Analyze this.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fnames = !ls members_*.csv\n",
    "df = pd.concat(\n",
    "    [pd.read_csv(fname, index_col=0) for fname in fnames]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total users 80923\n",
      "Total uniq users 70966\n"
     ]
    }
   ],
   "source": [
    "print('Total users', len(df))\n",
    "df = df.drop_duplicates(subset='id')\n",
    "print('Total uniq users', len(df.id.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['is_male'] = df['sex'].map({0: pd.NA, 1: 'Ж', 2: 'М'}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.to_csv('all_users_2020feb15.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>first_name</th>\n",
       "      <th>sex</th>\n",
       "      <th>is_male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jullich</td>\n",
       "      <td>1</td>\n",
       "      <td>Ж</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Elena</td>\n",
       "      <td>1</td>\n",
       "      <td>Ж</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tonya</td>\n",
       "      <td>1</td>\n",
       "      <td>Ж</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Evgenia</td>\n",
       "      <td>1</td>\n",
       "      <td>Ж</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Katya</td>\n",
       "      <td>1</td>\n",
       "      <td>Ж</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  first_name  sex is_male\n",
       "0    Jullich    1       Ж\n",
       "1      Elena    1       Ж\n",
       "2      Tonya    1       Ж\n",
       "3    Evgenia    1       Ж\n",
       "4      Katya    1       Ж"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['first_name', 'sex', 'is_male']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ж    63532\n",
       "М     7425\n",
       "Name: is_male, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['is_male'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8953591611821243"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "63532 / (63532 + 7425)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '89% подписчиков -- женщины')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['is_male'].value_counts().plot(kind='bar')\n",
    "plt.grid(False)\n",
    "plt.title('89% подписчиков -- женщины')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pendulum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(1983, 4, 26, 0, 0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.strptime('26.4.1983', '%d.%m.%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "today = dt.today()\n",
    "\n",
    "def to_years(row):\n",
    "    born = dt.strptime(row['bdate'], '%d.%m.%Y')\n",
    "    return today.year - born.year - ((today.month, today.day) < (born.month, born.day))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "years = df[df['bdate'].str.len() > 5].apply(to_years, axis=1)\n",
    "\n",
    "sns.distplot(years, norm_hist=False, kde=False)\n",
    "sns.despine()\n",
    "plt.title(f'Raw медиана возраста {years.median():.0f}')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3      36\n",
       "5      30\n",
       "7      32\n",
       "8      34\n",
       "11     36\n",
       "       ..\n",
       "663    36\n",
       "665    38\n",
       "666    29\n",
       "670    38\n",
       "671    28\n",
       "Length: 20512, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\n",
    "    df['bdate'].str.len() > 5\n",
    "].apply(to_years, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years.mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aa1z9Z1+9gbZvfSussSkV72655RbuvfdeZszoPMXXNddcw+9+9zt/P/uePXsA62av0aNHY7PZeOmll3C73UMec1ea6GPA/lPNtHZ4mDoylZzUhD7L+xL99nt+QvMvfxnu8JSKa+PHj+80zNJnzZo1dHR0MG/ePObOncvPf/5zAG677TZefPFF5s+fz4EDB6Li7l7tuokBB047ATg/iO4YAU40tNHh9pBg189xpQaqvLy827Z58+Yxb948AFJSUnjiiSe6lTnvvPPYvn27//mDDz7Y6bVOp/X/+dFHHw1D1D3TTBADyk5bkxdNHdn3xFWjMhLxGDhe30be/lLsH+ms0EoNd5roY8CBfiT6/JxkAI7WtnL9HTeQec01YY1NKRX9tOsmihV9VIkxhk9OWDdB7T/lJCP57KfsnJxkdh5t8PfTK6WUtuijXGObm1aXh5QEG+m9zGkTaEK21aKvqNNEr5SyaKKPcqca2wEYmZ4Y1IoymckOslMctLuH5tZqpVT000Qf5U41WYl+VEbwK+34+umVUgo00Uc9X4t+VHo/En22JnqlBitU0xQvXbqUXbt2AbBy5Urq6+tDGmcwgkr0IrJYRPaJSJmI3NfD/iQRecm7/30RmRSw70IRKRaRUhHZKyKahfrh9ABa9Odoi16pQQucphgIyTTFL7/8MpFYE7vPRC8iduBXwBJgBnCziMzoUuw2oNYYMwV4AviF97UO4A/AHcaYmcBVQEfIoo9zbo+hqulMH32w8tISSEmwce2tv6TsL6+HKzyl4t5ApiluaWnhtttuY86cOXzzm9/0f1AAXHTRRf75cr7xjW9w9dVXM3fuXJ4NmIEzPz+fn/3sZ8yfP5+FCxdy6tSpQdcjmOGVlwFlvhWiRORFYBnwSUCZZcCD3sfrgKfEunL4ZWCPMWY3gDEmcB1Z1Yea5g7cBrJTHCQ5gu9lExHGZSXxcccUPhp1HoVhjFGpcMt9sv/TCwej5vt9T5S2fPlyHn30URYtWkRpaSm33HILxcXFwJlpip966inq6+v9Cf/ZZ58lJSWF999/n9LSUq666qoej/0f//Ef5OTk0NLSQmFhIddddx25ubk4nU4uvfRS/vVf/5UHHniA559/njVr1gyqrsEk+vFA4L3AFcCc3soYY1wiUg/kAecDRkQ2AyOBF40x3e77FZFVwCqAoqIimpub+1uPiGlubqakpCQsx2463QbA+BQX2U1H+ig9HcBfbmKyh4PAP3aXkePs67XdhbNekRSP9YrHOuXk5DBixAj/dAHh0NexjTFMmjSJw4cP86c//Ykrr7yS1tZW3G43TqeTN998k9dee40nn3wSsFryBw4c4J133uE73/kOTqeTSZMmMX36dFpaWnA6nRhj8Hg8OJ1OnnrqKV5/3frGfezYMUpLS5k9ezaJiYn+qRIKCgp45513usVaXV1NWVlZp22Fhb036cJ9w5QDmAd8EWgGtojIh8aYLYGFjDFrgbUAxcXFpqCgIMxhhU5JSQmzZ88Oy7GfOfgJUEd2djZ16cG1aurSJwKQndfEz3//Y8ZmJnHxX57t93uHs16RFI/1isc6NTQ0YLPZSEtLC6rlHQ4iQlpaGl/96ld5+OGH/dMU2+120tLSsNlsvPDCC92mKXY4HCQnJ/snM7PZbKSkpJCWloaIYLPZ2LVrF++++y5vvPEGqampLF261P9+CQkJpKdbs9Smpqb6twfKy8tj8uTJQdclmP6AY0B+wPMJ3m09lvH2y2cB1Vit/7eNMVXGmGasdWfj6y8yjHwjbkb340Ksz+iMRP6f3Zu5+p0NoQ5LqWGlv9MUz507l3Xr1gHwySefUFpa2u2YDQ0NZGdnk5qayv79+9m5c2dY6xBMot8BTBWRySKSCNwEdM0eG4BbvY9XAFuNVfvNwCwRSfV+AFxJ5759dRb+MfT9uBDrEzidcW2zXv9WaqD6O02xr9tmzpw5PPLII1x00UXdXltYWIjL5WLOnDk89NBDXHrppWGtQ59dN94+9zuxkrYdeMYYUyoiDwE7jTEbgKeBF0SkDKjB+jDAGFMrIo9jfVgYYKMx5rUw1SWuONvc1Le4sNuE3CDmoO/KFnAX7f5TTuZMyg5leErFvYFOU5ySksLTTz/d4zF3796N0+kkKSmJV155pc/3XbZsGcuWLRtI+J0E1UdvjNmI1e0SuO3+gMetwA29vPYPWEMsVT98Xm1dkB6RloDN1vfUB2ez76QmeqWGM70zNkodq7dG3OSkDP56+b5T4Ru5oJSKfproo9Sxemv2yayU/nfbdKWJXqnhTeejj1LH66wWffYgWvSnp86gsqGNw9UttHa4SU7oe5pjpVT80UQfpY57u26yB3Ah1ucvv13Hn3aewF3dwsGqFmaOTQ9VeEqpGKJdN1HK13UzmBY9wLTR1o0W2n2j1PCliT4KeYzhRIPVos/qY+nAvkwb5U30JzXRK9UfoZqm+KKLLqK6OrLTfGmij0JVTe10uA1piXYS+zGZWVe3XzODu5bMArRFr1R/hWOa4kjRRB+FfEMrs0IwtNJn/yknHqPLCyrVH2ebpri2tpZvfOMbzJs3j4ULF/qnOqipqWH58uXMnTuX73//+/4pEsCaj/7aa69lwYIF/PCHP8Ttdg9JPTTRR6FjdaHpn/cZmZ5IS4eH8lpdMFzFntzcnLD8BGP58uWsX7+e1tZWSktLueSSS/z7HnnkEWbNmsX27dv58Y9/zPe+9z0AHn30US6//HKKi4u59tprqaioAGDfvn28+uqrvPrqq7z99tvY7fZe744NNR11E4X8I25CMIYeYNqoVE43tbP/lJOJuSkhOaZSw8HMmTMpLy+nqKiIhQsXdtr33nvv8dxzzwGwYMECampqaGho4N133+X5558H4Mtf/jLZ2dZd6W+//Ta7d+/m2muvxWaz0drayogRI4akHproo9DxEHfdeLzfHDfsPUVDq4uvXzwmJMdVaijU1NRG9P0XL17M/fff75+meKCMMdx0003cc8893aYdDjftuolCoRpa6eOb5vikd9pjpVTwepumeO7cuf6ul+3bt5OXl0dmZiZXXHGFf5riN954g7q6OsBq9W/YsIGqqirA6uPvaeK0cNAWfRQ67p/nJjRdN5rolRq43qYp/ud//mf+6Z/+iXnz5pGSksJ//ud/AnDvvfdy++23M3fuXC677DImTJgAQEFBAf/yL//CLbfcAkBCQgKPPvoo+fn53Y4daproo4zLYzjZ0IYAmYNs0b9z94MA5KYm4LAJDa0uWjuG5iq/UrGur2mKc3Jy+MMfuk/Mm5uby/r163s85vLly1m0aFF0dt2IyGIR2SciZSJyXw/7k0TkJe/+90Vkknf7JBFpEZGPvD+/CW348edkQxtuY42UcQx2euJrV7Lv2pXYbMLIdOvbgbbqlRp++mwyiogd+BWwEGtpwB0issEYE7hS1G1ArTFmiojcBPwCuNG776Ax5uIQxx23fN0247OTQnrcURlJnGho9y9PqJQaPoJp0V8GlBljDhlj2oEXga5LniwDnvM+XgcUisjgmqPDlO9C7Lis5EEfa9rfXmba314GtJ9eqeEsmE7g8UBgZ1UFMKe3Mt6lB+uBPO++ySKyC2gA/tUY807XNxCRVcAqgKKiIpqbm/tViUhqbm6mpKQkZMf7sMxa39XWXEN2U3+Gck0HILvpiH/L/McfBODkVXM412GNsayubwwq3lDXK1rEY73isU6pqamMHj060mGEhcfjwekc+JQkHR0dVFZWUlZW1ml7YWFhr68J98XYE8A5xphqEbkE+LOIzDTGNAQWMsasBdYCFBcXm4KCgjCHFTolJSXMnj07ZMdbf/wAcJpLCiZR5+n/lAV16RN73JaW6AaOcKJZuOjiL2Dvo/8/1PWKFvFYr3iskzGGffv2MW7cuEiHEnLV1dXk5eX1XbAXdrudadOm0Z9Ok2AS/TEgcPzPBO+2nspUiIgDyAKqjTXJQxuAMeZDETkInA/sDDrCYcY3/cG4rGSO1raE7LgpiXYyku00tropr21lUp7eIauil4jQ3NxMZmZmpEMJubKyMiZPnjyk7xlMH/0OYKqITBaRROAmYEOXMhuAW72PVwBbjTFGREZ6L+YiIucCU4FDoQk9Ph0L08VYgNEZ1jH360yWSg0rfSZ6Y4wLuBPYDHwKvGyMKRWRh0TkOm+xp4E8ESkD7gZ8QzAXAHtE5COsi7R3GGMGfg9xnGt3eTjd1I5drFEyoea7IHvgdOxcA1FKDV5QffTGmI3Axi7b7g943Arc0MPrioCiQcY4bPgWGxmdmTToMfQ9GZVuJXpt0Ss1vOhcN1HEN4Z+XFboW/NwpkWviV6p4UWnQIgix31j6DNDk+h/v/WTTs9z06ypECob22lodZE5yGUKlVKxQVv0UcTXdROKm6V6YhNhpHbfKDXsaKKPIr6um7Fh6roB7b5RajjSRB9FToS4j/5rq1fwtdUrOm0bk2kl+s9OaqJXarjQTtoo4uu6GRuiPvoRBz7ptm2M99ia6JUaPrRFHyU63B5ONbZjExjl7V4Jh1HpiQhwqKqZNpcnbO+jlIoemuijxMnGdgxWIk6wh++0JDpsTMpLwW2gTG+cUmpY0EQfJYbiQqxPwWhrdZvPTjaF/b2UUpGniT5KnAjhPPR9KRjlS/TaT6/UcKCJPkqE+kLs2RSMSQdgnyZ6pYYFHXUTJcLRdfPZV7tNPwTAtFGpgDW5mctjwjKvjlIqemiijxL+u2JD2KLffs9PetyelZLA2MwkTjS0caS6hfNGpobsPZVS0SeorhsRWSwi+0SkTETu62F/koi85N3/vohM6rL/HBFpEpE1oQk7/gzlxVgIuCCrd8gqFff6TPTehUN+BSwBZgA3i8iMLsVuA2qNMVOAJ4BfdNn/OLBp8OHGp1d2naDS26J/7/Naij6qpOijykEfN29/KXn7S3vcpyNvlBo+gmnRXwaUGWMOGWPagReBZV3KLAOe8z5eBxSKd0FDEfka8DnQc8ZRNLa68RhIT7TjCOEY+uvvuIHr7+i5n96X6PWCrFLxL5g++vFAecDzCmBOb2WMMS4RqcdacaoV+GdgIdBrt42IrAJWARQVFdHcHDs38jQ3N1NSUjKoY7hr2gHIS3KT3XRkgEeZDtDj67tuKyk5TkertfB46fEGPvzwQ0SE2uYOfxm7p50t298nJzVhgPFEp1Ccr2gTj3UCrVd/FRYW9rov3BdjHwSeMMY0nW3FcmPMWmAtQHFxsSkoKAhzWKFTUlLC7NmzB3WMDRsPAKdJS0ujLn30oI5Vlz6xz211gEkxpCUexdnuZlfbKCuhp58pk910hLr0iRRePGZQ8USbUJyvaBOPdQKtVygFk+iPAfkBzyd4t/VUpkJEHEAWUI3V8l8hIo8C2YBHRFqNMU8NOvI4Ut/iAiBrCBcCERFGZyRyqLqFk43tcddyV0qdEUyH8A5gqohMFpFE4CZgQ5cyG4BbvY9XAFuNZb4xZpIxZhLwS+DnmuS7q/Mm+uyUoU22vhE+vhE/Sqn41GcT0tvnfiewGbADzxhjSkXkIWCnMWYD8DTwgoiUATVYHwYqSPWt3hZ9ytDe1uAbs6+JXqn4FlRmMcZsBDZ22XZ/wONWoOfhHWfKPDiA+IYFf9fNUCd6b4v+REMrxhjOdh1FKRW79M7YCPMYc6ZFH+I++ld/88pZ92ckO8hIstPY5qamuYO8tPDNg6+Uihyd1CzCapwduD2G1AQbiY7Qno7q82dSff7Ms5bxteqPafeNUnFLE32E+frHh7rbxmecXpBVKu5poo8wf6IPw9DKeY89wLzHHjhrGd/89yc00SsVtzTRR1hFnbXgSDjGsRe89goFr529n943/31lQxtujwl5DEqpyNNEH2HltS1AeBJ9MJITbOSlJeA21rq1Sqn4o4k+wsq9LfrcCN6Z6h9mqd03SsUlTfQRVl4bvq6bYPlunDrmXbdWKRVfNNFHUHO7mypnB3abkJlsj1gcOvJGqfimiT6CfN02OSmOiN6VOjojEZtAlbODNpcnYnEopcJDE30E+bptwtU/XzV1BlVTuy4G1p3DbmN0hm86BG3VKxVvdAqECAr3iJs//3Zd0GXHZVmLhR+va2NSbkpY4lFKRX8FsE4AABjnSURBVIa26CMo3C36/hifbbXofeP6lVLxQxN9BPn76FMj/8UqP9u6Q7aizprJUikVP4JK9CKyWET2iUiZiNzXw/4kEXnJu/99EZnk3X6ZiHzk/dktIteHNvzYFu6hlbdfM4Pbr+m7jx4gO8VBeqKd5g4PNQFrxyqlYl+fiV5E7MCvgCXADOBmEemaPW4Dao0xU4AngF94t38MXGqMuRhYDPzWu9TgsNfa4eZkYzsOmwzpEoK9EREm5Fitet8HkFIqPgTTor8MKDPGHDLGtAMvAsu6lFkGPOd9vA4oFBExxjQbY1ze7cmA9gl4HauzRreMy0rCZouOBT/yvf305XU68kapeBJMU3I8UB7wvAJr0e8ey3iXHqwH8oAqEZkDPANMBL4ZkPj9RGQVsAqgqKiI5ubm/tYjYpqbmykpKen363adcgOQZWsnu+lICCKZDtDjsYI9/vQkwxvA8epG7Oe4yW46QknJ8RDEFj0Ger6iWTzWCbRe/VVYWNjrvrD3GRhj3gdmish04DkR2eRdejCwzFpgLUBxcbEpKCgId1ghU1JSwuzZs/v9uo8/OAYcYeakUdSlp4Ysnrr0iUFt60laqsFhO8zpVkO9OwFX5kQKLx4TstiiwUDPVzSLxzqB1iuUgum6OQbkBzyf4N3WYxlvH3wWUB1YwBjzKdAEXDDQYOPJUW8/eH529IxZt9vEPx3C4YYIB6OUCplgEv0OYKqITBaRROAmYEOXMhuAW72PVwBbjTHG+xoHgIhMBAqAwyGJPMb5Lnjmey+ARgtfPIcb9XKKUvGiz64bb5/7ncBmwA48Y4wpFZGHgJ3GmA3A08ALIlIG1GB9GADMA+4TkQ7AA/y/xpiqcFQk1vgS/Tk5yZxqDM/Fz3fufrDfr/GNpz/cYJgX4niUUpERVB+9MWYjsLHLtvsDHrcCN/TwuheAFwYZY9xpd3mobGjDJmdmjgyHfdeu7PdrfHfIljeBy60TnCkVD/TO2Ag4Vt+KwVrGL8EeXacgJcHOyHRrxakTDbrilFLxILqyzDAxVP3z0/72MtP+9nK/XzfB231TrvPeKBUXIn9L5jB0pGZoEv38xx8E+t+Fc05OMrsqGjlS0xKGqJRSQ01b9BFwsMq6IWzKiNCNnw8l3zTFR2tb6dB+eqVinib6CDhw2gnAeSOjM9FnJDsYlQIdbkPpiaZIh6OUGiRN9EPMYwyHqqwukWht0QNMybLm3/ngSH2EI1FKDZYm+iF2rK6NVpeHkemJZKVEfsGR3kzNthL9jqOa6JWKdXoxdoi98IE1e0RGkp2ijyojHE3vzs20/t19rJHWDjfJCfbIBqSUGjBt0Q+xU03W2PRRGYkRjuTs0hKEMRmJdLgNu481RjocpdQgaIt+iJ32JvqR6eFP9L/f+smgXj8pL4XKxnY+OFLPnEnZIYpKKTXUtEU/xE41elv0Q5DoB8s3zHKHXpBVKqZpoh9C7S4P1d71WEekR++FWJ9zcpJx2ITSyiYa27qtF6OUihGa6IfQ4ZoWjIHc1IQhmePma6tX8LXVKwb8+kSHjQvGpuMxUFKuE9QrFauCyjYislhE9olImYjc18P+JBF5ybv/fRGZ5N2+UEQ+FJG93n+vCW34scV3R+zIIWrNjzjwCSMODK6f/osTswD44LB23ygVq/pM9CJiB34FLAFmADeLyIwuxW4Dao0xU4AngF94t1cBS40xs7AWJhnWUxYfOO1L9NHfP+8zd7J1EXb7odoIR6KUGqhgWvSXAWXGmEPGmHbgRWBZlzLLgOe8j9cBhSIixphdxhjf6tKlQIqIhG8C9ih30Jvoo31oZaBZ4zLISnZwtLaVw9U6yZlSsSiYRD8eKA94XuHd1mMZY4wLqAfyupT5OlBijAnPckoxwNeij4URNz4OmzDvvBwA3i6riXA0SqmBGJJx9CIyE6s758u97F8FrAIoKiqiubl5KMIKiebmZkpKSvos1+IynGhowy4w2RzH3iQhjmQ6ANlNR7rt6WlbX+yedrKbjlBScpwJNjcAG3cf5YKEk4MLM8KCPV+xJB7rBFqv/iosLOx1XzCJ/hiQH/B8gndbT2UqvIuBZwHVACIyAXgV+JYx5mBPb2CMWQusBSguLjYFBQVBhBUdSkpKmD17dp/lrLtL9zIiPZHGjAlhi6cufWJQ2/qS3XSEuvSJFF48hqltLp4u3UFZnWHKjAvJTI7d++yCPV+xJB7rBFqvUAqm62YHMFVEJotIItbC3xu6lNmAdbEVYAWw1RhjRCQbeA24zxjzP6EKOhYdjMCF2M++egOffbXbUr79lpHkYHZ+Jm4D/6MXZZWKOX0mem+f+53AZuBT4GVjTKmIPCQi13mLPQ3kiUgZcDfgG4J5JzAFuF9EPvL+jAp5LWLAJ5XWvO6jh/BC7PZ7fsL2e34SkmPN9/fTa6JXKtYE9R3cGLMR2Nhl2/0Bj1uBbk1HY8zPgJ8NMsa4sOe4NTHY+OzwLh8YLgum5PDY1sP8z6FaOtyeqFvUXCnVO/3fOgSa2lyUnW7GYRPGZg5diz5vfyl5+0tDcqxzclKYlJtCY5tbZ7NUKsZooh8CH59owgDTRqcNaUv4+jtu4Po7Bt9H77NgitV985YOs1QqpmiiHwK+FvBF4zIiHMngXDklF4A391XjMSbC0SilgqWJfgjs8Sb6C8fHdqK/eEIGYzISqWxoZ1eFTnKmVKyI3QHRMcJjDHuPn0n078bY8MSuyx2eOyKVysZ2NpZWcUl+VoSiUkr1h7bow+zz6hYa29yMSk9kTAzNcdObWePSAXjjsyraXZ4IR6OUCoYm+jAL7LYRCfW0B0NvZHoiozMSaWxz64yWSsUITfRh5hs/f2GMX4gNdMFYq1W/sfR0hCNRSgVD++jDzNeivygCF2Jf/c0rYTnuzLHpbN1fw9sHa2lsdZERw3PfKDUcaIs+jBpaXRyqbiHBLhSMThvy968+fybV588M+XEzkx18cWIWHW7Dm/uqQ358pVRoaaIPI99om+mj00l0xNevesmMEQBs2HsqwpEopfoSX9knynxUEbluG4B5jz3AvMceCMuxF04bQXqinY+ONfonbFNKRSdN9GHkG5XyxYmZEXn/gtdeoeC18PTTpyXZ+dpF1kSkf9p5IizvoZQKDU30YfJ0cTmfnXSSYBeO17VS9FFlt5uPYt3Nl4zFJrD50ypONbZHOhylVC+CSvQislhE9olImYjc18P+JBF5ybv/fRGZ5N2eJyLbRKRJRJ4KbejR7cApa6GRKSNSccTplL7jspK55vw8XB7Dy7u0Va9UtOozA4mIHfgVsASYAdwsIjO6FLsNqDXGTAGewFofFqAV+DGwJmQRx4h9p5wAnD8qNcKRhNctl44FYN2uk7R0uCMcjVKqJ8E0NS8Dyowxh4wx7cCLwLIuZZYBz3kfrwMKRUSMMU5jzHashD9s1Ld0cKS2FRGYMjK+E/1F4zO4YGw69a0uXtMbqJSKSsHc6TIeKA94XgHM6a2MMcYlIvVAHlAVTBAisgpYBVBUVERzc3MwL4sKPa3o/u5xN8bA1CwY21YBbUMRyXTAWtS7q5629cXuaT/r67ZsP7PvogwPH5+A37x1iHzXMRJs0TvVQ0/nK9bFY51A69VfhYWFve6LilsajTFrgbUAxcXFpqCgIMIRBa+nFd3/eOQzoIZzx+ZRlz60MzzWpU/0P66aOqPbtmBlNx0J+nUTUw0jjlVQ5eyg1DWa71w+od/vN1R6Ol+xLh7rBFqvUAqm6+YYkB/wfIJ3W49lRMQBZAHD8pbJ1g43xZ/XAXD+qKG/GzbQn3+7jj//dl3Y38dmExZNt26g+t27FZxsGJKvMEqpIAWT6HcAU0VksogkAjcBG7qU2QDc6n28AthqzPBcguiDI/W0dHgYk5lIVkpUfGEaEpPzUigYnUZrh4cn/tH/riKlVPj0meiNMS7gTmAz8CnwsjGmVEQeEpHrvMWeBvJEpAy4G/APwRSRw8DjwLdFpKKHETtx5fVPrcsS0yLcmo+EL03LJdlhY/OnVew8Wh/pcJRSXkE1OY0xG4GNXbbdH/C4FehxFWpjzKRBxBdTjta2sPnTKhw28U/lG0m3X2N9pv5+6ydD8n7ZKQn878vH8+vt5fz09YM8c8sF5KXF/mIrSsW6+LyTJ0KeKT6Gx8BXZ44kJzUh0uFExK1zxjN1ZCpHa1tZ9d+lVDv1jlmlIk0TfYhU1LXyt49PYRe4bW70jjoJtySHjd/cOIPzRqRwqLpFk71SUUATfYg8U1yB28BXZo4kPyc50uFEVG5aImtvmulP9t96YS9vfFbFML0+r1TEaaIPgeP1rfz149PYhnlrPpAv2U8blcbx+jbu/ct+bn1hLyXlDZEOTalhRxP9IBlj+OW2I7g8hiUzRjIxNyXSIUWN3LREXvjWLJbMGEFaop29J5q47U8fc/N/fcTv3i3v+wBKqZAYPgO9w2TdARdvHKkmJcHGd6/Q1nxPUzFfkp/JBWPTee9wHe8druezU80cON2Ms83N6nn5pCTYIxCpUsOHJvpBeP6DY7x+xI1N4GsXjmLn0fqoGj/+zt0PRjoEvySHjSun5PKFCZlsO1DD3uNNPPfBcf5RVsNDX5nKhRFahUup4UAT/QAYY1j30Ume2GbdAbr0gpGcNyL6Zqncd+3KSIfQTWayg2WzRnFpfiZvldVyqLqF//3Hvdw6ZzzfvWKCtu6VCgPto++nU41t3FX0GT//+yEArpskzBqnrdH+Gp+dzJ++fRHfnjMOY+C/3jvG8t/v0tE5SoWBtuiD1NLu5iebyvhHWS1tLg9JDhtfLshjQVYNdZEOrhfT/vYyEJ0te4C/fXyKCdnJ3DpnHJs+qaKyoZ17/7KfS/IzufmSsSyYkkNCnK7OpdRQ0kR/FsYYjta28uc9p1i/+yQNrS4Apo5M5SszRpCR7ICmmghH2bv5jz8IRG+i95mQncxtc8ezq6KR4s/r+LC8gQ/LG8hOcbCwII8LxmYwZWQq5+alkKxdO0r127BP9IGjRDrcHiob2hmZnsjuY43sOtZAVVOHf/+E7CQun5TNtFGpiETv4hqxyCbCJfmZ3LfwXP669xR/2XuKA6ebeWXXSV7ZddJfzmETkhNsJDlsGANuj8HtMSDWMWwCOakJjExPZFRGIpNyU5g2Ko1po9MYkTY8p6VQatgn+qY2F6UnnHxS2cTxhja6dg9npziYOzmbm2aP5cBpZ2SCHEbe+KyK5AQbN84eQ2VDGwdON3O6qZ3WDg9Ha1txeQxNbW6a2npfn7auxcXn1S3dtmenOBiZ5ObC0weZkJ3MyPRE6wPB+6GQmqjfFlR8GpaJ3u0xvH2wlnW7Kin+vA5fbhdgVHoi47KSGJeVxDk5yeSlJSAimuQjYExmEmMyk/zPjTF4jPXNq8NjEM604q394DaG5nYPjW0uGlpdnG5sp7KxnZON7dS1uKhrgQMfnezx/TKS7IzLSubcESmcNyKVc0ekMm1UKmMzk/QbnOqVy2PweAwGsAlReV0pqEQvIouBfwfswO+NMY902Z8EPA9cgrWy1I3GmMPefT8CbgPcwPeNMZtDFn0/GGM4cLqZt8pqWL/7JJUN1kRbNoGpI1K5YFw6U0akkuiIvpOkLCKCXcBus3O22YTSk2BURufpkY0xNLa5aa2q4LA7h/oWF41tLpra3DS2uWhsddPY5mbfKSf7TnX+UM9IsjNlZCrn5KSQn5PM+KxkslIcZCbbSU9yIN5uIwEM+L8V2m1WV5PDbiMlwUayw6YfGF00tLo4VNXMwapmKuraaGh1Ud/SQUuHh/bmdiaeOkhWioMRAd+88tISyE1NCOv1GmMMbS4P9S0uq6HQ0EZlQxsnGtuobGjnZGMbdc0u6ls7aG73dHptWqKdnFQHuakJLJkxkovGZzB1VBqOCK6l3GeiFxE78CtgIdbC4DtEZIMxJnCS89uAWmPMFBG5CfgFcKN3kZGbgJnAOOBNETnfGNP79+4Bqmpqp7KhjQ6Pod3loc3loaqpg5ONbRyrb+ODI/Wcbjozi2J+djI3fGEMYPQr+zAgImQmOzgnWxjVwzq+xhhaOjzUNHdwuqmdqqYOTjVZ/8Eb29zsqmhkV0XjoGKwCaQm2ElLspOeZCct0eH919qWkmAnJcFGSoKdBLsgAnYRPAZcHo+35Wh9awn8MDlV6WJPxzES7TYSHUKi3UaCXUjw/msTEKx/Pcb6MPIYg8ttcHkMHW6P91/rx/fNyWC835gEuw3sNu9j3zcorOO5PWeO43tsXTvxxWodt9X7/7Kh1UVlg5UwfQMcerPzZM/fvgBSEmykJzlIT7STmmQnwSbYfT8i3g/gM7H6rum0u61Y290eOlyGDo8Hl9vgNtbvpd3lobndjTvIUb4CiFj/egBnuxtnu5uKujb2HG8CIDnBxpQRqUzKSyGx1UW54yRpSdb5T7TbsInVKEhw2MKyaFEwLfrLgDJjzCEAEXkRWAYEJvplwIPex+uAp8RquiwDXjTGtAGfe1egugwoDk34Z/z141M8+dbRs5YZkZ7A/5qcw8KCPOZOzsYm0uMt+2r4ERFSE+2kJtqZkH3m+4Ix1jWB0852aptd1Dg7aGh10dLhoc3lps1lJTJ/S977Hx6sJOgxVtLr8CbVpnY3Te1uTg7uM6O7g7G5fGOCXRiRlsCI9ETyUhNITbSTnGAj0W7D3nSSGnsezR2B37pcONvdNLe5aenw0NLRzukwxWYXSE6wk5lsfXvLTHaQleIgK9lBRrKDNO/fS6Jd/N/UjDE0tLqpae6g2tmOw25j97FGymtb+fhEEx+fsBL/+rKDPb5nbmoCW/7piyGvi/R1c4qIrAAWG2Nu9z7/JjDHGHNnQJmPvWUqvM8PAnOwkv97xpg/eLc/DWwyxqzr8h6rgFUAr7322rSkpKR9oale+NXU1IzIzc2tinQcoab1ih3xWCfQeg1AVWFh4eKedkTFxVhjzFpgbaTjGAgR2WmMuTTScYSa1it2xGOdQOsVSsFceTwG5Ac8n+Dd1mMZEXEAWVgXZYN5rVJKqTAKJtHvAKaKyGQRScS6uLqhS5kNwK3exyuArcbqE9oA3CQiSSIyGZgKfBCa0JVSSgWjz64bY4xLRO4ENmMNr3zGGFMqIg8BO40xG4CngRe8F1trsD4M8JZ7GevCrQv4P+EYcRNhMdnlFAStV+yIxzqB1itk+rwYq5RSKrbp3UFKKRXnNNErpVSc00QfJBHJF5FtIvKJiJSKyA+823NF5A0ROeD9NyfSsQ6EiNhFZJeI/M37fLKIvC8iZSLykvdCfEwRkWwRWScin4nIpyIyNx7Ol4j80Ps3+LGI/LeIJMfi+RKRZ0TklPc+HN+2Hs+PWJ701m+PiMyOXORn10u9/j/v3+EeEXlVRLID9v3IW699IrIoHDFpog+eC7jHGDMDuBz4P94pHu4DthhjpgJbvM9j0Q+ATwOe/wJ4whgzBajFmuYi1vw78LoxpgC4CKt+MX2+RGQ88H3gUmPMBVgDJHzTjsTa+XoW6HqDT2/nZwnWqL2pWDdX/nqIYhyIZ+lerzeAC4wxFwL7gR8BdJkmZjHwn95pZ0JKE32QjDEnjDEl3seNWEljPNY0D895iz0HfC0yEQ6ciEwAvgr83vtcgGuwprOAGKyXiGQBC7BGhGGMaTfG1BEH5wtrtFyK956VVOAEMXi+jDFvY43SC9Tb+VkGPG8s7wHZIjJ2aCLtn57qZYz5uzHGN7HPe1j3FEHANDHGmM8B3zQxIaWJfgBEZBLwBeB9YLQx5oR3VyUwOkJhDcYvgXux5mQCyAPqAv4wK7A+1GLJZOA08F/eLqnfi0gaMX6+jDHHgH8DjmIl+HrgQ2L/fPn0dn7GA+UB5WK5jt8BNnkfD0m9NNH3k4ikA0XAXcaYhsB93pvEYmq8qohcC5wyxnwY6VhCzAHMBn5tjPkC4KRLN02Mnq8crFbgZKwZYdPo3k0QF2Lx/PRFRP4vVjfwH4fyfTXR94OIJGAl+T8aY9Z7N5/0fYX0/nsqUvEN0P8CrhORw8CLWF0A/4711dh3Q10sTl1RAVQYY973Pl+Hlfhj/Xx9CfjcGHPaGNMBrMc6h7F+vnx6Oz8xP52KiHwbuBa4xZy5gWlI6qWJPkjefuungU+NMY8H7Aqc/uFW4C9DHdtgGGN+ZIyZYIyZhHVRaKsx5hZgG9Z0FhCb9aoEykVkmndTIdYd2jF9vrC6bC4XkVTv36SvXjF9vgL0dn42AN/yjr65HKgP6OKJemIt3nQvcJ0xpjlg19BME2O8CwPoz9l/gHlYXyP3AB95f76C1Z+9BTgAvAnkRjrWQdTxKuBv3sfnev/gyoBXgKRIxzeA+lwM7PSesz8DOfFwvoCfAJ8BHwMvAEmxeL6A/8a6ztCB9Q3stt7OD9Y0/78CDgJ7sUYdRbwO/ahXGVZfvC93/Cag/P/11msfsCQcMekUCEopFee060YppeKcJnqllIpzmuiVUirOaaJXSqk4p4leKaXinCZ6pZSKc5rolVIqzv3/2SIfBw8IUKkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax_box, ax_hist) = plt.subplots(\n",
    "    2, sharex=True, gridspec_kw= {\"height_ratios\": (0.2, 1)}\n",
    ")\n",
    "mean = years.mean()\n",
    "median = years.median()\n",
    "mode = years.mode()[0]\n",
    "\n",
    "sns.boxplot(years, ax=ax_box)\n",
    "ax_box.axvline(mean, color='r', linestyle='--')\n",
    "ax_box.axvline(median, color='g', linestyle='-')\n",
    "ax_box.axvline(mode, color='b', linestyle='-')\n",
    "\n",
    "sns.distplot(years, ax=ax_hist)\n",
    "ax_hist.axvline(mean, color='r', linestyle='--')\n",
    "ax_hist.axvline(median, color='g', linestyle='-')\n",
    "ax_hist.axvline(mode, color='b', linestyle='-')\n",
    "\n",
    "plt.legend({'Mean':mean,'Median':median,'Mode':mode})\n",
    "\n",
    "ax_box.set(xlabel='')\n",
    "sns.despine()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3      36\n",
       "5      30\n",
       "7      32\n",
       "8      34\n",
       "11     36\n",
       "       ..\n",
       "663    36\n",
       "665    38\n",
       "666    29\n",
       "670    38\n",
       "671    28\n",
       "Length: 20512, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['bdate'].str.len() > 5].apply(to_years, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3     1983\n",
       "5     1989\n",
       "7     1987\n",
       "8     1985\n",
       "11    1983\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def to_datetime(row):\n",
    "    return dt.strptime(row['bdate'], '%d.%m.%Y').year\n",
    "\n",
    "df[df['bdate'].str.len() > 5].apply(to_datetime, axis=1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnt_males = (df['sex'] == 2).sum()\n",
    "cnt_fems = (df['sex'] == 1).sum()\n",
    "\n",
    "labels = [\"М\", \"Ж\"]\n",
    "sizes = [cnt_males, cnt_fems] # будет отмасштабировано\n",
    "colors = [\"lightblue\", \"pink\"]\n",
    "\n",
    "plt.pie(sizes, colors=colors, startangle=-40)\n",
    "plt.axis(\"equal\")\n",
    "plt.legend(labels, loc=(1, 0.5))\n",
    "plt.title(\"89% клуба -- женщины\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEJCAYAAACNNHw2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAXVUlEQVR4nO3df5Dc9X3f8ef7fkin0887wEIgIUAiyDYEfHawY1yX5mKXJjSkgyfBHjvgxJYniafulGmD3Zna08QTNzN12zQdU1pT08Z2yER2kW3SmmDXmFio4AsgfhgQIIEEQkJ3EjqdTvfr0z92VzrdD92vvd397D4fMxp2v/vd/b7vo69efO/z/Xw+GyklJEn5aap2AZKk+THAJSlTBrgkZcoAl6RMGeCSlKmWSh7skUceSZs3b67kIdm1axdXXnllRY9Z62yTyWyTqdkuk1WjTTo6OmKq7RW9Ah8ZGank4QAYHh6u+DFrnW0ymW0yNdtlslpqE7tQJClTBrgkZcoAl6RMGeCSlCkDXJIyZYBLUqYMcEnKlAEuSZkywCUpUxWdSi/Vqr6BYbY9dmDS9puuPr8K1Uiz4xW4JGXKAJekTBngkpQpA1ySMmWAS1KmDHBJypQBLkmZMsAlKVMGuCRlygCXpEwZ4JKUKQNckjJlgEtSpgxwScqUAS5JmTLAJSlTBrgkZcoAl6RMzRjgEbEhIn4YEU9HxFMR8Zni9s6IuD8ini/+t2Pxy5UklczmCnwEuC2l9DbgPcDvR8TbgNuBB1JKlwEPFJ9LkipkxgBPKb2WUuopPj4GPANcCNwI3F3c7W7g1xerSEnSZJFSmv3OERcDDwJXAC+nlNYUtwfQV3o+4T1bga0A27Zte+fq1asXXvUcDAwM0N7eXtFj1jrbZLI3+48z2rRk0vaO9tYqVFM7PFcmq0abdHd3x1TbZx3gEbEC+BHwxZTStyLiyPjAjoi+lNJZ+8F37NiRtmzZMoeyF66np4eurq6KHrPW2SaTPfDQTo6s2Dhp+01Xn1+FamqH58pk1WiTjo6OKQN8VqNQIqIV2AZ8PaX0reLm1yNiXfH1dcDBchQqSZqd2YxCCeCrwDMppS+Pe2k7cEvx8S3AveUvT5I0nZZZ7HMt8DFgV0Q8Vtz2OeBLwF9GxO8Ae4HfWJwSJUlTmTHAU0oPAVP2vwDd5S1HkjRbzsSUpEwZ4JKUKQNckjJlgEtSpgxwScqUAS5JmTLAJSlTBrgkZcoAl6RMGeCSlCkDXJIyZYBLUqYMcEnKlAEuSZkywCUpUwa4JGXKAJekTBngkpQpA1ySMmWAS1KmDHBJypQBLkmZMsAlKVMGuCRlygCXpEwZ4JKUKQNckjJlgEtSpgxwScqUAS5JmTLAJSlTBrgkZcoAl6RMGeCSlCkDXJIyZYBLUqYMcEnK1IwBHhF3RcTBiHhy3LYvRMT+iHis+OdXFrdMSdJEs7kC/xpw/RTb/31K6erin/vKW5YkaSYzBnhK6UGgtwK1SJLmIFJKM+8UcTHw3ZTSFcXnXwBuBd4EHgVuSyn1TfPercBWgG3btr1z9erVZSh79gYGBmhvb6/oMWudbTLZm/3HGW1aMml7R3trFaqpHZ4rk1WjTbq7u2Oq7fMN8LXAG0AC/hBYl1L67Zk+Z8eOHWnLli2zr7oMenp66Orqqugxa51tMtkDD+3kyIqNk7bfdPX5VaimdniuTFaNNuno6JgywOc1CiWl9HpKaTSlNAb8V+CahRQnSZq7eQV4RKwb9/SfAE9Ot68kaXG0zLRDRHwTuA44NyL2AZ8HrouIqyl0oewBPrWINUqSpjBjgKeUPjzF5q8uQi2SpDlwJqYkZcoAl6RMGeCSlCkDXJIyZYBLUqYMcEnKlAEuSZkywCUpUwa4JGXKAJekTBngkpQpA1ySMmWAS1KmDHBJypQBLkmZMsAlKVMGuCRlygCXpEwZ4JKUKQNckjJlgEtSpgxwScqUAS5JmTLAJSlTBrgkZcoAl6RMGeCSlCkDXJIyZYBLUqYMcEnKlAEuSZkywCUpUwa4JGXKAJekTBngkpQpA1ySMtVS7QKkanr6QD/7jwyy643EQH8/69e0sbLNfxbKw4xnakTcBdwAHEwpXVHc1gncA1wM7AF+I6XUt3hlSuX36tFBPnr3E6RTWw6yduUSPvne9VWsSpq92XShfA24fsK224EHUkqXAQ8Un0tZeblvkAR0trdyRWdh2+Hjw6SUzvo+qVbMGOAppQeB3gmbbwTuLj6+G/j1MtclLbre48MAvOuiVdz61mZam4ORscTQqAGuPMy3s29tSum14uMDwNrpdoyIrcBWgG3btjEwMDDPQ87PwMAAPT09FT1mrbNNCh7fOwLA6PEjNHcMsaKlmb5RaO57mTXLAoCenlerWWLVea5MVo026e7unnL7gu/WpJRSREx7yZJSuhO4E2DHjh1py5YtCz3knPT09NDV1VXRY9Y626Tgx2/uBfaz5eILGR3bT3tbC30nT/Jay/m0rGgDoPvq86tbZJV5rkxWS20y32GEr0fEOoDifw+WrySpMnqPDwHQubwVgPYlzQAcHxqtWk3SXMw3wLcDtxQf3wLcW55ypMrpHSj0gXe2FwJ8uQGuzMwY4BHxTWAHcHlE7IuI3wG+BHwgIp4Hfrn4XMrK4WKAn1O8Al++tBjgJw1w5WHGPvCU0oeneWnqXnUpE33HT1+BH+T0FfiAV+DKhFPp1ZBSSqeuwO1CUa4McDWk/qFRhkcTy1qbWFYMbgNcuTHA1ZBKk3hK/d8wrg/cAFcmDHA1pNIIlI72cQG+xJuYyosBroZ0+NQV+JJT25a1NhEBgyNjjI45nV61zwBXQypdgZ8z7go8IljeajeK8mGAqyGVZmGO70IB+8GVFwNcDal3YPJNTDg9nX7AfnBlwABXQzp8/Mwx4CWlG5n9XoErAwa4GlJpGGHn8qkD3C4U5cAAV0OauJBVSakP3On0yoEBroY01UQeGNeFYh+4MmCAq+GcHBmjf2iUlqaY9A30LmilnBjgajjjZ2E2RZzxmn3gyokBrobTO80IFHAcuPJigKvh9A4UJvFM7P+GM79WLSWn06u2GeBqONMNIQRoaQraWppICU4Mj1W6NGlODHA1nIlf5DCRX26sXBjgajinhhBOE+D2gysXBrgazqlRKFN0oYDrgisfBrgazoxX4I4FVyYMcDWcU9PoZ7gCd0Er1ToDXA3n9DooS6Z83fVQlAsDXA1ldCzRd2omZsuU+9gHrlwY4GooR08MM5ZgdVsLrc1Tn/5Op1cuDHA1lJn6v8EAVz4McDWU3oERYPJ3YY7nOHDlwgBXQ5lpCCHAkuaguSkYHk2cMMRVwwxwNZTZdKFEBMuXFP5p9J0Yrkhd0nwY4GoofePWAj+bUj946YpdqkUGuBrKdN+FOVFpQavS/lItMsDVUGZ7BV4K8D4DXDXMAFdDme0V+HKvwJUBA1wNxQBXPTHA1VD6zvJtPOOd7kIZWfSapPkywNUwhkbG6B8apaUpWFmcrDMdR6EoBwa4Gsb4G5gRcdZ9SwHuOHDVsqmXY5uliNgDHANGgZGU0rvKUZS0GHpnWIVwvHavwJWBBQV40T9IKb1Rhs+RFtVsb2ACtBdnYvYODJNSmvGKXaoGu1DUMOYS4K3NTSxpDkbGEv2uC64atdAAT8D3I+KnEbG1HAVJi6U0AmWmSTwlDiVUrVtoF8r7Ukr7I+ItwP0R8bOU0oPjdygG+1aAbdu2MTAwsMBDzs3AwAA9PT0VPWata9Q2eWZPIYgHjxyip6fvjNeax4ZY07/3jG2rWkbpAx5+7CkOr2nMX1Yb9Vw5m2q0SXd395TbFxTgKaX9xf8ejIhvA9cAD07Y507gToAdO3akLVu2LOSQc9bT00NXV1dFj1nrGrVN7n3teeAQb9+8ka6r1p7x2gMP7eTIio1nbFvadgCODXDe+kvo+rlzKlhp7WjUc+VsaqlN5n1ZERHLI2Jl6THwQeDJchUmldtslpIdzwWtVOsWcgW+Fvh28e58C/CNlNL/LktV0iKYy01MOP3NPA4lVK2ad4CnlF4EripjLdKimnOAewWuGteYd2bUcFJKHCmuazLbAHdJWdU6A1wN4cTwGIMjY7S1NLFsydnXQSkpTeYxwFWrDHA1hN5ZfpHDeHahqNYZ4GoIvbNcRnY8A1y1zgBXQ5jrDUyA9tZCgB8ZGGF0LC1KXdJCGOBqCH3zCPCmpmDNshYScHTQL3ZQ7THA1RDmspTseB3LCoHf51hw1SADXA1htt9GP1FHsc/cfnDVIgNcDWE+feDj9zfAVYsMcDWEU6NQDHDVEQNcDWGuC1mVnApw+8BVgwxwNYTSlxPP9Qq81AfulxurFhngqntj49ZBWbNsjgG+rDBqxen0qkUGuOrescERRsYSK5Y2s6Rlbqd8qcvFLhTVIgNcdW++I1DGv8ebmKpFBrjqngGuemWAq+4d7p/fJB6AlW0ttDQF/SdHGRweLXdp0oIs9FvppZr3Uu8JAC7qaJvze5siWL+mjT29J9jbO8jla5eXuzzVqG2PHZhy+yUVruNsvAJX3XvxjQEANp3XPq/3bzp3GQC7i58j1QoDXHVv96FC8G4+d34BvrkY/C8Y4KoxBrjq2tDIGC/3DRLAJecsm9dnbCoG/wuHDHDVFgNcdW1v3wlGxhLrO9poa53dd2FOVApwu1BUawxw1bUXDhVuYM63+wRgQ0cbrc3Bq0dPMjDkSBTVDgNcda3Ub71pAQHe2tzExZ2F7pcXvQpXDTHAVddK3R6b5zkCpaT0frtRVEsMcNW101fg87uBWXKqH9wbmaohBrjq1onhUfb1DdLSFGzsLE+AO5RQtcQAV9166fAJErCxs43W5oWd6qfHgp8oQ2VSeTiVXnWr1N2xkBuYJResXkpbaxOH+oc4emKY1XNcV1x5G0uJg8eGODY4wt7BEfa1HuSGK86jKaKqdRngqlsvlmEESklTBJees4ynDxznhTdO0LXBAG8Uo2OJP3/kNV45Mnh64zO7ef3YST753g3VKwy7UFTHyjUCpWSzE3oa0g+e6+WVI4Msa23i0nOW8e7zmwjgKz9+hZ+82FfV2gxw1a0XytiFAqcXw3JKfePYfWiAnXuPEgG/2XU+H3nXOrZeuYRPXbuBBHzuO8/z6tHBGT9nsRjgqkv9J0c4cGyIpS1NrF8z92Vkp+JIlMZy7OQI2588CMB1mzvPOI8+ee16/t6mDo4OjnDbt5+t2lrxBrjqUmm0yCXnLKO5qTw3mjaPC/CUUlk+U7VpdCyxfdchBobGuLhzGe+9ZPUZrzdF8Ec3XMb6NUv52evH+Y8/2luVOg1w1aWde44A5es+AXjLyiWsXNrMkRMjPHvweNk+V7Xnrof38dLhE7QvaeLGnz+PmGK0yaq2Fv7kxstpaQr+4qcH+PELle8PN8BVdw68eZKv7dwPwA1XnFe2z40IbrjiLQD8yd+85FV4nXr05aPc8dArANx45VtYuXT6wXpvPX8Fv//+iwD4wn276T0+VJEaSwxw1Z0v/2APJ4bH6P65Tt5z8Zqyfvbvvm8Dne2t/N2+Y3zvqUNl/WxVX+/xIT77necYS3DtpWtm9Rvcb11zAb9w0Sp6B4b5/H27K/o/dgNcdeXhPUe4/9nDtLU2cVt3+b+9cGVbC5+5biMA/+H/7uXYyZGyH0PVcfDYEP/i3ud4o3+Yd6xfxd/f1DGr9zVF8Ie/ehmr2lp46MUj3L79uYqdFwsK8Ii4PiKejYjdEXF7uYqS5mN4dIx/e/9LAHziF9ezbtXSRTnODVecx1UXruTw8WHu+PEri3IMVc5YSnzrsde56at/R88rb9LR3sIf/+PLaJrDze+1q5byxRsuY1lrE9//2WFu/u+P88T+Y4tYdcG8Z2JGRDPwn4EPAPuARyJie0rp6XIVV/LCoQFOjo6den7mbyjpjO0Tf3l58egYra+ebsgAzrwfcfpJlWfFVsyeN8doO9Bf7TLmrPD3XvgbHktwcmSM4dHEof4hdrx0hIf3HOHIiRE2drbxsV+4YNHqaIrgsx+4lI/c/Tjf+Olr/O1LfVx7SQddG1axfGkzS1uaaG2K4vlUOKlyPbdyPVdKSudMKRtGxhInR8YYGhlj35GTPH/oOE++2n9qctb7N3Xw2Q9eytp5/M//fZs6+OatV/HZ7c/xzOvH+fjXd7Fl7XKuvGAlV6xbwTvWr+LCMg1pLVnIVPprgN0ppRcBIuIvgBuBsgf4v7z3WV48vIBFhP7frvIVUy92PlHtChbFho42/uhXL2NJy+L2Dl6+djm3/dIlfOWhl9nbO8je3tf4xk9fW9RjVk2dnivjdbS38Ae/fCkf3HLOlCNOZmtj5zK+9tEr+bMHX+Ybj77K0weO8/SB49wDfOjqtfyrf7ipfEUDMd8O94j4EHB9SukTxecfA96dUvr0hP22AlsBvve9712+dOnSZxdW8tz09vae29nZ+UYlj1nrbJPJbJOp2S6TValN3uju7r5+4sZFX8wqpXQncOdiH2c6EfFoSuld1Tp+LbJNJrNNpma7TFZLbbKQ3zP3A+OX4lpf3CZJqoCFBPgjwGURcUlELAFuBraXpyxJ0kzm3YWSUhqJiE8D/wdoBu5KKT1VtsrKp2rdNzXMNpnMNpma7TJZzbTJvG9iSpKqy5mYkpQpA1ySMlU3AT7TtP6IWBoR9xRf3xkRF1e+ysqaRZvcGhGHIuKx4p9PVKPOSoqIuyLiYEQ8Oc3rERF/WmyzJyKiq9I1Vtos2uS6iDg67jz515WusdIiYkNE/DAino6IpyLiM1PsU/1zJaWU/R8KN1FfAC4FlgCPA2+bsM/vAXcUH98M3FPtumugTW4F/qzatVa4Xd4PdAFPTvP6rwB/TWEO/HuAndWuuQba5Drgu9Wus8Jtsg7oKj5eCTw3xb+fqp8r9XIFfmpaf0ppCChN6x/vRuDu4uO/ArpjIXNma99s2qThpJQeBHrPssuNwP9IBQ8DayJiXWWqq45ZtEnDSSm9llLqKT4+BjwDXDhht6qfK/US4BcC45eF28fkxj61T0ppBDgKnFOR6qpjNm0CcFPx17+/iogNU7zeaGbbbo3mFyPi8Yj464h4e7WLqaRid+s7gJ0TXqr6uVIvAa75+Q5wcUrp54H7Of0bijReD7AxpXQV8J+A/1XleiomIlYA24B/llJ6s9r1TFQvAT6baf2n9omIFmA1cLgi1VXHjG2SUjqcUjpZfPrfgHdWqLZa5hIRE6SU3kwp9Rcf3we0RsS5VS5r0UVEK4Xw/npK6VtT7FL1c6VeAnw20/q3A7cUH38I+EEq3omoUzO2yYT+ul+j0M/X6LYDv1UcYfAe4GhKqU7XiZ2diDi/dL8oIq6hkBv1fPFD8ef9KvBMSunL0+xW9XNl0VcjrIQ0zbT+iPg3wKMppe0U/jL+Z0TspnDD5ubqVbz4Ztkm/zQifg0YodAmt1at4AqJiG9SGFVxbkTsAz4PtAKklO4A7qMwumA3MAB8vDqVVs4s2uRDwO9GxAhwAri5zi9+AK4FPgbsiojHits+B1wEtXOuOJVekjJVL10oktRwDHBJypQBLkmZMsAlKVMGuCRlygCXpEwZ4JKUKQNckjJlgKshRMQfRMT+iDhW/JKL7ohoiojbI+KFiDgcEX8ZEZ3F/X8zIl6KiFXF5/8oIg5ExHnV/Umk05yJqboXEZcDfwO8O6X0anF50GbgBuDDFKaKHwL+FFiVUvpw8X1fB4aB24BdwNaU0ncr/gNI0zDAVfciYjPwE+AjwI9SSsPF7c8An04pPVB8vg54GVhWXEtmDfAEhbXjf5JS+lRVfgBpGga4GkJEfITC1+q9ncICX/+cwiJEI8DYuF3bgE0ppf3F9/274r6Xp5Seq2jR0gwMcDWUYp/2f6EQ3NcAv51S+ttp9r0a+CGF7z3sTCldX7FCpVnwJqbqXkRcHhG/FBFLgUEKS6KOAXcAX4yIjcX9zouIG4uP24A/p7CE6MeBCyPi96ryA0jTqIv1wKUZLAW+BLyVwk3JnwBbgQMUvlH8+xFxAXAQuAe4F/hj4JWU0lcAIuKjwA8j4v6U0vOV/xGkyexCkaRM2YUiSZkywCUpUwa4JGXKAJekTBngkpQpA1ySMmWAS1KmDHBJytT/BwrLgGLB2johAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['sex'])\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23165"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df['bdate'].isna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.violinplot(data=df, y='sex', df['id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Демографическая картина"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "today = dt(2020, 2, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_age(bdate: str) -> int:\n",
    "    born = dt.strptime(bdate, '%d.%m.%Y')\n",
    "    age_raw = today.year - born.year\n",
    "    age_corr = (today.month, today.day) < (born.month, born.day) \n",
    "    return age_raw - age_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alumna (Specialist)        2509\n",
       "Student (Specialist)        705\n",
       "Alumnus (Specialist)        296\n",
       "Alumna (Bachelor's)         270\n",
       "Alumna (Master's)           246\n",
       "Student (Bachelor's)        227\n",
       "Candidate of Sciences        57\n",
       "Student (Master's)           47\n",
       "PhD                          47\n",
       "Alumnus (Master's)           46\n",
       "Alumnus (Bachelor's)         39\n",
       "Undergraduate applicant      36\n",
       "Postgraduate Student         31\n",
       "Intern                        2\n",
       "Resident                      1\n",
       "Name: education_status, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['education_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4559"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['education_status'].value_counts().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = defaultdict(int)\n",
    "\n",
    "def has_upper_education(status):\n",
    "    if pd.isna(status):\n",
    "        return 0\n",
    "    has_upper_education = any(\n",
    "        status.startswith(cat)\n",
    "        for cat in ('Alumn', 'Candidate', 'PhD')\n",
    "    )\n",
    "    return int(has_upper_education)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "upper_edu_stats = [\n",
    "    has_upper_education(s) for s in df['education_status'].values\n",
    "]\n",
    "upper_edu_stats = pd.Series(upper_edu_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    67456\n",
       "1     3510\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper_edu_stats.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04971248902359573"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3510 / (67456 + 3150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_years_sex_edu(row):\n",
    "    first = get_age(row['bdate'])\n",
    "    second = row['sex']\n",
    "    third = has_upper_education(row['education_status'])\n",
    "    return pd.Series([first, second, third])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "years_sex_edu = df[\n",
    "    df['bdate'].str.len() > 5\n",
    "].apply(to_years_sex_edu, axis=1)\n",
    "years_sex_edu.columns = ['age', 'sex', 'has_upper_edu']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>sex</th>\n",
       "      <th>has_upper_edu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  sex  has_upper_edu\n",
       "3    36    1              0\n",
       "5    30    1              0\n",
       "7    32    1              0\n",
       "8    34    1              1\n",
       "11   36    2              1"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years_sex_edu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "years_sex_edu['sex_cat'] = (\n",
    "    years_sex_edu['sex']\n",
    "    .map({\n",
    "        0: pd.NA,\n",
    "        1: 'F',\n",
    "        2: 'M'\n",
    "    })\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_to_plot = years_sex_edu[years_sex_edu['age'] < 70]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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": [
    "\n",
    "sns.violinplot(\n",
    "    x='has_upper_edu',\n",
    "    y='age',\n",
    "    data=df_to_plot.dropna(),\n",
    "    inner='quartile',\n",
    "    scale='count',\n",
    "    hue='sex_cat',\n",
    "    split=True,\n",
    "    palette=\"Set1\",\n",
    "    height=40\n",
    ")\n",
    "sns.despine()\n",
    "plt.xticks([0, 1], [\"Нет\", \"Есть\"])\n",
    "plt.xlabel(\"Высшее образование\")\n",
    "plt.ylabel(\"Возраст\")\n",
    "plt.title(' '.join([\n",
    "    'Наличие высшего образования не вносит',\n",
    "    'существенных отличий в диаграму',\n",
    "]))\n",
    "# plt.legend([\"Жо\", \"Мэ\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "projector2020-poetry",
   "language": "python",
   "name": "projector2020-poetry"
  },
  "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"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}