SE-0000025_primer_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('masked_primers.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32934 entries, 0 to 32933\n",
      "Data columns (total 2 columns):\n",
      "primer_type    32934 non-null object\n",
      "primer_name    32934 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 514.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>primer_type</th>\n",
       "      <th>primer_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>32934</td>\n",
       "      <td>32934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>unique</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>top</td>\n",
       "      <td>vprimer</td>\n",
       "      <td>IGHA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>freq</td>\n",
       "      <td>16467</td>\n",
       "      <td>7003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       primer_type primer_name\n",
       "count        32934       32934\n",
       "unique           2          11\n",
       "top        vprimer        IGHA\n",
       "freq         16467        7003"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f83ec480ad0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DqCRJkoowiEqSJKkIg6gkSZKKqDWIRsSAiJgeEb+OiPkRcVhEDIyIGRHxWPW4c7VtRMQlEbEgIuZGxMh2/Uyotn8sIibUWbMkSZK6R90zot8Dbs3MfYADgfnAmcCdmTkMuLNaBzgWGFZ9TQQuB4iIgcDZwCHAKODstvAqSZKknqu2IBoROwLvBa4CyMzXMvMF4HhgSrXZFOCEavl44OpsuA8YEBG7AWOAGZm5PDOfB2YAY+uqW5IkSd2jzhnRvYBlwL9HxOyI+EFEbA+8LTOfAage31ptvzvwVLv9F1dt62tfQ0RMjIjWiGhdtmxZ80cjSZKkpqoziG4DjAQuz8wRwEv86TB8R6KDttxA+5oNmVdmZktmtgwaNGhT6pUkSVI3qjOILgYWZ+b91fp0GsF0SXXInepxabvth7TbfzDw9AbaJUmS1IPVFkQz81ngqYjYu2o6CngEuBlou/J9AvDjavlm4JPV1fOHAi9Wh+5vA46JiJ2ri5SOqdokSZLUg21Tc/9/B1wbEdsCjwOfohF+r4+IU4EngROrbW8BPggsAF6utiUzl0fEecAD1XbnZubymuuWJElSzWoNopk5B2jp4KmjOtg2gdPX088kYFJzq5MkSVJJ3llJkiRJRRhEJUmSVIRBVJIkSUUYRCVJklSEQVSSJElFGEQlSZJUhEFUkiRJRRhEJUmSVIRBVJIkSUUYRCVJklSEQVSSJElFGEQlSZJUhEFUkiRJRRhEJUmSVIRBVJIkSUUYRCVJklSEQVSSJElFGEQlSZJUhEFUkiRJRRhEJUmSVIRBVJIkSUUYRCVJklSEQVSSJElFGEQlSZJUhEFUkiRJRRhEJUmSVIRBVJIkSUUYRCVJklSEQVSSJElFGEQlSZJUhEFUkiRJRRhEJUmSVIRBVJIkSUUYRCVJklSEQVSSJElFGEQlSZJUhEFUkiRJRRhEJUmSVIRBVJIkSUUYRCVJklSEQVSSJElFGEQlSZJUhEFUkiRJRdQaRCNiUUTMi4g5EdFatQ2MiBkR8Vj1uHPVHhFxSUQsiIi5ETGyXT8Tqu0fi4gJddYsSZKk7tEdM6JHZuZBmdlSrZ8J3JmZw4A7q3WAY4Fh1ddE4HJoBFfgbOAQYBRwdlt4lSRJUs9V4tD88cCUankKcEK79quz4T5gQETsBowBZmTm8sx8HpgBjO3uoiVJktRcdQfRBG6PiFkRMbFqe1tmPgNQPb61at8deKrdvourtvW1ryEiJkZEa0S0Llu2rMnDkCRJUrNtU3P/h2fm0xHxVmBGRPx6A9tGB225gfY1GzKvBK4EaGlpWed5SZIkbV5qnRHNzKerx6XATTTO8VxSHXKnelxabb4YGNJu98HA0xtolyRJUg9WWxCNiO0jon/bMnAM8BBwM9B25fsE4MfV8s3AJ6ur5w8FXqwO3d8GHBMRO1cXKR1TtUmSJKkHq/PQ/NuAmyKi7XX+IzNvjYgHgOsj4lTgSeDEavtbgA8CC4CXgU8BZObyiDgPeKDa7tzMXF5j3ZIkSeoGtQXRzHwcOLCD9ueAozpoT+D09fQ1CZjU7BolSZJUjndWkiRJUhEGUUmSJBVhEJUkSVIRBlFJkiQVYRCVJElSEQZRSZIkFWEQlSRJUhF132tekqSt0thvTqul31vPO6mWfqUSnBGVJElSEQZRSZIkFWEQlSRJUhEGUUmSJBVhEJUkSVIRBlFJkiQVYRCVJElSEQZRSZIkFWEQlSRJUhEGUUmSJBVhEJUkSVIRBlFJkiQVYRCVJElSEQZRSZIkFWEQlSRJUhEGUUmSJBVhEJUkSVIRBlFJkiQVYRCVJElSEQZRSZIkFWEQlSRJUhEGUUmSJBVhEJUkSVIRBlFJkiQVYRCVJElSEQZRSZIkFWEQlSRJUhEGUUmSJBVhEJUkSVIRBlFJkiQVYRCVJElSEQZRSZIkFWEQlSRJUhHblC5AktSxsd+cVlvft553Um19S1JnOSMqSZKkImqfEY2I3kAr8LvMPC4i9gSmAgOBB4FPZOZrEdEXuBo4GHgOOCkzF1V9fBU4FXgD+EJm3lZ33ZIk9XStLaNq67uldWZtfWvr0R0zon8PzG+3/m3gu5k5DHieRsCkenw+M98FfLfajogYDowH9gPGApdV4VaSJEk9WK1BNCIGA38J/KBaD+D9wPRqkynACdXy8dU61fNHVdsfD0zNzFcz8wlgAVDff/EkSZLULeqeEf0X4B+BVdX6LsALmbmyWl8M7F4t7w48BVA9/2K1/er2DvZZLSImRkRrRLQuW7as2eOQJElSk9UWRCPiOGBpZs5q39zBpvkmz21onz81ZF6ZmS2Z2TJo0KCNrleSJEndq86LlQ4HPhwRHwT6ATvSmCEdEBHbVLOeg4Gnq+0XA0OAxRGxDbATsLxde5v2+0iSJKmHqm1GNDO/mpmDM3MojYuN7srMjwE/A8ZVm00Aflwt31ytUz1/V2Zm1T4+IvpWV9wPA7xUT5IkqYcr8YH2ZwBTI+J8YDZwVdV+FXBNRCygMRM6HiAzH46I64FHgJXA6Zn5RveXLUmSpGbqliCamXcDd1fLj9PBVe+Z+Qpw4nr2vwC4oL4KJUmS1N06dWg+Iu7sTJskSZLUWRucEY2IfsB2wK4RsTN/uoJ9R+DtNdcmSZKkLdibHZr/DPBFGqFzFn8Kov8D/GuNdUmSJGkLt8EgmpnfA74XEX+XmZd2U02SJEnaCnTqYqXMvDQi/hwY2n6fzLy6prokSZK0hetUEI2Ia4B3AnOAto9OSsAgKkmSpE3S2Y9vagGGVx8wL0mSJHVZZ++s9BDw/9RZiCRJkrYunZ0R3RV4JCJmAq+2NWbmh2upStIWpbVlnXtYNEVLq3f7laSerLNB9Jw6i5AkSdLWp7NXzf+87kIkSZK0densVfN/oHGVPMC2QB/gpczcsa7CJEmStGXr7Ixo//brEXECUM9JX5IkSdoqdPaq+TVk5o+A9ze5FkmSJG1FOnto/q/arfai8bmifqaoJEmSNllnr5r/ULvllcAi4PimVyNJkqStRmfPEf1U3YVIkiRp69Kpc0QjYnBE3BQRSyNiSUT8Z0QMrrs4SZIkbbk6e7HSvwM3A28Hdgf+b9UmSZIkbZLOBtFBmfnvmbmy+poMDKqxLkmSJG3hOhtEfx8RH4+I3tXXx4Hn6ixMkiRJW7bOBtFPAx8FngWeAcYBXsAkSZKkTdbZj286D5iQmc8DRMRA4Ds0AqokSZK00To7I3pAWwgFyMzlwIh6SpIkSdLWoLNBtFdE7Ny2Us2IdnY2VZIkSVpHZ8PkRcB/R8R0Grf2/ChwQW1VSZIkaYvX2TsrXR0RrcD7gQD+KjMfqbUySZIkbdE6fXi9Cp6GT0mSJDVFZ88RlSRJkprKICpJkqQiDKKSJEkqwiAqSZKkIgyikiRJKsIgKkmSpCIMopIkSSrCICpJkqQiDKKSJEkqwiAqSZKkIgyikiRJKsIgKkmSpCIMopIkSSrCICpJkqQiaguiEdEvImZGxK8i4uGI+Keqfc+IuD8iHouIaRGxbdXet1pfUD0/tF1fX63aH42IMXXVLEmSpO5T54zoq8D7M/NA4CBgbEQcCnwb+G5mDgOeB06ttj8VeD4z3wV8t9qOiBgOjAf2A8YCl0VE7xrrliRJUjeoLYhmw4pqtU/1lcD7gelV+xTghGr5+Gqd6vmjIiKq9qmZ+WpmPgEsAEbVVbckSZK6R63niEZE74iYAywFZgALgRcyc2W1yWJg92p5d+ApgOr5F4Fd2rd3sI8kSZJ6qFqDaGa+kZkHAYNpzGLu29Fm1WOs57n1ta8hIiZGRGtEtC5btmxTS5YkSVI32aY7XiQzX4iIu4FDgQERsU016zkYeLrabDEwBFgcEdsAOwHL27W3ab9P+9e4ErgSoKWlZZ2gKknaNK0t9ZwN1dI6s5Z+JfUcdV41PygiBlTLbwGOBuYDPwPGVZtNAH5cLd9crVM9f1dmZtU+vrqqfk9gGOC/XpIkST1cnTOiuwFTqivcewHXZ+ZPIuIRYGpEnA/MBq6qtr8KuCYiFtCYCR0PkJkPR8T1wCPASuD0zHyjxrolSZLUDWoLopk5FxjRQfvjdHDVe2a+Apy4nr4uAC5odo2SJEkqxzsrSZIkqQiDqCRJkoowiEqSJKkIg6gkSZKKMIhKkiSpCIOoJEmSijCISpIkqQiDqCRJkoowiEqSJKkIg6gkSZKKMIhKkiSpCIOoJEmSijCISpIkqQiDqCRJkoowiEqSJKkIg6gkSZKKMIhKkiSpCIOoJEmSijCISpIkqYhtShfQnVpbRtXSb0vrzFr6lSRJ2pI5IypJkqQiDKKSJEkqwiAqSZKkIgyikiRJKmKrulhJ2lzVdSEdeDGdJGnz5YyoJEmSijCISpIkqQiDqCRJkoowiEqSJKkIg6gkSZKKMIhKkiSpCIOoJEmSijCISpIkqQiDqCRJkoowiEqSJKkIg6gkSZKKMIhKkiSpCIOoJEmSijCISpIkqQiDqCRJkoowiEqSJKkIg6gkSZKKMIhKkiSpiNqCaEQMiYifRcT8iHg4Iv6+ah8YETMi4rHqceeqPSLikohYEBFzI2Jku74mVNs/FhET6qpZkiRJ3afOGdGVwJczc1/gUOD0iBgOnAncmZnDgDurdYBjgWHV10TgcmgEV+Bs4BBgFHB2W3iVJElSz1VbEM3MZzLzwWr5D8B8YHfgeGBKtdkU4IRq+Xjg6my4DxgQEbsBY4AZmbk8M58HZgBj66pbkiRJ3aNbzhGNiKHACOB+4G2Z+Qw0wirw1mqz3YGn2u22uGpbX/varzExIlojonXZsmXNHoIkSZKarPYgGhE7AP8JfDEz/2dDm3bQlhtoX7Mh88rMbMnMlkGDBm1asZIkSeo2tQbRiOhDI4Rem5k3Vs1LqkPuVI9Lq/bFwJB2uw8Gnt5AuyRJknqwOq+aD+AqYH5mXtzuqZuBtivfJwA/btf+yerq+UOBF6tD97cBx0TEztVFSsdUbZIkSerBtqmx78OBTwDzImJO1fY14FvA9RFxKvAkcGL13C3AB4EFwMvApwAyc3lEnAc8UG13bmYur7FuSZIkdYPagmhm3kPH53cCHNXB9gmcvp6+JgGTmledJEmSSvPOSpIkSSrCICpJkqQiDKKSJEkqwiAqSZKkIgyikiRJKsIgKkmSpCIMopIkSSrCICpJkqQiDKKSJEkqwiAqSZKkIgyikiRJKsIgKkmSpCIMopIkSSrCICpJkqQiDKKSJEkqwiAqSZKkIgyikiRJKsIgKkmSpCIMopIkSSrCICpJkqQiDKKSJEkqwiAqSZKkIgyikiRJKsIgKkmSpCIMopIkSSrCICpJkqQiDKKSJEkqwiAqSZKkIgyikiRJKsIgKkmSpCIMopIkSSrCICpJkqQiDKKSJEkqwiAqSZKkIgyikiRJKsIgKkmSpCIMopIkSSrCICpJkqQiDKKSJEkqwiAqSZKkIgyikiRJKsIgKkmSpCIMopIkSSqitiAaEZMiYmlEPNSubWBEzIiIx6rHnav2iIhLImJBRMyNiJHt9plQbf9YREyoq15JkiR1rzpnRCcDY9dqOxO4MzOHAXdW6wDHAsOqr4nA5dAIrsDZwCHAKODstvAqSZKknq22IJqZvwCWr9V8PDClWp4CnNCu/epsuA8YEBG7AWOAGZm5PDOfB2awbriVJElSD9Td54i+LTOfAage31q17w481W67xVXb+trXERETI6I1IlqXLVvW9MIlSZLUXJvLxUrRQVtuoH3dxswrM7MlM1sGDRrU1OIkSZLUfN0dRJdUh9ypHpdW7YuBIe22Gww8vYF2SZIk9XDdHURvBtqufJ8A/Lhd+yerq+cPBV6sDt3fBhwTETtXFykdU7VJkiSph9umro4j4jpgNLBrRCymcfX7t4DrI+JU4EngxGrzW4APAguAl4FPAWTm8og4D3ig2u7czFz7AihJkiT1QLUF0cw8eT1PHdXBtgmcvp5+JgGTmliaJEmSNgOby8VKkiRJ2soYRCVJklSEQVSSJElFGEQlSZJUhEFUkiRJRRhEJUmSVERtH98kNVtry6ha+m1pnVlLv5IkacOcEZUkSVIRBlFJkiQVYRCVJElSEQZRSZIkFWEQlSRJUkUZ6VsAAAsSSURBVBEGUUmSJBVhEJUkSVIRfo6oJElSO2O/Oa22vm8976Ta+u6JnBGVJElSEQZRSZIkFWEQlSRJUhEGUUmSJBVhEJUkSVIRBlFJkiQVYRCVJElSEQZRSZIkFWEQlSRJUhEGUUmSJBVhEJUkSVIRBlFJkiQVYRCVJElSEQZRSZIkFWEQlSRJUhEGUUmSJBVhEJUkSVIRBlFJkiQVYRCVJElSEQZRSZIkFWEQlSRJUhEGUUmSJBVhEJUkSVIRBlFJkiQVYRCVJElSEQZRSZIkFWEQlSRJUhEGUUmSJBXRY4JoRIyNiEcjYkFEnFm6HkmSJHVNjwiiEdEb+FfgWGA4cHJEDC9blSRJkrqiRwRRYBSwIDMfz8zXgKnA8YVrkiRJUhdEZpau4U1FxDhgbGaeVq1/AjgkM/+23TYTgYnV6t7Ao91Y4q7A77vx9bqb4+vZtuTxbcljA8fX0zm+nmtLHht0//jekZmDOnpim24soiuig7Y1EnRmXglc2T3lrCkiWjOzpcRrdwfH17NtyePbkscGjq+nc3w915Y8Nti8xtdTDs0vBoa0Wx8MPF2oFkmSJDVBTwmiDwDDImLPiNgWGA/cXLgmSZIkdUGPODSfmSsj4m+B24DewKTMfLhwWe0VOSWgGzm+nm1LHt+WPDZwfD2d4+u5tuSxwWY0vh5xsZIkSZK2PD3l0LwkSZK2MAZRSZIkFWEQbSciVrRbHhYRP4mIhRExKyJ+FhHvrZ47JSL+37X2vTsiWiJickR8Zq3nToiIW6rlARExPSJ+HRHzI+Kw7hjb2pox1nbrIyIiI2JM941gTXW/dxExpOpnfkQ8HBF/3z0jW11Hd/xsToqIpRHxUHeMaa066n7/+kXEzIj4VfX+/VP3jGxNzfq9i4hFEfFfaz0/Z0t876rlRRExrxpja3eMqyNNfP92iIjLq31nV/v/r+4dzeq6an//qvXe1Vh/UveYOtLk37151dcjEXF+RPTt3tF0rIYxzqm+LqmzboNoByKiH/BT4MrMfGdmHgz8HbBXJ3a/jsZV/e2Nr9oBvgfcmpn7AAcC85tT9abp4ljbnAzcUz0WVeN7txL4cmbuCxwKnB4FbjNb88/mZGBsk0rdJDWO71Xg/Zl5IHAQMDYiDm1e5RunSb93/SNiSNXfvjWUuVFq/tkEODIzD9ocPvuwCe/fD4DngWGZOYLG793AWortpG54//6ewn/voGm/e0dm5v407vq4F5vRhT/Q1DEeVH19oZZCKwbRjn0MuDczV39EVGY+lJmTO7HvHcA+EbEbQERsBxwN/CgidgTeC1xV9flaZr7Q7OI3UlfGSkQEMA44BTim+gUoqZb3LjOfycwHq/7+QOMf1N2bXXwn1DK+qp9fAMubXvHGqev9y8xsmy3oU32VvFKzS793leuBk6rlk1nzj34Jtf1sboY2eawR8U4aAeYbmbmq2ndZZn67rmI7qbb3LyIGA39JI4CX1ozfvbb9VgCfBU6IiKL/kVhL08bYHQyiHdsPePBNtjmp3bT1HKAFIDPfAG4EPlpt92HgZ1V42QtYBvx7dYjiBxGxfT1D6LRNHmvlcOCJzFwI3A18sJ4yO62u9261iBgKjADub2LdnVX7+AqrbXzVocE5wFJgRmaWeP/adPX3DmA68FfV8oeA/9vkGjdWnT+bCdxeHWKcuE6v3a8r799+wK/aQuhmpM7371+AfwQ2hzE343dvtcz8H+AJYFgTa+yqZozxZ+2e/4d6ymwwiHZCRNwUEQ9FxI3tmqe1m7Y+CGh/3lL7wxTtD09sA4wELq8Ox7wEnFlz+RtlE8Z6MjC1Wp7KZnB4vr0mvndt/e0A/CfwxeofoKKaPb7NTTPHl5lvVNsPBkZFxLvrrr+zNmGc0Ji9fj4ixtOYoX+5u+rtjCb/bB6emSOBY2mcFvPeWovfSJv4/rXt+/Xqj/1mdbfAZr1/EXEcsDQzZ3VL4RupK+9d+25qLLHLNnGM7Q/Nf7fO+gyiHXuYRmAEIDM/QuPQc2en3n8J7BYRBwJ/DrSdsL0YWNxuJmZ6+9cpZJPHGhG9gb8GzoqIRcClwLER0b+WSjunrveOiOhDI4Rem5k3rmf/utU2vs1E7eOrToe5m7Lnw3Z1nG2mAf/K5vEfitreu8x8unpcCtxE49B2SV0Z6yPAgRHRq9r3gioI7FhDnRujrvfvcODD1d+IqcD7I+KHTap5UzTrdw+A6u/dUOA3TaitWZo6xroZRDv2H8DhEfHhdm3bdXbnzEwa529NAW7JzFeq9meBpyJi72rTo2j8o1RSV8Z6NI1DTEMyc2hmvoNGUDuh2UVuhFreu+pc2KuA+Zl5cRPr3Vi1jG8zUtf7NygiBlTLb6Hxs/vrplW98bo0znZuAv4PjbvOlVbXe7d9239uq1OZjgG6/dMB1rLJY83MBTRmn86v/jPfdnFJ6Vm1uv7ufTUzB2fmUBozpXdl5sebV/ZGa9bvXtsRsstonIf+fDOKa5KmjbE79IhbfHa3zPxjdTjh4oj4F2AJ8Afg/I3o5jrgK6x76P3vgGsjYlvgceBTTSh5k3VxrCfT+EPY3n8CnwOuaWqhnVTje3c48AlgXnU+DcDXMrNbZxTr/NmMiOuA0cCuEbEYODszr2pK4Z1U4/h2A6ZUf/h7AddnZpGPkYGmjbPtwrlvAzT+r1ROje/d24CbqvFtA/xHZt7anKo3TRPGehrwz8CCiFgO/BE4o5ZiO6nmv3ubjSaN82fV5EQvGn8Dz2t+pZuuiWN8o1qem5mfbHadbbzFpyRJkorw0LwkSZKKMIhKkiSpCIOoJEmSijCISpIkqQiDqCRJkoowiEqSJKkIg6gkdVFEnBsRR5euQ5J6Gj9HVJK6ICJ6Z+Ybb77lRvcbNP6NXtXsviVpc+GMqCStR0QMjYhfR8SUiJgbEdMjYruIWBQRZ0XEPcCJETE5IsZV+yyKiAsj4t6IaI2IkRFxW0QsjIjPtuv7KxHxQNXvP7V7vfkRcRnwIDBkPXWtiIgLIuJXEXFfRLytav9QRNwfEbMj4o527edUY7i9qu+vIuL/RMS8iLg1IvpU2x0cET+PiFlVzbvV+g2WtNUziErShu0NXJmZBwD/A3y+an8lM4/IzKkd7PNUZh4G/BcwGRgHHAqcCxARxwDDgFHAQcDBEfHedq93dWaOyMzfrqem7YH7MvNA4BfA/6ra7wEOzcwRwFTgH9vt807gL4HjgR8CP8vM/WncXvIvqzB6KTAuMw8GJgEXdOYbJEmbynvNS9KGPZWZv6yWfwh8oVqetoF9bq4e5wE7VPeE/0NEvBIRA4Bjqq/Z1XY70AimTwK/zcz73qSm14CfVMuzgA9Uy4OBadVM5rbAE+32+f8y8/WImAf0Btru1z4PGEojAL8bmFHd17038Myb1CFJXWIQlaQNW/tE+rb1lzawz6vV46p2y23r2wAB/O/M/Lf2O0XE0Dfpt83r+acT/N/gT/+WXwpcnJk3R8Ro4Jy1a8rMVRHRfv/2NT1czeRKUrfw0LwkbdgeEdEWzk6mcfi7q24DPh0ROwBExO4R8dYm9LsT8LtqecJG7vsoMKhtrBHRJyL2a0JNkrReBlFJ2rD5wISImAsMBC7vaoeZeTvwH8C91aHy6UD/rvZLYwb0hoj4L+D3G1nTazTOZf12RPwKmAP8eRNqkqT18uObJGk9qkPlP8nMdxcuRZK2SM6ISpIkqQhnRCVpMxUR9wN912r+RGbOK1GPJDWbQVSSJElFeGhekiRJRRhEJUmSVIRBVJIkSUUYRCVJklTE/w8X2zCVp5TyjwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,7))\n",
    "sns.countplot(x='primer_name',hue='primer_type',data=df,palette='Set1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}