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pandas_20200205.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<module 'pandas' from '/Users/kei/anaconda3/lib/python3.7/site-packages/pandas/__init__.py'>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<module 'numpy' from '/Users/kei/anaconda3/lib/python3.7/site-packages/numpy/__init__.py'>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "module 'pandas' has no attribute 'any'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-7eea23420119>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhelp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/__init__.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(name)\u001b[0m\n\u001b[1;32m 212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 213\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mPanel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 214\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"module 'pandas' has no attribute '{}'\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 215\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 216\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: module 'pandas' has no attribute 'any'"
]
}
],
"source": [
"help(pd.any)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<module 'pandas' from '/Users/kei/anaconda3/lib/python3.7/site-packages/pandas/__init__.py'>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "module 'pandas' has no attribute 'any'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-28821a5f7027>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/__init__.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(name)\u001b[0m\n\u001b[1;32m 212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 213\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mPanel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 214\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"module 'pandas' has no attribute '{}'\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 215\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 216\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: module 'pandas' has no attribute 'any'"
]
}
],
"source": [
"pd.any "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function any in module numpy:\n",
"\n",
"any(a, axis=None, out=None, keepdims=<no value>)\n",
" Test whether any array element along a given axis evaluates to True.\n",
" \n",
" Returns single boolean unless `axis` is not ``None``\n",
" \n",
" Parameters\n",
" ----------\n",
" a : array_like\n",
" Input array or object that can be converted to an array.\n",
" axis : None or int or tuple of ints, optional\n",
" Axis or axes along which a logical OR reduction is performed.\n",
" The default (`axis` = `None`) is to perform a logical OR over all\n",
" the dimensions of the input array. `axis` may be negative, in\n",
" which case it counts from the last to the first axis.\n",
" \n",
" .. versionadded:: 1.7.0\n",
" \n",
" If this is a tuple of ints, a reduction is performed on multiple\n",
" axes, instead of a single axis or all the axes as before.\n",
" out : ndarray, optional\n",
" Alternate output array in which to place the result. It must have\n",
" the same shape as the expected output and its type is preserved\n",
" (e.g., if it is of type float, then it will remain so, returning\n",
" 1.0 for True and 0.0 for False, regardless of the type of `a`).\n",
" See `doc.ufuncs` (Section \"Output arguments\") for details.\n",
" \n",
" keepdims : bool, optional\n",
" If this is set to True, the axes which are reduced are left\n",
" in the result as dimensions with size one. With this option,\n",
" the result will broadcast correctly against the input array.\n",
" \n",
" If the default value is passed, then `keepdims` will not be\n",
" passed through to the `any` method of sub-classes of\n",
" `ndarray`, however any non-default value will be. If the\n",
" sub-class' method does not implement `keepdims` any\n",
" exceptions will be raised.\n",
" \n",
" Returns\n",
" -------\n",
" any : bool or ndarray\n",
" A new boolean or `ndarray` is returned unless `out` is specified,\n",
" in which case a reference to `out` is returned.\n",
" \n",
" See Also\n",
" --------\n",
" ndarray.any : equivalent method\n",
" \n",
" all : Test whether all elements along a given axis evaluate to True.\n",
" \n",
" Notes\n",
" -----\n",
" Not a Number (NaN), positive infinity and negative infinity evaluate\n",
" to `True` because these are not equal to zero.\n",
" \n",
" Examples\n",
" --------\n",
" >>> np.any([[True, False], [True, True]])\n",
" True\n",
" \n",
" >>> np.any([[True, False], [False, False]], axis=0)\n",
" array([ True, False])\n",
" \n",
" >>> np.any([-1, 0, 5])\n",
" True\n",
" \n",
" >>> np.any(np.nan)\n",
" True\n",
" \n",
" >>> o=np.array(False)\n",
" >>> z=np.any([-1, 4, 5], out=o)\n",
" >>> z, o\n",
" (array(True), array(True))\n",
" >>> # Check now that z is a reference to o\n",
" >>> z is o\n",
" True\n",
" >>> id(z), id(o) # identity of z and o # doctest: +SKIP\n",
" (191614240, 191614240)\n",
"\n"
]
}
],
"source": [
"help(np.any)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.any([[True, False]]) "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ True, False])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.any([[True, False]], axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ True])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.any([[True, False]], axis=1) "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"data = [[True, False, False], [True, False, False], [True, False, True]]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"data = np.array([[True, False, False], [True, False, False], [True, False, True]])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ True, False, False],\n",
" [ True, False, False],\n",
" [ True, False, True]])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.any(data) "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.any(data, axis=(0,1))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "'list' object cannot be interpreted as an integer",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-17-f4177b3ca390>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36many\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36many\u001b[0;34m(a, axis, out, keepdims)\u001b[0m\n\u001b[1;32m 2268\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2269\u001b[0m \"\"\"\n\u001b[0;32m-> 2270\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapreduction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogical_or\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'any'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeepdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2271\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2272\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapreduction\u001b[0;34m(obj, ufunc, method, axis, dtype, out, **kwargs)\u001b[0m\n\u001b[1;32m 88\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mreduction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpasskwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 90\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mufunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpasskwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: 'list' object cannot be interpreted as an integer"
]
}
],
"source": [
"np.any(data, axis=[0,1]) "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.any(data)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ True, False, False],\n",
" [ True, False, False],\n",
" [ True, False, True]])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data "
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ True, False, True])"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.any(data, axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ True, True, True])"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.any(data, axis=1) "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ True, False, True]])"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.any(data, axis=0, keepdims=True)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ True],\n",
" [ True],\n",
" [ True]])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.any(data, axis=1, keepdims=True) "
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(np.arange(5 * 4).reshape((5, 4)))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'nparray' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-25-fac5f0ba09da>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msampler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnparray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'nparray' is not defined"
]
}
],
"source": [
"sampler = nparray([3, 1, 4, 2, 0])"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"sampler = np.array([3, 1, 4, 2, 0])"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
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{
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]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "'[4] not in index'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-32-d101416d9913>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msampler\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2984\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2985\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2986\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_to_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2987\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2988\u001b[0m \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[0;34m(self, obj, axis, is_setter, raise_missing)\u001b[0m\n\u001b[1;32m 1283\u001b[0m \u001b[0;31m# When setting, missing keys are not allowed, even with .loc:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1284\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"raise_missing\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mTrue\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_setter\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1285\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1286\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1287\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[1;32m 1090\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1091\u001b[0m self._validate_read_indexer(\n\u001b[0;32m-> 1092\u001b[0;31m \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraise_missing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1093\u001b[0m )\n\u001b[1;32m 1094\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[1;32m 1183\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"loc\"\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1184\u001b[0m \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1185\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{} not in index\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnot_found\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1187\u001b[0m \u001b[0;31m# we skip the warning on Categorical/Interval\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: '[4] not in index'"
]
}
],
"source": [
"df[sampler]"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
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" <td>15</td>\n",
" </tr>\n",
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" <td>16</td>\n",
" <td>17</td>\n",
" <td>18</td>\n",
" <td>19</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" 0 1 2 3\n",
"0 0 1 2 3\n",
"1 4 5 6 7\n",
"2 8 9 10 11\n",
"3 12 13 14 15\n",
"4 16 17 18 19"
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"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df "
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
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],
"source": [
"df[[2,3]]"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
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{
"data": {
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"0 2\n",
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"3 14\n",
"4 18\n",
"Name: 2, dtype: int64"
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"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[2] "
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.92366697]])"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.sample([1,1])"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on built-in function sample in module numpy.random.mtrand:\n",
"\n",
"sample(...)\n",
" This is an alias of `random_sample`. See `random_sample` for the complete\n",
" documentation.\n",
"\n"
]
}
],
"source": [
"help(np.random.sample)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on built-in function random_sample:\n",
"\n",
"random_sample(...) method of numpy.random.mtrand.RandomState instance\n",
" random_sample(size=None)\n",
" \n",
" Return random floats in the half-open interval [0.0, 1.0).\n",
" \n",
" Results are from the \"continuous uniform\" distribution over the\n",
" stated interval. To sample :math:`Unif[a, b), b > a` multiply\n",
" the output of `random_sample` by `(b-a)` and add `a`::\n",
" \n",
" (b - a) * random_sample() + a\n",
" \n",
" Parameters\n",
" ----------\n",
" size : int or tuple of ints, optional\n",
" Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n",
" ``m * n * k`` samples are drawn. Default is None, in which case a\n",
" single value is returned.\n",
" \n",
" Returns\n",
" -------\n",
" out : float or ndarray of floats\n",
" Array of random floats of shape `size` (unless ``size=None``, in which\n",
" case a single float is returned).\n",
" \n",
" Examples\n",
" --------\n",
" >>> np.random.random_sample()\n",
" 0.47108547995356098 # random\n",
" >>> type(np.random.random_sample())\n",
" <class 'float'>\n",
" >>> np.random.random_sample((5,))\n",
" array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428]) # random\n",
" \n",
" Three-by-two array of random numbers from [-5, 0):\n",
" \n",
" >>> 5 * np.random.random_sample((3, 2)) - 5\n",
" array([[-3.99149989, -0.52338984], # random\n",
" [-2.99091858, -0.79479508],\n",
" [-1.23204345, -1.75224494]])\n",
"\n"
]
}
],
"source": [
"help(np.random.random_sample)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"import random "
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<bound method Random.sample of <random.Random object at 0x7fe980884c20>>"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"random.sample"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "sample() missing 1 required positional argument: 'k'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-41-eb7af90a1d91>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: sample() missing 1 required positional argument: 'k'"
]
}
],
"source": [
"random.sample([1,3]) "
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "Sample larger than population or is negative",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-42-48d45532ac96>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/anaconda3/lib/python3.7/random.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, population, k)\u001b[0m\n\u001b[1;32m 319\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 320\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mk\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 321\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Sample larger than population or is negative\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 322\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 323\u001b[0m \u001b[0msetsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m21\u001b[0m \u001b[0;31m# size of a small set minus size of an empty list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Sample larger than population or is negative"
]
}
],
"source": [
"random.sample([1,3], 4)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on method sample in module random:\n",
"\n",
"sample(population, k) method of random.Random instance\n",
" Chooses k unique random elements from a population sequence or set.\n",
" \n",
" Returns a new list containing elements from the population while\n",
" leaving the original population unchanged. The resulting list is\n",
" in selection order so that all sub-slices will also be valid random\n",
" samples. This allows raffle winners (the sample) to be partitioned\n",
" into grand prize and second place winners (the subslices).\n",
" \n",
" Members of the population need not be hashable or unique. If the\n",
" population contains repeats, then each occurrence is a possible\n",
" selection in the sample.\n",
" \n",
" To choose a sample in a range of integers, use range as an argument.\n",
" This is especially fast and space efficient for sampling from a\n",
" large population: sample(range(10000000), 60)\n",
"\n"
]
}
],
"source": [
"help(random.sample)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"import random"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "Sample larger than population or is negative",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-45-ccb5b8484189>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/anaconda3/lib/python3.7/random.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, population, k)\u001b[0m\n\u001b[1;32m 319\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 320\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mk\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 321\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Sample larger than population or is negative\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 322\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 323\u001b[0m \u001b[0msetsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m21\u001b[0m \u001b[0;31m# size of a small set minus size of an empty list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Sample larger than population or is negative"
]
}
],
"source": [
"random.sample([1,2], 3) "
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2]"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"random.sample([1,2], 2)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[3, 2]"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"random.sample([1,2,3], 2) "
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function get_dummies in module pandas.core.reshape.reshape:\n",
"\n",
"get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False, dtype=None)\n",
" Convert categorical variable into dummy/indicator variables.\n",
" \n",
" Parameters\n",
" ----------\n",
" data : array-like, Series, or DataFrame\n",
" Data of which to get dummy indicators.\n",
" prefix : str, list of str, or dict of str, default None\n",
" String to append DataFrame column names.\n",
" Pass a list with length equal to the number of columns\n",
" when calling get_dummies on a DataFrame. Alternatively, `prefix`\n",
" can be a dictionary mapping column names to prefixes.\n",
" prefix_sep : str, default '_'\n",
" If appending prefix, separator/delimiter to use. Or pass a\n",
" list or dictionary as with `prefix`.\n",
" dummy_na : bool, default False\n",
" Add a column to indicate NaNs, if False NaNs are ignored.\n",
" columns : list-like, default None\n",
" Column names in the DataFrame to be encoded.\n",
" If `columns` is None then all the columns with\n",
" `object` or `category` dtype will be converted.\n",
" sparse : bool, default False\n",
" Whether the dummy-encoded columns should be backed by\n",
" a :class:`SparseArray` (True) or a regular NumPy array (False).\n",
" drop_first : bool, default False\n",
" Whether to get k-1 dummies out of k categorical levels by removing the\n",
" first level.\n",
" \n",
" .. versionadded:: 0.18.0\n",
" \n",
" dtype : dtype, default np.uint8\n",
" Data type for new columns. Only a single dtype is allowed.\n",
" \n",
" .. versionadded:: 0.23.0\n",
" \n",
" Returns\n",
" -------\n",
" DataFrame\n",
" Dummy-coded data.\n",
" \n",
" See Also\n",
" --------\n",
" Series.str.get_dummies : Convert Series to dummy codes.\n",
" \n",
" Examples\n",
" --------\n",
" >>> s = pd.Series(list('abca'))\n",
" \n",
" >>> pd.get_dummies(s)\n",
" a b c\n",
" 0 1 0 0\n",
" 1 0 1 0\n",
" 2 0 0 1\n",
" 3 1 0 0\n",
" \n",
" >>> s1 = ['a', 'b', np.nan]\n",
" \n",
" >>> pd.get_dummies(s1)\n",
" a b\n",
" 0 1 0\n",
" 1 0 1\n",
" 2 0 0\n",
" \n",
" >>> pd.get_dummies(s1, dummy_na=True)\n",
" a b NaN\n",
" 0 1 0 0\n",
" 1 0 1 0\n",
" 2 0 0 1\n",
" \n",
" >>> df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'],\n",
" ... 'C': [1, 2, 3]})\n",
" \n",
" >>> pd.get_dummies(df, prefix=['col1', 'col2'])\n",
" C col1_a col1_b col2_a col2_b col2_c\n",
" 0 1 1 0 0 1 0\n",
" 1 2 0 1 1 0 0\n",
" 2 3 1 0 0 0 1\n",
" \n",
" >>> pd.get_dummies(pd.Series(list('abcaa')))\n",
" a b c\n",
" 0 1 0 0\n",
" 1 0 1 0\n",
" 2 0 0 1\n",
" 3 1 0 0\n",
" 4 1 0 0\n",
" \n",
" >>> pd.get_dummies(pd.Series(list('abcaa')), drop_first=True)\n",
" b c\n",
" 0 0 0\n",
" 1 1 0\n",
" 2 0 1\n",
" 3 0 0\n",
" 4 0 0\n",
" \n",
" >>> pd.get_dummies(pd.Series(list('abc')), dtype=float)\n",
" a b c\n",
" 0 1.0 0.0 0.0\n",
" 1 0.0 1.0 0.0\n",
" 2 0.0 0.0 1.0\n",
"\n"
]
}
],
"source": [
"help(pd.get_dummies)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2])"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.unique([1,1,2]) "
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"numpy.ndarray"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(pd.unique([1,1,2]))"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"random.choice([1,2,3]) "
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"random.choice([1,2,3])"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on method choice in module random:\n",
"\n",
"choice(seq) method of random.Random instance\n",
" Choose a random element from a non-empty sequence.\n",
"\n"
]
}
],
"source": [
"help(random.choice)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[2, 2, 2, 2, 2]"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"random.choices([1,2,3], k=5)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "'int' object is not iterable",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-55-6667b11b7ef1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/anaconda3/lib/python3.7/random.py\u001b[0m in \u001b[0;36mchoices\u001b[0;34m(self, population, weights, cum_weights, k)\u001b[0m\n\u001b[1;32m 355\u001b[0m \u001b[0mtotal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0m_int\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mtotal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 357\u001b[0;31m \u001b[0mcum_weights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_itertools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccumulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 358\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mweights\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 359\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Cannot specify both weights and cumulative weights'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable"
]
}
],
"source": [
"random.choices([1,2,3], 2) "
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[3]"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"random.choices([1,2,3], [1,1,1])"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"scrolled": false
},
"outputs": [
{
"ename": "ValueError",
"evalue": "The number of weights does not match the population",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-57-5490eea5d8e8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/anaconda3/lib/python3.7/random.py\u001b[0m in \u001b[0;36mchoices\u001b[0;34m(self, population, weights, cum_weights, k)\u001b[0m\n\u001b[1;32m 359\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Cannot specify both weights and cumulative weights'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcum_weights\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 361\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'The number of weights does not match the population'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 362\u001b[0m \u001b[0mbisect\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_bisect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbisect\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0mtotal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcum_weights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: The number of weights does not match the population"
]
}
],
"source": [
"random.choices([1,2,3], [1,2]) "
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[3]"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"random.choices([1,2,3])"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on method choices in module random:\n",
"\n",
"choices(population, weights=None, *, cum_weights=None, k=1) method of random.Random instance\n",
" Return a k sized list of population elements chosen with replacement.\n",
" \n",
" If the relative weights or cumulative weights are not specified,\n",
" the selections are made with equal probability.\n",
"\n"
]
}
],
"source": [
"help(random.choices)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[3, 1, 1, 3, 2]"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"random.choices([1,2,3], k=5)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on built-in function random_sample:\n",
"\n",
"random_sample(...) method of numpy.random.mtrand.RandomState instance\n",
" random_sample(size=None)\n",
" \n",
" Return random floats in the half-open interval [0.0, 1.0).\n",
" \n",
" Results are from the \"continuous uniform\" distribution over the\n",
" stated interval. To sample :math:`Unif[a, b), b > a` multiply\n",
" the output of `random_sample` by `(b-a)` and add `a`::\n",
" \n",
" (b - a) * random_sample() + a\n",
" \n",
" Parameters\n",
" ----------\n",
" size : int or tuple of ints, optional\n",
" Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n",
" ``m * n * k`` samples are drawn. Default is None, in which case a\n",
" single value is returned.\n",
" \n",
" Returns\n",
" -------\n",
" out : float or ndarray of floats\n",
" Array of random floats of shape `size` (unless ``size=None``, in which\n",
" case a single float is returned).\n",
" \n",
" Examples\n",
" --------\n",
" >>> np.random.random_sample()\n",
" 0.47108547995356098 # random\n",
" >>> type(np.random.random_sample())\n",
" <class 'float'>\n",
" >>> np.random.random_sample((5,))\n",
" array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428]) # random\n",
" \n",
" Three-by-two array of random numbers from [-5, 0):\n",
" \n",
" >>> 5 * np.random.random_sample((3, 2)) - 5\n",
" array([[-3.99149989, -0.52338984], # random\n",
" [-2.99091858, -0.79479508],\n",
" [-1.23204345, -1.75224494]])\n",
"\n"
]
}
],
"source": [
"help(np.random.random_sample)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
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