diff --git a/Aigrind/Analisys.ipynb b/Aigrind/Analisys.ipynb
index 22be580..2fb162b 100644
--- a/Aigrind/Analisys.ipynb
+++ b/Aigrind/Analisys.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 203,
+   "execution_count": 1,
    "id": "c6bb3ab6",
    "metadata": {},
    "outputs": [],
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": 2,
    "id": "038a62d8",
    "metadata": {},
    "outputs": [
@@ -133,7 +133,7 @@
        "4  2021-01-01 00:06:41  Payment_system_5   1.432550     7229767"
       ]
      },
-     "execution_count": 204,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -153,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 205,
+   "execution_count": 3,
    "id": "a4b72e40",
    "metadata": {},
    "outputs": [
@@ -221,7 +221,7 @@
        "4  Payment_system_5      24.0"
       ]
      },
-     "execution_count": 205,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -241,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 206,
+   "execution_count": 4,
    "id": "a0f238ba",
    "metadata": {},
    "outputs": [
@@ -315,7 +315,7 @@
        "4   3842384.0   2021-01-01  BRA_MS1_install"
       ]
      },
-     "execution_count": 206,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -335,7 +335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 207,
+   "execution_count": 5,
    "id": "bf031a83",
    "metadata": {
     "scrolled": true
@@ -490,7 +490,7 @@
        "8     1989.52           10.48  "
       ]
      },
-     "execution_count": 207,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -561,7 +561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 208,
+   "execution_count": 6,
    "id": "33a0a78b",
    "metadata": {},
    "outputs": [
@@ -571,7 +571,7 @@
        "['BRA_MS1_install']"
       ]
      },
-     "execution_count": 208,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -591,7 +591,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
+   "execution_count": 7,
    "id": "73fb9a74",
    "metadata": {},
    "outputs": [],
@@ -610,7 +610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 210,
+   "execution_count": 8,
    "id": "8353352e",
    "metadata": {},
    "outputs": [
@@ -802,7 +802,7 @@
        "[711 rows x 8 columns]"
       ]
      },
-     "execution_count": 210,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -816,7 +816,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 9,
    "id": "95da41ef",
    "metadata": {},
    "outputs": [
@@ -852,7 +852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 212,
+   "execution_count": 10,
    "id": "d570186f",
    "metadata": {},
    "outputs": [
@@ -882,7 +882,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 213,
+   "execution_count": 11,
    "id": "772ce00f",
    "metadata": {},
    "outputs": [
@@ -892,7 +892,7 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 213,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -938,7 +938,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 214,
+   "execution_count": 12,
    "id": "81414a66",
    "metadata": {},
    "outputs": [
@@ -969,7 +969,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 215,
+   "execution_count": 13,
    "id": "2821cb8e",
    "metadata": {},
    "outputs": [
@@ -979,7 +979,7 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 215,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1017,7 +1017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 216,
+   "execution_count": 14,
    "id": "3f22c78b",
    "metadata": {},
    "outputs": [],
@@ -1027,7 +1027,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 217,
+   "execution_count": 15,
    "id": "93819b37",
    "metadata": {},
    "outputs": [
@@ -1037,7 +1037,7 @@
        "<Axes: ylabel='Time'>"
       ]
      },
-     "execution_count": 217,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1069,7 +1069,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 218,
+   "execution_count": 16,
    "id": "588eab3e",
    "metadata": {},
    "outputs": [
@@ -1079,7 +1079,7 @@
        "<Axes: ylabel='Time'>"
       ]
      },
-     "execution_count": 218,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1122,7 +1122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 286,
+   "execution_count": 17,
    "id": "c122ab03",
    "metadata": {},
    "outputs": [],
@@ -1132,7 +1132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 244,
+   "execution_count": 18,
    "id": "2a3f7a9e",
    "metadata": {},
    "outputs": [],
@@ -1145,7 +1145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 256,
+   "execution_count": 19,
    "id": "7c089a10",
    "metadata": {},
    "outputs": [],
@@ -1161,7 +1161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 281,
+   "execution_count": 20,
    "id": "479d54b6",
    "metadata": {},
    "outputs": [
@@ -1280,7 +1280,7 @@
        "[2980 rows x 3 columns]"
       ]
      },
-     "execution_count": 281,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1293,7 +1293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 292,
+   "execution_count": 22,
    "id": "5f34432f",
    "metadata": {},
    "outputs": [
@@ -1303,7 +1303,7 @@
        "datetime.date(2024, 1, 1)"
       ]
      },
-     "execution_count": 292,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1311,6 +1311,539 @@
    "source": [
     "datetime(2024,1,1).date() + timedelta(days=0)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "3723a9b9",
+   "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>account_id</th>\n",
+       "      <th>created_date</th>\n",
+       "      <th>payment_date</th>\n",
+       "      <th>cost</th>\n",
+       "      <th>campaign</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>330</th>\n",
+       "      <td>3861811</td>\n",
+       "      <td>2021-01-07</td>\n",
+       "      <td>2021-01-01</td>\n",
+       "      <td>1.858740</td>\n",
+       "      <td>RUS_MS1_purchase</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>339</th>\n",
+       "      <td>3861811</td>\n",
+       "      <td>2021-01-07</td>\n",
+       "      <td>2021-01-01</td>\n",
+       "      <td>1.858740</td>\n",
+       "      <td>RUS_MS1_purchase</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1922</th>\n",
+       "      <td>3845613</td>\n",
+       "      <td>2021-01-02</td>\n",
+       "      <td>2021-01-02</td>\n",
+       "      <td>0.726463</td>\n",
+       "      <td>RUS_MS1_purchase</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2920</th>\n",
+       "      <td>3843913</td>\n",
+       "      <td>2021-01-01</td>\n",
+       "      <td>2021-01-02</td>\n",
+       "      <td>0.726463</td>\n",
+       "      <td>RUS_MS1_purchase</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4335</th>\n",
+       "      <td>3908313</td>\n",
+       "      <td>2021-01-22</td>\n",
+       "      <td>2021-01-03</td>\n",
+       "      <td>4.237699</td>\n",
+       "      <td>UKR_MS1_purchase</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>290371</th>\n",
+       "      <td>3864062</td>\n",
+       "      <td>2021-01-07</td>\n",
+       "      <td>2021-12-17</td>\n",
+       "      <td>0.619811</td>\n",
+       "      <td>RUS_MS1_purchase</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>295786</th>\n",
+       "      <td>3908063</td>\n",
+       "      <td>2021-01-22</td>\n",
+       "      <td>2021-12-24</td>\n",
+       "      <td>23.013104</td>\n",
+       "      <td>UKR_MS1_purchase</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>296561</th>\n",
+       "      <td>3908313</td>\n",
+       "      <td>2021-01-22</td>\n",
+       "      <td>2021-12-25</td>\n",
+       "      <td>36.344492</td>\n",
+       "      <td>UKR_MS1_purchase</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>298659</th>\n",
+       "      <td>3908063</td>\n",
+       "      <td>2021-01-22</td>\n",
+       "      <td>2021-12-26</td>\n",
+       "      <td>23.013104</td>\n",
+       "      <td>UKR_MS1_purchase</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>303332</th>\n",
+       "      <td>3861811</td>\n",
+       "      <td>2021-01-07</td>\n",
+       "      <td>2021-12-31</td>\n",
+       "      <td>1.754931</td>\n",
+       "      <td>RUS_MS1_purchase</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>325 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        account_id created_date payment_date       cost          campaign\n",
+       "330        3861811   2021-01-07   2021-01-01   1.858740  RUS_MS1_purchase\n",
+       "339        3861811   2021-01-07   2021-01-01   1.858740  RUS_MS1_purchase\n",
+       "1922       3845613   2021-01-02   2021-01-02   0.726463  RUS_MS1_purchase\n",
+       "2920       3843913   2021-01-01   2021-01-02   0.726463  RUS_MS1_purchase\n",
+       "4335       3908313   2021-01-22   2021-01-03   4.237699  UKR_MS1_purchase\n",
+       "...            ...          ...          ...        ...               ...\n",
+       "290371     3864062   2021-01-07   2021-12-17   0.619811  RUS_MS1_purchase\n",
+       "295786     3908063   2021-01-22   2021-12-24  23.013104  UKR_MS1_purchase\n",
+       "296561     3908313   2021-01-22   2021-12-25  36.344492  UKR_MS1_purchase\n",
+       "298659     3908063   2021-01-22   2021-12-26  23.013104  UKR_MS1_purchase\n",
+       "303332     3861811   2021-01-07   2021-12-31   1.754931  RUS_MS1_purchase\n",
+       "\n",
+       "[325 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = registrations.dropna()\n",
+    "df = payments.join(df.set_index(\"account_id\"), on=\"account_id\").dropna()\n",
+    "df = df.join(persents.set_index(\"Payment_types\"), on=\"Payment_types\")\n",
+    "df['cost'] = df['real_cost'] * (100 - df['Share, %']) / 100\n",
+    "df['payment_date'] = pd.to_datetime(df['payment_date'], format=\"%Y-%m-%d %H:%M:%S\").dt.date\n",
+    "df = df[df['campaign'].str.contains(\"purchase\")]\n",
+    "table = df[[\"account_id\", \"created_date\", \"payment_date\", \"cost\", \"campaign\"]]\n",
+    "table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "ae68e44e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "campaigns = list(table['campaign'].unique())\n",
+    "accounts = list(table['account_id'].unique())\n",
+    "payments = list(table['payment_date'].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "d3c680b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for c in campaign:\n",
+    "    for acc in accounts:\n",
+    "        cond1 = table['campaign'] == c\n",
+    "        cond2 = table['account_id'] == acc\n",
+    "        cond = np.logical_and(cond1, cond2)\n",
+    "        tmp_table = table[cond]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "id": "11ba3dae",
+   "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>campaign</th>\n",
+       "      <th>account_id</th>\n",
+       "      <th>created_date</th>\n",
+       "      <th>payment_date</th>\n",
+       "      <th>cost</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BRA_MS1_purchase</td>\n",
+       "      <td>3848706</td>\n",
+       "      <td>2021-01-02</td>\n",
+       "      <td>2021-01-09</td>\n",
+       "      <td>0.090000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BRA_MS1_purchase</td>\n",
+       "      <td>3849559</td>\n",
+       "      <td>2021-01-03</td>\n",
+       "      <td>2021-01-03</td>\n",
+       "      <td>0.730151</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BRA_MS1_purchase</td>\n",
+       "      <td>3859034</td>\n",
+       "      <td>2021-01-06</td>\n",
+       "      <td>2021-02-10</td>\n",
+       "      <td>0.260000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BRA_MS1_purchase</td>\n",
+       "      <td>3861933</td>\n",
+       "      <td>2021-01-07</td>\n",
+       "      <td>2021-01-08</td>\n",
+       "      <td>1.754407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BRA_MS1_purchase</td>\n",
+       "      <td>3863849</td>\n",
+       "      <td>2021-01-07</td>\n",
+       "      <td>2021-01-07</td>\n",
+       "      <td>3.656603</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>258</th>\n",
+       "      <td>UKR_MS1_purchase</td>\n",
+       "      <td>3908313</td>\n",
+       "      <td>2021-01-22</td>\n",
+       "      <td>2021-12-06</td>\n",
+       "      <td>18.035712</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>259</th>\n",
+       "      <td>UKR_MS1_purchase</td>\n",
+       "      <td>3908313</td>\n",
+       "      <td>2021-01-22</td>\n",
+       "      <td>2021-12-12</td>\n",
+       "      <td>1.961796</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>260</th>\n",
+       "      <td>UKR_MS1_purchase</td>\n",
+       "      <td>3908313</td>\n",
+       "      <td>2021-01-22</td>\n",
+       "      <td>2021-12-25</td>\n",
+       "      <td>36.344492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>261</th>\n",
+       "      <td>UKR_MS1_purchase</td>\n",
+       "      <td>3911977</td>\n",
+       "      <td>2021-01-24</td>\n",
+       "      <td>2021-01-28</td>\n",
+       "      <td>0.634062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>262</th>\n",
+       "      <td>UKR_MS1_purchase</td>\n",
+       "      <td>3911977</td>\n",
+       "      <td>2021-01-24</td>\n",
+       "      <td>2021-01-29</td>\n",
+       "      <td>1.977679</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>263 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             campaign  account_id created_date payment_date       cost\n",
+       "0    BRA_MS1_purchase     3848706   2021-01-02   2021-01-09   0.090000\n",
+       "1    BRA_MS1_purchase     3849559   2021-01-03   2021-01-03   0.730151\n",
+       "2    BRA_MS1_purchase     3859034   2021-01-06   2021-02-10   0.260000\n",
+       "3    BRA_MS1_purchase     3861933   2021-01-07   2021-01-08   1.754407\n",
+       "4    BRA_MS1_purchase     3863849   2021-01-07   2021-01-07   3.656603\n",
+       "..                ...         ...          ...          ...        ...\n",
+       "258  UKR_MS1_purchase     3908313   2021-01-22   2021-12-06  18.035712\n",
+       "259  UKR_MS1_purchase     3908313   2021-01-22   2021-12-12   1.961796\n",
+       "260  UKR_MS1_purchase     3908313   2021-01-22   2021-12-25  36.344492\n",
+       "261  UKR_MS1_purchase     3911977   2021-01-24   2021-01-28   0.634062\n",
+       "262  UKR_MS1_purchase     3911977   2021-01-24   2021-01-29   1.977679\n",
+       "\n",
+       "[263 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result_table = table.groupby(by=[\"campaign\",\"account_id\",\"created_date\",\"payment_date\"], as_index=False).sum()\n",
+    "result_table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "id": "139b348c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dates = [7,14,30]\n",
+    "dates = [i for i in range(1, 32)]\n",
+    "ARPUs = np.zeros((len(campaigns), len(dates)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "id": "8d3253d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.91 s ± 25.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "for i, c in enumerate(campaigns):\n",
+    "    cond1 = result_table['campaign'] == c\n",
+    "    num_accounts = len(result_table[cond1])\n",
+    "    \n",
+    "    for acc in accounts:\n",
+    "        cond2 = result_table['account_id'] == acc\n",
+    "        cond = np.logical_and(cond1, cond2)\n",
+    "        \n",
+    "        tmp_table = result_table[cond]\n",
+    "        \n",
+    "        \n",
+    "        s = np.zeros(len(dates))\n",
+    "        for j,d in enumerate(dates):\n",
+    "            cond = tmp_table['payment_date'] <= tmp_table['created_date'] + timedelta(days=d)\n",
+    "            s[j] += tmp_table[cond]['cost'].sum()\n",
+    "#         print(s)\n",
+    "        ARPUs[i] += s \n",
+    "    ARPUs[i] /= num_accounts\n",
+    "# print(ARPUs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "id": "905d7a36",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 960x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(8,6), dpi=120)\n",
+    "ax.grid()\n",
+    "# ax.set_xlim(1, 70)\n",
+    "# ax.set(xlabel='common xlabel', ylabel='common ylabel')\n",
+    "\n",
+    "for i, c in enumerate(campaigns):\n",
+    "    sns.lineplot(ax=ax, x=dates, y=ARPUs[i], label=c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b14810e",
+   "metadata": {},
+   "source": [
+    "# Фактический CPI (cost per install)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7cc81742",
+   "metadata": {},
+   "source": [
+    "$$\\Large\n",
+    "CPI = \n",
+    "\\frac{\\text{Количество установок}}{\\text{Бюджет}}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 239,
+   "id": "47fe30e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = {\n",
+    "    (\"Media_source_1\",       \"install\",            \"Brazil\"): \"BRA_MS1_install\",\n",
+    "    (\"Media_source_1\",      \"purchase\",            \"Brazil\"): \"BRA_MS1_purchase\",\n",
+    "    (\"Media_source_1\",       \"install\",            \"Russia\"): \"RUS_MS1_install\",\n",
+    "    (\"Media_source_1\",      \"purchase\",            \"Russia\"): \"RUS_MS1_purchase\",\n",
+    "    (\"Media_source_1\",       \"install\",            \"Ukrane\"): \"UKR_MS1_install\",\n",
+    "    (\"Media_source_1\",      \"purchase\",            \"Ukrane\"): \"UKR_MS1_purchase\",\n",
+    "    (\"Media_source_2\",       \"install\",            \"Brazil\"): \"BRA_MS2_install\",\n",
+    "    (\"Media_source_2\",       \"install\",  \"English_speaking\"): \"ENG_MS2_install\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 245,
+   "id": "3bfa10eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[('BRA_MS1_install', np.float64(0.008647705461525039)),\n",
+      " ('UKR_MS1_install', np.float64(0.01464359351988218)),\n",
+      " ('RUS_MS1_install', np.float64(0.019471895011463358)),\n",
+      " ('ENG_MS2_install', np.float64(0.24557739557739558)),\n",
+      " ('BRA_MS1_purchase', np.float64(0.350017559262511)),\n",
+      " ('RUS_MS1_purchase', np.float64(0.39011385199240983)),\n",
+      " ('BRA_MS2_install', np.float64(0.5338815789473684)),\n",
+      " ('UKR_MS1_purchase', np.float64(1.3389615384615385))]\n"
+     ]
+    }
+   ],
+   "source": [
+    "CPI = list()\n",
+    "tmp_budget = budget.groupby(by=[\"media_source\", \"Campaign_type\",'Target']).sum()\n",
+    "for (MS, Type, Target), c in d.items():\n",
+    "    cond = registrations['campaign'] == c\n",
+    "    num_installs = cond.sum()\n",
+    "    \n",
+    "    b = tmp_budget.loc[MS, Type, Target]['Spend, USD']\n",
+    "    \n",
+    "    CPI.append((c, b / num_installs))\n",
+    "    \n",
+    "CPI.sort(key=lambda x: x[1])\n",
+    "pprint(CPI)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 246,
+   "id": "db865d1c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 246,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(6,4), dpi=120)\n",
+    "ax.grid()\n",
+    "# ax.set_xlim(1, 70)\n",
+    "# ax.set(xlabel='common xlabel', ylabel='common ylabel')\n",
+    "\n",
+    "y = list(map(lambda x: x[0], CPI))\n",
+    "x = list(map(lambda x: x[1], CPI))\n",
+    "\n",
+    "# plt.xticks(rotation='vertical')\n",
+    "sns.barplot(ax=ax, x=x, y=y)"
+   ]
   }
  ],
  "metadata": {