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koldunovn/converter_for_natavrame

Created April 15, 2013 22:25
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Ответ на вопрос, заданный на [koldunov.net](http://koldunov.net/?p=833)
Raw
converter_for_natavrame
{
"metadata": {
"name": "converter_for_natavrame"
},
"nbformat": 3,
"nbformat_minor": 0,
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u041e\u0442\u0432\u0435\u0442 \u043d\u0430 \u0432\u043e\u043f\u0440\u043e\u0441, \u0437\u0430\u0434\u0430\u043d\u043d\u044b\u0439 \u043d\u0430 [koldunov.net](http://koldunov.net/?p=833)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0418\u043c\u043f\u043e\u0440\u0442\u0438\u0440\u0443\u0435\u043c \u043c\u043e\u0434\u0443\u043b\u044c numpy \u0438 \u0432\u043a\u043b\u044e\u0447\u0430\u0435\u043c \u043e\u0442\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0435 \u0433\u0440\u0430\u0444\u0438\u043a\u043e\u0432 \u0432\u043d\u0443\u0442\u0440\u0438 \u043d\u043e\u0443\u0442\u0431\u0443\u043a\u0430 (\u043f\u043e\u043d\u0430\u0434\u043e\u0431\u0438\u0442\u0441\u044f \u043d\u0430\u043c \u043f\u043e\u0437\u0436\u0435)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.kernel.zmq.pylab.backend_inline].\n",
"For more information, type 'help(pylab)'.\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u041d\u0430\u0437\u0432\u0430\u043d\u0438\u0435 \u0444\u0430\u0439\u043b\u0430, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043c\u044b \u0431\u0443\u0434\u0435\u043c \u0441\u0447\u0438\u0442\u044b\u0432\u0430\u0442\u044c \"sample.txt\""
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ifile = open('sample.txt', 'r')\n",
"lines = ifile.readlines()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0422\u0430\u043a \u0432\u044b\u0433\u043b\u044f\u0434\u0438\u0442 \u043f\u0440\u043e\u0447\u0438\u0442\u0430\u043d\u043d\u044b\u0439 \u043d\u0430\u043c\u0438 \u0441\u043f\u0438\u0441\u043e\u043a \u0441\u043e \u0441\u0442\u0440\u043e\u043a\u0430\u043c\u0438. \u0412\u043e \u0432\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u043d\u043e\u043c \u0432\u0430\u043c\u0438 \u043f\u0440\u0438\u043c\u0435\u0440\u0435 \u043d\u0435 \u0432\u0441\u0435 \u0441\u0442\u0440\u043e\u043a\u0438 \u0431\u044b\u043b\u0438 \u043e\u0434\u0438\u043d\u0430\u043a\u043e\u0432\u043e\u0439 \u0434\u043b\u0438\u043d\u043d\u044b. \u0422\u0430\u043a \u0441\u0442\u0440\u043e\u043a\u0430 \u0434\u043b\u044f 1977 \u0433\u043e\u0434\u0430 \u0437\u0430\u043a\u0430\u043d\u0447\u0438\u0432\u0430\u043b\u0430\u0441\u044c (\\n) \u043f\u0440\u044f\u043c\u043e \u043f\u043e\u0441\u043b\u0435 \u043e\u0431\u043e\u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0433\u043e\u0434\u0430. \u041d\u0430\u0434\u0435\u044e\u0441\u044c \u0432 \u0432\u0430\u0448\u0438\u0445 \u043e\u0440\u0438\u0433\u0438\u043d\u0430\u043b\u044c\u043d\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445 \u044d\u0442\u043e \u043d\u0435 \u0442\u0430\u043a, \u0438\u043d\u0430\u0447\u0435 \u0434\u043b\u044f \u0442\u043e\u0433\u043e \u0447\u0442\u043e\u0431\u044b \u043f\u0440\u0438\u043c\u0435\u0440 \u0440\u0430\u0431\u043e\u0442\u0430\u043b \u043f\u0440\u0438\u0434\u0451\u0442\u0441\u044f \u0432\u0440\u0443\u0447\u043d\u0443\u044e \u0432\u044b\u0440\u0430\u0432\u043d\u0438\u0432\u0430\u0442\u044c \u0432\u0441\u0435 \u0441\u0442\u0440\u043e\u043a\u0438 :)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lines"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 13,
"text": [
"['20674 1967 22.0 35.0 28.0 39.0 22.0 23.0 59.0 46.0 49.0 45.0 36.0 29.0\\n',\n",
" '20674 1968 17.0 21.0 28.0 17.0 14.0 25.0 47.0 68.0 22.0 22.0\\n',\n",
" '20674 1969 20.0 16.0 15.0 8.0 9.0 17.0 60.0 33.0 27.0 11.0 27.0 30.0\\n',\n",
" '20674 1970 11.0 17.0 24.0 10.0 19.0 14.0 56.0 10.0 24.0 25.0 36.0 43.0\\n',\n",
" '20674 1971 91.0 39.0 17.0 4.0 12.0 13.0 31.0 67.0 78.0 16.0 31.0 26.0\\n',\n",
" '20674 1972 32.0 47.0 11.0 28.0 19.0 23.0 51.0 43.0 34.0 35.0 4.0 69.0\\n',\n",
" '20674 1973 25.0 36.0 25.0 9.0 38.0 50.0 21.0 75.0 43.0 36.0 16.0 15.0\\n',\n",
" '20674 1974 26.0 40.0 30.0 21.0 44.0 31.0 44.0 22.0 61.0 32.0 27.0\\n',\n",
" '20674 1975 49.0 16.0 15.0 29.0 12.0 28.0 35.0 25.0 35.0 43.0 24.0 54.0\\n',\n",
" '20674 1976 24.0 34.0 17.0 36.0 19.0 53.0 40.0 63.0 47.0 19.0 21.0 14.0\\n',\n",
" '20674 1977 \\n',\n",
" '20674 1978 20.0 20.0 16.0 6.0 14.0 18.0 48.0 41.0 12.0 27.0 25.0 16.0\\n',\n",
" '20674 1979 22.0 10.0 9.0 8.0 48.0 38.0 34.0 24.0 13.0 19.0 26.0\\n',\n",
" '20674 1980 21.0 35.0 9.0 14.0 23.0 41.0 66.0 67.0 55.0 44.0 8.0 32.0\\n',\n",
" '20674 1981 105.0 14.0 14.0 23.0 40.0 14.0 47.0 27.0 26.0 28.0 \\n',\n",
" '20674 1982 8.0 13.0 13.0 24.0 12.0 58.0 13.0 71.0 40.0 23.0 5.0 26.0\\n']"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0422\u0430\u043a \u0432\u044b\u0433\u043b\u044f\u0434\u0438\u0442 \u0441\u0442\u0440\u043e\u043a\u0430 \u0441 \u043f\u0440\u043e\u043f\u0443\u0441\u043a\u0430\u043c\u0438:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lines[1] "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 14,
"text": [
"'20674 1968 17.0 21.0 28.0 17.0 14.0 25.0 47.0 68.0 22.0 22.0\\n'"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u041c\u044b \u0435\u0441\u0442\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u043e \u043c\u043e\u0436\u0435\u043c \u0441\u0447\u0438\u0442\u0430\u0442\u044c \u044d\u0442\u043e\u0442 \u043f\u0440\u043e\u043f\u0443\u0441\u043a:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lines[1][59:62]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 25,
"text": [
"' '"
]
}
],
"prompt_number": 25
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0417\u0430\u0441\u0443\u043d\u0443\u0442\u044c \u0435\u0433\u043e \u0432 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u0443\u044e:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = lines[1][59:62]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 26
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0418 \u043f\u0440\u043e\u0432\u0435\u0440\u0438\u0442\u044c \u043c\u043e\u0436\u0435\u0442 \u043b\u0438 \u043f\u0438\u0442\u043e\u043d \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0434\u043b\u044f \u0441\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u044f \u043f\u0440\u043e\u0441\u0442\u043e \u043f\u0440\u043e\u0431\u0435\u043b\u044b, \u0431\u0435\u0437 \u0432\u0441\u044f\u043a\u0438\u0445 \\t"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a == ' '"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 29,
"text": [
"True"
]
}
],
"prompt_number": 29
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u041c\u043e\u0436\u0435\u0442 :)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0422\u0435\u043f\u0435\u0440\u044c \u0434\u0430\u0432\u0430\u0439\u0442\u0435 \u043f\u043e\u0439\u043c\u0451\u043c \u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0443 \u043d\u0430\u0441 \u0441\u0442\u0440\u043e\u043a, \u0442\u043e \u0435\u0441\u0442\u044c \u043b\u0435\u0442:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(lines)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 32,
"text": [
"16"
]
}
],
"prompt_number": 32
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0418 \u0441\u043e\u0437\u0434\u0430\u0434\u0438\u043c \u043f\u0443\u0441\u0442\u043e\u0439 numpy \u043c\u0430\u0441\u0441\u0438\u0432:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data = numpy.empty((16,12))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 168
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u041f\u043e\u0441\u043a\u043e\u043b\u044c\u043a\u0443 \u043c\u044b \u0441\u043d\u0430\u0447\u0430\u043b\u0430 \u0431\u0443\u0434\u0435\u043c \u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c \u0441 \u0434\u0430\u043d\u043d\u044b\u043c\u0438 \u0432 \u0441\u0442\u0440\u043e\u043a\u043e\u0432\u043e\u043c \u0444\u043e\u0440\u043c\u0430\u0442\u0435, \u0438 \u043c\u044b \u0437\u043d\u0430\u0435\u043c, \u0447\u0442\u043e \u043a\u0430\u0436\u0434\u043e\u0435 \u0441\u0442\u0440\u043e\u043a\u043e\u0432\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0431\u0443\u0434\u0435\u0442 \u0441\u043e\u0441\u0442\u043e\u044f\u0442\u044c \u0438\u0437 4 \u0441\u0438\u043c\u0432\u043e\u043b\u043e\u0432, \u043c\u044b \u0441\u043f\u0435\u0440\u0432\u0430 \u0437\u0430\u043f\u043e\u043b\u043d\u044f\u0435\u043c \u043c\u0430\u0441\u0441\u0438\u0432 \u0446\u0438\u0444\u0440\u0430\u043c\u0438 \u0441\u043e\u0441\u0442\u043e\u044f\u0449\u0438\u043c\u0438 \u043a\u0430\u043a \u043c\u0438\u043d\u0438\u043c\u0443\u043c \u0438\u0437 4 \u0441\u0438\u043c\u0432\u043e\u043b\u043e\u0432 (\u043b\u044e\u0431\u044b\u043c\u0438). \u0417\u0430\u0442\u0435\u043c \u043c\u0435\u043d\u044f\u0435\u043c \u0444\u043e\u0440\u043c\u0430\u0442 \u0434\u0430\u043d\u043d\u044b\u0445 \u043d\u0430 \u0441\u0442\u0440\u043e\u043a\u043e\u0432\u044b\u0439. \u0427\u0435\u0442\u0432\u0451\u0440\u043a\u0430 \u043f\u043e\u0441\u043b\u0435 S \u043e\u0437\u043d\u0430\u0447\u0430\u0435\u0442 \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0441\u0438\u043c\u0432\u043e\u043b\u043e\u0432 \u0432 \u043a\u0430\u0436\u0434\u043e\u043c \u044d\u043b\u0435\u043c\u0435\u043d\u0442\u0435 \u043c\u0430\u0441\u0441\u0438\u0432\u0430."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data.fill(9999)\n",
"data = data.astype('|S4' )"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 169
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0422\u0430\u043a \u0441\u0435\u0439\u0447\u0430\u0441 \u0432\u044b\u0433\u043b\u044f\u0434\u044f\u0442 \u043d\u0430\u0448 \u043c\u0430\u0441\u0441\u0438\u0432:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 170,
"text": [
"array([['9999', '9999', '9999', '9999', '9999', '9999', '9999', '9999',\n",
" '9999', '9999', '9999', '9999'],\n",
" ['9999', '9999', '9999', '9999', '9999', '9999', '9999', '9999',\n",
" '9999', '9999', '9999', '9999'],\n",
" ['9999', '9999', '9999', '9999', '9999', '9999', '9999', '9999',\n",
" '9999', '9999', '9999', '9999'],\n",
" ['9999', '9999', '9999', '9999', '9999', '9999', '9999', '9999',\n",
" '9999', '9999', '9999', '9999'],\n",
" ['9999', '9999', '9999', '9999', '9999', '9999', '9999', '9999',\n",
" '9999', '9999', '9999', '9999'],\n",
" ['9999', '9999', '9999', '9999', '9999', '9999', '9999', '9999',\n",
" '9999', '9999', '9999', '9999'],\n",
" ['9999', '9999', '9999', '9999', '9999', '9999', '9999', '9999',\n",
" '9999', '9999', '9999', '9999'],\n",
" ['9999', '9999', '9999', '9999', '9999', '9999', '9999', '9999',\n",
" '9999', '9999', '9999', '9999'],\n",
" ['9999', '9999', '9999', '9999', '9999', '9999', '9999', '9999',\n",
" '9999', '9999', '9999', '9999'],\n",
" ['9999', '9999', '9999', '9999', '9999', '9999', '9999', '9999',\n",
" '9999', '9999', '9999', '9999'],\n",
" ['9999', '9999', '9999', '9999', '9999', '9999', '9999', '9999',\n",
" '9999', '9999', '9999', '9999'],\n",
" ['9999', '9999', '9999', '9999', '9999', '9999', '9999', '9999',\n",
" '9999', '9999', '9999', '9999'],\n",
" ['9999', '9999', '9999', '9999', '9999', '9999', '9999', '9999',\n",
" '9999', '9999', '9999', '9999'],\n",
" ['9999', '9999', '9999', '9999', '9999', '9999', '9999', '9999',\n",
" '9999', '9999', '9999', '9999'],\n",
" ['9999', '9999', '9999', '9999', '9999', '9999', '9999', '9999',\n",
" '9999', '9999', '9999', '9999'],\n",
" ['9999', '9999', '9999', '9999', '9999', '9999', '9999', '9999',\n",
" '9999', '9999', '9999', '9999']], \n",
" dtype='|S4')"
]
}
],
"prompt_number": 170
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0422\u0435\u043f\u0435\u0440\u044c \u0437\u0430\u043f\u043e\u043b\u043d\u044f\u0435\u043c \u043d\u0430\u0448 \u043c\u0430\u0441\u0441\u0438\u0432 \u0438\u0437 \u0444\u0430\u0439\u043b\u0430, \u0438\u0441\u0445\u043e\u0434\u044f \u0438\u0437 \u043f\u0440\u0435\u0434\u043f\u043e\u043b\u043e\u0436\u0435\u043d\u0438\u044f, \u0447\u0442\u043e \u043f\u043e\u043b\u043e\u0436\u0435\u043d\u0438\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0432 \u0441\u0442\u0440\u043e\u043a\u0430\u0445 \u043d\u0435\u0438\u0437\u043c\u0435\u043d\u043d\u043e:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for year, line in enumerate(lines):\n",
" data[year,0] = line[12:16]\n",
" data[year,1] = line[18:22]\n",
" data[year,2] = line[24:28]\n",
" data[year,3] = line[30:34]\n",
" data[year,4] = line[36:40]\n",
" data[year,5] = line[42:46]\n",
" data[year,6] = line[48:52]\n",
" data[year,7] = line[54:58]\n",
" data[year,8] = line[60:64]\n",
" data[year,9] = line[66:70]\n",
" data[year,10] = line[72:76]\n",
" data[year,11] = line[78:82]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 171
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0422\u0435\u043f\u0435\u0440\u044c \u043c\u0430\u0441\u0441\u0438\u0432 \u0432\u044b\u0433\u043b\u044f\u0434\u0438\u0442 \u0432\u043e\u0442 \u0442\u0430\u043a:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 172,
"text": [
"array([['22.0', '35.0', '28.0', '39.0', '22.0', '23.0', '59.0', '46.0',\n",
" '49.0', '45.0', '36.0', '29.0'],\n",
" ['17.0', '21.0', '28.0', '17.0', '14.0', '25.0', '47.0', '68.0',\n",
" ' ', ' ', '22.0', '22.0'],\n",
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" [' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',\n",
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"\u041d\u0430\u043c \u043d\u0443\u0436\u043d\u043e \u043a\u0430\u043a \u0442\u043e \u0437\u0430\u043c\u0435\u043d\u0438\u0442\u044c \u043f\u0440\u043e\u0431\u0435\u043b\u044b. numpy \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u0435\u0442 \u043f\u0440\u0438\u043c\u0435\u043d\u0438\u0442\u044c \u0432\u0435\u043a\u0442\u043e\u0440\u0438\u0437\u043e\u0432\u0430\u043d\u043d\u0443\u044e \u043d\u043e\u0442\u0430\u0446\u0438\u044e, \u0442\u0430\u043a \u0447\u0442\u043e \u043d\u0430\u043c \u043d\u0435 \u043d\u0443\u0436\u043d\u043e \u0433\u043d\u0430\u0442\u044c \u0446\u0438\u043a\u043b \u0447\u0435\u0440\u0435\u0437 \u0432\u0435\u0441\u044c \u043c\u0430\u0441\u0441\u0438\u0432. \u0412\u044b\u0440\u0430\u0436\u0435\u043d\u0438\u0435 \u043d\u0438\u0436\u0435 \u0437\u043d\u0430\u0447\u0438\u0442 - \u0437\u0430\u043c\u0435\u043d\u0438\u0442\u044c \u0432\u0441\u0435 \u044d\u043b\u0435\u043c\u0435\u043d\u0442\u044b \u0440\u0430\u0432\u043d\u044b\u0435 4 \u043f\u0440\u043e\u0431\u0435\u043b\u0430\u043c \u043d\u0430 -999 :"
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{
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"language": "python",
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{
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"source": [
"\u0422\u0435\u043f\u0435\u0440\u044c \u043c\u0430\u0441\u0441\u0438\u0432 \u0432\u044b\u0433\u043b\u044f\u0434\u0438\u0442 \u0442\u0430\u043a:"
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{
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"array([['22.0', '35.0', '28.0', '39.0', '22.0', '23.0', '59.0', '46.0',\n",
" '49.0', '45.0', '36.0', '29.0'],\n",
" ['17.0', '21.0', '28.0', '17.0', '14.0', '25.0', '47.0', '68.0',\n",
" '-999', '-999', '22.0', '22.0'],\n",
" ['20.0', '16.0', '15.0', ' 8.0', ' 9.0', '17.0', '60.0', '33.0',\n",
" '27.0', '11.0', '27.0', '30.0'],\n",
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" '47.0', '19.0', '21.0', '14.0'],\n",
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" '12.0', '27.0', '25.0', '16.0'],\n",
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"\u041e\u0434\u043d\u0430\u043a\u043e \u044d\u043b\u0435\u043c\u0435\u043d\u0442\u044b \u0432 \u043c\u0430\u0441\u0441\u0438\u0432\u0435 \u0432\u0441\u0451 \u0435\u0449\u0451 \u0438\u043c\u0435\u044e\u0442 \u0442\u0438\u043f str, \u0447\u0442\u043e \u043d\u0430\u0441, \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e \u043d\u0435 \u043e\u0447\u0435\u043d\u044c \u0443\u0441\u0442\u0440\u0430\u0438\u0432\u0430\u0435\u0442, \u043e\u0441\u043e\u0431\u0435\u043d\u043d\u043e \u0432 \u0441\u043b\u0443\u0447\u0430\u0435, \u0435\u0441\u043b\u0438 \u043c\u044b \u0445\u043e\u0442\u0438\u043c \u0447\u0442\u043e-\u0442\u043e \u043f\u043e\u0441\u0447\u0438\u0442\u0430\u0442\u044c. \u041f\u0435\u0440\u0435\u0432\u0435\u0441\u0442\u0438 \u0441\u0442\u0440\u043e\u043a\u0438 \u0432 \u0447\u0438\u0441\u043b\u0430 \u0441 \u043f\u043b\u0430\u0432\u0430\u044e\u0449\u0435\u0439 \u0437\u0430\u043f\u044f\u0442\u043e\u0439 (\u0438\u043b\u0438 \u0432 \u043d\u0430\u0448\u0435\u043c \u0441\u043b\u0443\u0447\u0430\u0435 \u0442\u043e\u0447\u043a\u043e\u0439 :)) \u043c\u043e\u0436\u043d\u043e \u043f\u0440\u0438 \u043f\u043e\u043c\u043e\u0449\u0438 \u043a\u043e\u043c\u0430\u043d\u0434\u044b astype:"
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{
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"input": [
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"metadata": {},
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{
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{
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"text": [
"array([[ 22., 35., 28., 39., 22., 23., 59., 46., 49.,\n",
" 45., 36., 29.],\n",
" [ 17., 21., 28., 17., 14., 25., 47., 68., -999.,\n",
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" [ 20., 16., 15., 8., 9., 17., 60., 33., 27.,\n",
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" [ 11., 17., 24., 10., 19., 14., 56., 10., 24.,\n",
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" 32., -999., 27.],\n",
" [ 49., 16., 15., 29., 12., 28., 35., 25., 35.,\n",
" 43., 24., 54.],\n",
" [ 24., 34., 17., 36., 19., 53., 40., 63., 47.,\n",
" 19., 21., 14.],\n",
" [-999., -999., -999., -999., -999., -999., -999., -999., -999.,\n",
" -999., -999., -999.],\n",
" [ 20., 20., 16., 6., 14., 18., 48., 41., 12.,\n",
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" [ 22., -999., 10., 9., 8., 48., 38., 34., 24.,\n",
" 13., 19., 26.],\n",
" [ 21., 35., 9., 14., 23., 41., 66., 67., 55.,\n",
" 44., 8., 32.],\n",
" [ 5., 14., 14., 23., -999., 40., 14., 47., 27.,\n",
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"\u0422\u0435\u043f\u0435\u0440\u044c \u043c\u044b \u043c\u043e\u0436\u0435\u043c \u043d\u0430\u0448\u0438 \u0434\u0430\u043d\u043d\u044b\u0435 \u043e\u0442\u043e\u0431\u0440\u0430\u0437\u0438\u0442\u044c:"
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],
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{
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"text": [
"[<matplotlib.lines.Line2D at 0xc5f6ccc>]"
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{
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8XJQkGkn7wyb0nCh5TidR1+hmRa7FgCx/hYeLZK63dnxvjHLYhN7/oiHSG90k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\nOCZyIiKDYyInIjK4/weH6nuAdDNCjgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0xc2bc2cc>"
]
}
],
"prompt_number": 177
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u041d\u043e \u0447\u0442\u043e \u043f\u0440\u043e\u0438\u0437\u043e\u0439\u0434\u0451\u0442 \u0435\u0441\u043b\u0438 \u043c\u044b \u0432\u044b\u0431\u0435\u0440\u0435\u043c \u0433\u043e\u0434 \u0441 \u043e\u0442\u0441\u0443\u0442\u0441\u0442\u0432\u0443\u044e\u0449\u0438\u043c\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f\u043c\u0438?:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(data[1,:])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 190,
"text": [
"[<matplotlib.lines.Line2D at 0xcc342ac>]"
]
},
{
"output_type": "display_data",
"png": 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2KYprb2+XNmzYIGVmZkpZWVnSe++9p3ZJitm1a5fkcDikpUuXSiUlJdLly5fV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\nkgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0xc9d6a4c>"
]
}
],
"prompt_number": 190
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u041f\u0438\u0447\u0430\u043b\u044c\u043a\u0430... \u041d\u0430\u043c \u043d\u0443\u0436\u043d\u043e \u0434\u0430\u0442\u044c \u043f\u043e\u043d\u044f\u0442\u044c \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0435 \u0441\u0442\u0440\u043e\u044f\u0449\u0435\u0439 \u0433\u0440\u0430\u0444\u0438\u043a, \u0447\u0442\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f -999 \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u0435. \u0414\u043b\u044f \u044d\u0442\u043e\u0433\u043e \u043c\u043e\u0436\u043d\u043e \u0441\u043e\u0437\u0434\u0430\u0442\u044c \u0442\u0430\u043a \u043d\u0430\u0437\u044b\u0432\u0430\u0435\u043c\u044b\u0439 masked array, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0431\u0443\u0434\u0435\u0442 \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e \u0438\u043d\u0442\u0435\u0440\u043f\u0440\u0435\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c\u0441\u044f \u043a\u0430\u043a matplotlib, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0441\u0442\u0440\u043e\u0438\u0442 \u0433\u0440\u0430\u0444\u0438\u043a\u0438, \u0442\u0430\u043a \u0438 numpy \u043f\u0440\u0438 \u0440\u0430\u0441\u0447\u0451\u0442\u0430\u0445, \u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0441\u0440\u0435\u0434\u043d\u0438\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data_miss = numpy.ma.masked_equal(data, -999)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 178
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0422\u0435\u043f\u0435\u0440\u044c \u0433\u0440\u0430\u0444\u0438\u043a \u0432\u044b\u0433\u043b\u044f\u0434\u0438\u0442 \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(data_miss[1,:])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 191,
"text": [
"[<matplotlib.lines.Line2D at 0xcd45fcc>]"
]
},
{
"output_type": "display_data",
"png": 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5c6bd5Vjqueeeo7Kykp07d1JQUMAPfvADFi1aZHdZlklJSaFv376Ul5cDsGbN\nGgYOHGhzVdbJyMjgo48+4vDhwxiGwZo1a8jMzLS7LMu5/SzLypUrmT9/PkuXLuWcc84J75sisaVm\n+fLlRnp6utGvXz/jueeei8RL2Gb9+vVGQkKCcdVVVxlZWVlGVlaWsWLFCrvLslwwGDTGjh1rdxmW\n++STT4xhw4a1a2uXk8ybN69l++GUKVOMo0eP2l1Sp9x5551G7969ja5duxqpqanGwoULjf379xuj\nRo1yxfbDk9/f66+/bvTv39+45JJLWvJl+vTpbV6nzaFZIiIS29y3NUFEJM4oyEVEHE5BLiLicApy\nERGHU5CLiDicglxExOH+Dw4KE60JoS7uAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0xcb881ec>"
]
}
],
"prompt_number": 191
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data_miss[1,:].mean()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 200,
"text": [
"28.100000000000001"
]
}
],
"prompt_number": 200
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"\u0418\u043c\u043f\u043e\u0440\u0442 \u0434\u0430\u043d\u043d\u044b\u0445 \u043f\u0440\u0438 \u043f\u043e\u043c\u043e\u0449\u0438 Pandas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0415\u0441\u043b\u0438 \u0434\u0430\u043d\u043d\u044b\u0435 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e \u0431\u0443\u0434\u0435\u0442 \u0430\u043d\u0430\u043b\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u0442\u044c, \u0442\u043e \u043b\u0443\u0447\u0448\u0435 \u043e\u0442\u043a\u0440\u044b\u0432\u0430\u0442\u044c \u0438\u0445 \u0441\u0440\u0430\u0437\u0443 \u043f\u0440\u0438 \u043f\u043e\u043c\u043e\u0449\u0438 pandas. \u041f\u0440\u0438\u043c\u0435\u0440\u044b \u043f\u043e \u0440\u0430\u0431\u043e\u0442\u0435 \u0441 \u044d\u0442\u0438\u043c \u043c\u043e\u0434\u0443\u043b\u0435\u043c \u0441 \u0433\u0438\u0434\u0440\u043e\u043c\u0435\u0442\u0435\u043e\u0440\u043e\u043b\u043e\u0433\u0438\u0447\u0435\u0441\u043a\u0438\u043c \u0443\u043a\u043b\u043e\u043d\u043e\u043c \u043c\u043e\u0436\u043d\u043e \u043d\u0430\u0439\u0442\u0438 \u043d\u0430 \u0435\u0449\u0451 \u043f\u043e\u043a\u0430 \u043d\u0435 \u043e\u0442\u043a\u0440\u044b\u0442\u043e\u043c \u0441\u0435\u043a\u0440\u0435\u0442\u043d\u043e\u043c \u0441\u0430\u0439\u0442\u0435 [Earthpy.org](http://earthpy.org/pandas-basics.html) :)"
]
},
{
"cell_type": "markdown",
"metadata": {},
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"\u0418\u043c\u043f\u043e\u0440\u0442\u0438\u0440\u0443\u0435\u043c \u043c\u043e\u0434\u0443\u043b\u044c:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas\n",
"pandas.__version__"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 193,
"text": [
"'0.10.1'"
]
}
],
"prompt_number": 193
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0422\u0435\u043f\u0435\u0440\u044c \u043c\u044b \u043c\u043e\u0436\u0435\u043c \u043e\u0442\u043a\u0440\u044b\u0442\u044c \u0432\u0435\u0441\u044c \u0444\u0430\u0439\u043b \u0434\u043b\u044f \u0440\u0430\u0431\u043e\u0442\u044b \u043f\u0440\u0438 \u043f\u043e\u043c\u043e\u0449\u0438 \u043e\u0434\u043d\u043e\u0439, \u0445\u043e\u0442\u044c \u0438 \u043e\u043e\u043e\u043e\u0447\u0435\u043d\u044c \u0434\u043b\u0438\u043d\u043d\u043e\u0439 \u043a\u043e\u043c\u0430\u043d\u0434\u044b. \u0417\u0434\u0435\u0441\u044c \n",
"\n",
"- **colspecs** - \u0441\u043f\u0438\u0441\u043e\u043a \u0441 \u0443\u043a\u0430\u0437\u0430\u043d\u0438\u0435\u043c \u043d\u0430 \u043d\u0430\u0447\u0430\u043b\u044c\u043d\u043e\u0435 \u0438 \u043a\u043e\u043d\u0435\u0447\u043d\u043e\u0435 \u043f\u043e\u043b\u043e\u0436\u0435\u043d\u0438\u0435 \u0434\u043b\u044f \u043a\u0430\u0436\u0434\u043e\u0433\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u0430 \n",
"- **names** - \u0437\u0430\u0434\u0430\u0451\u043c \u0438\u043c\u0435\u043d\u0430 \u043d\u0430\u0448\u0438\u043c \u0441\u0442\u043e\u043b\u0431\u0446\u0430\u043c\n",
"- **parse_dates** - \u0432\u043a\u043b\u044e\u0447\u0430\u0435\u043c \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u043e\u0432\u0430\u043d\u0438\u0435 \u0434\u0430\u0442 \u0434\u043b\u044f \u043a\u043e\u043b\u043e\u043d\u043a\u0438 Year\n",
"- **insex_col** - \u0434\u0435\u043b\u0430\u0435\u043c \u043a\u043e\u043b\u043e\u043d\u043a\u0443 \u0441 \u0434\u0430\u0442\u0430\u043c\u0438 \u0438\u043d\u0434\u0435\u043a\u0441\u043e\u043c \u043d\u0430\u0448\u0438\u0445 \u0434\u0430\u043d\u043d\u044b\u0445"
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{
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"pd = pandas.read_fwf('sample.txt',colspecs=[(6,10),(12,16), (18,22), (24,28),\n",
" (30,34), (36,40), (42,46), (48,52), \n",
" (54,58), (60, 64), (66,70), (72, 76), \n",
" (78,82) ], names=['Year', 'JAN', 'FEB',\n",
" 'MAR', 'APR', 'MAY', 'JUN', 'JUL', 'AUG',\n",
" 'SEP', 'OCT', 'NOV', 'DEC'], \n",
" parse_dates=['Year'], index_col=['Year'] )"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 194
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0412 \u0438\u0442\u043e\u0433\u0435 \u043f\u043e\u043b\u0443\u0447\u0430\u0435\u043c \u043a\u0440\u0430\u0441\u0438\u0432\u0443\u044e \u0442\u0430\u0431\u043b\u0438\u0447\u043a\u0443, \u0441 \u043f\u0440\u043e\u0431\u0435\u043b\u0430\u043c\u0438 \u043f\u0435\u0440\u0435\u0432\u0435\u0434\u0451\u043d\u043d\u044b\u043c\u0438 \u0432 NaN \u0430\u0432\u0442\u043e\u043c\u0430\u0442\u043e\u043c (\u043d\u0435 \u0432\u0441\u0435\u0433\u0434\u0430 \u0442\u0430\u043a \u0432\u0435\u0437\u0451\u0442 :)) \u0438 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u044c\u044e \u043b\u0435\u0433\u043a\u043e \u043f\u0440\u043e\u0432\u043e\u0434\u0438\u0442\u044c \u0440\u0430\u0437\u043d\u043e\u043e\u0431\u0440\u0430\u0437\u043d\u044b\u0439 \u0430\u043d\u0430\u043b\u0438\u0437 \u0434\u0430\u043d\u043d\u044b\u0445 "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pd"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
" <th>JAN</th>\n",
" <th>FEB</th>\n",
" <th>MAR</th>\n",
" <th>APR</th>\n",
" <th>MAY</th>\n",
" <th>JUN</th>\n",
" <th>JUL</th>\n",
" <th>AUG</th>\n",
" <th>SEP</th>\n",
" <th>OCT</th>\n",
" <th>NOV</th>\n",
" <th>DEC</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Year</th>\n",
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" <th></th>\n",
" <th></th>\n",
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" <tbody>\n",
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" <th>1967-01-01</th>\n",
" <td> 22</td>\n",
" <td> 35</td>\n",
" <td> 28</td>\n",
" <td> 39</td>\n",
" <td> 22</td>\n",
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" <td> 14</td>\n",
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" <td> 22</td>\n",
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" <td> 16</td>\n",
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" <td> 9</td>\n",
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" <td> 27</td>\n",
" <td> 30</td>\n",
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" <td> 17</td>\n",
" <td> 24</td>\n",
" <td> 10</td>\n",
" <td> 19</td>\n",
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" <td> 10</td>\n",
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" <td> 39</td>\n",
" <td> 17</td>\n",
" <td> 4</td>\n",
" <td> 12</td>\n",
" <td> 13</td>\n",
" <td> 31</td>\n",
" <td> 67</td>\n",
" <td> 78</td>\n",
" <td> 16</td>\n",
" <td> 31</td>\n",
" <td> 26</td>\n",
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" <th>1972-01-01</th>\n",
" <td> 32</td>\n",
" <td> 47</td>\n",
" <td> 11</td>\n",
" <td> 28</td>\n",
" <td> 19</td>\n",
" <td> 23</td>\n",
" <td> 51</td>\n",
" <td> 43</td>\n",
" <td> 34</td>\n",
" <td> 35</td>\n",
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" <th>1973-01-01</th>\n",
" <td> 25</td>\n",
" <td> 36</td>\n",
" <td> 25</td>\n",
" <td> 9</td>\n",
" <td> 38</td>\n",
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" <td> 21</td>\n",
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" <td> 43</td>\n",
" <td> 36</td>\n",
" <td> 16</td>\n",
" <td> 15</td>\n",
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" <td> 21</td>\n",
" <td> 44</td>\n",
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" <td> 49</td>\n",
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" <td> 15</td>\n",
" <td> 29</td>\n",
" <td> 12</td>\n",
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" <td> 43</td>\n",
" <td> 24</td>\n",
" <td> 54</td>\n",
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" <tr>\n",
" <th>1976-01-01</th>\n",
" <td> 24</td>\n",
" <td> 34</td>\n",
" <td> 17</td>\n",
" <td> 36</td>\n",
" <td> 19</td>\n",
" <td> 53</td>\n",
" <td> 40</td>\n",
" <td> 63</td>\n",
" <td> 47</td>\n",
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" <th>1977-01-01</th>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>1978-01-01</th>\n",
" <td> 20</td>\n",
" <td> 20</td>\n",
" <td> 16</td>\n",
" <td> 6</td>\n",
" <td> 14</td>\n",
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" <td> 41</td>\n",
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" <td> 25</td>\n",
" <td> 16</td>\n",
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" <tr>\n",
" <th>1979-01-01</th>\n",
" <td> 22</td>\n",
" <td>NaN</td>\n",
" <td> 10</td>\n",
" <td> 9</td>\n",
" <td> 8</td>\n",
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" <td> 38</td>\n",
" <td> 34</td>\n",
" <td> 24</td>\n",
" <td> 13</td>\n",
" <td> 19</td>\n",
" <td> 26</td>\n",
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" <tr>\n",
" <th>1980-01-01</th>\n",
" <td> 21</td>\n",
" <td> 35</td>\n",
" <td> 9</td>\n",
" <td> 14</td>\n",
" <td> 23</td>\n",
" <td> 41</td>\n",
" <td> 66</td>\n",
" <td> 67</td>\n",
" <td> 55</td>\n",
" <td> 44</td>\n",
" <td> 8</td>\n",
" <td> 32</td>\n",
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" <tr>\n",
" <th>1981-01-01</th>\n",
" <td> 5</td>\n",
" <td> 14</td>\n",
" <td> 14</td>\n",
" <td> 23</td>\n",
" <td>NaN</td>\n",
" <td> 40</td>\n",
" <td> 14</td>\n",
" <td> 47</td>\n",
" <td> 27</td>\n",
" <td> 26</td>\n",
" <td> 28</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>1982-01-01</th>\n",
" <td> 8</td>\n",
" <td> 13</td>\n",
" <td> 13</td>\n",
" <td> 24</td>\n",
" <td> 12</td>\n",
" <td> 58</td>\n",
" <td> 13</td>\n",
" <td> 71</td>\n",
" <td> 40</td>\n",
" <td> 23</td>\n",
" <td> 5</td>\n",
" <td> 26</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 196,
"text": [
" JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC\n",
"Year \n",
"1967-01-01 22 35 28 39 22 23 59 46 49 45 36 29\n",
"1968-01-01 17 21 28 17 14 25 47 68 NaN NaN 22 22\n",
"1969-01-01 20 16 15 8 9 17 60 33 27 11 27 30\n",
"1970-01-01 11 17 24 10 19 14 56 10 24 25 36 43\n",
"1971-01-01 91 39 17 4 12 13 31 67 78 16 31 26\n",
"1972-01-01 32 47 11 28 19 23 51 43 34 35 4 69\n",
"1973-01-01 25 36 25 9 38 50 21 75 43 36 16 15\n",
"1974-01-01 26 40 30 21 44 31 44 22 61 32 NaN 27\n",
"1975-01-01 49 16 15 29 12 28 35 25 35 43 24 54\n",
"1976-01-01 24 34 17 36 19 53 40 63 47 19 21 14\n",
"1977-01-01 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
"1978-01-01 20 20 16 6 14 18 48 41 12 27 25 16\n",
"1979-01-01 22 NaN 10 9 8 48 38 34 24 13 19 26\n",
"1980-01-01 21 35 9 14 23 41 66 67 55 44 8 32\n",
"1981-01-01 5 14 14 23 NaN 40 14 47 27 26 28 NaN\n",
"1982-01-01 8 13 13 24 12 58 13 71 40 23 5 26"
]
}
],
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},
{
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"\u041c\u043e\u0436\u0435\u043c, \u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u043d\u0430\u0440\u0438\u0441\u043e\u0432\u0430\u0442\u044c \u0445\u043e\u0434 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0434\u043b\u044f \u044f\u043d\u0432\u0430\u0440\u044f:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pd.JAN.plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 197,
"text": [
"<matplotlib.axes.AxesSubplot at 0xcd562cc>"
]
},
{
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izJkzIikpSYSHh4vk5GRx9uzZeq9zc3VO/QbQ3K/Ily/f2MKZOlWIL74QorLS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\nIh/Dwk9E5GNY+ImIfMz/AwN87aBKbs74AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0xcc49f6c>"
]
}
],
"prompt_number": 197
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0418\u043b\u0438 \u0432\u044b\u0431\u0440\u0430\u0442\u044c \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u0443\u044e\u0449\u0438\u0445 \u043d\u0430\u0441 \u043b\u0435\u0442, \u0438 \u043e\u0442\u043e\u0431\u0440\u0430\u0437\u0438\u0442\u044c \u0434\u0430\u043d\u043d\u044b\u0435 \u0432 \u0432\u0438\u0434\u0435 \u0431\u0430\u0440 \u0433\u0440\u0430\u0444\u0438\u043a\u0430:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pd.ix['1980':'1982'].plot(kind='barh')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 136,
"text": [
"<matplotlib.axes.AxesSubplot at 0xbcba76c>"
]
},
{
"output_type": "display_data",
"png": 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bmprKLkciIqIWxEXsiV+urIxjumRLeCOVKYveSEV3kARrB2ABCeCHCRFZFW9H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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0xbcdb64c>"
]
}
],
"prompt_number": 136
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0418\u043b\u0438 \u0432\u044b\u0431\u0440\u0430\u0442\u044c \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u044b\u0439 \u0433\u043e\u0434:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pd.ix[1].plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 198,
"text": [
"<matplotlib.axes.AxesSubplot at 0xce9460c>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0xce985ec>"
]
}
],
"prompt_number": 198
}
],
"metadata": {}
}
]
}
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