sem06_eda/sem_06_eda_solved_upd.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center> Exploratory Data Analysis </center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Знакомство с библиотекой numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NumPy** — библиотека языка Python, позволяющая [удобно] работать с многомерными массивами и матрицами, содержащая математические функции. Кроме того, NumPy позволяет векторизовать многие вычисления, имеющие место в машинном обучении. \n",
    "\n",
    " - [numpy](http://www.numpy.org)\n",
    " - [numpy tutorial](http://cs231n.github.io/python-numpy-tutorial/)\n",
    " - [100 numpy exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/)\n",
    " - [numpy data types](https://docs.scipy.org/doc/numpy/user/basics.types.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Основным типом данных NumPy является многомерный массив элементов одного типа — [numpy.ndarray](http://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.array.html). Каждый подобный массив имеет несколько *измерений* или *осей* — в частности, вектор (в классическом понимании) является одномерным массивом и имеет 1 ось, матрица является двумерным массивом и имеет 2 оси и т.д.\n",
    "\n",
    "Создать массив из списков или кортежей можно с помощью функции ```np.array()```. \n",
    "\n",
    "```np.array``` - функция, создающая объект типа ```np.ndarray```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix.ndim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы узнать длину массива по каждой из осей, можно воспользоваться атрибутом ```shape```. Общее количество элементов выводим с помощью ```size```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2, 3), 6)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix.shape, matrix.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Альтернативные способы задать матрицу:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[1, 2],\n",
       "        [3, 4]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.matrix('1 2; 3 4') # аргумент - строка или список"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.91633496e-310, 6.91633496e-310, 0.00000000e+000],\n",
       "       [0.00000000e+000, 0.00000000e+000, 0.00000000e+000],\n",
       "       [0.00000000e+000, 1.63041663e-322, 9.67272235e-317]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ndarray(shape=(3,3), dtype='float') # из случайных чисел"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Массивы специального вида можно создать при помощи функций zeros, ones, empty, identity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0.])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((3,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((3, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.identity(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.92100617e-310, 6.92100617e-310, 2.88171090e+214,\n",
       "        9.78701508e+199, 4.72159866e+257],\n",
       "       [2.42092166e-322, 6.92100617e-310, 6.92100617e-310,\n",
       "        9.76515890e+199, 3.47816709e+069]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.empty((2, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Еще можно создавать массивы при помощи функции arange:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  5,  8, 11, 14, 17])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(2, 20, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(9).reshape(3, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вместо значения длины массива по одному из измерений можно указать -1 — в этом случае значение будет рассчитано автоматически:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3],\n",
       "       [4, 5, 6, 7]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(8).reshape(2, -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь порешаем задачки!\n",
    "\n",
    "**Задание 1.** Реализуйте функцию, возвращающую максимальный элемент в векторе x среди элементов, перед которыми стоит нулевой. Для x = np.array([6, 2, 0, 3, 0, 0, 5, 7, 0]) ответом является 5. Если нулевых элементов нет, функция должна возвращать None."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Пример:\n",
      "[1 5 8 6 6 1 7 4 4 0]\n",
      "Максимальный элемент:\n",
      "None\n",
      "Случайный вектор:\n",
      "[3 1 8 0 7 3 8 6 4 5]\n",
      "Максимальный элемент:\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "x = np.array([1, 5, 8, 6, 6, 1, 7, 4, 4, 0])\n",
    "\n",
    "def max_element_before_zero(x):\n",
    "    shift_by_one = np.hstack((1, x))\n",
    "    bool_array = (shift_by_one == 0)[:np.size(shift_by_one) - 1]\n",
    "    if np.size(x[bool_array]) == 0:\n",
    "        return None\n",
    "    else:\n",
    "        return np.max(x[bool_array])\n",
    "    \n",
    "print ('Пример:', x, 'Максимальный элемент:', max_element_before_zero(x), sep='\\n')\n",
    "print ('Случайный вектор:', y, 'Максимальный элемент:', max_element_before_zero(y), sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Задание 2.** Реализуйте функцию, принимающую на вход матрицу и некоторое число и возвращающую ближайший к числу элемент матрицы. Например: для X = np.arange(0,10).reshape((2, 5)) и v = 3.6 ответом будет 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица:\n",
      "[[0 1 2 3 4]\n",
      " [5 6 7 8 9]]\n",
      "Число:\n",
      "3.6\n",
      "Ближайший к числу элемент матрицы:\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "def nearest_value(X, v):\n",
    "    Y = np.abs(X - v)\n",
    "    m = np.min(Y)\n",
    "    inds = np.where(Y == m)\n",
    "    return X[inds[0][0], inds[1][0]]\n",
    "\n",
    "X = np.arange(0,10).reshape((2, 5))\n",
    "v = 3.6\n",
    "print ('Матрица:', X, 'Число:', v, 'Ближайший к числу элемент матрицы:', nearest_value(X, v), sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица:\n",
      "[[0 1 2 3 4]\n",
      " [5 6 7 8 9]]\n",
      "Число:\n",
      "3.6\n",
      "Ближайший к числу элемент матрицы:\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "# Решение в одну строку\n",
    "def get_nearest_value(X, v):\n",
    "    return int(np.min(np.abs(X - v)) + v)\n",
    "\n",
    "print ('Матрица:', X, 'Число:', v, \n",
    "       'Ближайший к числу элемент матрицы:', get_nearest_value(X, v), sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Задание 3.** Реализуйте функцию scale(X), которая принимает на вход матрицу и масштабирует каждый ее столбец (вычитает выборочное среднее и делит на стандартное отклонение). Убедитесь, что в функции не будет происходить деления на ноль. Протестируйте на случайной матрице (для её генерации можно использовать, например, функцию [numpy.random.randint](http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.randint.html))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица:\n",
      "[[-4 -4  6  4  7]\n",
      " [ 4  5  6 -6  3]]\n",
      "Масштабированная матрица:\n",
      "[[-1. -1.  0.  1.  1.]\n",
      " [ 1.  1.  0. -1. -1.]]\n"
     ]
    }
   ],
   "source": [
    "def scale(X):\n",
    "    std_deviation = X.std(axis=0)\n",
    "    std_deviation[std_deviation == 0] = 1\n",
    "    X = X - X.mean(axis=0) # или X -= X.mean(axis=0) c преобразованием X = X.astype(np.float)\n",
    "    X /= std_deviation\n",
    "    return(X)\n",
    "\n",
    "X = np.random.randint(-7, 8, size=(2,5))\n",
    "print ('Матрица:', X, 'Масштабированная матрица:', scale(X), sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  2. Pandas и первичный анализ данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pandas** (Python Data Analysis Library) — библиотека языка Python для удобных обработки и анализа данных."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Небольшой список литературы:\n",
    "\n",
    "1. [Шпаргалка по pandas](https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf)\n",
    "\n",
    "2. [Введение в pandas](https://khashtamov.com/ru/pandas-introduction/)\n",
    "\n",
    "3. [Статься в блоге сообщества OpenDataScience на Habr](https://habr.com/ru/company/ods/blog/322626/)\n",
    "\n",
    "4. [Туториалы в официальной документации (на английском)](https://pandas.pydata.org/docs/getting_started/10min.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Качаем набор данных о футболистах из гитхаба курса, сохраняем в формате csv. Далее загружаем в Jupyter Notebook и внимательно указываем путь к файлу (в какие папки нужно перейти, чтобы увидеть файл)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/data_football_profile.csv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Age', 'Nationality', 'Club', 'Value', 'Wage'], dtype='object')"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Функция возвращает DataFrame (то есть таблицу), однако затем приобретает ещё много важных параметров, среди которых:\n",
    "\n",
    "* sep — разделитель данных, по умолчанию ',';\n",
    "* decimal — разделитель числа на целую и дробную часть, по умолчанию'.';\n",
    "* names — список с названиями колонок, не обязательный параметр;\n",
    "* skiprows — если файл содержит системную информацию, можно просто её пропустить. Необязательный параметр.\n",
    "\n",
    "Дополнительные параметры можно посмотреть в [официальной документации.](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html)\n",
    "\n",
    "С помощью функции ```head``` можем посмотреть на первые несколько строк нашего датасета:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Club</th>\n",
       "      <th>Value</th>\n",
       "      <th>Wage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L. Messi</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>110500000.0</td>\n",
       "      <td>565000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>77000000.0</td>\n",
       "      <td>405000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>118500000.0</td>\n",
       "      <td>290000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>De Gea</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>72000000.0</td>\n",
       "      <td>260000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>102000000.0</td>\n",
       "      <td>355000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name   Age Nationality                 Club        Value      Wage\n",
       "0      L. Messi  31.0   Argentina         FC Barcelona  110500000.0  565000.0\n",
       "1           NaN  33.0    Portugal             Juventus   77000000.0  405000.0\n",
       "2     Neymar Jr  26.0      Brazil  Paris Saint-Germain  118500000.0  290000.0\n",
       "3        De Gea  27.0       Spain    Manchester United   72000000.0  260000.0\n",
       "4  K. De Bruyne  27.0     Belgium      Manchester City  102000000.0  355000.0"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Удалим колонку Value, к которой мы не знаем точную интерпретацию:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Value'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Club</th>\n",
       "      <th>Wage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12894</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16.0</td>\n",
       "      <td>England</td>\n",
       "      <td>Cambridge United</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12895</th>\n",
       "      <td>D. Walker-Rice</td>\n",
       "      <td>17.0</td>\n",
       "      <td>England</td>\n",
       "      <td>Tranmere Rovers</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12896</th>\n",
       "      <td>G. Nugent</td>\n",
       "      <td>16.0</td>\n",
       "      <td>England</td>\n",
       "      <td>Tranmere Rovers</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Name   Age Nationality              Club    Wage\n",
       "12894             NaN  16.0     England  Cambridge United  1000.0\n",
       "12895  D. Walker-Rice  17.0     England   Tranmere Rovers  1000.0\n",
       "12896       G. Nugent  16.0     England   Tranmere Rovers  1000.0"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# последние несколько строк:\n",
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на размер нашего датасета. Первое число – количество строк (наблюдений), второе – количество столбцов (признаков):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12897, 5)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если вы хотите переименовать какую-то переменную, воспользуйтесь ```rename```:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename({'Wage' : 'Salary'}, axis='columns', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Age', 'Nationality', 'Club', 'Salary'], dtype='object')"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Давайте посмотрим на информацию о датасете. В .info() можно передать дополнительные параметры, среди которых:\n",
    "\n",
    "* verbose: печатать ли информацию о DataFrame полностью (если таблица очень большая, то некоторая информация может потеряться);\n",
    "* memory_usage: печатать ли потребление памяти (по умолчанию используется True, но можно поставить либо False, что уберёт потребление памяти, либо 'deep' , что подсчитает потребление памяти более точно);\n",
    "* null_counts: подсчитывать ли количество пустых элементов (по умолчанию True)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12897 entries, 0 to 12896\n",
      "Data columns (total 5 columns):\n",
      "Name           12266 non-null object\n",
      "Age            12242 non-null float64\n",
      "Nationality    12213 non-null object\n",
      "Club           12226 non-null object\n",
      "Salary         12263 non-null float64\n",
      "dtypes: float64(2), object(3)\n",
      "memory usage: 503.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можно вывести только тип данных в каждой колонке:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name            object\n",
       "Age            float64\n",
       "Nationality     object\n",
       "Club            object\n",
       "Salary         float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Метод describe показывает основные статистические характеристики данных по каждому числовому признаку (типы int64 и float64): число непропущенных значений, среднее, стандартное отклонение, диапазон, медиану, 0.25 и 0.75 квартили."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>Age</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12242.000000</td>\n",
       "      <td>12263.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>24.814900</td>\n",
       "      <td>7530.946750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.885492</td>\n",
       "      <td>23338.219396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>565000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Age         Salary\n",
       "count  12242.000000   12263.000000\n",
       "mean      24.814900    7530.946750\n",
       "std        4.885492   23338.219396\n",
       "min       16.000000    1000.000000\n",
       "25%       21.000000    1000.000000\n",
       "50%       24.000000    2000.000000\n",
       "75%       28.000000    4000.000000\n",
       "max       45.000000  565000.000000"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы посмотреть статистику по нечисловым признакам (например, по строчным (object) или булевым (bool) данным), нужно явно указать интересующие нас типы в параметре метода describe include:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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>Name</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Club</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12266</td>\n",
       "      <td>12213</td>\n",
       "      <td>12226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>11742</td>\n",
       "      <td>155</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>J. Rodríguez</td>\n",
       "      <td>England</td>\n",
       "      <td>Shonan Bellmare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>7</td>\n",
       "      <td>1297</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name Nationality             Club\n",
       "count          12266       12213            12226\n",
       "unique         11742         155              650\n",
       "top     J. Rodríguez     England  Shonan Bellmare\n",
       "freq               7        1297               30"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include = ['object'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выведем уникальные значения по возрасту и сколько раз каждое из них встречается в датасете (по убыванию). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.0    1011\n",
       "20.0     939\n",
       "22.0     916\n",
       "19.0     857\n",
       "24.0     855\n",
       "23.0     841\n",
       "26.0     812\n",
       "25.0     812\n",
       "27.0     720\n",
       "28.0     680\n",
       "18.0     629\n",
       "29.0     626\n",
       "30.0     517\n",
       "31.0     426\n",
       "32.0     360\n",
       "34.0     266\n",
       "33.0     261\n",
       "17.0     254\n",
       "35.0     153\n",
       "36.0     119\n",
       "37.0      68\n",
       "16.0      41\n",
       "38.0      35\n",
       "39.0      24\n",
       "40.0      12\n",
       "41.0       4\n",
       "44.0       2\n",
       "42.0       1\n",
       "45.0       1\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы вывести уникальные значения в столбце или их количество, нужно использовать ```unique``` и ```nunique``` соответственно. Посмотрим, сколько у нас уникальных футбольных клубов. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Всего 650 футбольных клубов\n"
     ]
    }
   ],
   "source": [
    "print('Всего {} футбольных клубов'.format(df['Club'].nunique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['FC Barcelona', 'Juventus', 'Paris Saint-Germain',\n",
       "       'Manchester United', 'Manchester City', 'Chelsea', 'Real Madrid',\n",
       "       'Atlético Madrid', 'FC Bayern München', 'Tottenham Hotspur'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Club'].unique()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посчитаем арифметическое среднее, моду и медиану возраста футболистов (количественной переменной):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Среднее: 24.81 Медиана: 24.0 Мода: 21.0\n"
     ]
    }
   ],
   "source": [
    "print('Среднее:', round(df['Age'].mean(), 2), \n",
    "      'Медиана:', df['Age'].median(), \n",
    "      'Мода:', df['Age'].mode()[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для качественных переменных с помощью pandas можно вывести моду. Посмотрим на самую часто встречающуюся национальность:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    England\n",
       "dtype: object"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Nationality'].mode()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Было бы полезно узнать, много ли у нас пропусков в датасете."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name           631\n",
       "Age            655\n",
       "Nationality    684\n",
       "Club           671\n",
       "Salary         634\n",
       "dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы удалить пропуски из данных, нужно вопспользоваться ```df.dropna()```, либо заполнить их значениями (например, средним) -  ```df.fillna(df['column_name'].mean())``` .\n",
    "Если в датасете содержатся дубликаты строк - воспользуйтесь методом ```df.drop_duplicates()```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name           0\n",
       "Age            0\n",
       "Nationality    0\n",
       "Club           0\n",
       "Salary         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# заполним количественные переменные средними значениями (медианой)\n",
    "for column in df.columns:\n",
    "    if df[column].dtype != 'object':\n",
    "        df[column] = df[column].fillna(df[column].median())\n",
    "        \n",
    "# у оставшихся переменных удалим строки с пропусками\n",
    "df.dropna(inplace=True)\n",
    "\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Часто возникает необходимость выбрать данные из DataFrame по определённому условию. Например, если в уже известном нам наборе данных о футболистах мы хотим выбрать только тех, у кого возраст больше 20 лет, используется следующий код:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Club</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L. Messi</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>565000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>290000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>De Gea</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>260000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>355000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>E. Hazard</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>340000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>L. Modrić</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>420000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>L. Suárez</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>455000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sergio Ramos</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>380000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>J. Oblak</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>Atlético Madrid</td>\n",
       "      <td>94000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>R. Lewandowski</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Poland</td>\n",
       "      <td>FC Bayern München</td>\n",
       "      <td>205000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>T. Kroos</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>355000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>D. Godín</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Atlético Madrid</td>\n",
       "      <td>125000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>David Silva</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>285000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>N. Kanté</td>\n",
       "      <td>27.0</td>\n",
       "      <td>France</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>225000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>P. Dybala</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>205000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>A. Griezmann</td>\n",
       "      <td>27.0</td>\n",
       "      <td>France</td>\n",
       "      <td>Atlético Madrid</td>\n",
       "      <td>145000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>M. ter Stegen</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Germany</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>240000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Sergio Busquets</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>315000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>E. Cavani</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>M. Neuer</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Germany</td>\n",
       "      <td>FC Bayern München</td>\n",
       "      <td>130000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>S. Agüero</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>G. Chiellini</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>215000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>M. Salah</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>255000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>J. Rodríguez</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>FC Bayern München</td>\n",
       "      <td>315000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>L. Insigne</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Napoli</td>\n",
       "      <td>165000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Isco</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>315000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>C. Eriksen</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>Tottenham Hotspur</td>\n",
       "      <td>205000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Coutinho</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>340000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>P. Aubameyang</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Gabon</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>265000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>M. Hummels</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Germany</td>\n",
       "      <td>FC Bayern München</td>\n",
       "      <td>160000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12682</th>\n",
       "      <td>Wang Xuanhong</td>\n",
       "      <td>28.0</td>\n",
       "      <td>China PR</td>\n",
       "      <td>Beijing Renhe FC</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12684</th>\n",
       "      <td>Yu Bin</td>\n",
       "      <td>22.0</td>\n",
       "      <td>China PR</td>\n",
       "      <td>Beijing Renhe FC</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12699</th>\n",
       "      <td>J. Heaton</td>\n",
       "      <td>21.0</td>\n",
       "      <td>England</td>\n",
       "      <td>St. Mirren</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12701</th>\n",
       "      <td>D. Schwizer</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>FC Thun</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12711</th>\n",
       "      <td>Han Zilong</td>\n",
       "      <td>24.0</td>\n",
       "      <td>China PR</td>\n",
       "      <td>Changchun Yatai FC</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12719</th>\n",
       "      <td>J. Miszczuk</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Jagiellonia Białystok</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12720</th>\n",
       "      <td>Chen Ji</td>\n",
       "      <td>21.0</td>\n",
       "      <td>China PR</td>\n",
       "      <td>Guizhou Hengfeng FC</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12726</th>\n",
       "      <td>Zhang Enge</td>\n",
       "      <td>21.0</td>\n",
       "      <td>China PR</td>\n",
       "      <td>Shanghai SIPG FC</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12737</th>\n",
       "      <td>A. Suzuki</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Japan</td>\n",
       "      <td>Yokohama F. Marinos</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12746</th>\n",
       "      <td>Teng Shangkun</td>\n",
       "      <td>27.0</td>\n",
       "      <td>China PR</td>\n",
       "      <td>Tianjin TEDA FC</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12747</th>\n",
       "      <td>H. Wulff</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Norway</td>\n",
       "      <td>Stabæk Fotball</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12756</th>\n",
       "      <td>Gao Dalun</td>\n",
       "      <td>21.0</td>\n",
       "      <td>China PR</td>\n",
       "      <td>Jiangsu Suning FC</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12762</th>\n",
       "      <td>O. Dzonlagic</td>\n",
       "      <td>23.0</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>FC Thun</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12764</th>\n",
       "      <td>Liu Yi</td>\n",
       "      <td>21.0</td>\n",
       "      <td>China PR</td>\n",
       "      <td>Tianjin Quanjian FC</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12767</th>\n",
       "      <td>Sun Qibin</td>\n",
       "      <td>26.0</td>\n",
       "      <td>China PR</td>\n",
       "      <td>Tianjin Quanjian FC</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12777</th>\n",
       "      <td>Kwon Ki Pyo</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Korea Republic</td>\n",
       "      <td>Pohang Steelers</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12780</th>\n",
       "      <td>M. Ichimaru</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Japan</td>\n",
       "      <td>Gamba Osaka</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12785</th>\n",
       "      <td>G. Barry</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Republic of Ireland</td>\n",
       "      <td>Limerick FC</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12790</th>\n",
       "      <td>M. Connor</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Republic of Ireland</td>\n",
       "      <td>Waterford FC</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12798</th>\n",
       "      <td>T. Johnson</td>\n",
       "      <td>24.0</td>\n",
       "      <td>England</td>\n",
       "      <td>Crewe Alexandra</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12806</th>\n",
       "      <td>R. Pasquel</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Deportivo Toluca</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12809</th>\n",
       "      <td>K. Moesgaard</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>Esbjerg fB</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12829</th>\n",
       "      <td>A. Kelsey</td>\n",
       "      <td>24.0</td>\n",
       "      <td>England</td>\n",
       "      <td>Scunthorpe United</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12835</th>\n",
       "      <td>J. Garcia Sossa</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Bolivia</td>\n",
       "      <td>CD Huachipato</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12836</th>\n",
       "      <td>M. Harsman</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Germany</td>\n",
       "      <td>SV Meppen</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12838</th>\n",
       "      <td>D. Mackay</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>Kilmarnock</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12855</th>\n",
       "      <td>H. Norris</td>\n",
       "      <td>24.0</td>\n",
       "      <td>England</td>\n",
       "      <td>Oldham Athletic</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12861</th>\n",
       "      <td>Y. Uchimura</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Japan</td>\n",
       "      <td>Hokkaido Consadole Sapporo</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12873</th>\n",
       "      <td>K. Pilkington</td>\n",
       "      <td>44.0</td>\n",
       "      <td>England</td>\n",
       "      <td>Cambridge United</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12889</th>\n",
       "      <td>M. Baldisimo</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Vancouver Whitecaps FC</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8716 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name   Age          Nationality                        Club  \\\n",
       "0             L. Messi  31.0            Argentina                FC Barcelona   \n",
       "2            Neymar Jr  26.0               Brazil         Paris Saint-Germain   \n",
       "3               De Gea  27.0                Spain           Manchester United   \n",
       "4         K. De Bruyne  27.0              Belgium             Manchester City   \n",
       "5            E. Hazard  27.0              Belgium                     Chelsea   \n",
       "6            L. Modrić  32.0              Croatia                 Real Madrid   \n",
       "7            L. Suárez  31.0              Uruguay                FC Barcelona   \n",
       "8         Sergio Ramos  32.0                Spain                 Real Madrid   \n",
       "9             J. Oblak  25.0             Slovenia             Atlético Madrid   \n",
       "10      R. Lewandowski  29.0               Poland           FC Bayern München   \n",
       "11            T. Kroos  28.0              Germany                 Real Madrid   \n",
       "12            D. Godín  32.0              Uruguay             Atlético Madrid   \n",
       "13         David Silva  32.0                Spain             Manchester City   \n",
       "14            N. Kanté  27.0               France                     Chelsea   \n",
       "15           P. Dybala  24.0            Argentina                    Juventus   \n",
       "17        A. Griezmann  27.0               France             Atlético Madrid   \n",
       "18       M. ter Stegen  26.0              Germany                FC Barcelona   \n",
       "20     Sergio Busquets  29.0                Spain                FC Barcelona   \n",
       "21           E. Cavani  31.0              Uruguay         Paris Saint-Germain   \n",
       "22            M. Neuer  32.0              Germany           FC Bayern München   \n",
       "23           S. Agüero  30.0            Argentina             Manchester City   \n",
       "24        G. Chiellini  24.0                Italy                    Juventus   \n",
       "26            M. Salah  26.0                Egypt                   Liverpool   \n",
       "28        J. Rodríguez  26.0             Colombia           FC Bayern München   \n",
       "29          L. Insigne  27.0                Italy                      Napoli   \n",
       "30                Isco  26.0                Spain                 Real Madrid   \n",
       "31          C. Eriksen  26.0              Denmark           Tottenham Hotspur   \n",
       "32            Coutinho  26.0               Brazil                FC Barcelona   \n",
       "33       P. Aubameyang  29.0                Gabon                     Arsenal   \n",
       "34          M. Hummels  29.0              Germany           FC Bayern München   \n",
       "...                ...   ...                  ...                         ...   \n",
       "12682    Wang Xuanhong  28.0             China PR            Beijing Renhe FC   \n",
       "12684           Yu Bin  22.0             China PR            Beijing Renhe FC   \n",
       "12699        J. Heaton  21.0              England                  St. Mirren   \n",
       "12701      D. Schwizer  22.0          Switzerland                     FC Thun   \n",
       "12711       Han Zilong  24.0             China PR          Changchun Yatai FC   \n",
       "12719      J. Miszczuk  27.0               Poland       Jagiellonia Białystok   \n",
       "12720          Chen Ji  21.0             China PR         Guizhou Hengfeng FC   \n",
       "12726       Zhang Enge  21.0             China PR            Shanghai SIPG FC   \n",
       "12737        A. Suzuki  31.0                Japan         Yokohama F. Marinos   \n",
       "12746    Teng Shangkun  27.0             China PR             Tianjin TEDA FC   \n",
       "12747         H. Wulff  24.0               Norway              Stabæk Fotball   \n",
       "12756        Gao Dalun  21.0             China PR           Jiangsu Suning FC   \n",
       "12762     O. Dzonlagic  23.0          Switzerland                     FC Thun   \n",
       "12764           Liu Yi  21.0             China PR         Tianjin Quanjian FC   \n",
       "12767        Sun Qibin  26.0             China PR         Tianjin Quanjian FC   \n",
       "12777      Kwon Ki Pyo  21.0       Korea Republic             Pohang Steelers   \n",
       "12780      M. Ichimaru  21.0                Japan                 Gamba Osaka   \n",
       "12785         G. Barry  24.0  Republic of Ireland                 Limerick FC   \n",
       "12790        M. Connor  21.0  Republic of Ireland                Waterford FC   \n",
       "12798       T. Johnson  24.0              England             Crewe Alexandra   \n",
       "12806       R. Pasquel  22.0               Mexico            Deportivo Toluca   \n",
       "12809     K. Moesgaard  24.0              Denmark                  Esbjerg fB   \n",
       "12829        A. Kelsey  24.0              England           Scunthorpe United   \n",
       "12835  J. Garcia Sossa  24.0              Bolivia               CD Huachipato   \n",
       "12836       M. Harsman  24.0              Germany                   SV Meppen   \n",
       "12838        D. Mackay  21.0             Scotland                  Kilmarnock   \n",
       "12855        H. Norris  24.0              England             Oldham Athletic   \n",
       "12861      Y. Uchimura  33.0                Japan  Hokkaido Consadole Sapporo   \n",
       "12873    K. Pilkington  44.0              England            Cambridge United   \n",
       "12889     M. Baldisimo  24.0               Canada      Vancouver Whitecaps FC   \n",
       "\n",
       "         Salary  \n",
       "0      565000.0  \n",
       "2      290000.0  \n",
       "3      260000.0  \n",
       "4      355000.0  \n",
       "5      340000.0  \n",
       "6      420000.0  \n",
       "7      455000.0  \n",
       "8      380000.0  \n",
       "9       94000.0  \n",
       "10     205000.0  \n",
       "11     355000.0  \n",
       "12     125000.0  \n",
       "13     285000.0  \n",
       "14     225000.0  \n",
       "15     205000.0  \n",
       "17     145000.0  \n",
       "18     240000.0  \n",
       "20     315000.0  \n",
       "21     200000.0  \n",
       "22     130000.0  \n",
       "23     300000.0  \n",
       "24     215000.0  \n",
       "26     255000.0  \n",
       "28     315000.0  \n",
       "29     165000.0  \n",
       "30     315000.0  \n",
       "31     205000.0  \n",
       "32     340000.0  \n",
       "33     265000.0  \n",
       "34     160000.0  \n",
       "...         ...  \n",
       "12682    2000.0  \n",
       "12684    2000.0  \n",
       "12699    1000.0  \n",
       "12701    2000.0  \n",
       "12711    1000.0  \n",
       "12719    1000.0  \n",
       "12720    2000.0  \n",
       "12726    1000.0  \n",
       "12737    1000.0  \n",
       "12746    2000.0  \n",
       "12747    1000.0  \n",
       "12756    2000.0  \n",
       "12762    2000.0  \n",
       "12764    2000.0  \n",
       "12767    2000.0  \n",
       "12777    1000.0  \n",
       "12780    1000.0  \n",
       "12785    1000.0  \n",
       "12790    1000.0  \n",
       "12798    1000.0  \n",
       "12806    2000.0  \n",
       "12809    1000.0  \n",
       "12829    1000.0  \n",
       "12835    1000.0  \n",
       "12836    1000.0  \n",
       "12838    1000.0  \n",
       "12855    1000.0  \n",
       "12861    1000.0  \n",
       "12873    1000.0  \n",
       "12889    1000.0  \n",
       "\n",
       "[8716 rows x 5 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Age > 20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Задание:** Выберите футболистов, возраст которых больше среднего возраста футболистов, при условии, что они принадлежат ФК Барселона (Club == 'FC Barcelona')."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Club</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L. Messi</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>565000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>L. Suárez</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>455000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>M. ter Stegen</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Germany</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>240000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Sergio Busquets</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>315000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Coutinho</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>340000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Jordi Alba</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>250000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Piqué</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>240000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>J. Cillessen</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>135000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>T. Vermaelen</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name   Age  Nationality          Club    Salary\n",
       "0           L. Messi  31.0    Argentina  FC Barcelona  565000.0\n",
       "7          L. Suárez  31.0      Uruguay  FC Barcelona  455000.0\n",
       "18     M. ter Stegen  26.0      Germany  FC Barcelona  240000.0\n",
       "20   Sergio Busquets  29.0        Spain  FC Barcelona  315000.0\n",
       "32          Coutinho  26.0       Brazil  FC Barcelona  340000.0\n",
       "49        Jordi Alba  29.0        Spain  FC Barcelona  250000.0\n",
       "54             Piqué  31.0        Spain  FC Barcelona  240000.0\n",
       "204     J. Cillessen  29.0  Netherlands  FC Barcelona  135000.0\n",
       "605     T. Vermaelen  32.0      Belgium  FC Barcelona    2000.0"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.Age > df.Age.mean()) & (df.Club == 'FC Barcelona')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы объединить данные из нескольких датасетов по ключу (общей колонке), в pandas можно воспользовать встроенными аналогами SQL методов. В метод ```join``` в качестве аргумента how нужно указать тип объединения датасетов: inner, outer, left или right. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Вот так: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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>Name</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Position</th>\n",
       "      <th>Crossing</th>\n",
       "      <th>Finishing</th>\n",
       "      <th>HeadingAccuracy</th>\n",
       "      <th>ShortPassing</th>\n",
       "      <th>Volleys</th>\n",
       "      <th>Dribbling</th>\n",
       "      <th>Curve</th>\n",
       "      <th>...</th>\n",
       "      <th>SlidingTackle</th>\n",
       "      <th>GKDiving</th>\n",
       "      <th>GKHandling</th>\n",
       "      <th>GKKicking</th>\n",
       "      <th>GKPositioning</th>\n",
       "      <th>GKReflexes</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Club</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A. Abang</td>\n",
       "      <td>8574.0</td>\n",
       "      <td>ST</td>\n",
       "      <td>30.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Cameroon</td>\n",
       "      <td>New York Red Bulls</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A. Abdellaoui</td>\n",
       "      <td>10604.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>65.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>FC Sion</td>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A. Abdi</td>\n",
       "      <td>2188.0</td>\n",
       "      <td>CM</td>\n",
       "      <td>68.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>...</td>\n",
       "      <td>57.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Sheffield Wednesday</td>\n",
       "      <td>13000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A. Abdu Jaber</td>\n",
       "      <td>7807.0</td>\n",
       "      <td>ST</td>\n",
       "      <td>39.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Eritrea</td>\n",
       "      <td>Al Wehda</td>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A. Abdulhameed</td>\n",
       "      <td>11820.0</td>\n",
       "      <td>GK</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>Al Hazem</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name  Unnamed: 0 Position  Crossing  Finishing  HeadingAccuracy  \\\n",
       "0        A. Abang      8574.0       ST      30.0       61.0             67.0   \n",
       "1   A. Abdellaoui     10604.0      NaN      56.0       25.0             56.0   \n",
       "2         A. Abdi      2188.0       CM      68.0       61.0             59.0   \n",
       "3   A. Abdu Jaber      7807.0       ST      39.0       66.0              NaN   \n",
       "4  A. Abdulhameed     11820.0       GK      10.0        9.0             13.0   \n",
       "\n",
       "   ShortPassing  Volleys  Dribbling  Curve  ...  SlidingTackle  GKDiving  \\\n",
       "0          52.0     58.0        NaN   30.0  ...           21.0       6.0   \n",
       "1           NaN     32.0       39.0   30.0  ...           65.0      15.0   \n",
       "2          74.0     64.0       70.0   68.0  ...           57.0       9.0   \n",
       "3          49.0     64.0       61.0   59.0  ...           15.0       7.0   \n",
       "4          26.0      6.0        6.0   11.0  ...           11.0      59.0   \n",
       "\n",
       "   GKHandling  GKKicking  GKPositioning  GKReflexes   Age   Nationality  \\\n",
       "0         6.0        7.0           13.0         9.0  21.0      Cameroon   \n",
       "1        10.0       14.0           13.0        14.0  25.0       Algeria   \n",
       "2         6.0        8.0           11.0         8.0  31.0   Switzerland   \n",
       "3        11.0        7.0            6.0        12.0  21.0       Eritrea   \n",
       "4        57.0       51.0           52.0        58.0  24.0  Saudi Arabia   \n",
       "\n",
       "                  Club   Salary  \n",
       "0   New York Red Bulls   2000.0  \n",
       "1              FC Sion   4000.0  \n",
       "2  Sheffield Wednesday  13000.0  \n",
       "3             Al Wehda   4000.0  \n",
       "4             Al Hazem   1000.0  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_info = pd.read_csv('data/data_football_info.csv', sep='\\t')\n",
    "\n",
    "joined_dfs = df_info.set_index('Name').join(df.set_index('Name'), how='inner').reset_index()\n",
    "joined_dfs.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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