Gale-Shapley_1.ipynb

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     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define men and women data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "women = pd.DataFrame({'A': [3,4,2,1], 'B': [3,1,4,2], 'C':[2,3,4,1], 'D':[3,2,1,4]})\n",
    "women.index = ['a','b','c','d']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "man = pd.DataFrame({'A': [1,1,2,4], 'B': [2,4,1,2], 'C':[3,3,3,3], 'D':[4,2,4,1]})\n",
    "man.index = ['a','b','c','d']"
   ]
  },
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   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>A</th>\n",
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      "text/plain": [
       "   A  B  C  D\n",
       "a  1  2  3  4\n",
       "b  1  4  3  2\n",
       "c  2  1  3  4\n",
       "d  4  2  3  1"
      ]
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   "source": [
    "man"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   A  B  C  D\n",
       "a  3  3  2  3\n",
       "b  4  1  3  2\n",
       "c  2  4  4  1\n",
       "d  1  2  1  4"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "women"
   ]
  }
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