400_by_internal_version.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "from scipy import stats\n",
    "import math\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ps = pd.read_csv('patch_seq_log_mouse.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>User</th>\n",
       "      <th>Rig #</th>\n",
       "      <th>File</th>\n",
       "      <th>Ephys</th>\n",
       "      <th>Trans</th>\n",
       "      <th>Morph</th>\n",
       "      <th>Pilot</th>\n",
       "      <th>Pilot Details</th>\n",
       "      <th>cell type</th>\n",
       "      <th>...</th>\n",
       "      <th>Plate</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Stacis call for reconstructions</th>\n",
       "      <th>Stacis call for class</th>\n",
       "      <th>Confidence</th>\n",
       "      <th>Bad dates</th>\n",
       "      <th>Notes.1</th>\n",
       "      <th>Unnamed: 76</th>\n",
       "      <th>Unnamed: 77</th>\n",
       "      <th>Unnamed: 78</th>\n",
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       "      <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",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <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>3</th>\n",
       "      <td>140610</td>\n",
       "      <td>BRL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WT - 140610_01a</td>\n",
       "      <td>x</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RNAseq - pilot</td>\n",
       "      <td>NaN</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Date User Rig #             File Ephys Trans Morph           Pilot  \\\n",
       "0  140604  BRL   NaN   WT - 140604_01     x   NaN   NaN  RNAseq - pilot   \n",
       "1  140606  BRL   NaN   WT - 140606_01     x   NaN   NaN  RNAseq - pilot   \n",
       "2  140606  BRL   NaN   WT - 140606_02     x   NaN   NaN  RNAseq - pilot   \n",
       "3  140610  BRL   NaN  WT - 140610_01a     x   NaN   NaN  RNAseq - pilot   \n",
       "4  140610  BRL   NaN   WT - 140610_01     x   NaN   NaN  RNAseq - pilot   \n",
       "\n",
       "  Pilot Details cell type     ...     Plate Comments  \\\n",
       "0           NaN      tdt-     ...       NaN      NaN   \n",
       "1           NaN      tdt-     ...       NaN      NaN   \n",
       "2           NaN      tdt-     ...       NaN      NaN   \n",
       "3           NaN      tdt-     ...       NaN      NaN   \n",
       "4           NaN      tdt-     ...       NaN      NaN   \n",
       "\n",
       "  Stacis call for reconstructions Stacis call for class Confidence Bad dates  \\\n",
       "0                             NaN                   NaN        NaN       NaN   \n",
       "1                             NaN                   NaN        NaN       NaN   \n",
       "2                             NaN                   NaN        NaN       NaN   \n",
       "3                             NaN                   NaN        NaN       NaN   \n",
       "4                             NaN                   NaN        NaN       NaN   \n",
       "\n",
       "  Notes.1  Unnamed: 76 Unnamed: 77 Unnamed: 78  \n",
       "0     NaN          NaN         NaN         NaN  \n",
       "1     NaN          NaN         NaN         NaN  \n",
       "2     NaN          NaN         NaN         NaN  \n",
       "3     NaN          NaN         NaN         NaN  \n",
       "4     NaN          NaN         NaN         NaN  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7567</th>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>P9</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>80.280</td>\n",
       "      <td>30%</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7568</th>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>PA</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>65.844</td>\n",
       "      <td>36%</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7569</th>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>PA</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>159.027</td>\n",
       "      <td>33%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7570</th>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>PA</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>79.530</td>\n",
       "      <td>33%</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7571</th>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>PA</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>466.560</td>\n",
       "      <td>64%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7572</th>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>PA</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>80.820</td>\n",
       "      <td>36%</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7573</th>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>PB</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>629.040</td>\n",
       "      <td>48%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7574</th>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>PB</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>410.571</td>\n",
       "      <td>49%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7575</th>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>PB</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>165.930</td>\n",
       "      <td>30%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7576</th>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>PB</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>656.268</td>\n",
       "      <td>51%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7577</th>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>PB</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>961.316</td>\n",
       "      <td>67%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7578</th>\n",
       "      <td>2017-10-17</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>126.048</td>\n",
       "      <td>32%</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7579</th>\n",
       "      <td>2017-10-17</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>189.403</td>\n",
       "      <td>37%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7580</th>\n",
       "      <td>2017-10-17</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>149.420</td>\n",
       "      <td>31%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7581</th>\n",
       "      <td>2017-10-17</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>123.396</td>\n",
       "      <td>26%</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7582</th>\n",
       "      <td>2017-10-17</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>415.701</td>\n",
       "      <td>51%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7583</th>\n",
       "      <td>2017-10-17</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>833.328</td>\n",
       "      <td>48%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7584</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>130.186</td>\n",
       "      <td>34%</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7585</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>77.376</td>\n",
       "      <td>31%</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7586</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>319.924</td>\n",
       "      <td>44%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7587</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>57.246</td>\n",
       "      <td>21%</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7588</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>180.124</td>\n",
       "      <td>28%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7589</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>76.868</td>\n",
       "      <td>22%</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7590</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>365.106</td>\n",
       "      <td>42%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7591</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>145.915</td>\n",
       "      <td>35%</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7592</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>9997.520</td>\n",
       "      <td>65%</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7593</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>265.732</td>\n",
       "      <td>31%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7594</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>2060.355</td>\n",
       "      <td>55%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7595</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>297.321</td>\n",
       "      <td>31%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7596</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>378.252</td>\n",
       "      <td>28%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1914 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date User     Internal version      >400 Ratio SM_QC_PF  PCR cycles\n",
       "4968 2017-04-19   P2                 AiV2     8.000  0.41     fail        21.0\n",
       "4969 2017-04-19   P2                 AiV2   461.000  0.54     pass        21.0\n",
       "4970 2017-04-19   P2                 AiV2     1.000     1     fail        21.0\n",
       "4971 2017-04-19   P2                 AiV2   979.000  0.71     pass        21.0\n",
       "4972 2017-04-19   P2                 AiV2   456.000  0.55     pass        21.0\n",
       "4973 2017-04-19   P2                 AiV2  1193.000  0.81     pass        21.0\n",
       "4974 2017-04-19   P2                 AiV2  1319.000  0.59     pass        21.0\n",
       "4975 2017-04-19   P2                 AiV2  1567.000  0.71     pass        21.0\n",
       "4978 2017-04-19   P6                 AiV2   411.000  0.33     fail        21.0\n",
       "4979 2017-04-19   P6                 AiV2   751.000  0.51     pass        21.0\n",
       "4980 2017-04-19   P6                 AiV2   593.000  0.75     pass        21.0\n",
       "4981 2017-04-19   P6                 AiV2   378.000  0.43     pass        21.0\n",
       "4988 2017-04-20   P1                 AiV2   894.000  0.66     pass        21.0\n",
       "4989 2017-04-20   P1                 AiV2   376.000  0.64     pass        21.0\n",
       "4990 2017-04-20   P1                 AiV2   338.000   0.6     pass        21.0\n",
       "4991 2017-04-20   P1                 AiV2   518.000   0.6     pass        21.0\n",
       "4992 2017-04-20   P1                 AiV2   439.000  0.56     pass        21.0\n",
       "4993 2017-04-20   P1                 AiV2   463.000  0.66     pass        21.0\n",
       "4994 2017-04-20   P1                 AiV2   504.000   0.7     pass        21.0\n",
       "4995 2017-04-20   P1                 AiV2   441.000  0.58     pass        21.0\n",
       "4996 2017-04-20   P1                 AiV2   845.000  0.62     pass        21.0\n",
       "4997 2017-04-20   P1                 AiV2   878.000  0.65     pass        21.0\n",
       "5000 2017-04-20   P2                 AiV2  3744.000  0.41     pass        21.0\n",
       "5001 2017-04-20   P2                 AiV2   447.000  0.46     pass        21.0\n",
       "5002 2017-04-20   P2                 AiV2  1166.000  0.64     pass        21.0\n",
       "5003 2017-04-20   P2                 AiV2  1991.000  0.43     pass        21.0\n",
       "5004 2017-04-20   P2                 AiV2   537.000  0.45     pass        21.0\n",
       "5005 2017-04-20   P2                 AiV2   558.000  0.57     pass        21.0\n",
       "5006 2017-04-20   P2                 AiV2   506.000  0.43     pass        21.0\n",
       "5007 2017-04-20   P2                 AiV2   737.000   0.4     pass        21.0\n",
       "...         ...  ...                  ...       ...   ...      ...         ...\n",
       "7567 2017-10-16   P9  AiV2-50uM alexa 488    80.280   30%     fail        21.0\n",
       "7568 2017-10-16   PA  AiV2-50uM alexa 488    65.844   36%     fail        21.0\n",
       "7569 2017-10-16   PA  AiV2-50uM alexa 488   159.027   33%     pass        21.0\n",
       "7570 2017-10-16   PA  AiV2-50uM alexa 488    79.530   33%     fail        21.0\n",
       "7571 2017-10-16   PA  AiV2-50uM alexa 488   466.560   64%     pass        21.0\n",
       "7572 2017-10-16   PA  AiV2-50uM alexa 488    80.820   36%     fail        21.0\n",
       "7573 2017-10-16   PB  AiV2-50uM alexa 488   629.040   48%     pass        21.0\n",
       "7574 2017-10-16   PB  AiV2-50uM alexa 488   410.571   49%     pass        21.0\n",
       "7575 2017-10-16   PB  AiV2-50uM alexa 488   165.930   30%     pass        21.0\n",
       "7576 2017-10-16   PB  AiV2-50uM alexa 488   656.268   51%     pass        21.0\n",
       "7577 2017-10-16   PB  AiV2-50uM alexa 488   961.316   67%     pass        21.0\n",
       "7578 2017-10-17   P2  AiV2-50uM alexa 488   126.048   32%     fail        21.0\n",
       "7579 2017-10-17   P2  AiV2-50uM alexa 488   189.403   37%     pass        21.0\n",
       "7580 2017-10-17   P8  AiV2-50uM alexa 488   149.420   31%     pass        21.0\n",
       "7581 2017-10-17   P8  AiV2-50uM alexa 488   123.396   26%     fail        21.0\n",
       "7582 2017-10-17   P8  AiV2-50uM alexa 488   415.701   51%     pass        21.0\n",
       "7583 2017-10-17   P8  AiV2-50uM alexa 488   833.328   48%     pass        21.0\n",
       "7584 2017-10-18   P2  AiV2-50uM alexa 488   130.186   34%     fail        21.0\n",
       "7585 2017-10-18   P2  AiV2-50uM alexa 488    77.376   31%     fail        21.0\n",
       "7586 2017-10-18   P2  AiV2-50uM alexa 488   319.924   44%     pass        21.0\n",
       "7587 2017-10-18   P2  AiV2-50uM alexa 488    57.246   21%     fail        21.0\n",
       "7588 2017-10-18   P2  AiV2-50uM alexa 488   180.124   28%     pass        21.0\n",
       "7589 2017-10-18   P2  AiV2-50uM alexa 488    76.868   22%     fail        21.0\n",
       "7590 2017-10-18   P2  AiV2-50uM alexa 488   365.106   42%     pass        21.0\n",
       "7591 2017-10-18   P2  AiV2-50uM alexa 488   145.915   35%     fail        21.0\n",
       "7592 2017-10-18   P8  AiV2-50uM alexa 488  9997.520   65%     fail        21.0\n",
       "7593 2017-10-18   P8  AiV2-50uM alexa 488   265.732   31%     pass        21.0\n",
       "7594 2017-10-18   P8  AiV2-50uM alexa 488  2060.355   55%     pass        21.0\n",
       "7595 2017-10-18   P8  AiV2-50uM alexa 488   297.321   31%     pass        21.0\n",
       "7596 2017-10-18   P8  AiV2-50uM alexa 488   378.252   28%     pass        21.0\n",
       "\n",
       "[1914 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps = ps[[\"Date\", \"User\", \"Internal version\", \">400\" , \"Ratio\", \"SM_QC_PF\" , \"PCR cycles\"]]\n",
    "ps.dropna(subset = [\">400\"], inplace = True)\n",
    "ps[\"User\"] = ps[\"User\"].astype(\"category\")\n",
    "#ps[\">400\"] = ps[\">400\"].astype(\"category\")\n",
    "ps[\"Date\"] = pd.to_datetime(ps[\"Date\"], format = '%y%m%d')\n",
    "ps = ps[ps[\"SM_QC_PF\"] != \"cnt\"]\n",
    "ps = ps[ps[\"PCR cycles\"] == 21]\n",
    "ps[\">400\"] = pd.to_numeric(ps[\">400\"].str.replace(' ',''), errors ='force')\n",
    "ps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AiV2                   1424\n",
       "AiV2-50uM alexa 488     474\n",
       "Name: Internal version, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps['Internal version'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_filter(df, col,cond):\n",
    "    filtr = df[df[col] == cond]\n",
    "    return filtr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>User</th>\n",
       "      <th>Internal version</th>\n",
       "      <th>&gt;400</th>\n",
       "      <th>Ratio</th>\n",
       "      <th>SM_QC_PF</th>\n",
       "      <th>PCR cycles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4968</th>\n",
       "      <td>2017-04-19</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.41</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4969</th>\n",
       "      <td>2017-04-19</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2</td>\n",
       "      <td>461.0</td>\n",
       "      <td>0.54</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4970</th>\n",
       "      <td>2017-04-19</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4971</th>\n",
       "      <td>2017-04-19</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2</td>\n",
       "      <td>979.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4972</th>\n",
       "      <td>2017-04-19</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2</td>\n",
       "      <td>456.0</td>\n",
       "      <td>0.55</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date User Internal version   >400 Ratio SM_QC_PF  PCR cycles\n",
       "4968 2017-04-19   P2             AiV2    8.0  0.41     fail        21.0\n",
       "4969 2017-04-19   P2             AiV2  461.0  0.54     pass        21.0\n",
       "4970 2017-04-19   P2             AiV2    1.0     1     fail        21.0\n",
       "4971 2017-04-19   P2             AiV2  979.0  0.71     pass        21.0\n",
       "4972 2017-04-19   P2             AiV2  456.0  0.55     pass        21.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps2 = ps[ps[\">400\"] < 8000]\n",
    "ps2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2017-04-19</td>\n",
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       "      <td>AiV2</td>\n",
       "      <td>8.0</td>\n",
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       "      <td>21.0</td>\n",
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       "    <tr>\n",
       "      <th>4969</th>\n",
       "      <td>2017-04-19</td>\n",
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       "      <td>AiV2</td>\n",
       "      <td>461.0</td>\n",
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       "    <tr>\n",
       "      <th>4970</th>\n",
       "      <td>2017-04-19</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
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       "    <tr>\n",
       "      <th>4971</th>\n",
       "      <td>2017-04-19</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2</td>\n",
       "      <td>979.0</td>\n",
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       "    <tr>\n",
       "      <th>4972</th>\n",
       "      <td>2017-04-19</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2</td>\n",
       "      <td>456.0</td>\n",
       "      <td>0.55</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date User Internal version   >400 Ratio SM_QC_PF  PCR cycles\n",
       "4968 2017-04-19   P2             AiV2    8.0  0.41     fail        21.0\n",
       "4969 2017-04-19   P2             AiV2  461.0  0.54     pass        21.0\n",
       "4970 2017-04-19   P2             AiV2    1.0     1     fail        21.0\n",
       "4971 2017-04-19   P2             AiV2  979.0  0.71     pass        21.0\n",
       "4972 2017-04-19   P2             AiV2  456.0  0.55     pass        21.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "standard_internal= gen_filter(ps2, \"Internal version\", \"AiV2\")\n",
    "standard_internal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>7094</th>\n",
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       "      <td>AiV2-50uM alexa 488</td>\n",
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       "      <th>7095</th>\n",
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       "      <th>7096</th>\n",
       "      <td>2017-08-30</td>\n",
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       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>371.000</td>\n",
       "      <td>415</td>\n",
       "      <td>pass</td>\n",
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       "    <tr>\n",
       "      <th>7099</th>\n",
       "      <td>2017-08-30</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>99.000</td>\n",
       "      <td>91</td>\n",
       "      <td>pass</td>\n",
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       "    <tr>\n",
       "      <th>7101</th>\n",
       "      <td>2017-08-30</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>248.000</td>\n",
       "      <td>296</td>\n",
       "      <td>pass</td>\n",
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       "    <tr>\n",
       "      <th>7108</th>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>1648.000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
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       "    <tr>\n",
       "      <th>7109</th>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>1405.000</td>\n",
       "      <td>0.74</td>\n",
       "      <td>pass</td>\n",
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       "    <tr>\n",
       "      <th>7110</th>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>1053.000</td>\n",
       "      <td>0.69</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
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       "    <tr>\n",
       "      <th>7113</th>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>1633.000</td>\n",
       "      <td>0.71</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
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       "    <tr>\n",
       "      <th>7114</th>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>513.000</td>\n",
       "      <td>0.44</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
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       "    <tr>\n",
       "      <th>7115</th>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>1090.000</td>\n",
       "      <td>0.38</td>\n",
       "      <td>pass</td>\n",
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       "    <tr>\n",
       "      <th>7116</th>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>1956.000</td>\n",
       "      <td>0.83</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
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       "    <tr>\n",
       "      <th>7117</th>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>488.000</td>\n",
       "      <td>0.56</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7118</th>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>1304.000</td>\n",
       "      <td>0.71</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7119</th>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>1581.000</td>\n",
       "      <td>0.65</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
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       "    <tr>\n",
       "      <th>7126</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>1187.000</td>\n",
       "      <td>0.7</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
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       "    <tr>\n",
       "      <th>7128</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>1994.000</td>\n",
       "      <td>0.63</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
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       "    <tr>\n",
       "      <th>7129</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>781.000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7132</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>413.000</td>\n",
       "      <td>0.28</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7133</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>1509.000</td>\n",
       "      <td>0.71</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7134</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>379.000</td>\n",
       "      <td>0.34</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7136</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>552.000</td>\n",
       "      <td>0.44</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
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       "    <tr>\n",
       "      <th>7145</th>\n",
       "      <td>2017-09-08</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>734.069</td>\n",
       "      <td>71%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7146</th>\n",
       "      <td>2017-09-08</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>706.800</td>\n",
       "      <td>62%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7147</th>\n",
       "      <td>2017-09-08</td>\n",
       "      <td>P9</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>81.130</td>\n",
       "      <td>35%</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7148</th>\n",
       "      <td>2017-09-11</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>642.460</td>\n",
       "      <td>65%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7149</th>\n",
       "      <td>2017-09-11</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>615.912</td>\n",
       "      <td>66%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7150</th>\n",
       "      <td>2017-09-11</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>544.665</td>\n",
       "      <td>55%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7151</th>\n",
       "      <td>2017-09-11</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>758.340</td>\n",
       "      <td>66%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7152</th>\n",
       "      <td>2017-09-11</td>\n",
       "      <td>P9</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>384.705</td>\n",
       "      <td>45%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</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>7566</th>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>P9</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>372.996</td>\n",
       "      <td>52%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7567</th>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>P9</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>80.280</td>\n",
       "      <td>30%</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7568</th>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>PA</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>65.844</td>\n",
       "      <td>36%</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7569</th>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>PA</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>159.027</td>\n",
       "      <td>33%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7570</th>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>PA</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>79.530</td>\n",
       "      <td>33%</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7571</th>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>PA</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>466.560</td>\n",
       "      <td>64%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7572</th>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>PA</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>80.820</td>\n",
       "      <td>36%</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7573</th>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>PB</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>629.040</td>\n",
       "      <td>48%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7574</th>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>PB</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>410.571</td>\n",
       "      <td>49%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7575</th>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>PB</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>165.930</td>\n",
       "      <td>30%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7576</th>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>PB</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>656.268</td>\n",
       "      <td>51%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7577</th>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>PB</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>961.316</td>\n",
       "      <td>67%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7578</th>\n",
       "      <td>2017-10-17</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>126.048</td>\n",
       "      <td>32%</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7579</th>\n",
       "      <td>2017-10-17</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>189.403</td>\n",
       "      <td>37%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7580</th>\n",
       "      <td>2017-10-17</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>149.420</td>\n",
       "      <td>31%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7581</th>\n",
       "      <td>2017-10-17</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>123.396</td>\n",
       "      <td>26%</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7582</th>\n",
       "      <td>2017-10-17</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>415.701</td>\n",
       "      <td>51%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7583</th>\n",
       "      <td>2017-10-17</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>833.328</td>\n",
       "      <td>48%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7584</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>130.186</td>\n",
       "      <td>34%</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7585</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>77.376</td>\n",
       "      <td>31%</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7586</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>319.924</td>\n",
       "      <td>44%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7587</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>57.246</td>\n",
       "      <td>21%</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7588</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>180.124</td>\n",
       "      <td>28%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7589</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>76.868</td>\n",
       "      <td>22%</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7590</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>365.106</td>\n",
       "      <td>42%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7591</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P2</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>145.915</td>\n",
       "      <td>35%</td>\n",
       "      <td>fail</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7593</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>265.732</td>\n",
       "      <td>31%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7594</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>2060.355</td>\n",
       "      <td>55%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7595</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>297.321</td>\n",
       "      <td>31%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7596</th>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>P8</td>\n",
       "      <td>AiV2-50uM alexa 488</td>\n",
       "      <td>378.252</td>\n",
       "      <td>28%</td>\n",
       "      <td>pass</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>473 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date User     Internal version      >400 Ratio SM_QC_PF  PCR cycles\n",
       "7094 2017-08-29   P8  AiV2-50uM alexa 488   637.000   172     pass        21.0\n",
       "7095 2017-08-29   P8  AiV2-50uM alexa 488   156.000    92     pass        21.0\n",
       "7096 2017-08-30   P8  AiV2-50uM alexa 488   371.000   415     pass        21.0\n",
       "7099 2017-08-30   P8  AiV2-50uM alexa 488    99.000    91     pass        21.0\n",
       "7101 2017-08-30   P8  AiV2-50uM alexa 488   248.000   296     pass        21.0\n",
       "7108 2017-08-31   P2  AiV2-50uM alexa 488  1648.000  0.75     pass        21.0\n",
       "7109 2017-08-31   P2  AiV2-50uM alexa 488  1405.000  0.74     pass        21.0\n",
       "7110 2017-08-31   P2  AiV2-50uM alexa 488  1053.000  0.69     pass        21.0\n",
       "7113 2017-08-31   P8  AiV2-50uM alexa 488  1633.000  0.71     pass        21.0\n",
       "7114 2017-08-31   P8  AiV2-50uM alexa 488   513.000  0.44     pass        21.0\n",
       "7115 2017-08-31   P8  AiV2-50uM alexa 488  1090.000  0.38     pass        21.0\n",
       "7116 2017-08-31   P8  AiV2-50uM alexa 488  1956.000  0.83     pass        21.0\n",
       "7117 2017-08-31   P8  AiV2-50uM alexa 488   488.000  0.56     pass        21.0\n",
       "7118 2017-08-31   P8  AiV2-50uM alexa 488  1304.000  0.71     pass        21.0\n",
       "7119 2017-08-31   P8  AiV2-50uM alexa 488  1581.000  0.65     pass        21.0\n",
       "7126 2017-09-01   P2  AiV2-50uM alexa 488  1187.000   0.7     pass        21.0\n",
       "7128 2017-09-01   P2  AiV2-50uM alexa 488  1994.000  0.63     pass        21.0\n",
       "7129 2017-09-01   P2  AiV2-50uM alexa 488   781.000   0.4     pass        21.0\n",
       "7132 2017-09-01   P8  AiV2-50uM alexa 488   413.000  0.28     pass        21.0\n",
       "7133 2017-09-01   P8  AiV2-50uM alexa 488  1509.000  0.71     pass        21.0\n",
       "7134 2017-09-01   P8  AiV2-50uM alexa 488   379.000  0.34     pass        21.0\n",
       "7136 2017-09-01   P8  AiV2-50uM alexa 488   552.000  0.44     pass        21.0\n",
       "7145 2017-09-08   P2  AiV2-50uM alexa 488   734.069   71%     pass        21.0\n",
       "7146 2017-09-08   P2  AiV2-50uM alexa 488   706.800   62%     pass        21.0\n",
       "7147 2017-09-08   P9  AiV2-50uM alexa 488    81.130   35%     fail        21.0\n",
       "7148 2017-09-11   P2  AiV2-50uM alexa 488   642.460   65%     pass        21.0\n",
       "7149 2017-09-11   P2  AiV2-50uM alexa 488   615.912   66%     pass        21.0\n",
       "7150 2017-09-11   P2  AiV2-50uM alexa 488   544.665   55%     pass        21.0\n",
       "7151 2017-09-11   P2  AiV2-50uM alexa 488   758.340   66%     pass        21.0\n",
       "7152 2017-09-11   P9  AiV2-50uM alexa 488   384.705   45%     pass        21.0\n",
       "...         ...  ...                  ...       ...   ...      ...         ...\n",
       "7566 2017-10-16   P9  AiV2-50uM alexa 488   372.996   52%     pass        21.0\n",
       "7567 2017-10-16   P9  AiV2-50uM alexa 488    80.280   30%     fail        21.0\n",
       "7568 2017-10-16   PA  AiV2-50uM alexa 488    65.844   36%     fail        21.0\n",
       "7569 2017-10-16   PA  AiV2-50uM alexa 488   159.027   33%     pass        21.0\n",
       "7570 2017-10-16   PA  AiV2-50uM alexa 488    79.530   33%     fail        21.0\n",
       "7571 2017-10-16   PA  AiV2-50uM alexa 488   466.560   64%     pass        21.0\n",
       "7572 2017-10-16   PA  AiV2-50uM alexa 488    80.820   36%     fail        21.0\n",
       "7573 2017-10-16   PB  AiV2-50uM alexa 488   629.040   48%     pass        21.0\n",
       "7574 2017-10-16   PB  AiV2-50uM alexa 488   410.571   49%     pass        21.0\n",
       "7575 2017-10-16   PB  AiV2-50uM alexa 488   165.930   30%     pass        21.0\n",
       "7576 2017-10-16   PB  AiV2-50uM alexa 488   656.268   51%     pass        21.0\n",
       "7577 2017-10-16   PB  AiV2-50uM alexa 488   961.316   67%     pass        21.0\n",
       "7578 2017-10-17   P2  AiV2-50uM alexa 488   126.048   32%     fail        21.0\n",
       "7579 2017-10-17   P2  AiV2-50uM alexa 488   189.403   37%     pass        21.0\n",
       "7580 2017-10-17   P8  AiV2-50uM alexa 488   149.420   31%     pass        21.0\n",
       "7581 2017-10-17   P8  AiV2-50uM alexa 488   123.396   26%     fail        21.0\n",
       "7582 2017-10-17   P8  AiV2-50uM alexa 488   415.701   51%     pass        21.0\n",
       "7583 2017-10-17   P8  AiV2-50uM alexa 488   833.328   48%     pass        21.0\n",
       "7584 2017-10-18   P2  AiV2-50uM alexa 488   130.186   34%     fail        21.0\n",
       "7585 2017-10-18   P2  AiV2-50uM alexa 488    77.376   31%     fail        21.0\n",
       "7586 2017-10-18   P2  AiV2-50uM alexa 488   319.924   44%     pass        21.0\n",
       "7587 2017-10-18   P2  AiV2-50uM alexa 488    57.246   21%     fail        21.0\n",
       "7588 2017-10-18   P2  AiV2-50uM alexa 488   180.124   28%     pass        21.0\n",
       "7589 2017-10-18   P2  AiV2-50uM alexa 488    76.868   22%     fail        21.0\n",
       "7590 2017-10-18   P2  AiV2-50uM alexa 488   365.106   42%     pass        21.0\n",
       "7591 2017-10-18   P2  AiV2-50uM alexa 488   145.915   35%     fail        21.0\n",
       "7593 2017-10-18   P8  AiV2-50uM alexa 488   265.732   31%     pass        21.0\n",
       "7594 2017-10-18   P8  AiV2-50uM alexa 488  2060.355   55%     pass        21.0\n",
       "7595 2017-10-18   P8  AiV2-50uM alexa 488   297.321   31%     pass        21.0\n",
       "7596 2017-10-18   P8  AiV2-50uM alexa 488   378.252   28%     pass        21.0\n",
       "\n",
       "[473 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Alexa_internal= gen_filter(ps2, \"Internal version\", \"AiV2-50uM alexa 488\")\n",
    "Alexa_internal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xcd8a9e8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HC9DrBPMmRNvcA+CNtcd59MI+bEsrs5bFBriy70QFh3IrmRDrS4y/G4NCPHlm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc60b0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(x='Internal version', y='>400', data=ps2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xc9fa978>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sW7ac3bt3xzukbqWl5JGpql84jUFVFwKZrW1YVXeo6lI3XQl8itd3cgXwnFvs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\n/r1OjAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcccee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Internal version', y='>400', data=ps2, scale='area')\n",
    "plt.title(\">400 bp Concentration vs Internal Version\")\n",
    "plt.ylabel(\">400 bp Concentration\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}