test_cases/combine_columns_of_tables.ipynb

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    "def combine_columns_of_tables(dataframe1, dataframe2, index):\n",
    "    \"\"\"\n",
    "    This function combines to dataframes and makes sure the data is merged \n",
    "    using the given index column, which must be present in both dataframes.\n",
    "    The dataframes should be merged in a way that no data is lost and missing\n",
    "    fields are filled with NaN.\n",
    "    \"\"\"\n",
    "    import pandas as pd\n",
    "    return pd.merge(dataframe1, dataframe2, how='outer', on='label')"
   ]
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    "def check(candidate):\n",
    "    import pandas as pd\n",
    "    import numpy as np\n",
    "\n",
    "    table1 = pd.DataFrame({\n",
    "    \"label\":       [1,   2,   3],\n",
    "    \"circularity\": [0.3, 0.5, 0.7],\n",
    "    \"elongation\":  [2.3, 3.4, 1.2],\n",
    "    })\n",
    "\n",
    "    table2 = pd.DataFrame({\n",
    "    \"label\":    [3,   2,   1,   4],\n",
    "    \"area\":     [22,  32,  25,  18],\n",
    "    \"skewness\": [0.5, 0.6, 0.3, 0.3],\n",
    "    })\n",
    "\n",
    "    reference = pd.DataFrame({\n",
    "    \"label\":       [1,   2,   3,   4],\n",
    "    \"circularity\": [0.3, 0.5, 0.7, np.nan],\n",
    "    \"elongation\":  [2.3, 3.4, 1.2, np.nan],\n",
    "    \"area\":     [25,  32,  22,  18],\n",
    "    \"skewness\": [0.3, 0.6, 0.5, 0.3],\n",
    "    })\n",
    "    \n",
    "    result = candidate(table1, table2, \"label\")\n",
    "\n",
    "    comparison = result.compare(reference)\n",
    "    assert len(comparison.columns) == 0\n",
    "    assert len(comparison.index) == 0"
   ]
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   "cell_type": "code",
   "execution_count": 3,
   "id": "e4789ed1-dc01-46a6-80b2-dd98d8d7b875",
   "metadata": {},
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   "source": [
    "check(combine_columns_of_tables)"
   ]
  },
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   "execution_count": null,
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   "source": []
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