{
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"execution_count": 2,
"id": "cf46ff8d-e27b-43e0-b063-465806263ff9",
"metadata": {},
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"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "6ce3f68f-28ef-4eca-9047-0c7867649885",
"metadata": {},
"outputs": [],
"source": [
"corrects = pd.read_csv(\"../input/bagging/corrects.csv\", header=None)\n",
"incorrects = pd.read_csv(\"../input/bagging/incorrects.csv\", header=None)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "3ccfb3eb-6893-4f5d-8a45-daf17ae11ac0",
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},
"execution_count": 5,
"metadata": {},
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],
"source": [
"corrects"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "91f632ed-29e7-47de-85f1-c01052839def",
"metadata": {},
"outputs": [],
"source": [
"replace_method = lambda x: x.replace(\"D:\\Deep_Learning\\WasteNet\", \"/home/sato.yukiya\").replace(\"\\\\\", \"/\")"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "a70d2b8c-2c00-48a0-872d-e14dcbb8a988",
"metadata": {},
"outputs": [],
"source": [
"corrects[2] = corrects[2].map(replace_method)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "78f46053-39ac-4b46-8908-dbd70e347ff3",
"metadata": {},
"outputs": [],
"source": [
"for i in range(3):\n",
" incorrects[i] = incorrects[i].map(replace_method)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "cbadb302-2e35-4fcd-a6c9-f7826b4f935c",
"metadata": {},
"outputs": [],
"source": [
"incorrects.to_csv(\"../input/bagging/incorrects.csv\", header=None, index=False)\n",
"corrects.to_csv(\"../input/bagging/corrects.csv\", header=None, index=False)"
]
}
],
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