Lecture-1-Python-Data-Science-Guide.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Data Science Guide\n",
    "\n",
    "This notebook provides an introduction to the tools and libraries that we use in this GWAS lecture. These are: \n",
    "\n",
    "1. [NumPy](http://www.numpy.org/)\n",
    "2. [h5py](https://www.h5py.org/)\n",
    "3. [pandas](https://pandas.pydata.org/)\n",
    "4. [matplotlib](https://matplotlib.org/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NumPy\n",
    "\n",
    "NumPy stands for ‘Numerical Python’ or ‘Numeric Python’. It is an open source module of Python which provides fast mathematical computation on arrays and matrices.\n",
    "\n",
    "Numpy can be imported into the notebook using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating NumPy array \n",
    "\n",
    "NumPy’s main object is the homogeneous multidimensional array. It is a table with same type elements, i.e, integers or string or characters (homogeneous), usually integers. In NumPy, dimensions are called axes. The number of axes is called the rank.\n",
    "\n",
    "There are several ways to create an array in NumPy like np.array, np.zeros, no.ones, etc. Each of them provides some flexibility.\n",
    "\n",
    "| Numpy function | What it does ?                                                                                                        |\n",
    "|----------------|-----------------------------------------------------------------------------------------------------------------------|\n",
    "| np.array       | Convert array like python object to NumPy array                                                                       |\n",
    "| np.arange      | Create arrays with regularly incrementing values.                                                                     |\n",
    "| np.ones        | Create an array filled with 1 values                                                                                  |\n",
    "| np.zeros       | Create an array filled with 0 values                                                                                  |\n",
    "| np.full        | Create an array filled with the specified value                                                                       |\n",
    "| np.random.rand | Generate arrays of random values in the specified shape                                                               |\n",
    "| np.linspace    | Create arrays with a specified number of elements, and spaced equally between the specified beginning and end values. |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert array like python object to NumPy array\n",
    "np.array([2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2. , 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create arrays with regularly incrementing values.\n",
    "np.arange(2, 3, 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an array filled with 0 values\n",
    "np.zeros((2, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an array filled with 1 values\n",
    "np.ones((2, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.11, 0.11, 0.11, 0.11],\n",
       "       [0.11, 0.11, 0.11, 0.11],\n",
       "       [0.11, 0.11, 0.11, 0.11]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an array filled with the specified value\n",
    "np.full( (3,4), 0.11 )  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.75061919, 0.22210586, 0.39882487],\n",
       "       [0.16872907, 0.32425882, 0.44510628]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate arrays of random values in the specified shape\n",
    "np.random.rand(2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.2, 0.4, 0.6, 0.8, 1. ])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create arrays with a specified number of elements, and spaced equally between the specified beginning and end values\n",
    "np.linspace(0, 5/3, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the important attributes of a NumPy object are:\n",
    "\n",
    "- **ndim**: displays the dimension of the array\n",
    "- **shape**: returns a tuple of integers indicating the size of the array\n",
    "- **size**: returns the total number of elements in the NumPy array\n",
    "- **dtype**: returns the type of elements in the array, i.e., int64, character\n",
    "- **itemsize**: returns the size in bytes of each item\n",
    "- **reshape**: Reshapes the NumPy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1. 1.]\n",
      " [1. 1. 1.]]\n",
      "ndim: 2\n",
      "shape: (2,3)\n",
      "size: 6\n",
      "dtype: float64\n",
      "itemsize: 8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1., 1.],\n",
       "       [1., 1.],\n",
       "       [1., 1.]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.ones((2,3))\n",
    "print arr\n",
    "print(\"ndim: %s\" % arr.ndim)\n",
    "print(\"shape: (%s,%s)\" % arr.shape)\n",
    "print(\"size: %s\" % arr.size)\n",
    "print(\"dtype: %s\" % arr.dtype)\n",
    "print(\"itemsize: %s\" % arr.itemsize)\n",
    "arr.reshape((3,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default NumPy numeric arrays will be created using *float64* type. \n",
    "This can be overridden by providing an explicit dtype.\n",
    "For example to create a numpy array with 32bit integer elements use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1],\n",
       "       [1, 1, 1]], dtype=int32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((2,3), dtype=np.int32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessing NumPy array\n",
    "\n",
    "NumPy array elements can be accessed using indexing. Below are some of the useful examples:\n",
    "\n",
    "- arr[2:5] will print items 2 to 4. Index in NumPy arrays starts from 0\n",
    "- arr[2::2] will print items 2 to end skipping 2 items\n",
    "- arr[::-1] will print the array in the reverse order\n",
    "- arr[1:] will print from row 1 to end\n",
    "\n",
    "Accessing a multi-dimensional NumPy array can be done using following form:\n",
    "\n",
    "```Array[row_start_index:row_end_index, column_start_index: column_end_index]```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14],\n",
       "       [15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = a = np.arange(25).reshape(5, 5)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 11, 12, 13, 14],\n",
       "       [15, 16, 17, 18, 19]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access rows 2 to 4 and all columns\n",
    "arr[2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2,  3],\n",
       "       [ 7,  8],\n",
       "       [12, 13],\n",
       "       [17, 18],\n",
       "       [22, 23]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access colum 2 to 4 and all rows\n",
    "arr[:,2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12, 13],\n",
       "       [17, 18]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access colum 2 to 4 and row 2 to 4\n",
    "arr[2:4,2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 21, 22, 23, 24])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access last row \n",
    "arr[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  9, 14, 19, 24])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access last column \n",
    "arr[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access second row and third column\n",
    "arr[2,3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NumPy arrays can also be accessed using boolean indexing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(12).reshape(3, 4)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False,  True])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows_on = np.array([True, False, True])\n",
    "rows_on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[rows_on,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NumPy array manipulation \n",
    "\n",
    "NumPy arrays are capable of performing all basic operations such as addition, subtraction, element-wise product, matrix dot product, element-wise division, element-wise modulo, element-wise exponents and conditional operations.\n",
    "\n",
    "An important feature with NumPy arrays is broadcasting:  \n",
    "![Broadcasting Rules](https://jakevdp.github.io/PythonDataScienceHandbook/figures/02.05-broadcasting.png \"Broadcasting Rules\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Without broadcasting\n",
    "a = np.array([0, 1, 2])\n",
    "b = np.array([5, 5, 5])\n",
    "a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# With broadcasting\n",
    "a + 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NumPy provides basic mathematical and statistical functions like mean, min, max, sum, prod, std, var, summation across different axes, transposing of a matrix, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean: 6.0\n",
      "std: 0.816496580927726\n",
      "sum: 18\n",
      "var: 0.6666666666666666\n",
      "min: 5\n",
      "max: 7\n"
     ]
    }
   ],
   "source": [
    "c = a + b\n",
    "print(\"mean: %s\" % c.mean())\n",
    "print(\"std: %s\" % c.std())\n",
    "print(\"sum: %s\" % c.sum())\n",
    "print(\"var: %s\" % c.var())\n",
    "print(\"min: %s\" % c.min())\n",
    "print(\"max: %s\" % c.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A particular NumPy feature of interest is solving a system of linear equations. NumPy has a function to solve linear equations. \n",
    "For example these linear equations \n",
    "\n",
    "\\begin{align}\n",
    "2x + 6y & =  6 \\\\\n",
    "5x + 3y & =  -9 \n",
    "\\end{align}\n",
    "\n",
    "can be solved by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.,  2.])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeffs  = np.array([[2, 6], [5, 3]])\n",
    "depvars = np.array([6, -9])\n",
    "solution = np.linalg.solve(coeffs, depvars)\n",
    "solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## HDF5 & h5py\n",
    "\n",
    "The h5py package is a Pythonic interface to the HDF5 binary data format.\n",
    "\n",
    "It lets you store huge amounts of numerical data, and easily manipulate that data from NumPy. For example, you can slice into multi-terabyte datasets stored on disk, as if they were real NumPy arrays. Thousands of datasets can be stored in a single file, categorized and tagged however you want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file not found, downloading from https://www.ebi.ac.uk/~stegle/limix/tutorials/data/BYxRM/BYxRM.hdf5\n"
     ]
    }
   ],
   "source": [
    "# First we download the data\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": [
    "An HDF5 file is a container for two kinds of objects: datasets, which are array-like collections of data, and groups, which are folder-like containers that hold datasets and other groups. The most fundamental thing to remember when using h5py is:\n",
    "\n",
    "**Groups work like dictionaries, and datasets work like NumPy arrays**\n",
    "\n",
    "Open an existing HDF5 file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "f = h5py.File(file_name,'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**HDF** stands for *Hierarchical Data Format*.   \n",
    "Every object in an HDF5 file has a name, and they’re arranged in a POSIX-style hierarchy with `/`-separators.  \n",
    "The “folders” in this system are called groups. The **File** object we created is itself a *group*, in this case the *root* group, named `/`  \n",
    "\n",
    "![hdf5_structure](https://www.neonscience.org/sites/default/files/images/HDF5/hdf5_structure4.jpg \"HDF5 structure\")\n",
    "\n",
    "What is stored in this file? \n",
    "Remember **h5py.File** acts like a Python dictionary, thus we can check the keys,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'genotype', u'phenotype']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(f.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The root object contains two children (*genotype* and *phenotype*).  \n",
    "To find out what type (dataset or group) of object the *phenotype* is we can run: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<HDF5 group \"/phenotype\" (3 members)>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f['phenotype']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the *phenotype* object itself is a group with 3 children"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'col_header', u'matrix', u'row_header']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(f['phenotype'].keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An easy way to iterate over all elements of a HDF5 file is to use the visit method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "genotype: <HDF5 group \"/genotype\" (3 members)> \n",
      "genotype/col_header: <HDF5 group \"/genotype/col_header\" (3 members)> \n",
      "genotype/col_header/alleles: <HDF5 dataset \"alleles\": shape (11623, 2), type \"|S37\"> \n",
      "genotype/col_header/chrom: <HDF5 dataset \"chrom\": shape (11623,), type \"<i8\"> \n",
      "genotype/col_header/pos: <HDF5 dataset \"pos\": shape (11623,), type \"<i8\"> \n",
      "genotype/matrix: <HDF5 dataset \"matrix\": shape (1008, 11623), type \"|u1\"> \n",
      "genotype/row_header: <HDF5 group \"/genotype/row_header\" (1 members)> \n",
      "genotype/row_header/sample_ID: <HDF5 dataset \"sample_ID\": shape (1008,), type \"|S99\"> \n",
      "phenotype: <HDF5 group \"/phenotype\" (3 members)> \n",
      "phenotype/col_header: <HDF5 group \"/phenotype/col_header\" (1 members)> \n",
      "phenotype/col_header/phenotype_ID: <HDF5 dataset \"phenotype_ID\": shape (46,), type \"|S22\"> \n",
      "phenotype/matrix: <HDF5 dataset \"matrix\": shape (1008, 46), type \"<f8\"> \n",
      "phenotype/row_header: <HDF5 group \"/phenotype/row_header\" (1 members)> \n",
      "phenotype/row_header/sample_ID: <HDF5 dataset \"sample_ID\": shape (1008,), type \"|S22\"> \n"
     ]
    }
   ],
   "source": [
    "def printname(name):\n",
    "    print(\"%s: %s \" % (name, f[name]))\n",
    "f.visit(printname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every object in an HDF5 file can have custom attributes which can be checked like any other dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(f['phenotype']['matrix'].attrs.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us examine the *phenotype/matrix* dataset as a `Dataset` object.   \n",
    "The object we obtained isn’t an array, but an *HDF5* dataset. Like *NumPy* arrays, datasets have both a *shape* and a *data type*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape: (1008,46)\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "dset = f['phenotype/matrix']\n",
    "print('shape: (%s,%s)' % dset.shape)\n",
    "print('dtype: %s' % dset.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "They also support array-style slicing. This is how you read and write data from a dataset in the file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.24597261,  2.6897812 , -0.46048117,  0.03086018, -0.29291799,\n",
       "       -1.09117648,  0.70117087, -0.86986989, -0.13682744, -0.28441332])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# retrieve all rows between 10 and 20 for the the second column\n",
    "dset[10:20, 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When filtering/slicing/indexing rows and columns of a dataset, not the entire dataset is loaded into the main memory. This is very useful if you need to retrieve a subset of the data from a terabyte big HDF5 file.  \n",
    "To read the entire dataset into the memory run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-7.32351971e+00,  2.79992827e-01,  3.13118166e-01, ...,\n",
       "         8.90841948e-01,  4.11837231e+00,  8.59281836e+00],\n",
       "       [-8.09823582e+00, -2.06326076e-01, -5.34843783e-01, ...,\n",
       "         6.06164117e-03,  6.65150029e-02, -4.22047646e+00],\n",
       "       [ 7.60571968e+00, -1.27959825e-01, -3.11102424e-01, ...,\n",
       "         1.72315729e+00,  5.71408803e+00, -6.50651895e+00],\n",
       "       ...,\n",
       "       [            nan,  3.21844466e-01,             nan, ...,\n",
       "         1.80142187e+00,             nan, -4.99069190e-01],\n",
       "       [            nan, -1.00150686e+00,             nan, ...,\n",
       "        -1.74752575e+00,             nan, -7.43596147e+00],\n",
       "       [            nan,             nan,             nan, ...,\n",
       "                    nan,             nan,             nan]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'numpy.ndarray'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-7.32351971e+00,  2.79992827e-01,  3.13118166e-01, ...,\n",
       "         8.90841948e-01,  4.11837231e+00,  8.59281836e+00],\n",
       "       [-8.09823582e+00, -2.06326076e-01, -5.34843783e-01, ...,\n",
       "         6.06164117e-03,  6.65150029e-02, -4.22047646e+00],\n",
       "       [ 7.60571968e+00, -1.27959825e-01, -3.11102424e-01, ...,\n",
       "         1.72315729e+00,  5.71408803e+00, -6.50651895e+00],\n",
       "       ...,\n",
       "       [            nan,  3.21844466e-01,             nan, ...,\n",
       "         1.80142187e+00,             nan, -4.99069190e-01],\n",
       "       [            nan, -1.00150686e+00,             nan, ...,\n",
       "        -1.74752575e+00,             nan, -7.43596147e+00],\n",
       "       [            nan,             nan,             nan, ...,\n",
       "                    nan,             nan,             nan]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = dset[:]\n",
    "print(type(arr))\n",
    "arr"
   ]
  },
  {
   "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": 36,
   "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": 37,
   "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": 38,
   "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>birthyear</th>\n",
       "      <th>children</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         birthyear  children    hobby  weight\n",
       "alice         1985       NaN   Biking      68\n",
       "bob           1984       3.0  Dancing      83\n",
       "charles       1992       0.0      NaN     112"
      ]
     },
     "execution_count": 38,
     "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 (PyTasbles 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": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[u'col_header', u'matrix', u'row_header']\n",
      "<HDF5 dataset \"phenotype_ID\": shape (46,), type \"|S22\">\n",
      "<HDF5 dataset \"sample_ID\": shape (1008,), type \"|S22\">\n"
     ]
    }
   ],
   "source": [
    "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": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "pheno_group = f['phenotype']\n",
    "df = pd.DataFrame(pheno_group['matrix'][:], \n",
    "                        columns=pheno_group['col_header']['phenotype_ID'][:],\n",
    "                        index=pheno_group['row_header']['sample_ID'][:])\n"
   ]
  },
  {
   "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": 41,
   "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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# retrieve first 10 rows\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# retrieve last 5 rows\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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",
      "Cadmium_Chloride          799 non-null float64\n",
      "Caffeine                  1004 non-null float64\n",
      "Calcium_Chloride          949 non-null float64\n",
      "Cisplatin                 990 non-null float64\n",
      "Cobalt_Chloride           1007 non-null float64\n",
      "Congo_red                 979 non-null float64\n",
      "Copper                    972 non-null float64\n",
      "Cycloheximide             1004 non-null float64\n",
      "Diamide                   1003 non-null float64\n",
      "E6_Berbamine              1005 non-null float64\n",
      "Ethanol                   967 non-null float64\n",
      "Formamide                 999 non-null float64\n",
      "Galactose                 962 non-null float64\n",
      "Hydrogen_Peroxide         769 non-null float64\n",
      "Hydroquinone              960 non-null float64\n",
      "Hydroxyurea               944 non-null float64\n",
      "Indoleacetic_Acid         994 non-null float64\n",
      "Lactate                   973 non-null float64\n",
      "Lactose                   1004 non-null float64\n",
      "Lithium_Chloride          1002 non-null float64\n",
      "Magnesium_Chloride        964 non-null float64\n",
      "Magnesium_Sulfate         1006 non-null float64\n",
      "Maltose                   1005 non-null float64\n",
      "Mannose                   957 non-null float64\n",
      "Menadione                 1005 non-null float64\n",
      "Neomycin                  1005 non-null float64\n",
      "Paraquat                  1004 non-null float64\n",
      "Raffinose                 662 non-null float64\n",
      "SDS                       873 non-null float64\n",
      "Sorbitol                  599 non-null float64\n",
      "Trehalose                 1003 non-null float64\n",
      "Tunicamycin               1006 non-null float64\n",
      "x4-Hydroxybenzaldehyde    964 non-null float64\n",
      "x4NQO                     1005 non-null float64\n",
      "x5-Fluorocytosine         957 non-null float64\n",
      "x5-Fluorouracil           992 non-null float64\n",
      "x6-Azauracil              1001 non-null float64\n",
      "Xylose                    1003 non-null float64\n",
      "YNB                       1006 non-null float64\n",
      "YNB:ph3                   979 non-null float64\n",
      "YNB:ph8                   887 non-null float64\n",
      "YPD                       1006 non-null float64\n",
      "YPD:15C                   1005 non-null float64\n",
      "YPD:37C                   1003 non-null float64\n",
      "YPD:4C                    806 non-null float64\n",
      "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": 44,
   "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  \\\n",
       "count   1005.000000     ...       1001.000000  1003.000000  1006.000000   \n",
       "mean       0.264369     ...          0.112644     0.060641    16.315382   \n",
       "std        7.083140     ...          0.853002     0.939542     1.034526   \n",
       "min      -12.513969     ...         -3.327925    -2.911638    11.981303   \n",
       "25%       -4.880492     ...         -0.449021    -0.599287    15.661717   \n",
       "50%       -2.475193     ...          0.146145     0.047475    16.358272   \n",
       "75%        4.487006     ...          0.700031     0.717306    17.006638   \n",
       "max       23.116821     ...          2.816535     3.286086    19.067704   \n",
       "\n",
       "          YNB:ph3     YNB:ph8          YPD      YPD:15C      YPD:37C  \\\n",
       "count  979.000000  887.000000  1006.000000  1005.000000  1003.000000   \n",
       "mean     0.007996    0.007482    23.502212     0.047823    -0.140778   \n",
       "std      0.674851    0.902919     1.815418     2.541834     2.642301   \n",
       "min     -2.570029   -3.724701    16.979292    -6.493270    -7.377041   \n",
       "25%     -0.451187   -0.571156    22.380371    -1.879136    -2.168742   \n",
       "50%      0.024450   -0.003055    23.403000    -0.132709    -0.047865   \n",
       "75%      0.479301    0.628253    24.629824     1.832583     1.966580   \n",
       "max      1.773026    2.956204    29.985755     7.287769     7.474741   \n",
       "\n",
       "           YPD:4C      Zeocin  \n",
       "count  806.000000  957.000000  \n",
       "mean    -0.190777    0.166791  \n",
       "std      4.649077    6.433438  \n",
       "min     -9.588223  -13.359268  \n",
       "25%     -3.680280   -5.159223  \n",
       "50%     -0.977779   -2.015229  \n",
       "75%      2.605938    5.751830  \n",
       "max     15.552609   16.662919  \n",
       "\n",
       "[8 rows x 46 columns]"
      ]
     },
     "execution_count": 44,
     "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": 45,
   "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": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.127959825210201"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2].Caffeine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.127959825210201"
      ]
     },
     "execution_count": 47,
     "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": 48,
   "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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[1, 5, 6, 9]].Caffeine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[['A01_02', 'A01_06', 'A01_07', 'A01_10']].Caffeine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": 50,
     "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": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4.14189858085831"
      ]
     },
     "execution_count": 51,
     "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": 52,
   "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",
       "A01_06     True\n",
       "A01_07    False\n",
       "A01_08     True\n",
       "A01_09    False\n",
       "A01_10    False\n",
       "A01_11    False\n",
       "A01_12    False\n",
       "A01_13     True\n",
       "A01_14     True\n",
       "A01_15    False\n",
       "A01_16    False\n",
       "A01_17     True\n",
       "A01_18     True\n",
       "A01_19    False\n",
       "A01_20     True\n",
       "A01_21     True\n",
       "A01_22     True\n",
       "A01_23     True\n",
       "A01_24    False\n",
       "A01_25     True\n",
       "A01_26     True\n",
       "A01_27     True\n",
       "A01_28    False\n",
       "A01_29     True\n",
       "A01_30    False\n",
       "          ...  \n",
       "A11_67    False\n",
       "A11_68     True\n",
       "A11_69     True\n",
       "A11_70    False\n",
       "A11_71    False\n",
       "A11_72     True\n",
       "A11_73     True\n",
       "A11_74    False\n",
       "A11_75    False\n",
       "A11_76    False\n",
       "A11_77     True\n",
       "A11_78     True\n",
       "A11_79     True\n",
       "A11_80     True\n",
       "A11_81    False\n",
       "A11_82    False\n",
       "A11_83    False\n",
       "A11_84    False\n",
       "A11_85     True\n",
       "A11_86     True\n",
       "A11_87     True\n",
       "A11_88    False\n",
       "A11_89     True\n",
       "A11_90     True\n",
       "A11_91     True\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": 52,
     "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": 53,
   "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>A01_14</th>\n",
       "      <td>-6.128808</td>\n",
       "      <td>2.287569</td>\n",
       "      <td>0.030860</td>\n",
       "      <td>1.457627</td>\n",
       "      <td>2.658909</td>\n",
       "      <td>8.873294</td>\n",
       "      <td>-2.725993</td>\n",
       "      <td>2.081959</td>\n",
       "      <td>-0.640953</td>\n",
       "      <td>-2.948221</td>\n",
       "      <td>...</td>\n",
       "      <td>0.871464</td>\n",
       "      <td>0.239443</td>\n",
       "      <td>16.293701</td>\n",
       "      <td>0.306043</td>\n",
       "      <td>0.903620</td>\n",
       "      <td>23.053594</td>\n",
       "      <td>4.081697</td>\n",
       "      <td>3.783942</td>\n",
       "      <td>0.069884</td>\n",
       "      <td>2.976971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_17</th>\n",
       "      <td>-6.435934</td>\n",
       "      <td>0.331987</td>\n",
       "      <td>0.701171</td>\n",
       "      <td>0.330169</td>\n",
       "      <td>-3.157943</td>\n",
       "      <td>-1.098844</td>\n",
       "      <td>2.099880</td>\n",
       "      <td>3.261305</td>\n",
       "      <td>-10.892045</td>\n",
       "      <td>14.833187</td>\n",
       "      <td>...</td>\n",
       "      <td>0.778375</td>\n",
       "      <td>1.257294</td>\n",
       "      <td>16.432578</td>\n",
       "      <td>0.030953</td>\n",
       "      <td>1.637458</td>\n",
       "      <td>23.623720</td>\n",
       "      <td>3.763264</td>\n",
       "      <td>-3.649057</td>\n",
       "      <td>7.883571</td>\n",
       "      <td>-5.824656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_18</th>\n",
       "      <td>-1.744416</td>\n",
       "      <td>1.179753</td>\n",
       "      <td>-0.869870</td>\n",
       "      <td>-1.269823</td>\n",
       "      <td>3.048698</td>\n",
       "      <td>-6.121298</td>\n",
       "      <td>0.008026</td>\n",
       "      <td>-0.243670</td>\n",
       "      <td>3.129125</td>\n",
       "      <td>-0.310028</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.658192</td>\n",
       "      <td>0.014206</td>\n",
       "      <td>17.374168</td>\n",
       "      <td>-0.383067</td>\n",
       "      <td>-0.997588</td>\n",
       "      <td>23.653983</td>\n",
       "      <td>-2.572794</td>\n",
       "      <td>1.388221</td>\n",
       "      <td>-6.245010</td>\n",
       "      <td>-1.875007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_20</th>\n",
       "      <td>-6.362380</td>\n",
       "      <td>0.986639</td>\n",
       "      <td>-0.284413</td>\n",
       "      <td>0.014248</td>\n",
       "      <td>2.235446</td>\n",
       "      <td>-1.681283</td>\n",
       "      <td>-0.420546</td>\n",
       "      <td>0.076087</td>\n",
       "      <td>3.076295</td>\n",
       "      <td>-6.065038</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.904024</td>\n",
       "      <td>0.127447</td>\n",
       "      <td>14.506945</td>\n",
       "      <td>-0.817359</td>\n",
       "      <td>-0.907824</td>\n",
       "      <td>20.581287</td>\n",
       "      <td>0.125045</td>\n",
       "      <td>-0.928468</td>\n",
       "      <td>-2.542536</td>\n",
       "      <td>6.886391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_21</th>\n",
       "      <td>5.925445</td>\n",
       "      <td>1.238484</td>\n",
       "      <td>-0.180890</td>\n",
       "      <td>-0.785711</td>\n",
       "      <td>0.856511</td>\n",
       "      <td>-0.954674</td>\n",
       "      <td>1.886783</td>\n",
       "      <td>2.196050</td>\n",
       "      <td>2.436579</td>\n",
       "      <td>-0.643799</td>\n",
       "      <td>...</td>\n",
       "      <td>0.736404</td>\n",
       "      <td>1.220645</td>\n",
       "      <td>17.109577</td>\n",
       "      <td>0.002582</td>\n",
       "      <td>-0.689019</td>\n",
       "      <td>24.960265</td>\n",
       "      <td>-4.600436</td>\n",
       "      <td>-3.240098</td>\n",
       "      <td>-1.430459</td>\n",
       "      <td>4.630062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_22</th>\n",
       "      <td>3.015350</td>\n",
       "      <td>0.844175</td>\n",
       "      <td>0.640409</td>\n",
       "      <td>1.477814</td>\n",
       "      <td>2.287242</td>\n",
       "      <td>-0.343746</td>\n",
       "      <td>1.038470</td>\n",
       "      <td>-0.832654</td>\n",
       "      <td>2.717067</td>\n",
       "      <td>-4.706824</td>\n",
       "      <td>...</td>\n",
       "      <td>2.037066</td>\n",
       "      <td>1.738069</td>\n",
       "      <td>17.113177</td>\n",
       "      <td>0.625722</td>\n",
       "      <td>0.712437</td>\n",
       "      <td>24.854792</td>\n",
       "      <td>5.443331</td>\n",
       "      <td>-4.111030</td>\n",
       "      <td>5.459420</td>\n",
       "      <td>10.382066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_23</th>\n",
       "      <td>5.368651</td>\n",
       "      <td>1.648824</td>\n",
       "      <td>-0.311006</td>\n",
       "      <td>0.151763</td>\n",
       "      <td>2.089597</td>\n",
       "      <td>0.886900</td>\n",
       "      <td>-0.827784</td>\n",
       "      <td>-0.076783</td>\n",
       "      <td>2.241636</td>\n",
       "      <td>-5.830545</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.369967</td>\n",
       "      <td>-0.887642</td>\n",
       "      <td>15.680565</td>\n",
       "      <td>-0.132719</td>\n",
       "      <td>0.793680</td>\n",
       "      <td>23.211814</td>\n",
       "      <td>1.160337</td>\n",
       "      <td>3.339040</td>\n",
       "      <td>0.908244</td>\n",
       "      <td>13.591021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_25</th>\n",
       "      <td>2.361884</td>\n",
       "      <td>1.207676</td>\n",
       "      <td>-1.382897</td>\n",
       "      <td>1.224807</td>\n",
       "      <td>-1.735648</td>\n",
       "      <td>-0.200892</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.155111</td>\n",
       "      <td>-6.857530</td>\n",
       "      <td>-3.846204</td>\n",
       "      <td>...</td>\n",
       "      <td>0.909605</td>\n",
       "      <td>-1.330609</td>\n",
       "      <td>13.144011</td>\n",
       "      <td>1.004012</td>\n",
       "      <td>0.127184</td>\n",
       "      <td>22.482902</td>\n",
       "      <td>-0.605416</td>\n",
       "      <td>0.017608</td>\n",
       "      <td>0.398050</td>\n",
       "      <td>-2.081629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_26</th>\n",
       "      <td>-5.888597</td>\n",
       "      <td>1.748776</td>\n",
       "      <td>-0.432020</td>\n",
       "      <td>-0.303939</td>\n",
       "      <td>-2.064806</td>\n",
       "      <td>-0.087783</td>\n",
       "      <td>0.418293</td>\n",
       "      <td>1.550241</td>\n",
       "      <td>1.862355</td>\n",
       "      <td>4.625362</td>\n",
       "      <td>...</td>\n",
       "      <td>0.572039</td>\n",
       "      <td>-0.514878</td>\n",
       "      <td>16.622170</td>\n",
       "      <td>-0.709576</td>\n",
       "      <td>-0.502412</td>\n",
       "      <td>23.804121</td>\n",
       "      <td>-4.416801</td>\n",
       "      <td>1.710262</td>\n",
       "      <td>-2.832793</td>\n",
       "      <td>-2.582201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_27</th>\n",
       "      <td>5.864971</td>\n",
       "      <td>0.085989</td>\n",
       "      <td>0.272710</td>\n",
       "      <td>0.687241</td>\n",
       "      <td>-2.272625</td>\n",
       "      <td>-6.032576</td>\n",
       "      <td>2.116587</td>\n",
       "      <td>0.717661</td>\n",
       "      <td>4.509182</td>\n",
       "      <td>8.784337</td>\n",
       "      <td>...</td>\n",
       "      <td>0.984612</td>\n",
       "      <td>0.663690</td>\n",
       "      <td>17.433361</td>\n",
       "      <td>-0.237397</td>\n",
       "      <td>-0.214977</td>\n",
       "      <td>22.998909</td>\n",
       "      <td>1.925353</td>\n",
       "      <td>4.612446</td>\n",
       "      <td>2.617287</td>\n",
       "      <td>7.365063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_29</th>\n",
       "      <td>8.291001</td>\n",
       "      <td>0.602952</td>\n",
       "      <td>-0.209818</td>\n",
       "      <td>0.176575</td>\n",
       "      <td>2.970487</td>\n",
       "      <td>11.674431</td>\n",
       "      <td>-5.015363</td>\n",
       "      <td>0.942569</td>\n",
       "      <td>-0.493707</td>\n",
       "      <td>10.374494</td>\n",
       "      <td>...</td>\n",
       "      <td>0.222568</td>\n",
       "      <td>-1.013385</td>\n",
       "      <td>16.763518</td>\n",
       "      <td>-0.206383</td>\n",
       "      <td>0.672626</td>\n",
       "      <td>26.448189</td>\n",
       "      <td>1.866508</td>\n",
       "      <td>0.239108</td>\n",
       "      <td>-0.638321</td>\n",
       "      <td>12.690415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_31</th>\n",
       "      <td>-6.398361</td>\n",
       "      <td>0.166271</td>\n",
       "      <td>-0.878706</td>\n",
       "      <td>-0.078068</td>\n",
       "      <td>-3.349890</td>\n",
       "      <td>-1.069199</td>\n",
       "      <td>1.163056</td>\n",
       "      <td>-0.895535</td>\n",
       "      <td>-0.087970</td>\n",
       "      <td>-4.928491</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.314301</td>\n",
       "      <td>-1.375641</td>\n",
       "      <td>15.544307</td>\n",
       "      <td>-0.004930</td>\n",
       "      <td>-0.744114</td>\n",
       "      <td>24.094502</td>\n",
       "      <td>-2.020393</td>\n",
       "      <td>-0.891901</td>\n",
       "      <td>-5.279599</td>\n",
       "      <td>1.193205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_32</th>\n",
       "      <td>7.130669</td>\n",
       "      <td>0.718797</td>\n",
       "      <td>0.518366</td>\n",
       "      <td>1.823296</td>\n",
       "      <td>0.429805</td>\n",
       "      <td>-1.103549</td>\n",
       "      <td>0.740313</td>\n",
       "      <td>2.506797</td>\n",
       "      <td>2.778662</td>\n",
       "      <td>-4.981721</td>\n",
       "      <td>...</td>\n",
       "      <td>0.632448</td>\n",
       "      <td>1.494387</td>\n",
       "      <td>16.222977</td>\n",
       "      <td>0.751728</td>\n",
       "      <td>-2.119434</td>\n",
       "      <td>22.723083</td>\n",
       "      <td>5.791591</td>\n",
       "      <td>-0.958975</td>\n",
       "      <td>1.065139</td>\n",
       "      <td>-2.015229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_33</th>\n",
       "      <td>-7.996359</td>\n",
       "      <td>0.922486</td>\n",
       "      <td>0.264327</td>\n",
       "      <td>-1.664310</td>\n",
       "      <td>-5.532087</td>\n",
       "      <td>-5.217066</td>\n",
       "      <td>1.188328</td>\n",
       "      <td>2.380337</td>\n",
       "      <td>3.479604</td>\n",
       "      <td>-2.757452</td>\n",
       "      <td>...</td>\n",
       "      <td>1.271010</td>\n",
       "      <td>1.306615</td>\n",
       "      <td>18.784166</td>\n",
       "      <td>0.187778</td>\n",
       "      <td>0.246367</td>\n",
       "      <td>29.168126</td>\n",
       "      <td>4.090078</td>\n",
       "      <td>0.995169</td>\n",
       "      <td>3.198552</td>\n",
       "      <td>-10.757366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_34</th>\n",
       "      <td>-7.061253</td>\n",
       "      <td>0.024929</td>\n",
       "      <td>1.033085</td>\n",
       "      <td>0.766978</td>\n",
       "      <td>3.873184</td>\n",
       "      <td>1.228287</td>\n",
       "      <td>-0.019795</td>\n",
       "      <td>-0.597636</td>\n",
       "      <td>-0.869106</td>\n",
       "      <td>-3.144067</td>\n",
       "      <td>...</td>\n",
       "      <td>1.212225</td>\n",
       "      <td>1.713806</td>\n",
       "      <td>17.565024</td>\n",
       "      <td>0.101327</td>\n",
       "      <td>-0.127652</td>\n",
       "      <td>23.749063</td>\n",
       "      <td>3.836577</td>\n",
       "      <td>-2.537776</td>\n",
       "      <td>6.718132</td>\n",
       "      <td>-4.968092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_35</th>\n",
       "      <td>7.125313</td>\n",
       "      <td>0.477383</td>\n",
       "      <td>0.203855</td>\n",
       "      <td>0.146072</td>\n",
       "      <td>4.542455</td>\n",
       "      <td>-4.875453</td>\n",
       "      <td>2.360665</td>\n",
       "      <td>-1.442190</td>\n",
       "      <td>0.693330</td>\n",
       "      <td>-6.215016</td>\n",
       "      <td>...</td>\n",
       "      <td>1.818472</td>\n",
       "      <td>0.151740</td>\n",
       "      <td>16.212551</td>\n",
       "      <td>0.662079</td>\n",
       "      <td>-0.147682</td>\n",
       "      <td>23.430682</td>\n",
       "      <td>-2.494964</td>\n",
       "      <td>3.580115</td>\n",
       "      <td>-2.705861</td>\n",
       "      <td>-4.434123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_38</th>\n",
       "      <td>7.221967</td>\n",
       "      <td>1.607407</td>\n",
       "      <td>1.136931</td>\n",
       "      <td>0.321945</td>\n",
       "      <td>-2.917663</td>\n",
       "      <td>6.128958</td>\n",
       "      <td>-0.439967</td>\n",
       "      <td>3.226792</td>\n",
       "      <td>4.648551</td>\n",
       "      <td>-3.809075</td>\n",
       "      <td>...</td>\n",
       "      <td>1.010044</td>\n",
       "      <td>0.545939</td>\n",
       "      <td>15.461926</td>\n",
       "      <td>0.202358</td>\n",
       "      <td>0.744799</td>\n",
       "      <td>22.786138</td>\n",
       "      <td>-0.951993</td>\n",
       "      <td>-1.479317</td>\n",
       "      <td>6.993651</td>\n",
       "      <td>-5.792154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_40</th>\n",
       "      <td>7.386030</td>\n",
       "      <td>0.685339</td>\n",
       "      <td>0.552385</td>\n",
       "      <td>1.627962</td>\n",
       "      <td>4.060814</td>\n",
       "      <td>-1.013393</td>\n",
       "      <td>-0.051946</td>\n",
       "      <td>0.805096</td>\n",
       "      <td>5.205418</td>\n",
       "      <td>0.235112</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.328904</td>\n",
       "      <td>0.065395</td>\n",
       "      <td>16.315874</td>\n",
       "      <td>0.539172</td>\n",
       "      <td>-0.718097</td>\n",
       "      <td>23.366239</td>\n",
       "      <td>3.277305</td>\n",
       "      <td>1.194231</td>\n",
       "      <td>0.670884</td>\n",
       "      <td>7.639392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_44</th>\n",
       "      <td>5.584843</td>\n",
       "      <td>1.063073</td>\n",
       "      <td>-0.673369</td>\n",
       "      <td>0.292690</td>\n",
       "      <td>-0.585619</td>\n",
       "      <td>-1.411756</td>\n",
       "      <td>0.982226</td>\n",
       "      <td>-0.333864</td>\n",
       "      <td>2.798290</td>\n",
       "      <td>0.193106</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.635281</td>\n",
       "      <td>-0.836197</td>\n",
       "      <td>15.972579</td>\n",
       "      <td>0.842179</td>\n",
       "      <td>1.050111</td>\n",
       "      <td>22.333949</td>\n",
       "      <td>-4.374102</td>\n",
       "      <td>1.138760</td>\n",
       "      <td>-5.530835</td>\n",
       "      <td>5.771627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_45</th>\n",
       "      <td>-6.886116</td>\n",
       "      <td>2.422459</td>\n",
       "      <td>2.094146</td>\n",
       "      <td>2.513203</td>\n",
       "      <td>6.714054</td>\n",
       "      <td>-0.397405</td>\n",
       "      <td>-6.722845</td>\n",
       "      <td>-1.037253</td>\n",
       "      <td>-0.875755</td>\n",
       "      <td>-5.357255</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.635183</td>\n",
       "      <td>0.518553</td>\n",
       "      <td>15.862309</td>\n",
       "      <td>1.405404</td>\n",
       "      <td>1.074609</td>\n",
       "      <td>20.873441</td>\n",
       "      <td>-2.156279</td>\n",
       "      <td>6.328426</td>\n",
       "      <td>1.883678</td>\n",
       "      <td>-3.488512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_46</th>\n",
       "      <td>-6.857821</td>\n",
       "      <td>0.372263</td>\n",
       "      <td>-1.493623</td>\n",
       "      <td>-2.658450</td>\n",
       "      <td>-0.895147</td>\n",
       "      <td>-5.366424</td>\n",
       "      <td>-0.115780</td>\n",
       "      <td>-0.501187</td>\n",
       "      <td>3.045313</td>\n",
       "      <td>-5.753381</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.029738</td>\n",
       "      <td>-0.399975</td>\n",
       "      <td>15.623093</td>\n",
       "      <td>-0.450903</td>\n",
       "      <td>-0.254102</td>\n",
       "      <td>22.505698</td>\n",
       "      <td>-3.920752</td>\n",
       "      <td>-2.202890</td>\n",
       "      <td>-1.563144</td>\n",
       "      <td>1.055169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_47</th>\n",
       "      <td>-4.268287</td>\n",
       "      <td>0.362259</td>\n",
       "      <td>0.310740</td>\n",
       "      <td>1.390570</td>\n",
       "      <td>0.966619</td>\n",
       "      <td>10.681893</td>\n",
       "      <td>0.633434</td>\n",
       "      <td>0.512259</td>\n",
       "      <td>1.973278</td>\n",
       "      <td>-6.997449</td>\n",
       "      <td>...</td>\n",
       "      <td>0.574923</td>\n",
       "      <td>0.748745</td>\n",
       "      <td>17.494740</td>\n",
       "      <td>-0.075021</td>\n",
       "      <td>0.094029</td>\n",
       "      <td>23.578347</td>\n",
       "      <td>-0.045091</td>\n",
       "      <td>-2.543260</td>\n",
       "      <td>1.207601</td>\n",
       "      <td>4.475345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_55</th>\n",
       "      <td>4.074521</td>\n",
       "      <td>1.005304</td>\n",
       "      <td>-0.840341</td>\n",
       "      <td>-0.156409</td>\n",
       "      <td>1.439703</td>\n",
       "      <td>-0.288215</td>\n",
       "      <td>-2.433246</td>\n",
       "      <td>-0.739372</td>\n",
       "      <td>4.679368</td>\n",
       "      <td>-5.967278</td>\n",
       "      <td>...</td>\n",
       "      <td>0.205970</td>\n",
       "      <td>0.869975</td>\n",
       "      <td>15.028113</td>\n",
       "      <td>0.364559</td>\n",
       "      <td>0.530884</td>\n",
       "      <td>22.119766</td>\n",
       "      <td>5.280116</td>\n",
       "      <td>-4.113606</td>\n",
       "      <td>-2.367438</td>\n",
       "      <td>0.865415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_58</th>\n",
       "      <td>4.092310</td>\n",
       "      <td>0.929132</td>\n",
       "      <td>-0.885145</td>\n",
       "      <td>0.854615</td>\n",
       "      <td>-1.651898</td>\n",
       "      <td>1.106198</td>\n",
       "      <td>2.308981</td>\n",
       "      <td>-1.584598</td>\n",
       "      <td>2.258720</td>\n",
       "      <td>6.459206</td>\n",
       "      <td>...</td>\n",
       "      <td>0.709761</td>\n",
       "      <td>0.793777</td>\n",
       "      <td>17.961373</td>\n",
       "      <td>0.583428</td>\n",
       "      <td>-0.001954</td>\n",
       "      <td>27.061697</td>\n",
       "      <td>-4.836116</td>\n",
       "      <td>3.824283</td>\n",
       "      <td>-2.061558</td>\n",
       "      <td>-3.699731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A01_59</th>\n",
       "      <td>-7.267147</td>\n",
       "      <td>0.317266</td>\n",
       "      <td>0.353902</td>\n",
       "      <td>0.173577</td>\n",
       "      <td>3.859513</td>\n",
       "      <td>-1.350063</td>\n",
       "      <td>0.048835</td>\n",
       "      <td>-1.142921</td>\n",
       "      <td>0.293651</td>\n",
       "      <td>-2.481264</td>\n",
       "      <td>...</td>\n",
       "      <td>0.165374</td>\n",
       "      <td>1.366814</td>\n",
       "      <td>16.484308</td>\n",
       "      <td>-0.533237</td>\n",
       "      <td>-0.487195</td>\n",
       "      <td>24.985005</td>\n",
       "      <td>0.075893</td>\n",
       "      <td>2.709230</td>\n",
       "      <td>2.936690</td>\n",
       "      <td>5.137446</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_44</th>\n",
       "      <td>-7.455436</td>\n",
       "      <td>0.065010</td>\n",
       "      <td>0.708960</td>\n",
       "      <td>-0.303781</td>\n",
       "      <td>1.814606</td>\n",
       "      <td>5.638196</td>\n",
       "      <td>0.224649</td>\n",
       "      <td>1.198512</td>\n",
       "      <td>-1.343692</td>\n",
       "      <td>-4.435628</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.749211</td>\n",
       "      <td>-0.148476</td>\n",
       "      <td>17.105216</td>\n",
       "      <td>0.123122</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.720722</td>\n",
       "      <td>-2.992506</td>\n",
       "      <td>-2.502814</td>\n",
       "      <td>-1.839947</td>\n",
       "      <td>5.751830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_45</th>\n",
       "      <td>2.907273</td>\n",
       "      <td>0.377561</td>\n",
       "      <td>0.008094</td>\n",
       "      <td>1.516315</td>\n",
       "      <td>0.471784</td>\n",
       "      <td>-6.231109</td>\n",
       "      <td>-3.230581</td>\n",
       "      <td>-0.941268</td>\n",
       "      <td>-11.222300</td>\n",
       "      <td>-4.671810</td>\n",
       "      <td>...</td>\n",
       "      <td>0.790916</td>\n",
       "      <td>0.722727</td>\n",
       "      <td>16.467980</td>\n",
       "      <td>1.132648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.521577</td>\n",
       "      <td>1.643338</td>\n",
       "      <td>2.459915</td>\n",
       "      <td>-5.901576</td>\n",
       "      <td>-4.664778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_46</th>\n",
       "      <td>-6.928941</td>\n",
       "      <td>0.077312</td>\n",
       "      <td>0.227317</td>\n",
       "      <td>1.109649</td>\n",
       "      <td>2.180708</td>\n",
       "      <td>-1.061959</td>\n",
       "      <td>0.614301</td>\n",
       "      <td>1.245527</td>\n",
       "      <td>4.237199</td>\n",
       "      <td>3.007887</td>\n",
       "      <td>...</td>\n",
       "      <td>0.791549</td>\n",
       "      <td>1.060520</td>\n",
       "      <td>15.742954</td>\n",
       "      <td>0.688456</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.512751</td>\n",
       "      <td>0.454164</td>\n",
       "      <td>-2.993857</td>\n",
       "      <td>2.341450</td>\n",
       "      <td>5.118265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_48</th>\n",
       "      <td>7.183339</td>\n",
       "      <td>0.983350</td>\n",
       "      <td>2.090029</td>\n",
       "      <td>0.001171</td>\n",
       "      <td>0.420704</td>\n",
       "      <td>-2.660331</td>\n",
       "      <td>1.417940</td>\n",
       "      <td>1.470723</td>\n",
       "      <td>1.243041</td>\n",
       "      <td>-7.825930</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.111029</td>\n",
       "      <td>-0.589826</td>\n",
       "      <td>15.758187</td>\n",
       "      <td>0.727027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.681465</td>\n",
       "      <td>-1.544399</td>\n",
       "      <td>1.773595</td>\n",
       "      <td>-3.088895</td>\n",
       "      <td>-3.579066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_51</th>\n",
       "      <td>7.633948</td>\n",
       "      <td>0.077103</td>\n",
       "      <td>0.703641</td>\n",
       "      <td>0.419895</td>\n",
       "      <td>0.559865</td>\n",
       "      <td>-6.942340</td>\n",
       "      <td>1.111722</td>\n",
       "      <td>0.174473</td>\n",
       "      <td>2.517062</td>\n",
       "      <td>-1.057514</td>\n",
       "      <td>...</td>\n",
       "      <td>0.772863</td>\n",
       "      <td>-0.873542</td>\n",
       "      <td>16.774581</td>\n",
       "      <td>-0.384493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.893079</td>\n",
       "      <td>-0.050615</td>\n",
       "      <td>-1.412976</td>\n",
       "      <td>-6.528134</td>\n",
       "      <td>7.276401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_52</th>\n",
       "      <td>-8.027743</td>\n",
       "      <td>0.342941</td>\n",
       "      <td>0.207606</td>\n",
       "      <td>0.777800</td>\n",
       "      <td>1.855766</td>\n",
       "      <td>-6.155187</td>\n",
       "      <td>0.929895</td>\n",
       "      <td>-0.289071</td>\n",
       "      <td>0.662585</td>\n",
       "      <td>6.697976</td>\n",
       "      <td>...</td>\n",
       "      <td>0.621308</td>\n",
       "      <td>0.035059</td>\n",
       "      <td>16.362545</td>\n",
       "      <td>0.487377</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.642174</td>\n",
       "      <td>-0.752784</td>\n",
       "      <td>-3.310424</td>\n",
       "      <td>4.157145</td>\n",
       "      <td>-5.710500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_54</th>\n",
       "      <td>-8.525728</td>\n",
       "      <td>1.328282</td>\n",
       "      <td>0.473115</td>\n",
       "      <td>0.388526</td>\n",
       "      <td>0.823639</td>\n",
       "      <td>-6.179502</td>\n",
       "      <td>0.822936</td>\n",
       "      <td>-1.103175</td>\n",
       "      <td>7.015429</td>\n",
       "      <td>-4.235858</td>\n",
       "      <td>...</td>\n",
       "      <td>0.399033</td>\n",
       "      <td>0.443633</td>\n",
       "      <td>16.321728</td>\n",
       "      <td>-0.326466</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.076351</td>\n",
       "      <td>-3.822596</td>\n",
       "      <td>-2.623999</td>\n",
       "      <td>-6.319933</td>\n",
       "      <td>-4.721783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_57</th>\n",
       "      <td>6.346367</td>\n",
       "      <td>0.167243</td>\n",
       "      <td>2.454003</td>\n",
       "      <td>0.309949</td>\n",
       "      <td>1.359860</td>\n",
       "      <td>-0.526673</td>\n",
       "      <td>2.606971</td>\n",
       "      <td>0.754974</td>\n",
       "      <td>1.809343</td>\n",
       "      <td>-4.754368</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.912437</td>\n",
       "      <td>0.611290</td>\n",
       "      <td>18.938192</td>\n",
       "      <td>1.074502</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.313950</td>\n",
       "      <td>-0.262279</td>\n",
       "      <td>4.058366</td>\n",
       "      <td>6.746140</td>\n",
       "      <td>-5.175894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_58</th>\n",
       "      <td>-7.285724</td>\n",
       "      <td>0.583391</td>\n",
       "      <td>-0.163537</td>\n",
       "      <td>-1.535646</td>\n",
       "      <td>0.151295</td>\n",
       "      <td>-6.545795</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.749599</td>\n",
       "      <td>1.642822</td>\n",
       "      <td>2.715098</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.324229</td>\n",
       "      <td>-1.474309</td>\n",
       "      <td>16.352664</td>\n",
       "      <td>-0.068981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.206375</td>\n",
       "      <td>-4.699755</td>\n",
       "      <td>0.792384</td>\n",
       "      <td>-3.585440</td>\n",
       "      <td>2.196232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_60</th>\n",
       "      <td>7.598761</td>\n",
       "      <td>0.730103</td>\n",
       "      <td>0.828262</td>\n",
       "      <td>0.347327</td>\n",
       "      <td>0.161159</td>\n",
       "      <td>-8.610313</td>\n",
       "      <td>2.052813</td>\n",
       "      <td>-0.986483</td>\n",
       "      <td>-8.726147</td>\n",
       "      <td>-7.935309</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000856</td>\n",
       "      <td>-0.865281</td>\n",
       "      <td>15.641259</td>\n",
       "      <td>1.351713</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.050501</td>\n",
       "      <td>-2.512252</td>\n",
       "      <td>2.951493</td>\n",
       "      <td>-1.999519</td>\n",
       "      <td>-8.702733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_61</th>\n",
       "      <td>-6.880364</td>\n",
       "      <td>0.862238</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.426329</td>\n",
       "      <td>4.559201</td>\n",
       "      <td>-8.452262</td>\n",
       "      <td>0.926557</td>\n",
       "      <td>2.548554</td>\n",
       "      <td>1.555138</td>\n",
       "      <td>-3.631661</td>\n",
       "      <td>...</td>\n",
       "      <td>0.768317</td>\n",
       "      <td>-1.471348</td>\n",
       "      <td>17.081808</td>\n",
       "      <td>-0.140308</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.479476</td>\n",
       "      <td>-0.750635</td>\n",
       "      <td>3.074695</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.849204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_62</th>\n",
       "      <td>-8.045053</td>\n",
       "      <td>0.069826</td>\n",
       "      <td>-0.942733</td>\n",
       "      <td>-0.277029</td>\n",
       "      <td>0.725625</td>\n",
       "      <td>-7.727957</td>\n",
       "      <td>-0.064185</td>\n",
       "      <td>-0.066813</td>\n",
       "      <td>0.169675</td>\n",
       "      <td>-3.480213</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.039708</td>\n",
       "      <td>-0.499990</td>\n",
       "      <td>19.067704</td>\n",
       "      <td>-0.319564</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.226514</td>\n",
       "      <td>-4.369941</td>\n",
       "      <td>3.546102</td>\n",
       "      <td>-7.820512</td>\n",
       "      <td>-6.362555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_64</th>\n",
       "      <td>-8.452961</td>\n",
       "      <td>1.313303</td>\n",
       "      <td>-0.180174</td>\n",
       "      <td>-0.395152</td>\n",
       "      <td>-3.073554</td>\n",
       "      <td>11.475177</td>\n",
       "      <td>-3.706662</td>\n",
       "      <td>1.467159</td>\n",
       "      <td>3.371451</td>\n",
       "      <td>-4.024469</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.072126</td>\n",
       "      <td>-0.430788</td>\n",
       "      <td>16.292964</td>\n",
       "      <td>0.687982</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.538142</td>\n",
       "      <td>2.667982</td>\n",
       "      <td>2.440611</td>\n",
       "      <td>6.249088</td>\n",
       "      <td>8.189521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_66</th>\n",
       "      <td>9.448724</td>\n",
       "      <td>0.311918</td>\n",
       "      <td>0.164296</td>\n",
       "      <td>1.179131</td>\n",
       "      <td>1.776497</td>\n",
       "      <td>-6.723817</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.236887</td>\n",
       "      <td>3.533954</td>\n",
       "      <td>-2.685802</td>\n",
       "      <td>...</td>\n",
       "      <td>1.692752</td>\n",
       "      <td>0.418787</td>\n",
       "      <td>15.430309</td>\n",
       "      <td>1.211189</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.224755</td>\n",
       "      <td>0.956364</td>\n",
       "      <td>-0.765028</td>\n",
       "      <td>-0.505661</td>\n",
       "      <td>-4.559104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_68</th>\n",
       "      <td>-8.824487</td>\n",
       "      <td>0.908554</td>\n",
       "      <td>0.693707</td>\n",
       "      <td>0.258778</td>\n",
       "      <td>3.071328</td>\n",
       "      <td>12.956824</td>\n",
       "      <td>0.565678</td>\n",
       "      <td>0.980528</td>\n",
       "      <td>-4.295115</td>\n",
       "      <td>-3.894834</td>\n",
       "      <td>...</td>\n",
       "      <td>1.005061</td>\n",
       "      <td>0.611755</td>\n",
       "      <td>18.096924</td>\n",
       "      <td>-0.249243</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.928409</td>\n",
       "      <td>4.742218</td>\n",
       "      <td>-2.630358</td>\n",
       "      <td>5.647873</td>\n",
       "      <td>9.762167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_69</th>\n",
       "      <td>-5.625713</td>\n",
       "      <td>0.622860</td>\n",
       "      <td>-0.599341</td>\n",
       "      <td>-0.301774</td>\n",
       "      <td>0.571811</td>\n",
       "      <td>11.847581</td>\n",
       "      <td>0.201365</td>\n",
       "      <td>0.066659</td>\n",
       "      <td>2.859140</td>\n",
       "      <td>-4.665133</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.211785</td>\n",
       "      <td>-0.645943</td>\n",
       "      <td>16.710638</td>\n",
       "      <td>-0.341715</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.724599</td>\n",
       "      <td>1.796232</td>\n",
       "      <td>0.607982</td>\n",
       "      <td>0.693608</td>\n",
       "      <td>6.862298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_72</th>\n",
       "      <td>-9.219612</td>\n",
       "      <td>0.579977</td>\n",
       "      <td>0.901603</td>\n",
       "      <td>0.680549</td>\n",
       "      <td>1.497327</td>\n",
       "      <td>1.119451</td>\n",
       "      <td>-2.112560</td>\n",
       "      <td>-1.144666</td>\n",
       "      <td>-0.532813</td>\n",
       "      <td>-9.513557</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.582535</td>\n",
       "      <td>-1.300570</td>\n",
       "      <td>16.447684</td>\n",
       "      <td>0.858761</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.037218</td>\n",
       "      <td>0.305084</td>\n",
       "      <td>1.587400</td>\n",
       "      <td>-1.889495</td>\n",
       "      <td>-10.516313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_73</th>\n",
       "      <td>-5.636734</td>\n",
       "      <td>0.453751</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.238875</td>\n",
       "      <td>-1.676514</td>\n",
       "      <td>3.115243</td>\n",
       "      <td>-5.534217</td>\n",
       "      <td>-0.918070</td>\n",
       "      <td>-6.196405</td>\n",
       "      <td>-4.306319</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.095414</td>\n",
       "      <td>0.419458</td>\n",
       "      <td>15.222614</td>\n",
       "      <td>-0.870604</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.546315</td>\n",
       "      <td>2.459713</td>\n",
       "      <td>3.235806</td>\n",
       "      <td>-3.884509</td>\n",
       "      <td>4.981810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_77</th>\n",
       "      <td>-8.798892</td>\n",
       "      <td>0.301123</td>\n",
       "      <td>-0.323830</td>\n",
       "      <td>0.432050</td>\n",
       "      <td>2.895317</td>\n",
       "      <td>10.033640</td>\n",
       "      <td>0.595472</td>\n",
       "      <td>-0.957188</td>\n",
       "      <td>4.284933</td>\n",
       "      <td>-3.969501</td>\n",
       "      <td>...</td>\n",
       "      <td>0.783732</td>\n",
       "      <td>2.010373</td>\n",
       "      <td>18.009701</td>\n",
       "      <td>-0.453162</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.167154</td>\n",
       "      <td>-1.830648</td>\n",
       "      <td>-2.582527</td>\n",
       "      <td>6.232993</td>\n",
       "      <td>-2.549221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_78</th>\n",
       "      <td>-4.061045</td>\n",
       "      <td>0.153991</td>\n",
       "      <td>0.749857</td>\n",
       "      <td>0.981008</td>\n",
       "      <td>0.952446</td>\n",
       "      <td>-7.626006</td>\n",
       "      <td>0.546279</td>\n",
       "      <td>1.417278</td>\n",
       "      <td>2.061099</td>\n",
       "      <td>-4.090833</td>\n",
       "      <td>...</td>\n",
       "      <td>0.792770</td>\n",
       "      <td>2.142495</td>\n",
       "      <td>16.595233</td>\n",
       "      <td>0.516697</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.390230</td>\n",
       "      <td>5.181662</td>\n",
       "      <td>-2.575708</td>\n",
       "      <td>12.377388</td>\n",
       "      <td>8.398114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_79</th>\n",
       "      <td>-0.940648</td>\n",
       "      <td>0.348818</td>\n",
       "      <td>0.462085</td>\n",
       "      <td>0.467866</td>\n",
       "      <td>5.958515</td>\n",
       "      <td>5.921017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.267799</td>\n",
       "      <td>2.001825</td>\n",
       "      <td>2.009163</td>\n",
       "      <td>...</td>\n",
       "      <td>0.331876</td>\n",
       "      <td>2.086463</td>\n",
       "      <td>17.236369</td>\n",
       "      <td>1.208932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.567289</td>\n",
       "      <td>4.332028</td>\n",
       "      <td>-2.421702</td>\n",
       "      <td>2.236019</td>\n",
       "      <td>8.882389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_80</th>\n",
       "      <td>6.014177</td>\n",
       "      <td>0.634675</td>\n",
       "      <td>-0.431921</td>\n",
       "      <td>1.064627</td>\n",
       "      <td>-4.663311</td>\n",
       "      <td>-7.117807</td>\n",
       "      <td>1.670899</td>\n",
       "      <td>-0.385115</td>\n",
       "      <td>-4.388628</td>\n",
       "      <td>-5.171900</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.042121</td>\n",
       "      <td>-0.469607</td>\n",
       "      <td>16.598856</td>\n",
       "      <td>0.260251</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.133193</td>\n",
       "      <td>-1.978838</td>\n",
       "      <td>2.192267</td>\n",
       "      <td>-1.833746</td>\n",
       "      <td>-3.248339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_85</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.327573</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.645948</td>\n",
       "      <td>-2.031381</td>\n",
       "      <td>7.609264</td>\n",
       "      <td>-0.660415</td>\n",
       "      <td>1.255510</td>\n",
       "      <td>5.098531</td>\n",
       "      <td>-4.039771</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.470322</td>\n",
       "      <td>0.624881</td>\n",
       "      <td>15.913935</td>\n",
       "      <td>0.221976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.968383</td>\n",
       "      <td>1.705758</td>\n",
       "      <td>6.654459</td>\n",
       "      <td>10.226067</td>\n",
       "      <td>-1.580349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_86</th>\n",
       "      <td>3.189294</td>\n",
       "      <td>2.307381</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.684777</td>\n",
       "      <td>0.510847</td>\n",
       "      <td>-1.193390</td>\n",
       "      <td>-1.424114</td>\n",
       "      <td>-0.993333</td>\n",
       "      <td>-3.922912</td>\n",
       "      <td>1.348560</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.327925</td>\n",
       "      <td>0.331371</td>\n",
       "      <td>15.066564</td>\n",
       "      <td>0.038268</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.979292</td>\n",
       "      <td>-0.394850</td>\n",
       "      <td>5.130092</td>\n",
       "      <td>3.422867</td>\n",
       "      <td>-0.946073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11_87</th>\n",
       "      <td>-5.011391</td>\n",
       "      <td>0.101890</td>\n",
       "      <td>1.089817</td>\n",
       "      <td>-1.230890</td>\n",
       "      <td>-3.135374</td>\n",
       "      <td>-7.961377</td>\n",
       "      <td>0.164554</td>\n",
       "      <td>0.359548</td>\n",
       "      <td>-6.488684</td>\n",
       "      <td>-4.194792</td>\n",
       "      <td>...</td>\n",
       "      <td>0.180122</td>\n",
       "      <td>-0.699413</td>\n",
       "      <td>16.162823</td>\n",
       "      <td>-0.427215</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.939438</td>\n",
       "      <td>1.632431</td>\n",
       "      <td>-4.138147</td>\n",
       "      <td>-3.984612</td>\n",
       "      <td>-3.358585</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",
       "A01_14         -6.128808  2.287569          0.030860   1.457627   \n",
       "A01_17         -6.435934  0.331987          0.701171   0.330169   \n",
       "A01_18         -1.744416  1.179753         -0.869870  -1.269823   \n",
       "A01_20         -6.362380  0.986639         -0.284413   0.014248   \n",
       "A01_21          5.925445  1.238484         -0.180890  -0.785711   \n",
       "A01_22          3.015350  0.844175          0.640409   1.477814   \n",
       "A01_23          5.368651  1.648824         -0.311006   0.151763   \n",
       "A01_25          2.361884  1.207676         -1.382897   1.224807   \n",
       "A01_26         -5.888597  1.748776         -0.432020  -0.303939   \n",
       "A01_27          5.864971  0.085989          0.272710   0.687241   \n",
       "A01_29          8.291001  0.602952         -0.209818   0.176575   \n",
       "A01_31         -6.398361  0.166271         -0.878706  -0.078068   \n",
       "A01_32          7.130669  0.718797          0.518366   1.823296   \n",
       "A01_33         -7.996359  0.922486          0.264327  -1.664310   \n",
       "A01_34         -7.061253  0.024929          1.033085   0.766978   \n",
       "A01_35          7.125313  0.477383          0.203855   0.146072   \n",
       "A01_38          7.221967  1.607407          1.136931   0.321945   \n",
       "A01_40          7.386030  0.685339          0.552385   1.627962   \n",
       "A01_44          5.584843  1.063073         -0.673369   0.292690   \n",
       "A01_45         -6.886116  2.422459          2.094146   2.513203   \n",
       "A01_46         -6.857821  0.372263         -1.493623  -2.658450   \n",
       "A01_47         -4.268287  0.362259          0.310740   1.390570   \n",
       "A01_55          4.074521  1.005304         -0.840341  -0.156409   \n",
       "A01_58          4.092310  0.929132         -0.885145   0.854615   \n",
       "A01_59         -7.267147  0.317266          0.353902   0.173577   \n",
       "...                  ...       ...               ...        ...   \n",
       "A11_44         -7.455436  0.065010          0.708960  -0.303781   \n",
       "A11_45          2.907273  0.377561          0.008094   1.516315   \n",
       "A11_46         -6.928941  0.077312          0.227317   1.109649   \n",
       "A11_48          7.183339  0.983350          2.090029   0.001171   \n",
       "A11_51          7.633948  0.077103          0.703641   0.419895   \n",
       "A11_52         -8.027743  0.342941          0.207606   0.777800   \n",
       "A11_54         -8.525728  1.328282          0.473115   0.388526   \n",
       "A11_57          6.346367  0.167243          2.454003   0.309949   \n",
       "A11_58         -7.285724  0.583391         -0.163537  -1.535646   \n",
       "A11_60          7.598761  0.730103          0.828262   0.347327   \n",
       "A11_61         -6.880364  0.862238               NaN  -0.426329   \n",
       "A11_62         -8.045053  0.069826         -0.942733  -0.277029   \n",
       "A11_64         -8.452961  1.313303         -0.180174  -0.395152   \n",
       "A11_66          9.448724  0.311918          0.164296   1.179131   \n",
       "A11_68         -8.824487  0.908554          0.693707   0.258778   \n",
       "A11_69         -5.625713  0.622860         -0.599341  -0.301774   \n",
       "A11_72         -9.219612  0.579977          0.901603   0.680549   \n",
       "A11_73         -5.636734  0.453751               NaN  -1.238875   \n",
       "A11_77         -8.798892  0.301123         -0.323830   0.432050   \n",
       "A11_78         -4.061045  0.153991          0.749857   0.981008   \n",
       "A11_79         -0.940648  0.348818          0.462085   0.467866   \n",
       "A11_80          6.014177  0.634675         -0.431921   1.064627   \n",
       "A11_85               NaN  0.327573               NaN  -0.645948   \n",
       "A11_86          3.189294  2.307381               NaN   1.684777   \n",
       "A11_87         -5.011391  0.101890          1.089817  -1.230890   \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",
       "A01_14         2.658909   8.873294 -2.725993       2.081959  -0.640953   \n",
       "A01_17        -3.157943  -1.098844  2.099880       3.261305 -10.892045   \n",
       "A01_18         3.048698  -6.121298  0.008026      -0.243670   3.129125   \n",
       "A01_20         2.235446  -1.681283 -0.420546       0.076087   3.076295   \n",
       "A01_21         0.856511  -0.954674  1.886783       2.196050   2.436579   \n",
       "A01_22         2.287242  -0.343746  1.038470      -0.832654   2.717067   \n",
       "A01_23         2.089597   0.886900 -0.827784      -0.076783   2.241636   \n",
       "A01_25        -1.735648  -0.200892       NaN      -2.155111  -6.857530   \n",
       "A01_26        -2.064806  -0.087783  0.418293       1.550241   1.862355   \n",
       "A01_27        -2.272625  -6.032576  2.116587       0.717661   4.509182   \n",
       "A01_29         2.970487  11.674431 -5.015363       0.942569  -0.493707   \n",
       "A01_31        -3.349890  -1.069199  1.163056      -0.895535  -0.087970   \n",
       "A01_32         0.429805  -1.103549  0.740313       2.506797   2.778662   \n",
       "A01_33        -5.532087  -5.217066  1.188328       2.380337   3.479604   \n",
       "A01_34         3.873184   1.228287 -0.019795      -0.597636  -0.869106   \n",
       "A01_35         4.542455  -4.875453  2.360665      -1.442190   0.693330   \n",
       "A01_38        -2.917663   6.128958 -0.439967       3.226792   4.648551   \n",
       "A01_40         4.060814  -1.013393 -0.051946       0.805096   5.205418   \n",
       "A01_44        -0.585619  -1.411756  0.982226      -0.333864   2.798290   \n",
       "A01_45         6.714054  -0.397405 -6.722845      -1.037253  -0.875755   \n",
       "A01_46        -0.895147  -5.366424 -0.115780      -0.501187   3.045313   \n",
       "A01_47         0.966619  10.681893  0.633434       0.512259   1.973278   \n",
       "A01_55         1.439703  -0.288215 -2.433246      -0.739372   4.679368   \n",
       "A01_58        -1.651898   1.106198  2.308981      -1.584598   2.258720   \n",
       "A01_59         3.859513  -1.350063  0.048835      -1.142921   0.293651   \n",
       "...                 ...        ...       ...            ...        ...   \n",
       "A11_44         1.814606   5.638196  0.224649       1.198512  -1.343692   \n",
       "A11_45         0.471784  -6.231109 -3.230581      -0.941268 -11.222300   \n",
       "A11_46         2.180708  -1.061959  0.614301       1.245527   4.237199   \n",
       "A11_48         0.420704  -2.660331  1.417940       1.470723   1.243041   \n",
       "A11_51         0.559865  -6.942340  1.111722       0.174473   2.517062   \n",
       "A11_52         1.855766  -6.155187  0.929895      -0.289071   0.662585   \n",
       "A11_54         0.823639  -6.179502  0.822936      -1.103175   7.015429   \n",
       "A11_57         1.359860  -0.526673  2.606971       0.754974   1.809343   \n",
       "A11_58         0.151295  -6.545795       NaN      -0.749599   1.642822   \n",
       "A11_60         0.161159  -8.610313  2.052813      -0.986483  -8.726147   \n",
       "A11_61         4.559201  -8.452262  0.926557       2.548554   1.555138   \n",
       "A11_62         0.725625  -7.727957 -0.064185      -0.066813   0.169675   \n",
       "A11_64        -3.073554  11.475177 -3.706662       1.467159   3.371451   \n",
       "A11_66         1.776497  -6.723817       NaN       3.236887   3.533954   \n",
       "A11_68         3.071328  12.956824  0.565678       0.980528  -4.295115   \n",
       "A11_69         0.571811  11.847581  0.201365       0.066659   2.859140   \n",
       "A11_72         1.497327   1.119451 -2.112560      -1.144666  -0.532813   \n",
       "A11_73        -1.676514   3.115243 -5.534217      -0.918070  -6.196405   \n",
       "A11_77         2.895317  10.033640  0.595472      -0.957188   4.284933   \n",
       "A11_78         0.952446  -7.626006  0.546279       1.417278   2.061099   \n",
       "A11_79         5.958515   5.921017       NaN       0.267799   2.001825   \n",
       "A11_80        -4.663311  -7.117807  1.670899      -0.385115  -4.388628   \n",
       "A11_85        -2.031381   7.609264 -0.660415       1.255510   5.098531   \n",
       "A11_86         0.510847  -1.193390 -1.424114      -0.993333  -3.922912   \n",
       "A11_87        -3.135374  -7.961377  0.164554       0.359548  -6.488684   \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",
       "A01_14     -2.948221    ...          0.871464  0.239443  16.293701  0.306043   \n",
       "A01_17     14.833187    ...          0.778375  1.257294  16.432578  0.030953   \n",
       "A01_18     -0.310028    ...         -0.658192  0.014206  17.374168 -0.383067   \n",
       "A01_20     -6.065038    ...         -1.904024  0.127447  14.506945 -0.817359   \n",
       "A01_21     -0.643799    ...          0.736404  1.220645  17.109577  0.002582   \n",
       "A01_22     -4.706824    ...          2.037066  1.738069  17.113177  0.625722   \n",
       "A01_23     -5.830545    ...         -0.369967 -0.887642  15.680565 -0.132719   \n",
       "A01_25     -3.846204    ...          0.909605 -1.330609  13.144011  1.004012   \n",
       "A01_26      4.625362    ...          0.572039 -0.514878  16.622170 -0.709576   \n",
       "A01_27      8.784337    ...          0.984612  0.663690  17.433361 -0.237397   \n",
       "A01_29     10.374494    ...          0.222568 -1.013385  16.763518 -0.206383   \n",
       "A01_31     -4.928491    ...         -1.314301 -1.375641  15.544307 -0.004930   \n",
       "A01_32     -4.981721    ...          0.632448  1.494387  16.222977  0.751728   \n",
       "A01_33     -2.757452    ...          1.271010  1.306615  18.784166  0.187778   \n",
       "A01_34     -3.144067    ...          1.212225  1.713806  17.565024  0.101327   \n",
       "A01_35     -6.215016    ...          1.818472  0.151740  16.212551  0.662079   \n",
       "A01_38     -3.809075    ...          1.010044  0.545939  15.461926  0.202358   \n",
       "A01_40      0.235112    ...         -0.328904  0.065395  16.315874  0.539172   \n",
       "A01_44      0.193106    ...         -0.635281 -0.836197  15.972579  0.842179   \n",
       "A01_45     -5.357255    ...         -1.635183  0.518553  15.862309  1.405404   \n",
       "A01_46     -5.753381    ...         -1.029738 -0.399975  15.623093 -0.450903   \n",
       "A01_47     -6.997449    ...          0.574923  0.748745  17.494740 -0.075021   \n",
       "A01_55     -5.967278    ...          0.205970  0.869975  15.028113  0.364559   \n",
       "A01_58      6.459206    ...          0.709761  0.793777  17.961373  0.583428   \n",
       "A01_59     -2.481264    ...          0.165374  1.366814  16.484308 -0.533237   \n",
       "...              ...    ...               ...       ...        ...       ...   \n",
       "A11_44     -4.435628    ...         -0.749211 -0.148476  17.105216  0.123122   \n",
       "A11_45     -4.671810    ...          0.790916  0.722727  16.467980  1.132648   \n",
       "A11_46      3.007887    ...          0.791549  1.060520  15.742954  0.688456   \n",
       "A11_48     -7.825930    ...         -0.111029 -0.589826  15.758187  0.727027   \n",
       "A11_51     -1.057514    ...          0.772863 -0.873542  16.774581 -0.384493   \n",
       "A11_52      6.697976    ...          0.621308  0.035059  16.362545  0.487377   \n",
       "A11_54     -4.235858    ...          0.399033  0.443633  16.321728 -0.326466   \n",
       "A11_57     -4.754368    ...         -2.912437  0.611290  18.938192  1.074502   \n",
       "A11_58      2.715098    ...         -0.324229 -1.474309  16.352664 -0.068981   \n",
       "A11_60     -7.935309    ...         -0.000856 -0.865281  15.641259  1.351713   \n",
       "A11_61     -3.631661    ...          0.768317 -1.471348  17.081808 -0.140308   \n",
       "A11_62     -3.480213    ...         -0.039708 -0.499990  19.067704 -0.319564   \n",
       "A11_64     -4.024469    ...         -1.072126 -0.430788  16.292964  0.687982   \n",
       "A11_66     -2.685802    ...          1.692752  0.418787  15.430309  1.211189   \n",
       "A11_68     -3.894834    ...          1.005061  0.611755  18.096924 -0.249243   \n",
       "A11_69     -4.665133    ...         -0.211785 -0.645943  16.710638 -0.341715   \n",
       "A11_72     -9.513557    ...         -0.582535 -1.300570  16.447684  0.858761   \n",
       "A11_73     -4.306319    ...         -0.095414  0.419458  15.222614 -0.870604   \n",
       "A11_77     -3.969501    ...          0.783732  2.010373  18.009701 -0.453162   \n",
       "A11_78     -4.090833    ...          0.792770  2.142495  16.595233  0.516697   \n",
       "A11_79      2.009163    ...          0.331876  2.086463  17.236369  1.208932   \n",
       "A11_80     -5.171900    ...         -0.042121 -0.469607  16.598856  0.260251   \n",
       "A11_85     -4.039771    ...         -0.470322  0.624881  15.913935  0.221976   \n",
       "A11_86      1.348560    ...         -3.327925  0.331371  15.066564  0.038268   \n",
       "A11_87     -4.194792    ...          0.180122 -0.699413  16.162823 -0.427215   \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",
       "A01_14  0.903620  23.053594  4.081697  3.783942   0.069884   2.976971  \n",
       "A01_17  1.637458  23.623720  3.763264 -3.649057   7.883571  -5.824656  \n",
       "A01_18 -0.997588  23.653983 -2.572794  1.388221  -6.245010  -1.875007  \n",
       "A01_20 -0.907824  20.581287  0.125045 -0.928468  -2.542536   6.886391  \n",
       "A01_21 -0.689019  24.960265 -4.600436 -3.240098  -1.430459   4.630062  \n",
       "A01_22  0.712437  24.854792  5.443331 -4.111030   5.459420  10.382066  \n",
       "A01_23  0.793680  23.211814  1.160337  3.339040   0.908244  13.591021  \n",
       "A01_25  0.127184  22.482902 -0.605416  0.017608   0.398050  -2.081629  \n",
       "A01_26 -0.502412  23.804121 -4.416801  1.710262  -2.832793  -2.582201  \n",
       "A01_27 -0.214977  22.998909  1.925353  4.612446   2.617287   7.365063  \n",
       "A01_29  0.672626  26.448189  1.866508  0.239108  -0.638321  12.690415  \n",
       "A01_31 -0.744114  24.094502 -2.020393 -0.891901  -5.279599   1.193205  \n",
       "A01_32 -2.119434  22.723083  5.791591 -0.958975   1.065139  -2.015229  \n",
       "A01_33  0.246367  29.168126  4.090078  0.995169   3.198552 -10.757366  \n",
       "A01_34 -0.127652  23.749063  3.836577 -2.537776   6.718132  -4.968092  \n",
       "A01_35 -0.147682  23.430682 -2.494964  3.580115  -2.705861  -4.434123  \n",
       "A01_38  0.744799  22.786138 -0.951993 -1.479317   6.993651  -5.792154  \n",
       "A01_40 -0.718097  23.366239  3.277305  1.194231   0.670884   7.639392  \n",
       "A01_44  1.050111  22.333949 -4.374102  1.138760  -5.530835   5.771627  \n",
       "A01_45  1.074609  20.873441 -2.156279  6.328426   1.883678  -3.488512  \n",
       "A01_46 -0.254102  22.505698 -3.920752 -2.202890  -1.563144   1.055169  \n",
       "A01_47  0.094029  23.578347 -0.045091 -2.543260   1.207601   4.475345  \n",
       "A01_55  0.530884  22.119766  5.280116 -4.113606  -2.367438   0.865415  \n",
       "A01_58 -0.001954  27.061697 -4.836116  3.824283  -2.061558  -3.699731  \n",
       "A01_59 -0.487195  24.985005  0.075893  2.709230   2.936690   5.137446  \n",
       "...          ...        ...       ...       ...        ...        ...  \n",
       "A11_44       NaN  22.720722 -2.992506 -2.502814  -1.839947   5.751830  \n",
       "A11_45       NaN  22.521577  1.643338  2.459915  -5.901576  -4.664778  \n",
       "A11_46       NaN  22.512751  0.454164 -2.993857   2.341450   5.118265  \n",
       "A11_48       NaN  22.681465 -1.544399  1.773595  -3.088895  -3.579066  \n",
       "A11_51       NaN  23.893079 -0.050615 -1.412976  -6.528134   7.276401  \n",
       "A11_52       NaN  22.642174 -0.752784 -3.310424   4.157145  -5.710500  \n",
       "A11_54       NaN  23.076351 -3.822596 -2.623999  -6.319933  -4.721783  \n",
       "A11_57       NaN  22.313950 -0.262279  4.058366   6.746140  -5.175894  \n",
       "A11_58       NaN  23.206375 -4.699755  0.792384  -3.585440   2.196232  \n",
       "A11_60       NaN  22.050501 -2.512252  2.951493  -1.999519  -8.702733  \n",
       "A11_61       NaN  25.479476 -0.750635  3.074695        NaN   5.849204  \n",
       "A11_62       NaN  25.226514 -4.369941  3.546102  -7.820512  -6.362555  \n",
       "A11_64       NaN  24.538142  2.667982  2.440611   6.249088   8.189521  \n",
       "A11_66       NaN  22.224755  0.956364 -0.765028  -0.505661  -4.559104  \n",
       "A11_68       NaN  24.928409  4.742218 -2.630358   5.647873   9.762167  \n",
       "A11_69       NaN  23.724599  1.796232  0.607982   0.693608   6.862298  \n",
       "A11_72       NaN  23.037218  0.305084  1.587400  -1.889495 -10.516313  \n",
       "A11_73       NaN  21.546315  2.459713  3.235806  -3.884509   4.981810  \n",
       "A11_77       NaN  25.167154 -1.830648 -2.582527   6.232993  -2.549221  \n",
       "A11_78       NaN  25.390230  5.181662 -2.575708  12.377388   8.398114  \n",
       "A11_79       NaN  24.567289  4.332028 -2.421702   2.236019   8.882389  \n",
       "A11_80       NaN  23.133193 -1.978838  2.192267  -1.833746  -3.248339  \n",
       "A11_85       NaN  20.968383  1.705758  6.654459  10.226067  -1.580349  \n",
       "A11_86       NaN  16.979292 -0.394850  5.130092   3.422867  -0.946073  \n",
       "A11_87       NaN  22.939438  1.632431 -4.138147  -3.984612  -3.358585  \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": 53,
     "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": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0032293928665531"
      ]
     },
     "execution_count": 54,
     "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": 55,
   "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": 56,
   "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": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7efe7107be50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efe6e0bda10>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 57,
     "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": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7efe6e034950>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zj252ew2YlJkrImIocG9E/CfwFeCSzLwhIr4PnAJcWbz+PjP/OiKOBy7CKcWlweP87Wo8thMFVqE3czGt0wIcC2zf3Q7FvE3rnkI3tPhJYBKwbrrJa2nchHclcBR/viFvFvCvERHO/yRJ9WjqHERmLuvysyQzLwWO7Gm/iBgSEYuAl4A7gaeBP2TmmmKTDhr3VFC8Pl8cbw2NuYN3KPnMqRGxICIWdHZ2NlO+JOltaHaIaWyX1S1o9Ch63Dcz3wD2jYhhwK3AXm+nyLd85kxgJkB7e7u9C0mqSLNDTP/SZXkN8CxwXLMHycw/RMRc4MPAsIjYsugltAJLis2WALsBHcV9FtvROFktSapBs1cxHdLbD46I4cDqIhzeARxK48TzXOCTNK5kmgLcVuwyu1j/ZfH+zz3/IEn1aXaI6SvdvZ+ZF5c07wJcGxFDaAxL3ZSZt0fEY8ANEXEB8CBwdbH91cAPImIx8N/A8U1+B0lSBXpzFdP+NP7KB/g7YB7w1IZ2yMyHgf1K2p8Bxpe0r6JxdZQkaQBoNiBagbGZ+UeAiDgf+ElmnlhVYZKkejU71cYI4PUu668XbZKkTVSzPYjrgHkRcWux/gkaN7lJkjZRzV7F9O1imoyDiqbPZuaD1ZUlSapbs0NMAO8EXsnMy2jcqzCyopokSQNAs48cPQ84CzinaBoK/O+qipIk1a/ZHsTRwMeBlQCZ+Tvg3VUVJUmqX7MB8XpxV3MCRMS7qitJkjQQNBsQN0XEv9GYR+lzwBx68fAgSdLg0+xVTDOKZ1G/AowCvpGZd1ZamSSpVj0GRDGX0pxiwj5DQZI2Ez0OMRXPdFgbETU+T1CS1N+avZN6BfDriLiT4komgMw8vZKqJEm1azYgbil+JEmbiW4DIiJ2z8zfZqbzLknSZqancxA/XrcQETdXXIskaQDpKSCiy/IeVRYiSRpYegqI3MCyJGkT19NJ6jER8QqNnsQ7imWK9czMbSutTpJUm24DIjOH9FchkqSBpTfPg5AkbUYMCElSKQNCklTKgJAklTIgJEmlDAhJUqnKAiIidouIuRHxWEQ8GhFfKtq3j4g7I+Kp4vU9RXtExOURsTgiHo6IsVXVJknqWZU9iDXAGZm5N3AAcFpE7A2cDdyVmXsCdxXrAEcAexY/U4ErK6xNktSDygIiM1/IzAeK5T8CjwO7AkcB62aHvRb4RLF8FHBdNtxP4/nXu1RVnySpe/1yDiIi2oD9gF8BIzLzheKtF4ERxfKuwPNdduso2t76WVMjYkFELOjs7KysZkna3FUeEBGxDXAzMD0zX+n6XmYmvZwEMDNnZmZ7ZrYPHz68DyuVJHXV7BPl3paIGEojHK7PzHVPpFsaEbtk5gvFENJLRfsSYLcuu7cWbdKg03b2T2o79rMttR1am5gqr2IK4Grg8cy8uMtbs4EpxfIU4LYu7Z8prmY6AFjeZShKktTPquxBfAT4NPDriFhUtJ0LXAjcFBGnAM8BxxXv3QF8DFgMvAp8tsLaJEk9qCwgMvNe3vxEuq4ml2yfwGlV1SNJ6h3vpJYklTIgJEmlDAhJUikDQpJUyoCQJJUyICRJpQwISVIpA0KSVMqAkCSVMiAkSaUMCElSKQNCklTKgJAklTIgJEmlDAhJUikDQpJUyoCQJJUyICRJpQwISVKpyp5JLW3Onm35+7pLkDaaPQhJUikDQpJUyoCQJJUyICRJpQwISVIpA0KSVKqygIiIayLipYh4pEvb9hFxZ0Q8Vby+p2iPiLg8IhZHxMMRMbaquiRJzamyB/G/gMPf0nY2cFdm7gncVawDHAHsWfxMBa6ssC5JUhMqC4jMvAf477c0HwVcWyxfC3yiS/t12XA/MCwidqmqNklSz/r7HMSIzHyhWH4RGFEs7wo832W7jqJNklST2k5SZ2YC2dv9ImJqRCyIiAWdnZ0VVCZJgv4PiKXrho6K15eK9iXAbl22ay3a/kJmzszM9sxsHz58eKXFStLmrL8DYjYwpVieAtzWpf0zxdVMBwDLuwxFSZJqUNlsrhHxf4CJwI4R0QGcB1wI3BQRpwDPAccVm98BfAxYDLwKfLaquiRJzaksIDLzhA28Nblk2wROq6oWSVLveSe1JKmUASFJKmVASJJKGRCSpFIGhCSplAEhSSpV2WWuktRvzt+upuMur+e4/cQehCSplD0Ibdrq+stS2gTYg5AklTIgJEmlDAhJUikDQpJUyoCQJJUyICRJpQwISVIp74NQ//B+BGnQsQchSSplQEiSShkQkqRSBoQkqZQBIUkqZUBIkkoZEJKkUgaEJKmUASFJKjWg7qSOiMOBy4AhwFWZeWHNJW1avJtZUi8MmICIiCHA/wQOBTqA+RExOzMfq7eyCviLWtIgMJCGmMYDizPzmcx8HbgBOKrmmiRpszVgehDArsDzXdY7gA9VdjT/ipe0ser8PXL+8soPMZACoikRMRWYWqyuiIgn66znLXYEXq67iF6w3mpZb3UGU61QRb3fjI3Z+6+a2WggBcQSYLcu661F25tk5kxgZn8V1RsRsSAz2+uuo1nWWy3rrc5gqhUGX73rDKRzEPOBPSNiZERsBRwPzK65JknabA2YHkRmromILwD/ReMy12sy89Gay5KkzdaACQiAzLwDuKPuOjbCgBz66ob1Vst6qzOYaoXBVy8AkZl11yBJGoAG0jkISdIAYkD0sYjYNyLuj4hFEbEgIsbXXVNPIuKLEfFERDwaEd+tu55mRMQZEZERsWPdtWxIRPxz8e/6cETcGhHD6q6pTEQcHhFPRsTiiDi77nq6ExG7RcTciHis+P/6pbprakZEDImIByPi9rpr6Q0Dou99F/hmZu4LfKNYH7Ai4hAad6yPyczRwIyaS+pRROwG/A3w27pr6cGdwAcy84PA/wPOqbmev9BlipsjgL2BEyJi73qr6tYa4IzM3Bs4ADhtgNe7zpeAx+suorcMiL6XwLbF8nbA72qspRmnAhdm5msAmflSzfU04xLgqzT+rQeszPxZZq4pVu+ncW/PQDOoprjJzBcy84Fi+Y80funuWm9V3YuIVuBI4Kq6a+ktA6LvTQf+OSKep/HX+ID7q/Et3gccFBG/iohfRMT+dRfUnYg4CliSmQ/VXUsvnQz8Z91FlCib4mZA/8JdJyLagP2AX9VbSY8upfEHzdq6C+mtAXWZ62AREXOAnUve+howGfhyZt4cEccBVwMf7c/63qqHercEtqfRXd8fuCki9sgaL2/rod5zaQwvDQjd1ZqZtxXbfI3G0Mj1/VnbpiwitgFuBqZn5it117MhEfG3wEuZuTAiJtZdT295mWsfi4jlwLDMzIgIYHlmbtvTfnWJiJ8CF2Xm3GL9aeCAzOyst7K/FBH7AHcBrxZNrTSG8MZn5ou1FdaNiDgJ+Edgcma+2sPm/S4iPgycn5mHFevnAGTmd2otrBsRMRS4HfivzLy47nq6ExHfAT5N4w+EFhrDz7dk5om1FtYkh5j63u+ACcXyJOCpGmtpxo+BQwAi4n3AVgzQSdAy89eZuVNmtmVmG43hkLEDOBwOpzG08PGBGA6FQTXFTfFH19XA4wM9HAAy85zMbC3+vx4P/HywhAM4xFSFzwGXRcSWwCr+PPPsQHUNcE1EPAK8Dkypc3hpE/OvwNbAnY3fa9yfmdPqLenNBuEUNx+h8Rf5ryNiUdF2bjELg/qYQ0ySpFIOMUmSShkQkqRSBoQkqZQBIUkqZUBIkkoZEJKkUgaEJKmUASFJKvX/ATjJmLepCdhuAAAAAElFTkSuQmCC\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": [
    "---\n",
    "## Matplotlib\n",
    "\n",
    "*Matplotlib* is a Python package used for data plotting and visualisation. It is a useful complement to Pandas, and like Pandas, is a very feature-rich library which can produce a large variety of plots, charts, maps, and other visualisations.  \n",
    "`matplotlib.pyplot` is a collection of functions that make matplotlib work like *MATLAB*. Majority of plotting commands in `pyplot` have *MATLAB* analogs with similar arguments. Let us take a couple of examples.  \n",
    "\n",
    "Typically, if we are going to work on some plotting, we would import matplotlib like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4czSLEpHIfbl6J+OSU1i7cz8/GtSBcef1pkk99SuSIxPJ3keFZjaZ0DYFB5arFYVI8PZk5/Ho3GX8bf63JDSvz99vHMxJ3VoGXZbEuEj2PrqA0N5GqwADOpvZze4+N9rFicjBzVu2jfHTUtmalc2Np3TmN+f0oH5tNbCToxfJX9FTwBnungFgZl2B2YBCQaSC7dqXywMz05n+zSZ6tGnIc1eexPEJamAn5SeSUNjzfSCErQb2RKkeETkId2dWymbum5FOVnYevxzanVvP6EbtWmpRIeXrkKFgZpeELy4wsznAm4S2KVwGfF0BtYkIsDUrm7umpfH+0q0c274Jj40aTK9j1MBOouNwSwoXFru8FTgtfHk7oMMiRaLM3Xnj6/X8ds5S8goKuev83lx/SmdqqkWFRNEhQ8Hdr6vIQkTkP9bt3MedU1P5fNVOhnRpzqOX9KdTywZBlyXVQCR7H3UGfgF0Kj5erbNFyl9BofPKZ2t48t3lxNWowcMjE7liYAc1sJMKE8mG5umEzpA2EyiMbjki1dfyLaEGdt+s383QXq15aGQ/4ptoTa1UrEhCIdvd/xj1SkSqqdz8Qp77MINn52XQqG4cf/zR8VzYP14N7CQQkYTC02Y2AXgXyPn+RndfFLWqRKqJxet3M2ZKCsu37mHEcW2ZcGFfmjeoHXRZUo1FEgqJwFXAmfxn9ZGHr4vIETiQW8Dv3lvOS5+uoXWjurx0TRJDe6uBnQQvklC4DOji7rnRLkakOvh81Q7unJrKup37+fHgBMad14vGddXATiqHSEIhDWgKbItyLSJVWlZ2Ho/MWcbrX31Lxxb1ef2mIZzYtUXQZYn8l0hCoSmwzMy+5r+3KWiXVJEIvb9kK3dNT2X7nhxGn9qFX5/Vg3q1dapzqXwiCYUJUa9CpIrauTeH+2cuYcbiTfQ6phGTrkri2A5Ngy5L5JAiOZ/CR0fyxGbWAXgVaENow/Qkd3+6xJjTgbeANeGbprr7A0fyeiKVibszY/Em7puRzt6cfH5zdg9+elpXNbCTSi+SI5r38J9zMtcG4oB97l5aR6584P/cfZGZNQIWmtl77r6kxLhP3H14WQsXqaw2Zx7g7mlp/GvZNo7r0JTHR/WnR5tGQZclEpFIlhSK/potdDTNCGBIBI/bDGwOX95jZkuBdkDJUBCpEgoLnde//pZH5iyjoNC5Z3gfrj2pkxrYSUwp06mawqfhnB4+mG1cpI8zs07A8YRO6VnSiWa2GNgE3O7u6WWpSaQyWLNjH+OSU5i/Zhcnd2vBIyP7k9CiftBliZRZJKuPLil2tQaQBGRH+gJm1hBIBn7l7lkl7l4EdHT3vWZ2PqE+S90P8hyjgdEACQkJkb60SNTlFxTy8mdreOrdFdSuVYPHLk3kh0kd1KJCYpaFvvwfZoDZK8Wu5gNrgRfdvdTjFswsDpgFvOPuv4tg/Fogyd13HGpMUlKSL1iwoLSnEom6pZuzGJucQsqGTM7u04aHLu5Hm8Z1gy5L5KDMbKG7J5U2LpJtCkd0XoXw9oeXgKWHCgQzOwbY6u5uZoMILYnsPJLXE6koOfkFPDtvFc/Ny6BJvTie+fHxXJCoBnZSNUSy+qgVcBP/ez6F60t56MmEeialmtk34dvGAwnhx08ERgG3mFk+cAC4wktbdBEJ0KJvv2PslBRWbtvLJce3457hfWimBnZShUSyofkt4BPgfaAg0id290+Bw351cvdngGcifU6RoOzPzefJd1bwyudriG9cl1euG8gZPVsHXZZIuYskFOq7+9ioVyJSSX2WsYNxU1NYv+sAVw3pyJhhPWmkBnZSRUUSCrPM7Hx3nxP1akQqkcwDeTw8eylvLFhP55YNeGP0EAZ3UQM7qdoiCYVfAuPNLAfII7RKyCM4olkkZr2bvoW7p6exc18uPz2tK786qzt149TATqq+Mh3RLFLVbd+Tw30z05mdspne8Y156ZqBJLZvEnRZIhWmTEc0i1RV7s60f2/kgVlL2J9TwO3n9ODm07oSV1MN7KR6UShItbdx9wHumpbKh8u3MyAh1MCuW2stIEv1pFCQaquw0Pnb/HU8OncZDtx3YR+uOlEN7KR6iygUzOwUoLu7vxI+mK2hu68p7XEildXq7XsZl5zKV2t38YPuLXl4ZCIdmquBnUgkRzRPINQEryfwCqHzKfyV0BHLIjElv6CQFz9Zw+/fX0HdWjV4YlR/Rp3QXi0qRMIiWVIYSajt9SIAd98UPmmOSExJ35TJ2OQU0jZmcW7fNjw4oh+t1cBO5L9EEgq54YZ1DmBmDaJck0i5ys4r4E8frGTiR6tpVr82z185gPMS44MuS6RSiiQU3jSzF4CmZnYTcD3wYnTLEikfC9ftYsyUFFZt38elA9pzz/DeNK2vBnYihxLJwWtPmtnZQBah7Qr3uvt7Ua9M5Cjsy8nniXeWM/mLtbRtUo/J1w/itB6tgi5LpNKLaO8jd3/PzOZ/P97Mmrv7rqhWJnKEPl6xnTunprIp8wBXD+nIHcN60bCO9r4WiUQkex/dDNxP6BSchYR7HwFdoluaSNlk7s/jwdlLmLJwA11aNeDNm09kYKfmQZclElMi+fp0O9DvcKfIFAna22mbueetdHbty+Vnp3fltqFqYCdyJCIJhVXA/mgXInIktu3JZsJb6cxN20Kf+Ma8cu1A+rVTAzuRIxVJKNwJfB7eppDz/Y3uflvUqhIphbszZeEGHpq9lAN5Bdxxbk9Gn9pFDexEjlIkofAC8AGQSmibgkig1u/az/hpqXyycgdJHZvx6KX96da6YdBliVQJkYRCnLv/JuqViJSisNB59Yu1PP7Ocgx4YERffjK4IzXUwE6k3EQSCnPNbDQwk/9efaRdUqXCZGzby7jkFBas+45Te7Ti4ZH9aN9MDexEylskofCj8O87i92mXVKlQuQVFDLp49U8/f5K6tWuyVOXHcslA9qpgZ1IlERyRHPniihEpKS0jZmMmZLCks1ZnJ94DPdf1I9WjeoEXZZIlRbJwWtxwC3AqeGbPgRecPe8KNYl1Vh2XgFP/2slkz5eTfMGtZn4kwEM66cGdiIVIZLVR88TOofCc+HrV4Vvu/FwDzKzDsCrQBtCq5smufvTJcYY8DRwPqFjIa5190VlmYBULV+v3cXYKSms3rGPy05oz90X9KFJ/bigyxKpNiIJhYHufmyx6x+Y2eIIHpcP/J+7Lwqff2Ghmb3n7kuKjTkP6B7+GUwobAZHWLtUIXtz8nn87WW8+sU62jerx2s3DOIH3dXATqSiRRIKBWbW1d1XAZhZF6CgtAe5+2Zgc/jyHjNbCrQDiofCCOBVd3fgSzNrambx4cdKNTFv+TbumprK5qxsrju5E7ef05MGamAnEohI/ufdAcwzs9WEmuF1BK4ry4uYWSdCZ2+bX+KudsD6Ytc3hG9TKFQD3+3L5cFZS5j67410a92QKT89iRM6Ngu6LJFqLZK9j/5lZt0JnUsBYLm75xzuMcWZWUMgGfiVu2cdSZHh4yRGAyQkJBzJU0gl4u7MSd3ChBlp7N6fx8/P6MYvhnajTi01sBMJWqmNYszsMqC2u6cAFwGvm9mASJ48vOdSMvA3d596kCEbgQ7FrrcP3/Zf3H2Suye5e1KrVlrPHMu2ZWVz82sLufXvi4hvUo8ZPz+F28/tqUAQqSQi6R52T3ibwCnAUOAlQhuEDyu8Z9FLwFJ3/90hhs0ArraQIUCmtidUTe7Om1+vZ+jvPuKjFdsZd14vpv3sJPq0bRx0aSJSTEQbmsO/LwBedPfZZvZQBI87mdDuq6lm9k34tvFAAoC7TwTmENodNYPQLqll2lYhseHbnaEGdp9m7GBQ5+Y8ekkiXVqpgZ1IZRRJKGw0sxeAs4HHzKwOESxhuPunhDZMH26MA7dGUqjEnoJC5y+fr+XJd5ZTs4bx0MX9+PGgBDWwE6nEIgmFHwLDgCfdfbeZxRPaI0nkkFZu3cOY5BT+/e1uTu/ZiodHJtK2ab2gyxKRUkSy99F+YGqx60XHH4iUlJtfyMSPVvHMBxk0qFOTP1x+HCOOa6sGdiIxQkcISblJ2bCbMVNSWLZlD8P7x3PfRX1p2VAN7ERiiUJBjlp2XgG/f28FL36ympYN6zDpqhM4p+8xQZclIkdAoSBH5cvVOxmXnMLanfv50aAOjDuvN03qqYGdSKxSKMgR2ZOdx6Nzl/G3+d+S0Lw+f79xMCd1axl0WSJylBQKUmYfLNvKXdPS2JqVzY2ndOY35/Sgfm39KYlUBfqfLBHbtS+XB2amM/2bTXRv3ZDnbjmJ4xPUwE6kKlEoSKncnZkpm7lvRjpZB/L45dDu/OyMrupXJFIFKRTksLZkZnP39DTeX7qVY9s34bGbBtPrGPUrEqmqFApyUO7OP75ez8Ozl5JXWMhd5/fm+lM6U1MtKkSqNIWC/I91O/cxLjmVL1bvZEiX5jx6SX86tWwQdFkiUgEUClKkoNB55bM1PPnucuJq1ODhkYlcMbCDGtiJVCMKBQFg+ZZQA7vF63cztFdrHhrZj/gmamAnUt0oFKq53PxCnvswg2fnZdCobhxPX3EcFx2rBnYi1ZVCoRr7Zv1uxk5JYfnWPYw4ri33Du9DCzWwE6nWFArV0IHcAp56dzkvf7aG1o3q8tI1SQzt3SboskSkElAoVDOfr9rBuORUvt21nx8PTmDceb1oXFcN7EQkRKFQTWRl5/HInKW8/tV6Oraoz+s3DeHEri2CLktEKhmFQjXw/pKt3DU9le17chh9ahd+fVYP6tVWiwoR+V8KhSps594c7pu5hJmLN9HrmEZMuiqJYzs0DbosEanEFApVkLvz1jebuH9mOntz8vnN2T346WldqV2rRtCliUglp1CoYjbtPsDd09P4YNk2juvQlMdH9adHm0ZBlyUiMUKhUEUUFjp//+pbHp27jIJC557hfbj2pE5qYCciZRK1UDCzl4HhwDZ373eQ+08H3gLWhG+a6u4PRKueqmzNjn2MS05h/ppdnNytBY+M7E9Ci/pBlyUiMSiaSwp/AZ4BXj3MmE/cfXgUa6jS8gsKeenTNfzuvRXUrlWDxy5N5IdJHdSiQkSOWNRCwd0/NrNO0Xr+6m7JpizGJqeQujGTs/u04aGL+9Gmcd2gyxKRGBf0NoUTzWwxsAm43d3TA66n0svJL+CZDzJ4/sNVNK0fx7M/HsD5icdo6UBEykWQobAI6Ojue83sfGA60P1gA81sNDAaICEhoeIqrGQWrvuOsckpZGzbyyXHt+Oe4X1o1qB20GWJSBUSWCi4e1axy3PM7Dkza+nuOw4ydhIwCSApKckrsMxKYX9uPk+8s5y/fL6W+MZ1eeW6gZzRs3XQZYlIFRRYKJjZMcBWd3czGwTUAHYGVU9l9enKHYybmsKG7w5w1ZCOjBnWk0ZqYCciURLNXVJfB04HWprZBmACEAfg7hOBUcAtZpYPHACucPdqtxRwKJkH8vjt7CW8uWADnVs24I3RQxjcRQ3sRCS6orn30Y9Kuf8ZQrusSgnvpG/hnulp7NyXyy2nd+WXQ7tTN04N7EQk+oLe+0iK2b4nh/tmpDM7dTO94xvz0jUDSWzfJOiyRKQaUShUAu7O1EUbeWDWEg7kFnDHuT0ZfWoX4mqqgZ2IVCyFQsA27j7A+KmpfLRiOwMSQg3surVWAzsRCYZCISCFhc5f56/jsbnLcOC+C/tw1YlqYCciwVIoBGDV9r2MS07h67Xf8YPuLXl4ZCIdmquBnYgET6FQgfIKCnnxk9X84f2V1K1VgydG9WfUCe3VokJEKg2FQgVJ25jJ2OQU0jdlMazvMTxwcV9aN1IDOxGpXBQKUZadV8CfPljJxI9W06x+bZ6/cgDnJcYHXZaIyEEpFKJowdpdjElOYfX2fVw6oD33DO9N0/pqYCcilZdCIQr25YQa2E3+Yi1tm9Rj8vWDOK1Hq6DLEhEplUKhnH20Yjvjp6ayKfMA15zYiTvO7UmDOvpnFpHYoE+rcrJ7fy4PzlpK8qINdGnVgH/efCJJnZoHXZaISJkoFMrB3NTN3PNWOt/tz+XWM7ryizPVwE5EYpNC4Shsy8rm3rfSeTt9C33bNmby9QPp21YN7EQkdikUjoC7M2XhBh6ctYT5evR5AAAGlklEQVTs/ELGDOvJTT9QAzsRiX0KhTJav2s/46el8snKHQzs1IxHL+1P11YNgy5LRKRcKBQiVFDovPbFWh5/ZzkGPDiiL1cO7kgNNbATkSpEoRCBjG17GJucysJ133Faj1b8dmQ/2jdTAzsRqXoUCoeRV1DICx+t4o//yqB+nZr87ofHMvL4dmpgJyJVlkLhENI2ZnLHlBSWbs7igsR47ruoL60a1Qm6LBGRqFIolJCdV8Af3l/Ji5+spnmD2kz8yQkM63dM0GWJiFQIhUIxX63ZxbjkFFbv2MflSR0Yf35vmtSPC7osEZEKo1AA9mTn8fjby3nty3W0b1aPv94wmFO6twy6LBGRClftQ2He8m3cNTWVzVnZXH9yZ24/twf1a1f7fxYRqaai9ulnZi8Dw4Ft7t7vIPcb8DRwPrAfuNbdF0WrnpK+25fLg7OWMPXfG+nWuiFTfnoSJ3RsVlEvLyJSKUXzK/FfgGeAVw9x/3lA9/DPYOD58O+ocndmp25mwlvpZB7I47Yzu3Hrmd2oU0sN7EREohYK7v6xmXU6zJARwKvu7sCXZtbUzOLdfXO0atqalc0909N4d8lWEts14a83DqZ3fONovZyISMwJcuV5O2B9sesbwrdFJRTmLdvGbf/4N7n5hdx5Xi9uOKUztdTATkTkv8TEFlUzGw2MBkhISDii5+jcsgEDEppx30V96dyyQXmWJyJSZQT5VXkj0KHY9fbh2/6Hu09y9yR3T2rV6sjOddypZQMmXz9IgSAichhBhsIM4GoLGQJkRnN7goiIlC6au6S+DpwOtDSzDcAEIA7A3ScCcwjtjppBaJfU66JVi4iIRCaaex/9qJT7Hbg1Wq8vIiJlp91vRESkiEJBRESKKBRERKSIQkFERIooFEREpIiFdgKKHWa2HVh3hA9vCewox3KCpLlUTlVlLlVlHqC5fK+ju5d69G/MhcLRMLMF7p4UdB3lQXOpnKrKXKrKPEBzKSutPhIRkSIKBRERKVLdQmFS0AWUI82lcqoqc6kq8wDNpUyq1TYFERE5vOq2pCAiIodRJUPBzIaZ2XIzyzCzcQe5v46ZvRG+f34ppw0NVARzudbMtpvZN+GfG4OoszRm9rKZbTOztEPcb2b2x/A8U8xsQEXXGKkI5nK6mWUWe0/uregaI2FmHcxsnpktMbN0M/vlQcbExPsS4Vxi5X2pa2Zfmdni8FzuP8iY6H2GuXuV+gFqAquALkBtYDHQp8SYnwETw5evAN4Iuu6jmMu1wDNB1xrBXE4FBgBph7j/fGAuYMAQYH7QNR/FXE4HZgVdZwTziAcGhC83AlYc5O8rJt6XCOcSK++LAQ3Dl+OA+cCQEmOi9hlWFZcUBgEZ7r7a3XOBfwAjSowZAUwOX54CDDUzq8AaIxXJXGKCu38M7DrMkBHAqx7yJdDUzOIrprqyiWAuMcHdN7v7ovDlPcBSQudJLy4m3pcI5xITwv/We8NX48I/JTf+Ru0zrCqGQjtgfbHrG/jfP46iMe6eD2QCLSqkurKJZC4Al4YX7aeYWYeD3B8LIp1rrDgxvPg/18z6Bl1MacKrH44n9K20uJh7Xw4zF4iR98XMaprZN8A24D13P+T7Ut6fYVUxFKqbmUAnd+8PvMd/vj1IcBYRailwLPAnYHrA9RyWmTUEkoFfuXtW0PUcjVLmEjPvi7sXuPtxhM5dP8jM+lXUa1fFUNgIFP+23D5820HHmFktoAmws0KqK5tS5+LuO909J3z1z8AJFVRbeYvkfYsJ7p71/eK/u88B4sysZcBlHZSZxRH6EP2bu089yJCYeV9Km0ssvS/fc/fdwDxgWIm7ovYZVhVD4Wugu5l1NrPahDbCzCgxZgZwTfjyKOADD2+xqWRKnUuJ9bsXEVqXGotmAFeH93YZAmS6++agizoSZnbM9+t3zWwQof9nle5LR7jGl4Cl7v67QwyLifclkrnE0PvSysyahi/XA84GlpUYFrXPsKidozko7p5vZj8H3iG0987L7p5uZg8AC9x9BqE/ntfMLIPQBsMrgqv40CKcy21mdhGQT2gu1wZW8GGY2euE9v5oaWYbgAmENqDh7hOBOYT2dMkA9gPXBVNp6SKYyyjgFjPLBw4AV1TSLx0nA1cBqeH11wDjgQSIufclkrnEyvsSD0w2s5qEgutNd59VUZ9hOqJZRESKVMXVRyIicoQUCiIiUkShICIiRRQKIiJSRKEgIiJFFAoiIlJEoSAiIkUUCiIiUuT/AUMEHwHu2ZYdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example 1: Plotting a line graph\n",
    "plt.plot([1,2,3,4])\n",
    "plt.ylabel('some numbers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Example 2: Plotting a histogram\n",
    "x = [21,22,23,4,5,6,77,8,9,10,31,32,33,34,35,36,37,18,49,50,100]\n",
    "num_bins = 5\n",
    "plt.hist(x, num_bins, facecolor='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us try to plot 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": 62,
   "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": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7efe5fb95b50>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9dsq813jbx+q2FmBbtVbH9y7+JPJDYl1+CNOz5dW1BbcHRa/9W1D2MLBnRJBKll7K7/qNt3mszQuwcfLKsZeKBTzx0NaWReAdn7i1Y8PTux/UV2fkE2MlLBseFL30b0HZw8CeEUFmtb3UNc9vvEMiLemKibESJndvQj5ne++ov+aj514+sAuvT+3Bywd24cR/+CXXv9PmGTk7GFIaGNh7XNCjw9pnte3hr9e65jnjLRbyrt93y0UfO3sFtXryi/1e5Zl+m7ySKCklasdyxx4W9rCJ5rLCJJp7Rb2+M97Hpy+1nFzkaG4XMD1bDt2lMWpLXcDcK93ht7OWHQwpDVYCu4jcD+CPAOQA/C9VnbJx3UEXpZdL0D7e3QRo22dVnru8YPye0y7AuX4YYYN6+4MgSLOuID3Fvf4t4n4A02CKnIoRkRyAbwP4VQB3AXhYRO6Kel2Kv5dLt90do55u1M7r/QyJ4JGTc5Hqy52Gi17Z+eH8EKStgsW9jVerKDtrk+iuSYPJxoz90wB+rKr/AAAi8hcAPgfg7y1ce6DFfZhxt58IgjxwwsxETe8TsHN26bI2Ng1Nz5Zx5Pl5XFtsrX7J5wTV2nLHJqj6suLI8/PWPh2142HOFBcbi6clAG82ff3WymstRGS/iMyIyMzCgvmjN90Q98KbKUCXK1XPhVq/Sg+vmajbYvDGf5Fchcjw2sZcxqkvXz+cB9TcIKz9IWATD3OmuCS2eKqqxwEcBxotBZK6bz+Le+HNa6bcnDdvH4Pb9vvmB45pJvrYs5fw7gc3Xi9Xqvja03ORFzj9DK9sGmrOhddVIYg3cPt9aon7ExkNLhuBvQzg9qavP77yGlkQ52HGfgc+V2t1HHl+Hu/VllsWSp3t96Yj6kwPi+ag7og7qAPA2jU5z5YLXkzlmH6CLDDzMGeKi43A/rcAPikid6IR0H8DwL+zcF2KWZADn91mtM72+7lD7odG5CyfUhRVpVrD9S46N+aHBIcfvLurewbJn7MUkuISObCr6pKIfAXAWTTKHb+jqvORR0aJcD4R7Jh6KVSdeKVaW902385WUM8PAf/yI+Z0UVCCxmEcYdryRj3CLmj+PM5PZDS4rOw8VdXvq+q/VtVPqOo3bVyTkmVaqPVKRZjKG231dKktN8YV5Hrrh/ONhVAXCkAEgdoROD1h5g7dFyngspUApYktBQiAuR77s/feZvwd06zUZjfGI8/P4933lzx/RgDsuec2z/NEK4s13LTW/AFVAHx5+yhePrAr0AYtvzYPbCVAaWJLAVrllhbw2nSkALYeecE1ZbEuP2Tl0IogVSsK4NSFMsbvuBUlj0oTrzLC5mt4Bfagu26ZP6c08QQl8uR1epEjPyQ49sV7V/u+nDh/1frB0UE4J0W5VZoc3bvFc5G4+RovH9hl/L5pLcLv94hsCHqCEmfs5Mmr1t1RW9bVmX1aQR1opIb8Zspe5Z3ONfzu0c3vESWJOXbyFDRfXq5UcezsFatBPWzn9Q3FguemoCAHdvgtbnJRlPoBAzv5+tAa//9MBOaNSd0o5HP40vbRwIuwhXwOOzeP+DbVcg7LeOKhrV0tbnJRlPoBA/sACnp4h7NQGKT+W4HIB0E7ciI4uncLvjGxpWWG7Vy/VCzgy9tHOyp4zl1eCNx1stuujFG6ORIlhYunA6a9qgO4sbjYHpzCblpyrhW1GkYAvD61J/TvmRZ6u70eUa/h4mnG2DqQIUyr2LALgk5VSpDqEy9B89XtfyfF4bxreSTz3zRomIrpAzYPZAhT1REmILYf+NytoPlqt7+Td95b6thdyvw3DSIG9j5g88SiIL3Unfz74gdLyA8Fy5vv29a6uambfHupWMC+bSUcO3vFN//v9ndSW1bctHYN89808JiK6QNhZtl+KRuvVrHt+fdrizXkcwKBf4vb9l2bYRuBicvYypUqHjk5hyPPz+PQA627W01/J9erNWPXSaJBwRl7HwhaOx0kZeNV1eE6C64rhtf6lxw6vdudcXRTg+52f6DxgGl/H6wnJzJjYO8DQWung6ZsnDz461N7WppemWbBbgdkuLm22Gjle/i5+VAblZz34rVY2/4+WE9OZMbA3geC1k5H3e5uY7Z7+Ln50H3Pnffid//m98F6ciIz1rH3sfZ8+rvvL7kG1eYyRK9yyenZMiafuYhavfW/ieH8EBZry1bHXnIZg1uNffvvsNEWDTLWsWecW/vYfE6QHxLUmg4Sbd5q79dqFgDq9c4H/XtL9oK6aTNU81jcZv1MsxAFx1RMnzItdN68rrPcz7TV/tGnL7YsSB47ewVuIdzmgdP/dvQWz3TJxFgJc4fuwxMPbWWahahLTMX0qTDb5716queHBDevW4PKYi2xdrtf3j6Kb0xsSehuRNkRNBXDGXufMi00Fl3O/fRalKwtK64lGNSBRs92IooPA3ufmty9CTmXXaHvvLfUsVvT5hmkNqR1EAfRoODiaR9proIpDudRd0l+N59mdOT5+dWmWIX8EEQA25m39cN57LnnNpy7vIC3K1UUuqygaX5vtxTyEGkcQM2zQonC44y9T7TvKvU65LlcqWLymYstP1OtLUOAQL1fSsUC/nFqD554aGtHU6121xZrePL8VZRXHjb/be89vte/qW0na/t7q1Rrq+mhKA3PiAYVA3ufMG23d5MT6ahFBxrVLc1VM8VC3rMb4sRYCce+cC/Wu+Tt3VxbrGHymYsoFsw/nxsSfPPzrQunfu+t24ZnRIOKqZg+EXT3qMC7AVdlsYbZr99okuXXNCzoIdCOWl0h4n7gxvrhfEczLyDYe+Nh0UTBRQrsIvJFAIcB/BsAn1ZV1jB2yS/AbigWfA+vEABf2j6Kc5cXjD+raJyM5Fzf+eMlzKcFoPHw+B8PbQ18MEiQ98bmXkTBRZ2x/wjAXgB/YmEsA8ttF+nB05cw88bPVhclb1lJmzSnWPI5wU1r1+B6tXWR8fHpS3jSo6TQtPPU9HAJO1veUCwEemA43FoJN+OuU6JwIgV2VX0NAMTSIcaDytSV8cT5q6ulgZVqDfkhwfrhvGe1yPRsGacu+C80OjtPv3pyDhuKBezcPIJTF8qubQeCzKgd+ZyEDsLOe2BVDJEdVnaeisj/BfAfvVIxIrIfwH4AGB0d3fbGG29Evm9WeO0MbZcTwcO/ePvqTL498HVzADUA42EaxZUg216Fkx8SrF0z1NLS15RDJyI7rDUBE5EfAPiYy7ceU9XvBh2Qqh4HcBxotBQI+nuDIMyMuK7akmZpT6t0u8ho+gdx6xZZyA9haVlbgnohn2NQJ+oRvoFdVT+TxEAGmVuOOchxdA4nrQKYHxJhrufngyXtqLxxShIZ2InSxzr2HuB2aMSXto+GagNQV8XB05ewc/OI68lCX9o+unp900HTQVdKTOWULEkk6g1Ryx0/D+B/AhgBcEZE5lR1t5WRZZSp8sStimT8jlvx6NMXAx8MXa3Vce7yAo7u3eJZauh2oEUhn8O+bSU89cqbvvfLibj+DEsSiXpD1KqYZwE8a2ksmWcqawQ6D7xofi3o5iDnmn6lhu1VKM3B36/zovMAaK6gcV5nSSJRb+DO0wR5HTZtCsRuQXjn5hHPmfX0bNk3120K/l4Luc3H2Y3fcWvgDUhElCwG9gQFPWzaLV3TfNbn9GwZZ179ibER2OHn5rsOsm4LuW7H2YXZgEREyWJgT5BpNtycm/ZL1/gd+Ay4lygG5ZWmIaL+wMCeINNsuDk3bUrXHH5uHgBCLaZ2i7Nxov7GwJ6gILNhU7qmUq1h8i+DBfWgbXaJKJsY2BPmNxv2WrysuZyY1C6fExx64O6ux0dE/Y8blHpMlJLBUrGAY1+4l2kUogHHwN5jJsZKXaVSmnP1O6Zewp0HzmDH1Es8Uo5oADEVE4LfYRi2HHrgbjxyci7U71RrdTxycq6lJ4zfBqg4JPV3RERmnLEHMD1bxtYjL+CRk3OrBy7HecjyxFjJeG6o31nU7Vn4JM8LbT+UmgdRE6WDgd2HE6zcasPjDJqHH7zbtZnXR9aFT9Mk1ZzLa2ctESWHgd2H33mfcQVNt46PR/duwfUuNh8l1Zwr6M5aIooXc+w+/IJSnEHTrTTy2NkroU5ISrI5V5CdtUQUP87YfXgFpTQ6Gk7u3uTbp91Jwzuz/KQWL93Gxq6PRMmzcuZpWOPj4zozYzwetaeYerPctDaHfG4I16vJH7jcXnmyc/OI8QzUpLEqhig+Qc88ZWAPwC2QuvUjDzo7ZvAjom5YO8yaOnPdO6ZeCt1X3RH0sA0GfyLqFnPsXYhS/RGkJJD14EQUBQN7F0wLqkGqP4I8FFgPTkRRMLB3wa36Q9CYWfv1ZwnyUGA9OBFFwcDehebNQwBc+7OYgrupJHDn5pHV5l1D4t43gPXgRBQEF09DcFvQdNsw5LWQajqcurnKxu0wDdaDE1FQDOwBmapZTO0GvNImQapsACAngmVVVsUQUSgM7AGZFjRzIq4z7DBpE9NDYFkVr0/tCTdQIhp4zLEHZAq+ddXI2+ijVNkQEbWLFNhF5JiIXBaRV0XkWREp2hpYrzEFWacfS3sXxjBpE/ZYISKboqZiXgRwUFWXROS/AzgI4D9FH1bvmdy9qSOn7gRfvwOq/bgtqDKnTkTdihTYVfWFpi/PA/hCtOH0rriDb9SHAxGRw+bi6W8DOGn6pojsB7AfAEZHRy3eNjkMvkTUD3wDu4j8AMDHXL71mKp+d+VnHgOwBOCE6TqqehzAcaDR3bGr0caEDbeIKEt8A7uqfsbr+yLyWwA+C+CXNY0ewBEF7bZIRNQvolbF3A/g9wE8qKqLdoaULDbcIqKsiVrH/i0AHwbwoojMicgfWxhTothwi4iyJmpVzC/YGkhaeAAzEWXNwO885eYgIsqage8Vw81BRJQ1Ax/YAdanE1G2DHwqhogoaxjYiYgyhoGdiChj+irHzq3/RET++iawc+s/EVEwfZOK4dZ/IqJg+iawc+s/EVEwfRPYeS4oEVEwfRPYufWfiCiYvlk85dZ/IqJg+iawA9z6T0QURN+kYoiIKBgGdiKijGFgJyLKGAZ2IqKMYWAnIsoYBnYiooxhYCciyhgGdiKijGFgJyLKGAZ2IqKMYWAnIsqYSIFdRP6riLwqInMi8oKIbLA1MCIi6k7UGfsxVb1HVbcC+B6Ar1sYExERRRCpu6Oq/rzpy5sAaLThUDMe3k1E3YjctldEvgng3wO4DmCnx8/tB7AfAEZHR6PeNvN4eDcRdcs3FSMiPxCRH7n8+RwAqOpjqno7gBMAvmK6jqoeV9VxVR0fGRmx9w4yiod3E1G3fGfsqvqZgNc6AeD7AA5FGhEB4OHdRNS9qFUxn2z68nMALkcbDjl4eDcRdStqVczUSlrmVQD3Afg9C2Mi8PBuIupe1KqYfbYGQq14eDcRdauvDrMeNDy8m4i6wZYCREQZw8BORJQxDOxERBnDwE5ElDEM7EREGcPATkSUMaKafENGEVkA8EbiN77howD+KcX7x43vr7/x/fW/uN7jHarq22wrlcCeNhGZUdXxtMcRF76//sb31//Sfo9MxRARZQwDOxFRxgxqYD+e9gBixvfX3/j++l+q73Egc+xERFk2qDN2IqLMGsjALiLHROSyiLwqIs+KSDHtMdkkIl8UkXkRWRaRzFQfiMj9InJFRH4sIgfSHo9tIvIdEfmpiPwo7bHEQURuF5FzIvL3K/99Zur8BhFZJyJ/IyIXV97fkbTGMpCBHcCLAD6lqvcA+H8ADqY8Htt+BGAvgB+mPRBbRCQH4NsAfhXAXQAeFpG70h2VdX8G4P60BxGjJQCPqupdALYD+N2M/Ru+D2CXqt4LYCuA+0VkexoDGcjArqovqOrSypfnAXw8zfHYpqqvqWrWTr3+NIAfq+o/qOoHAP4CjeMYM0NVfwjgZ2mPIy6q+hNV/buV//3PAF4DkJkDB7ThnZUv8yt/UlnEHMjA3ua3AfyftAdBvkoA3mz6+i1kKCgMGhHZCGAMwCvpjsQuEcmJyByAnwJ4UVVTeX+ZPUFJRH4A4GMu33pMVb+78jOPofHx8ESSY7MhyPsj6kUicjOAUwAeUdWfpz0em1S1DmDryrrdsyLyKVVNfM0ks4FdVT/j9X3fOhCrAAABGElEQVQR+S0AnwXwy9qHNZ9+7y+DygBub/r64yuvUR8RkTwaQf2Eqp5OezxxUdWKiJxDY80k8cA+kKkYEbkfwO8DeFBVF9MeDwXytwA+KSJ3ishaAL8B4LmUx0QhiIgA+FMAr6nqH6Y9HttEZMSpsBORAoBfAXA5jbEMZGAH8C0AHwbwoojMicgfpz0gm0Tk8yLyFoBfAnBGRM6mPaaoVha7vwLgLBqLbk+r6ny6o7JLRJ4C8NcANonIWyLyO2mPybIdAH4TwK6V/9/NicivpT0oi24DcE5EXkVjIvKiqn4vjYFw5ykRUcYM6oydiCizGNiJiDKGgZ2IKGMY2ImIMoaBnYgoYxjYiYgyhoGdiChjGNiJiDLm/wPClKx/0i87gAAAAABJRU5ErkJggg==\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": 64,
   "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": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=0.9622331502773507, intercept=-0.011936070484259736, rvalue=0.9064525118355159, pvalue=1.0446912214962477e-114, stderr=0.025839319479636295)"
      ]
     },
     "execution_count": 65,
     "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": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7efdfaa2fed0>]"
      ]
     },
     "execution_count": 66,
     "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": 67,
   "metadata": {},
   "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": "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": "2.7.15rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}