Tworzenie plików z wynikami
This commit is contained in:
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SysInf.ipynb
244
SysInf.ipynb
@ -16,7 +16,7 @@
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 74,
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"outputs": [
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{
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@ -40,7 +40,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 75,
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"outputs": [],
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"source": [
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@ -57,7 +57,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 76,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -66,7 +66,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 77,
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"metadata": {},
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"outputs": [
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{
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@ -452,7 +452,7 @@
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"[2547 rows x 26 columns]"
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]
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},
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"execution_count": 12,
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"execution_count": 77,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -470,7 +470,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 78,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -479,7 +479,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 79,
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"metadata": {},
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"outputs": [
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{
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@ -865,7 +865,7 @@
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"[462 rows x 25 columns]"
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]
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},
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"execution_count": 14,
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"execution_count": 79,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -876,7 +876,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 80,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -885,7 +885,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 81,
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"metadata": {},
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"outputs": [
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{
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@ -979,7 +979,7 @@
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"[462 rows x 1 columns]"
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]
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},
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"execution_count": 16,
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"execution_count": 81,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -997,7 +997,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 82,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1006,7 +1006,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"execution_count": 83,
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"metadata": {},
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"outputs": [
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{
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@ -1392,7 +1392,7 @@
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"[418 rows x 25 columns]"
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]
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},
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"execution_count": 18,
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"execution_count": 83,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -1410,7 +1410,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"execution_count": 84,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1419,7 +1419,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"execution_count": 85,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1428,7 +1428,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"execution_count": 86,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1437,7 +1437,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 87,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1453,7 +1453,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": 88,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1462,7 +1462,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": 89,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1471,7 +1471,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"execution_count": 90,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1494,7 +1494,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"execution_count": 91,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1503,7 +1503,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"execution_count": 92,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1512,7 +1512,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"execution_count": 93,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1521,7 +1521,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"execution_count": 94,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -1530,7 +1530,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"execution_count": 95,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"execution_count": 96,
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"metadata": {},
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"outputs": [
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{
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@ -1643,7 +1643,7 @@
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"max 6.000000e+06 11.000000 404.000000 2022.000000"
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]
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},
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"execution_count": 31,
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"execution_count": 96,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"execution_count": 97,
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"metadata": {},
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"outputs": [
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{
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"[1763 rows x 4 columns]"
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]
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},
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"execution_count": 32,
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"execution_count": 97,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"execution_count": 98,
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"metadata": {},
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"outputs": [
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{
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"max 2.318580e+06 6.000000 234.200000 2021.000000"
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]
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},
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"execution_count": 33,
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"execution_count": 98,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"execution_count": 99,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"execution_count": 100,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"execution_count": 101,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"execution_count": 102,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"execution_count": 103,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"execution_count": 104,
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"metadata": {},
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"outputs": [
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{
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"[320 rows x 4 columns]"
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]
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},
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"execution_count": 39,
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"execution_count": 104,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"execution_count": 105,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"execution_count": 106,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"execution_count": 107,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"execution_count": 108,
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"metadata": {},
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"outputs": [
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{
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"max 6.000000e+06 11.000000 404.000000 2022.000000"
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]
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},
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"execution_count": 43,
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"execution_count": 108,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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"execution_count": 109,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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"execution_count": 110,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 46,
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"execution_count": 111,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 47,
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"execution_count": 112,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 48,
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"execution_count": 113,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 49,
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"execution_count": 114,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
|
||||
"<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
|
||||
],
|
||||
"text/plain": [
|
||||
"LinearRegression()"
|
||||
]
|
||||
},
|
||||
"execution_count": 49,
|
||||
"execution_count": 114,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -2350,16 +2350,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"execution_count": 115,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.metrics import mean_squared_error, r2_score"
|
||||
"from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"execution_count": 116,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -2368,7 +2368,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 52,
|
||||
"execution_count": 117,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -2377,7 +2377,7 @@
|
||||
"0.6264197107092035"
|
||||
]
|
||||
},
|
||||
"execution_count": 52,
|
||||
"execution_count": 117,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -2388,7 +2388,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 53,
|
||||
"execution_count": 118,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -2397,7 +2397,7 @@
|
||||
"22930468691.882328"
|
||||
]
|
||||
},
|
||||
"execution_count": 53,
|
||||
"execution_count": 118,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -2408,7 +2408,27 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 54,
|
||||
"execution_count": 119,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"74283.11238859448"
|
||||
]
|
||||
},
|
||||
"execution_count": 119,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mean_absolute_error(y_true=y_train, y_pred=y_train_pred)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 120,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -2417,7 +2437,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 55,
|
||||
"execution_count": 121,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -2426,7 +2446,7 @@
|
||||
"0.5872189061243203"
|
||||
]
|
||||
},
|
||||
"execution_count": 55,
|
||||
"execution_count": 121,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -2437,7 +2457,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 56,
|
||||
"execution_count": 122,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -2446,7 +2466,7 @@
|
||||
"12603444942.321444"
|
||||
]
|
||||
},
|
||||
"execution_count": 56,
|
||||
"execution_count": 122,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -2455,6 +2475,26 @@
|
||||
"mean_squared_error(y_true=y_val, y_pred=y_val_pred)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 123,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"68895.89161086899"
|
||||
]
|
||||
},
|
||||
"execution_count": 123,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mean_absolute_error(y_true=y_val, y_pred=y_val_pred)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@ -2464,7 +2504,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 57,
|
||||
"execution_count": 124,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -2473,7 +2513,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 58,
|
||||
"execution_count": 125,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -2567,7 +2607,7 @@
|
||||
"[292 rows x 1 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 58,
|
||||
"execution_count": 125,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -2586,7 +2626,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 59,
|
||||
"execution_count": 126,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -2602,7 +2642,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 60,
|
||||
"execution_count": 127,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -2633,7 +2673,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 61,
|
||||
"execution_count": 128,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -2664,7 +2704,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 62,
|
||||
"execution_count": 129,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -2695,7 +2735,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 63,
|
||||
"execution_count": 130,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -2726,7 +2766,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 64,
|
||||
"execution_count": 131,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -2757,7 +2797,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 65,
|
||||
"execution_count": 132,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -2788,7 +2828,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 66,
|
||||
"execution_count": 133,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -2800,7 +2840,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 70,
|
||||
"execution_count": 134,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -2809,7 +2849,7 @@
|
||||
"184531.94"
|
||||
]
|
||||
},
|
||||
"execution_count": 70,
|
||||
"execution_count": 134,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -2820,7 +2860,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 69,
|
||||
"execution_count": 135,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -2829,6 +2869,54 @@
|
||||
"filename = 'mieszkania_model'\n",
|
||||
"pickle.dump(model, open(filename, 'wb'))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Zapisywanie wyników"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Zapisywanie wyników zbioru walidacyjnego"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 136,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_val_pred = pd.DataFrame(y_val_pred)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 137,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_val_pred.to_csv(r'mieszkania5\\dev-0\\out.tsv', sep='\\t', header=None, index=None)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Zapisywanie wyników zbioru testowego"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 138,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_test_pred_df.to_csv(r'mieszkania5\\test-A\\out.tsv', sep='\\t', header=None, index=None)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
462
dev-0/expected.tsv
Normal file
462
dev-0/expected.tsv
Normal file
@ -0,0 +1,462 @@
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462
dev-0/in.tsv
Normal file
462
dev-0/in.tsv
Normal file
File diff suppressed because one or more lines are too long
320
dev-0/out.tsv
Normal file
320
dev-0/out.tsv
Normal file
@ -0,0 +1,320 @@
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||||
414006.21879144595
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418
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292
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292
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@ -0,0 +1,292 @@
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|
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|
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|
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|
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|
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|
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|
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|
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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||||
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|
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|
||||
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|
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|
||||
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|
||||
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||||
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|
|
2547
train/train.tsv
Normal file
2547
train/train.tsv
Normal file
File diff suppressed because one or more lines are too long
Loading…
Reference in New Issue
Block a user