{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#  matplotlib.style.use('ggplot')\n",
    "%matplotlib inline\n",
    "from scipy import stats\n",
    "import os\n",
    "# plt.style.use('dark_background')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data_test.csv\", sep = ',', encoding='cp1251')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>battle_id</th>\n",
       "      <th>unit_1</th>\n",
       "      <th>unit_2</th>\n",
       "      <th>unit_3</th>\n",
       "      <th>unit_4</th>\n",
       "      <th>len</th>\n",
       "      <th>balance_mark</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>103921</td>\n",
       "      <td>топор</td>\n",
       "      <td>лук</td>\n",
       "      <td>булава</td>\n",
       "      <td>лук</td>\n",
       "      <td>17.35</td>\n",
       "      <td>before</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>117214</td>\n",
       "      <td>посох</td>\n",
       "      <td>посох</td>\n",
       "      <td>лук</td>\n",
       "      <td>лук</td>\n",
       "      <td>21.84</td>\n",
       "      <td>before</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>115502</td>\n",
       "      <td>меч</td>\n",
       "      <td>посох</td>\n",
       "      <td>булава</td>\n",
       "      <td>топор</td>\n",
       "      <td>16.00</td>\n",
       "      <td>before</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>110102</td>\n",
       "      <td>булава</td>\n",
       "      <td>лук</td>\n",
       "      <td>лук</td>\n",
       "      <td>булава</td>\n",
       "      <td>17.22</td>\n",
       "      <td>before</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104989</td>\n",
       "      <td>меч</td>\n",
       "      <td>посох</td>\n",
       "      <td>булава</td>\n",
       "      <td>посох</td>\n",
       "      <td>19.20</td>\n",
       "      <td>before</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   battle_id  unit_1 unit_2  unit_3  unit_4    len balance_mark\n",
       "0     103921   топор    лук  булава     лук  17.35       before\n",
       "1     117214   посох  посох     лук     лук  21.84       before\n",
       "2     115502     меч  посох  булава   топор  16.00       before\n",
       "3     110102  булава    лук     лук  булава  17.22       before\n",
       "4     104989     меч  посох  булава   посох  19.20       before"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>battle_id</th>\n",
       "      <th>unit_1</th>\n",
       "      <th>unit_2</th>\n",
       "      <th>unit_3</th>\n",
       "      <th>unit_4</th>\n",
       "      <th>len</th>\n",
       "      <th>balance_mark</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>103921</td>\n",
       "      <td>топор</td>\n",
       "      <td>лук</td>\n",
       "      <td>булава</td>\n",
       "      <td>лук</td>\n",
       "      <td>17.35</td>\n",
       "      <td>before</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>117214</td>\n",
       "      <td>посох</td>\n",
       "      <td>посох</td>\n",
       "      <td>лук</td>\n",
       "      <td>лук</td>\n",
       "      <td>21.84</td>\n",
       "      <td>before</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>115502</td>\n",
       "      <td>меч</td>\n",
       "      <td>посох</td>\n",
       "      <td>булава</td>\n",
       "      <td>топор</td>\n",
       "      <td>16.00</td>\n",
       "      <td>before</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>110102</td>\n",
       "      <td>булава</td>\n",
       "      <td>лук</td>\n",
       "      <td>лук</td>\n",
       "      <td>булава</td>\n",
       "      <td>17.22</td>\n",
       "      <td>before</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104989</td>\n",
       "      <td>меч</td>\n",
       "      <td>посох</td>\n",
       "      <td>булава</td>\n",
       "      <td>посох</td>\n",
       "      <td>19.20</td>\n",
       "      <td>before</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19995</th>\n",
       "      <td>108834</td>\n",
       "      <td>посох</td>\n",
       "      <td>булава</td>\n",
       "      <td>лук</td>\n",
       "      <td>посох</td>\n",
       "      <td>26.37</td>\n",
       "      <td>after</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>119941</td>\n",
       "      <td>посох</td>\n",
       "      <td>меч</td>\n",
       "      <td>лук</td>\n",
       "      <td>посох</td>\n",
       "      <td>23.73</td>\n",
       "      <td>after</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>112911</td>\n",
       "      <td>топор</td>\n",
       "      <td>посох</td>\n",
       "      <td>булава</td>\n",
       "      <td>топор</td>\n",
       "      <td>27.73</td>\n",
       "      <td>after</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>111684</td>\n",
       "      <td>меч</td>\n",
       "      <td>топор</td>\n",
       "      <td>посох</td>\n",
       "      <td>меч</td>\n",
       "      <td>24.20</td>\n",
       "      <td>after</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>103437</td>\n",
       "      <td>лук</td>\n",
       "      <td>лук</td>\n",
       "      <td>булава</td>\n",
       "      <td>булава</td>\n",
       "      <td>22.83</td>\n",
       "      <td>after</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       battle_id  unit_1  unit_2  unit_3  unit_4    len balance_mark\n",
       "0         103921   топор     лук  булава     лук  17.35       before\n",
       "1         117214   посох   посох     лук     лук  21.84       before\n",
       "2         115502     меч   посох  булава   топор  16.00       before\n",
       "3         110102  булава     лук     лук  булава  17.22       before\n",
       "4         104989     меч   посох  булава   посох  19.20       before\n",
       "...          ...     ...     ...     ...     ...    ...          ...\n",
       "19995     108834   посох  булава     лук   посох  26.37        after\n",
       "19996     119941   посох     меч     лук   посох  23.73        after\n",
       "19997     112911   топор   посох  булава   топор  27.73        after\n",
       "19998     111684     меч   топор   посох     меч  24.20        after\n",
       "19999     103437     лук     лук  булава  булава  22.83        after\n",
       "\n",
       "[20000 rows x 7 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['топор', 'посох', 'меч', 'булава', 'лук'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# посмотрим на оружие\n",
    "df.unit_1.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# проверим, что нашли все оружие\n",
    "set(df.unit_1.unique()) == set(df.unit_2.unique()) and set(df.unit_1.unique()) == set(df.unit_3.unique()) \\\n",
    "     and set(df.unit_1.unique()) == set(df.unit_4.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "unit_1  unit_2  unit_3  unit_4  balance_mark\n",
       "булава  булава  булава  булава  after           19.811667\n",
       "                                before          18.518125\n",
       "                        лук     after           24.562381\n",
       "                                before          21.400588\n",
       "                        меч     after           20.456667\n",
       "                                                  ...    \n",
       "топор   топор   топор   меч     before          20.541500\n",
       "                        посох   after           24.838462\n",
       "                                before          20.475500\n",
       "                        топор   after           19.969167\n",
       "                                before          18.891667\n",
       "Name: len, Length: 1250, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['unit_1', 'unit_2', 'unit_3', 'unit_4', 'balance_mark'])['len'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Функции определяются так.\n",
    "def two_histograms(x, y):\n",
    "# После определения функции полезно бывает добавить её описание в таком стиле.\n",
    "# Тройные кавычки -- для обрамления многострочных строковых литералов.\n",
    "    \"\"\"\n",
    "    Функция, которая построит две гистограммы на одной картинке.\n",
    "    Дополнительно пунктирными линиями указываются средние значения выборок.\n",
    "    x: вектор pd.Series,\n",
    "    y: вектор pd.Series\n",
    "    \"\"\"\n",
    "    x.hist(alpha=0.5, weights=[1./len(x)]*len(x))\n",
    "    y.hist(alpha=0.5, weights=[1./len(y)]*len(y))\n",
    "    plt.axvline(x.mean(), color='red', alpha=0.8, linestyle='dashed')\n",
    "    plt.axvline(y.mean(), color='blue', alpha=0.8, linestyle='dashed')\n",
    "    plt.legend([x.name, y.name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "18\n"
     ]
    }
   ],
   "source": [
    "mask = np.logical_and(df.unit_1 == 'булава', df.unit_2 == 'булава')\n",
    "mask1 = np.logical_and(mask, df.unit_3 == 'булава')\n",
    "mask2 = np.logical_and(mask1, df.unit_4 == 'булава')\n",
    "\n",
    "conf = df[mask2]\n",
    "conf_before = conf[conf.balance_mark == 'before']\n",
    "conf_after = conf[conf.balance_mark == 'after']\n",
    "\n",
    "x = conf_before.len\n",
    "y = conf_after.len\n",
    "\n",
    "print(conf_before.shape[0])\n",
    "print(conf_after.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "two_histograms(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Посох__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "mask = np.logical_and(df.unit_1 == 'посох', df.unit_2 == 'посох')\n",
    "mask1 = np.logical_and(mask, df.unit_3 == 'посох')\n",
    "mask2 = np.logical_and(mask1, df.unit_4 == 'посох')\n",
    "\n",
    "conf = df[mask2]\n",
    "conf_before = conf[conf.balance_mark == 'before']\n",
    "conf_after = conf[conf.balance_mark == 'after']\n",
    "\n",
    "x = conf_before.len\n",
    "y = conf_after.len\n",
    "\n",
    "print(conf_before.shape[0])\n",
    "print(conf_after.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "two_histograms(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arturignatev/opt/anaconda3/lib/python3.8/site-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot([x, y])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Топор__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "mask = np.logical_and(df.unit_1 == 'топор', df.unit_2 == 'топор')\n",
    "mask1 = np.logical_and(mask, df.unit_3 == 'топор')\n",
    "mask2 = np.logical_and(mask1, df.unit_4 == 'топор')\n",
    "\n",
    "conf = df[mask2]\n",
    "conf_before = conf[conf.balance_mark == 'before']\n",
    "conf_after = conf[conf.balance_mark == 'after']\n",
    "\n",
    "x = conf_before.len\n",
    "y = conf_after.len\n",
    "\n",
    "print(conf_before.shape[0])\n",
    "print(conf_after.shape[0])"
   ]
  },
  {
   "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": [
    "two_histograms(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3tpKdywCXNHBJrgMeBO6uqstbXc9OZYBLGqgku1gK79NVdWar69nJDHBJA5MkLK3YtVBV9291PTudAd6gzjqljwA3JbmQZHKra5I6bgXuAN6Q5Fzn9aatLmqnchqhJDXKK3BJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhr1/5GP29RoQm1WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot([x, y])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Лук__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "21\n"
     ]
    }
   ],
   "source": [
    "mask = np.logical_and(df.unit_1 == 'лук', df.unit_2 == 'лук')\n",
    "mask1 = np.logical_and(mask, df.unit_3 == 'лук')\n",
    "mask2 = np.logical_and(mask1, df.unit_4 == 'лук')\n",
    "\n",
    "conf = df[mask2]\n",
    "conf_before = conf[conf.balance_mark == 'before']\n",
    "conf_after = conf[conf.balance_mark == 'after']\n",
    "\n",
    "x = conf_before.len\n",
    "y = conf_after.len\n",
    "\n",
    "print(conf_before.shape[0])\n",
    "print(conf_after.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "two_histograms(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arturignatev/opt/anaconda3/lib/python3.8/site-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n"
     ]
    },
    {
     "data": {
      "image/png": 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gQFW9MO56NioDXNJQJdnCYng/XFVHx13PRmaASxqaJAEOA/NVde+469noDPAGdXeI/DbwO0meSbJv3DVJnRuAW4Cbk5zsXh8Yd1EblcMIJalRnoFLUqMMcElqlAEuSY0ywCWpUQa4JDXKAJekRhngktQoA1ySGvX//EEs7pmxM/sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot([x, y])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Меч__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n",
      "20\n"
     ]
    }
   ],
   "source": [
    "mask = np.logical_and(df.unit_1 == 'меч', df.unit_2 == 'меч')\n",
    "mask1 = np.logical_and(mask, df.unit_3 == 'меч')\n",
    "mask2 = np.logical_and(mask1, df.unit_4 == 'меч')\n",
    "\n",
    "conf = df[mask2]\n",
    "conf_before = conf[conf.balance_mark == 'before']\n",
    "conf_after = conf[conf.balance_mark == 'after']\n",
    "\n",
    "x = conf_before.len\n",
    "y = conf_after.len\n",
    "\n",
    "print(conf_before.shape[0])\n",
    "print(conf_after.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "two_histograms(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arturignatev/opt/anaconda3/lib/python3.8/site-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot([x, y])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Гипотеза: посох достаточно сильно меняет продолжительность боя"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# посмотрим на бои, где был посох\n",
    "\n",
    "conf = df[df.unit_1 == 'посох']\n",
    "conf = conf.append(df[df.unit_2 == 'посох'])\n",
    "conf = conf.append(df[df.unit_3 == 'посох'])\n",
    "conf = conf.append(df[df.unit_4 == 'посох'])\n",
    "\n",
    "conf = conf.drop_duplicates(keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>battle_id</th>\n",
       "      <th>unit_1</th>\n",
       "      <th>unit_2</th>\n",
       "      <th>unit_3</th>\n",
       "      <th>unit_4</th>\n",
       "      <th>len</th>\n",
       "      <th>balance_mark</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>117214</td>\n",
       "      <td>посох</td>\n",
       "      <td>посох</td>\n",
       "      <td>лук</td>\n",
       "      <td>лук</td>\n",
       "      <td>21.84</td>\n",
       "      <td>before</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>108639</td>\n",
       "      <td>посох</td>\n",
       "      <td>лук</td>\n",
       "      <td>булава</td>\n",
       "      <td>лук</td>\n",
       "      <td>18.73</td>\n",
       "      <td>before</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>107293</td>\n",
       "      <td>посох</td>\n",
       "      <td>лук</td>\n",
       "      <td>лук</td>\n",
       "      <td>меч</td>\n",
       "      <td>17.18</td>\n",
       "      <td>before</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>115757</td>\n",
       "      <td>посох</td>\n",
       "      <td>лук</td>\n",
       "      <td>меч</td>\n",
       "      <td>топор</td>\n",
       "      <td>19.45</td>\n",
       "      <td>before</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>100579</td>\n",
       "      <td>посох</td>\n",
       "      <td>меч</td>\n",
       "      <td>лук</td>\n",
       "      <td>лук</td>\n",
       "      <td>22.75</td>\n",
       "      <td>before</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19933</th>\n",
       "      <td>110647</td>\n",
       "      <td>булава</td>\n",
       "      <td>булава</td>\n",
       "      <td>топор</td>\n",
       "      <td>посох</td>\n",
       "      <td>23.97</td>\n",
       "      <td>after</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19956</th>\n",
       "      <td>116932</td>\n",
       "      <td>меч</td>\n",
       "      <td>лук</td>\n",
       "      <td>лук</td>\n",
       "      <td>посох</td>\n",
       "      <td>25.03</td>\n",
       "      <td>after</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19959</th>\n",
       "      <td>117390</td>\n",
       "      <td>булава</td>\n",
       "      <td>меч</td>\n",
       "      <td>булава</td>\n",
       "      <td>посох</td>\n",
       "      <td>24.44</td>\n",
       "      <td>after</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19964</th>\n",
       "      <td>112010</td>\n",
       "      <td>меч</td>\n",
       "      <td>лук</td>\n",
       "      <td>булава</td>\n",
       "      <td>посох</td>\n",
       "      <td>22.77</td>\n",
       "      <td>after</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19981</th>\n",
       "      <td>102801</td>\n",
       "      <td>лук</td>\n",
       "      <td>топор</td>\n",
       "      <td>лук</td>\n",
       "      <td>посох</td>\n",
       "      <td>23.47</td>\n",
       "      <td>after</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11617 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       battle_id  unit_1  unit_2  unit_3 unit_4    len balance_mark\n",
       "1         117214   посох   посох     лук    лук  21.84       before\n",
       "11        108639   посох     лук  булава    лук  18.73       before\n",
       "17        107293   посох     лук     лук    меч  17.18       before\n",
       "21        115757   посох     лук     меч  топор  19.45       before\n",
       "22        100579   посох     меч     лук    лук  22.75       before\n",
       "...          ...     ...     ...     ...    ...    ...          ...\n",
       "19933     110647  булава  булава   топор  посох  23.97        after\n",
       "19956     116932     меч     лук     лук  посох  25.03        after\n",
       "19959     117390  булава     меч  булава  посох  24.44        after\n",
       "19964     112010     меч     лук  булава  посох  22.77        after\n",
       "19981     102801     лук   топор     лук  посох  23.47        after\n",
       "\n",
       "[11617 rows x 7 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5834\n",
      "5783\n"
     ]
    }
   ],
   "source": [
    "conf_before = conf[conf.balance_mark == 'before']\n",
    "conf_after = conf[conf.balance_mark == 'after']\n",
    "\n",
    "x = conf_before.len\n",
    "y = conf_after.len\n",
    "\n",
    "print(conf_before.shape[0])\n",
    "print(conf_after.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": [
    "two_histograms(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arturignatev/opt/anaconda3/lib/python3.8/site-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot([x, y])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       21.84\n",
       "11      18.73\n",
       "17      17.18\n",
       "21      19.45\n",
       "22      22.75\n",
       "        ...  \n",
       "9943    16.24\n",
       "9952    15.71\n",
       "9990    22.80\n",
       "9993    20.71\n",
       "9998    20.08\n",
       "Name: len, Length: 5834, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "expectation_x = []\n",
    "expectation_y = []\n",
    "\n",
    "for i in range(10000):\n",
    "    sample_x = np.random.choice(x, replace=True, size=len(x))\n",
    "    sample_y = np.random.choice(y, replace=True, size=len(y))\n",
    "    \n",
    "    expectation_x.append(sample_x.mean())\n",
    "    expectation_y.append(sample_y.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": [
    "two_histograms(pd.Series(expectation_x), pd.Series(expectation_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[19.984214946863215,\n",
       " 19.937716832362014,\n",
       " 19.92249400068564,\n",
       " 19.95007199177237,\n",
       " 20.035035995886183,\n",
       " 19.943388755570794,\n",
       " 19.93931264998286,\n",
       " 19.91475317106616,\n",
       " 19.94220603359616,\n",
       " 19.955306822077475,\n",
       " 19.91356187864244,\n",
       " 19.93993829276654,\n",
       " 19.887845389098388,\n",
       " 20.036674665752486,\n",
       " 20.005603359616043,\n",
       " 19.954285224545767,\n",
       " 19.920778196777512,\n",
       " 19.906856359273227,\n",
       " 19.897050051422692,\n",
       " 19.905095989029824,\n",
       " 19.97172951662667,\n",
       " 19.980773054508056,\n",
       " 19.92652896811793,\n",
       " 19.958222488858418,\n",
       " 19.90515255399383,\n",
       " 19.959883441892355,\n",
       " 19.909190949605758,\n",
       " 19.987816249571477,\n",
       " 20.01818135070278,\n",
       " 19.94153753856702,\n",
       " 19.98694377785396,\n",
       " 19.952773397326023,\n",
       " 19.994782310593074,\n",
       " 19.897386013027084,\n",
       " 19.96725745629071,\n",
       " 19.919060678779566,\n",
       " 19.945210833047653,\n",
       " 20.060377099760025,\n",
       " 20.02730716489544,\n",
       " 19.949122386013027,\n",
       " 19.93239801165581,\n",
       " 20.023865272540284,\n",
       " 19.932872814535482,\n",
       " 19.9618820706205,\n",
       " 19.9395594789167,\n",
       " 19.92301679808022,\n",
       " 19.95420809050394,\n",
       " 19.961194720603356,\n",
       " 20.034084676037022,\n",
       " 19.99086390126843,\n",
       " 19.89694549194378,\n",
       " 19.913476174151526,\n",
       " 19.885363387041483,\n",
       " 20.014958861844363,\n",
       " 19.973455605073703,\n",
       " 19.905714775454236,\n",
       " 19.91692320877614,\n",
       " 20.0181367843675,\n",
       " 19.953866986630103,\n",
       " 19.920114844017824,\n",
       " 19.964592046623242,\n",
       " 19.922829962290024,\n",
       " 19.969833733287622,\n",
       " 19.91735687350017,\n",
       " 19.92257284881728,\n",
       " 19.993635584504627,\n",
       " 19.983575591360985,\n",
       " 19.93375899897155,\n",
       " 19.905512512855676,\n",
       " 19.960027425437094,\n",
       " 19.877723688721293,\n",
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