PythonIntro2021.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b04b5d76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 + 5 * 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "89d63284",
   "metadata": {},
   "outputs": [],
   "source": [
    "length_km = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "99c0d8b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14\n"
     ]
    }
   ],
   "source": [
    "print(length_km)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "36623e4d",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'LENGTH_KM' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-cb9e69959716>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLENGTH_KM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'LENGTH_KM' is not defined"
     ]
    }
   ],
   "source": [
    "print(LENGTH_KM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a148d83e",
   "metadata": {},
   "outputs": [],
   "source": [
    "length_km = 14.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "666f0bf0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length in kilometers is 14.0\n"
     ]
    }
   ],
   "source": [
    "print(\"length in kilometers is\", length_km)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "80dec0fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length in miles is 8.75\n"
     ]
    }
   ],
   "source": [
    "print(\"length in miles is\", length_km / 1.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e639c15b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5d11df94",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = numpy.loadtxt(fname=\"inflammation-01.csv\", delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1cb2489f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 1. ... 3. 0. 0.]\n",
      " [0. 1. 2. ... 1. 0. 1.]\n",
      " [0. 1. 1. ... 2. 1. 1.]\n",
      " ...\n",
      " [0. 1. 1. ... 1. 1. 1.]\n",
      " [0. 0. 0. ... 0. 2. 0.]\n",
      " [0. 0. 1. ... 1. 1. 0.]]\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a048ee8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5d58cb9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "135fb0e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 40)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d5691057",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 60 rows and 40 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f10fd9ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first value in data: 0.0\n"
     ]
    }
   ],
   "source": [
    "print('first value in data:', data[0, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "02864403",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "middle value in data: 13.0\n"
     ]
    }
   ],
   "source": [
    "print('middle value in data:', data[30, 20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fa669e7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 1. 3. 1. 2. 4. 7. 8. 3.]\n",
      " [0. 1. 2. 1. 2. 1. 3. 2. 2. 6.]\n",
      " [0. 1. 1. 3. 3. 2. 6. 2. 5. 9.]\n",
      " [0. 0. 2. 0. 4. 2. 2. 1. 6. 7.]]\n"
     ]
    }
   ],
   "source": [
    "print(data[0:4, 0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ac827b1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 1. 2. 2. 4. 2. 1. 6. 4.]\n",
      " [0. 0. 2. 2. 4. 2. 2. 5. 5. 8.]\n",
      " [0. 0. 1. 2. 3. 1. 2. 3. 5. 3.]\n",
      " [0. 0. 0. 3. 1. 5. 6. 5. 5. 8.]\n",
      " [0. 1. 1. 2. 1. 3. 5. 3. 5. 8.]]\n"
     ]
    }
   ],
   "source": [
    "print(data[5:10, 0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9a16fcbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "small = data[:3, 36:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2736f1ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "small is:\n",
      "[[2. 3. 0. 0.]\n",
      " [1. 1. 0. 1.]\n",
      " [2. 2. 1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "print(\"small is:\")\n",
    "print(small)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d23013b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[-1, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4163731f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.14875\n"
     ]
    }
   ],
   "source": [
    "print(numpy.mean(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a6d6ca20",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "13d53627",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mon Jun 21 11:29:37 2021\n"
     ]
    }
   ],
   "source": [
    "print(time.ctime())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8cd25ce1",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'numpy.ndarray' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-30-a28c71a07a91>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'numpy.ndarray' object is not callable"
     ]
    }
   ],
   "source": [
    "data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "320c77e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[3, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "538789c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "maxval, minval, stdval = numpy.max(data), numpy.min(data), numpy.std(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "aad1fff3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maximum inflammation: 20.0\n",
      "minimum inflammation: 0.0\n",
      "standard deviation: 4.613833197118566\n"
     ]
    }
   ],
   "source": [
    "print('maximum inflammation:', maxval)\n",
    "print('minimum inflammation:', minval)\n",
    "print('standard deviation:', stdval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ec6394bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "numpy.mean?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3a6aa275",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function amax in module numpy:\n",
      "\n",
      "amax(a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)\n",
      "    Return the maximum of an array or maximum along an axis.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    a : array_like\n",
      "        Input data.\n",
      "    axis : None or int or tuple of ints, optional\n",
      "        Axis or axes along which to operate.  By default, flattened input is\n",
      "        used.\n",
      "    \n",
      "        .. versionadded:: 1.7.0\n",
      "    \n",
      "        If this is a tuple of ints, the maximum is selected over multiple axes,\n",
      "        instead of a single axis or all the axes as before.\n",
      "    out : ndarray, optional\n",
      "        Alternative output array in which to place the result.  Must\n",
      "        be of the same shape and buffer length as the expected output.\n",
      "        See :ref:`ufuncs-output-type` for more details.\n",
      "    \n",
      "    keepdims : bool, optional\n",
      "        If this is set to True, the axes which are reduced are left\n",
      "        in the result as dimensions with size one. With this option,\n",
      "        the result will broadcast correctly against the input array.\n",
      "    \n",
      "        If the default value is passed, then `keepdims` will not be\n",
      "        passed through to the `amax` method of sub-classes of\n",
      "        `ndarray`, however any non-default value will be.  If the\n",
      "        sub-class' method does not implement `keepdims` any\n",
      "        exceptions will be raised.\n",
      "    \n",
      "    initial : scalar, optional\n",
      "        The minimum value of an output element. Must be present to allow\n",
      "        computation on empty slice. See `~numpy.ufunc.reduce` for details.\n",
      "    \n",
      "        .. versionadded:: 1.15.0\n",
      "    \n",
      "    where : array_like of bool, optional\n",
      "        Elements to compare for the maximum. See `~numpy.ufunc.reduce`\n",
      "        for details.\n",
      "    \n",
      "        .. versionadded:: 1.17.0\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    amax : ndarray or scalar\n",
      "        Maximum of `a`. If `axis` is None, the result is a scalar value.\n",
      "        If `axis` is given, the result is an array of dimension\n",
      "        ``a.ndim - 1``.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    amin :\n",
      "        The minimum value of an array along a given axis, propagating any NaNs.\n",
      "    nanmax :\n",
      "        The maximum value of an array along a given axis, ignoring any NaNs.\n",
      "    maximum :\n",
      "        Element-wise maximum of two arrays, propagating any NaNs.\n",
      "    fmax :\n",
      "        Element-wise maximum of two arrays, ignoring any NaNs.\n",
      "    argmax :\n",
      "        Return the indices of the maximum values.\n",
      "    \n",
      "    nanmin, minimum, fmin\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    NaN values are propagated, that is if at least one item is NaN, the\n",
      "    corresponding max value will be NaN as well. To ignore NaN values\n",
      "    (MATLAB behavior), please use nanmax.\n",
      "    \n",
      "    Don't use `amax` for element-wise comparison of 2 arrays; when\n",
      "    ``a.shape[0]`` is 2, ``maximum(a[0], a[1])`` is faster than\n",
      "    ``amax(a, axis=0)``.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> a = np.arange(4).reshape((2,2))\n",
      "    >>> a\n",
      "    array([[0, 1],\n",
      "           [2, 3]])\n",
      "    >>> np.amax(a)           # Maximum of the flattened array\n",
      "    3\n",
      "    >>> np.amax(a, axis=0)   # Maxima along the first axis\n",
      "    array([2, 3])\n",
      "    >>> np.amax(a, axis=1)   # Maxima along the second axis\n",
      "    array([1, 3])\n",
      "    >>> np.amax(a, where=[False, True], initial=-1, axis=0)\n",
      "    array([-1,  3])\n",
      "    >>> b = np.arange(5, dtype=float)\n",
      "    >>> b[2] = np.NaN\n",
      "    >>> np.amax(b)\n",
      "    nan\n",
      "    >>> np.amax(b, where=~np.isnan(b), initial=-1)\n",
      "    4.0\n",
      "    >>> np.nanmax(b)\n",
      "    4.0\n",
      "    \n",
      "    You can use an initial value to compute the maximum of an empty slice, or\n",
      "    to initialize it to a different value:\n",
      "    \n",
      "    >>> np.max([[-50], [10]], axis=-1, initial=0)\n",
      "    array([ 0, 10])\n",
      "    \n",
      "    Notice that the initial value is used as one of the elements for which the\n",
      "    maximum is determined, unlike for the default argument Python's max\n",
      "    function, which is only used for empty iterables.\n",
      "    \n",
      "    >>> np.max([5], initial=6)\n",
      "    6\n",
      "    >>> max([5], default=6)\n",
      "    5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(numpy.max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3ac239ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ALLOW_THREADS',\n",
       " 'AxisError',\n",
       " 'BUFSIZE',\n",
       " 'Bytes0',\n",
       " 'CLIP',\n",
       " 'ComplexWarning',\n",
       " 'DataSource',\n",
       " 'Datetime64',\n",
       " 'ERR_CALL',\n",
       " 'ERR_DEFAULT',\n",
       " 'ERR_IGNORE',\n",
       " 'ERR_LOG',\n",
       " 'ERR_PRINT',\n",
       " 'ERR_RAISE',\n",
       " 'ERR_WARN',\n",
       " 'FLOATING_POINT_SUPPORT',\n",
       " 'FPE_DIVIDEBYZERO',\n",
       " 'FPE_INVALID',\n",
       " 'FPE_OVERFLOW',\n",
       " 'FPE_UNDERFLOW',\n",
       " 'False_',\n",
       " 'Inf',\n",
       " 'Infinity',\n",
       " 'MAXDIMS',\n",
       " 'MAY_SHARE_BOUNDS',\n",
       " 'MAY_SHARE_EXACT',\n",
       " 'MachAr',\n",
       " 'ModuleDeprecationWarning',\n",
       " 'NAN',\n",
       " 'NINF',\n",
       " 'NZERO',\n",
       " 'NaN',\n",
       " 'PINF',\n",
       " 'PZERO',\n",
       " 'RAISE',\n",
       " 'RankWarning',\n",
       " 'SHIFT_DIVIDEBYZERO',\n",
       " 'SHIFT_INVALID',\n",
       " 'SHIFT_OVERFLOW',\n",
       " 'SHIFT_UNDERFLOW',\n",
       " 'ScalarType',\n",
       " 'Str0',\n",
       " 'Tester',\n",
       " 'TooHardError',\n",
       " 'True_',\n",
       " 'UFUNC_BUFSIZE_DEFAULT',\n",
       " 'UFUNC_PYVALS_NAME',\n",
       " 'Uint64',\n",
       " 'VisibleDeprecationWarning',\n",
       " 'WRAP',\n",
       " '_NoValue',\n",
       " '_UFUNC_API',\n",
       " '__NUMPY_SETUP__',\n",
       " '__all__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__config__',\n",
       " '__deprecated_attrs__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__expired_functions__',\n",
       " '__file__',\n",
       " '__getattr__',\n",
       " '__git_revision__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " '_add_newdoc_ufunc',\n",
       " '_distributor_init',\n",
       " '_financial_names',\n",
       " '_globals',\n",
       " '_mat',\n",
       " '_pytesttester',\n",
       " 'abs',\n",
       " 'absolute',\n",
       " 'add',\n",
       " 'add_docstring',\n",
       " 'add_newdoc',\n",
       " 'add_newdoc_ufunc',\n",
       " 'alen',\n",
       " 'all',\n",
       " 'allclose',\n",
       " 'alltrue',\n",
       " 'amax',\n",
       " 'amin',\n",
       " 'angle',\n",
       " 'any',\n",
       " 'append',\n",
       " 'apply_along_axis',\n",
       " 'apply_over_axes',\n",
       " 'arange',\n",
       " 'arccos',\n",
       " 'arccosh',\n",
       " 'arcsin',\n",
       " 'arcsinh',\n",
       " 'arctan',\n",
       " 'arctan2',\n",
       " 'arctanh',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argpartition',\n",
       " 'argsort',\n",
       " 'argwhere',\n",
       " 'around',\n",
       " 'array',\n",
       " 'array2string',\n",
       " 'array_equal',\n",
       " 'array_equiv',\n",
       " 'array_repr',\n",
       " 'array_split',\n",
       " 'array_str',\n",
       " 'asanyarray',\n",
       " 'asarray',\n",
       " 'asarray_chkfinite',\n",
       " 'ascontiguousarray',\n",
       " 'asfarray',\n",
       " 'asfortranarray',\n",
       " 'asmatrix',\n",
       " 'asscalar',\n",
       " 'atleast_1d',\n",
       " 'atleast_2d',\n",
       " 'atleast_3d',\n",
       " 'average',\n",
       " 'bartlett',\n",
       " 'base_repr',\n",
       " 'binary_repr',\n",
       " 'bincount',\n",
       " 'bitwise_and',\n",
       " 'bitwise_not',\n",
       " 'bitwise_or',\n",
       " 'bitwise_xor',\n",
       " 'blackman',\n",
       " 'block',\n",
       " 'bmat',\n",
       " 'bool8',\n",
       " 'bool_',\n",
       " 'broadcast',\n",
       " 'broadcast_arrays',\n",
       " 'broadcast_shapes',\n",
       " 'broadcast_to',\n",
       " 'busday_count',\n",
       " 'busday_offset',\n",
       " 'busdaycalendar',\n",
       " 'byte',\n",
       " 'byte_bounds',\n",
       " 'bytes0',\n",
       " 'bytes_',\n",
       " 'c_',\n",
       " 'can_cast',\n",
       " 'cast',\n",
       " 'cbrt',\n",
       " 'cdouble',\n",
       " 'ceil',\n",
       " 'cfloat',\n",
       " 'char',\n",
       " 'character',\n",
       " 'chararray',\n",
       " 'choose',\n",
       " 'clip',\n",
       " 'clongdouble',\n",
       " 'clongfloat',\n",
       " 'column_stack',\n",
       " 'common_type',\n",
       " 'compare_chararrays',\n",
       " 'compat',\n",
       " 'complex128',\n",
       " 'complex256',\n",
       " 'complex64',\n",
       " 'complex_',\n",
       " 'complexfloating',\n",
       " 'compress',\n",
       " 'concatenate',\n",
       " 'conj',\n",
       " 'conjugate',\n",
       " 'convolve',\n",
       " 'copy',\n",
       " 'copysign',\n",
       " 'copyto',\n",
       " 'core',\n",
       " 'corrcoef',\n",
       " 'correlate',\n",
       " 'cos',\n",
       " 'cosh',\n",
       " 'count_nonzero',\n",
       " 'cov',\n",
       " 'cross',\n",
       " 'csingle',\n",
       " 'ctypeslib',\n",
       " 'cumprod',\n",
       " 'cumproduct',\n",
       " 'cumsum',\n",
       " 'datetime64',\n",
       " 'datetime_as_string',\n",
       " 'datetime_data',\n",
       " 'deg2rad',\n",
       " 'degrees',\n",
       " 'delete',\n",
       " 'deprecate',\n",
       " 'deprecate_with_doc',\n",
       " 'diag',\n",
       " 'diag_indices',\n",
       " 'diag_indices_from',\n",
       " 'diagflat',\n",
       " 'diagonal',\n",
       " 'diff',\n",
       " 'digitize',\n",
       " 'disp',\n",
       " 'divide',\n",
       " 'divmod',\n",
       " 'dot',\n",
       " 'double',\n",
       " 'dsplit',\n",
       " 'dstack',\n",
       " 'dtype',\n",
       " 'e',\n",
       " 'ediff1d',\n",
       " 'einsum',\n",
       " 'einsum_path',\n",
       " 'emath',\n",
       " 'empty',\n",
       " 'empty_like',\n",
       " 'equal',\n",
       " 'errstate',\n",
       " 'euler_gamma',\n",
       " 'exp',\n",
       " 'exp2',\n",
       " 'expand_dims',\n",
       " 'expm1',\n",
       " 'extract',\n",
       " 'eye',\n",
       " 'fabs',\n",
       " 'fastCopyAndTranspose',\n",
       " 'fft',\n",
       " 'fill_diagonal',\n",
       " 'find_common_type',\n",
       " 'finfo',\n",
       " 'fix',\n",
       " 'flatiter',\n",
       " 'flatnonzero',\n",
       " 'flexible',\n",
       " 'flip',\n",
       " 'fliplr',\n",
       " 'flipud',\n",
       " 'float128',\n",
       " 'float16',\n",
       " 'float32',\n",
       " 'float64',\n",
       " 'float_',\n",
       " 'float_power',\n",
       " 'floating',\n",
       " 'floor',\n",
       " 'floor_divide',\n",
       " 'fmax',\n",
       " 'fmin',\n",
       " 'fmod',\n",
       " 'format_float_positional',\n",
       " 'format_float_scientific',\n",
       " 'format_parser',\n",
       " 'frexp',\n",
       " 'frombuffer',\n",
       " 'fromfile',\n",
       " 'fromfunction',\n",
       " 'fromiter',\n",
       " 'frompyfunc',\n",
       " 'fromregex',\n",
       " 'fromstring',\n",
       " 'full',\n",
       " 'full_like',\n",
       " 'gcd',\n",
       " 'generic',\n",
       " 'genfromtxt',\n",
       " 'geomspace',\n",
       " 'get_array_wrap',\n",
       " 'get_include',\n",
       " 'get_printoptions',\n",
       " 'getbufsize',\n",
       " 'geterr',\n",
       " 'geterrcall',\n",
       " 'geterrobj',\n",
       " 'gradient',\n",
       " 'greater',\n",
       " 'greater_equal',\n",
       " 'half',\n",
       " 'hamming',\n",
       " 'hanning',\n",
       " 'heaviside',\n",
       " 'histogram',\n",
       " 'histogram2d',\n",
       " 'histogram_bin_edges',\n",
       " 'histogramdd',\n",
       " 'hsplit',\n",
       " 'hstack',\n",
       " 'hypot',\n",
       " 'i0',\n",
       " 'identity',\n",
       " 'iinfo',\n",
       " 'imag',\n",
       " 'in1d',\n",
       " 'index_exp',\n",
       " 'indices',\n",
       " 'inexact',\n",
       " 'inf',\n",
       " 'info',\n",
       " 'infty',\n",
       " 'inner',\n",
       " 'insert',\n",
       " 'int0',\n",
       " 'int16',\n",
       " 'int32',\n",
       " 'int64',\n",
       " 'int8',\n",
       " 'int_',\n",
       " 'intc',\n",
       " 'integer',\n",
       " 'interp',\n",
       " 'intersect1d',\n",
       " 'intp',\n",
       " 'invert',\n",
       " 'is_busday',\n",
       " 'isclose',\n",
       " 'iscomplex',\n",
       " 'iscomplexobj',\n",
       " 'isfinite',\n",
       " 'isfortran',\n",
       " 'isin',\n",
       " 'isinf',\n",
       " 'isnan',\n",
       " 'isnat',\n",
       " 'isneginf',\n",
       " 'isposinf',\n",
       " 'isreal',\n",
       " 'isrealobj',\n",
       " 'isscalar',\n",
       " 'issctype',\n",
       " 'issubclass_',\n",
       " 'issubdtype',\n",
       " 'issubsctype',\n",
       " 'iterable',\n",
       " 'ix_',\n",
       " 'kaiser',\n",
       " 'kernel_version',\n",
       " 'kron',\n",
       " 'lcm',\n",
       " 'ldexp',\n",
       " 'left_shift',\n",
       " 'less',\n",
       " 'less_equal',\n",
       " 'lexsort',\n",
       " 'lib',\n",
       " 'linalg',\n",
       " 'linspace',\n",
       " 'little_endian',\n",
       " 'load',\n",
       " 'loads',\n",
       " 'loadtxt',\n",
       " 'log',\n",
       " 'log10',\n",
       " 'log1p',\n",
       " 'log2',\n",
       " 'logaddexp',\n",
       " 'logaddexp2',\n",
       " 'logical_and',\n",
       " 'logical_not',\n",
       " 'logical_or',\n",
       " 'logical_xor',\n",
       " 'logspace',\n",
       " 'longcomplex',\n",
       " 'longdouble',\n",
       " 'longfloat',\n",
       " 'longlong',\n",
       " 'lookfor',\n",
       " 'ma',\n",
       " 'mafromtxt',\n",
       " 'mask_indices',\n",
       " 'mat',\n",
       " 'math',\n",
       " 'matmul',\n",
       " 'matrix',\n",
       " 'matrixlib',\n",
       " 'max',\n",
       " 'maximum',\n",
       " 'maximum_sctype',\n",
       " 'may_share_memory',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'memmap',\n",
       " 'meshgrid',\n",
       " 'mgrid',\n",
       " 'min',\n",
       " 'min_scalar_type',\n",
       " 'minimum',\n",
       " 'mintypecode',\n",
       " 'mod',\n",
       " 'modf',\n",
       " 'moveaxis',\n",
       " 'msort',\n",
       " 'multiply',\n",
       " 'nan',\n",
       " 'nan_to_num',\n",
       " 'nanargmax',\n",
       " 'nanargmin',\n",
       " 'nancumprod',\n",
       " 'nancumsum',\n",
       " 'nanmax',\n",
       " 'nanmean',\n",
       " 'nanmedian',\n",
       " 'nanmin',\n",
       " 'nanpercentile',\n",
       " 'nanprod',\n",
       " 'nanquantile',\n",
       " 'nanstd',\n",
       " 'nansum',\n",
       " 'nanvar',\n",
       " 'nbytes',\n",
       " 'ndarray',\n",
       " 'ndenumerate',\n",
       " 'ndfromtxt',\n",
       " 'ndim',\n",
       " 'ndindex',\n",
       " 'nditer',\n",
       " 'negative',\n",
       " 'nested_iters',\n",
       " 'newaxis',\n",
       " 'nextafter',\n",
       " 'nonzero',\n",
       " 'not_equal',\n",
       " 'numarray',\n",
       " 'number',\n",
       " 'obj2sctype',\n",
       " 'object0',\n",
       " 'object_',\n",
       " 'ogrid',\n",
       " 'oldnumeric',\n",
       " 'ones',\n",
       " 'ones_like',\n",
       " 'os',\n",
       " 'outer',\n",
       " 'packbits',\n",
       " 'pad',\n",
       " 'partition',\n",
       " 'percentile',\n",
       " 'pi',\n",
       " 'piecewise',\n",
       " 'place',\n",
       " 'poly',\n",
       " 'poly1d',\n",
       " 'polyadd',\n",
       " 'polyder',\n",
       " 'polydiv',\n",
       " 'polyfit',\n",
       " 'polyint',\n",
       " 'polymul',\n",
       " 'polynomial',\n",
       " 'polysub',\n",
       " 'polyval',\n",
       " 'positive',\n",
       " 'power',\n",
       " 'printoptions',\n",
       " 'prod',\n",
       " 'product',\n",
       " 'promote_types',\n",
       " 'ptp',\n",
       " 'put',\n",
       " 'put_along_axis',\n",
       " 'putmask',\n",
       " 'quantile',\n",
       " 'r_',\n",
       " 'rad2deg',\n",
       " 'radians',\n",
       " 'random',\n",
       " 'ravel',\n",
       " 'ravel_multi_index',\n",
       " 'real',\n",
       " 'real_if_close',\n",
       " 'rec',\n",
       " 'recarray',\n",
       " 'recfromcsv',\n",
       " 'recfromtxt',\n",
       " 'reciprocal',\n",
       " 'record',\n",
       " 'remainder',\n",
       " 'repeat',\n",
       " 'require',\n",
       " 'reshape',\n",
       " 'resize',\n",
       " 'result_type',\n",
       " 'right_shift',\n",
       " 'rint',\n",
       " 'roll',\n",
       " 'rollaxis',\n",
       " 'roots',\n",
       " 'rot90',\n",
       " 'round',\n",
       " 'round_',\n",
       " 'row_stack',\n",
       " 's_',\n",
       " 'safe_eval',\n",
       " 'save',\n",
       " 'savetxt',\n",
       " 'savez',\n",
       " 'savez_compressed',\n",
       " 'sctype2char',\n",
       " 'sctypeDict',\n",
       " 'sctypes',\n",
       " 'searchsorted',\n",
       " 'select',\n",
       " 'set_numeric_ops',\n",
       " 'set_printoptions',\n",
       " 'set_string_function',\n",
       " 'setbufsize',\n",
       " 'setdiff1d',\n",
       " 'seterr',\n",
       " 'seterrcall',\n",
       " 'seterrobj',\n",
       " 'setxor1d',\n",
       " 'shape',\n",
       " 'shares_memory',\n",
       " 'short',\n",
       " 'show_config',\n",
       " 'sign',\n",
       " 'signbit',\n",
       " 'signedinteger',\n",
       " 'sin',\n",
       " 'sinc',\n",
       " 'single',\n",
       " 'singlecomplex',\n",
       " 'sinh',\n",
       " 'size',\n",
       " 'sometrue',\n",
       " 'sort',\n",
       " 'sort_complex',\n",
       " 'source',\n",
       " 'spacing',\n",
       " 'split',\n",
       " 'sqrt',\n",
       " 'square',\n",
       " 'squeeze',\n",
       " 'stack',\n",
       " 'std',\n",
       " 'str0',\n",
       " 'str_',\n",
       " 'string_',\n",
       " 'subtract',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'sys',\n",
       " 'take',\n",
       " 'take_along_axis',\n",
       " 'tan',\n",
       " 'tanh',\n",
       " 'tensordot',\n",
       " 'test',\n",
       " 'testing',\n",
       " 'tile',\n",
       " 'timedelta64',\n",
       " 'trace',\n",
       " 'tracemalloc_domain',\n",
       " 'transpose',\n",
       " 'trapz',\n",
       " 'tri',\n",
       " 'tril',\n",
       " 'tril_indices',\n",
       " 'tril_indices_from',\n",
       " 'trim_zeros',\n",
       " 'triu',\n",
       " 'triu_indices',\n",
       " 'triu_indices_from',\n",
       " 'true_divide',\n",
       " 'trunc',\n",
       " 'typeDict',\n",
       " 'typecodes',\n",
       " 'typename',\n",
       " 'ubyte',\n",
       " 'ufunc',\n",
       " 'uint',\n",
       " 'uint0',\n",
       " 'uint16',\n",
       " 'uint32',\n",
       " 'uint64',\n",
       " 'uint8',\n",
       " 'uintc',\n",
       " 'uintp',\n",
       " 'ulonglong',\n",
       " 'unicode_',\n",
       " 'union1d',\n",
       " 'unique',\n",
       " 'unpackbits',\n",
       " 'unravel_index',\n",
       " 'unsignedinteger',\n",
       " 'unwrap',\n",
       " 'use_hugepage',\n",
       " 'ushort',\n",
       " 'vander',\n",
       " 'var',\n",
       " 'vdot',\n",
       " 'vectorize',\n",
       " 'version',\n",
       " 'void',\n",
       " 'void0',\n",
       " 'vsplit',\n",
       " 'vstack',\n",
       " 'warnings',\n",
       " 'where',\n",
       " 'who',\n",
       " 'zeros',\n",
       " 'zeros_like']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(numpy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ba6875a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maximum inflammation for patient 0: 18.0\n"
     ]
    }
   ],
   "source": [
    "patient_0 = data[0, :]\n",
    "print('maximum inflammation for patient 0:', numpy.max(patient_0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "259005cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maximum inflammation for patient 2: 19.0\n"
     ]
    }
   ],
   "source": [
    "print('maximum inflammation for patient 2:', numpy.max(data[2, :]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d3fc6292",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.          0.45        1.11666667  1.75        2.43333333  3.15\n",
      "  3.8         3.88333333  5.23333333  5.51666667  5.95        5.9\n",
      "  8.35        7.73333333  8.36666667  9.5         9.58333333 10.63333333\n",
      " 11.56666667 12.35       13.25       11.96666667 11.03333333 10.16666667\n",
      " 10.          8.66666667  9.15        7.25        7.33333333  6.58333333\n",
      "  6.06666667  5.95        5.11666667  3.6         3.3         3.56666667\n",
      "  2.48333333  1.5         1.13333333  0.56666667]\n"
     ]
    }
   ],
   "source": [
    "print(numpy.mean(data, axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8f1e68b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60,)\n"
     ]
    }
   ],
   "source": [
    "print(numpy.mean(data, axis=1).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "904d89ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "39928155",
   "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": [
    "image = matplotlib.pyplot.imshow(data)\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1d4a31bc",
   "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": [
    "ave_inflammation = numpy.mean(data, axis=0)\n",
    "ave_plot = matplotlib.pyplot.plot(ave_inflammation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4ec14b25",
   "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": [
    "max_plot = matplotlib.pyplot.plot(numpy.max(data, axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "598da8a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAD4CAYAAADFAawfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAbvUlEQVR4nO3de2xc6Xke8OedC+8zR6JISZyhuFyttKsVh/FuQq9Tb9Ham9Rdbxw7N6Q24MBFgwpJa9RGUjgxAgQx0KJF2gYuCiOIEhtxkzTbpOtF003i2PAltgPHm9XeZkbUrnZXe+EZUhSX0hkOySE5M2//mDkURc2QIy7POd/wPD9AEEVRZ949Sz48/N7vIqoKIiIyVyToAoiIaGcMaiIiwzGoiYgMx6AmIjIcg5qIyHAxLy46NDSk4+PjXlyaiOhAunDhwoKqDjf7O0+Cenx8HM8884wXlyYiOpBE5I1Wf8ehDyIiwzGoiYgMx6AmIjIcg5qIyHAMaiIiw7U160NEXgewBKAKoKKqU14WRUREN93J9Lz3q+qCZ5UQEVFTnsyjJtoPq+tVfOnvrmBto+rZazz2QyM4czzp2fWJ9kO7Qa0AviYiCuD3VPX89g8QkXMAzgHA2NjY/lVIofW1i3P4L3/zEgBAZP+vrwpcnF3CH3yCI3lktnaD+mFVLYjIUQBfF5FLqvqdrR/QCO/zADA1NcXTCOgdy8446I5FkP/cP0csuv99708//hz+/rXFfb8u0X5r67NfVQuN3+cBPAngIS+LIgKAXMHBmZGkJyENAJm0hbliGdeW1jy5PtF+2fUrQET6RSThvg3gAwByXhdG4VarKfJ2EZNp78aPM2kLQP0bApHJ2nlUOQbgeyLyAoCnAfylqn7V27Io7N5cXMHSWgWTjTD1wtlU/ZtA3mZQk9l2HaNW1dcAvMuHWog2ZRvhOZHyLqiTPXHcPdS/+VpEpuLKRDJSruCgKxrBvccSnr5OJm0hZxc9fQ2id4pBTUbK2Q7uO55AV8zbT9FMKgn7xiquL697+jpE7wSDmoyjqsjZxc1mn5cm2VCkDsCgJuPMXF+Fs7qBjIczPlzuGDjHqclkDGoyTq4Rml7O+HBZfXGMDfYhz3FqMhiDmoyTtR3EIuJ5I9GVSSf5RE1GY1CTcbK2g3uPJdATj/ryepm0hTcXV+CsbPjyekR3ikFNRlFV5AtFX4Y9XJnGOHWeDUUyFIOajFJwylhcXvelkejiUnIyHYOajOI2Ev2Ymuca7O9C+lAvsmwokqEY1GSUnO0gGhHcP+LvZv6ZdHLzmwSRaRjUZJSc7eD00QHfGomuTMrClYVlLJXZUCTzMKjJGKqKrF30dCOmVjKjbkORwx9kHgY1GWN+aQ0LpTVP96BuxZ35weEPMhGDmoyRnfG/kegaTnTjeLKHQU1GYlCTMXIFBxG5uaG/3zLpJHIc+iADMajJGDnbwT3DA+jravfM5f2VSVt49VoJy2uVQF6fqBUGNRnDr61NW8mkLKgC07N8qiazMKjJCNeW1jBXLAca1JOj3PKUzMSgJiNsrkgMaHwaAI4lezCc6ObRXGQcBjUZwQ3qiQCfqIH6NwrO/CDTMKjJCFnbwcmhfgx0B9NIdE2mLVyeX8LqejXQOoi2YlCTEfKFYBuJrom0hZoC03Mc/iBzMKgpcIvL67BvrPq6tWkr7j7YeQ5/kEEY1BS4ILY2bWXE6sFgfxdnfpBRGNQUODcUg9iMaTsRQSZtceYHGYVBTYHLFxzcdaQPVm886FIA1Gd+vHx1CeUNNhTJDAxqClzWdowY9nBNpi1UaoqXry4FXQoRAAY1BcxZ2cBbi6ub24yawP2mwXFqMgWDmgLlHijr56njuxk93AurN86FL2QMBjUFanNFYoBLx7erNxSTbCiSMdoOahGJishzIvKUlwVRuGRtB6OHe3G4vyvoUm6RSVt4aW4J65Va0KUQ3dET9acATHtVCIVTznaMGp92ZVIW1qs1NhTJCG1trCAiowB+AsB/BPArnlZEoVEsb+D1t1fwcz8yGnQpt3HHzD/yhb9DVMST13j/mWH83i9MeXJtOlja3QHn8wA+AyDR6gNE5ByAcwAwNjb2jgujgy/fGAM2aWqe664jffjchycwVyx7cv0Lb1zHNy/NY61SRXcs6slr0MGxa1CLyIcAzKvqBRF5X6uPU9XzAM4DwNTUlO5XgXRw5QvmLB3fTkTwifeOe3b9p14s4Okri7h8tWTkfz+ZpZ0x6ocBfFhEXgfwOIBHROSPPa2KQiFrOxixejA00B10Kb6b5FxtugO7BrWqflZVR1V1HMBHAXxTVT/ueWV04OUMW5Hop7HBPiR6YpyrTW3hPGoKRGmtgtcWlo2c8eEHEUEmZTGoqS13FNSq+m1V/ZBXxVB4TM8WoQpMjpqz0MVvk6MWpueWsFHlXG3aGZ+oKRDZGXMbiX6ZSCWxXqnh8tVS0KWQ4RjUFIhcwcHRRDeOJnqCLiUwbkPR3e+EqBUGNQUiZztGbcQUhPEj9cN8OU5Nu2FQk+9W1it4Zb6EiZAHdSQiOJtKcooe7YpBTb6bnl1CTc3a2jQomZSF6dkiKmwo0g4Y1OS7m4fZhnfGh2tyNInyRg2vXlsOuhQyGIOafJezHQwNdOF4MryNRJc7j5zj1LQTBjX5Lms7mEhZEI92peskJ4cH0NcV5Tg17YhBTb4qb1Rxeb7E8emGaERwdiS5uUEVUTMMavLVpbklVGvK8ektMmkL+UIR1Ro3naTmGNTkq5uNRD5RuzJpCyvrVVxZYEORmmNQk69ytoNDfXGkD/UGXYox3J8u2FCkVhjU5KtsY0UiG4k3nRoeQHcswoYitcSgJt+sVap4+eoSJkK6tWkrsWgE948k+URNLTGoyTcvz5WwUVXO+GhistFQrLGhSE0wqMk3uQJXJLaSSSdRWqvgjcWVoEshAzGoyTdZ20GiJ4axwb6gSzFOhmco0g4Y1OSbvO0gwxWJTd17LIGuaAR5BjU1waAmX2xUa5ieW8LkKMenm4lHIzgzkuATNTXFoCZfXL5awnqlhokUx6dbyaTrh92qsqFIt2JQky/cqWec8dFaJmWhWK7grcXVoEshwzCoyRdZ28FAdwzjR/qDLsVYk2woUgsMavJFruDgbCqJSISNxFbuPT6AeFR42C3dhkFNnqtUa5ieLW5ukk/NdceiuPdYgisU6TYMavLcq9eWUd6oYXKUjcTdZFJsKNLtGNTkOXfMlU/Uu8uMWri+sgH7BhuKdBODmjyXsx30xqM4OTwQdCnGcxuKObsYcCVkEgY1eS5n1xuJUTYSd3XmeALRiHCcmm7BoCZPVWuKi7NFzp9uU088itNHBzjzg27BoCZPXVkoYWW9yhWJd4ArFGm7XYNaRHpE5GkReUFE8iLyOT8Ko4PBbSRyj4/2TaYtLJTWMVcsB10KGaKdJ+o1AI+o6rsAPADgURH5UU+rogMjZxfRHYvgFBuJbbt5hiIbilQX2+0DtP7zV6nxx3jjF38mOyDKG1U88ewMyhs1T67/ty9fw/0jScSiHGVr1/0jSUQEePzpN/GmRwcJpKwefHByxJNr0/7bNagBQESiAC4AOAXgC6r6gyYfcw7AOQAYGxvbzxrJQ1/NzeE3nsx5+hqffP8pT69/0PR1xTB11yC+cWke37g079nrfP+zj2DE4mnwnaCtoFbVKoAHROQQgCdFJKOquW0fcx7AeQCYmpriE3eHeHHGQU88gu//+o95sg+HCJDobuvTjLb4X//6PVher3py7eyMg49/8QfIzjgM6g5xR19BqnpDRL4N4FEA3j6GkS9ytoOzI0kc7u8KuhTaIhaNwOr1ZrjoR+46jIjU/99/YOK4J69B+6udWR/DjSdpiEgvgB8HcMnjusgHtZoiX3A2z+ujcOjtiuLU0QHkCmxWdop2nqhHAHy5MU4dAfBnqvqUt2WRH668vYzl9SqDOoQyaQvfvbwQdBnUpnZmfbwI4EEfaiGf5bhZUmhlUha+8qyN+WIZR5M9QZdDu+CcqRDL2Q66YhGcPsY5zmHjLkDiaTKdgUEdYjm7iPtHkohzjnPonB1JQoSLajoFv0JDSlWRKzjIcA+OUOrvjuHkUD+fqDsEgzqk3lxcwVK5wl3tQmwybSHPXfo6AoM6pDZPXWFQh1YmbWHWKWOhtBZ0KbQLBnVI5ewi4lHBvccSQZdCAclsnibDp2rTMahDKmc7uO94Al0xfgqE1dmUu0sfg9p0/CoNIbeRyPHpcEv2xHH3UD9nfnQABnUIzVxfxY2VDUxwoUvoTaSSnPnRARjUIeR2+vlETZNpC/aNVVxfXg+6FNoBgzqEsraDWERw33E2EsNus6HIaXpGY1CHUM4u4vSxBHri0aBLoYC5+7xwnNpsDOqQUVXkbK5IpDqrL44Tg72c+WE4BnXIzDplvL28zlPBadNk2mJD0XAM6pBxn5w444NcEykLby6uwFnZCLoUaoFBHTI520FE6runEQE3Z/9w3w9zMahDJlco4tTRAfR2sZFIdZz5YT4GdchkbZ6RSLca7O9C+lAvspz5YSwGdYjMF8u4trTGo7foNhOpJPJsKBqLQR0ibmefMz5ou8m0hdcWlrFUZkPRRAzqEMnZRQgbidREpvHN+2KBwx8mYlCHSNZ2cHKoH/3dux4+TyHjDodxPrWZGNQhkufWptTCcKIbx5M9yPOJ2kgM6pBYKK1h1ilzxge1lElzy1NTMahDIsczEmkXmbSFV6+VsLJeCboU2oZBHRJuUJ/lZkzUQiZlQZUNRRMxqEMiZxdx91A/kj3xoEshQ7nTNrmTnnkY1CGRtR1M8GmadnA00Y2hgW6uUDQQgzoEri+vw76xyhkftCMRwWQ6yc2ZDMSgDgF3sx02Emk3mbSFy/MllDeqQZdCWzCoQ8CdcsU9Pmg3mbSFak1xcZbDHybZNahF5ISIfEtEpkUkLyKf8qMw2j95u4gTg72w+thIpJ25P3VxgyaztLOWuALgV1X1WRFJALggIl9X1Yse10b7JGtzRSK1J2X1YLC/iwtfDLNrUKvqLIDZxttLIjINIA2AQb1P/t8LBfyHv7yImnpz/WtLa/gX7z7hzcXpQBERTKSSePI5G9966Zonr9Ebj+LL/+oh3D3U78n1D6I72p1HRMYBPAjgB03+7hyAcwAwNja2H7WFxl/nZrFeqeHRzIgn1++KCn72h0c9uTYdPP/ux05j9HCfJ9feqNbwfy7M4LuXrzGo70DbQS0iAwCeAPBpVb2t06Cq5wGcB4CpqSmPng0Ppqzt4L33DOE//cxk0KUQ4d3jg3j3+KAn11ZVfOvSPLIzHFq5E23N+hCROOoh/Seq+hVvSwoXZ2UDby2uYiLNxSh08IkIJtIWclymfkfamfUhAL4IYFpVf8f7ksLFnePMZh+FxWQ6ictXlzhX+w6080T9MIBfAPCIiDzf+PWYx3WFRo5znClkMikLlZripbmloEvpGO3M+vgeAPGhllDK2g7Sh3pxuL8r6FKIfOHO1c7aDt514lCwxXQIrkwMWL5QRIbj0xQio4d7cagvzj1F7gCDOkDF8gauLCxzfJpCRUSQSVlcVHMHGNQBcjdo52ZJFDaZtIWX5pawXqkFXUpHYFAHiMdjUVhl0klsVBUvX2VDsR0M6gBlbQcjVg+GBrqDLoXIV5NbGoq0OwZ1gHK2gwlOy6MQGhvsQ6InxmO/2sSgDkhprYLX2EikkHIbigzq9jCoAzI9W4QqODWPQiuTTmJ6bgkbVTYUd8OgDoi7KQ2fqCmsMmkL65UaLl8tBV2K8RjUAckVHAwnunE02RN0KUSBcB9Sclz4sisGdUByPHWFQm78SD8GutlQbAeDOgCr61W8Ml9CJsXxaQqvSERwNpVkULeBQR2Ai7NF1JQLXYgyKQsXZ4uosKG4IwZ1ALgikahucjSJ8kYNr15bDroUozGoA5CzHRzp78KIxUYihZu7DzuHP3bGoA5A1nYwkbZQPzyHKLxODg+gNx7lUvJdMKh9Vt6o4vJ8CZNc6EKEaKOhyL2pd8ag9tmluSVUa8qjt4gaJtMW8oUiqjUNuhRjMah9xkYi0a0yaQsr61VcWWBDsRUGtc9ytgOrN47Rw71Bl0JkBHe/GzYUW2NQ+yxXqK9IZCORqO7U8AC6YxEG9Q4Y1D5aq1Tx0twShz2ItohFI7h/JMmZHztgUPvo5bkSNqrKrU2JtnEbijU2FJtiUPvI3SWMmzER3SqTTqK0VsEbiytBl2IkBrWPsraDRE8MY4N9QZdCZJQMz1DcEYPaR3nbQSbFRiLRdqePJtAVjSDPoG6KQe2TjWoN03NLHJ8maqIrFsGZkQSfqFtgUPvk8tUS1is1zvggaiGTrh92q8qG4nYMap9wRSLRzjIpC8VyBW8trgZdinEY1D7JFRz0d0Vx95H+oEshMhLPUGxt16AWkS+JyLyI5Pwo6KDK2g4mUhYiETYSiZq59/gA4lHhOHUT7TxR/yGARz2u40CrVGuYni1y2INoB92xKO49luBS8iZiu32Aqn5HRMZ9qCVQVxaW8ewb1z259tvLayhv1Djjg2gXmZSFr+bn8MSFGU+uH4kAj9x3DFZf3JPre2XXoG6XiJwDcA4AxsbG9uuyvvn3f/4CLngU1AAQEWDqrkHPrk90ELzn5CD+9zNv4Vf//AXPXuPfvO8efObRM55d3wv7FtSqeh7AeQCYmprqqPk1lWoNOdvBxx46gV/+p6c8eY3+7iiODHR7cm2ig+KnH0zjR08eQaXqTYSc+6Nn8OJM5w2t7FtQd7JXrpWwVqnhPXcfwdgRLu8mCoqIIHXIu73aHzhxCF/Nz0FVO2qFMKfnAcjOcI4zURhk0hZurGxg5npnzdVuZ3renwL4PoD7RGRGRH7R+7L8lS8U0dcVxd1DnONMdJC5D2OddphuO7M+PuZHIUGqz3FOIso5zkQH2pnjCUQj9bnaj2ZGgi6nbaEf+qjWFBcLRUzwVHCiA68nHsXpowPI2cWgS7kjoQ/q166VsLpR5Wb+RCEx2YGbP4U+qN19BdhIJAqHTNrC28vrmCuWgy6lbaEP6uxMET3xCO4ZZiORKAw2T5PpoPnUoQ/qXMHB2ZEkYtHQ3wqiUDg7kkREgFyhc8apQ51OtUYjkcMeROHR2xXFqaMDHbX5U6iD+vW3l1FaqzCoiULGPU2mU4Q6qN19bzOcmkcUKpmUhfmlNcx3SEMx1EGdLxTRFYvg9LGBoEshIh9NjnbWaTKhDursjIP7jycQZyORKFTOjiQhUp/11QlCm1CqilzB4fg0UQj1d8dwcqifT9Sme3NxBUtlNhKJwqqTGoqhDWp3rT+XjhOF02TawqxTxkJpLehSdhXaoM7aDuJRYSORKKTcjdg64ak6tEGdsx3cdzyB7lg06FKIKAATjcOmGdSG2mwkcv40UWgle+IYP9LXEVuehjKoZ66v4sbKBhuJRCGXSVubC99MFsqgznNrUyJCPQPsG6u4vrwedCk7CmVQZ20H0YjgzPFE0KUQUYDcWV+mz6cOZVDn7CJOHx1AT5yNRKIwm0i5DUWzx6lDF9SqipztcP40EeFQXxdODPYaP/MjdEE9Vyzj7eV1jk8TEYD6Tnoc+jCMe/wOg5qIgHoWvPH2CpzVjaBLaSl0QZ0rFBGR+u5ZRETuMGje4Kfq8AW17eDU0QH0drGRSEQ3f7o2eZw6lEHNYQ8icg32dyF9qNfomR+hCur5YhnzS2tcOk5Et5hIJflEbQq3s+sew0NEBNTHqV9bWMZS2cyGYqiCOjtThLCRSETbuMOhFwtmDn+EKqhzBQcnh/rR3x0LuhQiMshmQ5FBHTw2EomomeFEN44lu40dp24rqEXkURF5SUReEZFf97ooLyyU1jDrlLl0nIiamjT4DMVdg1pEogC+AOCDAM4C+JiInPW6sP3m/g+Y4IwPImpiImXh1WslrKxXgi7lNu0M1j4E4BVVfQ0ARORxAB8BcHG/i/nJ//E9lDeq+31ZANhcHuoev0NEtNVk2kJNgQ/+9++iK7q3UeHDfV34s1/6R/tcWXtBnQbw1pY/zwB4z/YPEpFzAM4BwNjY2J6KuWe4H+vV2p7+bTvOHE8i2RP37PpE1Lnee+oIfn5qFKW1vT9Re5Uv7QS1NHmf3vYO1fMAzgPA1NTUbX/fjs9/9MG9/DMionesryuG3/65dwVdRlPtPN/PADix5c+jAArelENERNu1E9T/AOC0iNwtIl0APgrgL7wti4iIXLsOfahqRUQ+CeBvAEQBfElV855XRkREANobo4aq/hWAv/K4FiIiaiJUKxOJiDoRg5qIyHAMaiIiwzGoiYgMJ6p7Wpuy80VFrgF4Y4//fAjAwj6Ws59Y296wtr1hbXvTqbXdparDzf7Ck6B+J0TkGVWdCrqOZljb3rC2vWFte3MQa+PQBxGR4RjURESGMzGozwddwA5Y296wtr1hbXtz4GozboyaiIhuZeITNRERbcGgJiIynDFBbfIBuiLyuohkReR5EXnGgHq+JCLzIpLb8r5BEfm6iFxu/H7YoNp+S0Tsxv17XkQeC6CuEyLyLRGZFpG8iHyq8f7A79sOtZlw33pE5GkReaFR2+ca7zfhvrWqLfD7tqXGqIg8JyJPNf68p/tmxBh14wDdlwH8M9QPKvgHAB9T1X0/l3EvROR1AFOqasQkehH5JwBKAP6nqmYa7/ttAIuq+p8b3+gOq+qvGVLbbwEoqep/9bueLXWNABhR1WdFJAHgAoCfAvAvEfB926G2n0fw900A9KtqSUTiAL4H4FMAfgbB37dWtT2KgO+bS0R+BcAUgKSqfmivX6emPFFvHqCrqusA3AN0qQlV/Q6AxW3v/giALzfe/jLqX+i+a1Fb4FR1VlWfbby9BGAa9fNAA79vO9QWOK0rNf4Yb/xSmHHfWtVmBBEZBfATAP5gy7v3dN9MCepmB+ga8YnaoAC+JiIXGof4muiYqs4C9S98AEcDrme7T4rIi42hkUCGZVwiMg7gQQA/gGH3bVttgAH3rfHj+/MA5gF8XVWNuW8tagMMuG8APg/gMwC2nti9p/tmSlC3dYBugB5W1R8G8EEA/7bx4z2173cB3APgAQCzAP5bUIWIyACAJwB8WlWLQdXRTJPajLhvqlpV1QdQPy/1IRHJBFFHMy1qC/y+iciHAMyr6oX9uJ4pQW30AbqqWmj8Pg/gSdSHakxztTHW6Y55zgdczyZVvdr4gqoB+H0EdP8a45hPAPgTVf1K491G3LdmtZly31yqegPAt1EfAzbivrm21mbIfXsYwIcb/a3HATwiIn+MPd43U4La2AN0RaS/0eCBiPQD+ACA3M7/KhB/AeATjbc/AeD/BljLLdxPzIafRgD3r9F4+iKAaVX9nS1/Ffh9a1WbIfdtWEQONd7uBfDjAC7BjPvWtDYT7puqflZVR1V1HPU8+6aqfhx7vW+qasQvAI+hPvPjVQC/EXQ9W+o6CeCFxq+8CbUB+FPUf6TbQP2nkV8EcATANwBcbvw+aFBtfwQgC+DFxifqSAB1/WPUh9NeBPB849djJty3HWoz4b79EIDnGjXkAPxm4/0m3LdWtQV+37bV+T4AT72T+2bE9DwiImrNlKEPIiJqgUFNRGQ4BjURkeEY1EREhmNQExEZjkFNRGQ4BjURkeH+P3pTIq3i2oWWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_plot = matplotlib.pyplot.plot(numpy.min(data, axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f16c4ceb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy \n",
    "import matplotlib.pyplot\n",
    "\n",
    "data = numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')\n",
    "\n",
    "fig = matplotlib.pyplot.figure(figsize=(10, 3))\n",
    "\n",
    "# number of rows, number of columns, plot number\n",
    "axes1 = fig.add_subplot(1, 3, 1)\n",
    "axes2 = fig.add_subplot(1, 3, 2)\n",
    "axes3 = fig.add_subplot(1, 3, 3)\n",
    "\n",
    "axes1.set_ylabel('average')\n",
    "axes1.plot(numpy.mean(data, axis=0))\n",
    "\n",
    "axes2.set_ylabel('max')\n",
    "axes2.plot(numpy.max(data, axis=0))\n",
    "\n",
    "axes3.set_ylabel('min')\n",
    "axes3.plot(numpy.min(data, axis=0))\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f4a26e51",
   "metadata": {},
   "outputs": [],
   "source": [
    "word = \"tin\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cf89658d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t\n",
      "i\n",
      "n\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "string index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-e7131ac5c666>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m: string index out of range"
     ]
    }
   ],
   "source": [
    "print(word[0])\n",
    "print(word[1])\n",
    "print(word[2])\n",
    "print(word[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b25d6c78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t\n",
      "i\n",
      "n\n"
     ]
    }
   ],
   "source": [
    "word = \"tin\"\n",
    "for char in word:\n",
    "    print(char)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fc539e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "for variable in collection:\n",
    "    # do things with variable, such as print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "98867fe2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length =  5\n"
     ]
    }
   ],
   "source": [
    "length = 0\n",
    "for vowel in 'aeiou':\n",
    "    length = length + 1\n",
    "print(\"length = \", length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "75620e34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a d\n",
      "a e\n",
      "a f\n",
      "b d\n",
      "b e\n",
      "b f\n",
      "c d\n",
      "c e\n",
      "c f\n"
     ]
    }
   ],
   "source": [
    "for i in \"abc\":\n",
    "    for j in \"def\":\n",
    "        print(i, j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c3cd9ac4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vowels = \"aeiou\"\n",
    "len(vowels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9794c1d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds are: [1, 3, 5, 7]\n"
     ]
    }
   ],
   "source": [
    "odds = [1, 3, 5, 7]\n",
    "print(\"odds are:\", odds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c13a14a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first element is: 1\n",
      "last element is: 7\n"
     ]
    }
   ],
   "source": [
    "print(\"first element is:\", odds[0])\n",
    "print(\"last element is:\", odds[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8a6dfb2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "3\n",
      "5\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "for number in odds:\n",
    "    print(number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ae498e8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "names = ['Curie', 'Darwing', 'Turing']  # typo in Darwin's name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "79c9dd5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "names is originally: ['Curie', 'Darwing', 'Turing']\n",
      "final value of names: ['Curie', 'Darwin', 'Turing']\n"
     ]
    }
   ],
   "source": [
    "print('names is originally:', names)\n",
    "names[1] = 'Darwin'\n",
    "print('final value of names:', names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f5d8f7ea",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'str' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-9030064e45ad>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Darwin'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mname\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'd'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "name = 'Darwin'\n",
    "name[0] = 'd'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e62b7ca",
   "metadata": {},
   "source": [
    " - lists are mutable: we can change items in a list\n",
    " - strings are immutable: we cannot change characters in a string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "59abb11d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['hot peppers', 'onions', 'cilantro', 'tomatoes']\n"
     ]
    }
   ],
   "source": [
    "salsa = ['peppers', 'onions', 'cilantro', 'tomatoes']\n",
    "my_salsa = salsa\n",
    "salsa[0] = 'hot peppers'\n",
    "print(my_salsa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "333eb326",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['peppers', 'onions', 'cilantro', 'tomatoes']\n"
     ]
    }
   ],
   "source": [
    "salsa = ['peppers', 'onions', 'cilantro', 'tomatoes']\n",
    "my_salsa = list(salsa)  # <-- makes a copy of the list\n",
    "salsa[0] = 'hot peppers'\n",
    "print(my_salsa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a825fec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [ ['pepper', 'zucchini', 'onion'],\n",
    "      ['cabbage', 'lettuce', 'garlic'],\n",
    "      ['apple', 'pear', 'banana']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3c1de2f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['pepper', 'zucchini', 'onion']\n"
     ]
    }
   ],
   "source": [
    "print(x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7a045de1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pepper\n"
     ]
    }
   ],
   "source": [
    "print(x[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2cb9f3b6",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "list indices must be integers or slices, not tuple",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-ecdbdfb8bb2e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: list indices must be integers or slices, not tuple"
     ]
    }
   ],
   "source": [
    "x[1, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "56db9e03",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_ages = [10, 12.5, 'unknown']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1b90d543",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds after adding a value: [1, 3, 5, 7, 11]\n"
     ]
    }
   ],
   "source": [
    "odds.append(11)\n",
    "print(\"odds after adding a value:\", odds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0143eea8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds after removing the first element: [3, 5, 7, 11]\n",
      "removed element: 1\n"
     ]
    }
   ],
   "source": [
    "removed_element = odds.pop(0)\n",
    "print(\"odds after removing the first element:\", odds)\n",
    "print(\"removed element:\", removed_element)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0822afbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odds after reversing: [11, 7, 5, 3]\n"
     ]
    }
   ],
   "source": [
    "odds.reverse()\n",
    "print('odds after reversing:', odds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "94009c57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "primes: [3, 5, 7, 2]\n",
      "odds: [3, 5, 7]\n"
     ]
    }
   ],
   "source": [
    "odds = [3, 5, 7]\n",
    "primes = list(odds)\n",
    "primes.append(2)\n",
    "print(\"primes:\", primes)\n",
    "print(\"odds:\", odds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9e5a7c31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[1, 2, 3] + [4, 5, 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8f616383",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "subset is [2, 7, 17, 31]\n"
     ]
    }
   ],
   "source": [
    "primes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 31, 37]\n",
    "subset = primes[0:12:3]  # is the step size\n",
    "print(\"subset is\", subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f45838e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1417c491",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['inflammation-03.csv', 'inflammation-02.csv', 'inflammation-09.csv', 'inflammation-12.csv', 'inflammation-06.csv', 'inflammation-05.csv', 'inflammation-04.csv', 'inflammation-08.csv', 'inflammation-01.csv', 'inflammation-11.csv', 'inflammation-10.csv', 'inflammation-07.csv']\n"
     ]
    }
   ],
   "source": [
    "print(glob.glob('inflammation*.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fd89fee2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-01.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-02.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-03.csv\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 720x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import glob\n",
    "import numpy\n",
    "import matplotlib.pyplot\n",
    "\n",
    "filenames = sorted(glob.glob('inflammation*.csv'))\n",
    "filenames = filenames[0:3]\n",
    "\n",
    "for filename in filenames:\n",
    "    print(filename)\n",
    "    \n",
    "    data = numpy.loadtxt(fname=filename, delimiter=',')\n",
    "    \n",
    "    fig = matplotlib.pyplot.figure(figsize=(10, 3))\n",
    "    \n",
    "    axes1 = fig.add_subplot(1, 3, 1)\n",
    "    axes2 = fig.add_subplot(1, 3, 2)\n",
    "    axes3 = fig.add_subplot(1, 3, 3)\n",
    "    \n",
    "    axes1.set_ylabel('average')\n",
    "    axes1.plot(numpy.mean(data, axis=0))\n",
    "    \n",
    "    axes2.set_ylabel('max')\n",
    "    axes2.plot(numpy.max(data, axis=0))\n",
    "    \n",
    "    axes3.set_ylabel('min')\n",
    "    axes3.plot(numpy.min(data, axis=0))\n",
    "    \n",
    "    fig.tight_layout()  # to make the plots prettier\n",
    "    matplotlib.pyplot.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "28b373b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not greater\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "num = 37\n",
    "if num > 100:\n",
    "    print('greater')\n",
    "else:\n",
    "    print('not greater')\n",
    "print('done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a168cd1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before conditional...\n",
      "...after conditional\n"
     ]
    }
   ],
   "source": [
    "num = 53\n",
    "print('before conditional...')\n",
    "if num > 100:\n",
    "    print(num, 'is greater than 100')\n",
    "print('...after conditional')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "66bf9945",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-3 is negative\n"
     ]
    }
   ],
   "source": [
    "num = -3\n",
    "\n",
    "if num > 0:\n",
    "    print(num, 'is positive')\n",
    "elif num == 0:\n",
    "    print(num, 'is zero')\n",
    "else:\n",
    "    print(num, 'is negative')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "23d0b9e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "at least one part is false\n"
     ]
    }
   ],
   "source": [
    "if (1 > 0) and (-1 > 0):\n",
    "    print('both parts are true')\n",
    "else:\n",
    "    print('at least one part is false')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "58cdf247",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "at least one test is true\n"
     ]
    }
   ],
   "source": [
    "if (1 < 0) or (-1 < 0):\n",
    "    print('at least one test is true')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2ab3d154",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minima add up to zero!\n"
     ]
    }
   ],
   "source": [
    "data = numpy.loadtxt(fname='inflammation-03.csv', delimiter=',')\n",
    "\n",
    "max_inflammation_0 = numpy.max(data, axis=0)[0]\n",
    "max_inflammation_20 = numpy.max(data, axis=0)[20]\n",
    "\n",
    "if max_inflammation_0 == 0 and max_inflammation_20 == 20:\n",
    "    print('suspicious looking maxima')\n",
    "elif numpy.sum(numpy.min(data, axis=0)) == 0:\n",
    "    print('minima add up to zero!')\n",
    "else:\n",
    "    print('looks okay!')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "225cf0e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "if '':\n",
    "    print('empty string is true')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "05e27a08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "word is true\n"
     ]
    }
   ],
   "source": [
    "if 'word':\n",
    "    print('word is true')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "804de788",
   "metadata": {},
   "outputs": [],
   "source": [
    "if []:\n",
    "    print('empty list is true')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "22d7dce3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "non-empty list is true\n"
     ]
    }
   ],
   "source": [
    "if [1, 2, 3]:\n",
    "    print('non-empty list is true')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4eb4e029",
   "metadata": {},
   "outputs": [],
   "source": [
    "if 0:\n",
    "    print('zero is true')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d2283140",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one is true\n"
     ]
    }
   ],
   "source": [
    "if 1:\n",
    "    print('one is true')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d65fba23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True is true\n"
     ]
    }
   ],
   "source": [
    "if True:\n",
    "    print('True is true')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f33b7240",
   "metadata": {},
   "outputs": [],
   "source": [
    "if False:\n",
    "    print('False is true')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2890d5ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fahr_to_celsius(temp):\n",
    "    return ((temp - 32) * (5/9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "240bc6f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fahr_to_celsius(32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ae1c250f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize(filename):\n",
    "\n",
    "    data = numpy.loadtxt(fname=filename, delimiter=',')\n",
    "    \n",
    "    fig = matplotlib.pyplot.figure(figsize=(10, 3))\n",
    "    \n",
    "    axes1 = fig.add_subplot(1, 3, 1)\n",
    "    axes2 = fig.add_subplot(1, 3, 2)\n",
    "    axes3 = fig.add_subplot(1, 3, 3)\n",
    "    \n",
    "    axes1.set_ylabel('average')\n",
    "    axes1.plot(numpy.mean(data, axis=0))\n",
    "    \n",
    "    axes2.set_ylabel('max')\n",
    "    axes2.plot(numpy.max(data, axis=0))\n",
    "    \n",
    "    axes3.set_ylabel('min')\n",
    "    axes3.plot(numpy.min(data, axis=0))\n",
    "    \n",
    "    fig.tight_layout()\n",
    "    matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b195d38f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-01.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-02.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-03.csv\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 720x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-04.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-05.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inflammation-06.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filenames = sorted(glob.glob('inflammation*.csv'))\n",
    "\n",
    "for filename in filenames[0:6]:\n",
    "    print(filename)\n",
    "    visualize(filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "599323d1",
   "metadata": {},
   "source": [
    "### Acknowledgments\n",
    "\n",
    "This tutorial was adapted from Software Carpentry's Progamming with Python tutorial under the terms of the CC BY 4.0 license.\n",
    "\n",
    "Thank you to everyone who participated!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}