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@@ -16,7 +16,7 @@
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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": 1,
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"metadata": {},
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"outputs": [
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{
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@@ -176,7 +176,7 @@
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"2 103660 12055 0.5 "
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]
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},
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- "execution_count": 13,
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+ "execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -193,7 +193,7 @@
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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": 2,
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"metadata": {},
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"outputs": [
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{
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@@ -310,7 +310,7 @@
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"2 2.0 2.5 "
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]
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},
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- "execution_count": 14,
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+ "execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -328,7 +328,7 @@
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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": 3,
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"metadata": {},
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"outputs": [
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{
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@@ -400,7 +400,7 @@
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"2 12055 0.5 "
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]
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},
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- "execution_count": 15,
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+ "execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -415,7 +415,7 @@
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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": 4,
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"metadata": {},
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"outputs": [
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{
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@@ -474,7 +474,7 @@
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"2 A Cure for Wellness 2016 3.0"
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]
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},
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- "execution_count": 16,
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+ "execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -529,7 +529,7 @@
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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": 5,
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"metadata": {},
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"outputs": [
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{
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@@ -637,7 +637,7 @@
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"31 Captain America: Civil War 2016 4.5"
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]
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},
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- "execution_count": 17,
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+ "execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -717,7 +717,7 @@
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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": 6,
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"metadata": {},
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"outputs": [
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{
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@@ -726,14 +726,13 @@
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"0"
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]
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},
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- "execution_count": 18,
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+ "execution_count": 6,
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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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"source": [
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- "sum(fandango_previous.Fandango_votes < 30) # `fandango_previous.Fandango_votes` is the same\n",
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- " # as `fandago_previous['Fandango_votes']`"
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+ "sum(fandango_previous['Fandango_votes'] < 30)"
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]
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},
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{
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@@ -745,7 +744,7 @@
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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": 7,
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"metadata": {},
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"outputs": [
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{
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@@ -807,7 +806,7 @@
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"1 12640 0.5 "
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]
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},
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- "execution_count": 19,
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+ "execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -825,7 +824,7 @@
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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": 8,
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"metadata": {},
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"outputs": [
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{
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@@ -890,19 +889,19 @@
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"1 12640 0.5 2015 "
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]
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},
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- "execution_count": 20,
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+ "execution_count": 8,
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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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"source": [
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- "fandango_previous['Year'] = fandango_previous.FILM.str[-5:-1]\n",
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+ "fandango_previous['Year'] = fandango_previous['FILM'].str[-5:-1]\n",
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"fandango_previous.head(2)"
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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": 21,
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+ "execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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@@ -913,18 +912,18 @@
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"Name: Year, dtype: int64"
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]
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},
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- "execution_count": 21,
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+ "execution_count": 9,
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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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"source": [
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- "fandango_previous.Year.value_counts()"
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+ "fandango_previous['Year'].value_counts()"
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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": 22,
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+ "execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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@@ -934,14 +933,14 @@
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"Name: Year, dtype: int64"
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]
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},
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- "execution_count": 22,
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+ "execution_count": 10,
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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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"source": [
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- "fandango_2015 = fandango_previous[fandango_previous.Year == '2015'].copy()\n",
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- "fandango_2015.Year.value_counts()"
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+ "fandango_2015 = fandango_previous[fandango_previous['Year'] == '2015'].copy()\n",
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+ "fandango_2015['Year'].value_counts()"
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]
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},
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{
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@@ -953,7 +952,7 @@
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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": 11,
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"metadata": {},
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"outputs": [
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{
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@@ -1005,7 +1004,7 @@
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"1 13 Hours 2016 4.5"
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]
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},
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- "execution_count": 23,
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+ "execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -1016,7 +1015,7 @@
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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": 12,
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"metadata": {},
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"outputs": [
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{
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@@ -1027,18 +1026,18 @@
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"Name: year, dtype: int64"
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]
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},
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- "execution_count": 24,
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+ "execution_count": 12,
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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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"source": [
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- "fandango_after.year.value_counts()"
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+ "fandango_after['year'].value_counts()"
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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": 25,
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+ "execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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@@ -1048,14 +1047,14 @@
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"Name: year, dtype: int64"
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]
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},
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- "execution_count": 25,
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+ "execution_count": 13,
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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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"source": [
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- "fandango_2016 = fandango_after[fandango_after.year == 2016].copy()\n",
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- "fandango_2016.year.value_counts()"
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+ "fandango_2016 = fandango_after[fandango_after['year'] == 2016].copy()\n",
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+ "fandango_2016['year'].value_counts()"
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]
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},
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{
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@@ -1071,7 +1070,7 @@
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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": 14,
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"metadata": {},
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"outputs": [
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{
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@@ -1091,8 +1090,8 @@
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"%matplotlib inline\n",
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"plt.style.use('fivethirtyeight')\n",
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"\n",
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- "fandango_2015.Fandango_Stars.plot.kde(label = '2015', legend = True, figsize = (8,5.5))\n",
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- "fandango_2016.fandango.plot.kde(label = '2016', legend = True)\n",
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+ "fandango_2015['Fandango_Stars'].plot.kde(label = '2015', legend = True, figsize = (8,5.5))\n",
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+ "fandango_2016['fandango'].plot.kde(label = '2016', legend = True)\n",
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"\n",
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"plt.title(\"Comparing distribution shapes for Fandango's ratings\\n(2015 vs 2016)\",\n",
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" y = 1.07) # the `y` parameter pads the title upward\n",
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@@ -1122,7 +1121,7 @@
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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": 15,
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"metadata": {},
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"outputs": [
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{
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@@ -1144,7 +1143,7 @@
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"Name: Fandango_Stars, dtype: float64"
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]
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},
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- "execution_count": 27,
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+ "execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -1152,12 +1151,12 @@
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"source": [
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"print('2015' + '\\n' + '-' * 16) # To help us distinguish between the two tables immediately and\n",
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" # avoid silly mistakes as we read to and fro\n",
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- "fandango_2015.Fandango_Stars.value_counts(normalize = True).sort_index() * 100"
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+ "fandango_2015['Fandango_Stars'].value_counts(normalize = True).sort_index() * 100"
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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": 28,
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+ "execution_count": 16,
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"metadata": {},
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"outputs": [
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{
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@@ -1180,14 +1179,14 @@
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"Name: fandango, dtype: float64"
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]
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},
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- "execution_count": 28,
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+ "execution_count": 16,
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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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"source": [
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"print('2016' + '\\n' + '-' * 16)\n",
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- "fandango_2016.fandango.value_counts(normalize = True).sort_index() * 100"
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+ "fandango_2016['fandango'].value_counts(normalize = True).sort_index() * 100"
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]
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},
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{
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@@ -1207,7 +1206,7 @@
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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": 17,
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"metadata": {},
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"outputs": [
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{
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@@ -1262,20 +1261,20 @@
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"mode 4.500000 4.000000"
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]
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},
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- "execution_count": 29,
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+ "execution_count": 17,
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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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"source": [
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- "mean_2015 = fandango_2015.Fandango_Stars.mean()\n",
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- "mean_2016 = fandango_2016.fandango.mean()\n",
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+ "mean_2015 = fandango_2015['Fandango_Stars'].mean()\n",
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+ "mean_2016 = fandango_2016['fandango'].mean()\n",
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"\n",
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- "median_2015 = fandango_2015.Fandango_Stars.median()\n",
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- "median_2016 = fandango_2016.fandango.median()\n",
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+ "median_2015 = fandango_2015['Fandango_Stars'].median()\n",
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+ "median_2016 = fandango_2016['fandango'].median()\n",
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"\n",
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- "mode_2015 = fandango_2015.Fandango_Stars.mode()[0] # the output of Series.mode() is a bit uncommon\n",
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- "mode_2016 = fandango_2016.fandango.mode()[0]\n",
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+ "mode_2015 = fandango_2015['Fandango_Stars'].mode()[0] # the output of Series.mode() is a bit uncommon\n",
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+ "mode_2016 = fandango_2016['fandango'].mode()[0]\n",
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"\n",
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"summary = pd.DataFrame()\n",
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"summary['2015'] = [mean_2015, median_2015, mode_2015]\n",
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@@ -1286,7 +1285,7 @@
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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": 18,
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"metadata": {},
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"outputs": [
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{
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@@ -1323,7 +1322,7 @@
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},
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{
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"cell_type": "code",
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- "execution_count": 77,
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+ "execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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@@ -1332,7 +1331,7 @@
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"0.048426835689519929"
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]
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},
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- "execution_count": 77,
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+ "execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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