04.09_numpy_structured_data.ipynb

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   "source": [
    "# structured data:numpy's structured arrays"
   ]
  },
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   "source": [
    "import numpy  as np\n"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "Imagine that we have several categories of data on a number of people (say, name, age, and weight), \n"
   ]
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   "source": [
    "name = ['Alice', 'Bob', 'Cathy', 'Doug']\n",
    "age = [25, 45, 37, 19]\n",
    "weight = [55.0, 85.5, 68.0, 61.5]"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "x = np.zeros(4, dtype=int)"
   ]
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   "cell_type": "code",
   "execution_count": 11,
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   },
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "[('name', '<U10'), ('age', '<i4'), ('weight', '<f8')]\n"
     ]
    }
   ],
   "source": [
    "# Use a compound data type for structured arrays\n",
    "data = np.zeros(4, dtype={'names':('name', 'age', 'weight'),\n",
    "                          'formats':('U10', 'i4', 'f8')})\n",
    "print(data.dtype)"
   ]
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   "cell_type": "code",
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "[('Alice', 25, 55. ) ('Bob', 45, 85.5) ('Cathy', 37, 68. )\n",
      " ('Doug', 19, 61.5)]\n"
     ]
    }
   ],
   "source": [
    "data['name'] = name\n",
    "data['age'] = age\n",
    "data['weight'] = weight\n",
    "print(data)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 14,
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   "outputs": [
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     "name": "stdout",
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     "text": [
      "['Alice' 'Bob' 'Cathy' 'Doug']\n",
      "Doug\n"
     ]
    }
   ],
   "source": [
    "print(data['name'])\n",
    "print(data[-1]['name'])"
   ]
  },
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   "cell_type": "code",
   "execution_count": 16,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Alice', 'Doug'], dtype='<U10')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data[data['age']<30]['name']"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## creating structured arrays"
   ]
  },
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   "cell_type": "code",
   "execution_count": 28,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype([('name', '<U10'), ('age', '<i8'), ('weight', '<f4')])"
      ]
     },
     "execution_count": 28,
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   ],
   "source": [
    "np.dtype({'names':('name', 'age', 'weight'),\n",
    "          'formats':('U10', 'i4', 'f8')})\n",
    "# equivalently\n",
    "np.dtype({'names':('name', 'age', 'weight'),\n",
    "          'formats':((np.str_,10), int, np.float32)})"
   ]
  },
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   "cell_type": "code",
   "execution_count": 29,
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype([('name', 'S10'), ('age', '<i4'), ('weight', '<f8')])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dtype([('name', 'S10'), ('age', 'i4'), ('weight', 'f8')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
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     "end_time": "2019-10-18T11:15:35.140996Z",
     "start_time": "2019-10-18T11:15:35.136432Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype([('f0', 'S10'), ('f1', '<i4'), ('f2', '<f8')])"
      ]
     },
     "execution_count": 31,
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   ],
   "source": [
    "np.dtype('S10,i4,f8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## more advanced compound type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-18T11:16:50.759907Z",
     "start_time": "2019-10-18T11:16:50.755090Z"
    }
   },
   "outputs": [],
   "source": [
    "tp = np.dtype([('id', 'i8'), ('mat', 'f8', (3,3))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-18T11:18:00.393353Z",
     "start_time": "2019-10-18T11:18:00.380429Z"
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      "(0, [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]])\n",
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      "  [0. 0. 0.]]]\n"
     ]
    }
   ],
   "source": [
    "x = np.zeros(1, dtype=tp)\n",
    "print(x[0])\n",
    "print(x['mat'])"
   ]
  }
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