03 - NumPy arrays.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NumPy arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nikolay Koldunov\n",
    "\n",
    "koldunovn@gmail.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "================"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img  height=\"100\" src=\"files/numpy.png\" >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-    a powerful N-dimensional array object\n",
    "-    sophisticated (broadcasting) functions\n",
    "-    tools for integrating C/C++ and Fortran code\n",
    "-    useful linear algebra, Fourier transform, and random number capabilities\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.set_printoptions(precision=3 , suppress= True) # this is just to make the output look better"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load data in to a variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = np.loadtxt('Ham_3column.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1891.,    1.,    1.,  -72.],\n",
       "       [1891.,    1.,    2.,  -43.],\n",
       "       [1891.,    1.,    3.,  -32.],\n",
       "       ...,\n",
       "       [2014.,    8.,   29.,  216.],\n",
       "       [2014.,    8.,   30.,  198.],\n",
       "       [2014.,    8.,   31.,  184.]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45168, 4)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img  height=\"100\" src=\"files/anatomyarray.png\" >"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180672"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So it's a *row-major* order. Matlab and Fortran use *column-major* order for arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy arrays are statically typed, which allow faster operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can't assign value of different type to element of the numpy array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Year'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-c2dbfa0bc312>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtemp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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'Year'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'Year'"
     ]
    }
   ],
   "source": [
    "temp[0,0] = 'Year'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slicing works similarly to Matlab:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1891.,    1.,    1.,  -72.],\n",
       "       [1891.,    1.,    2.,  -43.],\n",
       "       [1891.,    1.,    3.,  -32.],\n",
       "       [1891.,    1.,    4.,   12.],\n",
       "       [1891.,    1.,    5.,  -29.]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[0:5,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2014.,    8.,   27.,  219.],\n",
       "       [2014.,    8.,   28.,  234.],\n",
       "       [2014.,    8.,   29.,  216.],\n",
       "       [2014.,    8.,   30.,  198.]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[-5:-1,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can look at the data. This is done by matplotlib module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11b17be10>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pylab as plt\n",
    "plt.plot(temp[:,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Index slicing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general it is similar to Matlab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First 12 elements of **second** column (months). Remember that indexing starts with 0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[0:12,2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First raw:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1891.,    1.,    1.,  -72.])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[0,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    " - Plot only first 1000 values\n",
    " - Plot last 1000 values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create mask, selecting all raws where values in third raw (days) equals 10:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = (temp[:,2]==10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we apply this mask and show only first 5 raws of the array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1891.,    1.,   10.,  -89.],\n",
       "       [1891.,    2.,   10.,  -19.],\n",
       "       [1891.,    3.,   10.,   32.],\n",
       "       [1891.,    4.,   10.,   84.],\n",
       "       [1891.,    5.,   10.,  188.],\n",
       "       [1891.,    6.,   10.,  130.],\n",
       "       [1891.,    7.,   10.,  161.],\n",
       "       [1891.,    8.,   10.,  171.],\n",
       "       [1891.,    9.,   10.,  221.],\n",
       "       [1891.,   10.,   10.,  181.],\n",
       "       [1891.,   11.,   10.,   80.],\n",
       "       [1891.,   12.,   10.,  107.],\n",
       "       [1892.,    1.,   10.,   -4.],\n",
       "       [1892.,    2.,   10.,   36.],\n",
       "       [1892.,    3.,   10.,   16.],\n",
       "       [1892.,    4.,   10.,  146.],\n",
       "       [1892.,    5.,   10.,  195.],\n",
       "       [1892.,    6.,   10.,  205.],\n",
       "       [1892.,    7.,   10.,  209.],\n",
       "       [1892.,    8.,   10.,  155.]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[mask][:20,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You don't have to create separate variable for mask, but apply it directly. Here instead of first five rows I show five last rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2014.,    4.,   10.,  116.],\n",
       "       [2014.,    5.,   10.,   27.],\n",
       "       [2014.,    6.,   10.,  300.],\n",
       "       [2014.,    7.,   10.,  277.],\n",
       "       [2014.,    8.,   10.,  259.]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[temp[:,2]==10][-5:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can combine conditions. In this case we select days from 10 to 12 (only first 10 elements are shown):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1891.,    1.,   10.,  -89.],\n",
       "       [1891.,    1.,   11.,   16.],\n",
       "       [1891.,    1.,   12.,   21.],\n",
       "       [1891.,    2.,   10.,  -19.],\n",
       "       [1891.,    2.,   11.,   36.],\n",
       "       [1891.,    2.,   12.,   31.],\n",
       "       [1891.,    3.,   10.,   32.],\n",
       "       [1891.,    3.,   11.,   46.],\n",
       "       [1891.,    3.,   12.,   46.],\n",
       "       [1891.,    4.,   10.,   84.]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[(temp[:,2]>=10)&(temp[:,2]<=12)][0:10,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    "    Select only summer months\n",
    "    Select only first half of the year\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create example array from first 12 values of second column and perform some basic operations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12.])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days = temp[0:12,2]\n",
    "days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22.])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days+10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 20.,  40.,  60.,  80., 100., 120., 140., 160., 180., 200., 220.,\n",
       "       240.])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days*20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.,   4.,   9.,  16.,  25.,  36.,  49.,  64.,  81., 100., 121.,\n",
       "       144.])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days*days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's wrong with this figure?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11bfaee50>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(temp[:100,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    "- Create new array that will contain only temperatures\n",
    "\n",
    "- Convert temperature to deg C\n",
    "\n",
    "- Convert all temperatures to deg F\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create *temp_values* that will contain only data values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-7.2, -4.3, -3.2, ..., 21.6, 19.8, 18.4])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_values = temp[:,3]/10.\n",
    "temp_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simple statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-14.6"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_values.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.3"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_values.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.488779667020898"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_values.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.05358769929934"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_values.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "564093.2"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_values.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also use *sum* function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "564093.2"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(temp_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can make operations on the subsets:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    "    Calculate mean for first 1000 values of temperature\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can save your data as a text file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('temp_only_values.csv',temp[:, 3]/10., fmt='%.4f')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Python formatting options](https://pyformat.info/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Head of resulting file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-7.2000\r\n",
      "-4.3000\r\n",
      "-3.2000\r\n",
      "1.2000\r\n",
      "-2.9000\r\n",
      "-4.3000\r\n",
      "-3.7000\r\n",
      "-9.7000\r\n",
      "-9.9000\r\n",
      "-8.9000\r\n"
     ]
    }
   ],
   "source": [
    "!head temp_only_values.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also save it as binary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "f=open('temp_only_values.bin', 'w')\n",
    "temp[:,3].tofile(f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "* Select and plot only data for October\n",
    "* Calculate monthly means for years from 1990 to 1999 and plot them"
   ]
  }
 ],
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