"
]
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p.show()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import matplotlib.rcsetup as rcsetup\n",
"print(rcsetup.all_backends)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[u'GTK', u'GTKAgg', u'GTKCairo', u'MacOSX', u'Qt4Agg', u'TkAgg', u'WX', u'WXAgg', u'CocoaAgg', u'GTK3Cairo', u'GTK3Agg', u'WebAgg', u'agg', u'cairo', u'emf', u'gdk', u'pdf', u'pgf', u'ps', u'svg', u'template']\n"
]
}
],
"prompt_number": 27
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"4.3.1 \ubc30\uc5f4\uc5f0\uc0b0\uc73c\ub85c \uc870\uac74\uc808 \ud45c\ud604\ud558\uae30"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xarr = np.array([1.1, 1.2, 1.3, 1.4, 1.5])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"yarr = np.array([2.1, 2.2, 2.3, 2.4, 2.5])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cond = np.array([True, False, True, True, False])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cond"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 34,
"text": [
"array([ True, False, True, True, False], dtype=bool)"
]
}
],
"prompt_number": 34
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### \uc21c\uc218 \ud30c\uc774\uc36c \ubb38\uc81c\uc810\n",
"\n",
"- \ud070 \ubc30\uc5f4 \ube60\ub974\uac8c \ucc98\ub9ac \ubd88\uac00\n",
"- \ub2e4\ucc28\uc6d0 \ubc30\uc5f4 \uc0ac\uc6a9 \ubd88\uac00"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"result = [(x if c else y)\n",
" for x, y, c in zip(xarr, yarr, cond)]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"result"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 36,
"text": [
"[1.1000000000000001, 2.2000000000000002, 1.3, 1.3999999999999999, 2.5]"
]
}
],
"prompt_number": 36
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####np.where \n",
"\n",
"- \uc544\uc8fc \uac04\uacb0\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"result = np.where(cond, xarr, yarr)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"result"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 38,
"text": [
"array([ 1.1, 2.2, 1.3, 1.4, 2.5])"
]
}
],
"prompt_number": 38
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr = np.random.randn(4, 4)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"array([[-0.22738371, 1.00962082, -0.73187583, 0.00972816],\n",
" [-1.02646242, -0.56073136, 0.19004584, 0.29742716],\n",
" [ 0.93338441, 1.28053344, -1.13966449, -1.16674502],\n",
" [ 1.09996334, 0.65299153, 0.29683578, 0.82472068]])"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####np.where\n",
"\n",
"- 2\ubc88\uc9f8, 3\ubc88\uc9f8 \uc778\uc790\ub294 \ubc30\uc5f4\uc774 \uc544\ub2c8\ub77c\ub3c4 \ub428\n",
"- \ub458 \uc911 \ud558\ub098 \ud639\uc740 \ub458 \ub2e4 \uc2a4\uce7c\ub77c \uac12\ub3c4 \ub3d9\uc791\n",
"- \ub2e4\ub978 \ubc30\uc5f4\uc5d0 \uae30\ubc18\ud55c \uc0c8\ub85c\uc6b4 \ubc30\uc5f4 \uc0dd\uc131\n",
"- np.where(\uc870\uac74, \ucc38\uac12, \uac70\uc9d3\uac12)\n",
"- \uc870\uac74\uc774 True\uc77c \ub54c \ucc38\uac12\uc774 \uc2e4\ud589\ub418\uba70, \uc870\uac74\uc774 False\uc77c \ub54c \uac70\uc9d3\uac12\uc774 \uc2e4\ud589"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.where(arr > 0, 2, -2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"array([[-2, 2, -2, 2],\n",
" [-2, -2, 2, 2],\n",
" [ 2, 2, -2, -2],\n",
" [ 2, 2, 2, 2]])"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.where(arr > 0, 2, arr)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"array([[-0.22738371, 2. , -0.73187583, 2. ],\n",
" [-1.02646242, -0.56073136, 2. , 2. ],\n",
" [ 2. , 2. , -1.13966449, -1.16674502],\n",
" [ 2. , 2. , 2. , 2. ]])"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2\uac1c\uc758 \ubd88\ub9ac\uc5b8 \ubc30\uc5f4\ub85c \uc870\ud569 \uac00\ub2a5\ud55c 4\uac00\uc9c0 \uacbd\uc6b0\n",
"\n",
"result = []\n",
"for i in range(n):\n",
" if cond1[i] and cond2[i]:\n",
" result.append(0)\n",
" elif cond1[i]:\n",
" result.append(1)\n",
" elif cond2[i]:\n",
" result.append(2)\n",
" else:\n",
" result.append(3)\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####\uc911\ucca9 where"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cond1 = False\n",
"cond2 = True"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.where(cond1 & cond2, 0,\n",
" np.where(cond1, 1, \n",
" np.where(cond2, 2, 3)))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"array(2)"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"result = 1 * (cond1 & -cond2) + 2 * (cond2 & -cond1) + 3 * -(cond1 | cond2)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"result"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"-3"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"(cond1 & -cond2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"0"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"2 * (cond2 & -cond1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"0"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"3 * -(cond1 | cond2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"-3"
]
}
],
"prompt_number": 16
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"4.3.2 \uc218\ud559 \uba54\uc11c\ub4dc\uc640 \ud1b5\uacc4 \uba54\uc11c\ub4dc"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr = np.random.randn(5, 4)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 41,
"text": [
"array([[-0.63678953, -0.62168485, 0.02496119, -1.59948229],\n",
" [ 1.50743692, -0.55029167, 1.36859314, -1.07390982],\n",
" [-1.07246643, 1.66566843, 0.21434562, -0.11323983],\n",
" [ 0.15297739, -0.14143016, -1.76468442, 0.59774678],\n",
" [-0.936252 , 1.1050578 , -0.11289262, -0.36526419]])"
]
}
],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr.mean()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 42,
"text": [
"-0.11758002809084407"
]
}
],
"prompt_number": 42
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.mean(arr)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 43,
"text": [
"-0.11758002809084407"
]
}
],
"prompt_number": 43
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr.sum()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 44,
"text": [
"-2.3516005618168814"
]
}
],
"prompt_number": 44
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####mean\uc774\ub098 sum \uac19\uc740 \ud568\uc218\ub294 \uc120\ud0dd\uc801\uc73c\ub85c axis \uc778\uc790\ub97c \ubc1b\uc544 \ud574\ub2f9 axis\uc5d0 \ub300\ud55c \ud1b5\uacc4 \uacc4\uc0b0\ud558\uace0 \ud55c \ucc28\uc218 \ub0ae\uc740 \ubc30\uc5f4\uc744 \ubc18\ud658"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr.mean(axis=1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 45,
"text": [
"array([-0.70824887, 0.31295714, 0.17357695, -0.28884761, -0.07733775])"
]
}
],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# axis \uc778\uc790\ub97c \ub118\uae34 \uac83\uacfc \ub118\uae30\uc9c0 \uc54a\uc740 \uac83\uc758 \uacb0\uacfc\uac12\uc774 \uac19\ub2e4.\n",
"arr.sum(axis=0)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 49,
"text": [
"array([-0.98509366, 1.45731955, -0.2696771 , -2.55414935])"
]
}
],
"prompt_number": 49
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr.sum(0)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 46,
"text": [
"array([-0.98509366, 1.45731955, -0.2696771 , -2.55414935])"
]
}
],
"prompt_number": 46
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr.sum(axis=1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 50,
"text": [
"array([-2.83299548, 1.25182856, 0.6943078 , -1.15539042, -0.30935102])"
]
}
],
"prompt_number": 50
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr.sum(1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 47,
"text": [
"array([-2.83299548, 1.25182856, 0.6943078 , -1.15539042, -0.30935102])"
]
}
],
"prompt_number": 47
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr.sum?"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 51
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Type: builtin_function_or_method\n",
"String form: \n",
"Docstring:\n",
"a.sum(axis=None, dtype=None, out=None)\n",
"\n",
"Return the sum of the array elements over the given axis.\n",
"\n",
"Refer to `numpy.sum` for full documentation.\n",
"\n",
"See Also\n",
"--------\n",
"numpy.sum : equivalent function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####cumsum\uacfc cumprod \uba54\uc11c\ub4dc\ub294 \uc911\uac04 \uacc4\uc0b0 \uac12\uc744 \ub2f4\uace0 \uc788\ub294 \ubc30\uc5f4\uc744 \ubc18\ud658"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8]])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 24,
"text": [
"array([[0, 1, 2],\n",
" [3, 4, 5],\n",
" [6, 7, 8]])"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Return the cumulative sum of the elements along the given axis.\n",
"arr.cumsum?\n",
"np.cumsum?"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# \uc138\ub85c\ub85c \ub354\ud558\uae30\n",
"# 0\uc740 column\n",
"arr.cumsum(0)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 26,
"text": [
"array([[ 0, 1, 2],\n",
" [ 3, 5, 7],\n",
" [ 9, 12, 15]])"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# \uac00\ub85c\ub85c \uacf1\ud558\uae30\n",
"# 1\uc740 row\n",
"arr.cumprod(1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 27,
"text": [
"array([[ 0, 0, 0],\n",
" [ 3, 12, 60],\n",
" [ 6, 42, 336]])"
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr.cumprod(0)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 28,
"text": [
"array([[ 0, 1, 2],\n",
" [ 0, 4, 10],\n",
" [ 0, 28, 80]])"
]
}
],
"prompt_number": 28
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"4.3.3 \ubd88\ub9ac\uc5b8 \ubc30\uc5f4\uc744 \uc704\ud55c \uba54\uc11c\ub4dc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- \uc55e\uc758 \uba54\uc11c\ub4dc\uc5d0\uc11c \ubd88\ub9ac\uc5b8 \uac12\uc740 1(True), 0(False)\uc73c\ub85c \ucde8\uae09\n",
"- \ub530\ub77c\uc11c \ubd88\ub9ac\uc5b8 \ubc30\uc5f4\uc5d0 \ub300\ud55c sum \uba54\uc11c\ub4dc\ub97c \uc2e4\ud589\ud558\uba74 True\uc778 \uc6d0\uc18c\uc758 \uac1c\uc218\ub97c \ubc18\ud658"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr = randn(100)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"(arr > 0).sum()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 30,
"text": [
"51"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 31,
"text": [
"array([ -3.23981577e-01, 8.28471364e-01, -2.28531600e-01,\n",
" 7.81542231e-01, 7.65533575e-01, -2.30715331e-01,\n",
" -7.90815091e-01, -1.51521142e+00, 9.02727281e-01,\n",
" -7.96393829e-01, -1.48280372e+00, 2.14388106e+00,\n",
" 1.22935275e+00, 9.08580945e-01, -3.17050232e+00,\n",
" -3.19110230e-01, -7.02813098e-01, -1.33922041e-01,\n",
" 3.23867223e-01, -5.41750422e-01, 7.19855447e-01,\n",
" 1.53577806e+00, -5.33780671e-01, -3.06636492e-01,\n",
" 2.22113202e+00, 4.74560650e-01, 5.32113570e-02,\n",
" 4.08017943e-01, -1.34531648e+00, -1.04639008e+00,\n",
" -3.64408292e-01, -5.69251804e-01, -3.04232587e-01,\n",
" -9.34929999e-01, -1.22969197e+00, -2.70477054e-01,\n",
" 8.81988283e-01, 6.38303932e-02, -1.84218189e-01,\n",
" -9.37450358e-02, 1.43859306e+00, 2.28406301e-01,\n",
" -3.17556858e-01, -5.85153395e-01, -1.44669215e-02,\n",
" 1.57324249e-03, 1.18927149e+00, 6.92504354e-01,\n",
" 3.17347370e-01, -6.44348559e-01, -6.87929676e-01,\n",
" 1.06098783e+00, 3.41548590e-01, 9.72900494e-01,\n",
" -1.02978994e+00, 2.06980280e+00, 2.35221308e+00,\n",
" -1.42752411e+00, 1.44456522e+00, 5.16166548e-01,\n",
" 7.78068763e-03, -1.01988745e+00, -1.34798508e+00,\n",
" 5.30907112e-01, 1.32554042e+00, -1.14103792e+00,\n",
" -1.35376884e+00, -1.65932729e+00, -6.92428198e-01,\n",
" -3.76567403e-01, 4.58025510e-01, 1.70369468e+00,\n",
" -6.22888746e-01, -8.00883755e-01, -3.91857714e-01,\n",
" 2.04939933e-01, 8.23046230e-01, 3.93639430e-01,\n",
" 2.08406878e-01, -5.24268456e-02, 1.15119295e+00,\n",
" 8.63978530e-01, 4.14495650e-01, -2.29354833e+00,\n",
" 9.70715934e-01, 6.35288166e-01, 1.19324100e+00,\n",
" 2.40637663e-01, 8.95268524e-01, 7.65764883e-01,\n",
" -1.96315521e-02, 9.32871736e-02, -4.54868527e-01,\n",
" -3.47747570e-01, -1.68156608e+00, -6.87830148e-01,\n",
" 4.88761296e-02, 1.46764057e+00, -1.85496551e+00,\n",
" 8.81609302e-01])"
]
}
],
"prompt_number": 31
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- any: \ud558\ub098 \uc774\uc0c1\uc758 True\uac12 \uac80\uc0ac\n",
"- all: \ubaa8\ub4e0 \uc6d0\uc18c\uac00 True\uc778\uc9c0 \uac80\uc0ac"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bools = np.array([False, False, True, False])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bools.any()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 33,
"text": [
"True"
]
}
],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bools.all()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 34,
"text": [
"False"
]
}
],
"prompt_number": 34
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bools = np.array([True, True, True, True])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bools.any()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 36,
"text": [
"True"
]
}
],
"prompt_number": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bools.all()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 37,
"text": [
"True"
]
}
],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# 0\ub9cc \uc544\ub2c8\uba74 True\n",
"# \ubd88\ub9ac\uc5b8 \ubc30\uc5f4\uc774 \uc544\ub2c8\uc5b4\ub3c4 \ub3d9\uc791\n",
"arr.any()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 52,
"text": [
"True"
]
}
],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr.all()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 53,
"text": [
"True"
]
}
],
"prompt_number": 53
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"4.3.4 \uc815\ub82c"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr = np.random.randn(8)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 38
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 39,
"text": [
"array([-1.29426519, 0.14954979, 0.12856676, 0.98922155, 0.51783933,\n",
" 0.08145027, 1.34539112, -1.5399891 ])"
]
}
],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr.sort()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 41,
"text": [
"array([-1.5399891 , -1.29426519, 0.08145027, 0.12856676, 0.14954979,\n",
" 0.51783933, 0.98922155, 1.34539112])"
]
}
],
"prompt_number": 41
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### \ub2e4\ucc28\uc6d0 \ubc30\uc5f4\uc758 \uc815\ub82c\uc740 sort \uba54\uc11c\ub4dc\uc5d0 \ub118\uae34 \ucd95\uc758 \uac12\uc5d0 \ub530\ub77c 1\ucc28\uc6d0 \ubd80\ubd84 \uc815\ub82c"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr = randn(5, 3)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 65
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 66,
"text": [
"array([[-0.64670479, -0.61555955, 0.46512793],\n",
" [ 2.45875721, 0.30812392, -2.07493597],\n",
" [-1.93571858, 0.17247077, -1.41579888],\n",
" [-0.67120369, -0.6021445 , -0.47781563],\n",
" [-0.45793084, 0.21905382, -0.39171426]])"
]
}
],
"prompt_number": 66
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# row sort\n",
"arr.sort(1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 67
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 68,
"text": [
"array([[-0.64670479, -0.61555955, 0.46512793],\n",
" [-2.07493597, 0.30812392, 2.45875721],\n",
" [-1.93571858, -1.41579888, 0.17247077],\n",
" [-0.67120369, -0.6021445 , -0.47781563],\n",
" [-0.45793084, -0.39171426, 0.21905382]])"
]
}
],
"prompt_number": 68
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# column sort\n",
"arr.sort(0)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 69
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 70,
"text": [
"array([[-2.07493597, -1.41579888, -0.47781563],\n",
" [-1.93571858, -0.61555955, 0.17247077],\n",
" [-0.67120369, -0.6021445 , 0.21905382],\n",
" [-0.64670479, -0.39171426, 0.46512793],\n",
" [-0.45793084, 0.30812392, 2.45875721]])"
]
}
],
"prompt_number": 70
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# row sort == arr.sort(1)\n",
"arr.sort()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 63
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 64,
"text": [
"array([[ -2.04858580e+00, -1.17193396e+00, 1.32127939e-01],\n",
" [ -1.47844830e+00, -7.15492360e-01, 4.59896618e-01],\n",
" [ -1.28286231e+00, -5.63080466e-01, 9.40448815e-01],\n",
" [ -4.58628453e-01, 2.07478453e-01, 1.01902093e+00],\n",
" [ 1.35560421e-03, 5.62354352e-01, 2.26660736e+00]])"
]
}
],
"prompt_number": 64
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- \ubc30\uc5f4\uc758 \ubd84\uc704\uc218\uac00 \ubb50\uc9c0??\n",
"- \uc6b0\uc120 \ubc30\uc5f4\uc744 \uc815\ub82c\ud55c \ud6c4\uc5d0 \ud2b9\uc815 \ubd80\uc704\uc758 \uac12\uc744 \uc120\ud0dd"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"large_arr = randn(1000)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 71
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"large_arr"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 72,
"text": [
"array([ -4.29508395e-01, -1.36200493e+00, 4.95139033e-01,\n",
" 4.00047918e-01, -4.60536691e-01, -2.12402831e-01,\n",
" -9.06761307e-01, -4.58112023e-01, -1.89420493e-01,\n",
" 5.88505389e-01, 1.54879159e+00, 1.94523033e+00,\n",
" -1.10438705e+00, -2.00339405e-01, 4.40621245e-01,\n",
" -6.73178500e-01, -8.11953485e-01, -5.02833922e-01,\n",
" 1.10495039e-02, 5.13181343e-02, -3.05642333e-01,\n",
" 2.99366103e-01, 4.45071326e-01, -1.57670159e+00,\n",
" 2.41872881e-01, -3.03246248e-01, -2.69677731e+00,\n",
" -1.44213262e-01, 4.95691564e-01, 1.47744247e+00,\n",
" 2.47281292e+00, -5.58374769e-01, -5.48004074e-01,\n",
" 1.47523041e+00, -1.42483309e+00, 6.88823001e-01,\n",
" -2.32917572e-01, 7.93444413e-01, -6.87697145e-01,\n",
" 9.60635202e-02, -9.06197430e-01, 4.54872711e-02,\n",
" 1.79188637e-01, 8.69338847e-01, 9.68360788e-01,\n",
" -6.70549697e-02, -5.37811590e-01, 2.51251245e-01,\n",
" 1.32979384e+00, -9.09943189e-01, 1.61039789e+00,\n",
" -6.34625947e-01, -1.84791886e-01, -3.48497830e-02,\n",
" 4.63259730e-01, 2.30507960e-01, 1.22105211e-01,\n",
" 1.13231687e+00, 5.09248157e-01, -1.00935787e+00,\n",
" -6.99156259e-01, 6.92762353e-01, 1.07228328e+00,\n",
" 2.63261095e-01, -4.61268649e-01, -1.21295079e-02,\n",
" 1.79632812e+00, -9.65936552e-02, -4.08273329e-01,\n",
" -3.00067241e-01, -1.10413596e+00, -7.83237493e-01,\n",
" -2.22909413e-01, 1.03801629e+00, -2.09290504e-01,\n",
" -8.62668264e-01, 7.14459623e-01, -1.73361316e-01,\n",
" 1.07723630e+00, 2.23126720e-01, 3.72277818e-01,\n",
" 1.17767020e+00, -7.58352226e-01, -5.02207697e-01,\n",
" -3.60673507e-01, -2.23833716e+00, -1.13628708e+00,\n",
" 6.73188450e-01, -7.92321106e-01, 5.21635062e-01,\n",
" 5.44919937e-01, -1.47894173e+00, 5.16450627e-01,\n",
" 1.19299429e+00, 1.43446337e-01, -9.28864070e-01,\n",
" -8.76316357e-01, 3.73573015e-01, -8.04425224e-01,\n",
" 5.24236617e-01, -9.08890292e-01, -6.34736133e-01,\n",
" 1.34623284e+00, -9.29476165e-01, 1.91166008e+00,\n",
" 2.60012822e-01, -9.89962582e-01, -9.99338803e-02,\n",
" -6.12590213e-01, -5.95791161e-01, -8.59075224e-01,\n",
" -5.84090801e-02, -1.71179541e+00, -1.81882533e+00,\n",
" -1.67316878e+00, -2.03148775e+00, -3.99763191e-01,\n",
" 1.02217992e-01, -7.63591358e-01, 6.27405302e-01,\n",
" -6.48137038e-01, 8.02724324e-01, 7.29054021e-01,\n",
" 8.45711411e-01, -6.27447749e-01, 9.68040204e-01,\n",
" -1.20479407e+00, -6.84909828e-01, -1.32033351e+00,\n",
" -6.74435778e-01, -2.03561662e-01, -1.29237414e+00,\n",
" 2.33811627e+00, 1.83776057e+00, 1.11556763e-01,\n",
" -1.68339529e-01, 6.56059095e-02, 2.65158367e-01,\n",
" 2.66335457e+00, 7.03558990e-01, -2.69842939e-01,\n",
" -7.02982583e-01, -4.74552810e-01, 2.34925495e-02,\n",
" -1.37147570e+00, 1.58684535e-02, -5.67087413e-01,\n",
" 3.92901939e-03, -6.60641701e-01, 8.90034849e-01,\n",
" 2.44293395e+00, -1.88248419e+00, 1.14142368e-02,\n",
" 6.25897745e-02, 7.56576704e-01, 3.29939470e-01,\n",
" 2.20075664e-01, 9.79923464e-01, 5.32580306e-02,\n",
" -6.75553770e-01, 1.35057852e+00, -4.50302535e-01,\n",
" 1.68233640e+00, 2.79554382e-01, 5.38503551e-02,\n",
" 3.56232141e-01, -4.94321902e-01, -8.86454436e-01,\n",
" 1.44427854e+00, -2.02695546e-01, -1.39553176e-03,\n",
" 4.24066238e-01, -9.31776849e-01, -4.03702618e-01,\n",
" 1.24251395e+00, 1.80955269e+00, -5.71768210e-01,\n",
" 2.39689099e-01, 1.63196605e+00, 8.07407676e-01,\n",
" 1.53420395e+00, 1.42179316e+00, 2.27843853e+00,\n",
" 8.03832220e-01, -1.85708357e-01, -3.84618866e-01,\n",
" -9.73839901e-01, -6.40194272e-01, -1.11862402e+00,\n",
" -7.72480188e-01, -1.36718409e+00, -1.07401935e+00,\n",
" 1.59331230e+00, 8.72952920e-01, 1.42536690e-01,\n",
" 6.60584412e-01, 3.60638582e-01, 2.30646928e-03,\n",
" -9.49153500e-01, 7.48317714e-02, 1.17793492e+00,\n",
" -9.43259531e-01, -1.63496881e+00, -1.40158392e+00,\n",
" -1.88909547e+00, 1.04035212e+00, -7.34070917e-02,\n",
" 2.36539211e-01, 4.96466550e-01, 1.06531880e-01,\n",
" 7.68190763e-01, 1.62263596e+00, -2.16199725e-01,\n",
" 5.50272846e-01, -2.22164026e+00, -9.05457240e-01,\n",
" -8.25657951e-01, -2.35960958e-02, 1.57733609e+00,\n",
" -8.42690033e-01, 1.01029149e+00, -3.89522140e-01,\n",
" 2.24196358e+00, 1.40676970e+00, -7.23178560e-01,\n",
" 3.77128096e-01, 1.76395097e-01, -1.20027683e+00,\n",
" 1.68082983e+00, 9.36354944e-01, 3.28059562e-01,\n",
" 1.11334640e+00, -5.50203604e-01, -7.28600779e-01,\n",
" 3.77103373e-01, -7.55643567e-01, -8.83098102e-02,\n",
" 3.54852365e-01, -7.71534792e-01, -6.92987535e-03,\n",
" -4.46883339e-01, -1.13253883e+00, -1.74730807e+00,\n",
" -2.90835798e-01, -2.64816142e-01, -1.12801293e+00,\n",
" 8.87401625e-01, -2.02248839e-01, -1.07404348e+00,\n",
" -7.64295571e-01, -9.13545357e-01, -1.52115886e+00,\n",
" 8.35135084e-01, -2.63538561e-01, 2.91623652e-01,\n",
" -8.77624702e-01, -4.12720393e-02, 8.68549898e-01,\n",
" 2.87465694e-01, 6.16241466e-02, 2.49722884e-01,\n",
" -3.57049160e-01, 8.80203727e-01, 7.53353403e-01,\n",
" -2.64328904e-01, 9.55666238e-01, 2.39277732e-01,\n",
" 5.13352635e-02, 1.33661969e-01, 8.29789594e-02,\n",
" 2.83774160e-01, -1.43584702e+00, -9.07379596e-02,\n",
" 8.29396005e-01, -7.23347532e-01, 1.29933332e+00,\n",
" 3.16552961e-01, -2.90766277e-01, 1.02591928e+00,\n",
" 2.50048040e-01, 1.62370749e-01, 5.84664973e-01,\n",
" -3.78158131e-01, 1.81228938e+00, -8.33741222e-01,\n",
" -1.51988731e+00, -2.44823924e-01, -6.12378686e-01,\n",
" -1.91732894e+00, -6.30232869e-01, 3.16687225e-01,\n",
" 1.15717681e+00, -7.12191015e-01, 6.42571088e-01,\n",
" -1.49411865e+00, -8.70006521e-02, -6.10247820e-01,\n",
" -1.91302577e-01, 1.35799894e+00, -1.95041755e+00,\n",
" -3.51379530e-01, 3.71495503e-01, -2.00287859e+00,\n",
" 1.41282095e+00, 1.85591212e+00, -8.01882224e-01,\n",
" -6.98175787e-04, -3.05526912e-01, -3.90661627e-01,\n",
" -1.73071827e+00, -8.76039187e-01, -1.79323245e+00,\n",
" -4.51736762e-01, 9.80646954e-01, 9.38154844e-02,\n",
" 5.11654704e-01, 6.15532721e-01, -1.14700052e-01,\n",
" 4.85996352e-01, 7.51639064e-02, 5.35474476e-01,\n",
" -9.88417001e-02, -8.33385972e-01, -7.31626734e-01,\n",
" -6.08904215e-01, -9.21113698e-01, -2.13082227e-01,\n",
" 7.09102578e-01, -5.41494507e-01, 3.05555972e-01,\n",
" 9.55230292e-02, -3.69808547e-01, 1.92172171e+00,\n",
" -5.58850774e-01, -1.10528092e+00, 4.24916854e-01,\n",
" -8.72818054e-02, 6.21175240e-01, 8.25144871e-01,\n",
" -9.61513023e-01, -1.62155561e+00, 3.47142814e-02,\n",
" -1.69636901e-01, 2.47938480e+00, 4.80396091e-01,\n",
" 1.24418065e+00, 2.45412578e+00, -3.43163489e-02,\n",
" 3.62465604e-01, 2.42711151e-01, -4.74011779e-01,\n",
" 1.16674977e-01, 8.97977547e-01, -2.37991927e+00,\n",
" 2.21798841e-01, -1.10939751e+00, -1.11119638e+00,\n",
" 2.72332092e-01, -1.45628595e+00, 3.20351615e-02,\n",
" 4.07968884e-01, 1.01627667e+00, -2.58984149e-02,\n",
" -2.35832288e+00, -2.31968076e-02, -2.40170151e-01,\n",
" 1.62098680e+00, -5.75439430e-01, -6.09768274e-01,\n",
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{
"cell_type": "code",
"collapsed": false,
"input": [
"ints = np.array([3, 3, 3, 2, 2, 1, 1, 4, 4])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 54
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.unique(ints)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 55,
"text": [
"array([1, 2, 3, 4])"
]
}
],
"prompt_number": 55
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####\uc21c\uc218 \ud30c\uc774\uc36c \uad6c\ud604"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sorted(set(names))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 56,
"text": [
"['Bob', 'Joe', 'Will']"
]
}
],
"prompt_number": 56
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%timeit np.unique(names)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"100000 loops, best of 3: 11 \u00b5s per loop\n"
]
}
],
"prompt_number": 57
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%timeit sorted(set(names))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"100000 loops, best of 3: 2.4 \u00b5s per loop\n"
]
}
],
"prompt_number": 58
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"values = np.array([6, 0, 0, 3, 2, 5, 6])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 59
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####np.in1d\n",
"\n",
"- 2\uac1c\uc758 \ubc30\uc5f4\uc744 \uc778\uc790\ub85c \ubc1b\uc544 \uccab \ubc88\uc9f8 \ubc30\uc5f4\uc758 \uac01 \u3147\uc6d0\uc18c\uac00 \ub450 \ubc88\uc9f8 \ubc30\uc5f4\uc758 \uc6d0\uc18c\ub97c \ud3ec\ud568\ud558\ub294\uc9c0\ub97c \ub098\ud0c0\ub0b4\ub294 \ubd88\ub9ac\uc5b8 \ubc30\uc5f4\uc744 \ubc18\ud658"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Test whether each element of a 1-D array is also present in a second array.\n",
"# Returns a boolean array the same length as 'ar1' that is True\n",
"# where an element of `ar1` is in `ar2` and False otherwise.\n",
"# in 1 dictionary?\n",
"np.in1d?"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 60
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.in1d(values, [2, 3, 6])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 61,
"text": [
"array([ True, False, False, True, True, False, True], dtype=bool)"
]
}
],
"prompt_number": 61
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### \ubc30\uc5f4 \uc9d1\ud569 \uc5f0\uc0b0\n",
"\n",
"Method | Comment\n",
"--- |----\n",
"unique(x) | \ubc30\uc5f4 x\uc5d0\uc11c \uc911\ubcf5\ub41c \uc6d0\uc18c\ub97c \uc81c\uac70\ud55c \ud6c4 \uc815\ub82c\ud558\uc5ec \ubc18\ud658\ud55c\ub2e4.\n",
"intersect1d(x, y) | \ubc30\uc5f4 x\uc640 y\uc5d0 \uacf5\ud1b5\uc801\uc73c\ub85c \uc874\uc7ac\ud558\ub294 \uc6d0\uc18c\ub97c \uc815\ub82c\ud558\uc5ec \ubc18\ud658\ud55c\ub2e4.\n",
"union1d(x, y) | \ub450 \ubc30\uc5f4\uc758 \ud569\uc9d1\ud569\uc744 \ubc18\ud658\ud55c\ub2e4.\n",
"in1d(x, y) | x\uc758 \uc6d0\uc18c \uc911 y\uc758 \uc6d0\uc18c\ub97c \ud3ec\ud568\ud558\ub294\uc9c0\ub97c \ub098\ud0c0\ub0b4\ub294 \ubd88\ub9ac\uc5b8 \ubc30\uc5f4\uc744 \ubc18\ud658\ud55c\ub2e4.\n",
"setdiff1d(x, y) | x\uc640 y\uc758 \ucc28\uc9d1\ud569\uc744 \ubc18\ud658\ud55c\ub2e4.\n",
"setxor1d(x, y) | \ud55c \ubc30\uc5f4\uc5d0\ub294 \ud3ec\ud568\ub418\uc9c0\ub9cc \ub450 \ubc30\uc5f4 \ubaa8\ub450\uc5d0\ub294 \ud3ec\ud568\ub418\uc9c0 \uc54a\ub294 \uc6d0\uc18c\ub4e4\uc758 \uc9d1\ud569\uc778 \ub300\uce6d\ucc28\uc9d1\ud569\uc744 \ubc18\ud658\ud55c\ub2e4."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"4.4 \ubc30\uc5f4\uc758 \ud30c\uc77c \uc785\u2219\ucd9c\ub825"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- NumPy\ub294 \ub514\uc2a4\ud06c\uc5d0\uc11c \ud14d\uc2a4\ud2b8\ub098 \ubc14\uc774\ub108\ub9ac \ud615\uc2dd\uc758 \ud30c\uc77c\ub85c\ubd80\ud130 \ub370\uc774\ud130\ub97c \ubd88\ub7ec\uc624\uac70\ub098 \uc800\uc7a5 \uac00\ub2a5"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"4.4.1 \ubc30\uc5f4\uc744 \ubc14\uc774\ub108\ub9ac \ud615\uc2dd\uc73c\ub85c \ub514\uc2a4\ud06c\uc5d0 \uc800\uc7a5\ud558\uae30"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr = np.arange(10)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 62
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# add file extension '.npy'\n",
"np.save('some_array', arr)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 63
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!ls"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2014\ub144 5\uc6d4 \ub300\uc548\uc5b8\uc5b4 \ucd95\uc81c \uc815\ubaa8 \ud6c4\uae30.ipynb\r\n",
"AMINOCIDS.ipynb\r\n",
"CodeAcademy_Python.ipynb\r\n",
"Crawler_qna.ipynb\r\n",
"Hacker Challenge - Programming.ipynb\r\n",
"Learn to Program(Coursera).ipynb\r\n",
"Module 1_Python Language Essentials.ipynb\r\n",
"Module 2_System Programming.ipynb\r\n",
"Multiprocessing.ipynb\r\n",
"\u001b[1m\u001b[34mNewDirectory\u001b[m\u001b[m\r\n",
"Notebook_\uc790\ub3d9\ud654.ipynb\r\n",
"Part 3 - Plotting with Matplotlib.ipynb\r\n",
"Performance_MNR.xml\r\n",
"Python 3 Object Oriented Programming.ipynb\r\n",
"Requests test.ipynb\r\n",
"Unpythonic Python.ipynb\r\n",
"Untitled0.ipynb\r\n",
"Untitled1.ipynb\r\n",
"Untitled2.ipynb\r\n",
"Untitled3.ipynb\r\n",
"Untitled4.ipynb\r\n",
"Untitled5.ipynb\r\n",
"Untitled6.ipynb\r\n",
"Untitled7.ipynb\r\n",
"array_archive.npz\r\n",
"array_ex.txt\r\n",
"ch00_Feeling.ipynb\r\n",
"ch00_Python for Data Analysis - link.ipynb\r\n",
"ch01_Preliminaries.ipynb\r\n",
"\u001b[1m\u001b[34mch02\u001b[m\u001b[m\r\n",
"ch02_Introduction Examples.ipynb\r\n",
"ch02_w.py\r\n",
"\u001b[1m\u001b[34mch03\u001b[m\u001b[m\r\n",
"ch03_Introduction IPython.ipynb\r\n",
"ch04_Numpy.ipynb\r\n",
"ch05_Pandas.ipynb\r\n",
"\u001b[1m\u001b[34mch06\u001b[m\u001b[m\r\n",
"ch06_Data loading.ipynb\r\n",
"\u001b[1m\u001b[34mch07\u001b[m\u001b[m\r\n",
"ch07_Data loading_again.ipynb\r\n",
"ch07_Data_prepare_fixing_transform_merge.ipynb\r\n",
"\u001b[1m\u001b[34mch08\u001b[m\u001b[m\r\n",
"\u001b[1m\u001b[34mch09\u001b[m\u001b[m\r\n",
"\u001b[1m\u001b[34mch11\u001b[m\u001b[m\r\n",
"\u001b[1m\u001b[34mch13\u001b[m\u001b[m\r\n",
"cover.jpg\r\n",
"data.csv\r\n",
"debugger.csv\r\n",
"ex1.csv\r\n",
"foo.py\r\n",
"foo.py.prof\r\n",
"foo.pyc\r\n",
"ghostdriver.log\r\n",
"\u001b[1m\u001b[34mimages\u001b[m\u001b[m\r\n",
"ipython_log.py\r\n",
"ipython_log.py.001~\r\n",
"ipython_script_test.py\r\n",
"lambda_sorted.ipynb\r\n",
"\u001b[1m\u001b[34mmassspec\u001b[m\u001b[m\r\n",
"mydata.csv\r\n",
"mysql_test.ipynb\r\n",
"plfda_ch07.ipynb\r\n",
"rpy2.ipynb\r\n",
"some_array.npy\r\n",
"spse.txt\r\n",
"spse2.txt\r\n",
"test_filename\r\n",
"text.txt\r\n"
]
}
],
"prompt_number": 64
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.load('some_array.npy')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 65,
"text": [
"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
]
}
],
"prompt_number": 65
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.savez('array_archive.npz', a=arr, b=arr)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 66
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arch = np.load('array_archive.npz')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 67
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arch['b']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 68,
"text": [
"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
]
}
],
"prompt_number": 68
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arch['a']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 69,
"text": [
"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
]
}
],
"prompt_number": 69
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = randn(10)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 70
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 71,
"text": [
"array([ 1.44432567, 0.98120731, 0.11043039, 1.39949054, 0.71862609,\n",
" -1.66603304, 0.32605269, 0.02229856, 0.04036821, -0.53039062])"
]
}
],
"prompt_number": 71
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"4.4.2 \ud14d\uc2a4\ud2b8 \ud30c\uc77c \ubd88\ub7ec\uc624\uae30\uc640 \uc800\uc7a5\ud558\uae30"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%writefile array_ex.txt\n",
"2.23342715,-0.37376633,-1.05142871\n",
"-0.57247149,-1.35777871,0.28676036\n",
"-0.01042671,-0.0211314,-0.72049352"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Overwriting array_ex.txt\n"
]
}
],
"prompt_number": 72
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cat array_ex.txt"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2.23342715,-0.37376633,-1.05142871\r\n",
"-0.57247149,-1.35777871,0.28676036\r\n",
"-0.01042671,-0.0211314,-0.72049352"
]
}
],
"prompt_number": 73
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr = np.loadtxt('array_ex.txt', delimiter=',')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 74
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 75,
"text": [
"array([[ 2.23342715, -0.37376633, -1.05142871],\n",
" [-0.57247149, -1.35777871, 0.28676036],\n",
" [-0.01042671, -0.0211314 , -0.72049352]])"
]
}
],
"prompt_number": 75
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- np.savetxt: np.loadtxt\uc640 \ubc18\ub300\ub85c \ubc30\uc5f4\uc744 \ud30c\uc77c\ub85c \uc800\uc7a5\n",
"- genfromtxt: loadtxt\uc640 \uc720\uc0ac\ud558\uc9c0\ub9cc \uad6c\uc870\ud654\ub41c \ubc30\uc5f4\uacfc \ub204\ub77d\ub41c \ub370\uc774\ud130 \ucc98\ub9ac\ub97c \uc704\ud574 \uc124\uacc4\n",
"\n",
"####\ud30c\uc77c \uc785\u2219\ucd9c\ub825\uc5d0 \ub300\ud55c \uc790\uc138\ud55c \ub0b4\uc6a9, \ud2b9\ud788 \uc2a4\ud504\ub808\ub4dc\uc2dc\ud2b8 \ud615\uc2dd\uc758 \ub370\uc774\ud130 \uc785\u2219\ucd9c\ub825\uc5d0 \ub300\ud55c \ub0b4\uc6a9\uc740 \ub2e4\uc74c \uc7a5\uc5d0\uc11c pandas\uc640 DataFrame\uac1d\uccb4\uc640 \ud568\uaed8 \uc0b4\ud3b4\ubcf4\uaca0\ub2e4."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"4.5 \uc120\ud615\ub300\uc218"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- \ud589\ub82c\uc758 \uacf1\uc148, \ubd84\ud560, \ud589\ub82c\uc2dd, \uc815\uc0ac\uac01 \ud589\ub82c \uc218\ud559 \uac19\uc740 \uc120\ud615\ub300\uc218\ub294 \ubc30\uc5f4\uc744 \ub2e4\ub8e8\ub294 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc5d0\uc11c \uc911\uc694\ud55c \ubd80\ubd84\n",
"- MATLAB \uac19\uc740 \ub2e4\ub978 \uc5b8\uc5b4\uc640 \ub2ec\ub9ac 2\uac1c\uc758 2\ucc28\uc6d0 \ubc30\uc5f4\uc744 * \uc5f0\uc0b0\uc790\ub85c \uacf1\ud558\ub294 \uac74 \ud589\ub82c \uacf1\uc148\uc774 \uc544\ub2c8\ub77c \ub300\uc751\ud558\ub294 \uac01\uac01\uc758 \uc6d0\uc18c\uc758 \uacf1\uc744 \uacc4\uc0b0\ud558\ub294 \uac83\n",
"- \ud589\ub82c \uacf1\uc148\uc740 \ubc30\uc5f4 \uba54\uc11c\ub4dc\uc774\uc790 numpy \ub124\uc784\uc2a4\ud398\uc774\uc2a4 \uc548\uc5d0 \uc788\ub294 \ud568\uc218\uc778 dot \ud568\uc218\ub97c \uc0ac\uc6a9\ud574\uc11c \uacc4\uc0b0"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = np.array([[1., 2., 3.], [4., 5., 6.]])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 78
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y = np.array([[6., 23.], [-1, 7], [8, 9]])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 79
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 80,
"text": [
"array([[ 1., 2., 3.],\n",
" [ 4., 5., 6.]])"
]
}
],
"prompt_number": 80
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 93,
"text": [
"(2, 3)"
]
}
],
"prompt_number": 93
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Understand. 2 * 3 dot 3 * 1 -> 2 * 1\n",
"np.ones(3).shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 96,
"text": [
"(3,)"
]
}
],
"prompt_number": 96
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 81,
"text": [
"array([[ 6., 23.],\n",
" [ -1., 7.],\n",
" [ 8., 9.]])"
]
}
],
"prompt_number": 81
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x.dot(y) # np.dot(x, y) is same"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 82,
"text": [
"array([[ 28., 64.],\n",
" [ 67., 181.]])"
]
}
],
"prompt_number": 82
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"1*6 + 2*-1 + 3*8\n",
"#6 + -2 + 24"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 83,
"text": [
"28"
]
}
],
"prompt_number": 83
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.dot(x, np.ones(3))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 84,
"text": [
"array([ 6., 15.])"
]
}
],
"prompt_number": 84
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.ones(3)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 85,
"text": [
"array([ 1., 1., 1.])"
]
}
],
"prompt_number": 85
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"1*1 + 2*1 + 3*1"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 86,
"text": [
"6"
]
}
],
"prompt_number": 86
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- numpy.linalg\ub294 \ud589\ub82c\uc758 \ubd84\ud560\uacfc \uc5ed\ud589\ub82c, \ud589\ub82c\uc2dd \uac19\uc740 \uac83 \ud3ec\ud568\n",
"- \uc774\ub294 MATLAB, R \uac19\uc740 \uc5b8\uc5b4\uc5d0\uc11c \uc0ac\uc6a9\ud558\ub294 \ud45c\uc900 \ud3ec\ud2b8\ub780 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc778 BLAS, LAPACK \ub610\ub294 Intel MKL(NumPy \ube4c\ub4dc\uc5d0 \ub530\ub77c \ub2e4\ub974\ub2e4)\ub97c \uc0ac\uc6a9\ud574\uc11c \uad6c\ud604"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from numpy.linalg import inv, qr"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 84
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Core Linear Algebra Tools\n",
"numpy.linalg?"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 85
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Inverse of a square matrix\n",
"inv?"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 86
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# QR decomposition of a matrix\n",
"qr?"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 87
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X = randn(5, 5)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 88
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mat = X.T.dot(X)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 89
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mat"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 90,
"text": [
"array([[ 2.87598785, -3.21343638, -1.70180817, -0.49037638,\n",
" -0.08239518],\n",
" [ -3.21343638, 10.70211909, -0.02810709, 0.32085212,\n",
" 5.27753009],\n",
" [ -1.70180817, -0.02810709, 3.64969751, 0.8101601 ,\n",
" 3.00981921],\n",
" [ -0.49037638, 0.32085212, 0.8101601 , 1.55438619, 2.5745824 ],\n",
" [ -0.08239518, 5.27753009, 3.00981921, 2.5745824 ,\n",
" 15.12401822]])"
]
}
],
"prompt_number": 90
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"inv(mat)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 91,
"text": [
"array([[ 605.75721027, 286.68894835, 406.87869992, 298.32436502,\n",
" -228.49681949],\n",
" [ 286.68894835, 135.80319411, 192.6025786 , 141.24158107,\n",
" -108.20012647],\n",
" [ 406.87869992, 192.6025786 , 273.64302113, 200.30686605,\n",
" -153.54805702],\n",
" [ 298.32436502, 141.24158107, 200.30686605, 147.86204307,\n",
" -112.69469828],\n",
" [-228.49681949, -108.20012647, -153.54805702, -112.69469828,\n",
" 86.31939245]])"
]
}
],
"prompt_number": 91
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# \uc6d0\ub798 \ud589\ub82c * \uc5ed\ud589\ub82c = \ub2e8\uc704 \ud589\ub82c\n",
"# \uc120\ud615\ub300\uc218\uc5d0 \ub300\ud55c \uae30\ubcf8\uc801\uc778 \uc9c0\uc2dd\uc774 \uc788\uc5b4\uc57c \uc774\ud574\uac00 \ub41c\ub2e4.\n",
"mat.dot(inv(mat))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 92,
"text": [
"array([[ 1.00000000e+00, -1.24344979e-14, -1.86517468e-13,\n",
" -5.68434189e-14, 1.13686838e-13],\n",
" [ 0.00000000e+00, 1.00000000e+00, -1.13686838e-13,\n",
" 1.13686838e-13, 0.00000000e+00],\n",
" [ -2.27373675e-13, 0.00000000e+00, 1.00000000e+00,\n",
" 5.68434189e-14, 2.84217094e-14],\n",
" [ 0.00000000e+00, 5.68434189e-14, 5.68434189e-14,\n",
" 1.00000000e+00, -2.84217094e-14],\n",
" [ 4.54747351e-13, 6.82121026e-13, 0.00000000e+00,\n",
" 2.27373675e-13, 1.00000000e+00]])"
]
}
],
"prompt_number": 92
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"q, r = qr(mat)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 93
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"r"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 94,
"text": [
"array([[ 4.66271257e+00, -9.47447021e+00, -2.50078085e+00,\n",
" -1.02825530e+00, -5.32453372e+00],\n",
" [ 0.00000000e+00, 7.94052899e+00, -3.12819979e-01,\n",
" 1.17508664e+00, 1.09384797e+01],\n",
" [ 0.00000000e+00, 0.00000000e+00, 4.42502461e+00,\n",
" 2.39248980e+00, 1.10032116e+01],\n",
" [ 0.00000000e+00, 0.00000000e+00, -0.00000000e+00,\n",
" 1.37194037e+00, 1.79188567e+00],\n",
" [ 0.00000000e+00, -0.00000000e+00, -0.00000000e+00,\n",
" 0.00000000e+00, 3.04789951e-03]])"
]
}
],
"prompt_number": 94
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"q"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 95,
"text": [
"array([[ 0.61680573, 0.33127153, -0.01258385, -0.15693731, -0.69643534],\n",
" [-0.68917745, 0.52547227, -0.35868968, -0.10722938, -0.32978311],\n",
" [-0.36498243, -0.43902897, 0.58748134, -0.33148748, -0.46799905],\n",
" [-0.10516976, -0.08507943, 0.11763528, 0.92189088, -0.34348212],\n",
" [-0.01767108, 0.64354729, 0.71568916, 0.06407652, 0.26309283]])"
]
}
],
"prompt_number": 95
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[QR \ubd84\ud574 - wiki](http://ko.wikipedia.org/wiki/QR_%EB%B6%84%ED%95%B4)"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"4.6 \ub09c\uc218 \uc0dd\uc131"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####numpy \uc7a5\uc810\n",
"\n",
"- numpy.random \ubaa8\ub4c8\uc740 \ud30c\uc774\uc36c \ub0b4\uc7a5 random \ud568\uc218\ub97c \ubcf4\uac15\ud558\uc5ec \ub2e4\uc591\ud55c \uc885\ub958\uc758 \ud655\ub960\ubd84\ud3ec\ub85c\ubd80\ud130 \ud6a8\uacfc\uc801\uc73c\ub85c \ud45c\ubcf8 \uac12\uc744 \uc0dd\uc131\ud558\ub294 \ub370 \uc8fc\ub85c \uc0ac\uc6a9\n",
"- \uc608\ub97c \ub4e4\uc5b4 normal\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud45c\uc900\uc815\uaddc\ubd84\ud3ec\ub85c\ubd80\ud130 4 \\* 4 \ud06c\uae30\uc758 \ud45c\ubcf8 \uc0dd\uc131"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"samples = np.random.normal(size=(4, 4))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 96
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"samples"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 97,
"text": [
"array([[ -7.07105991e-01, 3.99861073e-01, -9.98456073e-02,\n",
" 3.17546390e-01],\n",
" [ -1.36656129e+00, -4.78290944e-01, -3.38881567e-01,\n",
" -1.00527089e+00],\n",
" [ -1.34366135e-03, 3.64203731e-01, 2.93014657e-01,\n",
" 1.13504417e+00],\n",
" [ -1.02982622e+00, -4.03534516e-01, -6.03830553e-01,\n",
" -3.05782586e-01]])"
]
}
],
"prompt_number": 97
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####\ud30c\uc774\uc36c \ub0b4\uc7a5 random \ub2e8\uc810\n",
"\n",
"- \ud30c\uc774\uc36c \ub0b4\uc7a5 random \ubaa8\ub4c8\uc740 \ud55c \ubc88\uc5d0 \ud558\ub098\uc758 \uac12\ub9cc \uc0dd\uc131\n",
"- \ud070 \ud45c\ubcf8\uc744 \uc0dd\uc131\ud560 \ub54c \ud6e8\uc52c \ube60\ub984"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from random import normalvariate"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 98
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"N = 1000000"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 99
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%timeit samples = [normalvariate(0, 1) for i in xrange(N)]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1 loops, best of 3: 1.22 s per loop\n"
]
}
],
"prompt_number": 101
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%timeit np.random.normal(size=N)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"10 loops, best of 3: 38.1 ms per loop\n"
]
}
],
"prompt_number": 102
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### [Random sampling (numpy.random)](http://docs.scipy.org/doc/numpy/reference/routines.random.html)"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"4.7 \uacc4\ub2e8 \uc624\ub974\ub0b4\ub9ac\uae30 \uc608\uc81c"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### \uc21c\uc218 \ud30c\uc774\uc36c \ubc84\uc804"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import random\n",
"position = 0\n",
"walk = [position]\n",
"steps = 1000\n",
"for i in xrange(steps):\n",
" step = 1 if random.randint(0, 1) else -1\n",
" position += step\n",
" walk.append(position)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 123
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from matplotlib.pyplot import plot as plt"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 124
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt(walk)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 125,
"text": [
"[]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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fdZCwuJgamqYsAJo1o6Ud9UlrAnruOXUA9KabKLSDfG+3bSMvn2AmoL/8RZ1I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\nO8Z6bBkDKC0tRW5uLgoKCrB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"text": [
""
]
}
],
"prompt_number": 125
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"walk"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 126,
"text": [
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{
"cell_type": "code",
"collapsed": false,
"input": [
"steps"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 132,
"text": [
"array([-1, -1, -1, 1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, 1,\n",
" 1, 1, 1, -1, -1, 1, 1, -1, -1, -1, -1, 1, -1, -1, -1, -1, 1,\n",
" 1, 1, 1, 1, 1, -1, -1, 1, 1, -1, -1, -1, 1, 1, -1, -1, 1,\n",
" -1, 1, 1, -1, 1, 1, 1, 1, 1, 1, -1, 1, 1, -1, -1, -1, -1,\n",
" 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, 1, -1, 1, -1, -1, 1, -1,\n",
" 1, 1, -1, 1, 1, -1, -1, 1, 1, 1, -1, 1, 1, 1, -1, -1, -1,\n",
" -1, -1, -1, -1, 1, -1, 1, 1, -1, -1, 1, -1, 1, -1, -1, 1, 1,\n",
" 1, -1, 1, -1, -1, -1, -1, 1, 1, 1, -1, 1, -1, -1, 1, 1, -1,\n",
" -1, -1, -1, -1, 1, -1, 1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1,\n",
" -1, -1, 1, -1, 1, -1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, -1,\n",
" 1, 1, -1, 1, -1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, -1,\n",
" 1, -1, -1, -1, 1, -1, -1, -1, 1, 1, 1, 1, 1, -1, -1, -1, -1,\n",
" -1, 1, 1, -1, 1, 1, 1, 1, 1, -1, 1, -1, 1, 1, -1, -1, 1,\n",
" -1, 1, 1, -1, -1, -1, -1, 1, 1, 1, -1, 1, 1, 1, -1, 1, -1,\n",
" -1, -1, 1, -1, -1, -1, -1, 1, 1, 1, -1, -1, 1, -1, 1, 1, -1,\n",
" -1, -1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, -1, -1, -1, -1,\n",
" 1, -1, 1, -1, 1, 1, -1, -1, 1, -1, 1, -1, 1, -1, -1, -1, -1,\n",
" -1, 1, 1, 1, 1, 1, 1, 1, -1, -1, 1, 1, 1, 1, 1, 1, 1,\n",
" -1, -1, -1, -1, 1, -1, 1, -1, -1, 1, 1, -1, 1, -1, 1, -1, -1,\n",
" 1, 1, 1, -1, 1, 1, 1, -1, 1, -1, -1, 1, 1, 1, 1, 1, -1,\n",
" 1, 1, -1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, -1, -1, -1, 1,\n",
" -1, -1, -1, 1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, 1, -1, -1,\n",
" 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, 1, -1, 1, -1, -1,\n",
" -1, 1, 1, 1, -1, 1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1,\n",
" 1, 1, -1, 1, 1, -1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1, 1,\n",
" 1, 1, 1, -1, -1, -1, 1, -1, -1, 1, -1, 1, 1, 1, 1, -1, 1,\n",
" 1, -1, 1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1, 1,\n",
" -1, -1, -1, 1, -1, -1, -1, -1, -1, 1, -1, 1, -1, -1, 1, 1, 1,\n",
" 1, -1, 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, 1, -1, 1, 1, 1,\n",
" 1, -1, 1, 1, -1, 1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, 1,\n",
" 1, -1, -1, 1, -1, 1, 1, 1, 1, -1, 1, 1, -1, 1, 1, -1, -1,\n",
" 1, -1, -1, -1, -1, -1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, -1,\n",
" -1, 1, -1, -1, -1, -1, -1, 1, -1, 1, -1, 1, -1, -1, 1, -1, 1,\n",
" 1, -1, 1, 1, -1, 1, 1, -1, -1, 1, 1, -1, 1, -1, -1, 1, -1,\n",
" 1, -1, 1, -1, 1, -1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, -1,\n",
" 1, -1, 1, -1, -1, 1, -1, 1, -1, -1, -1, 1, 1, 1, -1, -1, 1,\n",
" 1, 1, -1, -1, 1, 1, -1, 1, -1, -1, -1, 1, 1, 1, -1, 1, 1,\n",
" 1, 1, 1, -1, -1, 1, -1, -1, 1, 1, 1, -1, 1, -1, -1, -1, 1,\n",
" -1, -1, -1, 1, 1, 1, 1, -1, -1, 1, 1, 1, 1, 1, 1, 1, -1,\n",
" -1, -1, -1, 1, -1, -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, -1, -1,\n",
" -1, 1, -1, -1, 1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, 1,\n",
" -1, -1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, -1, 1, -1, 1,\n",
" 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, 1, -1, 1, -1, -1, 1,\n",
" -1, 1, -1, 1, -1, 1, -1, -1, 1, -1, 1, -1, -1, -1, 1, 1, -1,\n",
" -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1,\n",
" -1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, 1, -1, -1, -1,\n",
" 1, -1, 1, 1, 1, 1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1, -1,\n",
" -1, -1, 1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, 1, 1, -1, -1,\n",
" -1, 1, 1, 1, 1, 1, -1, -1, 1, -1, -1, -1, 1, -1, 1, 1, -1,\n",
" -1, 1, 1, -1, -1, 1, 1, 1, -1, -1, 1, 1, -1, 1, -1, -1, 1,\n",
" 1, -1, 1, -1, -1, -1, 1, 1, 1, -1, 1, -1, 1, 1, -1, -1, -1,\n",
" 1, 1, 1, -1, -1, 1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, -1,\n",
" 1, -1, 1, -1, -1, -1, 1, -1, -1, -1, -1, 1, 1, -1, 1, -1, -1,\n",
" 1, 1, 1, 1, 1, -1, 1, -1, -1, 1, -1, -1, -1, 1, -1, -1, -1,\n",
" -1, -1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, 1, 1, -1, 1, 1,\n",
" 1, -1, -1, -1, -1, 1, -1, -1, -1, -1, 1, -1, 1, 1, 1, -1, 1,\n",
" 1, 1, -1, -1, -1, -1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, 1,\n",
" 1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, 1, -1, 1, -1, 1, 1,\n",
" -1, 1, 1, 1, 1, 1, 1, 1, -1, 1, -1, -1, -1, -1])"
]
}
],
"prompt_number": 132
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"walk = steps.cumsum()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 133
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"walk"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 134,
"text": [
"array([ -1, -2, -3, -2, -3, -4, -5, -6, -7, -6, -7, -8, -9,\n",
" -10, -11, -12, -11, -10, -9, -8, -9, -10, -9, -8, -9, -10,\n",
" -11, -12, -11, -12, -13, -14, -15, -14, -13, -12, -11, -10, -9,\n",
" -10, -11, -10, -9, -10, -11, -12, -11, -10, -11, -12, -11, -12,\n",
" -11, -10, -11, -10, -9, -8, -7, -6, -5, -6, -5, -4, -5,\n",
" -6, -7, -8, -7, -6, -7, -6, -5, -4, -3, -4, -5, -4,\n",
" -3, -4, -3, -4, -5, -4, -5, -4, -3, -4, -3, -2, -3,\n",
" -4, -3, -2, -1, -2, -1, 0, 1, 0, -1, -2, -3, -4,\n",
" -5, -6, -5, -6, -5, -4, -5, -6, -5, -6, -5, -6, -7,\n",
" -6, -5, -4, -5, -4, -5, -6, -7, -8, -7, -6, -5, -6,\n",
" -5, -6, -7, -6, -5, -6, -7, -8, -9, -10, -9, -10, -9,\n",
" -8, -9, -10, -9, -10, -9, -8, -9, -8, -9, -10, -11, -10,\n",
" -11, -10, -11, -12, -11, -12, -11, -10, -9, -8, -7, -8, -9,\n",
" -10, -9, -8, -9, -8, -9, -10, -9, -8, -7, -8, -7, -6,\n",
" -7, -8, -7, -6, -7, -6, -7, -8, -9, -8, -9, -10, -11,\n",
" -10, -9, -8, -7, -6, -7, -8, -9, -10, -11, -10, -9, -10,\n",
" -9, -8, -7, -6, -5, -6, -5, -6, -5, -4, -5, -6, -5,\n",
" -6, -5, -4, -5, -6, -7, -8, -7, -6, -5, -6, -5, -4,\n",
" -3, -4, -3, -4, -5, -6, -5, -6, -7, -8, -9, -8, -7,\n",
" -6, -7, -8, -7, -8, -7, -6, -7, -8, -9, -10, -11, -10,\n",
" -9, -10, -11, -10, -11, -12, -13, -12, -13, -14, -15, -16, -15,\n",
" -16, -15, -16, -15, -14, -15, -16, -15, -16, -15, -16, -15, -16,\n",
" -17, -18, -19, -20, -19, -18, -17, -16, -15, -14, -13, -14, -15,\n",
" -14, -13, -12, -11, -10, -9, -8, -9, -10, -11, -12, -11, -12,\n",
" -11, -12, -13, -12, -11, -12, -11, -12, -11, -12, -13, -12, -11,\n",
" -10, -11, -10, -9, -8, -9, -8, -9, -10, -9, -8, -7, -6,\n",
" -5, -6, -5, -4, -5, -4, -3, -2, -1, -2, -1, -2, -1,\n",
" 0, 1, 0, -1, -2, -1, -2, -3, -4, -3, -2, -3, -4,\n",
" -5, -4, -5, -4, -5, -4, -5, -4, -5, -6, -5, -6, -7,\n",
" -6, -7, -6, -7, -8, -7, -8, -9, -8, -7, -8, -7, -8,\n",
" -9, -10, -9, -8, -7, -8, -7, -6, -7, -6, -7, -8, -9,\n",
" -8, -9, -8, -9, -8, -7, -6, -7, -6, -5, -6, -5, -6,\n",
" -5, -6, -7, -8, -9, -8, -7, -6, -5, -4, -3, -2, -3,\n",
" -4, -5, -4, -5, -6, -5, -6, -5, -4, -3, -2, -3, -2,\n",
" -1, -2, -1, 0, 1, 2, 3, 2, 1, 2, 1, 2, 3,\n",
" 2, 3, 2, 3, 2, 1, 0, 1, 0, -1, -2, -3, -4,\n",
" -3, -4, -3, -4, -5, -4, -3, -2, -1, -2, -1, 0, -1,\n",
" 0, 1, 2, 3, 2, 1, 2, 3, 2, 3, 4, 5, 6,\n",
" 5, 6, 7, 6, 7, 6, 7, 6, 7, 8, 9, 10, 11,\n",
" 10, 9, 10, 11, 10, 9, 10, 9, 10, 11, 12, 13, 12,\n",
" 13, 14, 13, 14, 15, 14, 13, 14, 13, 12, 11, 10, 9,\n",
" 10, 11, 10, 11, 12, 13, 14, 15, 16, 17, 16, 15, 16,\n",
" 15, 14, 13, 12, 11, 12, 11, 12, 11, 12, 11, 10, 11,\n",
" 10, 11, 12, 11, 12, 13, 12, 13, 14, 13, 12, 13, 14,\n",
" 13, 14, 13, 12, 13, 12, 13, 12, 13, 12, 13, 12, 13,\n",
" 14, 15, 16, 17, 18, 17, 18, 19, 20, 19, 20, 19, 20,\n",
" 19, 18, 19, 18, 19, 18, 17, 16, 17, 18, 19, 18, 17,\n",
" 18, 19, 20, 19, 18, 19, 20, 19, 20, 19, 18, 17, 18,\n",
" 19, 20, 19, 20, 21, 22, 23, 24, 23, 22, 23, 22, 21,\n",
" 22, 23, 24, 23, 24, 23, 22, 21, 22, 21, 20, 19, 20,\n",
" 21, 22, 23, 22, 21, 22, 23, 24, 25, 26, 27, 28, 27,\n",
" 26, 25, 24, 25, 24, 23, 22, 21, 22, 21, 22, 21, 22,\n",
" 23, 24, 23, 22, 21, 22, 21, 20, 21, 22, 23, 24, 25,\n",
" 24, 23, 24, 23, 24, 25, 26, 27, 26, 25, 24, 25, 26,\n",
" 27, 26, 25, 26, 27, 28, 27, 28, 27, 28, 27, 28, 29,\n",
" 30, 31, 30, 29, 28, 29, 30, 31, 32, 33, 34, 33, 34,\n",
" 33, 32, 33, 32, 33, 32, 33, 32, 33, 32, 31, 32, 31,\n",
" 32, 31, 30, 29, 30, 31, 30, 29, 28, 27, 28, 27, 28,\n",
" 27, 28, 29, 30, 29, 28, 27, 28, 29, 30, 31, 30, 29,\n",
" 30, 29, 30, 29, 30, 29, 30, 29, 30, 29, 30, 31, 30,\n",
" 29, 28, 29, 28, 29, 30, 31, 32, 31, 32, 33, 32, 33,\n",
" 32, 33, 32, 31, 32, 31, 30, 29, 30, 31, 30, 29, 30,\n",
" 29, 28, 29, 28, 27, 26, 27, 28, 27, 26, 25, 26, 27,\n",
" 28, 29, 30, 29, 28, 29, 28, 27, 26, 27, 26, 27, 28,\n",
" 27, 26, 27, 28, 27, 26, 27, 28, 29, 28, 27, 28, 29,\n",
" 28, 29, 28, 27, 28, 29, 28, 29, 28, 27, 26, 27, 28,\n",
" 29, 28, 29, 28, 29, 30, 29, 28, 27, 28, 29, 30, 29,\n",
" 28, 29, 28, 29, 30, 29, 28, 29, 28, 27, 26, 27, 26,\n",
" 27, 26, 27, 26, 25, 24, 25, 24, 23, 22, 21, 22, 23,\n",
" 22, 23, 22, 21, 22, 23, 24, 25, 26, 25, 26, 25, 24,\n",
" 25, 24, 23, 22, 23, 22, 21, 20, 19, 18, 17, 18, 17,\n",
" 18, 19, 20, 21, 22, 21, 20, 21, 22, 21, 22, 23, 24,\n",
" 23, 22, 21, 20, 21, 20, 19, 18, 17, 18, 17, 18, 19,\n",
" 20, 19, 20, 21, 22, 21, 20, 19, 18, 19, 18, 17, 16,\n",
" 15, 16, 15, 16, 17, 16, 17, 18, 17, 18, 19, 20, 19,\n",
" 18, 19, 18, 17, 18, 19, 18, 19, 18, 19, 20, 19, 20,\n",
" 21, 22, 23, 24, 25, 26, 25, 26, 25, 24, 23, 22])"
]
}
],
"prompt_number": 134
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"walk.min(), walk.max()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 135,
"text": [
"(-20, 34)"
]
}
],
"prompt_number": 135
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"(np.abs(walk) >= 10).argmax()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 172,
"text": [
"13"
]
}
],
"prompt_number": 172
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"walk.ndim"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 176,
"text": [
"1"
]
}
],
"prompt_number": 176
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"walk.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 177,
"text": [
"(1000,)"
]
}
],
"prompt_number": 177
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Error! \ub2f9\uc5f0\ud788 axis\uac00 1\uac1c \ubc16\uc5d0 \uc5c6\ub294 \ubc30\uc5f4\uc774\ub2c8\uae4c\n",
"(np.abs(walk) >= 10).argmax(1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "axis(=1) out of bounds",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Error! \ub2f9\uc5f0\ud788 axis\uac00 1\uac1c \ubc16\uc5d0 \uc5c6\ub294 \ubc30\uc5f4\uc774\ub2c8\uae4c\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwalk\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mValueError\u001b[0m: axis(=1) out of bounds"
]
}
],
"prompt_number": 175
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"4.7.1 \ud55c \ubc88\uc5d0 \uacc4\ub2e8 \uc624\ub974\ub0b4\ub9ac\uae30 \uc2dc\ubbac\ub808\uc774\uc158\ud558\uae30"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nwalks = 5000"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 137
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nsteps = 1000"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 138
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# randint\ub294 0\ubd80\ud130 2\uae4c\uc9c0(\ud3ec\ud568 \uc548\ub428)\uc758 \uc22b\uc790\ub97c \ub79c\ub364\uc73c\ub85c \uc0dd\uc131. \uc989 0\uacfc 1\ub9cc \uc0dd\uc131\n",
"# size\uc5d0 \ub118\uae30\ub294 \uac83\uc740 \ud589, \ub82c\uc744 \ud29c\ud50c \ud615\ud0dc\ub85c \ub118\uae40\n",
"draws = np.random.randint(0, 2, size=(nwalks, nsteps))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 168
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.random.randint(0, 2, size=(10,3))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 167,
"text": [
"array([[0, 1, 1],\n",
" [0, 1, 0],\n",
" [1, 1, 0],\n",
" [0, 0, 0],\n",
" [0, 1, 0],\n",
" [0, 0, 1],\n",
" [0, 0, 1],\n",
" [0, 0, 0],\n",
" [1, 0, 1],\n",
" [1, 1, 0]])"
]
}
],
"prompt_number": 167
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# size\uc5d0 \uc778\uc790 1\uac1c\ub9cc \ub118\uc5b4\uac08 \uc2dc \ud589\uc740 1\uac1c\ub85c \uace0\uc815 \uc9c0\uc815\ub418\uace0 1\uac1c \uc778\uc790\uac12\uc740 \uceec\ub7fc\uac12\uc73c\ub85c \uc124\uc815 \ub428.\n",
"np.random.randint(0, 2, size=5)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 171,
"text": [
"array([1, 1, 0, 0, 1])"
]
}
],
"prompt_number": 171
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"draws.ndim"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 157,
"text": [
"2"
]
}
],
"prompt_number": 157
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"draws.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 158,
"text": [
"(5000, 1000)"
]
}
],
"prompt_number": 158
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"steps = np.where(draws > 0, 1, -1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 140
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"walks = steps.cumsum(1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 141
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"walks"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 142,
"text": [
"array([[ -1, 0, -1, ..., 20, 19, 18],\n",
" [ -1, 0, 1, ..., -14, -13, -14],\n",
" [ 1, 2, 1, ..., 54, 55, 54],\n",
" ..., \n",
" [ -1, 0, -1, ..., -32, -31, -30],\n",
" [ -1, -2, -3, ..., 12, 11, 10],\n",
" [ -1, -2, -3, ..., -62, -61, -62]])"
]
}
],
"prompt_number": 142
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"walks.max(), walks.min()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 143,
"text": [
"(122, -122)"
]
}
],
"prompt_number": 143
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hits30 = (np.abs(walks) >= 30).any(1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 144
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hits30"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 145,
"text": [
"array([False, True, True, ..., True, True, True], dtype=bool)"
]
}
],
"prompt_number": 145
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(hits30)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 146,
"text": [
"5000"
]
}
],
"prompt_number": 146
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hits30.sum()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 147,
"text": [
"3371"
]
}
],
"prompt_number": 147
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"crossing_times = (np.abs(walks[hits30]) >= 30).argmax(1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 148
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"crossing_times.mean()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 149,
"text": [
"507.19875407890834"
]
}
],
"prompt_number": 149
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"steps = np.random.normal(loc=0, scale=0.25, size=(nwalks, nsteps))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 150
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"steps"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 151,
"text": [
"array([[ 0.08726702, 0.44660757, -0.20851034, ..., -0.32885954,\n",
" -0.10377829, -0.55204768],\n",
" [ 0.28219952, -0.15124127, -0.26097055, ..., 0.21983047,\n",
" -0.01899429, 0.12845647],\n",
" [ 0.10835411, 0.16839816, -0.0588874 , ..., -0.0136134 ,\n",
" 0.31643727, 0.09701192],\n",
" ..., \n",
" [-0.08377433, -0.4545433 , -0.04472277, ..., -0.00461127,\n",
" -0.03111517, 0.1789901 ],\n",
" [ 0.06285865, -0.19800213, -0.28502572, ..., -0.20323394,\n",
" 0.13771058, -0.11289319],\n",
" [ 0.16258237, 0.0869184 , 0.29934197, ..., -0.10236161,\n",
" -0.11827899, 0.00986353]])"
]
}
],
"prompt_number": 151
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"walks"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 152,
"text": [
"array([[ -1, 0, -1, ..., 20, 19, 18],\n",
" [ -1, 0, 1, ..., -14, -13, -14],\n",
" [ 1, 2, 1, ..., 54, 55, 54],\n",
" ..., \n",
" [ -1, 0, -1, ..., -32, -31, -30],\n",
" [ -1, -2, -3, ..., 12, 11, 10],\n",
" [ -1, -2, -3, ..., -62, -61, -62]])"
]
}
],
"prompt_number": 152
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"walks.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 155,
"text": [
"(5000, 1000)"
]
}
],
"prompt_number": 155
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"walks.ndim"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 156,
"text": [
"2"
]
}
],
"prompt_number": 156
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.abs(walks)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 153,
"text": [
"array([[ 1, 0, 1, ..., 20, 19, 18],\n",
" [ 1, 0, 1, ..., 14, 13, 14],\n",
" [ 1, 2, 1, ..., 54, 55, 54],\n",
" ..., \n",
" [ 1, 0, 1, ..., 32, 31, 30],\n",
" [ 1, 2, 3, ..., 12, 11, 10],\n",
" [ 1, 2, 3, ..., 62, 61, 62]])"
]
}
],
"prompt_number": 153
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [About my IPython in github](https://github.com/re4lfl0w/ipython)"
]
}
],
"metadata": {}
}
]
}![](\"files/images/slice.jpg\")
\n",
"\n",
"- img src=\"files/ch04/slice.jpg\" width=\"500\" \uc774\ub807\uac8c \ud574\uc57c \ub418\ub294\uad6c\ub098..\n",
"- \uc55e\uc5d0 **files**\ub97c \ubd99\uc5ec\uc57c \ub418\ub294\uac70\uc600\uc5b4.. \uc5c4\uccad\ub09c \uc0bd\uc9c8 \ub05d\uc5d0 \uc5bb\uc5b4\ub0b8 \uc815\ubcf4"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# names\uc5d0\uc11c \ubcf4\ub4ef\uc774 0, 3\ubc88\uc9f8 \uc5f4\uc774 True\n",
"names == 'Bob'"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 181,
"text": [
"array([ True, False, False, True, False, False, False], dtype=bool)"
]
}
],
"prompt_number": 181
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# names\uc5d0\uc11c Bob\ub294 0, 3\ubc88\uc9f8\uc5d0 \uc874\uc7ac\ud55c\ub2e4.\n",
"# \uadf8\ub7ec\ubbc0\ub85c 0, 3\ubc88 \ub85c\uc6b0\uac12\uc740 True\uac00 \ub418\uc5b4 data\uc758 0, 3\ubc88\uc9f8 \ub85c\uc6b0\uac12\uc774 \ubc18\ud658\ub41c\ub2e4.\n",
"# \uc774\ub807\uac8c \ub418\uba74 \ubc18\ubcf5\ubb38\uc744 \uc4f8 \ud544\uc694\ub3c4 \uc5c6\ub124..\n",
"data[names == 'Bob']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 182,
"text": [
"array([[ 1.77221108, 0.40225415, -1.13369729, 0.09811456],\n",
" [-0.84161162, -0.2952734 , -1.86067794, -0.18273331]])"
]
}
],
"prompt_number": 182
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### \ubd88\ub9ac\uc5b8 \uc8fc\uc758 \uc0ac\ud56d\n",
"\n",
"- **\ubd88\ub9ac\uc5b8 \ubc30\uc5f4\uc740 \ubc18\ub4dc\uc2dc \uc0c9\uc778\ud558\ub824\ub294 \ucd95\uc758 \uae38\uc774\uc640 \ub3d9\uc77c\ud55c \uae38\uc774???** \uc4f0\uae30\ub294 \ud588\ub294\ub370 \ubb34\uc2a8 \uc18c\ub9ac\uc778\uc9c0 \ub098\ub3c4 \uc774\ud574\uac00 \uc548\uac04\ub2e4..\n",
"- \ubd88\ub9ac\uc5b8 \ubc30\uc5f4 \uc0c9\uc778\ub3c4 \uc2ac\ub77c\uc774\uc2a4 \ub610\ub294 \uc22b\uc790 \uc0c9\uc778\uacfc \ud568\uaed8 \ud63c\uc6a9"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"names2 = np.array(['Bob', 'Joe', 'Will'])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 193
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data[names2 == 'Bob']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 196,
"text": [
"array([[ 1.77221108, 0.40225415, -1.13369729, 0.09811456]])"
]
}
],
"prompt_number": 196
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data[names == 'Bob', 2:]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 183,
"text": [
"array([[-1.13369729, 0.09811456],\n",
" [-1.86067794, -0.18273331]])"
]
}
],
"prompt_number": 183
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data[names == 'Bob', 3]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 184,
"text": [
"array([ 0.09811456, -0.18273331])"
]
}
],
"prompt_number": 184
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### !=, -, ~ \uc73c\ub85c \ubd80\uc815"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"names != 'Bob'"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 185,
"text": [
"array([False, True, True, False, True, True, True], dtype=bool)"
]
}
],
"prompt_number": 185
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data[-(names == 'Bob')]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 186,
"text": [
"array([[-0.8974136 , -1.68999202, 0.13735165, -0.34520354],\n",
" [ 0.28554592, 0.67873863, -0.28538987, -0.14202489],\n",
" [ 0.29432671, 0.29031815, 0.71247686, 1.08140858],\n",
" [-0.44048201, 0.76291664, -0.05339118, 0.08658191],\n",
" [-1.15092796, 0.05599653, 0.1364142 , -0.1197934 ]])"
]
}
],
"prompt_number": 186
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data[~(names == 'Bob')]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 187,
"text": [
"array([[-0.8974136 , -1.68999202, 0.13735165, -0.34520354],\n",
" [ 0.28554592, 0.67873863, -0.28538987, -0.14202489],\n",
" [ 0.29432671, 0.29031815, 0.71247686, 1.08140858],\n",
" [-0.44048201, 0.76291664, -0.05339118, 0.08658191],\n",
" [-1.15092796, 0.05599653, 0.1364142 , -0.1197934 ]])"
]
}
],
"prompt_number": 187
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mask = (names == 'Bob') | (names == 'Will')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 188
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mask"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 143,
"text": [
"array([ True, False, True, True, True, False, False], dtype=bool)"
]
}
],
"prompt_number": 143
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data[mask]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 144,
"text": [
"array([[-0.83835174, 0.60824064, -1.71982262, 0.98534612],\n",
" [ 0.18016504, -1.09498744, -0.7676533 , 0.52049666],\n",
" [ 0.47402157, -0.44598838, 0.96611024, -0.82171886],\n",
" [ 0.12207348, -1.35923971, 0.9028664 , -0.62213383]])"
]
}
],
"prompt_number": 144
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data[mask, 3:]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 145,
"text": [
"array([[ 0.98534612],\n",
" [ 0.52049666],\n",
" [-0.82171886],\n",
" [-0.62213383]])"
]
}
],
"prompt_number": 145
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### \ubd88\ub9ac\uc5b8 \uc0c9\uc778\uc2dc \ud56d\uc0c1 \ub370\uc774\ud130 \ubcf5\uc0ac(\ubc14\ub00c\uc9c0 \uc54a\ub354\ub77c\ub3c4)\n",
"\n",
"- \ud30c\uc774\uc36c \uc608\uc57d\uc5b4\uc778 and, or\ub294 \ubd88\ub9ac\uc5b8 \ubc30\uc5f4\uc5d0\uc11c\ub294 \uc0ac\uc6a9 \ubd88\uac00"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data[data < 0] = 0"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 146
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 147,
"text": [
"array([[ 0. , 0.60824064, 0. , 0.98534612],\n",
" [ 0. , 0.52443732, 0. , 0.80825771],\n",
" [ 0.18016504, 0. , 0. , 0.52049666],\n",
" [ 0.47402157, 0. , 0.96611024, 0. ],\n",
" [ 0.12207348, 0. , 0.9028664 , 0. ],\n",
" [ 0.61517386, 0.82523529, 0. , 0. ],\n",
" [ 0.33102606, 0. , 0. , 0. ]])"
]
}
],
"prompt_number": 147
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data[names != 'Joe'] = 7"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 148
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 149,
"text": [
"array([[ 7. , 7. , 7. , 7. ],\n",
" [ 0. , 0.52443732, 0. , 0.80825771],\n",
" [ 7. , 7. , 7. , 7. ],\n",
" [ 7. , 7. , 7. , 7. ],\n",
" [ 7. , 7. , 7. , 7. ],\n",
" [ 0.61517386, 0.82523529, 0. , 0. ],\n",
" [ 0.33102606, 0. , 0. , 0. ]])"
]
}
],
"prompt_number": 149
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"4.1.6 \ud32c\uc2dc \uc0c9\uc778"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- \ud32c\uc2dc \uc0c9\uc778: \uc815\uc218 \ubc30\uc5f4\uc744 \uc0ac\uc6a9\ud55c \uc0c9\uc778\uc744 \uc124\uba85\ud558\uae30 \uc704\ud574 NumPy\uc5d0\uc11c \ucc28\uc6a9\ud55c \ub2e8\uc5b4"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr = np.empty((8, 4))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 237
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i in range(8):\n",
" arr[i] = i"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 240
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 241,
"text": [
"array([[ 0., 0., 0., 0.],\n",
" [ 1., 1., 1., 1.],\n",
" [ 2., 2., 2., 2.],\n",
" [ 3., 3., 3., 3.],\n",
" [ 4., 4., 4., 4.],\n",
" [ 5., 5., 5., 5.],\n",
" [ 6., 6., 6., 6.],\n",
" [ 7., 7., 7., 7.]])"
]
}
],
"prompt_number": 241
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# \ud2b9\uc815\ud55c \uc21c\uc11c\ub85c \ub85c\uc6b0 \uc120\ud0dd\n",
"arr[[4, 3, 0, 6]]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 153,
"text": [
"array([[ 4., 4., 4., 4.],\n",
" [ 3., 3., 3., 3.],\n",
" [ 0., 0., 0., 0.],\n",
" [ 6., 6., 6., 6.]])"
]
}
],
"prompt_number": 153
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr[[-3, -5, -7]]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 154,
"text": [
"array([[ 5., 5., 5., 5.],\n",
" [ 3., 3., 3., 3.],\n",
" [ 1., 1., 1., 1.]])"
]
}
],
"prompt_number": 154
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.arange(32)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 242,
"text": [
"array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
" 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31])"
]
}
],
"prompt_number": 242
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr = np.arange(32).reshape((8, 4))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 244
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 245,
"text": [
"array([[ 0, 1, 2, 3],\n",
" [ 4, 5, 6, 7],\n",
" [ 8, 9, 10, 11],\n",
" [12, 13, 14, 15],\n",
" [16, 17, 18, 19],\n",
" [20, 21, 22, 23],\n",
" [24, 25, 26, 27],\n",
" [28, 29, 30, 31]])"
]
}
],
"prompt_number": 245
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# \uc774\uac74 1\uac1c\ub9cc \uc120\ud0dd\ud558\ub294 \uac83\n",
"arr[[1, 5, 7, 2], [0, 3, 1, 2]]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 246,
"text": [
"array([ 4, 23, 29, 10])"
]
}
],
"prompt_number": 246
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- \uacb0\uacfc\ub97c \ubcf4\uba74 (1, 0), (5, 3), (7, 1), (2, 2)\uc5d0 \ub300\uc751\ud558\ub294 \uc694\uc18c\uac00 \uc120\ud0dd"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# 1, 5, 7, 2 \ub85c\uc6b0\n",
"# \uceec\ub7fc \uc5f4\uc5d0\uc11c :,\ub85c \ubaa8\ub4e0 \ud589\uc744 \uc120\ud0dd\ud574 \uc8fc\uc5b4\uc57c \ud55c\ub2e4.\n",
"# 0, 3, 1, 2 \uc5f4\uc744 \uc21c\uc11c\ub300\ub85c \uc120\ud0dd\n",
"# \uc774\uac74 \uc5f4 \uc21c\uc11c\ub300\ub85c \uc120\ud0dd\ud574\uc11c \ubcf4\uc5ec\uc8fc\uae30\n",
"arr[[1, 5, 7, 2]][:, [0, 3, 1, 2]]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 248,
"text": [
"array([[ 4, 7, 5, 6],\n",
" [20, 23, 21, 22],\n",
" [28, 31, 29, 30],\n",
" [ 8, 11, 9, 10]])"
]
}
],
"prompt_number": 248
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Construct an open mesh from multiple sequences.\n",
"# \uc774\uac83\uc744 \uc368\uc57c \uc6b0\ub9ac\uac00 \uc608\uc0c1\ud588\ub358 \ub85c\uc6b0 \uc5f4\uacfc \uceec\ub7fc\uc758 \uc21c\uc11c\ub300\ub85c \ubcfc \uc218 \uc788\ub2e4.\n",
"np.ix_"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 159,
"text": [
"![](\"files/images/matrix_product.jpeg\")
"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Dot product of two arrays.\n",
"np.dot?"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 170
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.dot(arr.T, arr)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 171,
"text": [
"array([[ 7.70430453, -1.16826805, -4.30857517],\n",
" [-1.16826805, 2.43203161, 2.87115248],\n",
" [-4.30857517, 2.87115248, 5.02879605]])"
]
}
],
"prompt_number": 171
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Matrices are not aligned.\n",
"# ex) (3, 6) * (6, 3) is available\n",
"# but (3, 6) * (3, 6) is not avaiable.\n",
"np.dot(arr.T, arr.T)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "matrices are not aligned",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m