{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Demo: NumPy, Pandas, Matplotlib\n",
    "UW Geospatial Data Analysis  \n",
    "CEE498/CEWA599  \n",
    "David Shean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NumPy\n",
    "> NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib\n",
    ">Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.\n",
    "\n",
    ">Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits.\n",
    "\n",
    ">Matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc., with just a few lines of code. For examples, see the sample plots and thumbnail gallery.\n",
    "\n",
    ">For simple plotting the pyplot module provides a MATLAB-like interface, particularly when combined with IPython. For the power user, you have full control of line styles, font properties, axes properties, etc, via an object oriented interface or via a set of functions familiar to MATLAB users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#np.ndarray?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 1, 8, 6, 5, 4, 6, 5, 7, 5])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.random.randint(0,10,10)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 1, 8, 6, 5, 4, 6, 5, 7, 5], dtype=int8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.astype('int8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18446744073709551616"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2**64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "256"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2**8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.itemsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.random.random((10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.93725246, 0.15668888, 0.36611166, 0.28521612, 0.729094  ,\n",
       "        0.83046116, 0.55376036, 0.95884927, 0.29762467, 0.71853585],\n",
       "       [0.08302075, 0.82469381, 0.57287118, 0.70806906, 0.29734792,\n",
       "        0.45314182, 0.16445973, 0.91474639, 0.64656203, 0.06703322],\n",
       "       [0.81024205, 0.68601501, 0.63991827, 0.11388148, 0.88810797,\n",
       "        0.25755703, 0.69543896, 0.97644668, 0.6011786 , 0.86319739],\n",
       "       [0.72307131, 0.50361642, 0.32121823, 0.39273795, 0.54175156,\n",
       "        0.85948579, 0.67301454, 0.95202843, 0.98558777, 0.92096823],\n",
       "       [0.22072178, 0.14158838, 0.70495497, 0.35857463, 0.00207674,\n",
       "        0.77898224, 0.94767885, 0.43931853, 0.25459632, 0.12926945],\n",
       "       [0.98403695, 0.65750484, 0.95815873, 0.06120255, 0.70874163,\n",
       "        0.83894932, 0.8006265 , 0.77455562, 0.52743587, 0.75654806],\n",
       "       [0.08661299, 0.54225296, 0.99453582, 0.49692316, 0.91009936,\n",
       "        0.27296071, 0.46314554, 0.3789339 , 0.52389196, 0.24766733],\n",
       "       [0.13483696, 0.29410672, 0.39846689, 0.84681589, 0.67671481,\n",
       "        0.84317288, 0.85344483, 0.66011626, 0.51693959, 0.06508179],\n",
       "       [0.57092602, 0.36669827, 0.01014502, 0.48240653, 0.37714147,\n",
       "        0.76761243, 0.01684733, 0.56304474, 0.51408509, 0.83939443],\n",
       "       [0.98603552, 0.29201346, 0.25903222, 0.14021301, 0.16637503,\n",
       "        0.99199766, 0.87244072, 0.60820336, 0.65643397, 0.640062  ]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 10)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.93725246, 0.15668888, 0.36611166, 0.28521612, 0.729094  ,\n",
       "       0.83046116, 0.55376036, 0.95884927, 0.29762467, 0.71853585])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9372524570452767"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.93725246, 0.08302075, 0.81024205, 0.72307131, 0.22072178,\n",
       "       0.98403695, 0.08661299, 0.13483696, 0.57092602, 0.98603552])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.93725246, 0.15668888, 0.36611166, 0.28521612, 0.729094  ,\n",
       "        0.83046116, 0.55376036, 0.95884927, 0.29762467, 0.71853585],\n",
       "       [0.08302075, 0.82469381, 0.57287118, 0.70806906, 0.29734792,\n",
       "        0.45314182, 0.16445973, 0.91474639, 0.64656203, 0.06703322],\n",
       "       [0.81024205, 0.68601501, 0.63991827, 0.11388148, 0.88810797,\n",
       "        0.25755703, 0.69543896, 0.97644668, 0.6011786 , 0.86319739]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.93725246, 0.15668888, 0.36611166],\n",
       "       [0.08302075, 0.82469381, 0.57287118],\n",
       "       [0.81024205, 0.68601501, 0.63991827],\n",
       "       [0.72307131, 0.50361642, 0.32121823],\n",
       "       [0.22072178, 0.14158838, 0.70495497],\n",
       "       [0.98403695, 0.65750484, 0.95815873],\n",
       "       [0.08661299, 0.54225296, 0.99453582],\n",
       "       [0.13483696, 0.29410672, 0.39846689],\n",
       "       [0.57092602, 0.36669827, 0.01014502],\n",
       "       [0.98603552, 0.29201346, 0.25903222]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:,0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.93725246, 0.15668888, 0.36611166],\n",
       "       [0.08302075, 0.82469381, 0.57287118],\n",
       "       [0.81024205, 0.68601501, 0.63991827]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0:3,0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5534435458189786"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.15668888, 0.06703322, 0.11388148, 0.32121823, 0.00207674,\n",
       "       0.06120255, 0.08661299, 0.06508179, 0.01014502, 0.14021301])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.min(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.08302075, 0.14158838, 0.01014502, 0.06120255, 0.00207674,\n",
       "       0.25755703, 0.01684733, 0.3789339 , 0.25459632, 0.06508179])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.min(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6239a52c70>,\n",
       " <matplotlib.lines.Line2D at 0x7f6239a52ca0>,\n",
       " <matplotlib.lines.Line2D at 0x7f6239a52e20>,\n",
       " <matplotlib.lines.Line2D at 0x7f6239a52ee0>,\n",
       " <matplotlib.lines.Line2D at 0x7f6239a52fa0>,\n",
       " <matplotlib.lines.Line2D at 0x7f6239a640a0>,\n",
       " <matplotlib.lines.Line2D at 0x7f6239a64160>,\n",
       " <matplotlib.lines.Line2D at 0x7f6239a64220>,\n",
       " <matplotlib.lines.Line2D at 0x7f6239a642e0>,\n",
       " <matplotlib.lines.Line2D at 0x7f6239a643a0>]"
      ]
     },
     "execution_count": 25,
     "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(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6230154520>]"
      ]
     },
     "execution_count": 26,
     "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(a[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f62300c54f0>"
      ]
     },
     "execution_count": 27,
     "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.imshow(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.93725246, 0.15668888, 0.36611166, 0.28521612, 0.729094  ,\n",
       "        0.83046116, 0.55376036, 0.95884927, 0.29762467, 0.71853585],\n",
       "       [0.08302075, 0.82469381, 0.57287118, 0.70806906, 0.29734792,\n",
       "        0.45314182, 0.16445973, 0.91474639, 0.64656203, 0.06703322],\n",
       "       [0.81024205, 0.68601501, 0.63991827, 0.11388148, 0.88810797,\n",
       "        0.25755703, 0.69543896, 0.97644668, 0.6011786 , 0.86319739],\n",
       "       [0.72307131, 0.50361642, 0.32121823, 0.39273795, 0.54175156,\n",
       "        0.85948579, 0.67301454, 0.95202843, 0.98558777, 0.92096823],\n",
       "       [0.22072178, 0.14158838, 0.70495497, 0.35857463, 0.00207674,\n",
       "        0.77898224, 0.94767885, 0.43931853, 0.25459632, 0.12926945],\n",
       "       [0.98403695, 0.65750484, 0.95815873, 0.06120255, 0.70874163,\n",
       "        0.83894932, 0.8006265 , 0.77455562, 0.52743587, 0.75654806],\n",
       "       [0.08661299, 0.54225296, 0.99453582, 0.49692316, 0.91009936,\n",
       "        0.27296071, 0.46314554, 0.3789339 , 0.52389196, 0.24766733],\n",
       "       [0.13483696, 0.29410672, 0.39846689, 0.84681589, 0.67671481,\n",
       "        0.84317288, 0.85344483, 0.66011626, 0.51693959, 0.06508179],\n",
       "       [0.57092602, 0.36669827, 0.01014502, 0.48240653, 0.37714147,\n",
       "        0.76761243, 0.01684733, 0.56304474, 0.51408509, 0.83939443],\n",
       "       [0.98603552, 0.29201346, 0.25903222, 0.14021301, 0.16637503,\n",
       "        0.99199766, 0.87244072, 0.60820336, 0.65643397, 0.640062  ]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True, False, False, False,  True,  True,  True,  True, False,\n",
       "         True],\n",
       "       [False,  True,  True,  True, False, False, False,  True,  True,\n",
       "        False],\n",
       "       [ True,  True,  True, False,  True, False,  True,  True,  True,\n",
       "         True],\n",
       "       [ True,  True, False, False,  True,  True,  True,  True,  True,\n",
       "         True],\n",
       "       [False, False,  True, False, False,  True,  True, False, False,\n",
       "        False],\n",
       "       [ True,  True,  True, False,  True,  True,  True,  True,  True,\n",
       "         True],\n",
       "       [False,  True,  True, False,  True, False, False, False,  True,\n",
       "        False],\n",
       "       [False, False, False,  True,  True,  True,  True,  True,  True,\n",
       "        False],\n",
       "       [ True, False, False, False, False,  True, False,  True,  True,\n",
       "         True],\n",
       "       [ True, False, False, False, False,  True,  True,  True,  True,\n",
       "         True]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a > 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = (a > 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.93725246, 0.729094  , 0.83046116, 0.55376036, 0.95884927,\n",
       "       0.71853585, 0.82469381, 0.57287118, 0.70806906, 0.91474639,\n",
       "       0.64656203, 0.81024205, 0.68601501, 0.63991827, 0.88810797,\n",
       "       0.69543896, 0.97644668, 0.6011786 , 0.86319739, 0.72307131,\n",
       "       0.50361642, 0.54175156, 0.85948579, 0.67301454, 0.95202843,\n",
       "       0.98558777, 0.92096823, 0.70495497, 0.77898224, 0.94767885,\n",
       "       0.98403695, 0.65750484, 0.95815873, 0.70874163, 0.83894932,\n",
       "       0.8006265 , 0.77455562, 0.52743587, 0.75654806, 0.54225296,\n",
       "       0.99453582, 0.91009936, 0.52389196, 0.84681589, 0.67671481,\n",
       "       0.84317288, 0.85344483, 0.66011626, 0.51693959, 0.57092602,\n",
       "       0.76761243, 0.56304474, 0.51408509, 0.83939443, 0.98603552,\n",
       "       0.99199766, 0.87244072, 0.60820336, 0.65643397, 0.640062  ])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 10)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60,)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[idx].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx.nonzero()[0].size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = (a > 0.5) & (a < 0.7)\n",
    "#idx = (a < 0.5) | (a > 0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, False, False, False,  True, False, False,\n",
       "        False],\n",
       "       [False, False,  True, False, False, False, False, False,  True,\n",
       "        False],\n",
       "       [False,  True,  True, False, False, False,  True, False,  True,\n",
       "        False],\n",
       "       [False,  True, False, False,  True, False,  True, False, False,\n",
       "        False],\n",
       "       [False, False, False, False, False, False, False, False, False,\n",
       "        False],\n",
       "       [False,  True, False, False, False, False, False, False,  True,\n",
       "        False],\n",
       "       [False,  True, False, False, False, False, False, False,  True,\n",
       "        False],\n",
       "       [False, False, False, False,  True, False, False,  True,  True,\n",
       "        False],\n",
       "       [ True, False, False, False, False, False, False,  True,  True,\n",
       "        False],\n",
       "       [False, False, False, False, False, False, False,  True,  True,\n",
       "         True]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.55376036, 0.57287118, 0.64656203, 0.68601501, 0.63991827,\n",
       "       0.69543896, 0.6011786 , 0.50361642, 0.54175156, 0.67301454,\n",
       "       0.65750484, 0.52743587, 0.54225296, 0.52389196, 0.67671481,\n",
       "       0.66011626, 0.51693959, 0.57092602, 0.56304474, 0.51408509,\n",
       "       0.60820336, 0.65643397, 0.640062  ])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23,)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[idx].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True,  True,  True,  True,  True,  True, False,  True,  True,\n",
       "         True],\n",
       "       [ True,  True, False,  True,  True,  True,  True,  True, False,\n",
       "         True],\n",
       "       [ True, False, False,  True,  True,  True, False,  True, False,\n",
       "         True],\n",
       "       [ True, False,  True,  True, False,  True, False,  True,  True,\n",
       "         True],\n",
       "       [ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True],\n",
       "       [ True, False,  True,  True,  True,  True,  True,  True, False,\n",
       "         True],\n",
       "       [ True, False,  True,  True,  True,  True,  True,  True, False,\n",
       "         True],\n",
       "       [ True,  True,  True,  True, False,  True,  True, False, False,\n",
       "         True],\n",
       "       [False,  True,  True,  True,  True,  True,  True, False, False,\n",
       "         True],\n",
       "       [ True,  True,  True,  True,  True,  True,  True, False, False,\n",
       "        False]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "~idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.93725246, 0.15668888, 0.36611166, 0.28521612, 0.729094  ,\n",
       "       0.83046116, 0.95884927, 0.29762467, 0.71853585, 0.08302075,\n",
       "       0.82469381, 0.70806906, 0.29734792, 0.45314182, 0.16445973,\n",
       "       0.91474639, 0.06703322, 0.81024205, 0.11388148, 0.88810797,\n",
       "       0.25755703, 0.97644668, 0.86319739, 0.72307131, 0.32121823,\n",
       "       0.39273795, 0.85948579, 0.95202843, 0.98558777, 0.92096823,\n",
       "       0.22072178, 0.14158838, 0.70495497, 0.35857463, 0.00207674,\n",
       "       0.77898224, 0.94767885, 0.43931853, 0.25459632, 0.12926945,\n",
       "       0.98403695, 0.95815873, 0.06120255, 0.70874163, 0.83894932,\n",
       "       0.8006265 , 0.77455562, 0.75654806, 0.08661299, 0.99453582,\n",
       "       0.49692316, 0.91009936, 0.27296071, 0.46314554, 0.3789339 ,\n",
       "       0.24766733, 0.13483696, 0.29410672, 0.39846689, 0.84681589,\n",
       "       0.84317288, 0.85344483, 0.06508179, 0.36669827, 0.01014502,\n",
       "       0.48240653, 0.37714147, 0.76761243, 0.01684733, 0.83939443,\n",
       "       0.98603552, 0.29201346, 0.25903222, 0.14021301, 0.16637503,\n",
       "       0.99199766, 0.87244072])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[~idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77,)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[~idx].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Masked array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.93725246, 0.15668888, 0.36611166, 0.28521612, 0.729094  ,\n",
       "        0.83046116, 0.55376036, 0.95884927, 0.29762467, 0.71853585],\n",
       "       [0.08302075, 0.82469381, 0.57287118, 0.70806906, 0.29734792,\n",
       "        0.45314182, 0.16445973, 0.91474639, 0.64656203, 0.06703322],\n",
       "       [0.81024205, 0.68601501, 0.63991827, 0.11388148, 0.88810797,\n",
       "        0.25755703, 0.69543896, 0.97644668, 0.6011786 , 0.86319739],\n",
       "       [0.72307131, 0.50361642, 0.32121823, 0.39273795, 0.54175156,\n",
       "        0.85948579, 0.67301454, 0.95202843, 0.98558777, 0.92096823],\n",
       "       [0.22072178, 0.14158838, 0.70495497, 0.35857463, 0.00207674,\n",
       "        0.77898224, 0.94767885, 0.43931853, 0.25459632, 0.12926945],\n",
       "       [0.98403695, 0.65750484, 0.95815873, 0.06120255, 0.70874163,\n",
       "        0.83894932, 0.8006265 , 0.77455562, 0.52743587, 0.75654806],\n",
       "       [0.08661299, 0.54225296, 0.99453582, 0.49692316, 0.91009936,\n",
       "        0.27296071, 0.46314554, 0.3789339 , 0.52389196, 0.24766733],\n",
       "       [0.13483696, 0.29410672, 0.39846689, 0.84681589, 0.67671481,\n",
       "        0.84317288, 0.85344483, 0.66011626, 0.51693959, 0.06508179],\n",
       "       [0.57092602, 0.36669827, 0.01014502, 0.48240653, 0.37714147,\n",
       "        0.76761243, 0.01684733, 0.56304474, 0.51408509, 0.83939443],\n",
       "       [0.98603552, 0.29201346, 0.25903222, 0.14021301, 0.16637503,\n",
       "        0.99199766, 0.87244072, 0.60820336, 0.65643397, 0.640062  ]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, False, False, False,  True, False, False,\n",
       "        False],\n",
       "       [False, False,  True, False, False, False, False, False,  True,\n",
       "        False],\n",
       "       [False,  True,  True, False, False, False,  True, False,  True,\n",
       "        False],\n",
       "       [False,  True, False, False,  True, False,  True, False, False,\n",
       "        False],\n",
       "       [False, False, False, False, False, False, False, False, False,\n",
       "        False],\n",
       "       [False,  True, False, False, False, False, False, False,  True,\n",
       "        False],\n",
       "       [False,  True, False, False, False, False, False, False,  True,\n",
       "        False],\n",
       "       [False, False, False, False,  True, False, False,  True,  True,\n",
       "        False],\n",
       "       [ True, False, False, False, False, False, False,  True,  True,\n",
       "        False],\n",
       "       [False, False, False, False, False, False, False,  True,  True,\n",
       "         True]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "ma = np.ma.array(a, mask=~idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(\n",
       "  data=[[--, --, --, --, --, --, 0.5537603625060096, --, --, --],\n",
       "        [--, --, 0.5728711811469509, --, --, --, --, --,\n",
       "         0.6465620326513881, --],\n",
       "        [--, 0.6860150052162564, 0.6399182670598812, --, --, --,\n",
       "         0.6954389578430052, --, 0.6011785952124488, --],\n",
       "        [--, 0.5036164236165882, --, --, 0.5417515578959615, --,\n",
       "         0.67301453764201, --, --, --],\n",
       "        [--, --, --, --, --, --, --, --, --, --],\n",
       "        [--, 0.6575048391787636, --, --, --, --, --, --,\n",
       "         0.5274358710655832, --],\n",
       "        [--, 0.5422529561477778, --, --, --, --, --, --,\n",
       "         0.5238919606546085, --],\n",
       "        [--, --, --, --, 0.6767148052677167, --, --, 0.6601162558250676,\n",
       "         0.5169395925974186, --],\n",
       "        [0.5709260243416924, --, --, --, --, --, --, 0.5630447415496301,\n",
       "         0.5140850876133204, --],\n",
       "        [--, --, --, --, --, --, --, 0.60820335527965,\n",
       "         0.6564339696072983, 0.6400620038099053]],\n",
       "  mask=[[ True,  True,  True,  True,  True,  True, False,  True,  True,\n",
       "          True],\n",
       "        [ True,  True, False,  True,  True,  True,  True,  True, False,\n",
       "          True],\n",
       "        [ True, False, False,  True,  True,  True, False,  True, False,\n",
       "          True],\n",
       "        [ True, False,  True,  True, False,  True, False,  True,  True,\n",
       "          True],\n",
       "        [ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "          True],\n",
       "        [ True, False,  True,  True,  True,  True,  True,  True, False,\n",
       "          True],\n",
       "        [ True, False,  True,  True,  True,  True,  True,  True, False,\n",
       "          True],\n",
       "        [ True,  True,  True,  True, False,  True,  True, False, False,\n",
       "          True],\n",
       "        [False,  True,  True,  True,  True,  True,  True, False, False,\n",
       "          True],\n",
       "        [ True,  True,  True,  True,  True,  True,  True, False, False,\n",
       "         False]],\n",
       "  fill_value=1e+20)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5987712340751709"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ma.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f62300ac0d0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPUAAAD4CAYAAAA0L6C7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAKvElEQVR4nO3df6jV9R3H8dcrb1HaosC7ttR2DaQmbWE7hOWIof3Rr9UY27BRsP7If1aZNKI2Rn9sY/9EJSwCZ/VPrmAmES36tYoRMemobam3SMz0ltXpj1W0gYrv/XFP4NTr+d5zvx+/97z3fEDg+eG7N3Kffs8593u/OiIEII8Tml4AQL2IGkiGqIFkiBpIhqiBZIZKDJ09e3aMjIyUGI0B89bOj4rMPe+cM4vMHRSbNm36JCKGj/ZYkahHRkbUbrdLjMaAueTH9xSZ+9qff1Fk7qCw/d5Ej/HyG0iGqIFkiBpIhqiBZIgaSIaogWQqRW37cttv295h+87SSwHoX8+obc+Q9ICkKyQtlHSd7YWlFwPQnypH6osk7YiInRGxT9Ljkq4tuxaAflWJeo6kPYfcHuve9z9sr7Ddtt3udDp17QdgkqpE7aPcd8TlUiJiTUS0IqI1PHzUU1IBHAdVoh6TNO+Q23MlfVBmHQBTVSXq1yUtsD3f9kmSlkt6quxaAPrV86e0IuKA7ZslPSdphqSHI2Jb8c0A9KXSj15GxDOSnim8C4AacEYZkAxRA8kQNZAMUQPJEDWQTJELDw6ab6+8r/aZ/1y9qvaZg2iQLhD407/fVGTunxb/scjciXCkBpIhaiAZogaSIWogGaIGkiFqIBmiBpIhaiAZogaSIWogGaIGkiFqIBmiBpIhaiAZogaSIWogGaIGkiFqIBmiBpIhaiAZogaSGairie4a+3qRuWf98CdF5kI6+OGCInNP+No7tc987fXzap8pSVpcZuxEOFIDyRA1kAxRA8kQNZAMUQPJEDWQDFEDyfSM2vY82y/bHrW9zfbK47EYgP5UOfnkgKTbI2Kz7a9I2mT7hYjYXng3AH3oeaSOiL0Rsbn7688ljUqaU3oxAP2Z1Htq2yOSFknaeJTHVthu2253Op2a1gMwWZWjtn2qpCck3RYRnx3+eESsiYhWRLSGh4fr3BHAJFSK2vaJGg96XURsKLsSgKmo8um3JT0kaTQi7i2/EoCpqHKkXiLpBklLbb/R/e/KwnsB6FPPb2lFxKuSfBx2AVADzigDkiFqIBmiBpIhaiCZgbrw4E1nf7fI3BcOri4yt4Sly35fZO5Lf72ryNy//PvkInO/X2DmrltuLzD1+ONIDSRD1EAyRA0kQ9RAMkQNJEPUQDJEDSRD1EAyRA0kQ9RAMkQNJEPUQDJEDSRD1EAyRA0kQ9RAMkQNJEPUQDJEDSRD1EAyRA0k44iofWir1Yp2u137XADjbG+KiNbRHuNIDSRD1EAyRA0kQ9RAMkQNJEPUQDJEDSRTOWrbM2xvsf10yYUATM1kjtQrJY2WWgRAPSpFbXuupKskrS27DoCpqnqkvl/SHZIOTvQE2ytst223O51OHbsB6EPPqG1fLenjiNh0rOdFxJqIaEVEa3h4uLYFAUxOlSP1EknX2N4l6XFJS20/WnQrAH3rGXVE3BURcyNiRNJySS9FxPXFNwPQF75PDSQzNJknR8Qrkl4psgmAWnCkBpIhaiAZogaSIWogGaIGkpnUp99N+/WbPygy9zfferLIXAyWy8//VZG5z279XZG5E+FIDSRD1EAyRA0kQ9RAMkQNJEPUQDJEDSRD1EAyRA0kQ9RAMkQNJEPUQDJEDSRD1EAyRA0kQ9RAMkQNJEPUQDJEDSRD1EAyRA0kM1BXE3111eIyg18sMxYDZv+BpjeoBUdqIBmiBpIhaiAZogaSIWogGaIGkiFqIJlKUds+3fZ622/ZHrV9cenFAPSn6sknqyU9GxE/sn2SpJkFdwIwBT2jtn2apEsl/UySImKfpH1l1wLQryovv8+R1JH0iO0tttfannX4k2yvsN223e50OrUvCqCaKlEPSbpQ0oMRsUjSF5LuPPxJEbEmIloR0RoeHq55TQBVVYl6TNJYRGzs3l6v8cgBTEM9o46IDyXtsX1u965lkrYX3QpA36p++n2LpHXdT753Srqx3EoApqJS1BHxhqRW2VUA1IEzyoBkiBpIhqiBZIgaSIaogWQG6mqiL794xIls/3de2bWgyNzvjbxTZG4pv916de0z/flI7TObwJEaSIaogWSIGkiGqIFkiBpIhqiBZIgaSIaogWSIGkiGqIFkiBpIhqiBZIgaSIaogWSIGkiGqIFkiBpIhqiBZIgaSIaogWSKXHhw++6PdMHN99U+9x9/WFX7zEEzaBcILOXJe5fWPnP2/rdrn9kEjtRAMkQNJEPUQDJEDSRD1EAyRA0kQ9RAMpWitr3K9jbbW20/Zvvk0osB6E/PqG3PkXSrpFZEnC9phqTlpRcD0J+qL7+HJJ1ie0jSTEkflFsJwFT0jDoi3pd0j6TdkvZK+jQinj/8ebZX2G7bbh/4zxf1bwqgkiovv8+QdK2k+ZLOkjTL9vWHPy8i1kREKyJaQ6fMqn9TAJVUefl9maR3I6ITEfslbZB0Sdm1APSrStS7JS22PdO2JS2TNFp2LQD9qvKeeqOk9ZI2S3qz+3vWFN4LQJ8q/Tx1RNwt6e7CuwCoAWeUAckQNZAMUQPJEDWQDFEDyRS5mujCs89Umyt/oqDOxQdqn3nZyk9qnylJV/7t1tpnnnbuV78z0WMcqYFkiBpIhqiBZIgaSIaogWSIGkiGqIFkiBpIhqiBZIgaSIaogWSIGkiGqIFkiBpIhqiBZIgaSIaogWSIGkiGqIFkiBpIhqiBZBwR9Q+1O5Leq/DU2ZLKXMKxjEHad5B2lQZr3+mw6zciYvhoDxSJuirb7YhoNbbAJA3SvoO0qzRY+073XXn5DSRD1EAyTUc9aP94/SDtO0i7SoO177TetdH31ADq1/SRGkDNiBpIprGobV9u+23bO2zf2dQevdieZ/tl26O2t9le2fROVdieYXuL7aeb3uVYbJ9ue73tt7p/xhc3vdOx2F7V/TrYavsx2yc3vdPhGona9gxJD0i6QtJCSdfZXtjELhUckHR7RHxT0mJJP5/Gux5qpaTRppeoYLWkZyPiPEkXaBrvbHuOpFsltSLifEkzJC1vdqsjNXWkvkjSjojYGRH7JD0u6dqGdjmmiNgbEZu7v/5c4190c5rd6thsz5V0laS1Te9yLLZPk3SppIckKSL2RcS/Gl2qtyFJp9gekjRT0gcN73OEpqKeI2nPIbfHNM1DkSTbI5IWSdrY8Cq93C/pDkkHG96jl3MkdSQ90n2rsNb2rKaXmkhEvC/pHkm7Je2V9GlEPN/sVkdqKmof5b5p/b0126dKekLSbRHxWdP7TMT21ZI+johNTe9SwZCkCyU9GBGLJH0haTp/vnKGxl9Rzpd0lqRZtq9vdqsjNRX1mKR5h9yeq2n4MuZLtk/UeNDrImJD0/v0sETSNbZ3afxtzVLbjza70oTGJI1FxJevfNZrPPLp6jJJ70ZEJyL2S9og6ZKGdzpCU1G/LmmB7fm2T9L4hw1PNbTLMdm2xt/zjUbEvU3v00tE3BURcyNiRON/ri9FxLQ7mkhSRHwoaY/tc7t3LZO0vcGVetktabHtmd2vi2Wahh/sDTXxP42IA7ZvlvScxj9BfDgitjWxSwVLJN0g6U3bb3Tv+2VEPNPcSqncImld9y/3nZJubHifCUXERtvrJW3W+HdFtmganjLKaaJAMpxRBiRD1EAyRA0kQ9RAMkQNJEPUQDJEDSTzXyy2VWRhSrM5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(ma)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas\n",
    "> pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool,\n",
    "built on top of the Python programming language. \n",
    "\n",
    "> pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis/manipulation tool available in any language. It is already well on its way toward this goal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.937252</td>\n",
       "      <td>0.156689</td>\n",
       "      <td>0.366112</td>\n",
       "      <td>0.285216</td>\n",
       "      <td>0.729094</td>\n",
       "      <td>0.830461</td>\n",
       "      <td>0.553760</td>\n",
       "      <td>0.958849</td>\n",
       "      <td>0.297625</td>\n",
       "      <td>0.718536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.083021</td>\n",
       "      <td>0.824694</td>\n",
       "      <td>0.572871</td>\n",
       "      <td>0.708069</td>\n",
       "      <td>0.297348</td>\n",
       "      <td>0.453142</td>\n",
       "      <td>0.164460</td>\n",
       "      <td>0.914746</td>\n",
       "      <td>0.646562</td>\n",
       "      <td>0.067033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.810242</td>\n",
       "      <td>0.686015</td>\n",
       "      <td>0.639918</td>\n",
       "      <td>0.113881</td>\n",
       "      <td>0.888108</td>\n",
       "      <td>0.257557</td>\n",
       "      <td>0.695439</td>\n",
       "      <td>0.976447</td>\n",
       "      <td>0.601179</td>\n",
       "      <td>0.863197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.723071</td>\n",
       "      <td>0.503616</td>\n",
       "      <td>0.321218</td>\n",
       "      <td>0.392738</td>\n",
       "      <td>0.541752</td>\n",
       "      <td>0.859486</td>\n",
       "      <td>0.673015</td>\n",
       "      <td>0.952028</td>\n",
       "      <td>0.985588</td>\n",
       "      <td>0.920968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.220722</td>\n",
       "      <td>0.141588</td>\n",
       "      <td>0.704955</td>\n",
       "      <td>0.358575</td>\n",
       "      <td>0.002077</td>\n",
       "      <td>0.778982</td>\n",
       "      <td>0.947679</td>\n",
       "      <td>0.439319</td>\n",
       "      <td>0.254596</td>\n",
       "      <td>0.129269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.984037</td>\n",
       "      <td>0.657505</td>\n",
       "      <td>0.958159</td>\n",
       "      <td>0.061203</td>\n",
       "      <td>0.708742</td>\n",
       "      <td>0.838949</td>\n",
       "      <td>0.800627</td>\n",
       "      <td>0.774556</td>\n",
       "      <td>0.527436</td>\n",
       "      <td>0.756548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.086613</td>\n",
       "      <td>0.542253</td>\n",
       "      <td>0.994536</td>\n",
       "      <td>0.496923</td>\n",
       "      <td>0.910099</td>\n",
       "      <td>0.272961</td>\n",
       "      <td>0.463146</td>\n",
       "      <td>0.378934</td>\n",
       "      <td>0.523892</td>\n",
       "      <td>0.247667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.134837</td>\n",
       "      <td>0.294107</td>\n",
       "      <td>0.398467</td>\n",
       "      <td>0.846816</td>\n",
       "      <td>0.676715</td>\n",
       "      <td>0.843173</td>\n",
       "      <td>0.853445</td>\n",
       "      <td>0.660116</td>\n",
       "      <td>0.516940</td>\n",
       "      <td>0.065082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.570926</td>\n",
       "      <td>0.366698</td>\n",
       "      <td>0.010145</td>\n",
       "      <td>0.482407</td>\n",
       "      <td>0.377141</td>\n",
       "      <td>0.767612</td>\n",
       "      <td>0.016847</td>\n",
       "      <td>0.563045</td>\n",
       "      <td>0.514085</td>\n",
       "      <td>0.839394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.986036</td>\n",
       "      <td>0.292013</td>\n",
       "      <td>0.259032</td>\n",
       "      <td>0.140213</td>\n",
       "      <td>0.166375</td>\n",
       "      <td>0.991998</td>\n",
       "      <td>0.872441</td>\n",
       "      <td>0.608203</td>\n",
       "      <td>0.656434</td>\n",
       "      <td>0.640062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0  0.937252  0.156689  0.366112  0.285216  0.729094  0.830461  0.553760   \n",
       "1  0.083021  0.824694  0.572871  0.708069  0.297348  0.453142  0.164460   \n",
       "2  0.810242  0.686015  0.639918  0.113881  0.888108  0.257557  0.695439   \n",
       "3  0.723071  0.503616  0.321218  0.392738  0.541752  0.859486  0.673015   \n",
       "4  0.220722  0.141588  0.704955  0.358575  0.002077  0.778982  0.947679   \n",
       "5  0.984037  0.657505  0.958159  0.061203  0.708742  0.838949  0.800627   \n",
       "6  0.086613  0.542253  0.994536  0.496923  0.910099  0.272961  0.463146   \n",
       "7  0.134837  0.294107  0.398467  0.846816  0.676715  0.843173  0.853445   \n",
       "8  0.570926  0.366698  0.010145  0.482407  0.377141  0.767612  0.016847   \n",
       "9  0.986036  0.292013  0.259032  0.140213  0.166375  0.991998  0.872441   \n",
       "\n",
       "          7         8         9  \n",
       "0  0.958849  0.297625  0.718536  \n",
       "1  0.914746  0.646562  0.067033  \n",
       "2  0.976447  0.601179  0.863197  \n",
       "3  0.952028  0.985588  0.920968  \n",
       "4  0.439319  0.254596  0.129269  \n",
       "5  0.774556  0.527436  0.756548  \n",
       "6  0.378934  0.523892  0.247667  \n",
       "7  0.660116  0.516940  0.065082  \n",
       "8  0.563045  0.514085  0.839394  \n",
       "9  0.608203  0.656434  0.640062  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = ['a','b','c','d','e','f','g','h','i','j']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.937252</td>\n",
       "      <td>0.156689</td>\n",
       "      <td>0.366112</td>\n",
       "      <td>0.285216</td>\n",
       "      <td>0.729094</td>\n",
       "      <td>0.830461</td>\n",
       "      <td>0.553760</td>\n",
       "      <td>0.958849</td>\n",
       "      <td>0.297625</td>\n",
       "      <td>0.718536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.083021</td>\n",
       "      <td>0.824694</td>\n",
       "      <td>0.572871</td>\n",
       "      <td>0.708069</td>\n",
       "      <td>0.297348</td>\n",
       "      <td>0.453142</td>\n",
       "      <td>0.164460</td>\n",
       "      <td>0.914746</td>\n",
       "      <td>0.646562</td>\n",
       "      <td>0.067033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.810242</td>\n",
       "      <td>0.686015</td>\n",
       "      <td>0.639918</td>\n",
       "      <td>0.113881</td>\n",
       "      <td>0.888108</td>\n",
       "      <td>0.257557</td>\n",
       "      <td>0.695439</td>\n",
       "      <td>0.976447</td>\n",
       "      <td>0.601179</td>\n",
       "      <td>0.863197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.723071</td>\n",
       "      <td>0.503616</td>\n",
       "      <td>0.321218</td>\n",
       "      <td>0.392738</td>\n",
       "      <td>0.541752</td>\n",
       "      <td>0.859486</td>\n",
       "      <td>0.673015</td>\n",
       "      <td>0.952028</td>\n",
       "      <td>0.985588</td>\n",
       "      <td>0.920968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.220722</td>\n",
       "      <td>0.141588</td>\n",
       "      <td>0.704955</td>\n",
       "      <td>0.358575</td>\n",
       "      <td>0.002077</td>\n",
       "      <td>0.778982</td>\n",
       "      <td>0.947679</td>\n",
       "      <td>0.439319</td>\n",
       "      <td>0.254596</td>\n",
       "      <td>0.129269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>0.984037</td>\n",
       "      <td>0.657505</td>\n",
       "      <td>0.958159</td>\n",
       "      <td>0.061203</td>\n",
       "      <td>0.708742</td>\n",
       "      <td>0.838949</td>\n",
       "      <td>0.800627</td>\n",
       "      <td>0.774556</td>\n",
       "      <td>0.527436</td>\n",
       "      <td>0.756548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>0.086613</td>\n",
       "      <td>0.542253</td>\n",
       "      <td>0.994536</td>\n",
       "      <td>0.496923</td>\n",
       "      <td>0.910099</td>\n",
       "      <td>0.272961</td>\n",
       "      <td>0.463146</td>\n",
       "      <td>0.378934</td>\n",
       "      <td>0.523892</td>\n",
       "      <td>0.247667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>0.134837</td>\n",
       "      <td>0.294107</td>\n",
       "      <td>0.398467</td>\n",
       "      <td>0.846816</td>\n",
       "      <td>0.676715</td>\n",
       "      <td>0.843173</td>\n",
       "      <td>0.853445</td>\n",
       "      <td>0.660116</td>\n",
       "      <td>0.516940</td>\n",
       "      <td>0.065082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>0.570926</td>\n",
       "      <td>0.366698</td>\n",
       "      <td>0.010145</td>\n",
       "      <td>0.482407</td>\n",
       "      <td>0.377141</td>\n",
       "      <td>0.767612</td>\n",
       "      <td>0.016847</td>\n",
       "      <td>0.563045</td>\n",
       "      <td>0.514085</td>\n",
       "      <td>0.839394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>0.986036</td>\n",
       "      <td>0.292013</td>\n",
       "      <td>0.259032</td>\n",
       "      <td>0.140213</td>\n",
       "      <td>0.166375</td>\n",
       "      <td>0.991998</td>\n",
       "      <td>0.872441</td>\n",
       "      <td>0.608203</td>\n",
       "      <td>0.656434</td>\n",
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      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "a  0.937252  0.156689  0.366112  0.285216  0.729094  0.830461  0.553760   \n",
       "b  0.083021  0.824694  0.572871  0.708069  0.297348  0.453142  0.164460   \n",
       "c  0.810242  0.686015  0.639918  0.113881  0.888108  0.257557  0.695439   \n",
       "d  0.723071  0.503616  0.321218  0.392738  0.541752  0.859486  0.673015   \n",
       "e  0.220722  0.141588  0.704955  0.358575  0.002077  0.778982  0.947679   \n",
       "f  0.984037  0.657505  0.958159  0.061203  0.708742  0.838949  0.800627   \n",
       "g  0.086613  0.542253  0.994536  0.496923  0.910099  0.272961  0.463146   \n",
       "h  0.134837  0.294107  0.398467  0.846816  0.676715  0.843173  0.853445   \n",
       "i  0.570926  0.366698  0.010145  0.482407  0.377141  0.767612  0.016847   \n",
       "j  0.986036  0.292013  0.259032  0.140213  0.166375  0.991998  0.872441   \n",
       "\n",
       "          7         8         9  \n",
       "a  0.958849  0.297625  0.718536  \n",
       "b  0.914746  0.646562  0.067033  \n",
       "c  0.976447  0.601179  0.863197  \n",
       "d  0.952028  0.985588  0.920968  \n",
       "e  0.439319  0.254596  0.129269  \n",
       "f  0.774556  0.527436  0.756548  \n",
       "g  0.378934  0.523892  0.247667  \n",
       "h  0.660116  0.516940  0.065082  \n",
       "i  0.563045  0.514085  0.839394  \n",
       "j  0.608203  0.656434  0.640062  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.553676\n",
       "1    0.446518\n",
       "2    0.522541\n",
       "3    0.388604\n",
       "4    0.529745\n",
       "5    0.689432\n",
       "6    0.604086\n",
       "7    0.722624\n",
       "8    0.552434\n",
       "9    0.524776\n",
       "dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.583359\n",
       "b    0.473195\n",
       "c    0.653198\n",
       "d    0.687348\n",
       "e    0.397776\n",
       "f    0.706776\n",
       "g    0.491702\n",
       "h    0.528970\n",
       "i    0.450830\n",
       "j    0.561281\n",
       "dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading files\n",
    "Most of the time, you will read in tabular data and let Pandas do the work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_fn = '../01_Shell_Github/data/GLAH14_tllz_conus_lulcfilt_demfilt.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "decyear,ordinal,lat,lon,glas_z,dem_z,dem_z_std,lulc\n",
      "2003.13957078,731266.9433448168,44.157897,-105.356562,1398.51,1400.52,0.33,31\n",
      "2003.13957081,731266.9433462636,44.150175,-105.358116,1387.11,1384.64,0.43,31\n",
      "2003.13957081,731266.9433465529,44.148632,-105.358427,1392.83,1383.49,0.28,31\n",
      "2003.13957081,731266.9433468423,44.147087,-105.358738,1384.24,1382.85,0.84,31\n",
      "2003.13957081,731266.9433471316,44.145542,-105.359048,1369.21,1380.24,1.73,31\n",
      "2003.13957081,731266.9433474210,44.143996,-105.359359,1366.60,1375.23,1.60,31\n",
      "2003.13957081,731266.9433506038,44.126969,-105.362876,1355.14,1379.38,2.17,31\n",
      "2003.13957084,731266.9433604418,44.074358,-105.373549,1369.53,1391.71,2.88,31\n",
      "2003.13957084,731266.9433607311,44.072806,-105.373864,1380.02,1387.79,0.45,31\n"
     ]
    }
   ],
   "source": [
    "!head $csv_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>decyear</th>\n",
       "      <th>ordinal</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>glas_z</th>\n",
       "      <th>dem_z</th>\n",
       "      <th>dem_z_std</th>\n",
       "      <th>lulc</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003.139571</td>\n",
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       "      <td>1398.51</td>\n",
       "      <td>1400.52</td>\n",
       "      <td>0.33</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003.139571</td>\n",
       "      <td>731266.943346</td>\n",
       "      <td>44.150175</td>\n",
       "      <td>-105.358116</td>\n",
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       "      <td>1384.64</td>\n",
       "      <td>0.43</td>\n",
       "      <td>31</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003.139571</td>\n",
       "      <td>731266.943347</td>\n",
       "      <td>44.148632</td>\n",
       "      <td>-105.358427</td>\n",
       "      <td>1392.83</td>\n",
       "      <td>1383.49</td>\n",
       "      <td>0.28</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003.139571</td>\n",
       "      <td>731266.943347</td>\n",
       "      <td>44.147087</td>\n",
       "      <td>-105.358738</td>\n",
       "      <td>1384.24</td>\n",
       "      <td>1382.85</td>\n",
       "      <td>0.84</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003.139571</td>\n",
       "      <td>731266.943347</td>\n",
       "      <td>44.145542</td>\n",
       "      <td>-105.359048</td>\n",
       "      <td>1369.21</td>\n",
       "      <td>1380.24</td>\n",
       "      <td>1.73</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65231</th>\n",
       "      <td>2009.775995</td>\n",
       "      <td>733691.238340</td>\n",
       "      <td>37.896222</td>\n",
       "      <td>-117.044399</td>\n",
       "      <td>1556.16</td>\n",
       "      <td>1556.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65232</th>\n",
       "      <td>2009.775995</td>\n",
       "      <td>733691.238340</td>\n",
       "      <td>37.897769</td>\n",
       "      <td>-117.044675</td>\n",
       "      <td>1556.02</td>\n",
       "      <td>1556.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65233</th>\n",
       "      <td>2009.775995</td>\n",
       "      <td>733691.238340</td>\n",
       "      <td>37.899319</td>\n",
       "      <td>-117.044952</td>\n",
       "      <td>1556.19</td>\n",
       "      <td>1556.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65234</th>\n",
       "      <td>2009.775995</td>\n",
       "      <td>733691.238340</td>\n",
       "      <td>37.900869</td>\n",
       "      <td>-117.045230</td>\n",
       "      <td>1556.18</td>\n",
       "      <td>1556.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65235</th>\n",
       "      <td>2009.775995</td>\n",
       "      <td>733691.238341</td>\n",
       "      <td>37.902420</td>\n",
       "      <td>-117.045508</td>\n",
       "      <td>1556.32</td>\n",
       "      <td>1556.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65236 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           decyear        ordinal        lat         lon   glas_z    dem_z  \\\n",
       "0      2003.139571  731266.943345  44.157897 -105.356562  1398.51  1400.52   \n",
       "1      2003.139571  731266.943346  44.150175 -105.358116  1387.11  1384.64   \n",
       "2      2003.139571  731266.943347  44.148632 -105.358427  1392.83  1383.49   \n",
       "3      2003.139571  731266.943347  44.147087 -105.358738  1384.24  1382.85   \n",
       "4      2003.139571  731266.943347  44.145542 -105.359048  1369.21  1380.24   \n",
       "...            ...            ...        ...         ...      ...      ...   \n",
       "65231  2009.775995  733691.238340  37.896222 -117.044399  1556.16  1556.43   \n",
       "65232  2009.775995  733691.238340  37.897769 -117.044675  1556.02  1556.43   \n",
       "65233  2009.775995  733691.238340  37.899319 -117.044952  1556.19  1556.44   \n",
       "65234  2009.775995  733691.238340  37.900869 -117.045230  1556.18  1556.44   \n",
       "65235  2009.775995  733691.238341  37.902420 -117.045508  1556.32  1556.44   \n",
       "\n",
       "       dem_z_std  lulc  \n",
       "0           0.33    31  \n",
       "1           0.43    31  \n",
       "2           0.28    31  \n",
       "3           0.84    31  \n",
       "4           1.73    31  \n",
       "...          ...   ...  \n",
       "65231       0.00    31  \n",
       "65232       0.00    31  \n",
       "65233       0.00    31  \n",
       "65234       0.00    31  \n",
       "65235       0.00    31  \n",
       "\n",
       "[65236 rows x 8 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(csv_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "glas_df = pd.read_csv(csv_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Multiply index to demonstrate difference between loc and iloc\n",
    "glas_df.set_index(glas_df.index*10, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>decyear</th>\n",
       "      <th>ordinal</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>glas_z</th>\n",
       "      <th>dem_z</th>\n",
       "      <th>dem_z_std</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2003.139571</td>\n",
       "      <td>731266.943345</td>\n",
       "      <td>44.157897</td>\n",
       "      <td>-105.356562</td>\n",
       "      <td>1398.51</td>\n",
       "      <td>1400.52</td>\n",
       "      <td>0.33</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2003.139571</td>\n",
       "      <td>731266.943346</td>\n",
       "      <td>44.150175</td>\n",
       "      <td>-105.358116</td>\n",
       "      <td>1387.11</td>\n",
       "      <td>1384.64</td>\n",
       "      <td>0.43</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2003.139571</td>\n",
       "      <td>731266.943347</td>\n",
       "      <td>44.148632</td>\n",
       "      <td>-105.358427</td>\n",
       "      <td>1392.83</td>\n",
       "      <td>1383.49</td>\n",
       "      <td>0.28</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2003.139571</td>\n",
       "      <td>731266.943347</td>\n",
       "      <td>44.147087</td>\n",
       "      <td>-105.358738</td>\n",
       "      <td>1384.24</td>\n",
       "      <td>1382.85</td>\n",
       "      <td>0.84</td>\n",
       "      <td>31</td>\n",
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       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2003.139571</td>\n",
       "      <td>731266.943347</td>\n",
       "      <td>44.145542</td>\n",
       "      <td>-105.359048</td>\n",
       "      <td>1369.21</td>\n",
       "      <td>1380.24</td>\n",
       "      <td>1.73</td>\n",
       "      <td>31</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>652310</th>\n",
       "      <td>2009.775995</td>\n",
       "      <td>733691.238340</td>\n",
       "      <td>37.896222</td>\n",
       "      <td>-117.044399</td>\n",
       "      <td>1556.16</td>\n",
       "      <td>1556.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652320</th>\n",
       "      <td>2009.775995</td>\n",
       "      <td>733691.238340</td>\n",
       "      <td>37.897769</td>\n",
       "      <td>-117.044675</td>\n",
       "      <td>1556.02</td>\n",
       "      <td>1556.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31</td>\n",
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       "    <tr>\n",
       "      <th>652330</th>\n",
       "      <td>2009.775995</td>\n",
       "      <td>733691.238340</td>\n",
       "      <td>37.899319</td>\n",
       "      <td>-117.044952</td>\n",
       "      <td>1556.19</td>\n",
       "      <td>1556.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31</td>\n",
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       "    <tr>\n",
       "      <th>652340</th>\n",
       "      <td>2009.775995</td>\n",
       "      <td>733691.238340</td>\n",
       "      <td>37.900869</td>\n",
       "      <td>-117.045230</td>\n",
       "      <td>1556.18</td>\n",
       "      <td>1556.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31</td>\n",
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       "    <tr>\n",
       "      <th>652350</th>\n",
       "      <td>2009.775995</td>\n",
       "      <td>733691.238341</td>\n",
       "      <td>37.902420</td>\n",
       "      <td>-117.045508</td>\n",
       "      <td>1556.32</td>\n",
       "      <td>1556.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65236 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            decyear        ordinal        lat         lon   glas_z    dem_z  \\\n",
       "0       2003.139571  731266.943345  44.157897 -105.356562  1398.51  1400.52   \n",
       "10      2003.139571  731266.943346  44.150175 -105.358116  1387.11  1384.64   \n",
       "20      2003.139571  731266.943347  44.148632 -105.358427  1392.83  1383.49   \n",
       "30      2003.139571  731266.943347  44.147087 -105.358738  1384.24  1382.85   \n",
       "40      2003.139571  731266.943347  44.145542 -105.359048  1369.21  1380.24   \n",
       "...             ...            ...        ...         ...      ...      ...   \n",
       "652310  2009.775995  733691.238340  37.896222 -117.044399  1556.16  1556.43   \n",
       "652320  2009.775995  733691.238340  37.897769 -117.044675  1556.02  1556.43   \n",
       "652330  2009.775995  733691.238340  37.899319 -117.044952  1556.19  1556.44   \n",
       "652340  2009.775995  733691.238340  37.900869 -117.045230  1556.18  1556.44   \n",
       "652350  2009.775995  733691.238341  37.902420 -117.045508  1556.32  1556.44   \n",
       "\n",
       "        dem_z_std  lulc  \n",
       "0            0.33    31  \n",
       "10           0.43    31  \n",
       "20           0.28    31  \n",
       "30           0.84    31  \n",
       "40           1.73    31  \n",
       "...           ...   ...  \n",
       "652310       0.00    31  \n",
       "652320       0.00    31  \n",
       "652330       0.00    31  \n",
       "652340       0.00    31  \n",
       "652350       0.00    31  \n",
       "\n",
       "[65236 rows x 8 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glas_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>decyear</th>\n",
       "      <th>ordinal</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>glas_z</th>\n",
       "      <th>dem_z</th>\n",
       "      <th>dem_z_std</th>\n",
       "      <th>lulc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>65236.000000</td>\n",
       "      <td>65236.000000</td>\n",
       "      <td>65236.000000</td>\n",
       "      <td>65236.000000</td>\n",
       "      <td>65236.000000</td>\n",
       "      <td>65236.000000</td>\n",
       "      <td>65236.000000</td>\n",
       "      <td>65236.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2005.945322</td>\n",
       "      <td>732291.890372</td>\n",
       "      <td>40.946798</td>\n",
       "      <td>-115.040612</td>\n",
       "      <td>1791.494167</td>\n",
       "      <td>1792.260964</td>\n",
       "      <td>5.504748</td>\n",
       "      <td>30.339444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.729573</td>\n",
       "      <td>631.766682</td>\n",
       "      <td>3.590476</td>\n",
       "      <td>5.465065</td>\n",
       "      <td>1037.183482</td>\n",
       "      <td>1037.925371</td>\n",
       "      <td>7.518558</td>\n",
       "      <td>3.480576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2003.139571</td>\n",
       "      <td>731266.943345</td>\n",
       "      <td>34.999455</td>\n",
       "      <td>-124.482406</td>\n",
       "      <td>-115.550000</td>\n",
       "      <td>-114.570000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2004.444817</td>\n",
       "      <td>731743.803182</td>\n",
       "      <td>38.101451</td>\n",
       "      <td>-119.257599</td>\n",
       "      <td>1166.970000</td>\n",
       "      <td>1168.240000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2005.846896</td>\n",
       "      <td>732256.116938</td>\n",
       "      <td>39.884541</td>\n",
       "      <td>-115.686241</td>\n",
       "      <td>1555.730000</td>\n",
       "      <td>1556.380000</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2007.223249</td>\n",
       "      <td>732758.486046</td>\n",
       "      <td>43.453565</td>\n",
       "      <td>-109.816475</td>\n",
       "      <td>2399.355000</td>\n",
       "      <td>2400.072500</td>\n",
       "      <td>9.530000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2009.775995</td>\n",
       "      <td>733691.238341</td>\n",
       "      <td>48.999727</td>\n",
       "      <td>-104.052336</td>\n",
       "      <td>4340.310000</td>\n",
       "      <td>4252.940000</td>\n",
       "      <td>49.900000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            decyear        ordinal           lat           lon        glas_z  \\\n",
       "count  65236.000000   65236.000000  65236.000000  65236.000000  65236.000000   \n",
       "mean    2005.945322  732291.890372     40.946798   -115.040612   1791.494167   \n",
       "std        1.729573     631.766682      3.590476      5.465065   1037.183482   \n",
       "min     2003.139571  731266.943345     34.999455   -124.482406   -115.550000   \n",
       "25%     2004.444817  731743.803182     38.101451   -119.257599   1166.970000   \n",
       "50%     2005.846896  732256.116938     39.884541   -115.686241   1555.730000   \n",
       "75%     2007.223249  732758.486046     43.453565   -109.816475   2399.355000   \n",
       "max     2009.775995  733691.238341     48.999727   -104.052336   4340.310000   \n",
       "\n",
       "              dem_z     dem_z_std          lulc  \n",
       "count  65236.000000  65236.000000  65236.000000  \n",
       "mean    1792.260964      5.504748     30.339444  \n",
       "std     1037.925371      7.518558      3.480576  \n",
       "min     -114.570000      0.000000     12.000000  \n",
       "25%     1168.240000      0.070000     31.000000  \n",
       "50%     1556.380000      1.350000     31.000000  \n",
       "75%     2400.072500      9.530000     31.000000  \n",
       "max     4252.940000     49.900000     31.000000  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glas_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexing and selecting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "decyear        2003.139571\n",
       "ordinal      731266.943345\n",
       "lat              44.157897\n",
       "lon            -105.356562\n",
       "glas_z         1398.510000\n",
       "dem_z          1400.520000\n",
       "dem_z_std         0.330000\n",
       "lulc             31.000000\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glas_df.loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "decyear        2003.139571\n",
       "ordinal      731266.943345\n",
       "lat              44.157897\n",
       "lon            -105.356562\n",
       "glas_z         1398.510000\n",
       "dem_z          1400.520000\n",
       "dem_z_std         0.330000\n",
       "lulc             31.000000\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glas_df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>decyear</th>\n",
       "      <th>ordinal</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>glas_z</th>\n",
       "      <th>dem_z</th>\n",
       "      <th>dem_z_std</th>\n",
       "      <th>lulc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003.139571</td>\n",
       "      <td>731266.943345</td>\n",
       "      <td>44.157897</td>\n",
       "      <td>-105.356562</td>\n",
       "      <td>1398.51</td>\n",
       "      <td>1400.52</td>\n",
       "      <td>0.33</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2003.139571</td>\n",
       "      <td>731266.943346</td>\n",
       "      <td>44.150175</td>\n",
       "      <td>-105.358116</td>\n",
       "      <td>1387.11</td>\n",
       "      <td>1384.64</td>\n",
       "      <td>0.43</td>\n",
       "      <td>31</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        decyear        ordinal        lat         lon   glas_z    dem_z  \\\n",
       "0   2003.139571  731266.943345  44.157897 -105.356562  1398.51  1400.52   \n",
       "10  2003.139571  731266.943346  44.150175 -105.358116  1387.11  1384.64   \n",
       "\n",
       "    dem_z_std  lulc  \n",
       "0        0.33    31  \n",
       "10       0.43    31  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glas_df.loc[0:10]"
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  {
   "cell_type": "code",
   "execution_count": 64,
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       "      <td>-105.358427</td>\n",
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       "      <td>-105.362876</td>\n",
       "      <td>1355.14</td>\n",
       "      <td>1379.38</td>\n",
       "      <td>2.17</td>\n",
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       "      <th>70</th>\n",
       "      <td>2003.139571</td>\n",
       "      <td>731266.943360</td>\n",
       "      <td>44.074358</td>\n",
       "      <td>-105.373549</td>\n",
       "      <td>1369.53</td>\n",
       "      <td>1391.71</td>\n",
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       "      <td>731266.943361</td>\n",
       "      <td>44.072806</td>\n",
       "      <td>-105.373864</td>\n",
       "      <td>1380.02</td>\n",
       "      <td>1387.79</td>\n",
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       "      <td>-105.374177</td>\n",
       "      <td>1391.47</td>\n",
       "      <td>1396.90</td>\n",
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      "text/plain": [
       "        decyear        ordinal        lat         lon   glas_z    dem_z  \\\n",
       "0   2003.139571  731266.943345  44.157897 -105.356562  1398.51  1400.52   \n",
       "10  2003.139571  731266.943346  44.150175 -105.358116  1387.11  1384.64   \n",
       "20  2003.139571  731266.943347  44.148632 -105.358427  1392.83  1383.49   \n",
       "30  2003.139571  731266.943347  44.147087 -105.358738  1384.24  1382.85   \n",
       "40  2003.139571  731266.943347  44.145542 -105.359048  1369.21  1380.24   \n",
       "50  2003.139571  731266.943347  44.143996 -105.359359  1366.60  1375.23   \n",
       "60  2003.139571  731266.943351  44.126969 -105.362876  1355.14  1379.38   \n",
       "70  2003.139571  731266.943360  44.074358 -105.373549  1369.53  1391.71   \n",
       "80  2003.139571  731266.943361  44.072806 -105.373864  1380.02  1387.79   \n",
       "90  2003.139571  731266.943361  44.071256 -105.374177  1391.47  1396.90   \n",
       "\n",
       "    dem_z_std  lulc  \n",
       "0        0.33    31  \n",
       "10       0.43    31  \n",
       "20       0.28    31  \n",
       "30       0.84    31  \n",
       "40       1.73    31  \n",
       "50       1.60    31  \n",
       "60       2.17    31  \n",
       "70       2.88    31  \n",
       "80       0.45    31  \n",
       "90       1.56    31  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "glas_df.iloc[0:10]"
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  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=652360, step=10)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "glas_df.index"
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   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "array([[2.00313957e+03, 7.31266943e+05, 4.41578970e+01, ...,\n",
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       "       [2.00313957e+03, 7.31266943e+05, 4.41501750e+01, ...,\n",
       "        1.38464000e+03, 4.30000000e-01, 3.10000000e+01],\n",
       "       [2.00313957e+03, 7.31266943e+05, 4.41486320e+01, ...,\n",
       "        1.38349000e+03, 2.80000000e-01, 3.10000000e+01],\n",
       "       ...,\n",
       "       [2.00977600e+03, 7.33691238e+05, 3.78993190e+01, ...,\n",
       "        1.55644000e+03, 0.00000000e+00, 3.10000000e+01],\n",
       "       [2.00977600e+03, 7.33691238e+05, 3.79008690e+01, ...,\n",
       "        1.55644000e+03, 0.00000000e+00, 3.10000000e+01],\n",
       "       [2.00977600e+03, 7.33691238e+05, 3.79024200e+01, ...,\n",
       "        1.55644000e+03, 0.00000000e+00, 3.10000000e+01]])"
      ]
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     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "glas_df.values"
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  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['decyear', 'ordinal', 'lat', 'lon', 'glas_z', 'dem_z', 'dem_z_std',\n",
       "       'lulc'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "glas_df.columns"
   ]
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  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         1398.51\n",
       "10        1387.11\n",
       "20        1392.83\n",
       "30        1384.24\n",
       "40        1369.21\n",
       "           ...   \n",
       "652310    1556.16\n",
       "652320    1556.02\n",
       "652330    1556.19\n",
       "652340    1556.18\n",
       "652350    1556.32\n",
       "Name: glas_z, Length: 65236, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "glas_df['glas_z']"
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  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         1398.51\n",
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       "652310    1556.16\n",
       "652320    1556.02\n",
       "652330    1556.19\n",
       "652340    1556.18\n",
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      ]
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     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "glas_df.glas_z"
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  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0         1398.51\n",
       "10        1387.11\n",
       "20        1392.83\n",
       "30        1384.24\n",
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       "           ...   \n",
       "652310    1556.16\n",
       "652320    1556.02\n",
       "652330    1556.19\n",
       "652340    1556.18\n",
       "652350    1556.32\n",
       "Name: glas_z, Length: 65236, dtype: float64"
      ]
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     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "glas_df.iloc[:,4]"
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  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         1398.51\n",
       "10        1387.11\n",
       "20        1392.83\n",
       "30        1384.24\n",
       "40        1369.21\n",
       "           ...   \n",
       "652310    1556.16\n",
       "652320    1556.02\n",
       "652330    1556.19\n",
       "652340    1556.18\n",
       "652350    1556.32\n",
       "Name: glas_z, Length: 65236, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "glas_df.loc[:,'glas_z']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx2 = (glas_df['lulc'] == 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65236, 8)"
      ]
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     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
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    "glas_df.shape"
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   "execution_count": 74,
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>649850</th>\n",
       "      <td>2009.771998</td>\n",
       "      <td>733689.779258</td>\n",
       "      <td>47.907275</td>\n",
       "      <td>-123.628697</td>\n",
       "      <td>1782.52</td>\n",
       "      <td>1828.93</td>\n",
       "      <td>4.41</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2268 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            decyear        ordinal        lat         lon   glas_z    dem_z  \\\n",
       "230     2003.139573  731266.944184  39.669291 -106.225142  3505.12  3508.25   \n",
       "300     2003.139573  731266.944316  38.961190 -106.355153  4046.47  4047.25   \n",
       "4890    2003.147846  731269.963718  48.587233 -113.484046  2135.76  2123.37   \n",
       "4920    2003.147846  731269.963811  48.091352 -113.595790  1632.52  1615.77   \n",
       "7560    2003.157366  731273.438572  43.897412 -114.457131  2886.39  2889.82   \n",
       "...             ...            ...        ...         ...      ...      ...   \n",
       "647240  2009.764964  733687.211708  40.689722 -105.918309  3267.33  3267.62   \n",
       "647250  2009.764964  733687.211709  40.694371 -105.919164  3235.77  3238.94   \n",
       "649830  2009.771998  733689.779258  47.910365 -123.628017  1671.86  1711.73   \n",
       "649840  2009.771998  733689.779258  47.908820 -123.628357  1737.70  1776.17   \n",
       "649850  2009.771998  733689.779258  47.907275 -123.628697  1782.52  1828.93   \n",
       "\n",
       "        dem_z_std  lulc  \n",
       "230          5.74    12  \n",
       "300          7.14    12  \n",
       "4890         1.18    12  \n",
       "4920        11.43    12  \n",
       "7560        20.31    12  \n",
       "...           ...   ...  \n",
       "647240       1.83    12  \n",
       "647250       3.78    12  \n",
       "649830       8.44    12  \n",
       "649840       7.70    12  \n",
       "649850       4.41    12  \n",
       "\n",
       "[2268 rows x 8 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glas_df[idx2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2268, 8)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glas_df[idx2].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "decyear        2006.008627\n",
       "ordinal      732315.035881\n",
       "lat              43.065223\n",
       "lon            -112.936499\n",
       "glas_z         2918.746261\n",
       "dem_z          2920.785754\n",
       "dem_z_std         9.719951\n",
       "lulc             12.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glas_df[idx2].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f62bffe0d60>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glas_df.groupby('lulc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>decyear</th>\n",
       "      <th>ordinal</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>glas_z</th>\n",
       "      <th>dem_z</th>\n",
       "      <th>dem_z_std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lulc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2006.008627</td>\n",
       "      <td>732315.035881</td>\n",
       "      <td>43.065223</td>\n",
       "      <td>-112.936499</td>\n",
       "      <td>2918.746261</td>\n",
       "      <td>2920.785754</td>\n",
       "      <td>9.719951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2005.943042</td>\n",
       "      <td>732291.056710</td>\n",
       "      <td>40.870496</td>\n",
       "      <td>-115.116398</td>\n",
       "      <td>1750.892469</td>\n",
       "      <td>1751.613426</td>\n",
       "      <td>5.352924</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          decyear        ordinal        lat         lon       glas_z  \\\n",
       "lulc                                                                   \n",
       "12    2006.008627  732315.035881  43.065223 -112.936499  2918.746261   \n",
       "31    2005.943042  732291.056710  40.870496 -115.116398  1750.892469   \n",
       "\n",
       "            dem_z  dem_z_std  \n",
       "lulc                          \n",
       "12    2920.785754   9.719951  \n",
       "31    1751.613426   5.352924  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glas_df.groupby('lulc').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "planets = sns.load_dataset('planets')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>number</th>\n",
       "      <th>orbital_period</th>\n",
       "      <th>mass</th>\n",
       "      <th>distance</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>269.300000</td>\n",
       "      <td>7.10</td>\n",
       "      <td>77.40</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>874.774000</td>\n",
       "      <td>2.21</td>\n",
       "      <td>56.95</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>763.000000</td>\n",
       "      <td>2.60</td>\n",
       "      <td>19.84</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>326.030000</td>\n",
       "      <td>19.40</td>\n",
       "      <td>110.62</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>516.220000</td>\n",
       "      <td>10.50</td>\n",
       "      <td>119.47</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>3.941507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>172.00</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>2.615864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>148.00</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>3.191524</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.00</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>4.125083</td>\n",
       "      <td>NaN</td>\n",
       "      <td>293.00</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1034</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>4.187757</td>\n",
       "      <td>NaN</td>\n",
       "      <td>260.00</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1035 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               method  number  orbital_period   mass  distance  year\n",
       "0     Radial Velocity       1      269.300000   7.10     77.40  2006\n",
       "1     Radial Velocity       1      874.774000   2.21     56.95  2008\n",
       "2     Radial Velocity       1      763.000000   2.60     19.84  2011\n",
       "3     Radial Velocity       1      326.030000  19.40    110.62  2007\n",
       "4     Radial Velocity       1      516.220000  10.50    119.47  2009\n",
       "...               ...     ...             ...    ...       ...   ...\n",
       "1030          Transit       1        3.941507    NaN    172.00  2006\n",
       "1031          Transit       1        2.615864    NaN    148.00  2007\n",
       "1032          Transit       1        3.191524    NaN    174.00  2007\n",
       "1033          Transit       1        4.125083    NaN    293.00  2008\n",
       "1034          Transit       1        4.187757    NaN    260.00  2008\n",
       "\n",
       "[1035 rows x 6 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "method\n",
       "Astrometry                         631.180000\n",
       "Eclipse Timing Variations         4343.500000\n",
       "Imaging                          27500.000000\n",
       "Microlensing                      3300.000000\n",
       "Orbital Brightness Modulation        0.342887\n",
       "Pulsar Timing                       66.541900\n",
       "Pulsation Timing Variations       1170.000000\n",
       "Radial Velocity                    360.200000\n",
       "Transit                              5.714932\n",
       "Transit Timing Variations           57.011000\n",
       "Name: orbital_period, dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.groupby('method')['orbital_period'].median()"
   ]
  }
 ],
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