2020_2021/lesson_10_2020.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generator comprehensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [x/2 for x in range(1000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = (x/2 for x in range(1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "249750.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum( x/2 for x in range(1000) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "249750.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum([ x/2 for x in range(1000) ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 20, 1: 20, 2: 20, 3: 20, 4: 20})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "Counter( x%5 for x in range(100)  )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10f7c5d30>]"
      ]
     },
     "execution_count": 15,
     "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": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot( [1,2,3,4]  , [2,6,4,9] )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11ddc6860>]"
      ]
     },
     "execution_count": 24,
     "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": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot( [1,2,3,4]  , [2,6,4,9], '-', c='green' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11df2c668>]"
      ]
     },
     "execution_count": 28,
     "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": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot( [1,2,3,4]  , [2,6,4,9], '-', c='#ff0000' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11dd0de10>]"
      ]
     },
     "execution_count": 30,
     "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": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot( [1,2,3,4]  , [2,6,4,9], '-', c='#ff0000' )\n",
    "ax.plot( [1,2,3,4]  , [-1,5,8,3], '-', c='orange' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FTQbCjgUwtiUc2OxPHP36wXXXQb16rmdvyf4fLOEbczwObILpd8Co+rBnlUvyF02DUvG9CNdRVQhevN29DMa2cCWokfT113DDDW61y1GjoFixyLYfAyzhG5MbgUNuNcshp8MfX7n69Mt/d8M48bTAWE6VuwiaDYM9f8DY5m5550j44gu4+Wa3jv3w4VAkBstdIyCB/zKNyaUNv8Dwmm7j8FKN4NIFbs34vEX9jiw6lG0JLUbCvr9gTDPYu9bb9j7+GG6/3W06PnQonBCF5aFRwhK+MaHaswomXgnjL3Y9/GZDXKll0cRajyUkZZq4i7cHN8GYpq7H74X33oN773W19j/9BAULetNOnLCEb8yxHNrjFg0bWg02joFar8JlC92YdawsieCH0g2h5Vg4tNP19HevCO/zv/46dOsGV18N/fu7BdFMtizhG5MVVVj9HQw9y233V/k6aPs7VHvcrRFvjq1kXWg1DtL3u57+zqXhed6XX4bHH4cOHVxlTr44nOPgAUv4xhzNtrkuQU3t5EorW0+FRl9BofJ+RxZ7TqwFrca7eQpjm8GOhbl/LlV49ll45hm46Sbo2xfyxuAyzT6xhG9MRge2wIy7YWQdtwPV+b3g4hlueMLkXvFzoFUKSLIr2dw+P+fPoQpPPAEvveQu0vbuDclxsPtWBFnCNwbcRuHLPnBllit7wZndXJnlabcndpllOBU7C1pNcMspj23hJquFShUeesiN299zD3z2mSX7XLC/ZGM2joMRtd0a7yXrwqW/QZ23o2NNmHhT9HS4cCLkLQbjWsGWX4/9mEAA7rvPVeQ8+CD06OG2JzQ5Zr81k7j2rIZJ7V3iSdsLTQa5UsJi1fyOLL4VPgUunAD5S8O41rBpctbHBgJw112u1v6xx+Cdd6wy6jhYwjeJJ20f/PYcDDvbrVV/7svQdjFUutKSSaScUNn19AtVcPMa/h7/72PS0+HWW6FXL3eR9tVX7fU5Tp4mfBEpLiL9RWSpiCwREbvyZfyjCn/+6MosF74IFa+Cy5fBOU/HxjZ98aZQeTemX/gUt2fAhl+O3HfoENx4I3z1Fbz4ovuyZH/cvO7hvweMVNWzgJrAEo/bM+botv/mLhROvg7ylXC9y8bfQqHE3e4uKhQ8yZVsFjkTJlwOfw2D1NQj9fWvveZ69yYsPFsPX0SKAk2BWwBUNRVI9ao9E2XS0mDrVjjpJH/jOLgVfnsWVnwC+U6Eep/AaXdAklV4RI0Cpd3krPEXwcSrYGJNGDjLjdd36+Z3dHHFyx7+qcBmoLeIzBWRXiJiqxolgtWroUEDKFsWGjVyKxnu2RPZGALpsPxjGHIGrPgUTr/PzZI9/S5L9tEofwloOAQ2FYJGs6Dn7ZbsPeBlws8DnAd8rKq1gb3AE5kPEpEuIjJLRGZt3uzTpgkmfEaOhDp1YMUKN/V9xw43SaZcOejSBWbMcGPpXvp7Aow8D2beCyfWhEvmQt33/d2FyWRv71645iZ4aidwOpzQG/74xu+o4o6XCX8dsE5Vpwdv98e9AfyDqn6mqnVVtW7p0qU9DMd4KhBwMyAvvRQqVoRZs1xVxaJFMGUKXHstfPMNnH8+nHuuq6neujW8MexdC5M7uHXYU3fABf3d4l3Fa4S3HRNeu3e7v5vx4+GTr6DDHCjTDKbdBCt7+x1dXPEs4avqRmCtiBxeO7YVsNir9oyPtm+Hdu3cGic33ADTpkHVqu4+kSPDOhs2uBmShQq5j+vly0PHjjBmjHvDyK20/bDgJRh6Jvz1M9R4HtougcrXWGVHtNu5061jP2WK6xDcdBPkLQzNhkLZC2H6bbD8U7+jjB+q6tkXUAuYBfwG/AScmN3xderUURNj5sxRPeUU1bx5VXv0UA0EQnvc/PmqXbuqliihCqpVqqi+9JLq2rWhtx0IqP45QPWnKqrfoDrpWtU9q3N3Hibytm1TrVdPNU8e1f79/31/2n7V8Ze513bp+5GPL0YAszTUnBzqgZH4soQfY3r3Vi1QQLVCBdVp03L3HPv3q373nWqrVu7PMSlJ9dJLVQcOVE1Nzfpx2xeqjmnlksGwGqobx+eufeOPzZtVa9dWzZdPdfDgrI9LO6g64Ur3Oi9+M3LxxZCcJHybaWty7uBBuPtuNwuyYUOYM8dV5eRGgQKu5nrMGFi5Ep56CubNc5taVKzoLvz+/vuR41O3w6wHYURN2D4H6n4IbebASc3DcmomAjZtgpYtYfFi+Plnt1tVVpLzwQU/QOVrYe6jbl8Ck2uiXldM5EDdunV11qwcrKBnIu/PP6F9e5g50yXjl1+GPGGezpGWBqNGuSn1Q4e6200vgDuqQsGhcGgbVL0Lzn0J8pcMb9vGWxs2QKtWrnR3yBD3fSgCafDrLbD6GzjnOajxnF2fCRKR2apaN5RjPZt4ZeLQmDGuN56aCgMHwlVXedNOnjxw2WXua+NG6PcipH8ByZNhYTLsuRrOuN3NmDWxY90617Nfvx5GjIBmzUJ/bFIeaNAHkvLCwhcgcBBqdrekn0M2pGOOLRCA7t1dNUXZsq7k0qtkn9G+v2DlI1DmYzitNJR4Dv7oBB8Ng7p14bzz3FK527d7H4s5PqtXQ9Om8Pff8MsvOUv2hyUlw/mfu093i191QzxRNEIRCyzhm+zt2AFXXglPPw3XXw/Tp8MZZ3jbZvoBN1Y79ExYOwDOeQbaLoU2z0Ofr1wP8aOP3Jro99/vyjtvvBFSUiwBRKOVK12C377dfUps1Cj3zyVJUO9jOOMBWPq228NAj6OkN9GEenU3El9WpRNl5s9XPe00Vzb33nuhl1zmViCguvZn1Z9PdVUZE69W3b0q+8fMmaN6332qxYq5Kp+qVVW7d1ddv97bWE1oli51VVwlSrjXKlwCAdXZj7i/k1/vVA2kh++5YwxWpWOOW9++rvJm3z7Xc+7a1dvx0p1LYHwbmHiFW6q45WhoMsAtnZud2rXhww/dxcCvv4YKFVylT6VKcMUV7sJgWpp3cZusLV7sevapqe5vqHbt8D23CNR+A6o/BSt7wq+3ufWTTLYs4Zt/Sk11wyQ33QT167uSy8aNPWxvJ8x+GIafC1unQ5334JJ5bpZlThQseGRY5/ff4T//cev2tGsHlSu7N4EVKzw5BXMUv/0GzZu7xJySAjU8WN5CxG1eU+MF+KMPTOvsqnlM1kL9KBCJLxvS8dnataoNGrihkUcfVT10yLu2AumqKz5XHVBG9RtRnd5Fdf+m8LaRmqr600+ql1/uJnSBaosWqn37qu7bF962zBGzZ7shnAoVVJcti0ybC18JDgO2V03PZsJeHMKGdEyOjR/vql4WLoQff4Q33gh/ff1hm6fBqPNh+u1QuCq0mQX1P3XroodT3rxuWGfwYDd/4L//hTVr3CeB8uXhgQdg/vzwtpnoZsxwtfVFisDEid5f4D+s+hNw3tuwtj9MvhbSD0am3RhjCT/RqcLrr8OFF0KpUm5CVfv23rS1fwNMuxlGN4L966HRN9B6MpT41yKq4Xd4bH/5chg3zq3O2LMn1KrlSjw/+cQt5GVyb8oU93dUogRMmACnnhrZ9s96yM28XvczTLraVXuZf7CEn8h27YJrrnEzZq+5xpVcnnVW+NtJPwiLX3ebkazpB9WehLbLoEqnyE+cSUqCFi3cyozr18P777v9U++5x63Zf8stMGmSlXfmVEqKm6dRrpxL9ief7E8cZ9znPi2uH+G2TEzb508c0SrUsZ9IfNkYfgQtXKh6xhmqycmqb73lXcnluqGqP1d146sp7VR3rfCmneMRCKjOnKl6112qRYq4sf4zzlB9/XXVjRv9ji76jR6tWrCgarVq0VMOu7K3uzY0urlq6m6/o/EUNoZvsvXdd64CZ+dON7zx8MPh72nvXQspl8GEtm6GZPOR0OxnKHJaeNsJB5EjwzobNsCXX0KZMvDYY24Bt6uvhuHDId3K/v5lxAho29btfzB+vOvhR4NTb4FGfWHzJEhpA4d2+R1RVLCEn0hSU+HBB6FTJ1cTPWeOm+4e9na2uw2pN02E2m/BJb9B+YvD344XTjgBbr7ZDessWeI2apk82a3rU6WK2+Rl9Wq/o4wOgwe7WdjVqrlkX6aM3xH9U5VO0LgfbJkO4y5yu6AlOEv4iWL9erdw1fvvuyQ2fryrVAm39FSY1B72rITmw+Dsh90St7HorLNctdK6dTBggKslf/lldzHyoovg++/dUtGJqH9/d92nVi0YOxZKRumqpZXbQ5P+bintsa3gYJi31YwxlvATwYQJruRy3jw3nPPOO65kMdxUYeY98Pc4OP8LKOPBpwc/5Mt3ZFhnzRp44QU3uatDB1f989BDrpw1UXz7rTv3+vVh9Gg48US/I8pexSugyU+wcxGMbQkHNvkdkW8s4cczVXjrLVcXXayYq8Lp0MG79ha/Bqu+gHOehVNu9K4dP1WqBM88A6tWuVUfW7VyC7nVqOGWoujVy23KHa/69HGzsC+4wO1ZULSo3xGFpsKl0GwI7F4OY1vA/o1+R+QLS/jxavduuO46ePRRN/lo5kyoXt279v78EeY/CSd3cpuIx7ukJGjd2g3r/PWX+9S0Zw/ceae7cHnHHW4z93gq7+zVy+1y1rKl+7RTuLDfEeVMudbQfDjsXQNjmrnltxOMJfx4tGSJ+7g9cKCbVNW/v7c9sS2/unVMSjeGBp8n3qYUpUq56yILFrgk37Ej9OvnlgE+5xz3ZrBli99RHp8ePdybWZs2bkG6QoX8jih3TmoOLUa5SYBjmsHeP/2OKKJCSvgi8qaIeNg9NGHz448u2W/b5tYe/89/vE3Ae1a7FS4LlnfjpMkFvGsr2om4YZ2ePV15Z69e7o324YfdBfLrrnPDQIEYW7/9nXfcgnrt2sGgQW4f4lhWurFbjfXgFhjTFPas8juiiAm1h78U+ExEpovI3SJSzMugTC4cOgSPPOKSSo0aruSyRQtv20zdARMuc5U5zYZBgVLethdLihSB2293Pf6FC13CHDfOzUY99VR48UVYu9bvKI/ttdfcG9Y117jORP78fkcUHqXOh1Zj4dBu19PftdzviCIj1BlabkIXZwKvAmuAb4EWOXn8sb5spm0ubdig2rSpmyF6//2qBw9632Z6qurYC1W/zaO6cZz37cWDAwdUv/9e9aKLVEXcV5s2qv37R+Y1y6kXX3R/Ux07ertyqp+2zVPtX0p1YDnVHYv9jiZX8GKmrYgkA2cFv7YA84GHRaRfuN+ETA5MnuxKLmfOdJuWfPCBKyP0kirMvA82joHze8JJHn+SiBf587tPYKNGuSqfZ55xvf/27d2M3kcfdddf/KYK//d/bpJZ585uYxmvVk7124k1oVWK2yZxbHPYEefltaG8KwBvAyuAT4H6me5bFuq7y7G+rIefA4GA6rvvuu0Hq1Z12xFGyqLX3do4856KXJvxKi1NdcQI1Wuuca8lqDZurNq7t+qePZGPJxBweyGA6h13qKYnyNaBO5eqDiyv2r+k6tYwbsUYAeSghx9qwr8NKJTFfcVCbexYX5bwQ7R7t2qHDu7lu+IK1e3bI9f2nwPcolSTrk/ofUQ98fffqm+8oXrWWe61LVJEtUsX1RkzvN9PWNW10bWra/veexMn2R+2a7nqoEqqPxRX3TLD72hC5kXCTwJuBJ4N3q6cuacfji9L+CFYutStSpiU5DbrjuR/yi0zVPsVVB3ZQPWQ7RjlmUBAdfJk1VtuUS1UyP03Pfdct5H81q3etJmernr33a6thx6KzBtMNNr9h+pPp6j+UFR101S/owlJThJ+qGP4PYCGQMfg7d3Bn5lIGjgQ6tWDTZvcOPCTT7oJQJGwd41bX7xAWbfqZZ6CkWk3EYm4fYR793blnZ9+6sb/H3zQlXd26uQqfsJV3pme7mrsP/kEnnjCzc5OtLkUhxWuAhdOgAInHVkAMI6Emi3OV9X7gAMAqrodiNEVsWJQWtqRTUrOPtuVXF6Yw02+j0fqTkhp63YQaj4MCkTZqojxrGhR6NLFbR04b577fuRIt6RD1apu28a/jmPGaFqa2/Tliy/cRdru3RM32R92QiWX9AtVhPGXwMaxfkcUNqEm/EPBKh0FEJHSQIzNHolRf//tVmZ8/XW4+263T2grSuo+AAAWX0lEQVSlSpFrP5AGU66HXUuhyQAodnbk2jb/VLOmW+10/Xq3gNkpp7hqmsqV4fLL4aef3HyMUB06BDfc4Kq7Xn7ZLQqX6Mn+sILlXPVO4VPdng7rR/kdUXiEMu4D3AAMBtYB/wWWAdeGOm4U6peN4WcydapqhQqqBQqofvll5NsPBFSn3+0qclb0inz75thWrFB9+mnV8uXd+PtJJ6k+/rjq779n/7iDB1Wvuso95o03IhNrLNq/WXV4LdXv8qmuHex3NEdFuC/auufkLOA+4H7g7Bw8LhmYCww91rGW8IMCAdUPP1TNm1f11FNV5871J47Fb7lkP/dxf9o3oTt0SHXIEFe1lZzs/ms3bar61Veqe/f+89j9+1Uvu8wd8957/sQbSw5sVR1R100y/HOA39H8S04Sfk6u+C0HBgV7+ntFpHKIj3sQiILZJDFi71432eX++91QzqxZbpOJSFv7E8x9FCq1h5rdI9++yZk8edxWgz/95JZsePVVN/TTubO70HvffTB3Luzf71ZPHTbMXaTt2tXvyKNf/hLQcgyUrAeTr4M13/sdUe6F8q4APICbXbsI+A1YAPwWwuMqAmOBllgP/9iWL1etUcNNuX/pJf/qoLfOUu1XSHVkfSu/jGWBgGpKiupNN7lhQVAtUcL9fX3+ud/RxZ7UXaqjm6p+m6S66iu/o/kfctDDF3d89kRkBa5SJ0f7g4lIf+AVoAjwqKq2PcoxXYAuAJUrV66zZs2anDQRPwYPdr2x5GR3Qe5in/aA3bsWfjkfkvLBRdOh4En+xGHCa8cO93f1ww+u0qdTJ78jik1pe2FCO/h7PJzfC067ze+IEJHZqlo3lGNDHdJZC+zMYRBtgU2qOju741T1M1Wtq6p1S5cunZMm4kN6Ojz9tPuYXbUqzJ7tX7I/tNtVJKTtdatfWrKPH8WLw733QkqKJfvjkecEaDYUyl0E02+H5R/7HVGOhLoi0iogRUSGAf/btVlV387mMY2BdiJyKVAAKCoifVU1Tve+y4XNm91/vjFj3MSX99/3b63xQBpMvt7t+9l8BBS37Q+MOao8BaHpTzDpWph5r1se/KwH/Y4qJKH28P8ERuMmWxXJ8JUlVX1SVSuqahWgAzDOkn0GM2ZAnTowaZLbKOOzz/xL9qowuxtsGAH1PnJbwRljspZcwM1LqXQ1zOkGi9/wO6KQhNTDV9UXAESkiLupezyNKp6puqnyh6fJT5niEr+flr0Py3vA2Y9C1S7+xmJMrEjOB437wdSbYN5jEDgI5/yf31FlK6SELyLnAF8DJYK3twCdVXVRKI9X1RQgJXchxpH9++Gee6BPH7c3aN++ULKkvzGtGwJzHoKKV0Gt1/yNxZhYk5QXGvV1RQ6/PQOBVKgRvTOWQx3D/wx4WFXHA4hIc6An0MijuOLPqlVuLZz58+G559zmF8nJ/sa0bS5M7Qgl6rg/WrE97Y3JsaQ80KC3S/4LX3JJv+YrUZn0Q034JxxO9uB67CJygkcxxZ9hw+DG4OWLoUPh0kv9jQdg3zpXkZOvBDQbDHkK+R2RMbErKdnt/pacHxa/BukH4by3oy7ph1ylIyLP4IZ1wK2N/4c3IcWR9HS3INVLL7nZsgMGuA2s/XZoj1vq+NBuuGiKWyjKGHN8JAnq9nDDO8vedT39uh9E1SfnUBP+bcALwEBAgInArV4FFRe2bnUrEY4a5Zaf/egjKBgFa8gH0mFKR9ixwNUTF6/hd0TGxA8ROO8dl/SXvOGSfv1Poybph1qlsx2wRTdCNXu2G68/vHnFnXdGz0e7OQ/D+qGu/LJ8G7+jMSb+iLgCiKT8sOhll/TP/8IN+/gs24QvIoOzu19V24U3nDjQq5db+KxMGZg82e1QFS2WfQi/vw9nPgSn3+N3NMbELxGo+ZLr6S941iX9hl+7C7w+OlbrDXHLKnwHTMcN55ijOXDAJfrPP4fWrd26JaVK+R3VEX8NgzkPQoV2UDs2JokYE/NqPOMu5M57HAKHoNG3rn7fJ8caWCoLPAWcA7wHtAa2qOoEVZ3gdXAxY/Vqtwfp55+7dXFGjIiuZL99PkzpAMVrQeNvo+KjpTEJo9pjblx/7QCY3N5V8Pgk24SvqumqOlJVbwYaACtwa+o8EJHoYsHIkW6m7MqVbsXLl1/2v74+o33rg+WXxaHZELf4kzEmss7q5ip4/hoCE6+EtP2+hHHMS8cikl9Ergb64na8eh9XrZPYAgF48UVXU1+xotuo5PLL/Y7qn9L2uvLL1B2uIqdQeb8jMiZxnXEv1O8JG0a5/5dpeyMewrEu2vbBDeeMAF5Q1YURiSrabd/uJlINH+7+/fRTKBRlE5cC6TClE+yYB02HwIk1/Y7IGFP1Dnchd/qtkHKp64jlzXYdyrA61kXbm4C9wBlAVzlSWii4RdSKehhbdJo715VcrlsHPXq4tXGipeQyo7n/gb8GQ50PoEIUzOw1xjindnZJf9qNML4NNB8O+YpFpOlsE76qRsdsgWjx5ZcuwZcsCRMnQoMGfkd0dMs/hmXvwBld4cz7/Y7GGJNZlQ5u7Z0pHWBca2g5CvKd6HmzltBDcfAg3HUX3HorNGoEc+ZEb7JfPxJmPQDl27q1PIwx0anyNdBkIOyYD2NbuSVPPGYJ/1j+/BOaNHEblDzxhFsqoUwZv6M6uh0LYPJ1brmExt9Z+aUx0a7i5dD0ZyjbKiIVdP5O+4p2o0dDx45w6BAMGgRXXul3RFnbvxFS2roLQM2GQN7CfkdkjAlF+TYRW+bEevhHEwhA9+5uM/GyZWHmzOhO9mn7YEI7SN0aLL+s6HdExpgoZD38zHbsgM6dYcgQ17vv2RNOiOLJShqAaTfB9tnQ5CcoUdvviIwxUcoSfka//QZXXw1r1sD777u1caKx5DKjeU/A2oFw3rtuPNAYY7JgQzqH9e3rKm/274eUFHjggehP9it6ujW3T78PzrTVq40x2bOEn5oK990HN90E9eu7tewbN/Y7qmPbMBpm3gPlLoE670b/m5MxxneJnfDXrYNmzdxuVI8+CmPGuIu00W7HIrfqXrHqcMH3vq+xbYyJDYmbKcaNgw4d3BDOjz9C+/Z+RxSa/X/DhMtczW6E1+EwxsS2xOvhq8Lrr7tNSkqVciWXsZLs0/bDxCvgwGZXa39CJb8jMsbEkMTq4e/c6ZZHGDQIrr3WbVhSJEZ6yBqAaZ1h6wxoOghK1PE7ImNMjEmchL9woSu5XLUK3n4bunWLrQud85+Gtf2h9ltQ8Qq/ozHGxKDESPjffQd33AFFi7qx+6ZN/Y4oZ1Z+AYtfhap3w1kP+R2NMSZGxfcYfmoqPPggdOoE553nVrmMtWS/cSzMuAvKXQx1P4itTyXGmKgSvwl//Xpo0cLNmO3WzfXsy5XzO6qc2bkEJl0DRc+CC36w8ktjzHGJzwwyYQJcfz3s2QP9+rnvY82BTZByGSQXgOZDIW/ibS5mjAkvz3r4IlJJRMaLyBIRWSQiD3rV1v+owltvQatWULw4TJ8em8k+/YDb2f7AxmD55cl+R2SMiQNe9vDTgEdUdY6IFAFmi8hoVV3sSWu7d8Ntt0H//q4ap3dvd5E21mgApt0CW36FJv2hZD2/IzLGxAnPeviqukFV5wS/3w0sASp40tiSJW4dnIED3aSq/v1jM9kD/PYs/Pk91HoNKl3tdzTGmDgSkTF8EakC1Aamh/3Jt22Dhg0hf363Fk6LFmFvImJWfQmL/gun3QlnP+p3NMaYOON5wheRwsAAoJuq7jrK/V2ALgCVK1fOeQMlSkCPHtC8OVTw5gNERPydAjO6QNnWUK+HlV8aY8JOVNW7JxfJCwwFRqnq28c6vm7dujpr1izP4olau5bBLw2hYDloPRXyFfM7ImNMjBCR2apaN5RjvazSEeBzYEkoyT5hHdjiyi+T8kKzYZbsjTGe8XLiVWPgJqCliMwLfl3qYXuxJ/0ATLoS9v8FTX+GwlX8jsgYE8c8G8NX1cmADURnRRV+vR02T3GzaEs18DsiY0yci9+lFaLdgudhzbdQsztUvtbvaIwxCcASvh/++BoWvgin3gbVnvA7GmNMgrCEH2mbJsL02+GkFlDvYyu/NMZEjCX8SNq1HCZeBYVPgyYDIDmf3xEZYxKIJfxIObgVUi4FSYLmwyDfiX5HZIxJMPG5PHK0ST/oevb71kKrcVD4VL8jMsYkIEv4XlOF6XfA5knQ6Dso3cjviIwxCcqGdLy28CVY3RfOfQmqdPA7GmNMArOE76XV38KC5+CUm6H6035HY4xJcJbwvbJpMvx6K5RpBvU/s/JLY4zvLOF7YfcKt0bOCVWgyUArvzTGRAVL+OF2cJtb/RJc+WX+Ev7GY4wxQValE07pqTDpGti7GlqOhSJV/Y7IGGP+xxJ+uKi6Has2pUDDvlDmAr8jMsaYf7AhnXBZ1B3+6AM1nodTbvA7GmOM+RdL+OGw5nv47f+gyo1wzrN+R2OMMUdlCf94bZ4K026G0k3g/F5WfmmMiVqW8I/HnlUw8QooVAmaDoLk/H5HZIwxWbKEn1up2135pQaC5Zcl/Y7IGGOyZVU6uZGeCpPaw56V0HIMFD3D74iMMeaYLOHnlCrMvAf+HgcN+kCZpn5HZIwxIbEhnZxa/Bqs+gLOeQZO7ex3NMYYEzJL+Dnx548w/0k4uSPUeMHvaIwxJkcs4Ydqy68wrTOUbgwNvrDyS2NMzLGEH4o9q135ZcHy0GQQJBfwOyJjjMkxS/jHkroDJlzmKnOaDYMCpf2OyBhjcsWqdLITOASTr4Vdv0PLX6DYWX5HZIwxuWYJPyuqMPM+2DgGGvSGk1r4HZExxhwXG9LJypI3YWVPqP4UnHqL39EYY8xxs4R/NGsHwrzHofL1cO5LfkdjjDFhYQk/s60zYeqNUPJ8N5Qj9isyxsQHT7OZiLQRkWUiskJEnvCyrbDYuwYmXA4FykKznyFPQb8jMsaYsPEs4YtIMtADuASoBnQUkWpetXfcUndCSltIP+BWvyxQxu+IjDEmrLzs4dcHVqjqKlVNBfoBV3jYXu4F0mDK9bBrKTQZAMXO9jsiY4wJOy8TfgVgbYbb64I/+wcR6SIis0Rk1ubNmz0MJwuqMOsB2DAK6n8CZVtFPgZjjIkALxP+0Rab0X/9QPUzVa2rqnVLl/ZhFuvSd2DFJ1DtcTjt9si3b4wxEeJlwl8HVMpwuyKw3sP2cm7tTzD3UajUHmp29zsaY4zxlJcJfyZwuoicIiL5gA7AYA/by5lts2HqDVCyHjT8ysovjTFxz7OlFVQ1TUTuB0YBycAXqrrIq/ZyZO/aYPllaWg62MovjTEJwdO1dFR1ODDcyzZy7NBumNAW0vZC69FQ8CS/IzLGmIhIrMXTAmkw+XrYuQiaj4Di1f2OyBhjIiZxEr4qzO4GG0ZA/U+hXGu/IzLGmIhKnCuVy96H5T3g7Eehahe/ozHGmIhLjIS/bgjMeQgqXgW1XvM7GmOM8UX8J/xtc2FqRyhRBxr1tfJLY0zCiu/st2+dq8jJVwKaDYY8hfyOyBhjfBO/F20P7XG19od2w0VToGA5vyMyxhhfxWfCD6TDlI6wYwE0GwrFa/gdkTHG+C4+E/6ch2H9UKj3EZRv43c0xhgTFeJvDH/Zh/D7+3DmQ3D6PX5HY4wxUSO+Ev5fw2DOg1ChHdR+w+9ojDEmqsRPwt8+H6Z0gOK1oPG3kJTsd0TGGBNV4iPh71sfLL8sDs2GQJ4T/I7IGGOiTuxftE3b68ovU3dA68lQqLzfERljTFSK/R6+JEOxatC4H5xY0+9ojDEmasV+Dz+5ADT62u8ojDEm6sV+D98YY0xILOEbY0yCsIRvjDEJwhK+McYkCEv4xhiTICzhG2NMgrCEb4wxCcISvjHGJAhRVb9j+B8R2QysyeXDSwFbwhiOn+LlXOLlPMDOJRrFy3nA8Z3LyapaOpQDoyrhHw8RmaWqdf2OIxzi5Vzi5TzAziUaxct5QOTOxYZ0jDEmQVjCN8aYBBFPCf8zvwMIo3g5l3g5D7BziUbxch4QoXOJmzF8Y4wx2YunHr4xxphsWMI3xpgEEVMJX0S+EJFNIrIwi/tFRN4XkRUi8puInBfpGEMVwrk0F5GdIjIv+PVspGMMhYhUEpHxIrJERBaJyINHOSYmXpcQzyVWXpcCIjJDROYHz+WFoxyTX0S+D74u00WkSuQjzV6I53GLiGzO8Jrc4UesoRKRZBGZKyJDj3Kft6+JqsbMF9AUOA9YmMX9lwIjAAEaANP9jvk4zqU5MNTvOEM4j3LAecHviwC/A9Vi8XUJ8Vxi5XURoHDw+7zAdKBBpmPuBT4Jft8B+N7vuHN5HrcAH/odaw7O6WHg26P9HXn9msRUD19VJwLbsjnkCuArdX4FiotIuchElzMhnEtMUNUNqjon+P1uYAlQIdNhMfG6hHguMSH4u94TvJk3+JW5QuMKoE/w+/5AKxGRCIUYkhDPI2aISEXgMqBXFod4+prEVMIPQQVgbYbb64jR/7BBDYMfZUeISHW/gzmW4MfP2rheWEYx97pkcy4QI69LcOhgHrAJGK2qWb4uqpoG7ARKRjbKYwvhPACuCQ4X9heRShEOMSfeBR4DAlnc7+lrEm8J/2jvhLHaG5iDWyOjJvAB8JPP8WRLRAoDA4Buqror891HeUjUvi7HOJeYeV1UNV1VawEVgfoick6mQ2LidQnhPIYAVVT1XGAMR3rIUUVE2gKbVHV2docd5Wdhe03iLeGvAzK+u1cE1vsUy3FR1V2HP8qq6nAgr4iU8jmsoxKRvLgE+Y2qDjzKITHzuhzrXGLpdTlMVXcAKUCbTHf973URkTxAMaJ4mDGr81DVrap6MHizJ1AnwqGFqjHQTkRWA/2AliLSN9Mxnr4m8ZbwBwOdg1UhDYCdqrrB76ByQ0TKHh67E5H6uNdqq79R/Vswxs+BJar6dhaHxcTrEsq5xNDrUlpEige/LwhcCCzNdNhg4Obg9+2BcRq8WhgtQjmPTNeD2uGuvUQdVX1SVSuqahXcBdlxqnpjpsM8fU3yhOuJIkFEvsNVSZQSkXXAc7iLOKjqJ8BwXEXICmAfcKs/kR5bCOfSHrhHRNKA/UCHaPvPGNQYuAlYEBxnBXgKqAwx97qEci6x8rqUA/qISDLuTekHVR0qIi8Cs1R1MO7N7WsRWYHrRXbwL9wshXIeXUWkHZCGO49bfIs2FyL5mtjSCsYYkyDibUjHGGNMFizhG2NMgrCEb4wxCcISvjHGJAhL+MYYkyBiqizTmHARkZLA2ODNskA6sDl4e5+qNvIlMGM8ZGWZJuGJyPPAHlV90+9YjPGSDekYk4mI7An+21xEJojIDyLyu4i8KiI3BNdnXyAipwWPKy0iA0RkZvCrsb9nYMzRWcI3Jns1gQeBGrhZuGeoan3c8rYPBI95D3hHVesB15D10rfG+MrG8I3J3szD6/6IyErgl+DPFwAtgt9fCFTLsGx5UREpElxT35ioYQnfmOwdzPB9IMPtAEf+/yQBDVV1fyQDMyanbEjHmOP3C3D/4RsiUsvHWIzJkiV8Y45fV6BucMelxcDdfgdkzNFYWaYxxiQI6+EbY0yCsIRvjDEJwhK+McYkCEv4xhiTICzhG2NMgrCEb4wxCcISvjHGJIj/B3FxYFvNHQ3uAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot( [1,2,3,4]  , [2,6,4,9], '-', c='#ff0000' )\n",
    "ax.plot( [1,2,3,4]  , [-1,5,8,3], '-', c='orange' )\n",
    "\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('Money')\n",
    "ax.set_title('Amount of Time/Money')\n",
    "\n",
    "#plt.savefig('figure_2.png', dpi=300)\n",
    "\n",
    "plt.savefig('figure_3.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Amount of Time/Money')"
      ]
     },
     "execution_count": 48,
     "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": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot( [1,2,3,4]  , [2,6,4,9], '+', c='#ff0000' )\n",
    "ax.plot( [1,2,3,4]  , [-1,5,8,3], '-', c='orange' )\n",
    "\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('Money')\n",
    "ax.set_title('Amount of Time/Money')\n",
    "\n",
    "#plt.savefig('figure_2.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Amount of Time/Money')"
      ]
     },
     "execution_count": 66,
     "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": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot( [1,2,3,4]  , [2,6,4,9], '-', c='0.2', linewidth=5 )\n",
    "ax.plot( [1,2,3,4]  , [-1,5,8,3], '-', c='orange' )\n",
    "\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('Money')\n",
    "ax.set_title('Amount of Time/Money')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in /Users/alexandroskanterakis/anaconda3/lib/python3.7/site-packages (0.9.0)\n",
      "Requirement already satisfied: matplotlib>=1.4.3 in /Users/alexandroskanterakis/anaconda3/lib/python3.7/site-packages (from seaborn) (2.2.3)\n",
      "Requirement already satisfied: numpy>=1.9.3 in /Users/alexandroskanterakis/anaconda3/lib/python3.7/site-packages (from seaborn) (1.15.1)\n",
      "Requirement already satisfied: pandas>=0.15.2 in /Users/alexandroskanterakis/anaconda3/lib/python3.7/site-packages (from seaborn) (0.23.4)\n",
      "Requirement already satisfied: scipy>=0.14.0 in /Users/alexandroskanterakis/anaconda3/lib/python3.7/site-packages (from seaborn) (1.1.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/alexandroskanterakis/anaconda3/lib/python3.7/site-packages (from matplotlib>=1.4.3->seaborn) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Users/alexandroskanterakis/anaconda3/lib/python3.7/site-packages (from matplotlib>=1.4.3->seaborn) (2.2.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /Users/alexandroskanterakis/anaconda3/lib/python3.7/site-packages (from matplotlib>=1.4.3->seaborn) (2.7.3)\n",
      "Requirement already satisfied: pytz in /Users/alexandroskanterakis/anaconda3/lib/python3.7/site-packages (from matplotlib>=1.4.3->seaborn) (2018.5)\n",
      "Requirement already satisfied: six>=1.10 in /Users/alexandroskanterakis/anaconda3/lib/python3.7/site-packages (from matplotlib>=1.4.3->seaborn) (1.11.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/alexandroskanterakis/anaconda3/lib/python3.7/site-packages (from matplotlib>=1.4.3->seaborn) (1.0.1)\n",
      "Requirement already satisfied: setuptools in /Users/alexandroskanterakis/anaconda3/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=1.4.3->seaborn) (40.2.0)\n",
      "\u001b[33mWARNING: You are using pip version 20.1; however, version 20.3.1 is available.\n",
      "You should consider upgrading via the '/Users/alexandroskanterakis/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x120208080>"
      ]
     },
     "execution_count": 75,
     "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": [
    "impo\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "lg_1 = ax.plot( [1,2,3,4]  , [2,6,4,9], '-', c='0.2', linewidth=5 )\n",
    "lg_2 = ax.plot( [1,2,3,4]  , [-1,5,8,3], '-', c='orange' )\n",
    "\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('Money')\n",
    "ax.set_title('Amount of Time/Money')\n",
    "ax.grid(True)\n",
    "\n",
    "plt.legend([lg_1[0], lg_2[0]], ['Value', 'Cost'], loc=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a252b2f28>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#sns.set_theme()\n",
    "#sns.set()\n",
    "#sns.set('whitegrid')\n",
    "sns.set_style(\"dark\")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "lg_1 = ax.plot( [1,2,3,4]  , [2,6,4,9], '-', c='0.2', linewidth=5 )\n",
    "lg_2 = ax.plot( [1,2,3,4]  , [-1,5,8,3], '-', c='orange' )\n",
    "\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('Money')\n",
    "ax.set_title('Amount of Time/Money')\n",
    "ax.grid(True)\n",
    "\n",
    "ax.text(3,0,\"Python\")\n",
    "plt.legend([lg_1[0], lg_2[0]], ['Value', 'Cost'], loc=2)\n",
    "\n",
    "\n",
    "new_axes = fig.add_axes([0.32, 0.22, 0.3, 0.2])\n",
    "new_axes.plot([1,2,3,4], [2,4,6,6])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# sin(x)  -2 pi   2pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11f735710>]"
      ]
     },
     "execution_count": 58,
     "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": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "#np.linspace(3,8,20) # (8-3)/20\n",
    "X = np.linspace(-2*np.pi, 2*np.pi, 100)\n",
    "Y = np.sin(X)\n",
    "ax.plot(X,Y)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0,'-7'), Text(0,0,'0'), Text(0,0,'test')]"
      ]
     },
     "execution_count": 65,
     "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": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "#np.linspace(3,8,20) # (8-3)/20\n",
    "X = np.linspace(-2*np.pi, 2*np.pi, 100)\n",
    "Y = np.sin(X)\n",
    "ax.plot(X,Y)\n",
    "ax.set_xlim(-10, 10)\n",
    "ax.set_ylim(-2, 2)\n",
    "ax.set_xlabel('Time', fontsize=25)\n",
    "\n",
    "ax.set_xticks([-7, 0, 5])\n",
    "tick_labels = ax.get_xticks().tolist()\n",
    "tick_labels[2] = \"test\"\n",
    "ax.set_xticklabels(tick_labels)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a25ab4dd8>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2)\n",
    "\n",
    "ax[0].plot([1,2,3,4], [5,3,6,4])\n",
    "ax[1].plot([1,2,3,4], [5,-1,6,-4])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a25720470>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,2)\n",
    "\n",
    "ax[0,0].plot([1,2,3,4], [5,3,6,4])\n",
    "ax[1,0].plot([1,2,3,4], [5,-1,6,-4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/alexandroskanterakis/anaconda3/lib/python3.7/site-packages/matplotlib/figure.py:2299: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "fig = plt.figure(tight_layout=True)\n",
    "gs = gridspec.GridSpec(2, 2)\n",
    "\n",
    "ax = fig.add_subplot(gs[0, :]) # Ενώνει τια δύο στήλες σε μία\n",
    "ax.plot(np.arange(0, 1e6, 1000))\n",
    "ax.set_ylabel('YLabel0')\n",
    "ax.set_xlabel('XLabel0')\n",
    "\n",
    "for i in range(2):\n",
    "    ax = fig.add_subplot(gs[1, i])\n",
    "    ax.plot(np.arange(1., 0., -0.1) * 2000., np.arange(1., 0., -0.1))\n",
    "    ax.set_ylabel('YLabel1 %d' % i)\n",
    "    ax.set_xlabel('XLabel1 %d' % i)\n",
    "    if i == 0:\n",
    "        for tick in ax.get_xticklabels():\n",
    "            tick.set_rotation(55)\n",
    "fig.align_labels()  # same as fig.align_xlabels(); fig.align_ylabels()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/alexandroskanterakis/anaconda3/lib/python3.7/site-packages/matplotlib/figure.py:2299: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "fig = plt.figure(tight_layout=True)\n",
    "gs = gridspec.GridSpec(2, 2)\n",
    "\n",
    "ax = fig.add_subplot(gs[:, 0]) # Ενώνει τια δύο στήλες σε μία\n",
    "ax.plot(np.arange(0, 1e6, 1000))\n",
    "ax.set_ylabel('YLabel0')\n",
    "ax.set_xlabel('XLabel0')\n",
    "\n",
    "for i in range(2):\n",
    "    ax = fig.add_subplot(gs[i, 1])\n",
    "    ax.plot(np.arange(1., 0., -0.1) * 2000., np.arange(1., 0., -0.1))\n",
    "    ax.set_ylabel('YLabel1 %d' % i)\n",
    "    ax.set_xlabel('XLabel1 %d' % i)\n",
    "    if i == 0:\n",
    "        for tick in ax.get_xticklabels():\n",
    "            tick.set_rotation(55)\n",
    "fig.align_labels()  # same as fig.align_xlabels(); fig.align_ylabels()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install pandas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = pd.read_csv('example.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a['MORIA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(512, 5)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['IDRYMA', 'ONOMA_SXOLHS', 'EIDOS_THESIS', 'EPITYXONTES', 'MORIA'], dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Α.Ε.Ν.', 'ΑΕΑ ΑΘΗΝΑΣ', 'ΑΕΑ ΘΕΣ/ΝΙΚΗΣ', 'ΑΕΑ ΙΩΑΝΝΙΝΩΝ',\n",
       "       'ΑΕΑ ΚΡΗΤΗΣ', 'ΑΠΘ', 'ΑΣΚΤ', 'ΑΣΠΑΙΤΕ', 'ΑΣΤ. ΣΧΟΛΕΣ', 'ΑΣΤΕ',\n",
       "       'ΓΕΩΠΟΝΙΚΟ ΠΑΝ.', 'ΔΙ.ΠΑ.Ε.', 'ΔΠΘ', 'ΕΚΠΑ', 'ΕΛ.ΜΕ.ΠΑ.', 'ΕΜΠ',\n",
       "       'ΙΟΝΙΟ ΠΑΝ/ΜΙΟ', 'Λ.Σ. - ΕΛ. ΑΚΤ.', 'ΟΠΑ', 'ΠΑΝ. ΑΙΓΑΙΟΥ',\n",
       "       'ΠΑΝ. ΔΥΤ. ΑΤΤΙΚΗΣ', 'ΠΑΝ. ΔΥΤ. ΜΑΚ.', 'ΠΑΝ. ΘΕΣΣΑΛΙΑΣ',\n",
       "       'ΠΑΝ. ΙΩΑΝΝΙΝΩΝ', 'ΠΑΝ. ΚΡΗΤΗΣ', 'ΠΑΝ. ΜΑΚΕΔ.', 'ΠΑΝ. ΠΑΤΡΩΝ',\n",
       "       'ΠΑΝ. ΠΕΙΡΑΙΑ', 'ΠΑΝ. ΠΕΛ/ΝΗΣΟΥ', 'ΠΑΝΤΕΙΟ', 'ΠΟΛ/ΧΝΕΙΟ ΚΡΗΤΗΣ',\n",
       "       'Σ.Π.Α.', 'ΣΑΝ', 'ΣΙ', 'ΣΜΥ', 'ΣΜΥΑ', 'ΣΜΥΝ', 'ΣΝΔ', 'ΣΣΑΣ', 'ΣΣΕ',\n",
       "       'ΧΑΡΟΚΟΠΕΙΟ'], dtype=object)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a['IDRYMA'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ΑΠΘ                  41\n",
       "ΕΚΠΑ                 41\n",
       "ΠΑΝ. ΠΑΤΡΩΝ          35\n",
       "ΠΑΝ. ΘΕΣΣΑΛΙΑΣ       35\n",
       "ΔΙ.ΠΑ.Ε.             31\n",
       "ΠΑΝ. ΔΥΤ. ΑΤΤΙΚΗΣ    26\n",
       "ΠΑΝ. ΙΩΑΝΝΙΝΩΝ       22\n",
       "ΠΑΝ. ΠΕΛ/ΝΗΣΟΥ       22\n",
       "ΠΑΝ. ΔΥΤ. ΜΑΚ.       21\n",
       "ΔΠΘ                  20\n",
       "ΠΑΝ. ΑΙΓΑΙΟΥ         18\n",
       "ΠΑΝ. ΚΡΗΤΗΣ          17\n",
       "ΣΣΑΣ                 13\n",
       "ΕΛ.ΜΕ.ΠΑ.            11\n",
       "ΠΑΝ. ΠΕΙΡΑΙΑ         10\n",
       "ΙΟΝΙΟ ΠΑΝ/ΜΙΟ        10\n",
       "ΓΕΩΠΟΝΙΚΟ ΠΑΝ.        9\n",
       "ΕΜΠ                   9\n",
       "ΠΑΝΤΕΙΟ               9\n",
       "ΑΣΤ. ΣΧΟΛΕΣ           9\n",
       "ΠΑΝ. ΜΑΚΕΔ.           9\n",
       "Α.Ε.Ν.                8\n",
       "ΟΠΑ                   8\n",
       "ΣΙ                    8\n",
       "Σ.Π.Α.                7\n",
       "Λ.Σ. - ΕΛ. ΑΚΤ.       7\n",
       "ΣΣΕ                   6\n",
       "ΣΜΥΑ                  6\n",
       "ΠΟΛ/ΧΝΕΙΟ ΚΡΗΤΗΣ      5\n",
       "ΣΝΔ                   4\n",
       "ΑΕΑ ΚΡΗΤΗΣ            4\n",
       "ΧΑΡΟΚΟΠΕΙΟ            4\n",
       "ΑΣΠΑΙΤΕ               4\n",
       "ΑΕΑ ΑΘΗΝΑΣ            4\n",
       "ΣΜΥ                   4\n",
       "ΑΕΑ ΘΕΣ/ΝΙΚΗΣ         4\n",
       "ΑΕΑ ΙΩΑΝΝΙΝΩΝ         4\n",
       "ΣΑΝ                   2\n",
       "ΣΜΥΝ                  2\n",
       "ΑΣΤΕ                  2\n",
       "ΑΣΚΤ                  1\n",
       "Name: IDRYMA, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a['IDRYMA'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a['IDRYMA'].value_counts().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>IDRYMA</th>\n",
       "      <th>ONOMA_SXOLHS</th>\n",
       "      <th>EIDOS_THESIS</th>\n",
       "      <th>EPITYXONTES</th>\n",
       "      <th>MORIA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>248</td>\n",
       "      <td>7550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ</td>\n",
       "      <td>ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ.ΠΟΛΥΤΕΚΝΟΙ 10% (ΝΕΟ)</td>\n",
       "      <td>24</td>\n",
       "      <td>4400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   IDRYMA     ONOMA_SXOLHS                          EIDOS_THESIS  EPITYXONTES  \\\n",
       "0  Α.Ε.Ν.  ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ            ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)          248   \n",
       "1  Α.Ε.Ν.  ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ  ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ.ΠΟΛΥΤΕΚΝΟΙ 10% (ΝΕΟ)           24   \n",
       "\n",
       "    MORIA  \n",
       "0  7550.0  \n",
       "1  4400.0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.iloc[:2]"
   ]
  },
  {
   "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",
       "    }\n",
       "\n",
       "    .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>IDRYMA</th>\n",
       "      <th>ONOMA_SXOLHS</th>\n",
       "      <th>EIDOS_THESIS</th>\n",
       "      <th>EPITYXONTES</th>\n",
       "      <th>MORIA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ΑΠΘ</td>\n",
       "      <td>ΑΓΓΛΙΚΗΣ ΓΛΩΣΣΑΣ ΚΑΙ ΦΙΛΟΛΟΓΙΑΣ (ΘΕΣΣΑΛΟΝΙΚΗ)</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>138</td>\n",
       "      <td>19075.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ΑΠΘ</td>\n",
       "      <td>ΑΡΧΙΤΕΚΤΟΝΩΝ ΜΗΧΑΝΙΚΩΝ (ΘΕΣΣΑΛΟΝΙΚΗ)</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>86</td>\n",
       "      <td>17885.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ΑΠΘ</td>\n",
       "      <td>ΒΙΟΛΟΓΙΑΣ (ΘΕΣΣΑΛΟΝΙΚΗ)</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>90</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ΑΠΘ</td>\n",
       "      <td>ΔΗΜΟΣΙΟΓΡΑΦΙΑΣ ΚΑΙ ΜΕΣΩΝ ΜΑΖΙΚΗΣ ΕΠΙΚΟΙΝΩΝΙΑΣ ...</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>98</td>\n",
       "      <td>15415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>ΑΠΘ</td>\n",
       "      <td>ΕΠΙΣΤΗΜΗΣ ΦΥΣΙΚΗΣ ΑΓΩΓΗΣ ΚΑΙ ΑΘΛΗΤΙΣΜΟΥ (ΘΕΣΣΑ...</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>119</td>\n",
       "      <td>17116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>ΑΠΘ</td>\n",
       "      <td>ΗΛΕΚΤΡΟΛΟΓΩΝ ΜΗΧΑΝΙΚΩΝ ΚΑΙ ΜΗΧΑΝΙΚΩΝ ΥΠΟΛΟΓΙΣΤ...</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>151</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>ΑΠΘ</td>\n",
       "      <td>ΙΑΤΡΙΚΗΣ (ΘΕΣΣΑΛΟΝΙΚΗ)</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>119</td>\n",
       "      <td>18075.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>ΑΠΘ</td>\n",
       "      <td>ΚΤΗΝΙΑΤΡΙΚΗΣ (ΘΕΣΣΑΛΟΝΙΚΗ)</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>69</td>\n",
       "      <td>16875.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>ΑΠΘ</td>\n",
       "      <td>ΜΗΧΑΝΟΛΟΓΩΝ ΜΗΧΑΝΙΚΩΝ (ΘΕΣΣΑΛΟΝΙΚΗ)</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>85</td>\n",
       "      <td>16950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>ΑΠΘ</td>\n",
       "      <td>ΝΟΜΙΚΗΣ (ΘΕΣΣΑΛΟΝΙΚΗ)</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>302</td>\n",
       "      <td>17825.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>ΑΠΘ</td>\n",
       "      <td>ΟΔΟΝΤΙΑΤΡΙΚΗΣ (ΘΕΣΣΑΛΟΝΙΚΗ)</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>64</td>\n",
       "      <td>17175.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>ΑΠΘ</td>\n",
       "      <td>ΠΑΙΔΑΓΩΓΙΚΟ ΔΗΜΟΤΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ (ΘΕΣΣΑΛΟΝΙΚΗ)</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>189</td>\n",
       "      <td>15275.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>ΑΠΘ</td>\n",
       "      <td>ΠΛΗΡΟΦΟΡΙΚΗΣ (ΘΕΣΣΑΛΟΝΙΚΗ)</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>105</td>\n",
       "      <td>16800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>ΑΠΘ</td>\n",
       "      <td>ΠΟΛΙΤΙΚΩΝ ΕΠΙΣΤΗΜΩΝ (ΘΕΣΣΑΛΟΝΙΚΗ)</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>104</td>\n",
       "      <td>15800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>ΑΠΘ</td>\n",
       "      <td>ΦΑΡΜΑΚΕΥΤΙΚΗΣ (ΘΕΣΣΑΛΟΝΙΚΗ)</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>69</td>\n",
       "      <td>16975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>ΑΠΘ</td>\n",
       "      <td>ΧΗΜΕΙΑΣ (ΘΕΣΣΑΛΟΝΙΚΗ)</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>151</td>\n",
       "      <td>15625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>ΑΠΘ</td>\n",
       "      <td>ΧΗΜΙΚΩΝ ΜΗΧΑΝΙΚΩΝ (ΘΕΣΣΑΛΟΝΙΚΗ)</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>87</td>\n",
       "      <td>16450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>ΑΠΘ</td>\n",
       "      <td>ΨΥΧΟΛΟΓΙΑΣ (ΘΕΣΣΑΛΟΝΙΚΗ)</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>147</td>\n",
       "      <td>17725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>ΑΣΤ. ΣΧΟΛΕΣ</td>\n",
       "      <td>ΑΞΙΩΜΑΤΙΚΩΝ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ (ΜΟΝΟ ΓΙΑ ΑΣΤ...</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>34</td>\n",
       "      <td>17425.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>ΑΣΤ. ΣΧΟΛΕΣ</td>\n",
       "      <td>ΑΞΙΩΜΑΤΙΚΩΝ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ (ΜΟΝΟ ΓΙΑ ΑΣΤ...</td>\n",
       "      <td>ΓΕΛ ΑΣΤΥΝΟΜΙΑ ΕΙΔ.ΚΑΤ. ΠΟΛΥΤΕΚΝΟΙ 10% (ΝΕΟ)</td>\n",
       "      <td>3</td>\n",
       "      <td>17125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>ΑΣΤ. ΣΧΟΛΕΣ</td>\n",
       "      <td>ΑΞΙΩΜΑΤΙΚΩΝ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ (ΜΟΝΟ ΓΙΑ ΑΣΤ...</td>\n",
       "      <td>ΓΕΛ ΑΣΤΥΝΟΜΙΑ ΕΙΔ.ΚΑΤ. ΤΡΙΤΕΚΝΟΙ 4% (ΝΕΟ)</td>\n",
       "      <td>1</td>\n",
       "      <td>17250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>ΑΣΤ. ΣΧΟΛΕΣ</td>\n",
       "      <td>ΑΞΙΩΜΑΤΙΚΩΝ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ (ΜΟΝΟ ΓΙΑ ΠΟΛ...</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>48</td>\n",
       "      <td>17625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>ΑΣΤ. ΣΧΟΛΕΣ</td>\n",
       "      <td>ΑΞΙΩΜΑΤΙΚΩΝ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ (ΜΟΝΟ ΓΙΑ ΠΟΛ...</td>\n",
       "      <td>ΓΕΛ ΑΣΤΥΝΟΜΙΑ ΕΙΔ.ΚΑΤ. ΠΟΛΥΤΕΚΝΟΙ 10% (ΝΕΟ)</td>\n",
       "      <td>5</td>\n",
       "      <td>17050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>ΑΣΤ. ΣΧΟΛΕΣ</td>\n",
       "      <td>ΑΞΙΩΜΑΤΙΚΩΝ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ (ΜΟΝΟ ΓΙΑ ΠΟΛ...</td>\n",
       "      <td>ΓΕΛ ΑΣΤΥΝΟΜΙΑ ΕΙΔ.ΚΑΤ. ΤΡΙΤΕΚΝΟΙ 4% (ΝΕΟ)</td>\n",
       "      <td>2</td>\n",
       "      <td>17500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>ΓΕΩΠΟΝΙΚΟ ΠΑΝ.</td>\n",
       "      <td>ΒΙΟΤΕΧΝΟΛΟΓΙΑΣ (ΑΘΗΝΑ)</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>98</td>\n",
       "      <td>15125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>ΔΠΘ</td>\n",
       "      <td>ΙΑΤΡΙΚΗΣ (ΑΛΕΞΑΝΔΡΟΥΠΟΛΗ)</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>103</td>\n",
       "      <td>17400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>ΔΠΘ</td>\n",
       "      <td>ΚΟΙΝΩΝΙΚΗΣ ΕΡΓΑΣΙΑΣ (ΚΟΜΟΤΗΝΗ)</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>87</td>\n",
       "      <td>15375.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>ΔΠΘ</td>\n",
       "      <td>ΜΟΡΙΑΚΗΣ ΒΙΟΛΟΓΙΑΣ ΚΑΙ ΓΕΝΕΤΙΚΗΣ (ΑΛΕΞΑΝΔΡΟΥΠΟΛΗ)</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>105</td>\n",
       "      <td>15750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>ΔΠΘ</td>\n",
       "      <td>ΝΟΜΙΚΗΣ (ΚΟΜΟΤΗΝΗ)</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>388</td>\n",
       "      <td>17250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>ΕΚΠΑ</td>\n",
       "      <td>ΑΓΓΛΙΚΗΣ ΓΛΩΣΣΑΣ ΚΑΙ ΦΙΛΟΛΟΓΙΑΣ (ΑΘΗΝΑ)</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>182</td>\n",
       "      <td>18385.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>ΣΙ</td>\n",
       "      <td>ΙΚΑΡΩΝ (ΣΙ) ΙΠΤΑΜΕΝΟΙ</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>22</td>\n",
       "      <td>16875.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>ΣΙ</td>\n",
       "      <td>ΙΚΑΡΩΝ (ΣΙ) ΙΠΤΑΜΕΝΟΙ</td>\n",
       "      <td>ΓΕΛ ΣΤΡΑΤΙΩΤΙΚΕΣ ΕΙΔ.ΚΑΤ.3648/α (ΝΕΟ)</td>\n",
       "      <td>12</td>\n",
       "      <td>15425.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>ΣΙ</td>\n",
       "      <td>ΙΚΑΡΩΝ (ΣΙ) ΜΗΧΑΝΙΚΟΙ (ΣΜΑ)</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>9</td>\n",
       "      <td>18250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>ΣΙ</td>\n",
       "      <td>ΙΚΑΡΩΝ (ΣΙ) ΜΗΧΑΝΙΚΟΙ (ΣΜΑ)</td>\n",
       "      <td>ΓΕΛ ΣΤΡΑΤΙΩΤΙΚΕΣ ΕΙΔ.ΚΑΤ.3648/α (ΝΕΟ)</td>\n",
       "      <td>4</td>\n",
       "      <td>17700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>ΣΜΥ</td>\n",
       "      <td>ΜΟΝΙΜΩΝ ΥΠΑΞΙΩΜΑΤΙΚΩΝ ΣΤΡΑΤΟΥ (Σ.Μ.Υ.) – ΣΩΜΑΤΑ</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>26</td>\n",
       "      <td>15825.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>ΣΜΥΑ</td>\n",
       "      <td>ΜΟΝΙΜΩΝ ΥΠΑΞΙΩΜΑΤΙΚΩΝ ΑΕΡΟΠΟΡΙΑΣ (ΣΜΥΑ) - ΚΑΤΕ...</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>15</td>\n",
       "      <td>17025.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>ΣΜΥΑ</td>\n",
       "      <td>ΜΟΝΙΜΩΝ ΥΠΑΞΙΩΜΑΤΙΚΩΝ ΑΕΡΟΠΟΡΙΑΣ (ΣΜΥΑ) - ΚΑΤΕ...</td>\n",
       "      <td>ΓΕΛ ΣΤΡΑΤΙΩΤΙΚΕΣ ΕΙΔ.ΚΑΤ.3648/α (ΝΕΟ)</td>\n",
       "      <td>7</td>\n",
       "      <td>16675.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>ΣΜΥΑ</td>\n",
       "      <td>ΜΟΝΙΜΩΝ ΥΠΑΞΙΩΜΑΤΙΚΩΝ ΑΕΡΟΠΟΡΙΑΣ (ΣΜΥΑ) - ΚΑΤΕ...</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>15</td>\n",
       "      <td>16400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>ΣΜΥΑ</td>\n",
       "      <td>ΜΟΝΙΜΩΝ ΥΠΑΞΙΩΜΑΤΙΚΩΝ ΑΕΡΟΠΟΡΙΑΣ (ΣΜΥΑ) - ΚΑΤΕ...</td>\n",
       "      <td>ΓΕΛ ΣΤΡΑΤΙΩΤΙΚΕΣ ΕΙΔ.ΚΑΤ.3648/α (ΝΕΟ)</td>\n",
       "      <td>6</td>\n",
       "      <td>15850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>ΣΜΥΑ</td>\n",
       "      <td>ΜΟΝΙΜΩΝ ΥΠΑΞΙΩΜΑΤΙΚΩΝ ΑΕΡΟΠΟΡΙΑΣ (ΣΜΥΑ) - ΚΑΤΕ...</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>58</td>\n",
       "      <td>15375.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>ΣΝΔ</td>\n",
       "      <td>ΝΑΥΤΙΚΩΝ ΔΟΚΙΜΩΝ (ΣΝΔ) ΜΑΧΙΜΟΙ</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>13</td>\n",
       "      <td>16175.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>ΣΝΔ</td>\n",
       "      <td>ΝΑΥΤΙΚΩΝ ΔΟΚΙΜΩΝ (ΣΝΔ) ΜΑΧΙΜΟΙ</td>\n",
       "      <td>ΓΕΛ ΣΤΡΑΤΙΩΤΙΚΕΣ ΕΙΔ.ΚΑΤ.3648/α (ΝΕΟ)</td>\n",
       "      <td>5</td>\n",
       "      <td>15650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>ΣΝΔ</td>\n",
       "      <td>ΝΑΥΤΙΚΩΝ ΔΟΚΙΜΩΝ (ΣΝΔ) ΜΗΧΑΝΙΚΟΙ</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>6</td>\n",
       "      <td>16700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>ΣΝΔ</td>\n",
       "      <td>ΝΑΥΤΙΚΩΝ ΔΟΚΙΜΩΝ (ΣΝΔ) ΜΗΧΑΝΙΚΟΙ</td>\n",
       "      <td>ΓΕΛ ΣΤΡΑΤΙΩΤΙΚΕΣ ΕΙΔ.ΚΑΤ.3648/α (ΝΕΟ)</td>\n",
       "      <td>3</td>\n",
       "      <td>15500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>ΣΣΑΣ</td>\n",
       "      <td>ΙΑΤΡΙΚΟ (ΣΣΑΣ) ΘΕΣ/ΝΙΚΗΣ</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>16</td>\n",
       "      <td>17650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>ΣΣΑΣ</td>\n",
       "      <td>ΙΑΤΡΙΚΟ (ΣΣΑΣ) ΘΕΣ/ΝΙΚΗΣ</td>\n",
       "      <td>ΓΕΛ ΣΤΡΑΤΙΩΤΙΚΕΣ ΕΙΔ.ΚΑΤ.3648/α (ΝΕΟ)</td>\n",
       "      <td>7</td>\n",
       "      <td>17100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>ΣΣΑΣ</td>\n",
       "      <td>ΚΤΗΝΙΑΤΡΙΚΟ (ΣΣΑΣ) ΘΕΣ/ΝΙΚΗΣ</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>3</td>\n",
       "      <td>17250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>ΣΣΑΣ</td>\n",
       "      <td>ΟΔΟΝΤΙΑΤΡΙΚΟ (ΣΣΑΣ) ΘΕΣ/ΝΙΚΗΣ</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>1</td>\n",
       "      <td>18075.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>ΣΣΑΣ</td>\n",
       "      <td>ΟΔΟΝΤΙΑΤΡΙΚΟ (ΣΣΑΣ) ΘΕΣ/ΝΙΚΗΣ</td>\n",
       "      <td>ΓΕΛ ΣΤΡΑΤΙΩΤΙΚΕΣ ΕΙΔ.ΚΑΤ.3648/β (ΝΕΟ)</td>\n",
       "      <td>1</td>\n",
       "      <td>15200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>ΣΣΑΣ</td>\n",
       "      <td>ΟΙΚΟΝΟΜΙΚΟ (ΣΣΑΣ) ΘΕΣ/ΝΙΚΗ</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>10</td>\n",
       "      <td>18350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>ΣΣΑΣ</td>\n",
       "      <td>ΟΙΚΟΝΟΜΙΚΟ (ΣΣΑΣ) ΘΕΣ/ΝΙΚΗ</td>\n",
       "      <td>ΓΕΛ ΣΤΡΑΤΙΩΤΙΚΕΣ ΕΙΔ.ΚΑΤ.3648/α (ΝΕΟ)</td>\n",
       "      <td>4</td>\n",
       "      <td>17975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>ΣΣΑΣ</td>\n",
       "      <td>ΣΤΡΑΤΟΛΟΓΙΚΟ - ΣΤΡΑΤΙΩΤ. ΝΟΜ. ΣΥΜΒ. (ΣΣΑΣ) ΘΕΣ...</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>8</td>\n",
       "      <td>18325.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>ΣΣΑΣ</td>\n",
       "      <td>ΣΤΡΑΤΟΛΟΓΙΚΟ - ΣΤΡΑΤΙΩΤ. ΝΟΜ. ΣΥΜΒ. (ΣΣΑΣ) ΘΕΣ...</td>\n",
       "      <td>ΓΕΛ ΣΤΡΑΤΙΩΤΙΚΕΣ ΕΙΔ.ΚΑΤ.3648/α (ΝΕΟ)</td>\n",
       "      <td>4</td>\n",
       "      <td>18250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>ΣΣΑΣ</td>\n",
       "      <td>ΦΑΡΜΑΚΕΥΤΙΚΟ (ΣΣΑΣ) ΘΕΣ/ΝΙΚΗΣ</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>3</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>ΣΣΑΣ</td>\n",
       "      <td>ΨΥΧΟΛΟΓΩΝ (ΣΣΑΣ) ΘΕΣ/ΝΙΚΗ</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>4</td>\n",
       "      <td>18400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>ΣΣΕ</td>\n",
       "      <td>ΕΥΕΛΠΙΔΩΝ (ΣΣΕ) – ΟΠΛΑ</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>74</td>\n",
       "      <td>15200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>ΣΣΕ</td>\n",
       "      <td>ΕΥΕΛΠΙΔΩΝ (ΣΣΕ) – ΣΩΜΑΤΑ</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>17</td>\n",
       "      <td>16725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>ΣΣΕ</td>\n",
       "      <td>ΕΥΕΛΠΙΔΩΝ (ΣΣΕ) – ΣΩΜΑΤΑ</td>\n",
       "      <td>ΓΕΛ ΣΤΡΑΤΙΩΤΙΚΕΣ ΕΙΔ.ΚΑΤ.3648/α (ΝΕΟ)</td>\n",
       "      <td>8</td>\n",
       "      <td>16000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>ΧΑΡΟΚΟΠΕΙΟ</td>\n",
       "      <td>ΕΠΙΣΤΗΜΗΣ ΔΙΑΙΤΟΛΟΓΙΑΣ ΚΑΙ ΔΙΑΤΡΟΦΗΣ (ΑΘΗΝΑ)</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>58</td>\n",
       "      <td>15700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>ΧΑΡΟΚΟΠΕΙΟ</td>\n",
       "      <td>ΠΛΗΡΟΦΟΡΙΚΗΣ ΚΑΙ ΤΗΛΕΜΑΤΙΚΗΣ (ΑΘΗΝΑ)</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>90</td>\n",
       "      <td>15575.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>141 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             IDRYMA                                       ONOMA_SXOLHS  \\\n",
       "24              ΑΠΘ      ΑΓΓΛΙΚΗΣ ΓΛΩΣΣΑΣ ΚΑΙ ΦΙΛΟΛΟΓΙΑΣ (ΘΕΣΣΑΛΟΝΙΚΗ)   \n",
       "26              ΑΠΘ               ΑΡΧΙΤΕΚΤΟΝΩΝ ΜΗΧΑΝΙΚΩΝ (ΘΕΣΣΑΛΟΝΙΚΗ)   \n",
       "27              ΑΠΘ                            ΒΙΟΛΟΓΙΑΣ (ΘΕΣΣΑΛΟΝΙΚΗ)   \n",
       "33              ΑΠΘ  ΔΗΜΟΣΙΟΓΡΑΦΙΑΣ ΚΑΙ ΜΕΣΩΝ ΜΑΖΙΚΗΣ ΕΠΙΚΟΙΝΩΝΙΑΣ ...   \n",
       "34              ΑΠΘ  ΕΠΙΣΤΗΜΗΣ ΦΥΣΙΚΗΣ ΑΓΩΓΗΣ ΚΑΙ ΑΘΛΗΤΙΣΜΟΥ (ΘΕΣΣΑ...   \n",
       "37              ΑΠΘ  ΗΛΕΚΤΡΟΛΟΓΩΝ ΜΗΧΑΝΙΚΩΝ ΚΑΙ ΜΗΧΑΝΙΚΩΝ ΥΠΟΛΟΓΙΣΤ...   \n",
       "41              ΑΠΘ                             ΙΑΤΡΙΚΗΣ (ΘΕΣΣΑΛΟΝΙΚΗ)   \n",
       "45              ΑΠΘ                         ΚΤΗΝΙΑΤΡΙΚΗΣ (ΘΕΣΣΑΛΟΝΙΚΗ)   \n",
       "48              ΑΠΘ                ΜΗΧΑΝΟΛΟΓΩΝ ΜΗΧΑΝΙΚΩΝ (ΘΕΣΣΑΛΟΝΙΚΗ)   \n",
       "50              ΑΠΘ                              ΝΟΜΙΚΗΣ (ΘΕΣΣΑΛΟΝΙΚΗ)   \n",
       "51              ΑΠΘ                        ΟΔΟΝΤΙΑΤΡΙΚΗΣ (ΘΕΣΣΑΛΟΝΙΚΗ)   \n",
       "53              ΑΠΘ    ΠΑΙΔΑΓΩΓΙΚΟ ΔΗΜΟΤΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ (ΘΕΣΣΑΛΟΝΙΚΗ)   \n",
       "54              ΑΠΘ                         ΠΛΗΡΟΦΟΡΙΚΗΣ (ΘΕΣΣΑΛΟΝΙΚΗ)   \n",
       "56              ΑΠΘ                  ΠΟΛΙΤΙΚΩΝ ΕΠΙΣΤΗΜΩΝ (ΘΕΣΣΑΛΟΝΙΚΗ)   \n",
       "58              ΑΠΘ                        ΦΑΡΜΑΚΕΥΤΙΚΗΣ (ΘΕΣΣΑΛΟΝΙΚΗ)   \n",
       "62              ΑΠΘ                              ΧΗΜΕΙΑΣ (ΘΕΣΣΑΛΟΝΙΚΗ)   \n",
       "63              ΑΠΘ                    ΧΗΜΙΚΩΝ ΜΗΧΑΝΙΚΩΝ (ΘΕΣΣΑΛΟΝΙΚΗ)   \n",
       "64              ΑΠΘ                           ΨΥΧΟΛΟΓΙΑΣ (ΘΕΣΣΑΛΟΝΙΚΗ)   \n",
       "70      ΑΣΤ. ΣΧΟΛΕΣ  ΑΞΙΩΜΑΤΙΚΩΝ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ (ΜΟΝΟ ΓΙΑ ΑΣΤ...   \n",
       "71      ΑΣΤ. ΣΧΟΛΕΣ  ΑΞΙΩΜΑΤΙΚΩΝ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ (ΜΟΝΟ ΓΙΑ ΑΣΤ...   \n",
       "72      ΑΣΤ. ΣΧΟΛΕΣ  ΑΞΙΩΜΑΤΙΚΩΝ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ (ΜΟΝΟ ΓΙΑ ΑΣΤ...   \n",
       "73      ΑΣΤ. ΣΧΟΛΕΣ  ΑΞΙΩΜΑΤΙΚΩΝ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ (ΜΟΝΟ ΓΙΑ ΠΟΛ...   \n",
       "74      ΑΣΤ. ΣΧΟΛΕΣ  ΑΞΙΩΜΑΤΙΚΩΝ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ (ΜΟΝΟ ΓΙΑ ΠΟΛ...   \n",
       "75      ΑΣΤ. ΣΧΟΛΕΣ  ΑΞΙΩΜΑΤΙΚΩΝ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ (ΜΟΝΟ ΓΙΑ ΠΟΛ...   \n",
       "83   ΓΕΩΠΟΝΙΚΟ ΠΑΝ.                             ΒΙΟΤΕΧΝΟΛΟΓΙΑΣ (ΑΘΗΝΑ)   \n",
       "129             ΔΠΘ                          ΙΑΤΡΙΚΗΣ (ΑΛΕΞΑΝΔΡΟΥΠΟΛΗ)   \n",
       "131             ΔΠΘ                     ΚΟΙΝΩΝΙΚΗΣ ΕΡΓΑΣΙΑΣ (ΚΟΜΟΤΗΝΗ)   \n",
       "135             ΔΠΘ  ΜΟΡΙΑΚΗΣ ΒΙΟΛΟΓΙΑΣ ΚΑΙ ΓΕΝΕΤΙΚΗΣ (ΑΛΕΞΑΝΔΡΟΥΠΟΛΗ)   \n",
       "136             ΔΠΘ                                 ΝΟΜΙΚΗΣ (ΚΟΜΟΤΗΝΗ)   \n",
       "141            ΕΚΠΑ            ΑΓΓΛΙΚΗΣ ΓΛΩΣΣΑΣ ΚΑΙ ΦΙΛΟΛΟΓΙΑΣ (ΑΘΗΝΑ)   \n",
       "..              ...                                                ...   \n",
       "468              ΣΙ                              ΙΚΑΡΩΝ (ΣΙ) ΙΠΤΑΜΕΝΟΙ   \n",
       "469              ΣΙ                              ΙΚΑΡΩΝ (ΣΙ) ΙΠΤΑΜΕΝΟΙ   \n",
       "471              ΣΙ                        ΙΚΑΡΩΝ (ΣΙ) ΜΗΧΑΝΙΚΟΙ (ΣΜΑ)   \n",
       "472              ΣΙ                        ΙΚΑΡΩΝ (ΣΙ) ΜΗΧΑΝΙΚΟΙ (ΣΜΑ)   \n",
       "475             ΣΜΥ    ΜΟΝΙΜΩΝ ΥΠΑΞΙΩΜΑΤΙΚΩΝ ΣΤΡΑΤΟΥ (Σ.Μ.Υ.) – ΣΩΜΑΤΑ   \n",
       "477            ΣΜΥΑ  ΜΟΝΙΜΩΝ ΥΠΑΞΙΩΜΑΤΙΚΩΝ ΑΕΡΟΠΟΡΙΑΣ (ΣΜΥΑ) - ΚΑΤΕ...   \n",
       "478            ΣΜΥΑ  ΜΟΝΙΜΩΝ ΥΠΑΞΙΩΜΑΤΙΚΩΝ ΑΕΡΟΠΟΡΙΑΣ (ΣΜΥΑ) - ΚΑΤΕ...   \n",
       "479            ΣΜΥΑ  ΜΟΝΙΜΩΝ ΥΠΑΞΙΩΜΑΤΙΚΩΝ ΑΕΡΟΠΟΡΙΑΣ (ΣΜΥΑ) - ΚΑΤΕ...   \n",
       "480            ΣΜΥΑ  ΜΟΝΙΜΩΝ ΥΠΑΞΙΩΜΑΤΙΚΩΝ ΑΕΡΟΠΟΡΙΑΣ (ΣΜΥΑ) - ΚΑΤΕ...   \n",
       "481            ΣΜΥΑ  ΜΟΝΙΜΩΝ ΥΠΑΞΙΩΜΑΤΙΚΩΝ ΑΕΡΟΠΟΡΙΑΣ (ΣΜΥΑ) - ΚΑΤΕ...   \n",
       "485             ΣΝΔ                     ΝΑΥΤΙΚΩΝ ΔΟΚΙΜΩΝ (ΣΝΔ) ΜΑΧΙΜΟΙ   \n",
       "486             ΣΝΔ                     ΝΑΥΤΙΚΩΝ ΔΟΚΙΜΩΝ (ΣΝΔ) ΜΑΧΙΜΟΙ   \n",
       "487             ΣΝΔ                   ΝΑΥΤΙΚΩΝ ΔΟΚΙΜΩΝ (ΣΝΔ) ΜΗΧΑΝΙΚΟΙ   \n",
       "488             ΣΝΔ                   ΝΑΥΤΙΚΩΝ ΔΟΚΙΜΩΝ (ΣΝΔ) ΜΗΧΑΝΙΚΟΙ   \n",
       "489            ΣΣΑΣ                           ΙΑΤΡΙΚΟ (ΣΣΑΣ) ΘΕΣ/ΝΙΚΗΣ   \n",
       "490            ΣΣΑΣ                           ΙΑΤΡΙΚΟ (ΣΣΑΣ) ΘΕΣ/ΝΙΚΗΣ   \n",
       "491            ΣΣΑΣ                       ΚΤΗΝΙΑΤΡΙΚΟ (ΣΣΑΣ) ΘΕΣ/ΝΙΚΗΣ   \n",
       "492            ΣΣΑΣ                      ΟΔΟΝΤΙΑΤΡΙΚΟ (ΣΣΑΣ) ΘΕΣ/ΝΙΚΗΣ   \n",
       "493            ΣΣΑΣ                      ΟΔΟΝΤΙΑΤΡΙΚΟ (ΣΣΑΣ) ΘΕΣ/ΝΙΚΗΣ   \n",
       "494            ΣΣΑΣ                         ΟΙΚΟΝΟΜΙΚΟ (ΣΣΑΣ) ΘΕΣ/ΝΙΚΗ   \n",
       "495            ΣΣΑΣ                         ΟΙΚΟΝΟΜΙΚΟ (ΣΣΑΣ) ΘΕΣ/ΝΙΚΗ   \n",
       "497            ΣΣΑΣ  ΣΤΡΑΤΟΛΟΓΙΚΟ - ΣΤΡΑΤΙΩΤ. ΝΟΜ. ΣΥΜΒ. (ΣΣΑΣ) ΘΕΣ...   \n",
       "498            ΣΣΑΣ  ΣΤΡΑΤΟΛΟΓΙΚΟ - ΣΤΡΑΤΙΩΤ. ΝΟΜ. ΣΥΜΒ. (ΣΣΑΣ) ΘΕΣ...   \n",
       "499            ΣΣΑΣ                      ΦΑΡΜΑΚΕΥΤΙΚΟ (ΣΣΑΣ) ΘΕΣ/ΝΙΚΗΣ   \n",
       "501            ΣΣΑΣ                          ΨΥΧΟΛΟΓΩΝ (ΣΣΑΣ) ΘΕΣ/ΝΙΚΗ   \n",
       "502             ΣΣΕ                             ΕΥΕΛΠΙΔΩΝ (ΣΣΕ) – ΟΠΛΑ   \n",
       "505             ΣΣΕ                           ΕΥΕΛΠΙΔΩΝ (ΣΣΕ) – ΣΩΜΑΤΑ   \n",
       "506             ΣΣΕ                           ΕΥΕΛΠΙΔΩΝ (ΣΣΕ) – ΣΩΜΑΤΑ   \n",
       "509      ΧΑΡΟΚΟΠΕΙΟ       ΕΠΙΣΤΗΜΗΣ ΔΙΑΙΤΟΛΟΓΙΑΣ ΚΑΙ ΔΙΑΤΡΟΦΗΣ (ΑΘΗΝΑ)   \n",
       "511      ΧΑΡΟΚΟΠΕΙΟ               ΠΛΗΡΟΦΟΡΙΚΗΣ ΚΑΙ ΤΗΛΕΜΑΤΙΚΗΣ (ΑΘΗΝΑ)   \n",
       "\n",
       "                                    EIDOS_THESIS  EPITYXONTES    MORIA  \n",
       "24                    ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)          138  19075.0  \n",
       "26                    ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           86  17885.0  \n",
       "27                    ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           90  16500.0  \n",
       "33                    ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           98  15415.0  \n",
       "34                    ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)          119  17116.0  \n",
       "37                    ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)          151  17450.0  \n",
       "41                    ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)          119  18075.0  \n",
       "45                    ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           69  16875.0  \n",
       "48                    ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           85  16950.0  \n",
       "50                    ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)          302  17825.0  \n",
       "51                    ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           64  17175.0  \n",
       "53                    ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)          189  15275.0  \n",
       "54                    ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)          105  16800.0  \n",
       "56                    ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)          104  15800.0  \n",
       "58                    ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           69  16975.0  \n",
       "62                    ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)          151  15625.0  \n",
       "63                    ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           87  16450.0  \n",
       "64                    ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)          147  17725.0  \n",
       "70                    ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           34  17425.0  \n",
       "71   ΓΕΛ ΑΣΤΥΝΟΜΙΑ ΕΙΔ.ΚΑΤ. ΠΟΛΥΤΕΚΝΟΙ 10% (ΝΕΟ)            3  17125.0  \n",
       "72     ΓΕΛ ΑΣΤΥΝΟΜΙΑ ΕΙΔ.ΚΑΤ. ΤΡΙΤΕΚΝΟΙ 4% (ΝΕΟ)            1  17250.0  \n",
       "73                    ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           48  17625.0  \n",
       "74   ΓΕΛ ΑΣΤΥΝΟΜΙΑ ΕΙΔ.ΚΑΤ. ΠΟΛΥΤΕΚΝΟΙ 10% (ΝΕΟ)            5  17050.0  \n",
       "75     ΓΕΛ ΑΣΤΥΝΟΜΙΑ ΕΙΔ.ΚΑΤ. ΤΡΙΤΕΚΝΟΙ 4% (ΝΕΟ)            2  17500.0  \n",
       "83                    ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           98  15125.0  \n",
       "129                   ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)          103  17400.0  \n",
       "131                   ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           87  15375.0  \n",
       "135                   ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)          105  15750.0  \n",
       "136                   ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)          388  17250.0  \n",
       "141                   ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)          182  18385.0  \n",
       "..                                           ...          ...      ...  \n",
       "468                   ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           22  16875.0  \n",
       "469        ΓΕΛ ΣΤΡΑΤΙΩΤΙΚΕΣ ΕΙΔ.ΚΑΤ.3648/α (ΝΕΟ)           12  15425.0  \n",
       "471                   ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)            9  18250.0  \n",
       "472        ΓΕΛ ΣΤΡΑΤΙΩΤΙΚΕΣ ΕΙΔ.ΚΑΤ.3648/α (ΝΕΟ)            4  17700.0  \n",
       "475                   ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           26  15825.0  \n",
       "477                   ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           15  17025.0  \n",
       "478        ΓΕΛ ΣΤΡΑΤΙΩΤΙΚΕΣ ΕΙΔ.ΚΑΤ.3648/α (ΝΕΟ)            7  16675.0  \n",
       "479                   ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           15  16400.0  \n",
       "480        ΓΕΛ ΣΤΡΑΤΙΩΤΙΚΕΣ ΕΙΔ.ΚΑΤ.3648/α (ΝΕΟ)            6  15850.0  \n",
       "481                   ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           58  15375.0  \n",
       "485                   ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           13  16175.0  \n",
       "486        ΓΕΛ ΣΤΡΑΤΙΩΤΙΚΕΣ ΕΙΔ.ΚΑΤ.3648/α (ΝΕΟ)            5  15650.0  \n",
       "487                   ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)            6  16700.0  \n",
       "488        ΓΕΛ ΣΤΡΑΤΙΩΤΙΚΕΣ ΕΙΔ.ΚΑΤ.3648/α (ΝΕΟ)            3  15500.0  \n",
       "489                   ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           16  17650.0  \n",
       "490        ΓΕΛ ΣΤΡΑΤΙΩΤΙΚΕΣ ΕΙΔ.ΚΑΤ.3648/α (ΝΕΟ)            7  17100.0  \n",
       "491                   ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)            3  17250.0  \n",
       "492                   ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)            1  18075.0  \n",
       "493        ΓΕΛ ΣΤΡΑΤΙΩΤΙΚΕΣ ΕΙΔ.ΚΑΤ.3648/β (ΝΕΟ)            1  15200.0  \n",
       "494                   ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           10  18350.0  \n",
       "495        ΓΕΛ ΣΤΡΑΤΙΩΤΙΚΕΣ ΕΙΔ.ΚΑΤ.3648/α (ΝΕΟ)            4  17975.0  \n",
       "497                   ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)            8  18325.0  \n",
       "498        ΓΕΛ ΣΤΡΑΤΙΩΤΙΚΕΣ ΕΙΔ.ΚΑΤ.3648/α (ΝΕΟ)            4  18250.0  \n",
       "499                   ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)            3  17450.0  \n",
       "501                   ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)            4  18400.0  \n",
       "502                   ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           74  15200.0  \n",
       "505                   ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           17  16725.0  \n",
       "506        ΓΕΛ ΣΤΡΑΤΙΩΤΙΚΕΣ ΕΙΔ.ΚΑΤ.3648/α (ΝΕΟ)            8  16000.0  \n",
       "509                   ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           58  15700.0  \n",
       "511                   ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           90  15575.0  \n",
       "\n",
       "[141 rows x 5 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[a['MORIA'] > 15000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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>MORIA</th>\n",
       "      <th>EPITYXONTES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7550.0</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4400.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7000.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6550.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10025.0</td>\n",
       "      <td>284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6075.0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9375.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8675.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4750.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     MORIA  EPITYXONTES\n",
       "0   7550.0          248\n",
       "1   4400.0           24\n",
       "2   7000.0            7\n",
       "3   6550.0            4\n",
       "4  10025.0          284\n",
       "5   6075.0           28\n",
       "6   9375.0            8\n",
       "7   8675.0            5\n",
       "8   4750.0            8\n",
       "9      NaN            0"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[['MORIA', 'EPITYXONTES']][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17100.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[a['ONOMA_SXOLHS'].str.contains('^ΙΑΤΡΙΚ')]['MORIA'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "490"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[a['ONOMA_SXOLHS'].str.contains('^ΙΑΤΡΙΚ')]['MORIA'].idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IDRYMA                                           ΣΣΑΣ\n",
       "ONOMA_SXOLHS                 ΙΑΤΡΙΚΟ (ΣΣΑΣ) ΘΕΣ/ΝΙΚΗΣ\n",
       "EIDOS_THESIS    ΓΕΛ ΣΤΡΑΤΙΩΤΙΚΕΣ ΕΙΔ.ΚΑΤ.3648/α (ΝΕΟ)\n",
       "EPITYXONTES                                         7\n",
       "MORIA                                           17100\n",
       "Name: 490, dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.iloc[a[a['ONOMA_SXOLHS'].str.contains('^ΙΑΤΡΙΚ')]['MORIA'].idxmin()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a26888eb8>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a['IDRYMA'].value_counts()[:15].plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a25528048>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a['IDRYMA'].value_counts()[:10].plot(kind=\"pie\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>IDRYMA</th>\n",
       "      <th>ONOMA_SXOLHS</th>\n",
       "      <th>EIDOS_THESIS</th>\n",
       "      <th>EPITYXONTES</th>\n",
       "      <th>MORIA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>248</td>\n",
       "      <td>7550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ</td>\n",
       "      <td>ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ.ΠΟΛΥΤΕΚΝΟΙ 10% (ΝΕΟ)</td>\n",
       "      <td>24</td>\n",
       "      <td>4400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ</td>\n",
       "      <td>ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ. ΤΡΙΤΕΚΝΟΙ 3% (ΝΕΟ)</td>\n",
       "      <td>7</td>\n",
       "      <td>7000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ</td>\n",
       "      <td>ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ. ΚΟΙΝ. ΚΡΙΤΗΡΙΑ (ΝΕΟ)</td>\n",
       "      <td>4</td>\n",
       "      <td>6550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΠΛΟΙΑΡΧΩΝ</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>284</td>\n",
       "      <td>10025.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΠΛΟΙΑΡΧΩΝ</td>\n",
       "      <td>ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ.ΠΟΛΥΤΕΚΝΟΙ 10% (ΝΕΟ)</td>\n",
       "      <td>28</td>\n",
       "      <td>6075.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΠΛΟΙΑΡΧΩΝ</td>\n",
       "      <td>ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ. ΤΡΙΤΕΚΝΟΙ 3% (ΝΕΟ)</td>\n",
       "      <td>8</td>\n",
       "      <td>9375.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΠΛΟΙΑΡΧΩΝ</td>\n",
       "      <td>ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ. ΚΟΙΝ. ΚΡΙΤΗΡΙΑ (ΝΕΟ)</td>\n",
       "      <td>5</td>\n",
       "      <td>8675.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ΑΕΑ ΑΘΗΝΑΣ</td>\n",
       "      <td>ΠΡΟΓΡΑΜΜΑ ΔΙΑΧΕΙΡΙΣΗΣ ΕΚΚΛΗΣΙΑΣΤΙΚΩΝ ΚΕΙΜΗΛΙΩΝ...</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>8</td>\n",
       "      <td>4750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ΑΕΑ ΑΘΗΝΑΣ</td>\n",
       "      <td>ΠΡΟΓΡΑΜΜΑ ΔΙΑΧΕΙΡΙΣΗΣ ΕΚΚΛΗΣΙΑΣΤΙΚΩΝ ΚΕΙΜΗΛΙΩΝ...</td>\n",
       "      <td>ΓΕΛ ΕΚΚΛΗΣΙΑΣΤΙΚΩΝ (ΝΕΟ)</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       IDRYMA                                       ONOMA_SXOLHS  \\\n",
       "0      Α.Ε.Ν.                                    ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ   \n",
       "1      Α.Ε.Ν.                                    ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ   \n",
       "2      Α.Ε.Ν.                                    ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ   \n",
       "3      Α.Ε.Ν.                                    ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ   \n",
       "4      Α.Ε.Ν.                                    ΣΧΟΛΗ ΠΛΟΙΑΡΧΩΝ   \n",
       "5      Α.Ε.Ν.                                    ΣΧΟΛΗ ΠΛΟΙΑΡΧΩΝ   \n",
       "6      Α.Ε.Ν.                                    ΣΧΟΛΗ ΠΛΟΙΑΡΧΩΝ   \n",
       "7      Α.Ε.Ν.                                    ΣΧΟΛΗ ΠΛΟΙΑΡΧΩΝ   \n",
       "8  ΑΕΑ ΑΘΗΝΑΣ  ΠΡΟΓΡΑΜΜΑ ΔΙΑΧΕΙΡΙΣΗΣ ΕΚΚΛΗΣΙΑΣΤΙΚΩΝ ΚΕΙΜΗΛΙΩΝ...   \n",
       "9  ΑΕΑ ΑΘΗΝΑΣ  ΠΡΟΓΡΑΜΜΑ ΔΙΑΧΕΙΡΙΣΗΣ ΕΚΚΛΗΣΙΑΣΤΙΚΩΝ ΚΕΙΜΗΛΙΩΝ...   \n",
       "\n",
       "                            EIDOS_THESIS  EPITYXONTES    MORIA  \n",
       "0             ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)          248   7550.0  \n",
       "1   ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ.ΠΟΛΥΤΕΚΝΟΙ 10% (ΝΕΟ)           24   4400.0  \n",
       "2    ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ. ΤΡΙΤΕΚΝΟΙ 3% (ΝΕΟ)            7   7000.0  \n",
       "3  ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ. ΚΟΙΝ. ΚΡΙΤΗΡΙΑ (ΝΕΟ)            4   6550.0  \n",
       "4             ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)          284  10025.0  \n",
       "5   ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ.ΠΟΛΥΤΕΚΝΟΙ 10% (ΝΕΟ)           28   6075.0  \n",
       "6    ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ. ΤΡΙΤΕΚΝΟΙ 3% (ΝΕΟ)            8   9375.0  \n",
       "7  ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ. ΚΟΙΝ. ΚΡΙΤΗΡΙΑ (ΝΕΟ)            5   8675.0  \n",
       "8             ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)            8   4750.0  \n",
       "9               ΓΕΛ ΕΚΚΛΗΣΙΑΣΤΙΚΩΝ (ΝΕΟ)            0      NaN  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = a.groupby('IDRYMA').aggregate('mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "b.to_excel('b.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>IDRYMA</th>\n",
       "      <th>ONOMA_SXOLHS</th>\n",
       "      <th>EIDOS_THESIS</th>\n",
       "      <th>EPITYXONTES</th>\n",
       "      <th>MORIA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>248</td>\n",
       "      <td>7550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ</td>\n",
       "      <td>ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ.ΠΟΛΥΤΕΚΝΟΙ 10% (ΝΕΟ)</td>\n",
       "      <td>24</td>\n",
       "      <td>4400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ</td>\n",
       "      <td>ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ. ΤΡΙΤΕΚΝΟΙ 3% (ΝΕΟ)</td>\n",
       "      <td>7</td>\n",
       "      <td>7000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ</td>\n",
       "      <td>ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ. ΚΟΙΝ. ΚΡΙΤΗΡΙΑ (ΝΕΟ)</td>\n",
       "      <td>4</td>\n",
       "      <td>6550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΠΛΟΙΑΡΧΩΝ</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>284</td>\n",
       "      <td>10025.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΠΛΟΙΑΡΧΩΝ</td>\n",
       "      <td>ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ.ΠΟΛΥΤΕΚΝΟΙ 10% (ΝΕΟ)</td>\n",
       "      <td>28</td>\n",
       "      <td>6075.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΠΛΟΙΑΡΧΩΝ</td>\n",
       "      <td>ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ. ΤΡΙΤΕΚΝΟΙ 3% (ΝΕΟ)</td>\n",
       "      <td>8</td>\n",
       "      <td>9375.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΠΛΟΙΑΡΧΩΝ</td>\n",
       "      <td>ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ. ΚΟΙΝ. ΚΡΙΤΗΡΙΑ (ΝΕΟ)</td>\n",
       "      <td>5</td>\n",
       "      <td>8675.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ΑΕΑ ΑΘΗΝΑΣ</td>\n",
       "      <td>ΠΡΟΓΡΑΜΜΑ ΔΙΑΧΕΙΡΙΣΗΣ ΕΚΚΛΗΣΙΑΣΤΙΚΩΝ ΚΕΙΜΗΛΙΩΝ...</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>8</td>\n",
       "      <td>4750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ΑΕΑ ΑΘΗΝΑΣ</td>\n",
       "      <td>ΠΡΟΓΡΑΜΜΑ ΙΕΡΑΤΙΚΩΝ ΣΠΟΥΔΩΝ ΑΘΗΝΑΣ</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>33</td>\n",
       "      <td>6650.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        IDRYMA                                       ONOMA_SXOLHS  \\\n",
       "0       Α.Ε.Ν.                                    ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ   \n",
       "1       Α.Ε.Ν.                                    ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ   \n",
       "2       Α.Ε.Ν.                                    ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ   \n",
       "3       Α.Ε.Ν.                                    ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ   \n",
       "4       Α.Ε.Ν.                                    ΣΧΟΛΗ ΠΛΟΙΑΡΧΩΝ   \n",
       "5       Α.Ε.Ν.                                    ΣΧΟΛΗ ΠΛΟΙΑΡΧΩΝ   \n",
       "6       Α.Ε.Ν.                                    ΣΧΟΛΗ ΠΛΟΙΑΡΧΩΝ   \n",
       "7       Α.Ε.Ν.                                    ΣΧΟΛΗ ΠΛΟΙΑΡΧΩΝ   \n",
       "8   ΑΕΑ ΑΘΗΝΑΣ  ΠΡΟΓΡΑΜΜΑ ΔΙΑΧΕΙΡΙΣΗΣ ΕΚΚΛΗΣΙΑΣΤΙΚΩΝ ΚΕΙΜΗΛΙΩΝ...   \n",
       "10  ΑΕΑ ΑΘΗΝΑΣ                 ΠΡΟΓΡΑΜΜΑ ΙΕΡΑΤΙΚΩΝ ΣΠΟΥΔΩΝ ΑΘΗΝΑΣ   \n",
       "\n",
       "                             EIDOS_THESIS  EPITYXONTES    MORIA  \n",
       "0              ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)          248   7550.0  \n",
       "1    ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ.ΠΟΛΥΤΕΚΝΟΙ 10% (ΝΕΟ)           24   4400.0  \n",
       "2     ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ. ΤΡΙΤΕΚΝΟΙ 3% (ΝΕΟ)            7   7000.0  \n",
       "3   ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ. ΚΟΙΝ. ΚΡΙΤΗΡΙΑ (ΝΕΟ)            4   6550.0  \n",
       "4              ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)          284  10025.0  \n",
       "5    ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ.ΠΟΛΥΤΕΚΝΟΙ 10% (ΝΕΟ)           28   6075.0  \n",
       "6     ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ. ΤΡΙΤΕΚΝΟΙ 3% (ΝΕΟ)            8   9375.0  \n",
       "7   ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ. ΚΟΙΝ. ΚΡΙΤΗΡΙΑ (ΝΕΟ)            5   8675.0  \n",
       "8              ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)            8   4750.0  \n",
       "10             ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)           33   6650.0  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[~a['MORIA'].isna()][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = a[a['ONOMA_SXOLHS'].str.contains('^ΙΑΤΡΙΚ')]['MORIA'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "not_iatrikes = a[~a['ONOMA_SXOLHS'].str.contains('^ΙΑΤΡΙΚ')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3608"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_iatrikes[not_iatrikes['MORIA'] > b]['EPITYXONTES'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17100.0"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ΑΠΘ                  41\n",
       "ΕΚΠΑ                 41\n",
       "ΠΑΝ. ΠΑΤΡΩΝ          35\n",
       "ΠΑΝ. ΘΕΣΣΑΛΙΑΣ       35\n",
       "ΔΙ.ΠΑ.Ε.             31\n",
       "ΠΑΝ. ΔΥΤ. ΑΤΤΙΚΗΣ    26\n",
       "ΠΑΝ. ΙΩΑΝΝΙΝΩΝ       22\n",
       "ΠΑΝ. ΠΕΛ/ΝΗΣΟΥ       22\n",
       "ΠΑΝ. ΔΥΤ. ΜΑΚ.       21\n",
       "ΔΠΘ                  20\n",
       "Name: IDRYMA, dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a['IDRYMA'].value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IDRYMA</th>\n",
       "      <th>ONOMA_SXOLHS</th>\n",
       "      <th>EIDOS_THESIS</th>\n",
       "      <th>EPITYXONTES</th>\n",
       "      <th>MORIA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>248</td>\n",
       "      <td>7550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ</td>\n",
       "      <td>ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ.ΠΟΛΥΤΕΚΝΟΙ 10% (ΝΕΟ)</td>\n",
       "      <td>24</td>\n",
       "      <td>4400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ</td>\n",
       "      <td>ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ. ΤΡΙΤΕΚΝΟΙ 3% (ΝΕΟ)</td>\n",
       "      <td>7</td>\n",
       "      <td>7000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ</td>\n",
       "      <td>ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ. ΚΟΙΝ. ΚΡΙΤΗΡΙΑ (ΝΕΟ)</td>\n",
       "      <td>4</td>\n",
       "      <td>6550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΠΛΟΙΑΡΧΩΝ</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>284</td>\n",
       "      <td>10025.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΠΛΟΙΑΡΧΩΝ</td>\n",
       "      <td>ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ.ΠΟΛΥΤΕΚΝΟΙ 10% (ΝΕΟ)</td>\n",
       "      <td>28</td>\n",
       "      <td>6075.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΠΛΟΙΑΡΧΩΝ</td>\n",
       "      <td>ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ. ΤΡΙΤΕΚΝΟΙ 3% (ΝΕΟ)</td>\n",
       "      <td>8</td>\n",
       "      <td>9375.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Α.Ε.Ν.</td>\n",
       "      <td>ΣΧΟΛΗ ΠΛΟΙΑΡΧΩΝ</td>\n",
       "      <td>ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ. ΚΟΙΝ. ΚΡΙΤΗΡΙΑ (ΝΕΟ)</td>\n",
       "      <td>5</td>\n",
       "      <td>8675.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ΑΕΑ ΑΘΗΝΑΣ</td>\n",
       "      <td>ΠΡΟΓΡΑΜΜΑ ΔΙΑΧΕΙΡΙΣΗΣ ΕΚΚΛΗΣΙΑΣΤΙΚΩΝ ΚΕΙΜΗΛΙΩΝ...</td>\n",
       "      <td>ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)</td>\n",
       "      <td>8</td>\n",
       "      <td>4750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ΑΕΑ ΑΘΗΝΑΣ</td>\n",
       "      <td>ΠΡΟΓΡΑΜΜΑ ΔΙΑΧΕΙΡΙΣΗΣ ΕΚΚΛΗΣΙΑΣΤΙΚΩΝ ΚΕΙΜΗΛΙΩΝ...</td>\n",
       "      <td>ΓΕΛ ΕΚΚΛΗΣΙΑΣΤΙΚΩΝ (ΝΕΟ)</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       IDRYMA                                       ONOMA_SXOLHS  \\\n",
       "0      Α.Ε.Ν.                                    ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ   \n",
       "1      Α.Ε.Ν.                                    ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ   \n",
       "2      Α.Ε.Ν.                                    ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ   \n",
       "3      Α.Ε.Ν.                                    ΣΧΟΛΗ ΜΗΧΑΝΙΚΩΝ   \n",
       "4      Α.Ε.Ν.                                    ΣΧΟΛΗ ΠΛΟΙΑΡΧΩΝ   \n",
       "5      Α.Ε.Ν.                                    ΣΧΟΛΗ ΠΛΟΙΑΡΧΩΝ   \n",
       "6      Α.Ε.Ν.                                    ΣΧΟΛΗ ΠΛΟΙΑΡΧΩΝ   \n",
       "7      Α.Ε.Ν.                                    ΣΧΟΛΗ ΠΛΟΙΑΡΧΩΝ   \n",
       "8  ΑΕΑ ΑΘΗΝΑΣ  ΠΡΟΓΡΑΜΜΑ ΔΙΑΧΕΙΡΙΣΗΣ ΕΚΚΛΗΣΙΑΣΤΙΚΩΝ ΚΕΙΜΗΛΙΩΝ...   \n",
       "9  ΑΕΑ ΑΘΗΝΑΣ  ΠΡΟΓΡΑΜΜΑ ΔΙΑΧΕΙΡΙΣΗΣ ΕΚΚΛΗΣΙΑΣΤΙΚΩΝ ΚΕΙΜΗΛΙΩΝ...   \n",
       "\n",
       "                            EIDOS_THESIS  EPITYXONTES    MORIA  \n",
       "0             ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)          248   7550.0  \n",
       "1   ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ.ΠΟΛΥΤΕΚΝΟΙ 10% (ΝΕΟ)           24   4400.0  \n",
       "2    ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ. ΤΡΙΤΕΚΝΟΙ 3% (ΝΕΟ)            7   7000.0  \n",
       "3  ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ. ΚΟΙΝ. ΚΡΙΤΗΡΙΑ (ΝΕΟ)            4   6550.0  \n",
       "4             ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)          284  10025.0  \n",
       "5   ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ.ΠΟΛΥΤΕΚΝΟΙ 10% (ΝΕΟ)           28   6075.0  \n",
       "6    ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ. ΤΡΙΤΕΚΝΟΙ 3% (ΝΕΟ)            8   9375.0  \n",
       "7  ΓΕΛ ΑΕΝ ΕΙΔ.ΚΑΤ. ΚΟΙΝ. ΚΡΙΤΗΡΙΑ (ΝΕΟ)            5   8675.0  \n",
       "8             ΓΕΛ ΓΕΝIKH ΣΕΙΡΑ ΗΜ. (ΝΕΟ)            8   4750.0  \n",
       "9               ΓΕΛ ΕΚΚΛΗΣΙΑΣΤΙΚΩΝ (ΝΕΟ)            0      NaN  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_index = a.to_dict('index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_records = a.to_dict('records')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.DataFrame(a_index)\n",
    "#pd.DataFrame(a_records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_dict = {\n",
    "    'A': [1,2,3,4],\n",
    "    'B': [5,6,7,8],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]}"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_df = pd.DataFrame(my_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   A  B\n",
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_df.to_excel('my_df.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   A  B\n",
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