materials/tv19/misc_data/les4/Lesson4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dictionary (again)\n",
    "<strong>Recall</strong>: dictionaries are a convenient way to store data for later retrieval by key. Keys must be unique, immutable objects; the values can be anything."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "seq = 'TAGGATTACAGGCATGAGCTACCGTATAATGGCCAGGCCCCCTGCCTTTGTAAATAAATTTTCACTGGAACCTGGACACACTTGTTTATGTGTTGTTTGTGCCTGTTTTCACGCTGCGGCAGGAAAGTTGAGTCGTTGTGTCAGAGACCAGAGAGAGAGCCTGCAGAACCTCAAATACTATCTGGCCCTTGCCAGAAAAAGTTTACCAACCCCCTGCCTCCCTGGAATGGGTGGAGGGTGGTTGTAAAGGTACTGGAGGATCTGAAGACATAATAGGGTCCGTGACCCTTGTGAGGTTGTGAAGCTCCCTTAAGGCACATGGTGGCTGGGCTGTGGATTTGGGGTATGGGCAGAGAGTGTGGAGAGCACTTCCAGGGGCCATGTCTGAGAGACTACATGATGCCACTTTGAATGCCCAGTTTGTTCATCCTTTTCTGTTTTCCCCACTTCCCCAGATGGGTGATCTACAATGACCAGAAAGTGTGTGCCTCCGAGAAGCCGCCCAAGGATATAATACATCTACTTCTACCAGAGAGTGGCCAGCTAAGAGCCTGCCTCACCCCTTACCAATGAGGGCAGGGGAAGACCACCTGGCATGAGGGAGAGGGGCTGAGGGATGGACTTCAGCCCCTCTGCTCTGTACCCTTTTTCCTTTTGTCCCCGGCAGCAGGGAAGAAGCTGGAGGCCGTGGGAGAATGGCTGGGCAGAGCAGAGGGGCAGCGATAGACTCTGGGGATGGAGCAGGACGGGGACGGGAGGGGCCGGCCACCTGTCTGTAAGGAGACTTTGTTGCTTCCCCTGCCCCCGGAATCCACAGTGCTCTGCTTCTCTGTGTCGCCCCGCCCAGCCCCCTGGTGTGGAGGGAGGGGTCTCGTTTGTGCGCGTGGGTGTAGCTTTGTGCATCCTCTCCCAGTGGAGCGATCACCTGTGCCTCCCCTCCCCCTTTGTTTGCCCCTGTGTGGTTGGTCAAGGAGGGATGTGAGGGAAATAGGGACCCCCCGACTTGCCCTCCTGCCTCAGTCTTTCCCCCACCCTGTCTCTTCCTTGTCCTTCTCTGGAAAATGCCAAAATACACGATGTGAATAAAAGTACAACGGCTAAATTGTGTCCTGTTTGATACCTTGGGGGAGAGGCTTACCTTCCTGGGGTTAGCAGGAGGGCGCTTAAGAAAACTCCTAACTCTGGCCGCCTCCCTGCCAAAGTCAAGTCTCCACTTTTCACTGGTTCTAGAGCTCTAGGAAAATTGGGGTTGGGTGGGGAGGTGGAGTAGAGTGACTAAATGCCGACACAAAGCCAAGGAAAGATGGAGTGAAGAACCCTTCCCTCTCTTTATTCACACAGGAGTGGAGGATTTCCCAAATGTCCCTAACTGGCTAGCTGGCTTCAGGCTGGGACTCAGTCCCTGCAGTTCCTGCCAGGCCTTGCCAGCCGGGGCGAGGGTTGGGATGATCCTGGCGGCCTATGCCTTATAATGCTGCCCCTCCCGCTGTGAACCCTGCATTTGTCCCGCAAGTTTTCACTCAGGTAGACTCCCTGGGTACAAGGGTGCCTGCTCAGCAGTCGGGCATGAGCTGCTCCGATGGGCGAAGGAGGTTGTCTATCCCACAGTTGGAGAGGGGCCCTCTCTGCCCCAGTGGGCGATCTGGGCTACGGCCAAGTTGCCACCAGCTAGTTCCGCTTGAAAACCACTTCTGGCCCCGTGGGGGACTCAAGTCGCCAAGCGAGGGTTCCCCTGAGCGCCGGAGCTCACAGGTCTCGCCTTGTCCCGAAAGCCCCGCAATCGAGGCGGAGGCGACCGAGCCCCCGACTCTCCTAGAACGTTGCCACAAGAAGGGGGAACGTCGGAACAGTGCATCATCGGGCGGCGGCCGGGGCGGCGGCAGGAGGGCGGGCGGGGGGCAGGGCTCCGGGGGACTGGGCGGGCCATGGCGGAGGACGGCGAGGAGGCGGAGTTCCACTTCGCGGCGCTCTATATAAGTGGGCAGTGGCCGCGACTGCGCGCAGACACTGACCTTCAGCGCCTCGGCTCCAGCGCCATGGCGCCCTCCAGGAAGTTCTTCGTTGGGGGAAACTGGAAGATGAACGGGCGGAAGCAGAGTCTGGGGGAGCTCATCGGCACTCTGAACGCGGCCAAGGTGCCGGCCGACACCG'\n",
    "\n",
    "consensus_motifs = {\n",
    "    'Shine_Dalgarno': 'AGGAGG',\n",
    "    'TATA_BOX': 'TATAAT',\n",
    "    'CAT_BOX': 'GGGCGG',\n",
    "    'FAKE_BOX_1': 'ATGGAAGGCA',\n",
    "    'FAKE_BOX_2': 'TTTTA'\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slicing operation\n",
    "The slicing operation allows us to extract a contiguous piece of a string or list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "example[0] 0\n",
      "\n",
      "example[2:5] 234\n",
      "\n",
      "example[0:2] 01\n",
      "example[:2] 01\n",
      "\n",
      "example[2:len(example)] 123456\n",
      "example[2:] 123456\n"
     ]
    }
   ],
   "source": [
    "example = '0123456'\n",
    "\n",
    "print('example[0]', example[0]) # Indexing\n",
    "\n",
    "print('\\nexample[2:5]', example[2:5])\n",
    "\n",
    "print('\\nexample[0:2]', example[0:2])\n",
    "print('example[:2]', example[:2])\n",
    "\n",
    "print('\\nexample[2:len(example)]', example[1:len(example)])\n",
    "print('example[2:]', example[1:len(example)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AGGAGG': {969, 1153, 1587, 1881, 1941},\n",
       " 'TATAAT': {24, 509, 1467},\n",
       " 'GGGCGG': {1859, 1871, 1885, 1889, 1916, 2084}}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def find_motifs_v1(sequence, motif_list):\n",
    "    motifs_dict = {}\n",
    "    \n",
    "    for motif in motif_list:\n",
    "        motif_len = len(motif)\n",
    "\n",
    "        for pos in range(len(sequence) - motif_len):\n",
    "            # Returns a slice of the string, starting at index pos,\n",
    "            # and going up to, but not including, index pos+motif_len.\n",
    "            kmer = sequence[pos:pos+motif_len]\n",
    "            \n",
    "            if motif == kmer:\n",
    "                if motif not in motifs_dict.keys():\n",
    "                    motifs_dict[motif] = set()\n",
    "                motifs_dict[motif].add(pos)\n",
    "\n",
    "    return motifs_dict\n",
    "\n",
    "find_motifs_v1(seq, consensus_motifs.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.63 ms ± 78.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "944 µs ± 8.72 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "def find_motifs_v2(sequence, motif_list):\n",
    "    motifs_dict = {}\n",
    "    \n",
    "    for motif in motif_list:\n",
    "        if motif in seq:\n",
    "            motif_len = len(motif)\n",
    "\n",
    "            for pos in range(len(sequence) - motif_len + 1):\n",
    "                kmer = sequence[pos:pos+motif_len]\n",
    "                if motif == kmer:\n",
    "                    if motif not in motifs_dict.keys():\n",
    "                        motifs_dict[motif] = set()\n",
    "                    motifs_dict[motif].add(pos)\n",
    "\n",
    "    return motifs_dict\n",
    "\n",
    "%timeit find_motifs_v1(seq, consensus_motifs.values())\n",
    "%timeit find_motifs_v2(seq, consensus_motifs.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Python dictionary throws a KeyError if you try to get an item with a key that is not currently in the dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "motifs_dict = {}\n",
    "\n",
    "#motifs_dict['TATAAT'].add(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The <code>defaultdict</code> allows to specify the default value when the container is initialized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(set,\n",
       "            {'AGGAGG': {969, 1153, 1587, 1881, 1941},\n",
       "             'TATAAT': {24, 509, 1467},\n",
       "             'GGGCGG': {1859, 1871, 1885, 1889, 1916, 2084}})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "def find_motifs_v3(sequence, motif_list):\n",
    "    motifs_dict = defaultdict(set)\n",
    "    \n",
    "    for motif in motif_list:\n",
    "        if motif in seq:\n",
    "            motif_len = len(motif)\n",
    "\n",
    "            for pos in range(len(sequence) - motif_len + 1):\n",
    "                kmer = sequence[pos:pos+motif_len]\n",
    "                if motif == kmer:\n",
    "                    # Now it's useless\n",
    "                    #if motif not in motifs_dict.keys():\n",
    "                    #    motifs_dict[motif] = set()\n",
    "                    motifs_dict[motif].add(pos)\n",
    "\n",
    "    return motifs_dict\n",
    "\n",
    "find_motifs_v3(seq, consensus_motifs.values())"
   ]
  },
  {
   "attachments": {
    "IMAGE%202019-11-21%2014:53:44.jpg": {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy (from <a href=\"https://github.com/noise42/datastructures/blob/master/materials/python-data-science-handbook.pdf\">here</a> and <a href=\"https://python4bioinformaticsblog.wordpress.com/index/python-bits/numpy/\">here</a>)\n",
    "[NumPy](https://numpy.org/) (short for *Numerical Python*) is a numerical library for Python which provides an efficient interface to store and operate on data.\n",
    "\n",
    "While a statically-typed language like C requires each variable to be explicitly declared, a dynamically-typed language like Python skips this specification (in Python the types are dynamically inferred).\n",
    "\n",
    "### A Python Integer Is More Than Just an Integer\n",
    "The standard Python implementation is written in C. This means that every Python object is simply a C structure, which contains not only its value, but other information as well. For example, a Python integer is a pointer to a position in memory containing all the Python object information, including the bytes that contain the integer value.\n",
    "\n",
    "![IMAGE%202019-11-21%2014:53:44.jpg](attachment:IMAGE%202019-11-21%2014:53:44.jpg)\n",
    "\n",
    "This extra information in the Python integer structure is what allows Python to be coded so freely and dynamically."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A Python List Is More Than Just a List\n",
    "Because of Python's dynamic typing, we can create heterogeneous lists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[bool, str, float, int]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list = [True, \"2\", 3.0, 4]\n",
    "#how do I create a mirror list with types referring to the original?\n",
    "[type(item) for item in my_list]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But this flexibility comes at a <strong>cost</strong>: to allow these flexible types, each item in the list must contain its own type info, reference count, and other information, that is, each item is a complete Python object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the implementation level, the array essentially contains a single pointer to one contiguous block of data. The Python list, on the other hand, contains a pointer to a block of pointers, each of which in turn points to a full Python object. In the special case that all variables are of the <strong>same type</strong>, much of this information is redundant: it can be much more efficient to store data in a <strong>fixed-type</strong> array.\n",
    "\n",
    "\n",
    "\n",
    "### Fixed-Type Arrays in Python\n",
    "The built-in ``array`` module can be used to create arrays of a uniform type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array('i', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import array\n",
    "\n",
    "L = list(range(10))\n",
    "A = array.array('i', L) # i: indicates integer values\n",
    "A"
   ]
  },
  {
   "attachments": {
    "IMAGE%202019-11-21%2014:54:21.jpg": {
     "image/jpeg": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much more useful, however, is the ``numpy.ndarray`` object of the NumPy package which adds to this efficient *operations* on that data.\n",
    "\n",
    "![IMAGE%202019-11-21%2014:54:21.jpg](attachment:IMAGE%202019-11-21%2014:54:21.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(numpy.ndarray, array([9, 2, 0, 5, 3, 3, 0, 7, 2]))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x_np = np.random.randint(10, size=9)  # One-dimensional array\n",
    "\n",
    "type(x_np), x_np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can \"mentally map\" Numpy arrays to standard python lists, for they have similarities:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x1[3]: 5\n",
      "x1[2:5]: [0 5 3]\n",
      "9\n",
      "2\n",
      "0\n",
      "5\n",
      "3\n",
      "3\n",
      "0\n",
      "7\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "print('x1[3]:', x_np[3])     # Array Indexing\n",
    "print('x1[2:5]:', x_np[2:5]) # Array Slicing\n",
    "\n",
    "# Iteration\n",
    "for element in x_np:\n",
    "    print(element)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``numpy.ndarray`` stands for <strong>N-dimensional array</strong> which means that this object is built to be multi-dimensional, with attributes and methods specifically designed for this feature. For example, if you want to put the values in the ``x_np`` array in a 3x3 grid, you can use the ``reshape`` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grid.ndim:  2\n",
      "grid.shape: (3, 3)\n",
      "grid.size:  9\n",
      "grid.dtype: int64\n",
      "[9 2 0 5 3 3 0 7 2]\n",
      "[[9 2 0]\n",
      " [5 3 3]\n",
      " [0 7 2]]\n"
     ]
    }
   ],
   "source": [
    "grid = x_np.reshape((3, 3))  # Two-dimensional array\n",
    "\n",
    "print(\"grid.ndim: \", grid.ndim)\n",
    "print(\"grid.shape:\", grid.shape)\n",
    "print(\"grid.size: \", grid.size)\n",
    "print(\"grid.dtype:\", grid.dtype)\n",
    "\n",
    "print(x_np)\n",
    "print(grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that for this to work, the size of the initial array must match the size of the reshaped array."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorized Operations\n",
    "Operation between arrays are carried out with a different logic than that of standard lists. For example, the operator ``+`` on lists concatenates two lists, while if applied to two ``ndarray``s they get summed in an element-wise fashion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_list + x_list: [9, 2, 0, 5, 3, 3, 0, 7, 2, 9, 2, 0, 5, 3, 3, 0, 7, 2]\n",
      "x_np + x_np: [18  4  0 10  6  6  0 14  4]\n"
     ]
    }
   ],
   "source": [
    "x_list = list(x_np)\n",
    "\n",
    "print('x_list + x_list:', x_list + x_list)\n",
    "print('x_np + x_np:', x_np + x_np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Actually, each arithmetic operator is simply a convenient wrapper around specific functions built into NumPy:\n",
    "\n",
    "| Operator\t    | Equivalent func     | Description                           |\n",
    "|---------------|---------------------|---------------------------------------|\n",
    "|``+``          |``np.add``           |Addition (e.g., ``1 + 1 = 2``)         |\n",
    "|``-``          |``np.subtract``      |Subtraction (e.g., ``3 - 2 = 1``)      |\n",
    "|``-``          |``np.negative``      |Unary negation (e.g., ``-2``)          |\n",
    "|``*``          |``np.multiply``      |Multiplication (e.g., ``2 * 3 = 6``)   |\n",
    "|``/``          |``np.divide``        |Division (e.g., ``3 / 2 = 1.5``)       |\n",
    "|``//``         |``np.floor_divide``  |Floor division (e.g., ``3 // 2 = 1``)  |\n",
    "|``**``         |``np.power``         |Exponentiation (e.g., ``2 ** 3 = 8``)  |\n",
    "|``%``          |``np.mod``           |Modulus/remainder (e.g., ``9 % 4 = 1``)|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python's default implementation does some operations very slowly. In a loop, for each iteration Python first examines the object's type and does a dynamic lookup of the correct function to use for that type. If we were working in compiled code instead, this type specification would be known before the code executes and the result could be computed much more efficiently. Numpy provides a way to do this through the <strong>vectorized operations</strong>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.89 s ± 32.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "4.74 ms ± 9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "big_array = np.random.rand(10000000)\n",
    "\n",
    "%timeit sum(big_array)\n",
    "%timeit np.sum(big_array) # or big_array.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the ``sum`` function and the ``np.sum`` function are not identical! In particular, their optional arguments have different meanings, and ``np.sum`` is aware of multiple array dimensions.\n",
    "\n",
    "<strong>Important</strong>: whenever possible, make sure that you are using the NumPy version of these operations when operating on NumPy arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boolean indexing\n",
    "Numpy arrays can be sliced with vectors of booleans (``list``s or other ``ndarray``s) with the same dimensions.\n",
    "\n",
    "A boolean vector is usually created starting from the ``ndarray`` itself by applying a condition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_np: [9 2 0 5 3 3 0 7 2]\n",
      "boolean_np: [ True False False  True False False False  True False]\n",
      "[9 5 7]\n",
      "[9 5 7]\n",
      "3.23 s ± 98.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "7.45 ms ± 675 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "boolean_np = x_np > 3\n",
    "\n",
    "print('x_np:', x_np)\n",
    "print('boolean_np:', boolean_np) # It states if the element in the elements in the same position are > 3.\n",
    "\n",
    "print(x_np[boolean_np])\n",
    "print(x_np[x_np > 3])\n",
    "\n",
    "%timeit [x for x in big_array if x > 3]\n",
    "%timeit big_array[big_array > 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas  (from <a href=\"https://github.com/noise42/datastructures/blob/master/materials/python-data-science-handbook.pdf\">here</a> and <a href=\"http://bioinformationx.com/index.php/index/python-bits/pandas/\">here</a>)\n",
    "\n",
    "[Pandas](https://pandas.pydata.org/) is a package built on top of NumPy, and provides an efficient implementation of a ``DataFrame``. ``DataFrame``s are essentially multidimensional arrays with attached row and column labels, and often with heterogeneous types and/or missing data.\n",
    "\n",
    "### The Pandas Series Object\n",
    "A Pandas ``Series`` is a one-dimensional array of indexed data. It can be created from a list or array as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        RNA\n",
       "1       gene\n",
       "2    protein\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.Series(['RNA', 'gene', 'protein'])\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ``Series`` wraps both a sequence of values and a sequence of indices, which we can access with the ``values`` and ``index`` attributes. The ``values`` are simply a NumPy array, while the ``index`` is an array-like object of type ``pd.Index``. Note that the index needs not be an integer, but can consist of values of any desired type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ENST        RNA\n",
       "ENSG       gene\n",
       "ENSP    protein\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series(\n",
    "    ['RNA', 'gene', 'protein'],\n",
    "    index=['ENST', 'ENSG', 'ENSP']\n",
    ")\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last part, the `dtype`, is not part of the elements you access when cycling through the values of the ``Series``, it is just another attribute.\n",
    "\n",
    "We can construct a ``Series`` from a dictionary and the way we access the values are similar to dictionaries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'gene'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_dict = {'ENST': 'RNA', 'ENSG': 'gene', 'ENSP': 'protein'}\n",
    "data = pd.Series(map_dict)\n",
    "\n",
    "data['ENSG']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``Series`` support slicing just like other arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ENSG       gene\n",
       "ENSP    protein\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['ENSG':]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Pandas DataFrame Object\n",
    "The ``DataFrame`` can be thought as a generalization of a mix of both a NumPy array and a dictionary. It can be constructed from 2 or more dictionary with the same keys (or from 2 ``Series`` with the same indexes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mapping type</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENST</th>\n",
       "      <td>RNA</td>\n",
       "      <td>3300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG</th>\n",
       "      <td>gene</td>\n",
       "      <td>18435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSP</th>\n",
       "      <td>protein</td>\n",
       "      <td>12034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     mapping type  counts\n",
       "ENST          RNA    3300\n",
       "ENSG         gene   18435\n",
       "ENSP      protein   12034"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_dict = {'ENST': 3300, 'ENSG':18435, 'ENSP': 12034}\n",
    " \n",
    "df = pd.DataFrame({'mapping type': map_dict, 'counts': count_dict})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can access the index labels with the ``DataFrame`` attribute ``index``. Additionally, the ``DataFrame`` has a ``columns`` attribute, which holds the labels for all columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Index(['ENST', 'ENSG', 'ENSP'], dtype='object'),\n",
       " Index(['mapping type', 'counts'], dtype='object'))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index, df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can access a colum like a dictionary or in a Pandas way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ENST     3300\n",
       "ENSG    18435\n",
       "ENSP    12034\n",
       "Name: counts, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['counts']  # like a dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ENST     3300\n",
       "ENSG    18435\n",
       "ENSP    12034\n",
       "Name: counts, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.counts  # The Pandas way"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The only difference is that the dictionary way supports labels with spaces and special characters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ENST        RNA\n",
       "ENSG       gene\n",
       "ENSP    protein\n",
       "Name: mapping type, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mapping type']\n",
    "#df.mapping type  # I can't do it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexers: loc, iloc\n",
    "Pandas provides some special *indexer* attributes that explicitly expose certain indexing/slicing schemes.\n",
    "\n",
    "ToDo"
   ]
  }
 ],
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