{
 "cells": [
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   "cell_type": "code",
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   "id": "5c5b0c64",
   "metadata": {},
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     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>3398</th>\n",
       "      <th>3399</th>\n",
       "      <th>3400</th>\n",
       "      <th>3401</th>\n",
       "      <th>3402</th>\n",
       "      <th>3403</th>\n",
       "      <th>3404</th>\n",
       "      <th>3405</th>\n",
       "      <th>3406</th>\n",
       "      <th>3407</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Note: All these datasets are for non-commercia...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sample_id</td>\n",
       "      <td>A2M</td>\n",
       "      <td>A4GNT</td>\n",
       "      <td>AARS</td>\n",
       "      <td>ABCA1</td>\n",
       "      <td>ABCB1</td>\n",
       "      <td>ABCB6</td>\n",
       "      <td>ABCC5</td>\n",
       "      <td>ABCC8</td>\n",
       "      <td>ABCF1</td>\n",
       "      <td>...</td>\n",
       "      <td>ZNF185</td>\n",
       "      <td>ZNF274</td>\n",
       "      <td>ZNF281</td>\n",
       "      <td>ZNF318</td>\n",
       "      <td>ZNF395</td>\n",
       "      <td>ZNF451</td>\n",
       "      <td>ZNF586</td>\n",
       "      <td>ZNF589</td>\n",
       "      <td>ZW10</td>\n",
       "      <td>ZYX</td>\n",
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       "</table>\n",
       "<p>2 rows × 3408 columns</p>\n",
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      "text/plain": [
       "                                                0    1      2     3      4     \\\n",
       "0  Note: All these datasets are for non-commercia...  NaN    NaN   NaN    NaN   \n",
       "1                                          Sample_id  A2M  A4GNT  AARS  ABCA1   \n",
       "\n",
       "    5      6      7      8      9     ...    3398    3399    3400    3401  \\\n",
       "0    NaN    NaN    NaN    NaN    NaN  ...     NaN     NaN     NaN     NaN   \n",
       "1  ABCB1  ABCB6  ABCC5  ABCC8  ABCF1  ...  ZNF185  ZNF274  ZNF281  ZNF318   \n",
       "\n",
       "     3402    3403    3404    3405  3406 3407  \n",
       "0     NaN     NaN     NaN     NaN   NaN  NaN  \n",
       "1  ZNF395  ZNF451  ZNF586  ZNF589  ZW10  ZYX  \n",
       "\n",
       "[2 rows x 3408 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "file_path = \"/data/boom/CIGS/MCE_Bioactive_Compounds_MDA_MB_231_10μM_Counts.xlsx\"\n",
    "\n",
    "# 第 1 步：只把前两行当表头读进来，不读数据\n",
    "header = pd.read_excel(file_path, nrows=0)          # 0 行数据，只解析列名\n",
    "# 第 2 步：再读一次，跳过中间所有行，只拿前两行数据\n",
    "df = pd.read_excel(file_path,\n",
    "                   skiprows=0,       # 从第 0 行开始\n",
    "                   nrows=2,          # 只要 2 行\n",
    "                   header=None)      # 不再用第一行当表头\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c660f0f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3407"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_name = df.iloc[1].to_list()[1:]\n",
    "\n",
    "len(gene_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "25708d2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "978"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LINCS = pd.read_csv('/home/bob/boom/VCBench/data/LINCS2020/landmark_geneinfo.csv')\n",
    "gene_info_LINCS = LINCS['gene_symbol'].astype(str)\n",
    "len(gene_info_LINCS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee20f28a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "942"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取交集：gene_name & gene_info_LINCS\n",
    "gene_name = set(gene_name)\n",
    "gene_info_LINCS = set(gene_info_LINCS)\n",
    "commen = gene_name & gene_info_LINCS\n",
    "\n",
    "len(commen) # 就用原始的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "663f0a39",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92377cdc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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