Lecture-6-Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from warnings import simplefilter \n",
    "simplefilter(action='ignore', category=FutureWarning)\n",
    "\n",
    "import h5py\n",
    "import numpy as np\n",
    "import pylab as plt\n",
    "import sys\n",
    "sys.path.append('./..')\n",
    "import data as tutorial_data\n",
    "file_name = tutorial_data.get_file('BYxRM')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Pandas\n",
    "\n",
    "Similar to *NumPy*, *Pandas* is one of the most widely used python libraries in data science. It provides high-performance, easy to use structures and data analysis tools. Unlike *NumPy* library which provides objects for multi-dimensional arrays, *Pandas* provides in-memory 2d table object called *Dataframe*. It is like a spreadsheet with column names and row label.  \n",
    "\n",
    "Some commonly used data structures in pandas are:\n",
    "\n",
    "- *Series* objects: 1D array, similar to a column in a spreadsheet\n",
    "- *DataFrame* objects: 2D table, similar to a spreadsheet\n",
    "- *Panel* objects: Dictionary of DataFrames, similar to sheet in MS Excel\n",
    "\n",
    "Pandas can be imported into Python using:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas Series\n",
    "\n",
    "Pandas Series object is created using *pd.Series* function. Each row is provided with an index and by defaults is assigned numerical values starting from *0*. Like *NumPy*, Pandas also provide the basic mathematical functionalities like addition, subtraction and conditional operations and broadcasting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alice       68\n",
      "bob         83\n",
      "charles    112\n",
      "dtype: int64\n",
      "---\n",
      "[ 68  83 112]\n"
     ]
    }
   ],
   "source": [
    "weight = pd.Series([68, 83, 112],index=[\"alice\", \"bob\", \"charles\"])\n",
    "birthyear = pd.Series([1984, 1985, 1992], index=[\"bob\", \"alice\", \"charles\"], name=\"year\")\n",
    "children = pd.Series([0, 3], index=[\"charles\", \"bob\"])\n",
    "hobby = pd.Series([\"Biking\", \"Dancing\"], index=[\"alice\", \"bob\"])\n",
    "print(weight)\n",
    "print('---')\n",
    "print(weight.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a lot of applications, a plain old *Series* is probably not a lot of use, but it is the core component of the Pandas workhorse, the *DataFrame*, so it’s useful to know about.   \n",
    "\n",
    "The *DataFrame* represents tabular data, a bit like a spreadsheet. *DataFrames* are organised into colums (each of which is a *Series*), and each column can store a single data-type, such as floating point numbers, strings, boolean values etc. *DataFrames* can be indexed by either their row or column names. (They are similar in many ways to R’s `data.frame`.).  \n",
    "\n",
    "We can create a *DataFrame* in *Pandas* from a Python dictionary, or by loading in a text file containing tabular data. First we are going to look at how to create one from a dictionary.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>weight</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>children</th>\n",
       "      <th>hobby</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>68</td>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>83</td>\n",
       "      <td>1984</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Dancing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>112</td>\n",
       "      <td>1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         weight  birthyear  children    hobby\n",
       "alice        68       1985       NaN   Biking\n",
       "bob          83       1984       3.0  Dancing\n",
       "charles     112       1992       0.0      NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'weight': weight, 'birthyear': birthyear, 'children': children, 'hobby': hobby})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An alternative way to create a *DataFrame* is to load it from existing files. There are various loading function to load data from different file sources which can be found [here](https://pandas.pydata.org/pandas-docs/stable/api.html#input-output). The most important ones are:\n",
    "- *pd.read_pickle*: Load pickled pandas object (or any object) from file\n",
    "- *pd.read_csv*: Read CSV (comma-separated) file into DataFrame\n",
    "- *pd.read_json*: Convert a JSON string to pandas object\n",
    "- *pd.read_excel*: Read an Excel table into a pandas DataFrame\n",
    "- *pd.read_hdf*: Read from the HDF5 file (PyTables format)\n",
    "\n",
    "Let's return now to our HDF5 file that we previously examined.\n",
    "The *phenotype* group contains 3 datasets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['col_header', 'matrix', 'row_header']\n",
      "<HDF5 dataset \"phenotype_ID\": shape (46,), type \"|S22\">\n",
      "<HDF5 dataset \"sample_ID\": shape (1008,), type \"|S22\">\n"
     ]
    }
   ],
   "source": [
    "f = h5py.File(file_name, 'r')\n",
    "print(list(f['phenotype'].keys()))\n",
    "print(f['phenotype/col_header/phenotype_ID'])\n",
    "print(f['phenotype/row_header/sample_ID'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This seems to be a phenotype matrix where the *matrix* dataset contains the 2d array with the phenotype values and the *col_header/phenotype_ID* contains the phenotype names and the *row_header/sample_ID* contains the accession ids.  \n",
    "We can construct a Panda *dataframe* in the following way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pheno_group = f['phenotype']\n",
    "df = pd.DataFrame(pheno_group['matrix'][:], \n",
    "                        columns=np.char.decode(pheno_group['col_header']['phenotype_ID'][:]),\n",
    "                        index=np.char.decode(pheno_group['row_header']['sample_ID'][:]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dataframes** have some essential methods to access the data:\n",
    "- **head()**: returns the top 5 rows in the dataframe object\n",
    "- **tail()**: returns the bottom 5 rows in the dataframe\n",
    "- **info()**: prints the summary of the dataframe\n",
    "- **describe()**: gives a nice overview of the main aggregated values over each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Cadmium_Chloride</th>\n",
       "      <th>Caffeine</th>\n",
       "      <th>Calcium_Chloride</th>\n",
       "      <th>Cisplatin</th>\n",
       "      <th>Cobalt_Chloride</th>\n",
       "      <th>Congo_red</th>\n",
       "      <th>Copper</th>\n",
       "      <th>Cycloheximide</th>\n",
       "      <th>Diamide</th>\n",
       "      <th>E6_Berbamine</th>\n",
       "      <th>...</th>\n",
       "      <th>x6-Azauracil</th>\n",
       "      <th>Xylose</th>\n",
       "      <th>YNB</th>\n",
       "      <th>YNB:ph3</th>\n",
       "      <th>YNB:ph8</th>\n",
       "      <th>YPD</th>\n",
       "      <th>YPD:15C</th>\n",
       "      <th>YPD:37C</th>\n",
       "      <th>YPD:4C</th>\n",
       "      <th>Zeocin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A01_01</th>\n",
       "      <td>-7.323520</td>\n",
       "      <td>0.279993</td>\n",
       "      <td>0.313118</td>\n",
       "      <td>1.658179</td>\n",
       "      <td>-1.604442</td>\n",
       "      <td>5.841617</td>\n",
       "      <td>-4.130950</td>\n",
       "      <td>0.821226</td>\n",
       "      <td>3.622602</td>\n",
       "      <td>-0.378747</td>\n",
       "      <td>...</td>\n",
       "      <td>1.199054</td>\n",
       "      <td>-0.309149</td>\n",
       "      <td>17.470822</td>\n",
       "      <td>0.055225</td>\n",
       "      <td>-0.184268</td>\n",
       "      <td>24.548971</td>\n",
       "      <td>0.712171</td>\n",
       "      <td>0.890842</td>\n",
       "      <td>4.118372</td>\n",
       "      <td>8.592818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_02</th>\n",
       "      <td>-8.098236</td>\n",
       "      <td>-0.206326</td>\n",
       "      <td>-0.534844</td>\n",
       "      <td>-0.918012</td>\n",
       "      <td>0.892198</td>\n",
       "      <td>-1.618172</td>\n",
       "      <td>1.131947</td>\n",
       "      <td>-0.764736</td>\n",
       "      <td>-2.946279</td>\n",
       "      <td>-2.475193</td>\n",
       "      <td>...</td>\n",
       "      <td>0.443182</td>\n",
       "      <td>0.697908</td>\n",
       "      <td>18.052925</td>\n",
       "      <td>0.283462</td>\n",
       "      <td>1.662201</td>\n",
       "      <td>26.808476</td>\n",
       "      <td>-1.522498</td>\n",
       "      <td>0.006062</td>\n",
       "      <td>0.066515</td>\n",
       "      <td>-4.220476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_03</th>\n",
       "      <td>7.605720</td>\n",
       "      <td>-0.127960</td>\n",
       "      <td>-0.311102</td>\n",
       "      <td>-2.712088</td>\n",
       "      <td>3.301709</td>\n",
       "      <td>-6.680571</td>\n",
       "      <td>-1.138056</td>\n",
       "      <td>-3.382532</td>\n",
       "      <td>-3.157866</td>\n",
       "      <td>-4.178616</td>\n",
       "      <td>...</td>\n",
       "      <td>1.472629</td>\n",
       "      <td>1.474329</td>\n",
       "      <td>18.163782</td>\n",
       "      <td>-0.951379</td>\n",
       "      <td>0.369565</td>\n",
       "      <td>26.183975</td>\n",
       "      <td>1.082289</td>\n",
       "      <td>1.723157</td>\n",
       "      <td>5.714088</td>\n",
       "      <td>-6.506519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_04</th>\n",
       "      <td>-6.147649</td>\n",
       "      <td>0.878392</td>\n",
       "      <td>-0.563331</td>\n",
       "      <td>-0.827358</td>\n",
       "      <td>-1.291270</td>\n",
       "      <td>2.753851</td>\n",
       "      <td>-2.268121</td>\n",
       "      <td>0.867456</td>\n",
       "      <td>-6.832079</td>\n",
       "      <td>-1.970630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.054694</td>\n",
       "      <td>-0.739040</td>\n",
       "      <td>15.575856</td>\n",
       "      <td>-1.690867</td>\n",
       "      <td>-0.840344</td>\n",
       "      <td>20.893646</td>\n",
       "      <td>-0.636280</td>\n",
       "      <td>0.053258</td>\n",
       "      <td>-3.196936</td>\n",
       "      <td>0.294462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_05</th>\n",
       "      <td>9.379060</td>\n",
       "      <td>-1.353169</td>\n",
       "      <td>0.405204</td>\n",
       "      <td>-1.137234</td>\n",
       "      <td>4.260286</td>\n",
       "      <td>-7.391216</td>\n",
       "      <td>-3.222742</td>\n",
       "      <td>1.586376</td>\n",
       "      <td>1.048391</td>\n",
       "      <td>-3.971730</td>\n",
       "      <td>...</td>\n",
       "      <td>0.267463</td>\n",
       "      <td>0.959138</td>\n",
       "      <td>17.808444</td>\n",
       "      <td>0.127621</td>\n",
       "      <td>1.967267</td>\n",
       "      <td>28.621507</td>\n",
       "      <td>-3.720999</td>\n",
       "      <td>-0.768724</td>\n",
       "      <td>-4.002786</td>\n",
       "      <td>7.045575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_06</th>\n",
       "      <td>9.394562</td>\n",
       "      <td>1.655436</td>\n",
       "      <td>0.531654</td>\n",
       "      <td>-2.044903</td>\n",
       "      <td>1.577325</td>\n",
       "      <td>-5.769939</td>\n",
       "      <td>1.355905</td>\n",
       "      <td>2.336068</td>\n",
       "      <td>4.038331</td>\n",
       "      <td>-2.917101</td>\n",
       "      <td>...</td>\n",
       "      <td>0.591233</td>\n",
       "      <td>-0.134019</td>\n",
       "      <td>16.085601</td>\n",
       "      <td>-0.100294</td>\n",
       "      <td>0.710547</td>\n",
       "      <td>23.549116</td>\n",
       "      <td>-2.680231</td>\n",
       "      <td>2.025716</td>\n",
       "      <td>-0.889829</td>\n",
       "      <td>-4.108786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_07</th>\n",
       "      <td>-7.229972</td>\n",
       "      <td>-1.423605</td>\n",
       "      <td>-0.007546</td>\n",
       "      <td>-1.714243</td>\n",
       "      <td>0.954948</td>\n",
       "      <td>2.111495</td>\n",
       "      <td>0.590067</td>\n",
       "      <td>1.415252</td>\n",
       "      <td>-4.861309</td>\n",
       "      <td>-4.082216</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.330839</td>\n",
       "      <td>0.294128</td>\n",
       "      <td>16.472448</td>\n",
       "      <td>-0.612281</td>\n",
       "      <td>-0.242591</td>\n",
       "      <td>23.901629</td>\n",
       "      <td>-1.834152</td>\n",
       "      <td>-3.487390</td>\n",
       "      <td>3.618048</td>\n",
       "      <td>-0.035142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_08</th>\n",
       "      <td>-7.585476</td>\n",
       "      <td>1.750342</td>\n",
       "      <td>0.941181</td>\n",
       "      <td>0.306306</td>\n",
       "      <td>-2.997501</td>\n",
       "      <td>0.063007</td>\n",
       "      <td>-4.352137</td>\n",
       "      <td>2.634605</td>\n",
       "      <td>3.147146</td>\n",
       "      <td>8.292883</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043294</td>\n",
       "      <td>-0.826648</td>\n",
       "      <td>16.210856</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.538390</td>\n",
       "      <td>23.595423</td>\n",
       "      <td>-1.449024</td>\n",
       "      <td>1.679693</td>\n",
       "      <td>-2.227455</td>\n",
       "      <td>-5.773160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_09</th>\n",
       "      <td>-7.890982</td>\n",
       "      <td>-0.559696</td>\n",
       "      <td>-1.396937</td>\n",
       "      <td>-0.879233</td>\n",
       "      <td>-1.044798</td>\n",
       "      <td>-6.214873</td>\n",
       "      <td>0.211404</td>\n",
       "      <td>-0.006420</td>\n",
       "      <td>-9.470146</td>\n",
       "      <td>-4.480685</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.048497</td>\n",
       "      <td>-0.483113</td>\n",
       "      <td>15.161760</td>\n",
       "      <td>-0.035535</td>\n",
       "      <td>-0.398174</td>\n",
       "      <td>22.693341</td>\n",
       "      <td>-1.580512</td>\n",
       "      <td>-1.320567</td>\n",
       "      <td>-4.863302</td>\n",
       "      <td>9.024782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_10</th>\n",
       "      <td>-8.144131</td>\n",
       "      <td>-2.787328</td>\n",
       "      <td>-1.111711</td>\n",
       "      <td>-0.239611</td>\n",
       "      <td>-2.933533</td>\n",
       "      <td>1.231604</td>\n",
       "      <td>1.397043</td>\n",
       "      <td>2.405056</td>\n",
       "      <td>8.532335</td>\n",
       "      <td>14.355684</td>\n",
       "      <td>...</td>\n",
       "      <td>0.685319</td>\n",
       "      <td>-0.594544</td>\n",
       "      <td>16.388451</td>\n",
       "      <td>-0.742849</td>\n",
       "      <td>-0.427930</td>\n",
       "      <td>23.697733</td>\n",
       "      <td>-2.967864</td>\n",
       "      <td>-1.553708</td>\n",
       "      <td>-5.314155</td>\n",
       "      <td>-1.229565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Cadmium_Chloride  Caffeine  Calcium_Chloride  Cisplatin  \\\n",
       "A01_01         -7.323520  0.279993          0.313118   1.658179   \n",
       "A01_02         -8.098236 -0.206326         -0.534844  -0.918012   \n",
       "A01_03          7.605720 -0.127960         -0.311102  -2.712088   \n",
       "A01_04         -6.147649  0.878392         -0.563331  -0.827358   \n",
       "A01_05          9.379060 -1.353169          0.405204  -1.137234   \n",
       "A01_06          9.394562  1.655436          0.531654  -2.044903   \n",
       "A01_07         -7.229972 -1.423605         -0.007546  -1.714243   \n",
       "A01_08         -7.585476  1.750342          0.941181   0.306306   \n",
       "A01_09         -7.890982 -0.559696         -1.396937  -0.879233   \n",
       "A01_10         -8.144131 -2.787328         -1.111711  -0.239611   \n",
       "\n",
       "        Cobalt_Chloride  Congo_red    Copper  Cycloheximide   Diamide  \\\n",
       "A01_01        -1.604442   5.841617 -4.130950       0.821226  3.622602   \n",
       "A01_02         0.892198  -1.618172  1.131947      -0.764736 -2.946279   \n",
       "A01_03         3.301709  -6.680571 -1.138056      -3.382532 -3.157866   \n",
       "A01_04        -1.291270   2.753851 -2.268121       0.867456 -6.832079   \n",
       "A01_05         4.260286  -7.391216 -3.222742       1.586376  1.048391   \n",
       "A01_06         1.577325  -5.769939  1.355905       2.336068  4.038331   \n",
       "A01_07         0.954948   2.111495  0.590067       1.415252 -4.861309   \n",
       "A01_08        -2.997501   0.063007 -4.352137       2.634605  3.147146   \n",
       "A01_09        -1.044798  -6.214873  0.211404      -0.006420 -9.470146   \n",
       "A01_10        -2.933533   1.231604  1.397043       2.405056  8.532335   \n",
       "\n",
       "        E6_Berbamine  ...  x6-Azauracil    Xylose        YNB   YNB:ph3  \\\n",
       "A01_01     -0.378747  ...      1.199054 -0.309149  17.470822  0.055225   \n",
       "A01_02     -2.475193  ...      0.443182  0.697908  18.052925  0.283462   \n",
       "A01_03     -4.178616  ...      1.472629  1.474329  18.163782 -0.951379   \n",
       "A01_04     -1.970630  ...      0.054694 -0.739040  15.575856 -1.690867   \n",
       "A01_05     -3.971730  ...      0.267463  0.959138  17.808444  0.127621   \n",
       "A01_06     -2.917101  ...      0.591233 -0.134019  16.085601 -0.100294   \n",
       "A01_07     -4.082216  ...     -0.330839  0.294128  16.472448 -0.612281   \n",
       "A01_08      8.292883  ...      0.043294 -0.826648  16.210856       NaN   \n",
       "A01_09     -4.480685  ...     -0.048497 -0.483113  15.161760 -0.035535   \n",
       "A01_10     14.355684  ...      0.685319 -0.594544  16.388451 -0.742849   \n",
       "\n",
       "         YNB:ph8        YPD   YPD:15C   YPD:37C    YPD:4C    Zeocin  \n",
       "A01_01 -0.184268  24.548971  0.712171  0.890842  4.118372  8.592818  \n",
       "A01_02  1.662201  26.808476 -1.522498  0.006062  0.066515 -4.220476  \n",
       "A01_03  0.369565  26.183975  1.082289  1.723157  5.714088 -6.506519  \n",
       "A01_04 -0.840344  20.893646 -0.636280  0.053258 -3.196936  0.294462  \n",
       "A01_05  1.967267  28.621507 -3.720999 -0.768724 -4.002786  7.045575  \n",
       "A01_06  0.710547  23.549116 -2.680231  2.025716 -0.889829 -4.108786  \n",
       "A01_07 -0.242591  23.901629 -1.834152 -3.487390  3.618048 -0.035142  \n",
       "A01_08 -0.538390  23.595423 -1.449024  1.679693 -2.227455 -5.773160  \n",
       "A01_09 -0.398174  22.693341 -1.580512 -1.320567 -4.863302  9.024782  \n",
       "A01_10 -0.427930  23.697733 -2.967864 -1.553708 -5.314155 -1.229565  \n",
       "\n",
       "[10 rows x 46 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# retrieve first 10 rows\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Cadmium_Chloride</th>\n",
       "      <th>Caffeine</th>\n",
       "      <th>Calcium_Chloride</th>\n",
       "      <th>Cisplatin</th>\n",
       "      <th>Cobalt_Chloride</th>\n",
       "      <th>Congo_red</th>\n",
       "      <th>Copper</th>\n",
       "      <th>Cycloheximide</th>\n",
       "      <th>Diamide</th>\n",
       "      <th>E6_Berbamine</th>\n",
       "      <th>...</th>\n",
       "      <th>x6-Azauracil</th>\n",
       "      <th>Xylose</th>\n",
       "      <th>YNB</th>\n",
       "      <th>YNB:ph3</th>\n",
       "      <th>YNB:ph8</th>\n",
       "      <th>YPD</th>\n",
       "      <th>YPD:15C</th>\n",
       "      <th>YPD:37C</th>\n",
       "      <th>YPD:4C</th>\n",
       "      <th>Zeocin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A11_92</th>\n",
       "      <td>-4.262720</td>\n",
       "      <td>-0.893290</td>\n",
       "      <td>-0.329504</td>\n",
       "      <td>0.257061</td>\n",
       "      <td>-3.120957</td>\n",
       "      <td>2.556543</td>\n",
       "      <td>2.567092</td>\n",
       "      <td>2.054232</td>\n",
       "      <td>1.672347</td>\n",
       "      <td>5.465978</td>\n",
       "      <td>...</td>\n",
       "      <td>0.232614</td>\n",
       "      <td>1.130568</td>\n",
       "      <td>16.629005</td>\n",
       "      <td>0.531583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.520842</td>\n",
       "      <td>0.159946</td>\n",
       "      <td>-5.158120</td>\n",
       "      <td>-0.090273</td>\n",
       "      <td>-7.030154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_93</th>\n",
       "      <td>-10.147285</td>\n",
       "      <td>1.145573</td>\n",
       "      <td>0.048948</td>\n",
       "      <td>-1.089350</td>\n",
       "      <td>0.539833</td>\n",
       "      <td>2.720952</td>\n",
       "      <td>-3.445357</td>\n",
       "      <td>0.891066</td>\n",
       "      <td>3.003684</td>\n",
       "      <td>-1.667592</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.317916</td>\n",
       "      <td>1.977915</td>\n",
       "      <td>15.687951</td>\n",
       "      <td>-0.098451</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.553930</td>\n",
       "      <td>2.977722</td>\n",
       "      <td>-1.913176</td>\n",
       "      <td>4.279626</td>\n",
       "      <td>-4.650492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_94</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.321844</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.016712</td>\n",
       "      <td>1.801923</td>\n",
       "      <td>-4.157083</td>\n",
       "      <td>-0.097598</td>\n",
       "      <td>-1.954080</td>\n",
       "      <td>-1.739499</td>\n",
       "      <td>-6.520551</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.698334</td>\n",
       "      <td>1.363267</td>\n",
       "      <td>17.357844</td>\n",
       "      <td>0.390511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.977236</td>\n",
       "      <td>0.078992</td>\n",
       "      <td>1.801422</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.499069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_95</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.001507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.004191</td>\n",
       "      <td>4.675717</td>\n",
       "      <td>-10.240998</td>\n",
       "      <td>1.244410</td>\n",
       "      <td>-0.810857</td>\n",
       "      <td>2.305318</td>\n",
       "      <td>9.321186</td>\n",
       "      <td>...</td>\n",
       "      <td>0.259829</td>\n",
       "      <td>0.942454</td>\n",
       "      <td>17.098753</td>\n",
       "      <td>-0.432459</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.927686</td>\n",
       "      <td>-0.254905</td>\n",
       "      <td>-1.747526</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.435961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_96</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.155556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.603438</td>\n",
       "      <td>-0.026752</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.776737</td>\n",
       "      <td>17.924399</td>\n",
       "      <td>-0.165314</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.553372</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Cadmium_Chloride  Caffeine  Calcium_Chloride  Cisplatin  \\\n",
       "A11_92         -4.262720 -0.893290         -0.329504   0.257061   \n",
       "A11_93        -10.147285  1.145573          0.048948  -1.089350   \n",
       "A11_94               NaN  0.321844               NaN   1.016712   \n",
       "A11_95               NaN -1.001507               NaN   0.004191   \n",
       "A11_96               NaN       NaN               NaN        NaN   \n",
       "\n",
       "        Cobalt_Chloride  Congo_red    Copper  Cycloheximide   Diamide  \\\n",
       "A11_92        -3.120957   2.556543  2.567092       2.054232  1.672347   \n",
       "A11_93         0.539833   2.720952 -3.445357       0.891066  3.003684   \n",
       "A11_94         1.801923  -4.157083 -0.097598      -1.954080 -1.739499   \n",
       "A11_95         4.675717 -10.240998  1.244410      -0.810857  2.305318   \n",
       "A11_96        -0.155556        NaN  2.603438      -0.026752       NaN   \n",
       "\n",
       "        E6_Berbamine  ...  x6-Azauracil    Xylose        YNB   YNB:ph3  \\\n",
       "A11_92      5.465978  ...      0.232614  1.130568  16.629005  0.531583   \n",
       "A11_93     -1.667592  ...     -0.317916  1.977915  15.687951 -0.098451   \n",
       "A11_94     -6.520551  ...     -1.698334  1.363267  17.357844  0.390511   \n",
       "A11_95      9.321186  ...      0.259829  0.942454  17.098753 -0.432459   \n",
       "A11_96           NaN  ...           NaN  1.776737  17.924399 -0.165314   \n",
       "\n",
       "        YNB:ph8        YPD   YPD:15C   YPD:37C    YPD:4C    Zeocin  \n",
       "A11_92      NaN  23.520842  0.159946 -5.158120 -0.090273 -7.030154  \n",
       "A11_93      NaN  21.553930  2.977722 -1.913176  4.279626 -4.650492  \n",
       "A11_94      NaN  23.977236  0.078992  1.801422       NaN -0.499069  \n",
       "A11_95      NaN  22.927686 -0.254905 -1.747526       NaN -7.435961  \n",
       "A11_96      NaN        NaN  1.553372       NaN       NaN       NaN  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# retrieve last 5 rows\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1008 entries, A01_01 to A11_96\n",
      "Data columns (total 46 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   Cadmium_Chloride        799 non-null    float64\n",
      " 1   Caffeine                1004 non-null   float64\n",
      " 2   Calcium_Chloride        949 non-null    float64\n",
      " 3   Cisplatin               990 non-null    float64\n",
      " 4   Cobalt_Chloride         1007 non-null   float64\n",
      " 5   Congo_red               979 non-null    float64\n",
      " 6   Copper                  972 non-null    float64\n",
      " 7   Cycloheximide           1004 non-null   float64\n",
      " 8   Diamide                 1003 non-null   float64\n",
      " 9   E6_Berbamine            1005 non-null   float64\n",
      " 10  Ethanol                 967 non-null    float64\n",
      " 11  Formamide               999 non-null    float64\n",
      " 12  Galactose               962 non-null    float64\n",
      " 13  Hydrogen_Peroxide       769 non-null    float64\n",
      " 14  Hydroquinone            960 non-null    float64\n",
      " 15  Hydroxyurea             944 non-null    float64\n",
      " 16  Indoleacetic_Acid       994 non-null    float64\n",
      " 17  Lactate                 973 non-null    float64\n",
      " 18  Lactose                 1004 non-null   float64\n",
      " 19  Lithium_Chloride        1002 non-null   float64\n",
      " 20  Magnesium_Chloride      964 non-null    float64\n",
      " 21  Magnesium_Sulfate       1006 non-null   float64\n",
      " 22  Maltose                 1005 non-null   float64\n",
      " 23  Mannose                 957 non-null    float64\n",
      " 24  Menadione               1005 non-null   float64\n",
      " 25  Neomycin                1005 non-null   float64\n",
      " 26  Paraquat                1004 non-null   float64\n",
      " 27  Raffinose               662 non-null    float64\n",
      " 28  SDS                     873 non-null    float64\n",
      " 29  Sorbitol                599 non-null    float64\n",
      " 30  Trehalose               1003 non-null   float64\n",
      " 31  Tunicamycin             1006 non-null   float64\n",
      " 32  x4-Hydroxybenzaldehyde  964 non-null    float64\n",
      " 33  x4NQO                   1005 non-null   float64\n",
      " 34  x5-Fluorocytosine       957 non-null    float64\n",
      " 35  x5-Fluorouracil         992 non-null    float64\n",
      " 36  x6-Azauracil            1001 non-null   float64\n",
      " 37  Xylose                  1003 non-null   float64\n",
      " 38  YNB                     1006 non-null   float64\n",
      " 39  YNB:ph3                 979 non-null    float64\n",
      " 40  YNB:ph8                 887 non-null    float64\n",
      " 41  YPD                     1006 non-null   float64\n",
      " 42  YPD:15C                 1005 non-null   float64\n",
      " 43  YPD:37C                 1003 non-null   float64\n",
      " 44  YPD:4C                  806 non-null    float64\n",
      " 45  Zeocin                  957 non-null    float64\n",
      "dtypes: float64(46)\n",
      "memory usage: 370.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# Summary of dataframe\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Cadmium_Chloride</th>\n",
       "      <th>Caffeine</th>\n",
       "      <th>Calcium_Chloride</th>\n",
       "      <th>Cisplatin</th>\n",
       "      <th>Cobalt_Chloride</th>\n",
       "      <th>Congo_red</th>\n",
       "      <th>Copper</th>\n",
       "      <th>Cycloheximide</th>\n",
       "      <th>Diamide</th>\n",
       "      <th>E6_Berbamine</th>\n",
       "      <th>...</th>\n",
       "      <th>x6-Azauracil</th>\n",
       "      <th>Xylose</th>\n",
       "      <th>YNB</th>\n",
       "      <th>YNB:ph3</th>\n",
       "      <th>YNB:ph8</th>\n",
       "      <th>YPD</th>\n",
       "      <th>YPD:15C</th>\n",
       "      <th>YPD:37C</th>\n",
       "      <th>YPD:4C</th>\n",
       "      <th>Zeocin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>799.000000</td>\n",
       "      <td>1004.000000</td>\n",
       "      <td>949.000000</td>\n",
       "      <td>990.000000</td>\n",
       "      <td>1007.000000</td>\n",
       "      <td>979.000000</td>\n",
       "      <td>972.000000</td>\n",
       "      <td>1004.000000</td>\n",
       "      <td>1003.000000</td>\n",
       "      <td>1005.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1001.000000</td>\n",
       "      <td>1003.000000</td>\n",
       "      <td>1006.000000</td>\n",
       "      <td>979.000000</td>\n",
       "      <td>887.000000</td>\n",
       "      <td>1006.000000</td>\n",
       "      <td>1005.000000</td>\n",
       "      <td>1003.000000</td>\n",
       "      <td>806.000000</td>\n",
       "      <td>957.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.235763</td>\n",
       "      <td>0.056543</td>\n",
       "      <td>0.012039</td>\n",
       "      <td>-0.080343</td>\n",
       "      <td>-0.021220</td>\n",
       "      <td>-0.580926</td>\n",
       "      <td>0.067722</td>\n",
       "      <td>0.157040</td>\n",
       "      <td>0.139683</td>\n",
       "      <td>0.264369</td>\n",
       "      <td>...</td>\n",
       "      <td>0.112644</td>\n",
       "      <td>0.060641</td>\n",
       "      <td>16.315382</td>\n",
       "      <td>0.007996</td>\n",
       "      <td>0.007482</td>\n",
       "      <td>23.502212</td>\n",
       "      <td>0.047823</td>\n",
       "      <td>-0.140778</td>\n",
       "      <td>-0.190777</td>\n",
       "      <td>0.166791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.306318</td>\n",
       "      <td>0.910155</td>\n",
       "      <td>0.773159</td>\n",
       "      <td>1.045181</td>\n",
       "      <td>2.808820</td>\n",
       "      <td>6.411729</td>\n",
       "      <td>1.808578</td>\n",
       "      <td>1.683631</td>\n",
       "      <td>4.749402</td>\n",
       "      <td>7.083140</td>\n",
       "      <td>...</td>\n",
       "      <td>0.853002</td>\n",
       "      <td>0.939542</td>\n",
       "      <td>1.034526</td>\n",
       "      <td>0.674851</td>\n",
       "      <td>0.902919</td>\n",
       "      <td>1.815418</td>\n",
       "      <td>2.541834</td>\n",
       "      <td>2.642301</td>\n",
       "      <td>4.649077</td>\n",
       "      <td>6.433438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-10.381891</td>\n",
       "      <td>-4.141899</td>\n",
       "      <td>-2.452206</td>\n",
       "      <td>-4.557306</td>\n",
       "      <td>-6.184913</td>\n",
       "      <td>-13.488314</td>\n",
       "      <td>-8.944908</td>\n",
       "      <td>-5.980597</td>\n",
       "      <td>-17.291493</td>\n",
       "      <td>-12.513969</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.327925</td>\n",
       "      <td>-2.911638</td>\n",
       "      <td>11.981303</td>\n",
       "      <td>-2.570029</td>\n",
       "      <td>-3.724701</td>\n",
       "      <td>16.979292</td>\n",
       "      <td>-6.493270</td>\n",
       "      <td>-7.377041</td>\n",
       "      <td>-9.588223</td>\n",
       "      <td>-13.359268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-6.456470</td>\n",
       "      <td>-0.507907</td>\n",
       "      <td>-0.498740</td>\n",
       "      <td>-0.741802</td>\n",
       "      <td>-2.288283</td>\n",
       "      <td>-6.620026</td>\n",
       "      <td>-0.665649</td>\n",
       "      <td>-0.963134</td>\n",
       "      <td>-3.193845</td>\n",
       "      <td>-4.880492</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.449021</td>\n",
       "      <td>-0.599287</td>\n",
       "      <td>15.661717</td>\n",
       "      <td>-0.451187</td>\n",
       "      <td>-0.571156</td>\n",
       "      <td>22.380371</td>\n",
       "      <td>-1.879136</td>\n",
       "      <td>-2.168742</td>\n",
       "      <td>-3.680280</td>\n",
       "      <td>-5.159223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.022356</td>\n",
       "      <td>0.067418</td>\n",
       "      <td>0.042018</td>\n",
       "      <td>-0.019068</td>\n",
       "      <td>-0.377311</td>\n",
       "      <td>-0.568692</td>\n",
       "      <td>0.396090</td>\n",
       "      <td>0.234640</td>\n",
       "      <td>0.892107</td>\n",
       "      <td>-2.475193</td>\n",
       "      <td>...</td>\n",
       "      <td>0.146145</td>\n",
       "      <td>0.047475</td>\n",
       "      <td>16.358272</td>\n",
       "      <td>0.024450</td>\n",
       "      <td>-0.003055</td>\n",
       "      <td>23.403000</td>\n",
       "      <td>-0.132709</td>\n",
       "      <td>-0.047865</td>\n",
       "      <td>-0.977779</td>\n",
       "      <td>-2.015229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.813172</td>\n",
       "      <td>0.622523</td>\n",
       "      <td>0.503318</td>\n",
       "      <td>0.645479</td>\n",
       "      <td>1.998527</td>\n",
       "      <td>4.537839</td>\n",
       "      <td>1.235156</td>\n",
       "      <td>1.324370</td>\n",
       "      <td>3.327036</td>\n",
       "      <td>4.487006</td>\n",
       "      <td>...</td>\n",
       "      <td>0.700031</td>\n",
       "      <td>0.717306</td>\n",
       "      <td>17.006638</td>\n",
       "      <td>0.479301</td>\n",
       "      <td>0.628253</td>\n",
       "      <td>24.629824</td>\n",
       "      <td>1.832583</td>\n",
       "      <td>1.966580</td>\n",
       "      <td>2.605938</td>\n",
       "      <td>5.751830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12.876948</td>\n",
       "      <td>3.372236</td>\n",
       "      <td>2.689781</td>\n",
       "      <td>3.763224</td>\n",
       "      <td>8.467436</td>\n",
       "      <td>15.984204</td>\n",
       "      <td>4.924384</td>\n",
       "      <td>6.516210</td>\n",
       "      <td>11.349569</td>\n",
       "      <td>23.116821</td>\n",
       "      <td>...</td>\n",
       "      <td>2.816535</td>\n",
       "      <td>3.286086</td>\n",
       "      <td>19.067704</td>\n",
       "      <td>1.773026</td>\n",
       "      <td>2.956204</td>\n",
       "      <td>29.985755</td>\n",
       "      <td>7.287769</td>\n",
       "      <td>7.474741</td>\n",
       "      <td>15.552609</td>\n",
       "      <td>16.662919</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Cadmium_Chloride     Caffeine  Calcium_Chloride   Cisplatin  \\\n",
       "count        799.000000  1004.000000        949.000000  990.000000   \n",
       "mean          -0.235763     0.056543          0.012039   -0.080343   \n",
       "std            6.306318     0.910155          0.773159    1.045181   \n",
       "min          -10.381891    -4.141899         -2.452206   -4.557306   \n",
       "25%           -6.456470    -0.507907         -0.498740   -0.741802   \n",
       "50%            1.022356     0.067418          0.042018   -0.019068   \n",
       "75%            5.813172     0.622523          0.503318    0.645479   \n",
       "max           12.876948     3.372236          2.689781    3.763224   \n",
       "\n",
       "       Cobalt_Chloride   Congo_red      Copper  Cycloheximide      Diamide  \\\n",
       "count      1007.000000  979.000000  972.000000    1004.000000  1003.000000   \n",
       "mean         -0.021220   -0.580926    0.067722       0.157040     0.139683   \n",
       "std           2.808820    6.411729    1.808578       1.683631     4.749402   \n",
       "min          -6.184913  -13.488314   -8.944908      -5.980597   -17.291493   \n",
       "25%          -2.288283   -6.620026   -0.665649      -0.963134    -3.193845   \n",
       "50%          -0.377311   -0.568692    0.396090       0.234640     0.892107   \n",
       "75%           1.998527    4.537839    1.235156       1.324370     3.327036   \n",
       "max           8.467436   15.984204    4.924384       6.516210    11.349569   \n",
       "\n",
       "       E6_Berbamine  ...  x6-Azauracil       Xylose          YNB     YNB:ph3  \\\n",
       "count   1005.000000  ...   1001.000000  1003.000000  1006.000000  979.000000   \n",
       "mean       0.264369  ...      0.112644     0.060641    16.315382    0.007996   \n",
       "std        7.083140  ...      0.853002     0.939542     1.034526    0.674851   \n",
       "min      -12.513969  ...     -3.327925    -2.911638    11.981303   -2.570029   \n",
       "25%       -4.880492  ...     -0.449021    -0.599287    15.661717   -0.451187   \n",
       "50%       -2.475193  ...      0.146145     0.047475    16.358272    0.024450   \n",
       "75%        4.487006  ...      0.700031     0.717306    17.006638    0.479301   \n",
       "max       23.116821  ...      2.816535     3.286086    19.067704    1.773026   \n",
       "\n",
       "          YNB:ph8          YPD      YPD:15C      YPD:37C      YPD:4C  \\\n",
       "count  887.000000  1006.000000  1005.000000  1003.000000  806.000000   \n",
       "mean     0.007482    23.502212     0.047823    -0.140778   -0.190777   \n",
       "std      0.902919     1.815418     2.541834     2.642301    4.649077   \n",
       "min     -3.724701    16.979292    -6.493270    -7.377041   -9.588223   \n",
       "25%     -0.571156    22.380371    -1.879136    -2.168742   -3.680280   \n",
       "50%     -0.003055    23.403000    -0.132709    -0.047865   -0.977779   \n",
       "75%      0.628253    24.629824     1.832583     1.966580    2.605938   \n",
       "max      2.956204    29.985755     7.287769     7.474741   15.552609   \n",
       "\n",
       "           Zeocin  \n",
       "count  957.000000  \n",
       "mean     0.166791  \n",
       "std      6.433438  \n",
       "min    -13.359268  \n",
       "25%     -5.159223  \n",
       "50%     -2.015229  \n",
       "75%      5.751830  \n",
       "max     16.662919  \n",
       "\n",
       "[8 rows x 46 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate some summary statistics\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessing data in a DataFrame\n",
    "\n",
    "Our columns in the dataframe object are individual Series of data. We can access them by referring to the column name e.g. dataframe['column-name']. For example to access the *Caffeine* column run this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A01_01    0.279993\n",
      "A01_02   -0.206326\n",
      "A01_03   -0.127960\n",
      "A01_04    0.878392\n",
      "A01_05   -1.353169\n",
      "Name: Caffeine, dtype: float64\n",
      "============= or ===========\n",
      "A01_01    0.279993\n",
      "A01_02   -0.206326\n",
      "A01_03   -0.127960\n",
      "A01_04    0.878392\n",
      "A01_05   -1.353169\n",
      "Name: Caffeine, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df['Caffeine'].head())\n",
    "print('============= or ===========')\n",
    "print(df.Caffeine.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To filter by row we have to use the *iloc* or *loc* function on the *dataframe*. The *iloc* function allows to filter by the integer index of the row while the *loc* function allows to index by the index label (accession id):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.127959825210201"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2].Caffeine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.127959825210201"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['A01_03'].Caffeine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to retrieve multiple rows either by specifying the exact row index labels or integer positions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A01_02   -0.206326\n",
       "A01_06    1.655436\n",
       "A01_07   -1.423605\n",
       "A01_10   -2.787328\n",
       "Name: Caffeine, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[1, 5, 6, 9]].Caffeine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A01_02   -0.206326\n",
       "A01_06    1.655436\n",
       "A01_07   -1.423605\n",
       "A01_10   -2.787328\n",
       "Name: Caffeine, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[['A01_02', 'A01_06', 'A01_07', 'A01_10']].Caffeine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Caffeine</th>\n",
       "      <th>Cisplatin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A01_01</th>\n",
       "      <td>0.279993</td>\n",
       "      <td>1.658179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_02</th>\n",
       "      <td>-0.206326</td>\n",
       "      <td>-0.918012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_03</th>\n",
       "      <td>-0.127960</td>\n",
       "      <td>-2.712088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_04</th>\n",
       "      <td>0.878392</td>\n",
       "      <td>-0.827358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_05</th>\n",
       "      <td>-1.353169</td>\n",
       "      <td>-1.137234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_06</th>\n",
       "      <td>1.655436</td>\n",
       "      <td>-2.044903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_07</th>\n",
       "      <td>-1.423605</td>\n",
       "      <td>-1.714243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_08</th>\n",
       "      <td>1.750342</td>\n",
       "      <td>0.306306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_09</th>\n",
       "      <td>-0.559696</td>\n",
       "      <td>-0.879233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_10</th>\n",
       "      <td>-2.787328</td>\n",
       "      <td>-0.239611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Caffeine  Cisplatin\n",
       "A01_01  0.279993   1.658179\n",
       "A01_02 -0.206326  -0.918012\n",
       "A01_03 -0.127960  -2.712088\n",
       "A01_04  0.878392  -0.827358\n",
       "A01_05 -1.353169  -1.137234\n",
       "A01_06  1.655436  -2.044903\n",
       "A01_07 -1.423605  -1.714243\n",
       "A01_08  1.750342   0.306306\n",
       "A01_09 -0.559696  -0.879233\n",
       "A01_10 -2.787328  -0.239611"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# retrieve the first 10 rows for the columns Caffeine (column 1 ) and Cisplatin (column 2)\n",
    "df.iloc[:10,[1,3]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering data\n",
    "\n",
    "We can also apply conditions to the data we are inspecting, such as to filter our data.\n",
    "Let's examine the Caffeine column and check the lowest value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4.14189858085831"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Caffeine.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now filter the Caffeine column for values that are greater than 0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A01_01     True\n",
       "A01_02    False\n",
       "A01_03    False\n",
       "A01_04     True\n",
       "A01_05    False\n",
       "          ...  \n",
       "A11_92    False\n",
       "A11_93     True\n",
       "A11_94     True\n",
       "A11_95    False\n",
       "A11_96    False\n",
       "Name: Caffeine, Length: 1008, dtype: bool"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Caffeine > 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This returns a new *Series* of *True*/*False* values though. To actually filter the data, we need to use this *Series* to mask our original *DataFrame*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Cadmium_Chloride</th>\n",
       "      <th>Caffeine</th>\n",
       "      <th>Calcium_Chloride</th>\n",
       "      <th>Cisplatin</th>\n",
       "      <th>Cobalt_Chloride</th>\n",
       "      <th>Congo_red</th>\n",
       "      <th>Copper</th>\n",
       "      <th>Cycloheximide</th>\n",
       "      <th>Diamide</th>\n",
       "      <th>E6_Berbamine</th>\n",
       "      <th>...</th>\n",
       "      <th>x6-Azauracil</th>\n",
       "      <th>Xylose</th>\n",
       "      <th>YNB</th>\n",
       "      <th>YNB:ph3</th>\n",
       "      <th>YNB:ph8</th>\n",
       "      <th>YPD</th>\n",
       "      <th>YPD:15C</th>\n",
       "      <th>YPD:37C</th>\n",
       "      <th>YPD:4C</th>\n",
       "      <th>Zeocin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A01_01</th>\n",
       "      <td>-7.323520</td>\n",
       "      <td>0.279993</td>\n",
       "      <td>0.313118</td>\n",
       "      <td>1.658179</td>\n",
       "      <td>-1.604442</td>\n",
       "      <td>5.841617</td>\n",
       "      <td>-4.130950</td>\n",
       "      <td>0.821226</td>\n",
       "      <td>3.622602</td>\n",
       "      <td>-0.378747</td>\n",
       "      <td>...</td>\n",
       "      <td>1.199054</td>\n",
       "      <td>-0.309149</td>\n",
       "      <td>17.470822</td>\n",
       "      <td>0.055225</td>\n",
       "      <td>-0.184268</td>\n",
       "      <td>24.548971</td>\n",
       "      <td>0.712171</td>\n",
       "      <td>0.890842</td>\n",
       "      <td>4.118372</td>\n",
       "      <td>8.592818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_04</th>\n",
       "      <td>-6.147649</td>\n",
       "      <td>0.878392</td>\n",
       "      <td>-0.563331</td>\n",
       "      <td>-0.827358</td>\n",
       "      <td>-1.291270</td>\n",
       "      <td>2.753851</td>\n",
       "      <td>-2.268121</td>\n",
       "      <td>0.867456</td>\n",
       "      <td>-6.832079</td>\n",
       "      <td>-1.970630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.054694</td>\n",
       "      <td>-0.739040</td>\n",
       "      <td>15.575856</td>\n",
       "      <td>-1.690867</td>\n",
       "      <td>-0.840344</td>\n",
       "      <td>20.893646</td>\n",
       "      <td>-0.636280</td>\n",
       "      <td>0.053258</td>\n",
       "      <td>-3.196936</td>\n",
       "      <td>0.294462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_06</th>\n",
       "      <td>9.394562</td>\n",
       "      <td>1.655436</td>\n",
       "      <td>0.531654</td>\n",
       "      <td>-2.044903</td>\n",
       "      <td>1.577325</td>\n",
       "      <td>-5.769939</td>\n",
       "      <td>1.355905</td>\n",
       "      <td>2.336068</td>\n",
       "      <td>4.038331</td>\n",
       "      <td>-2.917101</td>\n",
       "      <td>...</td>\n",
       "      <td>0.591233</td>\n",
       "      <td>-0.134019</td>\n",
       "      <td>16.085601</td>\n",
       "      <td>-0.100294</td>\n",
       "      <td>0.710547</td>\n",
       "      <td>23.549116</td>\n",
       "      <td>-2.680231</td>\n",
       "      <td>2.025716</td>\n",
       "      <td>-0.889829</td>\n",
       "      <td>-4.108786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_08</th>\n",
       "      <td>-7.585476</td>\n",
       "      <td>1.750342</td>\n",
       "      <td>0.941181</td>\n",
       "      <td>0.306306</td>\n",
       "      <td>-2.997501</td>\n",
       "      <td>0.063007</td>\n",
       "      <td>-4.352137</td>\n",
       "      <td>2.634605</td>\n",
       "      <td>3.147146</td>\n",
       "      <td>8.292883</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043294</td>\n",
       "      <td>-0.826648</td>\n",
       "      <td>16.210856</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.538390</td>\n",
       "      <td>23.595423</td>\n",
       "      <td>-1.449024</td>\n",
       "      <td>1.679693</td>\n",
       "      <td>-2.227455</td>\n",
       "      <td>-5.773160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_13</th>\n",
       "      <td>6.099126</td>\n",
       "      <td>1.193630</td>\n",
       "      <td>-0.460481</td>\n",
       "      <td>-2.254822</td>\n",
       "      <td>-1.737934</td>\n",
       "      <td>-6.773013</td>\n",
       "      <td>1.847423</td>\n",
       "      <td>1.492433</td>\n",
       "      <td>5.377797</td>\n",
       "      <td>-2.763472</td>\n",
       "      <td>...</td>\n",
       "      <td>0.428124</td>\n",
       "      <td>-0.447422</td>\n",
       "      <td>15.707533</td>\n",
       "      <td>1.328875</td>\n",
       "      <td>-0.676125</td>\n",
       "      <td>22.880547</td>\n",
       "      <td>-4.488093</td>\n",
       "      <td>3.399301</td>\n",
       "      <td>-5.313911</td>\n",
       "      <td>-4.854207</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>A11_89</th>\n",
       "      <td>-0.781114</td>\n",
       "      <td>1.079321</td>\n",
       "      <td>0.219939</td>\n",
       "      <td>0.771153</td>\n",
       "      <td>1.955114</td>\n",
       "      <td>5.559785</td>\n",
       "      <td>4.924384</td>\n",
       "      <td>2.472066</td>\n",
       "      <td>2.813091</td>\n",
       "      <td>14.695409</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.372606</td>\n",
       "      <td>0.096194</td>\n",
       "      <td>16.820080</td>\n",
       "      <td>0.619160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.101895</td>\n",
       "      <td>3.574523</td>\n",
       "      <td>2.409968</td>\n",
       "      <td>4.127189</td>\n",
       "      <td>15.004739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_90</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.645842</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.890742</td>\n",
       "      <td>13.016383</td>\n",
       "      <td>-5.268835</td>\n",
       "      <td>-0.468322</td>\n",
       "      <td>2.211619</td>\n",
       "      <td>-4.457251</td>\n",
       "      <td>...</td>\n",
       "      <td>0.058875</td>\n",
       "      <td>-1.051974</td>\n",
       "      <td>17.411414</td>\n",
       "      <td>-0.035335</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.180528</td>\n",
       "      <td>-1.240811</td>\n",
       "      <td>3.017025</td>\n",
       "      <td>-2.024357</td>\n",
       "      <td>-5.818075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_91</th>\n",
       "      <td>-6.343767</td>\n",
       "      <td>0.438523</td>\n",
       "      <td>-0.322617</td>\n",
       "      <td>1.973411</td>\n",
       "      <td>4.515720</td>\n",
       "      <td>-8.495414</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.224670</td>\n",
       "      <td>3.258043</td>\n",
       "      <td>2.732642</td>\n",
       "      <td>...</td>\n",
       "      <td>0.114539</td>\n",
       "      <td>1.206215</td>\n",
       "      <td>16.871327</td>\n",
       "      <td>1.084758</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.730956</td>\n",
       "      <td>4.994928</td>\n",
       "      <td>-2.030958</td>\n",
       "      <td>-0.282208</td>\n",
       "      <td>8.456099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_93</th>\n",
       "      <td>-10.147285</td>\n",
       "      <td>1.145573</td>\n",
       "      <td>0.048948</td>\n",
       "      <td>-1.089350</td>\n",
       "      <td>0.539833</td>\n",
       "      <td>2.720952</td>\n",
       "      <td>-3.445357</td>\n",
       "      <td>0.891066</td>\n",
       "      <td>3.003684</td>\n",
       "      <td>-1.667592</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.317916</td>\n",
       "      <td>1.977915</td>\n",
       "      <td>15.687951</td>\n",
       "      <td>-0.098451</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.553930</td>\n",
       "      <td>2.977722</td>\n",
       "      <td>-1.913176</td>\n",
       "      <td>4.279626</td>\n",
       "      <td>-4.650492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_94</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.321844</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.016712</td>\n",
       "      <td>1.801923</td>\n",
       "      <td>-4.157083</td>\n",
       "      <td>-0.097598</td>\n",
       "      <td>-1.954080</td>\n",
       "      <td>-1.739499</td>\n",
       "      <td>-6.520551</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.698334</td>\n",
       "      <td>1.363267</td>\n",
       "      <td>17.357844</td>\n",
       "      <td>0.390511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.977236</td>\n",
       "      <td>0.078992</td>\n",
       "      <td>1.801422</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.499069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>534 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Cadmium_Chloride  Caffeine  Calcium_Chloride  Cisplatin  \\\n",
       "A01_01         -7.323520  0.279993          0.313118   1.658179   \n",
       "A01_04         -6.147649  0.878392         -0.563331  -0.827358   \n",
       "A01_06          9.394562  1.655436          0.531654  -2.044903   \n",
       "A01_08         -7.585476  1.750342          0.941181   0.306306   \n",
       "A01_13          6.099126  1.193630         -0.460481  -2.254822   \n",
       "...                  ...       ...               ...        ...   \n",
       "A11_89         -0.781114  1.079321          0.219939   0.771153   \n",
       "A11_90               NaN  0.645842               NaN        NaN   \n",
       "A11_91         -6.343767  0.438523         -0.322617   1.973411   \n",
       "A11_93        -10.147285  1.145573          0.048948  -1.089350   \n",
       "A11_94               NaN  0.321844               NaN   1.016712   \n",
       "\n",
       "        Cobalt_Chloride  Congo_red    Copper  Cycloheximide   Diamide  \\\n",
       "A01_01        -1.604442   5.841617 -4.130950       0.821226  3.622602   \n",
       "A01_04        -1.291270   2.753851 -2.268121       0.867456 -6.832079   \n",
       "A01_06         1.577325  -5.769939  1.355905       2.336068  4.038331   \n",
       "A01_08        -2.997501   0.063007 -4.352137       2.634605  3.147146   \n",
       "A01_13        -1.737934  -6.773013  1.847423       1.492433  5.377797   \n",
       "...                 ...        ...       ...            ...       ...   \n",
       "A11_89         1.955114   5.559785  4.924384       2.472066  2.813091   \n",
       "A11_90        -0.890742  13.016383 -5.268835      -0.468322  2.211619   \n",
       "A11_91         4.515720  -8.495414       NaN      -0.224670  3.258043   \n",
       "A11_93         0.539833   2.720952 -3.445357       0.891066  3.003684   \n",
       "A11_94         1.801923  -4.157083 -0.097598      -1.954080 -1.739499   \n",
       "\n",
       "        E6_Berbamine  ...  x6-Azauracil    Xylose        YNB   YNB:ph3  \\\n",
       "A01_01     -0.378747  ...      1.199054 -0.309149  17.470822  0.055225   \n",
       "A01_04     -1.970630  ...      0.054694 -0.739040  15.575856 -1.690867   \n",
       "A01_06     -2.917101  ...      0.591233 -0.134019  16.085601 -0.100294   \n",
       "A01_08      8.292883  ...      0.043294 -0.826648  16.210856       NaN   \n",
       "A01_13     -2.763472  ...      0.428124 -0.447422  15.707533  1.328875   \n",
       "...              ...  ...           ...       ...        ...       ...   \n",
       "A11_89     14.695409  ...     -0.372606  0.096194  16.820080  0.619160   \n",
       "A11_90     -4.457251  ...      0.058875 -1.051974  17.411414 -0.035335   \n",
       "A11_91      2.732642  ...      0.114539  1.206215  16.871327  1.084758   \n",
       "A11_93     -1.667592  ...     -0.317916  1.977915  15.687951 -0.098451   \n",
       "A11_94     -6.520551  ...     -1.698334  1.363267  17.357844  0.390511   \n",
       "\n",
       "         YNB:ph8        YPD   YPD:15C   YPD:37C    YPD:4C     Zeocin  \n",
       "A01_01 -0.184268  24.548971  0.712171  0.890842  4.118372   8.592818  \n",
       "A01_04 -0.840344  20.893646 -0.636280  0.053258 -3.196936   0.294462  \n",
       "A01_06  0.710547  23.549116 -2.680231  2.025716 -0.889829  -4.108786  \n",
       "A01_08 -0.538390  23.595423 -1.449024  1.679693 -2.227455  -5.773160  \n",
       "A01_13 -0.676125  22.880547 -4.488093  3.399301 -5.313911  -4.854207  \n",
       "...          ...        ...       ...       ...       ...        ...  \n",
       "A11_89       NaN  24.101895  3.574523  2.409968  4.127189  15.004739  \n",
       "A11_90       NaN  25.180528 -1.240811  3.017025 -2.024357  -5.818075  \n",
       "A11_91       NaN  22.730956  4.994928 -2.030958 -0.282208   8.456099  \n",
       "A11_93       NaN  21.553930  2.977722 -1.913176  4.279626  -4.650492  \n",
       "A11_94       NaN  23.977236  0.078992  1.801422       NaN  -0.499069  \n",
       "\n",
       "[534 rows x 46 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Caffeine > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check that there are no values below 0 run the *min()* function again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0032293928665531"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Caffeine > 0].Caffeine.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of our values have missing values that are represented in the output as `NaN`. Often we can't work with them. One way to deal with them is to drop the rows that contain them. First let's check how many missing values we have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A05_12   NaN\n",
      "A09_65   NaN\n",
      "A10_01   NaN\n",
      "A11_96   NaN\n",
      "Name: Caffeine, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df.Caffeine[df.Caffeine.isnull()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], Name: Caffeine, dtype: float64)\n"
     ]
    }
   ],
   "source": [
    "df.dropna(subset=['Caffeine'], inplace=True)\n",
    "print(df.Caffeine[df.Caffeine.isnull()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting pandas dataframes\n",
    "\n",
    "The *DataFrame* has a built in plotting functions (i.e `pd.hist()` or `pd.plot()`) that allows to create simple plots using matplotlib internally. For more advanced plotting functions, one can use the *matplotlib* library directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f8ca4a5ccf8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8ca4b633c8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['Caffeine','Copper']].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ca4cb8a90>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['Caffeine','Copper']].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting with matplotlib\n",
    "\n",
    "We don't have to rely on pandas plotting functionality but can also use matplot lib to visualize our phenotype data.  Let’s have a look at the relationship between two phenotypes in our dataframe. To save typing later on, we can extract the Series for “Caffeine” and “Copper” by assigning each to a new variable, x and y, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)\n",
    "x = df.Sorbitol\n",
    "y = df.Trehalose"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can plot them as a scatter chart by adding:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f8ca4acc748>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "# or plt.savefig(\"name.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to do some basic statistics so we can plot a line of linear regression on our chart.  \n",
    "Let’s plot a linear regression through the data. Python has a library called scipy that contains a lot of statistics routines. We can import it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the statistics for the linear regression we can use a function called `linregress`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=0.9622331502773507, intercept=-0.011936070484259736, rvalue=0.9064525118355159, pvalue=1.0446912214962477e-114, stderr=0.025839319479636295)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats = linregress(x, y)\n",
    "m = stats.slope\n",
    "b = stats.intercept\n",
    "stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The equation for the straight line that describes linear regression is `y = mx + b`, where m is the *slope* and `b` is the *intercept*.  \n",
    "(Hint: you have some extra information in the stats object - stats.rvalue and stats.pvalue.)\n",
    "\n",
    "Therefore, we can then plot the line of linear regression by adding the following line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8ca6181550>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "plt.plot(x, m * x + b, color=\"r\")   # I've added a color argument here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib figures are highly customisable, and there are so many options it is usually best to consult the [documentation]() first.  \n",
    "We will take the above plot and customize *fontsize*, *linewidth*, *color* for example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Change the default figure size\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "# Change the default marker for the scatter from circles to x's\n",
    "plt.scatter(x, y, marker='x')\n",
    "\n",
    "# Set the linewidth on the regression line to 3px\n",
    "plt.plot(x, m * x + b, color=\"red\", linewidth=3)\n",
    "\n",
    "# Add x and y lables, and set their font size\n",
    "plt.xlabel(\"Sorbitol\", fontsize=20)\n",
    "plt.ylabel(\"Trehalose\", fontsize=20)\n",
    "\n",
    "# Plot pvalue and r2 value\n",
    "plt.text(-2.5, 3, 'pvalue: %s' % stats.pvalue,\n",
    "         fontsize=14)\n",
    "plt.text(-2.5, 2.8, 'r2: %s' % stats.rvalue,\n",
    "         fontsize=14)\n",
    "\n",
    "# Set the font size of the number lables on the axes\n",
    "plt.xticks(fontsize=18)\n",
    "plt.yticks(fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "You can find more pandas excercices [here](https://github.com/guipsamora/pandas_exercises) and [here](https://www.machinelearningplus.com/python/101-pandas-exercises-python/)"
   ]
  },
  {
   "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}