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fixed assignment 2 to increase accuracy
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-174
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4 files changed

+220
-174
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‎ClassNotebooks/.ipynb_checkpoints/Assignment 2-checkpoint.ipynb‎

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@@ -23,7 +23,7 @@
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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": 1,
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"metadata": {
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"collapsed": false,
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"nbgrader": {
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"Australasia 12 ANZ "
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]
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},
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"execution_count": 45,
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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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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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"Name: Afghanistan, dtype: object"
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]
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},
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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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},
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{
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"cell_type": "code",
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"execution_count": 47,
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"execution_count": 3,
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"metadata": {
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"collapsed": false,
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"nbgrader": {
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"'United States'"
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]
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},
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"execution_count": 47,
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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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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false
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},
@@ -333,7 +333,7 @@
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"'United States'"
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]
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},
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"execution_count": 66,
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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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},
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{
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"cell_type": "code",
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"execution_count": 67,
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"'Bulgaria'"
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]
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},
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"execution_count": 67,
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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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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": false
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},
@@ -412,38 +412,38 @@
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"Argentina 130\n",
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"Armenia 16\n",
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"Australasia 22\n",
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"Australia 919\n",
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"Austria 488\n",
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"Australia 923\n",
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"Austria 569\n",
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"Azerbaijan 43\n",
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"Bahamas 24\n",
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"Bahrain 1\n",
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"Barbados 1\n",
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"Belarus 149\n",
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"Belgium 273\n",
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"Belarus 154\n",
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"Belgium 276\n",
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"Bermuda 1\n",
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"Bohemia 5\n",
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"Botswana 2\n",
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"Brazil 184\n",
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"British West Indies 2\n",
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"Bulgaria 408\n",
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"Bulgaria 411\n",
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"Burundi 3\n",
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"Cameroon 12\n",
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"Canada 794\n",
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"Canada 846\n",
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"Chile 24\n",
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"China 1101\n",
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"China 1120\n",
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"Colombia 29\n",
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"Costa Rica 7\n",
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"Ivory Coast 2\n",
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"Croatia 66\n",
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"Croatia 67\n",
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"Cuba 420\n",
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"Cyprus 2\n",
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" ... \n",
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"Spain 267\n",
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"Spain 268\n",
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"Sri Lanka 4\n",
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"Sudan 2\n",
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"Suriname 4\n",
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"Sweden 1163\n",
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"Switzerland 582\n",
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"Sweden 1217\n",
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"Switzerland 630\n",
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"Syria 6\n",
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"Chinese Taipei 32\n",
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"Tajikistan 4\n",
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"Tunisia 19\n",
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"Turkey 191\n",
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"Uganda 14\n",
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"Ukraine 216\n",
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"Ukraine 220\n",
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"United Arab Emirates 3\n",
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"United States 5600\n",
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"United States 5684\n",
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"Uruguay 16\n",
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"Uzbekistan 38\n",
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"Venezuela 18\n",
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"Vietnam 4\n",
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"Virgin Islands 2\n",
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"Yugoslavia 170\n",
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"Yugoslavia 171\n",
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"Independent Olympic Participants 4\n",
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"Zambia 3\n",
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"Zimbabwe 18\n",
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"Mixed team 38\n",
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"Name: Points, dtype: int64"
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]
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},
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"execution_count": 72,
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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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"def answer_four():\n",
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" df['Points'] = df['Gold.2'] * 3 + df['Silver.2'] * 2 + df['Bronze']\n",
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" df['Points'] = df['Gold.2'] * 3 + df['Silver.2'] * 2 + df['Bronze.2'] * 1\n",
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" return df['Points']\n",
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"\n",
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"answer_four()"
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},
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{
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"cell_type": "code",
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"execution_count": 148,
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"execution_count": 7,
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"metadata": {
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"collapsed": false
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},
@@ -702,7 +702,7 @@
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"[5 rows x 100 columns]"
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]
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},
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"execution_count": 148,
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -714,7 +714,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 182,
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"execution_count": 8,
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"metadata": {
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"collapsed": false
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},
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"'Texas'"
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]
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},
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"execution_count": 182,
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -749,27 +749,28 @@
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},
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{
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"cell_type": "code",
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"execution_count": 274,
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"execution_count": 9,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['California', 'Illinois', 'Texas']"
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"['California', 'Texas', 'Illinois']"
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]
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},
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"execution_count": 274,
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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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"def answer_six():\n",
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" d = census_df.where(census_df['SUMLEV'] == 50)\n",
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" \n",
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" return d.groupby(['STNAME', 'COUNTY']).POPESTIMATE2015.max().nlargest(3).reset_index()['STNAME'].tolist()\n",
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" df1 = pd.DataFrame(census_df.where(census_df['SUMLEV'] == 50).groupby(['STNAME'])['POPESTIMATE2015'].nlargest(3))\n",
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" df1 = df1.reset_index()\n",
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"\n",
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" return list(df1.groupby(['STNAME']).sum()['POPESTIMATE2015'].nlargest(3).index)\n",
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"\n",
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"answer_six()"
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]
@@ -786,7 +787,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 316,
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"execution_count": 10,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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"'Harris County'"
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]
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},
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"execution_count": 316,
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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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{
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"cell_type": "code",
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"execution_count": 336,
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"execution_count": 13,
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"metadata": {
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"collapsed": false
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},
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>1211</th>\n",
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" <td>Maine</td>\n",
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" <th>896</th>\n",
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" <td>Iowa</td>\n",
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" <td>Washington County</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1918</th>\n",
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" <td>New York</td>\n",
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" <th>1419</th>\n",
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" <td>Minnesota</td>\n",
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" <td>Washington County</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>Washington County</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2863</th>\n",
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" <td>Vermont</td>\n",
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" <th>3163</th>\n",
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" <td>Wisconsin</td>\n",
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" <td>Washington County</td>\n",
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" </tr>\n",
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" </tbody>\n",
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],
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"text/plain": [
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" STNAME CTYNAME\n",
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"1211 Maine Washington County\n",
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"1918 New York Washington County\n",
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"896 Iowa Washington County\n",
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"1419 Minnesota Washington County\n",
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"2345 Pennsylvania Washington County\n",
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"2355 Rhode Island Washington County\n",
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"2863 Vermont Washington County"
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"3163 Wisconsin Washington County"
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]
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},
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"execution_count": 336,
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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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"def answer_eight():\n",
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"\n",
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" return census_df[((census_df['REGION'] == 1) | (census_df['REGION'] == 1)) & (census_df['CTYNAME'].str.startswith(\"Washington\"))].loc[:,['STNAME','CTYNAME']]\n",
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" return census_df[((census_df['REGION'] == 1) | (census_df['REGION'] == 2)) & (census_df['CTYNAME'].str.startswith(\"Washington\") & (census_df['POPESTIMATE2015'] > census_df['POPESTIMATE2014']))].loc[:,['STNAME','CTYNAME']]\n",
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"\n",
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"answer_eight()"
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]

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