасетом русскоязычных твитов. Скачайте данные по ссылке: https://www.dropbox.com/s/ou55qwyhbj1pdav/tweets.csv?dl=1\n",
    "\n",
    "Корпус состоит из 12 столбцов:\n",
    "\n",
    "    – id: уникальный номер сообщения в системе twitter;\n",
    "    – tdate: дата публикации сообщения (твита);\n",
    "    – tmane: имя пользователя, опубликовавшего сообщение;\n",
    "    – ttext:  текст сообщения (твита);\n",
    "    – ttype: поле в котором в дальнейшем будет указано к кому классу относится твит (положительный, отрицательный, нейтральный);\n",
    "    – trep: количество реплаев к данному сообщению. В настоящий момент API твиттера не отдает эту информацию;\n",
    "    – tfav: число сколько раз данное сообщение было добавлено в избранное другими пользователями;\n",
    "    – tstcount: число всех сообщений пользователя в сети twitter;\n",
    "    – tfol: количество фолловеров пользователя (тех людей, которые читают пользователя);\n",
    "    – tfrien: количество друзей пользователя (те люди, которых читает пользователь);\n",
    "    – listcount: количество листов-подписок в которые добавлен твиттер-пользователь.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-02-05 19:38:04--  https://www.dropbox.com/s/ou55qwyhbj1pdav/tweets.csv?dl=1\n",
      "Loaded CA certificate '/etc/ssl/certs/ca-certificates.crt'\n",
      "Resolving www.dropbox.com (www.dropbox.com)... 2620:100:6026:1::a27d:4601, 162.125.70.1\n",
      "Connecting to www.dropbox.com (www.dropbox.com)|2620:100:6026:1::a27d:4601|:443... connected.\n",
      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
      "Location: /s/dl/ou55qwyhbj1pdav/tweets.csv [following]\n",
      "--2020-02-05 19:38:04--  https://www.dropbox.com/s/dl/ou55qwyhbj1pdav/tweets.csv\n",
      "Reusing existing connection to [www.dropbox.com]:443.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://uca7972a37096388729cde0a4e8b.dl.dropboxusercontent.com/cd/0/get/AxgDUcorVCbPTsK_PPh3tAuHkMxiIxRvJko3WOWn75sZ1ZDbQ33191Aq30sJ2r8jVQA-UMywVR_DYYe4KLDGBWSG79E5JOi6WD0VV6hKfl_GQ3Ff-YcOrGH-NPG1RK9qBjw/file?dl=1# [following]\n",
      "--2020-02-05 19:38:05--  https://uca7972a37096388729cde0a4e8b.dl.dropboxusercontent.com/cd/0/get/AxgDUcorVCbPTsK_PPh3tAuHkMxiIxRvJko3WOWn75sZ1ZDbQ33191Aq30sJ2r8jVQA-UMywVR_DYYe4KLDGBWSG79E5JOi6WD0VV6hKfl_GQ3Ff-YcOrGH-NPG1RK9qBjw/file?dl=1\n",
      "Resolving uca7972a37096388729cde0a4e8b.dl.dropboxusercontent.com (uca7972a37096388729cde0a4e8b.dl.dropboxusercontent.com)... 2620:100:6026:6::a27d:4606, 162.125.70.6\n",
      "Connecting to uca7972a37096388729cde0a4e8b.dl.dropboxusercontent.com (uca7972a37096388729cde0a4e8b.dl.dropboxusercontent.com)|2620:100:6026:6::a27d:4606|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 44812115 (43M) [application/binary]\n",
      "Saving to: ‘tweets.csv’\n",
      "\n",
      "tweets.csv          100%[===================>]  42.74M  12.8MB/s    in 3.8s    \n",
      "\n",
      "2020-02-05 19:38:09 (11.2 MB/s) - ‘tweets.csv’ saved [44812115/44812115]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# For linux/google colab users -- download directly from the notebook\n",
    "! wget -O tweets.csv https://www.dropbox.com/s/ou55qwyhbj1pdav/tweets.csv?dl=1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./tweets.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>tdate</th>\n",
       "      <th>tmane</th>\n",
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       "      <th>trep</th>\n",
       "      <th>tfav</th>\n",
       "      <th>tstcount</th>\n",
       "      <th>tfol</th>\n",
       "      <th>tfrien</th>\n",
       "      <th>listcount</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>410745723928731648</td>\n",
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       "      <td>chavdaridi</td>\n",
       "      <td>RT @dianochka6464ru: Россия отмочила! Смотреть...</td>\n",
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       "      <th>2</th>\n",
       "      <td>419794699738570752</td>\n",
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       "      <td>RomanenkoJulya</td>\n",
       "      <td>мне надо домой, @RomaRudenko1 не отпускает(</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>410625694087909376</td>\n",
       "      <td>1386735769</td>\n",
       "      <td>VizivayuDyavola</td>\n",
       "      <td>RT @boobear1236969: Линта,я слева:D http://t.c...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>410849586635628545</td>\n",
       "      <td>1386789149</td>\n",
       "      <td>Den_Byhovsky</td>\n",
       "      <td>@juliasvirid17 я имел ввиду, что для меня спос...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3248</td>\n",
       "      <td>61</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id       tdate            tmane  \\\n",
       "0  410745723928731648  1386764386       chavdaridi   \n",
       "1  409580147185315840  1386486491      pifelanybal   \n",
       "2  419794699738570752  1388921830   RomanenkoJulya   \n",
       "3  410625694087909376  1386735769  VizivayuDyavola   \n",
       "4  410849586635628545  1386789149     Den_Byhovsky   \n",
       "\n",
       "                                               ttext  ttype  trep  tfav  \\\n",
       "0  RT @dianochka6464ru: Россия отмочила! Смотреть...      1     0     1   \n",
       "1  Google сменил алгоритм чексуммы в загребании P...     -1     0     0   \n",
       "2        мне надо домой, @RomaRudenko1 не отпускает(     -1     0     0   \n",
       "3  RT @boobear1236969: Линта,я слева:D http://t.c...      1     0     3   \n",
       "4  @juliasvirid17 я имел ввиду, что для меня спос...      1     0     0   \n",
       "\n",
       "   tstcount  tfol  tfrien  listcount  \n",
       "0         0   285      11         11  \n",
       "1         0   130     100         86  \n",
       "2         0   730      28         18  \n",
       "3         0  1959     405        537  \n",
       "4         0  3248      61         48  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Задание 16__. Создайте колонку 'tdif', равную разнице числа друзей и числа фолловеров у пользователя."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"tdif\"] = df[\"tfrien\"] - df[\"tfol\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Den_Byhovsky</td>\n",
       "      <td>@juliasvirid17 я имел ввиду, что для меня спос...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3248</td>\n",
       "      <td>61</td>\n",
       "      <td>48</td>\n",
       "      <td>-3187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id       tdate            tmane  \\\n",
       "0  410745723928731648  1386764386       chavdaridi   \n",
       "1  409580147185315840  1386486491      pifelanybal   \n",
       "2  419794699738570752  1388921830   RomanenkoJulya   \n",
       "3  410625694087909376  1386735769  VizivayuDyavola   \n",
       "4  410849586635628545  1386789149     Den_Byhovsky   \n",
       "\n",
       "                                               ttext  ttype  trep  tfav  \\\n",
       "0  RT @dianochka6464ru: Россия отмочила! Смотреть...      1     0     1   \n",
       "1  Google сменил алгоритм чексуммы в загребании P...     -1     0     0   \n",
       "2        мне надо домой, @RomaRudenko1 не отпускает(     -1     0     0   \n",
       "3  RT @boobear1236969: Линта,я слева:D http://t.c...      1     0     3   \n",
       "4  @juliasvirid17 я имел ввиду, что для меня спос...      1     0     0   \n",
       "\n",
       "   tstcount  tfol  tfrien  listcount  tdif  \n",
       "0         0   285      11         11  -274  \n",
       "1         0   130     100         86   -30  \n",
       "2         0   730      28         18  -702  \n",
       "3         0  1959     405        537 -1554  \n",
       "4         0  3248      61         48 -3187  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Задание 16__. Создайте датафрейм, в котором остались только строки с чётными номерами, и пронумеруйте их начиная с 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "only_even_df = df.drop(range(1, df.shape[0], 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "only_even_df = only_even_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>tdate</th>\n",
       "      <th>tmane</th>\n",
       "      <th>ttext</th>\n",
       "      <th>ttype</th>\n",
       "      <th>trep</th>\n",
       "      <th>tfav</th>\n",
       "      <th>tstcount</th>\n",
       "      <th>tfol</th>\n",
       "      <th>tfrien</th>\n",
       "      <th>listcount</th>\n",
       "      <th>tdif</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>410745723928731648</td>\n",
       "      <td>1386764386</td>\n",
       "      <td>chavdaridi</td>\n",
       "      <td>RT @dianochka6464ru: Россия отмочила! Смотреть...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>285</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>-274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>419794699738570752</td>\n",
       "      <td>1388921830</td>\n",
       "      <td>RomanenkoJulya</td>\n",
       "      <td>мне надо домой, @RomaRudenko1 не отпускает(</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>730</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>-702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>410849586635628545</td>\n",
       "      <td>1386789149</td>\n",
       "      <td>Den_Byhovsky</td>\n",
       "      <td>@juliasvirid17 я имел ввиду, что для меня спос...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3248</td>\n",
       "      <td>61</td>\n",
       "      <td>48</td>\n",
       "      <td>-3187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>415545552320077824</td>\n",
       "      <td>1387908754</td>\n",
       "      <td>VitaVita52</td>\n",
       "      <td>Было неожиданно,все попадали на концерте,никог...</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>461</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>-445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>413821842823348224</td>\n",
       "      <td>1387497790</td>\n",
       "      <td>wyhuzezyg</td>\n",
       "      <td>RT @yvysogoga: мда. центос это стабильность......</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>641</td>\n",
       "      <td>385</td>\n",
       "      <td>386</td>\n",
       "      <td>-256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id       tdate           tmane  \\\n",
       "0  410745723928731648  1386764386      chavdaridi   \n",
       "1  419794699738570752  1388921830  RomanenkoJulya   \n",
       "2  410849586635628545  1386789149    Den_Byhovsky   \n",
       "3  415545552320077824  1387908754      VitaVita52   \n",
       "4  413821842823348224  1387497790       wyhuzezyg   \n",
       "\n",
       "                                               ttext  ttype  trep  tfav  \\\n",
       "0  RT @dianochka6464ru: Россия отмочила! Смотреть...      1     0     1   \n",
       "1        мне надо домой, @RomaRudenko1 не отпускает(     -1     0     0   \n",
       "2  @juliasvirid17 я имел ввиду, что для меня спос...      1     0     0   \n",
       "3  Было неожиданно,все попадали на концерте,никог...     -1     0     0   \n",
       "4  RT @yvysogoga: мда. центос это стабильность......     -1     0     3   \n",
       "\n",
       "   tstcount  tfol  tfrien  listcount  tdif  \n",
       "0         0   285      11         11  -274  \n",
       "1         0   730      28         18  -702  \n",
       "2         0  3248      61         48 -3187  \n",
       "3         0   461      16         19  -445  \n",
       "4         0   641     385        386  -256  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_even_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Задание 17__. Создайте датафрейм, в котором остались лишь твиты положительной тональности."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "only_positive_tweets = df[df[\"ttype\"] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>410745723928731648</td>\n",
       "      <td>1386764386</td>\n",
       "      <td>chavdaridi</td>\n",
       "      <td>RT @dianochka6464ru: Россия отмочила! Смотреть...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
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       "      <td>1386735769</td>\n",
       "      <td>VizivayuDyavola</td>\n",
       "      <td>RT @boobear1236969: Линта,я слева:D http://t.c...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1959</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>410849586635628545</td>\n",
       "      <td>1386789149</td>\n",
       "      <td>Den_Byhovsky</td>\n",
       "      <td>@juliasvirid17 я имел ввиду, что для меня спос...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3248</td>\n",
       "      <td>61</td>\n",
       "      <td>48</td>\n",
       "      <td>-3187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>411098495815131136</td>\n",
       "      <td>1386848494</td>\n",
       "      <td>KlimMasha</td>\n",
       "      <td>как всегда круто посидели на химии:) http://t....</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>583</td>\n",
       "      <td>22</td>\n",
       "      <td>37</td>\n",
       "      <td>-561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>410406761066405888</td>\n",
       "      <td>1386683571</td>\n",
       "      <td>Alexandrovna_07</td>\n",
       "      <td>Сегодняшнее творение ,больше половины уже нет ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1155</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>-1131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    id       tdate            tmane  \\\n",
       "0   410745723928731648  1386764386       chavdaridi   \n",
       "3   410625694087909376  1386735769  VizivayuDyavola   \n",
       "4   410849586635628545  1386789149     Den_Byhovsky   \n",
       "15  411098495815131136  1386848494        KlimMasha   \n",
       "17  410406761066405888  1386683571  Alexandrovna_07   \n",
       "\n",
       "                                                ttext  ttype  trep  tfav  \\\n",
       "0   RT @dianochka6464ru: Россия отмочила! Смотреть...      1     0     1   \n",
       "3   RT @boobear1236969: Линта,я слева:D http://t.c...      1     0     3   \n",
       "4   @juliasvirid17 я имел ввиду, что для меня спос...      1     0     0   \n",
       "15  как всегда круто посидели на химии:) http://t....      1     0     0   \n",
       "17  Сегодняшнее творение ,больше половины уже нет ...      1     0     0   \n",
       "\n",
       "    tstcount  tfol  tfrien  listcount  tdif  \n",
       "0          0   285      11         11  -274  \n",
       "3          0  1959     405        537 -1554  \n",
       "4          0  3248      61         48 -3187  \n",
       "15         0   583      22         37  -561  \n",
       "17         0  1155      24         20 -1131  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_positive_tweets.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Задание 18__. Посчитайте число уникальных пользователей (пользователей с различными tmane).\n",
    "\n",
    "__Хинт__: воспользуйтесь np.unique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Уникальных пользователей:\n",
      "139658\n"
     ]
    }
   ],
   "source": [
    "unique_users = np.unique(df[\"tmane\"])\n",
    "\n",
    "print(\"Уникальных пользователей:\")\n",
    "print(unique_users.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Задание 19__. Посчитайте основные статистики по числу подписчиков при помощи метода .describe(). Постройте гистограмму значений признака."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "follower_stat = df[\"tfol\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.268340e+05\n",
       "mean     7.584972e+03\n",
       "std      2.039972e+04\n",
       "min      0.000000e+00\n",
       "25%      4.730000e+02\n",
       "50%      2.026000e+03\n",
       "75%      7.562000e+03\n",
       "max      1.138639e+06\n",
       "Name: tfol, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "follower_stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "followers = df[\"tfol\"].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkiSNpOuEig2raqOWzP14Yr/n2CRJkjRNXdeWHdZ1nJ4kSZJWs67dsp9omzsD5/YXjiRJkmaiayvcUuAB4CvAWb1FI0mSpBnpOlv2pL4DkSRJ0syNMuZOkiRJ85TJnSRJ0hiZk+QuyR8nuSzJpUm+kGTdJJsmOT3J1e17k6Hzj0iyLMlVSV40VP7UJJe0Y0cnSStfJ8mXWvm5SRav/qeUJEla/Told0muTXLN0OfaJNeM8oNJtgLeCuxWVTsxWJ92CXA4cEZV7QCc0fZJsmM7/iRgb+CYJBNr2h4LHALs0D57t/KDgduq6vHAx4CjRolVkiRpTdO15e484CfAh4A9gd2Ap83gd9cG1kuyNrA+cAOwHzAxceMkYP+2vR/wxaq6p6quBZYBuyfZAtioqs6pqgI+O+maiXudCuw10aonSZI0zrquUPEq4GXAw4FPA2+uql+M8oNV9VPgIwySxRuB26vqu8BjqurGds6NwObtkq2A64dusbyVbdW2J5evcE1V3QfcDjx6lHglSZLWJNMZc/cAK64vO5I2lm4/YDtgS+CRSQ5c1SVTlNUqyld1zeRYDkmyNMnSW265ZdWBS5IkrQG6jrk7GfhH4D7gdcAnkmw64m/+NnBtVd1SVfcyeDHyM4Gfta5W2vfN7fzlwDZD12/NoBt3edueXL7CNa3rd2Pg1smBVNVxVbVbVe22aNGiER9HkiRp/ujacrcnsBg4Avh34HwGq1aM4ifAHknWb+Pg9gKuAE4DDmrnHAR8rW2fBixpM2C3YzBx4rzWdXtHkj3afV476ZqJe70COLONy5MkSRprXVeoWDxbP1hV5yY5FbiAQUvghcBxwAbAKUkOZpAAHtDOvyzJKcDl7fzDqur+drtDgc8A6wHfah+AE4DPJVnGoMVuyWzFL0mSNJ91Su6SvHaq8qr67Cg/WlXvBd47qfgeBq14U51/JHDkFOVLgZ2mKL+blhxKkiQtJJ2SO1Z87cnEZIaJ149IkiRpnujaLfsWgCSLgLczeCXKJ3qMS5IkSSOY7vJjf8fglSg3ACfPfjiSJEmaia7dshMeW1X7AyT5vR7ikSRJ0gx0nVCxa9tcN8kuDMbcPbK3qCRJkjSSri13f9O+bwI+2rZvn/1wJEmSNBNdJ1Q8v+9AJEmSNHNdu2XfM1V5VX1gdsORJEnSTHSdLXtn+/zh0PadfQUlSZKk0XTtlv0bgCQHTmxLkiRp/pnue+6qlygkSZI0K7qOufs6g8Ru+ySnTZRX1b59BSZJkqTp6/oqlI+0b7tkJUmS5rGuY+7O7jsQSZIkzVzXbtk7GHTLrgfcxWCFiqqqjXqMTZIkSdPUteVuQ4AkF1bVLv2GJEmSpFE5W1aSJGmMdO2W3bVtrpdkFwbdslTVBX0FJkmSpOnrOlt2YpbsTcBH23YBL5j1iCRJkjSyrmPunt93IJIkSZq5ri13JNkHeBKw7kRZVX2gj6AkSZI0mk4TKpJ8EngV8BYG4+0OAB7bY1ySJEkaQdfZss+sqtcCt1XV+4FnANv0F5YkSZJG0TW5u6t9/1eSLYF7ge36CUmSJEmj6jrm7htJHgV8GLiAwUzZ43uLSpIkSSPpOlv2g23zH5J8A1i3qm7vLyxJkiSNoutLjDedouwEYEPgY1V1zmwHJkmSpOnr2i17I/BT2soUzRZVte5KzpckSdIc6JrcXV5VuwwXJLmwh3gkSZI0A12Tuw2S7AncBvy0jber/sKSJEnSKLomd1cC7wY2ALZNcj2wZW9RSZIkaSRdZ8u+dHg/yR7At5KcCBxbVT/sIzhJkiRNT+e1ZYdV1Q+S7Ag8ArhldkOSJEnSqEZK7gCq6sbZDESSJEkz13X5MUmSJK0BTO4kSZLGSKfkLslhbW3Zif1NkvxRf2FJkiRpFF1b7v6wqv5zYqeqbgP+sJ+QJEmSNKquyd3Dkvxm6bEkazGYKStJkqR5pOts2e8ApyT5JIOVKd4EfLu3qCRJkjSSrsndu4BDgEOBAN8Fju8rKEmSJI2m6woVDwCfbB9JkiTNU74KRZIkaYyY3EmSJI0RkztJkqQx0mnMXZLTpiqvqn1nNxxJkiTNxCqTuyQfqqrDgU2ADYG/BH62OgKTJEnS9D1Uy90LAKrq2Un2Af4M+B7w11X1y76DkyRJ0vQ81Ji7myY2quqbVbUncBlwepI/6TUySZIkTdtDtdwdAJDkDgYrU8DgJcYPA3YDPtJfaJIkSZquVSZ3VXVP+95w9YQjSZKkmej0KpQMHJjkz9v+tkl27zc0SZIkTVfX99wdAzwD+P22fwfwt71EJEmSpJF1es8d8PSq2jXJhQBVdVuSR/QYlyRJkkbQteXu3iRr0SZVJFkEPNBbVJIkSRpJ1+TuaOCrwOZJjgT+lcELjUeS5FFJTk1yZZIrkjwjyaZJTk9ydfveZOj8I5IsS3JVkhcNlT81ySXt2NFJ0srXSfKlVn5uksWjxipJkrQm6ZTcVdXJwJ8CfwXcCOxfVV+ewe9+HPh2Vf0WsDNwBXA4cEZV7QCc0fZJsiOwBHgSsDdwTGtFBDgWOATYoX32buUHA7dV1eOBjwFHzSBWSZKkNUbX2bK7AusD5wD/DqzXyqYtyUbAc4ATAKrq11X1n8B+wEnttJOA/dv2fsAXq+qeqroWWAbsnmQLYKOqOqeqCvjspGsm7nUqsNdEq54kSdI46zqh4mzghwxeYDyhaMuTTdP2wC3Ap5PsDJwPvA14TFXdCFBVNybZvJ2/FfCDoeuXt7J72/bk8olrrm/3ui/J7cCjgZ+PEK8kSdIao2tyt6yqRknkVvabuwJvqapzk3yc1gW7ElO1uNUqyld1zYo3Tg5h0K3Ltttuu6qYJUmS1ghdJ1QsSvL2JG9Ksm+Sx8zgN5cDy6vq3LZ/KoNk72etq5X2ffPQ+dsMXb81cEMr33qK8hWuSbI2sDFw6+RAquq4qtqtqnZbtGjRDB5JkiRpfuia3P0dsCnwOOBVwHlJXjfKD1bVTcD1SZ7QivYCLgdOAw5qZQcBX2vbpwFL2gzY7RhMnDivdeHekWSPNp7utZOumbjXK4Az27g8SZKksdapW7aq3j+8n2Qz4F+Az4z4u28BTm4vQr4GeD2DRPOUJAcDPwEOaL99WZJTGCSA9wGHVdX97T6HthjWA77VPjCYrPG5JMsYtNgtGTFOSZKkNUrXMXcAJHlkVd1ZVT8Hnjjqj1bVRcBuUxzaayXnHwkcOUX5UmCnKcrvpiWHkiRJC0nXV6E8M8nlDN5HR5KdkxzTa2SSJEmatq5j7j4GvAj4BUBVXczgXXWSJEmaR7omd1TV9ZOK7p/yREmSJM2ZrmPurk/yTKDaJIi30rpoJUmSNH90bbl7E3AYg5UflgNPafuSJEmaR7q23G1bVa/pNRJJkiTNWNeWu+N7jUKSJEmzomvL3dpJNmHSmq1V9d+W9JIkSdLc6ZrcPQE4nxWTuwK2n/WIJEmSNLKuyd3lVbVLr5FIkiRpxjq/506SJEnzX9fk7hm9RiFJkqRZ0bVbdsMkfwHsCKw7UVhVL+glKkmSJI2ka8vdyQxWpNgOeD9wHfDDnmKSJEnSiLomd4+uqhOAe6vq7Kp6A7BHj3FJkiRpBF27Ze9t3zcm2Qe4Adi6n5AkSZI0qq7J3V8k2Rj4n8AngI2AP+4tKkmSJI2kU3JXVd9om7cDz+8vHEmSJM1EpzF3SZ6X5CNJnpTkO0mWJvmdvoOTJEnS9HTtlj0GOBH4HvBq4A7geODJPcUlSZKkEXSdLfvrqvoIcEtVnVFV5wH39RiXJEmSRtC15W6zJO8ANm7fARb1F5YkSZJG0TW5+ztgw6FvGHTLSpIkaR7pOlv2/QBJNhzs1q96jUqSJEkj6TpbdqckFwKXApclOT/Jk/oNTZIkSdPVdULFccA7quqxVfVYBi8z/rv+wpIkSdIouiZ3j6yq703sVNVZwCN7iUiSJEkj6zqh4pokfw58ru0fCFzbT0iSJEkaVdeWuzcwePXJV4Cvtu3X9xWUJEmSRtN1tuxtwFsn9pOsXVW+xFiSJGme6Tpb9tAkP01ycJLzgFuS/GHPsUmSJGmauo65ezPwPOAi4EnAvcA/44xZSZKkeaVrcnd3VV2d5Kqqug4gyd39hSVJkqRRdJ1QcS1AVe0KkGQD4IG+gpIkSdJoOiV3VfWKSfu/Ap7ZS0SSJEkaWdduWQCSbAa8HXgE8Ang+j6CkiRJ0mi6dstOOB4o4Abg5NkPR5IkSTMxrZY7YNuq2h8gye/1EI8kSZJmoFNyl2TXtrlekl2A4NqykiRJ807Xlru/ad83AR9t27fPfjiSJEmaia7Ljz2/70AkSZI0c127ZY+eqryq3jpVuSRJkuZG127Z/YD39BmIJEmSZq5rcveLqjqp10gkSZI0Y12Tu99KchFwN4N33P0b8LdV5fqykiRJ80jX5O6JwFrAesCWwAEMXmh8YE9xSZIkaQRdZ8v+x9DuZcDpSY7qJyRJkiSNqvMKFUl2Bp7ddv+lqt7VT0iSJEkaVae1ZZO8jcFaspu3z98neUufgUmSJGn6urbcHQw8varuBGhdsucAn+grMEmSJE1fp5Y7BmvJ3j+0f38rkyRJ0jzSteXu08C5Sb7a9vcHTugnJEmSJI2q62zZjyY5C3gWgxa711fVhX0GJkmSpOnrurbstsDPgX8cLquqn/QVmCRJkqava7fslcAyBq12NfT95J7ikiRJ0gi6Tqi4qqqeXFX/Y/h7Jj+cZK0kFyb5RtvfNMnpSa5u35sMnXtEkmVJrkryoqHypya5pB07Okla+TpJvtTKz02yeCaxSpIkrSm6JnfVw2+/DbhiaP9w4Iyq2gE4o+2TZEdgCfAkYG/gmCRrtWuOBQ4BdmifvVv5wcBtVfV44GOAq2lIkqQFoWty96gkL5/8GfVHk2wN7MNgfdoJ+wEnte2TGMzInSj/YlXdU1XXMuge3j3JFsBGVXVOVRXw2UnXTNzrVGCviVY9SZKkcdZ1zN3ZwEsnlRXwlRF/938DfwpsOFT2mKq6EaCqbkyyeSvfCvjB0HnLW9m9bXty+cQ117d73ZfkduDRDCaF/EaSQxi0/LHtttuO+CiSJEnzR9dXobx+tn4wyUuAm6vq/CTP63LJVCGtonxV16xYUHUccBzAbrvt1kfXsyRJ0mrVteVuNu0J7JvkxcC6wEZJ/h74WZItWqvdFsDN7fzlwDZD128N3NDKt56ifPia5UnWBjYGbu3rgSRJkuaLrmPuZk1VHVFVW1fVYgYTJc6sqgOB04CD2mkHAV9r26cBS9oM2O0YTJw4r3Xh3pFkjzae7rWTrpm41yvab9gyJ0mSxt5ctNytzIeAU5IcDPwEOACgqi5LcgpwOXAfcFhVTaxzeyjwGWA94FvtA4Ol0T6XZBmDFrslq+shJEmS5lLXFSo2Bt4HPLsVnQ18oKpun8mPV9VZwFlt+xfAXis570jgyCnKlwI7TVF+Ny05lCRJWki6dsueCPwSeGX7/BL4dF9BSZIkaTRdu2UfV1W/N7T//iQX9RGQJEmSRte15e6uJM+a2EmyJ3BXPyFJkiRpVF1b7g4FTmpj78JgksLr+gpKkiRJo+n6EuOLgJ2TbNT2f9lrVJIkSRpJp27ZJDsmeTODV458OMmpSXbpNzRJkiRNV9cxd58HngCcC5wHnAIc31dQkiRJGk3X5O5hVfUW4NdVdUJVnTKNayVJkrSadJ1QsUGSlwNrJ3kZg8Ruo/7CkiRJ0ii6JndnAy9t3/u2su/3EpEkSZJG1jW5+0RVXdBrJJIkSZqxruPmnDwhSZK0Bujacrd2kk0YvMD4N6rq1tkPSZIkSaPqmtw9ATifFZO7Araf9YgkSZI0sq7J3eVV5UuLJUmS5jnfVSdJkjRGuiZ3z+g1CkmSJM2Krsnd15M8amInySZJvtNTTJIkSRpR1+RuUVX958ROVd0GbN5PSJIkSRpV1+Tu/iTbTuwkeSyD2bKSJEmaR7rOln038K9Jzm77zwEO6SckSZIkjapTcldV306yK7AHg3fd/XFV/bzXyCRJkjRtnbplkwTYG9i1qr4OrJ9k914jkyRJ0rR1HXN3DIPXoby67d8B/G0vEUmSJGlkXcfcPb2qdk1yIQxmyyZ5RI9xSZIkadWz294AABGRSURBVARdk7t7k6xFmyGbZBHwQG9RicWHf7PTedd9aJ+eI5EkSWuSrt2yRwNfBTZPciTwr8Bf9haVJEmSRtJ1tuzJSc4H9mIwW3b/qrqi18gkSZI0bZ2SuySbAjcDXxguq6pb+wpMkiRJ09d1zN35DMbbBdgCuLHtb99TXJIkSRpB127Z7Sa2k1xYVbv0F5IkSZJG1XVCBQDt9Se+AkWSJGme6jrm7utt84nA5/sLR5IkSTPRdczdRxi81255VV3bYzySJEmaga7J3SUTG23mLADOlpUkSZpfuiZ3Pwd+BtzFYMYsOFtWkiRp3uk6oeIQYDnwN8AOVbVdVZnYSZIkzTOdkruqOh54FrAO8O9JXtNrVJIkSRpJp+QuycuBfYDrgGOBdyW5uMe4JEmSNIKuY+5eOmn//NkORJIkSTPXdYWK1/cdiCRJkmau60uMT5uqvKr2nd1wJEmSNBNdu2WfCLyxz0AkSZI0c12Tuzuq6uxeI5EkSdKMdX3P3c5J/jPJTUkuSPKJJJv1GpkkSZKmret77tYCNgUeB7wKuAk4qce4JEmSNIKuLXdU1QNVdWdVXV1VRwLf7jEuSZIkjaDrmDuS7As8p+2eXVWf6CckSZIkjarrChV/BbwNuLx93trKJEmSNI90bbnbB3hKVT0AkOQk4ELgiL4CkyRJ0vR1HnMHPGpoe+PZDkSSJEkz17Xl7q+AC5N8DwiDsXd/1ltUkiRJGknXtWW/kOQs4GkMkrt3VdVNfQYmSZKk6Vtlt2ySfSa2q+rGqjqtqr4G3JnE2bKSJEnzzEONuft4koOHC5L8PvAj4OZRfjDJNkm+l+SKJJcleVsr3zTJ6Umubt+bDF1zRJJlSa5K8qKh8qcmuaQdOzpJWvk6Sb7Uys9NsniUWCVJktY0D5XcPRs4LMl7kvy/Sf4ZOBD47ar64Ii/eR/wP6vqicAe7f47AocDZ1TVDsAZbZ92bAnwJGBv4Jgka7V7HQscAuzQPnu38oOB26rq8cDHgKNGjFWSJGmNssrkrqpuBJ7LIMn7EXB8Vb24qn486g+27t0L2vYdwBXAVsB+PLik2UnA/m17P+CLVXVPVV0LLAN2T7IFsFFVnVNVBXx20jUT9zoV2GuiVU+SJGmcPeSrUFoC9rvAKcDvJ1l3tn68dZfuApwLPKYlkxNJ5ebttK2A64cuW97Ktmrbk8tXuKaq7gNuBx49W3FLkiTNV6ucLZvkDqAmdoFHArcmuR+oqtpo1B9OsgHwD8Dbq+qXq2hYm+pAraJ8VddMjuEQBt26bLvttg8VsiRJ0rz3UN2yG1bVRu2zYVU9rKrWnygf9UeTPJxBYndyVX2lFf+sdbXSvicmbCwHthm6fGvghla+9RTlK1yTZG0GL12+dYrnO66qdquq3RYtWjTq40iSJM0b01mhYla0sW8nAFdU1UeHDp0GHNS2DwK+NlS+pM2A3Y7BxInzWtftHUn2aPd87aRrJu71CuDMNi5PkiRprHVdoWI27Qn8AXBJkota2Z8BHwJOaa9e+QlwAEBVXZbkFOByBjNtD6uq+9t1hwKfAdYDvtU+MEgeP5dkGYMWuyV9P5QkSdJ8sNqTu6r6V6YeEwew10quORI4corypcBOU5TfTUsOJUmSFpLV3i0rSZKk/pjcSZIkjRGTO0mSpDFicidJkjRGTO4kSZLGiMmdJEnSGJmL99xpFi0+/JudzrvuQ/v0HIkkSZoPbLmTJEkaIyZ3kiRJY8TkTpIkaYyY3EmSJI0RkztJkqQxYnInSZI0RkzuJEmSxojJnSRJ0hgxuZMkSRojJneSJEljxOROkiRpjJjcSZIkjRGTO0mSpDFicidJkjRGTO4kSZLGiMmdJEnSGDG5kyRJGiNrz3UAWj0WH/7NTudd96F9eo5EkiT1yZY7SZKkMWJyJ0mSNEZM7iRJksaIyZ0kSdIYMbmTJEkaIyZ3kiRJY8TkTpIkaYyY3EmSJI0RkztJkqQx4goVWkHXlSzA1SwkSZqPbLmTJEkaIyZ3kiRJY8TkTpIkaYyY3EmSJI0RkztJkqQx4mxZjazrzFpn1UqStPrYcidJkjRGTO4kSZLGiMmdJEnSGDG5kyRJGiNOqFDvnHghSdLqY8udJEnSGDG5kyRJGiN2y2resPtWkqSZs+VOkiRpjNhypzWOLXySJK2cLXeSJEljxJY7jS1b+CRJC5HJnRY8k0BJ0jgxuZM66poEgomgJGnujHVyl2Rv4OPAWsDxVfWhOQ5JC4StgZKkuTK2yV2StYC/BX4HWA78MMlpVXX53EYmPWg6rYFzweRTktY8Y5vcAbsDy6rqGoAkXwT2A0zupI7mMvk0sZSk0YxzcrcVcP3Q/nLg6XMUi6Rpmu+tmgvRQky452qIxVyO8XVYyZpvnJO7TFFWK5yQHAIc0nZ/leSq3qOCzYCfr4bfWRNYFyuyPlZkfTxoXtRFjprrCH5jXtTHsLmsmxw1N/Uxj/48DJt3fzZ69NiVHRjn5G45sM3Q/tbADcMnVNVxwHGrM6gkS6tqt9X5m/OVdbEi62NF1seDrIsVWR8rsj4eZF0MjPMKFT8EdkiyXZJHAEuA0+Y4JkmSpF6NbctdVd2X5M3Adxi8CuXEqrpsjsOSJEnq1dgmdwBV9U/AP811HJOs1m7gec66WJH1sSLr40HWxYqsjxVZHw+yLoBU1UOfJUmSpDXCOI+5kyRJWnBM7laTJHsnuSrJsiSHz3U8syXJNkm+l+SKJJcleVsr3zTJ6Umubt+bDF1zRKuHq5K8aKj8qUkuaceOTpJWvk6SL7Xyc5MsXt3POV1J1kpyYZJvtP0FWx9JHpXk1CRXtj8nz1io9ZHkj9u/J5cm+UKSdRdSXSQ5McnNSS4dKlstz5/koPYbVyc5aPU88aqtpD4+3P5d+VGSryZ51NCxsa2Pqepi6NifJKkkmw2VjW1dzIqq8tPzh8GEjh8D2wOPAC4GdpzruGbp2bYAdm3bGwL/F9gR+Gvg8FZ+OHBU296xPf86wHatXtZqx84DnsHgHYXfAn63lf8R8Mm2vQT40lw/d4d6eQfweeAbbX/B1gdwEvDGtv0I4FELsT4YvFj9WmC9tn8K8LqFVBfAc4BdgUuHynp/fmBT4Jr2vUnb3mSe1scLgbXb9lELpT6mqotWvg2DiZH/AWy2EOpiVupzrgNYCJ/2B+07Q/tHAEfMdVw9PevXGKznexWwRSvbArhqqmdv/9I+o51z5VD5q4FPDZ/Tttdm8ILKzPWzrqIOtgbOAF7Ag8ndgqwPYCMGCU0mlS+4+uDBVXM2bXF+g8F/yBdUXQCLWTGZ6f35h89pxz4FvHqu62Kq+ph07GXAyQulPqaqC+BUYGfgOh5M7sa+Lmb6sVt29ZhqKbSt5iiW3rRm7l2Ac4HHVNWNAO1783bayupiq7Y9uXyFa6rqPuB24NF9PMMs+d/AnwIPDJUt1PrYHrgF+HQG3dTHJ3kkC7A+quqnwEeAnwA3ArdX1XdZgHUxyep4/jX17+A3MGh9ggVYH0n2BX5aVRdPOrTg6mK6TO5Wj4dcCm1Nl2QD4B+At1fVL1d16hRltYryVV0z7yR5CXBzVZ3f9ZIpysamPhj8H/KuwLFVtQtwJ4Out5UZ2/poY8n2Y9CNtCXwyCQHruqSKcrGoi46ms3nX+PqJcm7gfuAkyeKpjhtbOsjyfrAu4H3THV4irKxrYtRmNytHg+5FNqaLMnDGSR2J1fVV1rxz5Js0Y5vAdzcyldWF8vb9uTyFa5JsjawMXDr7D/JrNgT2DfJdcAXgRck+XsWbn0sB5ZX1blt/1QGyd5CrI/fBq6tqluq6l7gK8AzWZh1MWx1PP8a9XdwG9T/EuA11foKWXj18TgG/yN0cfv7dGvggiT/DwuvLqbN5G71GNul0NpMpBOAK6rqo0OHTgMOatsHMRiLN1G+pM1c2g7YATivdcfckWSPds/XTrpm4l6vAM4c+gtvXqmqI6pq66pazOCf85lVdSALtz5uAq5P8oRWtBdwOQuzPn4C7JFk/fYMewFXsDDrYtjqeP7vAC9MsklrQX1hK5t3kuwNvAvYt6r+a+jQgqqPqrqkqjavqsXt79PlDCbv3cQCq4uRzPWgv4XyAV7MYCbpj4F3z3U8s/hcz2LQhP0j4KL2eTGDsQxnAFe3702Hrnl3q4eraDOZWvluwKXt2P/hwZdsrwt8GVjGYCbU9nP93B3r5nk8OKFiwdYH8BRgafsz8o8MZqQtyPoA3g9c2Z7jcwxm+y2YugC+wGC84b0M/mN98Op6fgbj15a1z+vnui5WUR/LGIwBm/j79JMLoT6mqotJx6+jTagY97qYjY8rVEiSJI0Ru2UlSZLGiMmdJEnSGDG5kyRJGiMmd5IkSWPE5E6SJGmMmNxJ6k2SXw1tb5Hkx0leOpcxSdK4M7mT1LskGwL/BBxVVV+f63gkaZyZ3EnqVVue7ivAaVV13FD5q5NckuTSJEdNuub+JBclWZbkG63sM0le0bbfmKSSbJbkeRPntGPXJdmsbR+Y5Lx2r08lWauV753kgiQXJzkjyXrtnIuS/LrFdVGS3drvXtvi/FGSndo9npLkB63sq+3t9pOffeLaiXvflWRxO/aOds9Lk7x96JrNWgyTn/95SW4futdPk7yvHTsryW5t+y8mtZj+aXuei5N8aIo6el+SP1nVfYbrOMlzk5ybZOMMfLg9wyVJXjVFrNckece0/tBImhGTO0l9OxF4LoM30AOQZEvgKOAFDFaweFqS/duxtYA7q+opwBsn3yzJusCbeHAN0geYYvHvJE8EXgXs2e51P/CaJIuAvwN+r6p2Bg6oqruq6intvBuA57f9pe1276yqnYDvt5gBPgu8q6qeDFwCvHclz//OoXv/uMX2VOD1wNOBPYA/TLJLO38tBuvxTvX8/zJ0r49N8cybM1jWbGL/d4H9gae3Z/3rVdXZyu4zVP4/gI8DL6uq24GXM/jntzODtXM/nLZO7ESsDP4ZHLiy35I0+0zuJPXpkcCmwOuAvx0qfxpwVlXdUlX3AScDz2nH1gPuXsU9DwNOAu5q+8uBJ7akb9hewFOBHya5qO1vzyCZ+n5VXQtQVbd2eI4PJ7ka2Bf4cpKNgUdV1dnt+ElD8XfxLOCrVXVnVf2KQcvms9uxDRgsaD6KPwf+cmj/t4FPV1ujdOhZlwO7sHKT7wOwJfAt4KSqmlhY/VnAF6rq/qr6GXA2g3+2AM9u9f494OgRn0fSCEzuJPXpHuCVVfV54N4kr2nlK201YpBE3LCSYxsBrwY+NVFQVdcAnwcuaMnElkO/cdJES1dVPaGq3tfKp7vu4juragfgAwzWh52pVT3/dgySr+laDOw0aUzjyp71z4C/bvX1pg73Afgt4I+A/6+1fk7cf2UmWu4WA++fIvmW1BOTO0l9uq+q7mzbbwaObK1e5wLPbePL1mKQsE20gr0S+LeV3O+PgaOr6tfDhVX1v6pqx6FuVRgsQv+K1sVIkk2TPBY4p/32dhPl03ieXzJYvPx24LYkE61tfzAUfxffB/ZPsn6SRwIvA/6lHTsA+MZKr1y59/Lfu4a/C7whyfrw4LNW1b8Ode9+ssN9AM6sqtMYtOh9fOg5XpVkrZbwPYfBouzD/otBa+w6IzyTpBGsPdcBSFoYqmpZkk8Df1lVhyU5gkGXXYB/qqqvJXkrsCdw0EpuE+DvO/7e5Un+F/DdJA8D7gUOq6ofJDkE+Eorvxn4nYe43YfbvYoHx8EdBHyyJU7XMBhD10lVXZDkMzyYCB1fVRcm+SPgEAbJ55sZdNEuSrIvg8RyVZZX1fcn/c63kzwFWJrk1wxmLP/ZdO8z6Z6fTfKaJC8Gvgo8A7iYQd38aVXdlOS3eLBbdl3goy0hlrQapGq6vROSpD602a9nVdVZQ2UvYdBa+Jk5CkvSGsaWO0maP07lwVnAEy7ALk1J02DLnSRJ0hhxQoUkSdIYMbmTJEkaIyZ3kiRJY8TkTpIkaYyY3EmSJI0RkztJkqQx8v8Dn9kgZ2PA+gMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(followers, bins=50, range=(0, 150000))\n",
    "plt.xlabel(\"Количество подписчиков\")\n",
    "plt.ylabel(\"Количество пользователей с данным числом подписчиков\")\n",
    "plt.rcParams['figure.figsize'] = [10, 8]\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Задание 20__. Посчитайте число ретвитов в датасете (твитов, начинающихся с \"RT @\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "retweets_indict = df[\"ttext\"].str.match(\"^RT\\s\\@.+\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество ретвитов:\n",
      "36462\n"
     ]
    }
   ],
   "source": [
    "print(\"Количество ретвитов:\")\n",
    "print(df[retweets_indict].shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Задание 21__. Назовём длинным такой твит, что его длина больше, чем не менее 80% всех твитов. Найдите число длинных твитов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "lens_of_tweets = df[\"ttext\"].str.len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "lens_quantile = lens_of_tweets.quantile(0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "is_long_tweet = lens_of_tweets >= lens_quantile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Число длинных твитов:\n",
      "46560\n"
     ]
    }
   ],
   "source": [
    "print(\"Число длинных твитов:\")\n",
    "print(df[is_long_tweet].shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Задание 22__. Постройте гистограмму распределения числа твитов по месяцам. В какой месяц какого года было опубликовано больше всего твитов?\n",
    "\n",
    "__Хинт__: Время в колонке tdate -- это Unix time, и оно отображает то, сколько секунд прошло с 00:00:00 1 января 1970 года. Для конвертации этого времени в удобный формат воспользуйтесь библиотекой `datetime`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import timezone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import calendar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_date = df[\"tdate\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Самая рання дата в Unix time:\n",
      "1386325927\n",
      "По UTC:\n",
      "06-12-2013 10:32:07\n"
     ]
    }
   ],
   "source": [
    "print(\"Самая рання дата в Unix time:\")\n",
    "print(min_date)\n",
    "print(\"По UTC:\")\n",
    "print(datetime.datetime.utcfromtimestamp(min_date).strftime('%d-%m-%Y %H:%M:%S'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_date = df[\"tdate\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Самая поздняя дата в Unix time\n",
      "1390195914\n",
      "По UTC:\n",
      "20-01-2014 05:31:54\n"
     ]
    }
   ],
   "source": [
    "print(\"Самая поздняя дата в Unix time\")\n",
    "print(max_date)\n",
    "print(\"По UTC:\")\n",
    "print(datetime.datetime.utcfromtimestamp(max_date).strftime('%d-%m-%Y %H:%M:%S'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "separation_time = int(datetime.datetime.utcfromtimestamp(calendar.timegm(datetime.date(2014, 1, 1).timetuple())).replace(tzinfo=timezone.utc).timestamp())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_in_december = df[df[\"tdate\"] < separation_time]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_in_january = df[df[\"tdate\"] >= separation_time]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество твитов в декабре 2013:\n",
      "187629\n"
     ]
    }
   ],
   "source": [
    "print(\"Количество твитов в декабре 2013:\")\n",
    "print(tweets_in_december.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество твитов в январе 2014:\n",
      "39205\n"
     ]
    }
   ],
   "source": [
    "print(\"Количество твитов в январе 2014:\")\n",
    "print(tweets_in_january.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Таким образом в декабре 2013 года было больше всего твитов__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def date_rewriter(d):\n",
    "    if d < separation_time:\n",
    "        return \"Декабрь 2013\"\n",
    "    else:\n",
    "        return \"Январь 2014\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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StIOY5LLvU5LcN7YeoKpqt4H6JEmSpIFMEv7WVNXTBu+JJEmSBudlX0mSpI5MEv5+afBeSJIkaUFsNfxV1RcXoiOSJEkanpd9JUmSOmL4kyRJ6shWw1+S3ZOck+SG9jo7ye4L0TlJkiTNr0lG/i4E7gN+ub3uA/7/ITslSZKkYUwS/n6iqk6rqi+21+8Bj99aoyQXJrk7ya1jtT2TXJPk8+19j7Ftb0iyNsntSY4eqx+WZE3b9tYkafWdk7y31a9PsmKszUntHJ9PctJkfwpJkqQd3yTh71tJfnZmJcnPAN+aoN1FwDGzaq8HPlJVBwEfaeskeRKwCji4tXl7kp1am/OA1cBB7TVzzJOBe6rqQOAc4Kx2rD2B04BnAIcDp42HTEmSpJ5NEv5OAf44ybokXwbeBrxia42q6uPAhlnlY4GL2/LFwHFj9fdU1QNV9SVgLXB4kn2A3arqk1VVwLtmtZk51vuAI9uo4NHANVW1oaruAa7h+0OoJElSl7b6eLequhl4apLd2vp9W2myJcur6s52nDuTPKbV9wWuG9tvfat9py3Prs+0+Wo71qYk9wJ7jdfnaCNJktS1rYa/dkn254A/A05PshdwZlXdNI/9yBy12kJ9W9s89KTJakaXlFm+fDnT09Nb7ejDtXwXOPXJmwY/z0JYiL+XJElLzcaNG7fr78ithj/g3cDfANcDZwD3A+8EDtuG892VZJ826rcPcHerrwf2H9tvP+COVt9vjvp4m/VJlgG7M7rMvB6YmtVmeq7OVNX5wPkAK1eurKmpqbl2m1fnXnoFZ6+Z5M++/Vt3wtRid0GSpO3O9PQ0C5EpttUk9/z9UFX9JvCvVXVBVV0+Ybu5XAnMzL49CbhirL6qzeA9gNHEjk+1S8T3Jzmi3c934qw2M8d6EXBtuy/ww8BRSfZoEz2OajVJkqTuTTIE9aNJfhFYluSFjILfbltrlOQyRiNweydZz2gG7v8ELk9yMvAV4MUAVXVbksuBzwKbgFdW1YPtUKcwmjm8C3B1ewFcAFySZC2jEb9V7VgbkpwBfLrtd3pVzZ54IkmS1KVJwt/HgP/U3l/Qah/fWqOqOn4zm47czP5nAmfOUb8BOGSO+rdp4XGObRcy+nFqSZIkjZkk/J1bVZ8ZvCeSJEka3CT37r1z8F5IkiRpQUwy8resTZx4yE+oeB+dJEnS0jNJ+Pv3wI08NPwVEzzfV5IkSduXScLfZ6vqaYP3RJIkSYPb1t/rkyRJ0hI0Sfj76cF7IUmSpAUxSfj7yyQ/NrPSnpzhEzMkSZKWoEnC36Or6p9nVqrqHuAxw3VJkiRJQ5kk/D2Y5LEzK0kex2i2ryRJkpaYSWb7/i7wt0k+1tafBawerkuSJEkaylbDX1X9VZKnA0cw+q2/36qqrw/eM0mSJM27rV72TRLgGODpVfWXwCOTHD54zyRJkjTvJrnn7+2Mfu7l+LZ+P/DHg/VIkiRJg5nknr9nVNXTk9wEo9m+SX5k4H5JkiRpAJOM/H0nyU60Gb5JHg18d9BeSZIkaRCThL+3Ah8AHpPkTOBvgf8xaK8kSZI0iElm+16a5EbgSEazfY+rqs8N3jNJkiTNu62GvyR7AncDl43XqmrDkB2TJEnS/JtkwseNjO73C7APcGdbf/yA/ZIkSdIAJrnse8DMcpKbquppw3ZJkiRJQ5lkwgcA7edd/IkXSZKkJWySe/7+si0+EXj3sN2RJEnSkCa55+8tjH7Xb31VfWng/kiSJGlAk4S/NTMLbeYvAM72lSRJWnomCX9fB+4CvsVoxi8421eSJGlJmmTCx2pgPXA2cFBVHVBVBj9JkqQlaKvhr6reCfwssDPwiSQnDN4rSZIkDWKr4S/JLwLPB9YB5wGvS/J3A/dLkiRJA5jknr//NGv9xiE6IkmSpOFN8oSPX1uIjkiSJGl4k/zI85Vz1avqBfPfHUmSJA1pksu+TwR+feiOSJIkaXiThL/7q+pjg/dEkiRJg5vkd/6emuSfk/xjks8kOTfJ3oP3TJIkSfNukt/52wnYE/gJ4CXAPwIXD9wvSZIkDWCSkT+q6rtV9c2q+nxVnQn81cD9kiRJ0gAmueePJC8AntVWP1ZV5w7XJUmSJA1lkid8/D7wX4HPtterW02SJElLzCQjf88HDq2q7wIkuRi4CXjDkB2TJEnS/Jvonj/gx8aWdx+iI5IkSRreJCN/vw/clOSjQBjd+/c7g/ZKkiRJg5jk2b6XJZkGfopR+HtdVf3j0B2TJEnS/NvsZd8kz59Zrqo7q+rKqroC+GYSZ/tKkiQtQVu65+9/JTl5vJDkpcAtwN2D9kqSJEmD2NJl32cCH0qyL/Ae4O3AvwLPqaovLETnJEmSNL82O/JXVXcC/5FRCLwFeGdVPc/gJ0mStHRt8adequp+4LnA5cBLkzzi4Z4wyb9PcvPY674kr0nypiRfG6s/b6zNG5KsTXJ7kqPH6oclWdO2vTVJWn3nJO9t9euTrHi4/ZYkSdoRbPayb5L7gZpZBXYFNiR5EKiq2m1bTlhVtwOHtnPsBHwN+ADwa8A5VfWWWf14ErAKOBj4d8D/TvKEqnoQOA9YDVwHXAUcA1wNnAzcU1UHJlkFnAW8ZFv6K0mStCPZ0mXfR1XVbu31qKr6oap65Ex9ns5/JPCFqvryFvY5FnhPVT1QVV8C1gKHJ9kH2K2qPllVBbwLOG6szcVt+X3AkTOjgpIkST2b5Eeeh7QKuGxs/VVJTgRuAE6tqnuAfRmN7M1Y32rfacuz67T3rwJU1aYk9wJ7AV8fP3mS1YxGDlm+fDnT09Pz86m2YPkucOqTNw1+noWwEH8vSZKWmo0bN27X35GLFv6S/AjwAr73jODzgDMYXWo+AzgbeDmjS86z1RbqbGXb9wpV5wPnA6xcubKmpqYm/wDb6NxLr+DsNYuduefHuhOmFrsLkiRtd6anp1mITLGtJn227xCeC3ymqu4CqKq7qurBqvou8A7g8LbfemD/sXb7AXe0+n5z1B/SJskyRs8j3jDQ55AkSVoyFjP8Hc/YJd92D9+MFwK3tuUrgVVtBu8BwEHAp9pP0dyf5Ih2P9+JwBVjbU5qyy8Crm33BUqSJHVtUa4/Jnkk8PPAb4yV/yDJoYwuz66b2VZVtyW5HPgssAl4ZZvpC3AKcBGwC6NZvle3+gXAJUnWMhrxWzXk55EkSVoqFiX8VdW/MJqAMV771S3sfyZw5hz1G4BD5qh/G3jxw++pJEnSjmUxL/tKkiRpgRn+JEmSOmL4kyRJ6ojhT5IkqSOGP0mSpI4Y/iRJkjpi+JMkSeqI4U+SJKkjhj9JkqSOGP4kSZI6YviTJEnqiOFPkiSpI4Y/SZKkjhj+JEmSOmL4kyRJ6ojhT5IkqSOGP0mSpI4Y/iRJkjpi+JMkSeqI4U+SJKkjhj9JkqSOGP4kSZI6YviTJEnqiOFPkiSpI4Y/SZKkjhj+JEmSOmL4kyRJ6ojhT5IkqSOGP0mSpI4Y/iRJkjpi+JMkSeqI4U+SJKkjhj9JkqSOGP4kSZI6YviTJEnqiOFPkiSpI4Y/SZKkjhj+JEmSOmL4kyRJ6ojhT5IkqSOGP0mSpI4Y/iRJkjpi+JMkSeqI4U+SJKkjhj9JkqSOGP4kSZI6sijhL8m6JGuS3JzkhlbbM8k1ST7f3vcY2/8NSdYmuT3J0WP1w9px1iZ5a5K0+s5J3tvq1ydZsdCfUZIkaXu0mCN/z66qQ6tqZVt/PfCRqjoI+EhbJ8mTgFXAwcAxwNuT7NTanAesBg5qr2Na/WTgnqo6EDgHOGsBPo8kSdJ2b3u67HsscHFbvhg4bqz+nqp6oKq+BKwFDk+yD7BbVX2yqgp416w2M8d6H3DkzKigJElSz5Yt0nkL+OskBfxpVZ0PLK+qOwGq6s4kj2n77gtcN9Z2fat9py3Prs+0+Wo71qYk9wJ7AV8f70SS1YxGDlm+fDnT09Pz9gE3Z/kucOqTNw1+noWwEH8vSZKWmo0bN27X35GLFf5+pqruaAHvmiR/v4V95xqxqy3Ut9TmoYVR6DwfYOXKlTU1NbXFTs+Hcy+9grPXLNaffX6tO2FqsbsgSdJ2Z3p6moXIFNtqUS77VtUd7f1u4APA4cBd7VIu7f3utvt6YP+x5vsBd7T6fnPUH9ImyTJgd2DDEJ9FkiRpKVnw8Jdk1ySPmlkGjgJuBa4ETmq7nQRc0ZavBFa1GbwHMJrY8al2ifj+JEe0+/lOnNVm5lgvAq5t9wVKkiR1bTGuPy4HPtDmXywD3l1Vf5Xk08DlSU4GvgK8GKCqbktyOfBZYBPwyqp6sB3rFOAiYBfg6vYCuAC4JMlaRiN+qxbig0mSJG3vFjz8VdUXgafOUf8GcORm2pwJnDlH/QbgkDnq36aFR0mSJH3P9vRTL5IkSRqY4U+SJKkjhj9JkqSOGP4kSZI6YviTJEnqiOFPkiSpI4Y/SZKkjhj+JEmSOmL4kyRJ6ojhT5IkqSOGP0mSpI4Y/iRJkjpi+JMkSeqI4U+SJKkjhj9JkqSOGP4kSZI6YviTJEnqiOFPkiSpI8sWuwOSJEkrXv+hxe7CvLnomF0Xuwtb5MifJElSRwx/kiRJHTH8SZIkdcTwJ0mS1BHDnyRJUkcMf5IkSR0x/EmSJHXE8CdJktQRw58kSVJHDH+SJEkdMfxJkiR1xPAnSZLUEcOfJElSRwx/kiRJHTH8SZIkdcTwJ0mS1BHDnyRJUkcMf5IkSR0x/EmSJHXE8CdJktQRw58kSVJHDH+SJEkdMfxJkiR1xPAnSZLUEcOfJElSRwx/kiRJHTH8SZIkdWTBw1+S/ZN8NMnnktyW5L+2+puSfC3Jze31vLE2b0iyNsntSY4eqx+WZE3b9tYkafWdk7y31a9PsmKhP6ckSdL2aDFG/jYBp1bVE4EjgFcmeVLbdk5VHdpeVwG0bauAg4FjgLcn2antfx6wGjiovY5p9ZOBe6rqQOAc4KwF+FySJEnbvQUPf1V1Z1V9pi3fD3wO2HcLTY4F3lNVD1TVl4C1wOFJ9gF2q6pPVlUB7wKOG2tzcVt+H3DkzKigJElSzxb1nr92OfZpwPWt9KoktyS5MMkerbYv8NWxZutbbd+2PLv+kDZVtQm4F9hrgI8gSZK0pCxbrBMn+VHg/cBrquq+JOcBZwDV3s8GXg7MNWJXW6izlW3jfVjN6LIxy5cvZ3p6+gf8FD+45bvAqU/eNPh5FsJC/L0kSX3YUb4bATZu3Lhdf0cuSvhL8sOMgt+lVfXnAFV119j2dwAfbKvrgf3Hmu8H3NHq+81RH2+zPskyYHdgw+x+VNX5wPkAK1eurKmpqYf70bbq3Euv4Ow1i5a559W6E6YWuwuSpB3Ey17/ocXuwry56JhdWYhMsa0WY7ZvgAuAz1XVH47V9xnb7YXArW35SmBVm8F7AKOJHZ+qqjuB+5Mc0Y55InDFWJuT2vKLgGvbfYGSJEldW4whqJ8BfhVYk+TmVvsd4PgkhzK6PLsO+A2AqrotyeXAZxnNFH5lVT3Y2p0CXATsAlzdXjAKl5ckWctoxG/VwJ9JkiRpSVjw8FdVf8vc9+RdtYU2ZwJnzlG/AThkjvq3gRc/jG5KkiTtkHzChyRJUkcMf5IkSR0x/EmSJHXE8CdJktQRw58kSVJHDH+SJEkdMfxJkiR1xPAnSZLUEcOfJElSRwx/kiRJHTH8SZIkdcTwJ0mS1BHDnyRJUkcMf5IkSR0x/EmSJHXE8CdJktQRw58kSVJHDH+SJEkdMfxJkiR1xPAnSZLUEcOfJElSRwx/kiRJHTH8SZIkdcTwJ0mS1BHDnyRJUkcMf5IkSR0x/EmSJHXE8CdJktQRw58kSVJHDH+SJEkdMfxJkiR1xPAnSZLUEcOfJElSRwx/kiRJHTH8SZIkdcTwJ0mS1BHDnyRJUkcMf5IkSR0x/EmSJHXE8CdJktQRw58kSVJHDH+SJEkdMfxJkiR1xPAnSZLUEcOfJElSRwx/kiRJHdmhw1+SY5LcnmRtktcvdn8kSZIW2w4b/pLsBPwx8FzgScDxSZ60uL2SJElaXDts+AMOB9ZW1Rer6l+B9wDHLnKfJEmSFtWOHP72Bb46tr6+1SRJkrq1bLE7MKDMUauH7JCsBla31Y1Jbh+8Vy6CeTEAAAgnSURBVLA38PUFOM/gctZi90CSpO3Ps89akO/6x21rwx05/K0H9h9b3w+4Y3yHqjofOH8hO5XkhqpauZDnlCRJC2d7/67fkS/7fho4KMkBSX4EWAVcuch9kiRJWlQ77MhfVW1K8irgw8BOwIVVddsid0uSJGlR7bDhD6CqrgKuWux+zLKgl5klSdKC266/61NVW99LkiRJO4Qd+Z4/SZIkzdJF+Etya5LPJrk5ydeSvGmAc+yZ5I+TfCrJmiRPmYdj/nbr9y1JPpLkcWPbTkry+fY6aaz+qvY4u0qy91j92Hacm5PckORnH27/JEnaHiV5fvsu/rv2vm6x+zQuyQntO/mWJJ9I8tSxbXM+mjbJi5PcluS7Sb5vJnGSxybZmOS1Wzt/F+GveW5VHQqcM9DxLwM+Avx0VT25qm6Zh2PeBKysqqcA7wP+AEZBEzgNeAajJ5mclmSP1ub/AM8BvjzrWB8Bntr+Bi8H3jkP/ZMkabuS5IeAS4BXV9VTgWcvcpfm8iXgP7bv9zNo9whu5dG0twK/CHx8M8c8B7h6kpP3Ev5+GHhgrg1J/luST7f0/XuttiLJrW35ie3/HPZv63+R5MaWvle32pMY/djiG4Gbk1yYZOe2bV2Ss9qI4KeSHNjqFyV50ZY6XVUfrap/aavXMfqtQoCjgWuqakNV3QNcAxzT2txUVevmONbG+t4Nnrsy6wevJUnaQewN7AF8YvaGJFNJPji2/tqZq4FJptuI22eTXJfk37X6G1tOuDXJ+Ukytv8ftZG7W5Mc3upv2troW1V9on1/w0O/3zf7aNqq+lxVzfkwiiTHAV8EJvpVk17C36OA+2cXkxwFHMToj30ocFiSZ41t35fRH/6lVTXzqLiXV9VhwErg1Un2Ah4NHAD8clU9mdEs6lPGTnVfVR0OvA34o238DCfzvUS/TY+uS/LCJH8PfIjR6J8kSTuafwK+xmj07Ad1AnBwO8bMpdW3VdVPVdUhwC7AL4ztv2tV/QfgvwAXbmN/H9b3e5JdgdcBvzfpCXf48NeGUB9VVd+cY/NR7XUT8BngJxmFQYAfBf4KmJ71+4CvTvJ3jJL6/m3/ANdX1T+0fS4GnjXW5rKx958eq7+53YP3kSRP2MJn+BVG/wjfPFOaY7etjuRV1Qeq6ieB4xgNM0uStENpV7meC/xOki8DH521yzPbd+/NwG/N2nYpo0uyjwP+d6s9O8n1SdYAP8coHM64rJ3z48BuSX6s1X+rneP/JDlic31N8mxG4e91M6W5PtIWPi6MQt85VbVxK/v9mx0+/AGPB/5hM9sC/H5VHdpeB1bVBW3b/sDvM/qP/kQYDRczup/up9t9BDcBjwDu20ofajPL/63dg3cZ8KY5O5g8B/hd4AVVNXPpequPrttiZ0b/SH9ifEKIJEk7kJlBm1/j++/5+5uZ732+fx7ACVW1gtETwV6T5BHA24EXtSt772D0vT9jdjCbWT+nHf804A/n6mCbGPpO4Niq+kYrb8v3+zOAP2iTWl7DKPS+aksNegh/vwx8cjPbPgy8PMmPwugyb5LHtG2fq6p3A78J/Gm7xr87cE9V/UuSnwRm0vztwBNm7ucDfhX42Nh5XjL2PldfvgH8yOxikqcBf8oo+N09q99HJdmjTfQ4qtU2K8mBY/cpPL2d7xtbaiNJ0hL1KuAfqurabWx/H6N7B2eC3tdbVph9r/5LANovaNxbVffO2r657/fHAn8O/OrYVUPYhkfTVtUzq2pFC61/BPyPqnrbltrs0E/4SHIKo8ubXxn7aZNHAzsl+UxVXdlG9T7ZctFG4FeAB2eOUVUfa/fJnQJcALwiyS2MAt91bZ9vJvnPwAfacT4F/MlYV3ZOcj2jsH38WP2MJK8BdgZ+Y46P8GZGl5//rB33K1X1gqrakOQMRv9IAE6vqg3tM78a+H+AHwduSXJVVf068EvAiUm+A3wLeMnYBBBJknYISfYDXgv81DY0vzTJtxh9T760qv45yTuANcA6vve9O+OeJJ8AduOh99K/sk3CeCTwhjnO80ZgL+Dt7ft9U1Wt3NKjaZO8EDiXUY75UJKbq+robfiMO/YTPtoMnnVVddEk9YH6sI7Rz7V8fehzSZKkhZFkGnhtVd2w2H35QfVw2VeSJEnNjj7yt4zRxJ8HJ6lLkiTt6Hbo8CdJkqSH8rKvJElSRwx/kiRJHTH8SZIkdcTwJ0lbkaSSXDK2vizJP40/IF6SlgrDnyRt3TeBQ5Ls0tZ/ntGD4yVpyTH8SdJkrgae35aPpz3QHSDJrkkuTPLpJDclObbVd0ryliRrktyS5Ddb/flJbmsPfv+nJC9r9XUzz9xOsnf7kXhJmleGP0mazHuAVe1B708Brh/b9rvAtVX1U4weIv/mJLsCq4EDgKdV1VOAS9v+pwMntQe/v3ehPoAkwQ7+bF9Jmi9VdUuSFYxG/a6atfko4AVJXtvWHwE8FngO8CdVtakdY0Pb/iDwqKH7LElzMfxJ0uSuBN4CTDF6KPuMAL9UVbeP75zRE9vn+iX9U4FLkny7HWfJPRtU0tLlZV9JmtyFwOlVtWZW/cPAb7awR5KntfpfA69oj5QkyZ6t/jXgTmAlXvaVtMAMf5I0oapaX1X/a45NZwA/DNyS5Na2DvBO4Cut/nfAS5PsDFwM/HpVbZzjWB9M8rfAB4EfT/I78/5BJHXNZ/tK0naq3WP4pqp62eL2RNKOxJE/Sdp+3QO8f7E7IWnH4sifJElSRxz5kyRJ6ojhT5IkqSOGP0mSpI4Y/iRJkjpi+JMkSerI/wWzr1Old3RkXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df[\"tdate\"].apply(date_rewriter), align=\"mid\")\n",
    "plt.rcParams['figure.figsize'] = [10, 8]\n",
    "plt.xlabel(\"Месяц\")\n",
    "plt.ylabel(\"Количество твитов\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Задание 23__. Вставьте ниже смешную картинку, желательно про машинное обучение. Лучшие картинки мы наградим бонусными баллами (но это не точно)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Picture](https://raw.githubusercontent.com/GlebChili/math-ml-hse-2019/master/8ccb5905351695a77e4d2a723d098761.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Задание 999__. Напишите ниже, чего вы ждёте от курса, какой формат лекций и семинаров вам более интересен, в какие дни хотели бы поставить семинары, темы, которые хотели бы изучить, промежуточный фидбек (при наличии), а также произвольные пожелания и предложения. Мы постараемся всё это учесть."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Хотелось бы больше про математические (вероятность, статистика) основания ML."
   ]
  }
 ],
 "metadata": {
  "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": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}