6.10 Mathematical and Statistical methods.ipynb

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    "![title](img/cover4.png)"
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   "source": [
    "<center><em>Copyright! This material is protected, please do not copy or distribute.    by:Taher Assaf</em></center>"
   ]
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  {
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   "source": [
    "***\n",
    "<h1 align=\"center\">Udemy course : Python Bootcamp for Data Science 2021 Numpy Pandas & Seaborn</h1> \n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.10 Mathematical and Statistical methods"
   ]
  },
  {
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   "source": [
    "First we import numpy library:"
   ]
  },
  {
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   "source": [
    "import numpy as np"
   ]
  },
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   "source": [
    "Here we create a two-dimensional array:"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "array([[-0.00320844,  0.07349777,  0.13016683,  2.31008908,  0.28560643],\n",
       "       [ 0.12461897, -1.32824579,  1.89111807, -0.12169976,  0.7842233 ],\n",
       "       [ 0.07253599,  2.01722233, -0.29318444,  0.50617902,  2.51987334],\n",
       "       [-1.34638161,  0.8941427 , -0.67649225, -0.75862097,  0.66861239]])"
      ]
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     "execution_count": 2,
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   ],
   "source": [
    "array1 = np.random.randn(4, 5)\n",
    "array1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can calculate the mean of the array using the mean() function:"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3875026479745699"
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     "execution_count": 3,
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   "source": [
    "array1.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also calculate the mean using the numpy function np.mean():"
   ]
  },
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   "execution_count": 4,
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     "data": {
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       "0.3875026479745699"
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     "execution_count": 4,
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   "source": [
    "np.mean(array1)"
   ]
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   "source": [
    "Similarly, we can calculate the minimum, maximum, standard deviation and cumulative sum, like this:"
   ]
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   "execution_count": 5,
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     "data": {
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       "-1.346381609978168"
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     "execution_count": 5,
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     "output_type": "execute_result"
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   "source": [
    "array1.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hide_input": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5198733420523496"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array1.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hide_input": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0789344654834638"
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     "execution_count": 7,
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     "output_type": "execute_result"
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   "source": [
    "array1.std()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.20843652e-03,  7.02893287e-02,  2.00456158e-01,  2.51054524e+00,\n",
       "        2.79615167e+00,  2.92077064e+00,  1.59252484e+00,  3.48364292e+00,\n",
       "        3.36194316e+00,  4.14616646e+00,  4.21870245e+00,  6.23592478e+00,\n",
       "        5.94274034e+00,  6.44891935e+00,  8.96879270e+00,  7.62241109e+00,\n",
       "        8.51655379e+00,  7.84006154e+00,  7.08144057e+00,  7.75005296e+00])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
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   "source": [
    "array1.cumsum()"
   ]
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   "source": [
    "____________________________________________________________________________\n",
    "\n",
    "### Here we practice two important functions, argmin() and argmax():\n",
    "\n",
    "argmin() returns the index of the minumum value.\n",
    "\n",
    "argmax() returns the index of the maximum value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([77, 46, 85, 22, 28, 89, 63, 43,  4, 76])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "array2 = np.array([77, 46, 85, 22, 28, 89, 63, 43,  4, 76])\n",
    "array2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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    {
     "data": {
      "text/plain": [
       "4"
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     "execution_count": 10,
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   ],
   "source": [
    "array2.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "array2.argmin()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 12,
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    "hide_input": false
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     "data": {
      "text/plain": [
       "5"
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     "execution_count": 12,
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   "source": [
    "array2.argmax()"
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   "source": [
    "Lets now create a two dimensional array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.00320844,  0.07349777,  0.13016683,  2.31008908,  0.28560643],\n",
       "       [ 0.12461897, -1.32824579,  1.89111807, -0.12169976,  0.7842233 ],\n",
       "       [ 0.07253599,  2.01722233, -0.29318444,  0.50617902,  2.51987334],\n",
       "       [-1.34638161,  0.8941427 , -0.67649225, -0.75862097,  0.66861239]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create statistics for rows or columns separately, using the argument axis = 0 or axis =1:"
   ]
  },
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   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hide_input": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.15243509,  1.65661701,  1.05160821,  1.93594737,  4.25831547])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "array1.sum(axis = 0)"
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   "cell_type": "code",
   "execution_count": 15,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.79615167,  1.35001479,  4.82262624, -1.21873974])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "array1.sum(axis = 1)"
   ]
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   "cell_type": "markdown",
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   "source": [
    "We can also use statistics for boolean values, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hide_input": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False, False,  True,  True])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array3 = np.array([True, False, False, True, True])\n",
    "array3"
   ]
  },
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   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hide_input": false
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    {
     "data": {
      "text/plain": [
       "3"
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     "execution_count": 17,
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   "source": [
    "array3.sum()"
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  },
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   "cell_type": "code",
   "execution_count": 18,
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    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array3.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hide_input": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array3.all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array4= np.array([True, True, True, True])\n",
    "array4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "hide_input": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array4.all()"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "***\n",
    "\n",
    "<h1 align=\"center\">Thank You</h1> \n",
    "\n",
    "***"
   ]
  }
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