python-data-2-numpy-sol.ipynb

   ],
   "source": [
    "A = np.random.randn(100)\n",
    "A_bool = A > 0\n",
    "A_bool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A_bool.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A_bool.all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 8\n",
    "Generate and normalise a random 5x5x5 matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.        , 0.00806452, 0.01612903, 0.02419355, 0.03225806],\n",
       "        [0.04032258, 0.0483871 , 0.05645161, 0.06451613, 0.07258065],\n",
       "        [0.08064516, 0.08870968, 0.09677419, 0.10483871, 0.11290323],\n",
       "        [0.12096774, 0.12903226, 0.13709677, 0.14516129, 0.15322581],\n",
       "        [0.16129032, 0.16935484, 0.17741935, 0.18548387, 0.19354839]],\n",
       "\n",
       "       [[0.2016129 , 0.20967742, 0.21774194, 0.22580645, 0.23387097],\n",
       "        [0.24193548, 0.25      , 0.25806452, 0.26612903, 0.27419355],\n",
       "        [0.28225806, 0.29032258, 0.2983871 , 0.30645161, 0.31451613],\n",
       "        [0.32258065, 0.33064516, 0.33870968, 0.34677419, 0.35483871],\n",
       "        [0.36290323, 0.37096774, 0.37903226, 0.38709677, 0.39516129]],\n",
       "\n",
       "       [[0.40322581, 0.41129032, 0.41935484, 0.42741935, 0.43548387],\n",
       "        [0.44354839, 0.4516129 , 0.45967742, 0.46774194, 0.47580645],\n",
       "        [0.48387097, 0.49193548, 0.5       , 0.50806452, 0.51612903],\n",
       "        [0.52419355, 0.53225806, 0.54032258, 0.5483871 , 0.55645161],\n",
       "        [0.56451613, 0.57258065, 0.58064516, 0.58870968, 0.59677419]],\n",
       "\n",
       "       [[0.60483871, 0.61290323, 0.62096774, 0.62903226, 0.63709677],\n",
       "        [0.64516129, 0.65322581, 0.66129032, 0.66935484, 0.67741935],\n",
       "        [0.68548387, 0.69354839, 0.7016129 , 0.70967742, 0.71774194],\n",
       "        [0.72580645, 0.73387097, 0.74193548, 0.75      , 0.75806452],\n",
       "        [0.76612903, 0.77419355, 0.78225806, 0.79032258, 0.7983871 ]],\n",
       "\n",
       "       [[0.80645161, 0.81451613, 0.82258065, 0.83064516, 0.83870968],\n",
       "        [0.84677419, 0.85483871, 0.86290323, 0.87096774, 0.87903226],\n",
       "        [0.88709677, 0.89516129, 0.90322581, 0.91129032, 0.91935484],\n",
       "        [0.92741935, 0.93548387, 0.94354839, 0.9516129 , 0.95967742],\n",
       "        [0.96774194, 0.97580645, 0.98387097, 0.99193548, 1.        ]]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M = np.arange(5*5*5)\n",
    "M = M.reshape(5,5,5)\n",
    "\n",
    "M = (M - M.min())/(M.max() - M.min())\n",
    "M"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 9\n",
    "Create a random vector of size 30 and find its mean value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.623688802625794"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.random.rand(30)\n",
    "np.mean(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 10\n",
    "\n",
    "Subrtract the mean of each row of a randomly generated matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.74543641,  0.22202193,  0.52370205,  1.87712214,  1.86512697],\n",
       "       [-0.41672625, -0.02944647,  0.17001214, -0.83433252,  2.63141455],\n",
       "       [-1.021981  , -0.17505632, -0.07618986, -0.07461297, -0.7511966 ],\n",
       "       [-2.26270537,  0.74184188,  0.39589936,  0.28847449,  0.17428801],\n",
       "       [-1.64970934, -1.64178761,  0.31676457,  0.73482004, -0.2623074 ]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.random.randn(5,5)\n",
    "x = x - x.mean(axis=1)\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sorting <a name=\"sorting\"></a>\n",
    "Similar to Python's built-in list type, NumyPy arrays can be sorted in place:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.6331158 , -0.9584933 ,  0.38867611, -1.57654646,  0.67797536,\n",
       "       -0.52135612,  0.02151883,  0.05561592, -0.31669069,  1.4695012 ])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.random.randn(10)\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.57654646, -0.9584933 , -0.52135612, -0.31669069,  0.02151883,\n",
       "        0.05561592,  0.38867611,  0.67797536,  1.4695012 ,  1.6331158 ])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.sort()\n",
    "A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another option is `unique()` which returns the sorted unique values in an array."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Algebra <a name=\"linear\"></a>\n",
    "Similar to other languages like MATLAB, NumyPy offers a set of standard linear algebra operations, like matrix multiplication, decompositions, determinants and etc.. Unlike some other languages though, the default operations like `*` peform element-wise operations. To perform matrix-wise operartions we need to use special functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = np.arange(16)\n",
    "A = temp[:8]\n",
    "B = temp[8:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "364"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.dot(B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also extend this with the `numpy.linalg` package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6.33842154,  1.27606607, -0.71658047, -0.61896202,  1.21103202],\n",
       "       [ 1.27606607,  1.98671195, -0.52193113, -1.76000421, -1.35723627],\n",
       "       [-0.71658047, -0.52193113,  1.36414106,  1.11827657,  2.05075293],\n",
       "       [-0.61896202, -1.76000421,  1.11827657,  2.31861401,  2.55103779],\n",
       "       [ 1.21103202, -1.35723627,  2.05075293,  2.55103779,  4.65956589]])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy.linalg import inv, qr\n",
    "A = np.random.randn(5, 5)\n",
    "mat = A.T.dot(A)\n",
    "mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  2.15448337,  -1.90845394,   6.15341037,   0.92062735,\n",
       "         -4.32809649],\n",
       "       [ -1.90845394,   3.86956814,  -5.78343753,   1.58641385,\n",
       "          3.29998914],\n",
       "       [  6.15341037,  -5.78343753,  19.79214728,   2.27318392,\n",
       "        -13.23926987],\n",
       "       [  0.92062735,   1.58641385,   2.27318392,   4.12565275,\n",
       "         -3.03637844],\n",
       "       [ -4.32809649,   3.29998914, -13.23926987,  -3.03637844,\n",
       "          9.78990685]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inv(mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.00000000e+00, -3.38911408e-16, -2.95287182e-15,\n",
       "         3.93818578e-16, -1.23910385e-15],\n",
       "       [-1.41601726e-15,  1.00000000e+00, -2.57502274e-15,\n",
       "        -1.01003533e-16,  2.53975941e-16],\n",
       "       [ 1.59331815e-15, -2.35551750e-16,  1.00000000e+00,\n",
       "        -3.90688727e-16, -2.40095760e-15],\n",
       "       [-2.79801471e-15,  1.21798097e-15, -4.40189198e-15,\n",
       "         1.00000000e+00,  2.53615120e-15],\n",
       "       [-1.28888178e-15, -5.44621316e-17, -3.76744772e-15,\n",
       "        -2.06240809e-15,  1.00000000e+00]])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat.dot(inv(mat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a set of commonly used `numpy.linalg` functions\n",
    "\n",
    "| Function | Description |\n",
    "| --- | --- |\n",
    "| diag | Return the diagonal (or off-diagonal) elements of a square matrix as a 1D array,<br>or convert a 1D array into a square matrix with zeros on the off-diagonal |\n",
    "| dot | Matrix multiplication |\n",
    "| trace | Compute the sum of the diagonal elements |\n",
    "| det | Compute the matrix determinant |\n",
    "| eig | Compute the eigenvalues and eigenvectors of a square matrix |\n",
    "| inv | Compute the inverse of a square matrix |\n",
    "| pinv | Compute the Moore-Penrose pseudo-inverse inverse of a square matrix |\n",
    "| qr | Compute the QR decomposition |\n",
    "| svd | Compute the singular value decomposition (SVD) |\n",
    "| solve | Solve the linear system Ax = b for x, where A is a square matrix |\n",
    "| lstsq | Compute the least-squares solution to y = Xb |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 11\n",
    "Obtain the digonal of a dot product of 2 random matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.4094936 , -0.76347609, -0.78950568, -0.38601468, -0.30973188])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.random.randn(5, 5)\n",
    "y = np.random.randn(5, 5)\n",
    "\n",
    "prod = np.dot(x, y)\n",
    "\n",
    "np.diag(prod)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## File IO <a name=\"file\"></a>\n",
    "NumPy offers its own set of File IO functions.\n",
    "\n",
    "The most common one is `genfromtxt()` which can load the common `.csv` and `.tsv` files.\n",
    "\n",
    "Now let us analyse temperature data from Stockholm over the years.\n",
    "\n",
    "First we have to load the file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77431, 7)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.genfromtxt(\"./data/stockholm_td_adj.dat\")\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first column of this array gives years, and the 6th gives temperature readings. We can extract these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "yrs = data[:, 0]\n",
    "temps = data[:, 5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having read in our data, we can now work with it - for example, we could produce a plot.\n",
    "We will cover plotting in more depth in notebook 4, so there's no need to get too caught up in the details right now - this is just an examle of something we might do having read in some data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 6)) # Create a 16x6 figure\n",
    "plt.plot(yrs, temps) # Plot temps vs yrs\n",
    "\n",
    "#Set some labels\n",
    "plt.title(\"Temperatures in Stockholm\")\n",
    "plt.xlabel(\"year\")\n",
    "plt.ylabel(\"Temperature (C)\")\n",
    "\n",
    "plt.show() # Show the plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 12\n",
    "Read in the file `daily_gas_price.csv`, which lists the daily price of natural gas since 1997. Each row contains a date and a price, separated by a comma. Find the minimum, maximum, and mean gas price over the dataset.\n",
    "\n",
    "(Hint: you will need to use the delimiter option in `np.genfromtxt` to specify that data is separated by commas. Also, NumPy will interpret the data in float format by default - we may need to set the dtype to a string format at first, then discard the dates, before turning the gas prices back into floats to process them! Otherwise, NumPy may find it confusing to try and interpret dates formatted as YYYY-MM-DD as floats and will probably complain.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean:  4.331772908366535\n",
      "Std :  2.2031375976175553\n",
      "Max :  18.48\n",
      "Min :  1.05\n",
      "[]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "5522"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.genfromtxt(\"./data/daily_gas_price.csv\", delimiter=\",\")\n",
    "\n",
    "# filter NaNs\n",
    "data = data[1:,1]\n",
    "data = data[~np.isnan(data)]\n",
    "\n",
    "# print stats\n",
    "print(\"Mean: \", data.mean())\n",
    "print(\"Std : \", data.std())\n",
    "print(\"Max : \", data.max())\n",
    "print(\"Min : \", data.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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