pandas_panel_from_dict.ipynb

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  "signature": "sha256:28a18d1ca966d70e1de5674969d1d663ef08797bd6b09af82f193b1bfe49072d"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "from datetime import datetime, timedelta\n",
      "import matplotlib.pyplot as plt\n",
      "from IPython.display import display\n",
      "from IPython.html.widgets import interactive"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "def generate_data(nrows = 20, nx = 10, ny = 15):\n",
      "    \"\"\"\n",
      "    Generate test data\n",
      "    \"\"\"\n",
      "    data = np.random.randn(nrows, nx, ny)\n",
      "    d0 = datetime(1985, 4, 23)\n",
      "    dt = timedelta(days=1)\n",
      "    dates = [d0 + i * dt for i in range(nrows)]\n",
      "    \n",
      "    return [{\"year\": d.year, \n",
      "             \"month\": d.month, \n",
      "             \"day\": d.day, \n",
      "             \"hour\": d.hour, \n",
      "             \"minute\": d.minute, \n",
      "             \"second\": d.second,\n",
      "             \"field\": data[i, :, :]} for i, d in enumerate(dates)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rows = generate_data()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_pandas_panel(the_rows):\n",
      "    date_keys = [\"year\", \"month\", \"day\", \"hour\", \"minute\", \"second\"]\n",
      "    return pd.Panel({datetime(*[row[k] for k in date_keys]): pd.DataFrame(row[\"field\"]) \n",
      "                     for row in the_rows})\n",
      "    \n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ppanel = get_pandas_panel(rows)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ppanel"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "<class 'pandas.core.panel.Panel'>\n",
        "Dimensions: 20 (items) x 10 (major_axis) x 15 (minor_axis)\n",
        "Items axis: 1985-04-23 00:00:00 to 1985-05-12 00:00:00\n",
        "Major_axis axis: 0 to 9\n",
        "Minor_axis axis: 0 to 14"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_frame(frame_index):\n",
      "    im = plt.pcolormesh(ppanel[frame_index].values.transpose(), vmin=-1, vmax=1)\n",
      "    plt.colorbar(im)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "w = interactive(plot_frame, frame_index=[str(d) for d in sorted(ppanel.items)])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "display(w)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9uoiINE2EdcwDsXxWaj3+W64MxGrLRcXXbYV1QWlYlMScw2bMvwQO8gZzPXAZVncpcWfJ\n9yOAfSKEFZHseAG7vhavoPrx8o4VrOhYUe2lG4BBZc8NxpJzrXMHe1/9zq1LLsJrt8fqyqWDPg6Y\nBexbcl6hMCpClDrk/pZ3EifvcG+0GXOchSJ3trsdy9jPXSi2dBPm/sc/7yYQMJ/xzmJdkdvkLJa3\nu1yUvARQuKAwPdSJs3OzyuMNANZgM8hiOeMW4EXggrKX34Z9skzzHo/DmrcOaWTQpaLUmFdjy0TO\nwcoYW2PF8EXVXiQikrRN9Al1+HgHuBu7kNcfOBQ4GkvO5W4GJgF7Y3Xl6cANcYw/SmIuYFcvj8aS\n9HPAJmxdn4hI03TSK9QRYDK2oGEVNgM+G1t5sRNWHRjqnTcfuBx4GKvHLANmxDH+qBf//gT8axwD\nERGJS8Q7/9YCx/s8v5LKi4A/9I5Y6ZZsEcmctN+SrcQsIpmj/ZhFRFqM9mMWEWkxKmWIiLSY9+PZ\n5K1plJhFJHNUYxYRaTGqMYuItBjVmEVEWowSs4hIi1GNOYTc3251EYZHCj92Euc6rnUSB2Bm7ixn\nsQoHR93UK7yrn5jkLNbq3FwncY78srvd+fK/cvf/auMAZ6Ho/04876Mas4hIi0n7crmoraVERFpO\nhNZSYdTTRfs0YCHW6eRFrJFIzcBKzCKSORG3/awlbBdtsO1Dvwl8DOv0NA74Vq0AKmWISOYkuCqj\n2EV7BJVdtM/3Ob+0B9ErWNeTmq1ulJhFJHMSTMxBXbTbQ77+MODpWicpMYtI5iSYmOvpol3ua8D+\n3teqVGMWkczZRN9Qh48O4MOA41EsCYftol3qOGA2cCTW7LWqOGbMJ2B9rj4JvAacDjwWw/uKiDQk\naMa8sePPbOxYWO2l7TXeegCWN3enq5wxkurliSOAa4GjgCU13h+InpgPBy4FvgIswNp2u1v5LiLi\nIygx920/mL7tB3/0eM3MOb7nVVHaRfsMrDRxNDAm4Pyx2AW/Y7Flc6FELWXM9I4F3uNXsSuPIiJN\nk/A65qAu2lDZSXsaVn++33t+PXBfrQBRZsxtwAHYUpHngC2B/wX+G1vjJyLSFAnfkh3URRsqO2mP\nbSRAlBnzjkBvYCJwKDAK2A/7hBARaZpO2kIdrSrKx8q73tcrgde973+AJeay5HxXyfd7E3yTjIj0\nJI922hG3Vk66YURJzGuBl0KdedfdEcKEN2brU5zEOXPdz5zEAbi8MNhZrFxuqrNYhYl5Z7FmOorj\ncsc3l/r3c7drHqvj+W+46f10b2IUtRBzA3Au8AB2N8xU4N6ogxIRiaJzc7rvnYs6+lnAdsBS7ILf\nncB3ow5KRCSKzs09t5QBNkv+uneIiLSEnp6YRURazuYPlJhFRFrKh53pTm3pHr2IiB+VMkREWsx7\n6U5t6R69iIifzc0eQDRKzCKSPSlPzNooX0SyZ3PIozH1dMku9SC24X7NvKsZs4hkzweJvntpl+z9\nsG08FwHPVHnNyVi+DXV/u2bMIpI9nSGP+hW7ZE+nskt2kMHARcC3CdlIRDNmEcme5GrMjXTJng1c\nQ9cunDW5ScwDnUTh+bfcxHn2qyPdBAJyN77gLBZ37ews1H4TxjuLdVUuqOtPvHZzuBPgT3OO/tiB\nfEw7voWKFdcbJdeqo94u2aOxtlPnYt1NQlEpQ0Syp/GLfx3E1yV7C2ymfJ73+qKan3QqZYhI9gSV\nMp7qgKc7qr2yvcY719MlexDWfu9O73HxdsSXgC9h9WlfSswikj1BiXnvdjuKfl53G4V6umSvA4aU\nPN4Ja1y9P7C6WhAlZhHJnmSXy00Grse6ZK+mskv2EqyH3kveOUX9seVyr9O9tFFBiVlEsieBPoIl\n6umSXWo5hGtGqMQsItmT8luylZhFJHuSWy7nRFzL5fbA/lPcEtP7iYg0Ltm9MhIX14z5auxqo8M+\n5yIiAVo46YYRR2I+ASuGP4Ot7RMRaa6UJ+aopYxBwExgKiE35xARSdwHIY8WFXXGPAu4DngFlTFE\npFUku1wucVES8yhgHLYfKVSZMZ/1+PYffT+6vT+j2wdECBts+Hw3nw358S43ddnFWazrJjznLNZx\njjYWgq6V/0k75KcONxZ61t08qDAtub/3jlXQ8UbJE9V2NK5HyldlREnMhwE7YwuqwXZdasPueBld\neuLZ+e0RESnXvoMdRTPjSswprzFHSczXAnd43+eAb2GJ+uyIYxIRiaaF68dhREnM73pH0Qbv8ZuR\nRiQiElUPrjGXq3ubJhGRRPTgUoaISGtKeWJWBxMRyZ5k1zFvC8zDyrfLgRNrnL8r8BusJdUbwGW1\nAmjGLCLZsynRd78aW5C3A7Zc+D6sIavfmpI+wO+AK4EvY9XvT9UKoMQsItmTXCljADABGAFsxNpD\n3QOcCpzvc/7p2Ib5V5Q891StICpliEj2JFfK2BNL+8+XPLcIS9R+DgZWAL/FyhgPA/vUCqLELCLZ\n0xnyqN9ArFZcaj3BXUuGYhu9/Qjr/3cfNsPuXS2IShkikj1BpYzVHfBmR7VXdgCfC/jZY8A3sM3b\nSg3GkrOfjcAfgPne4+8D04C9qFLSUGIWkewJSsxbt9tRtLTi9ov28ifKDMDy5u50lTNGAk8HnL8Y\nOKTkcaiNR1TKEJHsSa7G/A5wN3Ax1vX6UOBogrs33YrVmcdhewmdh9Waq+6t5WTGvH/uP1yEYU2h\nv5M4jxfGOYkD8O+5ac5ikbvNWagZpzsLxZwb3ATL5VxuE+PuRttU3tKb7HK5ycD1wCpgNbY/UDHR\n7gQswTZzewlYCpwCzMGW1/0FOIYa60ZUyhCR7En2zr+1wPEBP1tJ5YXAed4RmhKziGRPD95dTkSk\nNWl3ORGRFpPyTYyUmEUke5SYRURajGrMIiItJtnlcomLeoNJH2Autifp28CTwBER31NEJJrNIY8W\nFXXG3Atbt/c57+sXgV8A+2I7KomIuNfDSxkb6X5j0H3AC8D+KDGLSLNouVw3O2L7lS6J+X1FRMJr\n4TJFGHEm5t7AbcCN2P3hIiLNocQM2EXEW7A+WFNiek8Rkcb08Boz2P6ic4HtgaPwre48XPL9zsAu\nMYStNI1LEnnfctd84z+dxAEYWni+9kkxOSOXdxYrt0fBWSxyrvZHe8BRHJhU2M5ZrBmHJvfeHW/Z\n8ZEXY3rjZGfM22I573Bsd7nzgTuqnD8dOAvb3OhJ4Ov4N279SByJ+SfYbvxfIHD14OdjCCMiWdM+\n2I6imXEl5mTV0yX7GGxb0EOwlWuXYNWFA6oFiLqOeRj2STASeA1rr7IeODHi+4qItKJil+zpVHbJ\n9jMCa0m1HPgQuw43vFaQqDPmFagLioj0HEFdstsDzn8QOAfYA0vOpwH31wqiW7JFJIMSu/pXb5fs\nBcBNwLPY9beVWJupqpSYRSSDgq7+PeodgTqIt0v2FCwRD8XKvacCD2EljneDBqHELCIZFDRjHuMd\nRbPLT2iv8cb1dsk+Alux8Yr3+CbgCqwn4F+Dgqg+LCIZ9G7Io271dsleDHwFW8GxBTZj7kX3GnUF\nJWYRyaAPQh4NmQz0w7pk30pll+z1WOkCbHncs1iCXgt8E5hIZZ26G5UyRCSDEr3DpJ4u2RuBM+oN\noMQsIhmU7nuylZhFJIPSvYuRErOIZJBmzCIiLaahFRctw0liXlS43UUYRh71rJM4hfackzgAhTXO\nQrEFeXfBLnS14xtw1wwnYX490d3fxTEOdwK8boKzUDFSKUNEpMWolCEi0mI0YxYRaTGaMYuItBjN\nmEVEWoxmzCIiLUbL5UREWky6Z8xRd5fbFpgHbMDapqjXn4i0gM0hj4ZMARZiDVlvCHH+VOBV4C2s\nu3afWi+ImphLu8WejHXMrtloUEQkWYlu+/kyMAu4PsS544HvAGOx5tW7AjXvroqSmOvtFpspHcua\nPYKkvNDsAcTv6Y5mjyAhGfx/FZtEZ8zzsFz3ZohzTwOuw/ZrXodtsH96rRdFScxB3WJHRHjP1Oj4\nZ7NHkJTlzR5A/JZ0NHsECVne7AG0sERnzEVh7sEfjuXFosXAjsA21V4U5eJfvd1iRUQccbKOuRDi\nnIFYbbmomDO3wjbc9xVlxryB+rrFiog40nDPvw7gw4CjvL12mBlzeZ4c7H1NLE8OADZh3WKLbqGy\n7ezz2CeLDh06dNQ6qjYpDameeFV779Uwi9qrMm7D+v4VjcNWaCTqDuB2urrFrsPacouIZFUbsCXw\nP8DNQF/vOT/jsUS8N1ZX7qBy8hq7bei+jvmEpAOKiDRZnsoyx0Xez8q7ZIOtY36NrnXMvV0NVERE\nRERE6pXF27X7YP8UWY5dNHgSOKKZA4rZHtidnLc0eyAxOgFb3L8Bu7B0aHOHE4uhwL3YDQ6vAlcS\nXONsVdVuax4H/AN4B3gIKw9ITO7wjv7AIdiFwbTfrt0fmEHXH8oXsQQ9rGkjitf/YUuCbm72QGJy\nOPYh+hnv8RDgX5o2mvjcjSWzPtjNCouBc5s6ovodDxwLXEP3xLwdlismYr/f5cATzkeXUX5L6W7C\nrmJmzSLsjyztTgDuxD54sjJjfhz4arMHkYBn6f4vtcuBOU0aS1TlS87OAh4redwf2/JhT5eDarao\nmxgF6Sm3a++I/a5Lmj2QiAZhG6tMJdyi+TRoAw7ANth6DngR+yf/ls0cVEzmAycB/YBPAEcC9zd1\nRI0r/3sbQfdbmDdieWQfZyNqAUkl5p5wu3ZvbPH4jcDS5g4lslnYRiuvYIvus2BH7P/RRKyuPArY\nD5jWzEHFJI8lqrexD5w/Y5vqpFH539sAKnPH21hO6TGSSsxZv117C+yf++9hFzHSbBR2seUK73FW\nZszF+22vBF7HLpT9ADiqaSOKRw6bMf8S+2f+dtiF9suaOagIyv/esp47QkkqMS/FNkgqrTGPBJ5O\nKJ5LOWxlxvbYbKyzucOJ7DBgZ2AldoX/v7Dfa2ETxxSHtcBLzR5EArbDSjRXYdujrcH+1ZbWD5zy\nGfMSLFcUDQB2I/3lwpaR1du152BXiQc0eyAx6YfVYXfA/vn/PWw29rFmDiomM4EF2IfoNsAfCLFJ\neYvLYRu1fxuro2+NLUu9tZmDakDQbc3FVRkTvJ9fjl3ElZhk8XbtYdjtlxuxf1oVjyys0S6aQXaW\ny/XCuuysxf41cAUh2vqkwEHYh8xa4A3g59iHT5rkCb6teRy29nwjWscsIiIiIiIiIiIiIiIiIiIi\nIiIiIiIiIiIiIiIiTff/mrMaxBYYSgEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c1d43d0>"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ppanel4d = pd.Panel4D({i: ppanel for i in range(5)})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ppanel.groupby??"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    }
   ],
   "metadata": {}
  }
 ]
}