update,
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louiscklaw
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
|
||||
"source": [
|
||||
"# Supervised Learning\n",
|
||||
"## KNN\n",
|
||||
"> - KNN is a non-parametric learning algorithm (No assumption is made on the data) \n",
|
||||
"> - KNN can be used for classification (discrte) and regression (continuous label) \n",
|
||||
"> - All training data has to be present to determine the label of new data \n",
|
||||
"> - Sensitive to irrelavant features \n",
|
||||
"> - Sensitive to scale of data \n",
|
||||
"### Issues:\n",
|
||||
"> - Choose number of neighors *k* \n",
|
||||
"> - Choose distance metric "
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||||
]
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||||
},
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||||
{
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||||
"cell_type": "markdown",
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||||
"metadata": {},
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||||
"source": [
|
||||
"## Data\n",
|
||||
"### Breast Cancer\n",
|
||||
"> **Label**: Malignant or Benign \n",
|
||||
"> **30 Features**: Radius, Texture, Perimeter, Area, Smoothness, etc"
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||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
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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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"data": {
|
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"text/html": [
|
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"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
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" }\n",
|
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"\n",
|
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" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
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"\n",
|
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||||
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|
||||
" }\n",
|
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"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>mean radius</th>\n",
|
||||
" <th>mean texture</th>\n",
|
||||
" <th>mean perimeter</th>\n",
|
||||
" <th>mean area</th>\n",
|
||||
" <th>mean smoothness</th>\n",
|
||||
" <th>mean compactness</th>\n",
|
||||
" <th>mean concavity</th>\n",
|
||||
" <th>mean concave points</th>\n",
|
||||
" <th>mean symmetry</th>\n",
|
||||
" <th>mean fractal dimension</th>\n",
|
||||
" <th>...</th>\n",
|
||||
" <th>worst radius</th>\n",
|
||||
" <th>worst texture</th>\n",
|
||||
" <th>worst perimeter</th>\n",
|
||||
" <th>worst area</th>\n",
|
||||
" <th>worst smoothness</th>\n",
|
||||
" <th>worst compactness</th>\n",
|
||||
" <th>worst concavity</th>\n",
|
||||
" <th>worst concave points</th>\n",
|
||||
" <th>worst symmetry</th>\n",
|
||||
" <th>worst fractal dimension</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>17.99</td>\n",
|
||||
" <td>10.38</td>\n",
|
||||
" <td>122.80</td>\n",
|
||||
" <td>1001.0</td>\n",
|
||||
" <td>0.11840</td>\n",
|
||||
" <td>0.27760</td>\n",
|
||||
" <td>0.3001</td>\n",
|
||||
" <td>0.14710</td>\n",
|
||||
" <td>0.2419</td>\n",
|
||||
" <td>0.07871</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>25.38</td>\n",
|
||||
" <td>17.33</td>\n",
|
||||
" <td>184.60</td>\n",
|
||||
" <td>2019.0</td>\n",
|
||||
" <td>0.1622</td>\n",
|
||||
" <td>0.6656</td>\n",
|
||||
" <td>0.7119</td>\n",
|
||||
" <td>0.2654</td>\n",
|
||||
" <td>0.4601</td>\n",
|
||||
" <td>0.11890</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>20.57</td>\n",
|
||||
" <td>17.77</td>\n",
|
||||
" <td>132.90</td>\n",
|
||||
" <td>1326.0</td>\n",
|
||||
" <td>0.08474</td>\n",
|
||||
" <td>0.07864</td>\n",
|
||||
" <td>0.0869</td>\n",
|
||||
" <td>0.07017</td>\n",
|
||||
" <td>0.1812</td>\n",
|
||||
" <td>0.05667</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>24.99</td>\n",
|
||||
" <td>23.41</td>\n",
|
||||
" <td>158.80</td>\n",
|
||||
" <td>1956.0</td>\n",
|
||||
" <td>0.1238</td>\n",
|
||||
" <td>0.1866</td>\n",
|
||||
" <td>0.2416</td>\n",
|
||||
" <td>0.1860</td>\n",
|
||||
" <td>0.2750</td>\n",
|
||||
" <td>0.08902</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>19.69</td>\n",
|
||||
" <td>21.25</td>\n",
|
||||
" <td>130.00</td>\n",
|
||||
" <td>1203.0</td>\n",
|
||||
" <td>0.10960</td>\n",
|
||||
" <td>0.15990</td>\n",
|
||||
" <td>0.1974</td>\n",
|
||||
" <td>0.12790</td>\n",
|
||||
" <td>0.2069</td>\n",
|
||||
" <td>0.05999</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>23.57</td>\n",
|
||||
" <td>25.53</td>\n",
|
||||
" <td>152.50</td>\n",
|
||||
" <td>1709.0</td>\n",
|
||||
" <td>0.1444</td>\n",
|
||||
" <td>0.4245</td>\n",
|
||||
" <td>0.4504</td>\n",
|
||||
" <td>0.2430</td>\n",
|
||||
" <td>0.3613</td>\n",
|
||||
" <td>0.08758</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>11.42</td>\n",
|
||||
" <td>20.38</td>\n",
|
||||
" <td>77.58</td>\n",
|
||||
" <td>386.1</td>\n",
|
||||
" <td>0.14250</td>\n",
|
||||
" <td>0.28390</td>\n",
|
||||
" <td>0.2414</td>\n",
|
||||
" <td>0.10520</td>\n",
|
||||
" <td>0.2597</td>\n",
|
||||
" <td>0.09744</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>14.91</td>\n",
|
||||
" <td>26.50</td>\n",
|
||||
" <td>98.87</td>\n",
|
||||
" <td>567.7</td>\n",
|
||||
" <td>0.2098</td>\n",
|
||||
" <td>0.8663</td>\n",
|
||||
" <td>0.6869</td>\n",
|
||||
" <td>0.2575</td>\n",
|
||||
" <td>0.6638</td>\n",
|
||||
" <td>0.17300</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>20.29</td>\n",
|
||||
" <td>14.34</td>\n",
|
||||
" <td>135.10</td>\n",
|
||||
" <td>1297.0</td>\n",
|
||||
" <td>0.10030</td>\n",
|
||||
" <td>0.13280</td>\n",
|
||||
" <td>0.1980</td>\n",
|
||||
" <td>0.10430</td>\n",
|
||||
" <td>0.1809</td>\n",
|
||||
" <td>0.05883</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>22.54</td>\n",
|
||||
" <td>16.67</td>\n",
|
||||
" <td>152.20</td>\n",
|
||||
" <td>1575.0</td>\n",
|
||||
" <td>0.1374</td>\n",
|
||||
" <td>0.2050</td>\n",
|
||||
" <td>0.4000</td>\n",
|
||||
" <td>0.1625</td>\n",
|
||||
" <td>0.2364</td>\n",
|
||||
" <td>0.07678</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>5 rows × 30 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" mean radius mean texture mean perimeter mean area mean smoothness \\\n",
|
||||
"0 17.99 10.38 122.80 1001.0 0.11840 \n",
|
||||
"1 20.57 17.77 132.90 1326.0 0.08474 \n",
|
||||
"2 19.69 21.25 130.00 1203.0 0.10960 \n",
|
||||
"3 11.42 20.38 77.58 386.1 0.14250 \n",
|
||||
"4 20.29 14.34 135.10 1297.0 0.10030 \n",
|
||||
"\n",
|
||||
" mean compactness mean concavity mean concave points mean symmetry \\\n",
|
||||
"0 0.27760 0.3001 0.14710 0.2419 \n",
|
||||
"1 0.07864 0.0869 0.07017 0.1812 \n",
|
||||
"2 0.15990 0.1974 0.12790 0.2069 \n",
|
||||
"3 0.28390 0.2414 0.10520 0.2597 \n",
|
||||
"4 0.13280 0.1980 0.10430 0.1809 \n",
|
||||
"\n",
|
||||
" mean fractal dimension ... worst radius worst texture worst perimeter \\\n",
|
||||
"0 0.07871 ... 25.38 17.33 184.60 \n",
|
||||
"1 0.05667 ... 24.99 23.41 158.80 \n",
|
||||
"2 0.05999 ... 23.57 25.53 152.50 \n",
|
||||
"3 0.09744 ... 14.91 26.50 98.87 \n",
|
||||
"4 0.05883 ... 22.54 16.67 152.20 \n",
|
||||
"\n",
|
||||
" worst area worst smoothness worst compactness worst concavity \\\n",
|
||||
"0 2019.0 0.1622 0.6656 0.7119 \n",
|
||||
"1 1956.0 0.1238 0.1866 0.2416 \n",
|
||||
"2 1709.0 0.1444 0.4245 0.4504 \n",
|
||||
"3 567.7 0.2098 0.8663 0.6869 \n",
|
||||
"4 1575.0 0.1374 0.2050 0.4000 \n",
|
||||
"\n",
|
||||
" worst concave points worst symmetry worst fractal dimension \n",
|
||||
"0 0.2654 0.4601 0.11890 \n",
|
||||
"1 0.1860 0.2750 0.08902 \n",
|
||||
"2 0.2430 0.3613 0.08758 \n",
|
||||
"3 0.2575 0.6638 0.17300 \n",
|
||||
"4 0.1625 0.2364 0.07678 \n",
|
||||
"\n",
|
||||
"[5 rows x 30 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 1,
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||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"from sklearn.datasets import load_breast_cancer\n",
|
||||
"\n",
|
||||
"breast_cancer = load_breast_cancer()\n",
|
||||
"X = pd.DataFrame(breast_cancer.data, columns=breast_cancer.feature_names)\n",
|
||||
"X.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Create an X with 2 features only\n",
|
||||
"\n",
|
||||
"X = X[['mean area', 'mean compactness']]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array(['malignant', 'benign'], dtype='<U9')"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"breast_cancer.target_names"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"['malignant', 'malignant', 'malignant', 'malignant', 'malignant', ..., 'malignant', 'malignant', 'malignant', 'malignant', 'benign']\n",
|
||||
"Length: 569\n",
|
||||
"Categories (2, object): ['malignant', 'benign']"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"y = pd.Categorical.from_codes(codes=breast_cancer.target, categories=breast_cancer.target_names)\n",
|
||||
"y"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>benign</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" benign\n",
|
||||
"0 0\n",
|
||||
"1 0\n",
|
||||
"2 0\n",
|
||||
"3 0\n",
|
||||
"4 0"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"y = pd.get_dummies(y, drop_first=True) \n",
|
||||
"y.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"(426, 1)\n",
|
||||
"(143, 1)\n",
|
||||
"<class 'pandas.core.frame.DataFrame'>\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"\n",
|
||||
"Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, random_state=1)\n",
|
||||
"print(ytrain.shape)\n",
|
||||
"print(ytest.shape)\n",
|
||||
"print(type(ytest))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.neighbors import KNeighborsClassifier\n",
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"knn = KNeighborsClassifier(n_neighbors=5, metric='euclidean')\n",
|
||||
"knn.fit(Xtrain, ytrain.to_numpy().ravel())\n",
|
||||
"ypred = knn.predict(Xtest).reshape([143,1])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Prediction</th>\n",
|
||||
" <th>Actual</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Prediction Actual\n",
|
||||
"0 1 1\n",
|
||||
"1 1 0\n",
|
||||
"2 1 1\n",
|
||||
"3 0 0\n",
|
||||
"4 0 0"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.DataFrame(np.hstack([ypred,ytest]),columns=['Prediction','Actual']).head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Evaluation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[[42 13]\n",
|
||||
" [ 9 79]]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from sklearn.metrics import confusion_matrix\n",
|
||||
"print(confusion_matrix(ytest, ypred))"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
@@ -0,0 +1,151 @@
|
||||
Id,SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species
|
||||
1,5.1,3.5,1.4,0.2,Iris-setosa
|
||||
2,4.9,3.0,1.4,0.2,Iris-setosa
|
||||
3,4.7,3.2,1.3,0.2,Iris-setosa
|
||||
4,4.6,3.1,1.5,0.2,Iris-setosa
|
||||
5,5.0,3.6,1.4,0.2,Iris-setosa
|
||||
6,5.4,3.9,1.7,0.4,Iris-setosa
|
||||
7,4.6,3.4,1.4,0.3,Iris-setosa
|
||||
8,5.0,3.4,1.5,0.2,Iris-setosa
|
||||
9,4.4,2.9,1.4,0.2,Iris-setosa
|
||||
10,4.9,3.1,1.5,0.1,Iris-setosa
|
||||
11,5.4,3.7,1.5,0.2,Iris-setosa
|
||||
12,4.8,3.4,1.6,0.2,Iris-setosa
|
||||
13,4.8,3.0,1.4,0.1,Iris-setosa
|
||||
14,4.3,3.0,1.1,0.1,Iris-setosa
|
||||
15,5.8,4.0,1.2,0.2,Iris-setosa
|
||||
16,5.7,4.4,1.5,0.4,Iris-setosa
|
||||
17,5.4,3.9,1.3,0.4,Iris-setosa
|
||||
18,5.1,3.5,1.4,0.3,Iris-setosa
|
||||
19,5.7,3.8,1.7,0.3,Iris-setosa
|
||||
20,5.1,3.8,1.5,0.3,Iris-setosa
|
||||
21,5.4,3.4,1.7,0.2,Iris-setosa
|
||||
22,5.1,3.7,1.5,0.4,Iris-setosa
|
||||
23,4.6,3.6,1.0,0.2,Iris-setosa
|
||||
24,5.1,3.3,1.7,0.5,Iris-setosa
|
||||
25,4.8,3.4,1.9,0.2,Iris-setosa
|
||||
26,5.0,3.0,1.6,0.2,Iris-setosa
|
||||
27,5.0,3.4,1.6,0.4,Iris-setosa
|
||||
28,5.2,3.5,1.5,0.2,Iris-setosa
|
||||
29,5.2,3.4,1.4,0.2,Iris-setosa
|
||||
30,4.7,3.2,1.6,0.2,Iris-setosa
|
||||
31,4.8,3.1,1.6,0.2,Iris-setosa
|
||||
32,5.4,3.4,1.5,0.4,Iris-setosa
|
||||
33,5.2,4.1,1.5,0.1,Iris-setosa
|
||||
34,5.5,4.2,1.4,0.2,Iris-setosa
|
||||
35,4.9,3.1,1.5,0.1,Iris-setosa
|
||||
36,5.0,3.2,1.2,0.2,Iris-setosa
|
||||
37,5.5,3.5,1.3,0.2,Iris-setosa
|
||||
38,4.9,3.1,1.5,0.1,Iris-setosa
|
||||
39,4.4,3.0,1.3,0.2,Iris-setosa
|
||||
40,5.1,3.4,1.5,0.2,Iris-setosa
|
||||
41,5.0,3.5,1.3,0.3,Iris-setosa
|
||||
42,4.5,2.3,1.3,0.3,Iris-setosa
|
||||
43,4.4,3.2,1.3,0.2,Iris-setosa
|
||||
44,5.0,3.5,1.6,0.6,Iris-setosa
|
||||
45,5.1,3.8,1.9,0.4,Iris-setosa
|
||||
46,4.8,3.0,1.4,0.3,Iris-setosa
|
||||
47,5.1,3.8,1.6,0.2,Iris-setosa
|
||||
48,4.6,3.2,1.4,0.2,Iris-setosa
|
||||
49,5.3,3.7,1.5,0.2,Iris-setosa
|
||||
50,5.0,3.3,1.4,0.2,Iris-setosa
|
||||
51,7.0,3.2,4.7,1.4,Iris-versicolor
|
||||
52,6.4,3.2,4.5,1.5,Iris-versicolor
|
||||
53,6.9,3.1,4.9,1.5,Iris-versicolor
|
||||
54,5.5,2.3,4.0,1.3,Iris-versicolor
|
||||
55,6.5,2.8,4.6,1.5,Iris-versicolor
|
||||
56,5.7,2.8,4.5,1.3,Iris-versicolor
|
||||
57,6.3,3.3,4.7,1.6,Iris-versicolor
|
||||
58,4.9,2.4,3.3,1.0,Iris-versicolor
|
||||
59,6.6,2.9,4.6,1.3,Iris-versicolor
|
||||
60,5.2,2.7,3.9,1.4,Iris-versicolor
|
||||
61,5.0,2.0,3.5,1.0,Iris-versicolor
|
||||
62,5.9,3.0,4.2,1.5,Iris-versicolor
|
||||
63,6.0,2.2,4.0,1.0,Iris-versicolor
|
||||
64,6.1,2.9,4.7,1.4,Iris-versicolor
|
||||
65,5.6,2.9,3.6,1.3,Iris-versicolor
|
||||
66,6.7,3.1,4.4,1.4,Iris-versicolor
|
||||
67,5.6,3.0,4.5,1.5,Iris-versicolor
|
||||
68,5.8,2.7,4.1,1.0,Iris-versicolor
|
||||
69,6.2,2.2,4.5,1.5,Iris-versicolor
|
||||
70,5.6,2.5,3.9,1.1,Iris-versicolor
|
||||
71,5.9,3.2,4.8,1.8,Iris-versicolor
|
||||
72,6.1,2.8,4.0,1.3,Iris-versicolor
|
||||
73,6.3,2.5,4.9,1.5,Iris-versicolor
|
||||
74,6.1,2.8,4.7,1.2,Iris-versicolor
|
||||
75,6.4,2.9,4.3,1.3,Iris-versicolor
|
||||
76,6.6,3.0,4.4,1.4,Iris-versicolor
|
||||
77,6.8,2.8,4.8,1.4,Iris-versicolor
|
||||
78,6.7,3.0,5.0,1.7,Iris-versicolor
|
||||
79,6.0,2.9,4.5,1.5,Iris-versicolor
|
||||
80,5.7,2.6,3.5,1.0,Iris-versicolor
|
||||
81,5.5,2.4,3.8,1.1,Iris-versicolor
|
||||
82,5.5,2.4,3.7,1.0,Iris-versicolor
|
||||
83,5.8,2.7,3.9,1.2,Iris-versicolor
|
||||
84,6.0,2.7,5.1,1.6,Iris-versicolor
|
||||
85,5.4,3.0,4.5,1.5,Iris-versicolor
|
||||
86,6.0,3.4,4.5,1.6,Iris-versicolor
|
||||
87,6.7,3.1,4.7,1.5,Iris-versicolor
|
||||
88,6.3,2.3,4.4,1.3,Iris-versicolor
|
||||
89,5.6,3.0,4.1,1.3,Iris-versicolor
|
||||
90,5.5,2.5,4.0,1.3,Iris-versicolor
|
||||
91,5.5,2.6,4.4,1.2,Iris-versicolor
|
||||
92,6.1,3.0,4.6,1.4,Iris-versicolor
|
||||
93,5.8,2.6,4.0,1.2,Iris-versicolor
|
||||
94,5.0,2.3,3.3,1.0,Iris-versicolor
|
||||
95,5.6,2.7,4.2,1.3,Iris-versicolor
|
||||
96,5.7,3.0,4.2,1.2,Iris-versicolor
|
||||
97,5.7,2.9,4.2,1.3,Iris-versicolor
|
||||
98,6.2,2.9,4.3,1.3,Iris-versicolor
|
||||
99,5.1,2.5,3.0,1.1,Iris-versicolor
|
||||
100,5.7,2.8,4.1,1.3,Iris-versicolor
|
||||
101,6.3,3.3,6.0,2.5,Iris-virginica
|
||||
102,5.8,2.7,5.1,1.9,Iris-virginica
|
||||
103,7.1,3.0,5.9,2.1,Iris-virginica
|
||||
104,6.3,2.9,5.6,1.8,Iris-virginica
|
||||
105,6.5,3.0,5.8,2.2,Iris-virginica
|
||||
106,7.6,3.0,6.6,2.1,Iris-virginica
|
||||
107,4.9,2.5,4.5,1.7,Iris-virginica
|
||||
108,7.3,2.9,6.3,1.8,Iris-virginica
|
||||
109,6.7,2.5,5.8,1.8,Iris-virginica
|
||||
110,7.2,3.6,6.1,2.5,Iris-virginica
|
||||
111,6.5,3.2,5.1,2.0,Iris-virginica
|
||||
112,6.4,2.7,5.3,1.9,Iris-virginica
|
||||
113,6.8,3.0,5.5,2.1,Iris-virginica
|
||||
114,5.7,2.5,5.0,2.0,Iris-virginica
|
||||
115,5.8,2.8,5.1,2.4,Iris-virginica
|
||||
116,6.4,3.2,5.3,2.3,Iris-virginica
|
||||
117,6.5,3.0,5.5,1.8,Iris-virginica
|
||||
118,7.7,3.8,6.7,2.2,Iris-virginica
|
||||
119,7.7,2.6,6.9,2.3,Iris-virginica
|
||||
120,6.0,2.2,5.0,1.5,Iris-virginica
|
||||
121,6.9,3.2,5.7,2.3,Iris-virginica
|
||||
122,5.6,2.8,4.9,2.0,Iris-virginica
|
||||
123,7.7,2.8,6.7,2.0,Iris-virginica
|
||||
124,6.3,2.7,4.9,1.8,Iris-virginica
|
||||
125,6.7,3.3,5.7,2.1,Iris-virginica
|
||||
126,7.2,3.2,6.0,1.8,Iris-virginica
|
||||
127,6.2,2.8,4.8,1.8,Iris-virginica
|
||||
128,6.1,3.0,4.9,1.8,Iris-virginica
|
||||
129,6.4,2.8,5.6,2.1,Iris-virginica
|
||||
130,7.2,3.0,5.8,1.6,Iris-virginica
|
||||
131,7.4,2.8,6.1,1.9,Iris-virginica
|
||||
132,7.9,3.8,6.4,2.0,Iris-virginica
|
||||
133,6.4,2.8,5.6,2.2,Iris-virginica
|
||||
134,6.3,2.8,5.1,1.5,Iris-virginica
|
||||
135,6.1,2.6,5.6,1.4,Iris-virginica
|
||||
136,7.7,3.0,6.1,2.3,Iris-virginica
|
||||
137,6.3,3.4,5.6,2.4,Iris-virginica
|
||||
138,6.4,3.1,5.5,1.8,Iris-virginica
|
||||
139,6.0,3.0,4.8,1.8,Iris-virginica
|
||||
140,6.9,3.1,5.4,2.1,Iris-virginica
|
||||
141,6.7,3.1,5.6,2.4,Iris-virginica
|
||||
142,6.9,3.1,5.1,2.3,Iris-virginica
|
||||
143,5.8,2.7,5.1,1.9,Iris-virginica
|
||||
144,6.8,3.2,5.9,2.3,Iris-virginica
|
||||
145,6.7,3.3,5.7,2.5,Iris-virginica
|
||||
146,6.7,3.0,5.2,2.3,Iris-virginica
|
||||
147,6.3,2.5,5.0,1.9,Iris-virginica
|
||||
148,6.5,3.0,5.2,2.0,Iris-virginica
|
||||
149,6.2,3.4,5.4,2.3,Iris-virginica
|
||||
150,5.9,3.0,5.1,1.8,Iris-virginica
|
||||
|
@@ -0,0 +1,19 @@
|
||||
[[source]]
|
||||
url = "https://pypi.org/simple"
|
||||
verify_ssl = true
|
||||
name = "pypi"
|
||||
|
||||
[packages]
|
||||
jupyter = "*"
|
||||
notebook = "*"
|
||||
pandas = "*"
|
||||
quandl = "*"
|
||||
seaborn = "*"
|
||||
sklearn = "*"
|
||||
scikit-learn = "*"
|
||||
pydot = "*"
|
||||
|
||||
[dev-packages]
|
||||
|
||||
[requires]
|
||||
python_version = "3.11"
|
||||
1334
Man1130/jupyter/Man1130-python-comission/course_materials/Note/Pipfile.lock
generated
Normal file
1334
Man1130/jupyter/Man1130-python-comission/course_materials/Note/Pipfile.lock
generated
Normal file
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,8 @@
|
||||
10 + 10
|
||||
'10 + 10'
|
||||
'10 + 10'
|
||||
10 + 10
|
||||
10 + '10'
|
||||
'Hello world'
|
||||
type(10)
|
||||
type('abc')
|
||||
@@ -0,0 +1,6 @@
|
||||
# x = 5
|
||||
# x
|
||||
# x = 10
|
||||
# y = 20
|
||||
# x + y
|
||||
# x = y
|
||||
@@ -0,0 +1,29 @@
|
||||
# print()
|
||||
# print('hello world!')
|
||||
# year = 2017
|
||||
# print('This year is '+year)
|
||||
# print('This year is '+str(year))
|
||||
# yearStr = '2017'
|
||||
# print('This year is '+yearStr)
|
||||
# print('This year is '+(yearStr+1))
|
||||
# print('This year is '+str(int(yearStr)+1))
|
||||
#
|
||||
# format()
|
||||
# year = 2017
|
||||
# print('This year is '+year)
|
||||
# print('This year is '+str(year))
|
||||
# print('This year is {}'.format(year)
|
||||
# print('This year is {}, and next year is {}'.format(year,year+1))
|
||||
# print('This year is {}, and I will go abroad at the end of {}'.format(2017,2017))
|
||||
# print('This year is {y}, and I will go abroad at the end of {y}'.format(y=2017))
|
||||
#
|
||||
# input()
|
||||
# input('How tall are you? ')
|
||||
# height = input('How tall are you?')
|
||||
# print('You are {} meter tall'.format(int(height)/100))
|
||||
#
|
||||
#
|
||||
#
|
||||
height = input('What\'s your height? ')
|
||||
height = int(input('What\'s your height? '))
|
||||
|
||||
@@ -0,0 +1,30 @@
|
||||
def say_hello():
|
||||
print('Hello World!')
|
||||
|
||||
|
||||
def area_of_square(length):
|
||||
return length**2
|
||||
|
||||
|
||||
def area_of_circle(radius):
|
||||
pi = 3.1415926
|
||||
return pi*radius**2
|
||||
|
||||
# pi can also be defined outside of the function
|
||||
# good and bad?
|
||||
# what if someone change the value outside?
|
||||
|
||||
|
||||
def area_of_rectangle(length,width):
|
||||
return length*width
|
||||
|
||||
|
||||
|
||||
def app():
|
||||
age = int(input('What\'s your age?'))
|
||||
print('Your age is {}'.format(age))
|
||||
print('-------')
|
||||
app()
|
||||
|
||||
app()
|
||||
|
||||
@@ -0,0 +1,16 @@
|
||||
|
||||
|
||||
def bmi_test(bmi):
|
||||
if bmi < 18.5:
|
||||
print('You\'d better eat more!')
|
||||
elif bmi < 24:
|
||||
print('Good job!')
|
||||
else:
|
||||
print('You\'d better to some exercises')
|
||||
|
||||
|
||||
|
||||
bmi_test(20)
|
||||
|
||||
# multiple statement
|
||||
# multiple
|
||||
@@ -0,0 +1,52 @@
|
||||
# BMI = Weight / Height^2 kg and m
|
||||
|
||||
def convert_to_m(height):
|
||||
return height/100
|
||||
|
||||
|
||||
def calculate_bmi(height, weight):
|
||||
#print(weight)
|
||||
#print(height)
|
||||
return weight/height**2
|
||||
|
||||
|
||||
def bmi_test(bmi):
|
||||
print('Your bmi is {}'.format(round(bmi, 2)))
|
||||
if bmi < 18.5:
|
||||
print('You\'d better eat more!')
|
||||
elif bmi < 25:
|
||||
print('Good job!')
|
||||
elif bmi < 30:
|
||||
print('You\'d better do some exercises')
|
||||
else:
|
||||
print('You\'d better consult doctor')
|
||||
|
||||
|
||||
def bmi_app():
|
||||
try:
|
||||
age = int(input('What\'s your age?'))
|
||||
except ValueError:
|
||||
print("You need to enter 0 - 100 as your age")
|
||||
try:
|
||||
age = int(input('What\'s your age?'))
|
||||
except:
|
||||
print("Your input is still wrong. Quitting the app")
|
||||
return
|
||||
|
||||
if age < 18:
|
||||
print("Sorry I can't help you.")
|
||||
else:
|
||||
height = float(input('What\'s your height (in cm)? '))
|
||||
height = convert_to_m(height)
|
||||
weight = float(input('what\'s your weight (in kg) '))
|
||||
bmi = calculate_bmi(height, weight)
|
||||
bmi_test(bmi)
|
||||
|
||||
print('-------------------------------------------')
|
||||
bmi_app()
|
||||
|
||||
|
||||
bmi_app()
|
||||
|
||||
# how to improve?
|
||||
# Q1 what if the weight is not integer? e.g. 62.5
|
||||
@@ -0,0 +1,29 @@
|
||||
# you can download at
|
||||
# https://goo.gl/WKdcqo
|
||||
|
||||
|
||||
colors = ['red', 'blue', 'green']
|
||||
|
||||
colors
|
||||
|
||||
colors[0] # zero base indexing
|
||||
colors[1]
|
||||
colors[2]
|
||||
|
||||
colors.append('purple')
|
||||
colors
|
||||
|
||||
|
||||
colors.remove('green')
|
||||
colors
|
||||
|
||||
|
||||
colors.extend(['green', 'yellow'])
|
||||
|
||||
'green' in colors
|
||||
|
||||
colors[3]
|
||||
|
||||
type(colors)
|
||||
|
||||
help(colors)
|
||||
@@ -0,0 +1,13 @@
|
||||
# you can download at
|
||||
# https://goo.gl/WKdcqo
|
||||
|
||||
import random
|
||||
|
||||
random.randint(0,3)
|
||||
|
||||
|
||||
colors = [random.randint(0,2)]
|
||||
colors = [random.randint(0,len(colors))]
|
||||
|
||||
|
||||
random.choice(colors)
|
||||
@@ -0,0 +1,28 @@
|
||||
# you can download at
|
||||
# https://goo.gl/WKdcqo
|
||||
|
||||
colors = ['red','blue','green']
|
||||
|
||||
for color in colors:
|
||||
print('I love {}'.format(color))
|
||||
|
||||
range(3)
|
||||
|
||||
for i in range(3):
|
||||
print(i)
|
||||
|
||||
for i in range(5):
|
||||
print(i)
|
||||
|
||||
# break, pass, continue
|
||||
for i in range(3):
|
||||
cmd = input('Enter command: ')
|
||||
if cmd == 'break':
|
||||
break
|
||||
elif cmd == 'pass':
|
||||
pass
|
||||
print('command is pass')
|
||||
elif cmd == 'continue':
|
||||
continue
|
||||
print('command is continue')
|
||||
|
||||
@@ -0,0 +1,26 @@
|
||||
# you can download at
|
||||
# https://goo.gl/WKdcqo
|
||||
|
||||
import random
|
||||
|
||||
|
||||
def guess_lucky_number():
|
||||
colors = ['red', 'blue', 'green', 'purple', 'yellow']
|
||||
lucky_color = random.choice(colors)
|
||||
|
||||
for i in range(3):
|
||||
print('There are {} colors'.format(colors))
|
||||
guess = input('Guess your lucky color: ')
|
||||
if guess != lucky_color:
|
||||
colors.remove(guess)
|
||||
print('Seems like {} is not your lucky color :( \n'.format(guess))
|
||||
else:
|
||||
break
|
||||
|
||||
if guess == lucky_color:
|
||||
print('Great! {} is your lucky color!! '.format(lucky_color))
|
||||
else:
|
||||
print('Actually, {} is your lucky color '.format(lucky_color))
|
||||
|
||||
|
||||
guess_lucky_number()
|
||||
@@ -0,0 +1,19 @@
|
||||
vocabs = {}
|
||||
|
||||
|
||||
type(vocabs)
|
||||
|
||||
vocabs['programming'] = 'the activity of job of writing computer programs'
|
||||
vocabs
|
||||
|
||||
|
||||
vocabs['python'] = 'a very large snake that kills animals for food'
|
||||
|
||||
del(vocabs['programming'])
|
||||
|
||||
vocabs.clear()
|
||||
|
||||
vocabs['programming'] = ['the activity of job of writing computer programs','_______ming']
|
||||
|
||||
vocabs = {'programming': 'the activity of job of writing computer programs',
|
||||
'python': 'a very large snake'}
|
||||
@@ -0,0 +1,19 @@
|
||||
vocabs = {'programming': ['the activity or job of writing computer programs','_______ming'],
|
||||
'python': ['a very large snake that kills animals for food by wrapping itself around them and crushing them', '___hon'],
|
||||
'fun': ['pleasure, enjoyment, or entertainment', '__n']}
|
||||
|
||||
|
||||
vocabs.keys()
|
||||
|
||||
vocabs.values()
|
||||
|
||||
vocabs.items()
|
||||
|
||||
for key in vocabs.keys():
|
||||
print(key)
|
||||
|
||||
for value in vocabs.values():
|
||||
print(value)
|
||||
|
||||
for key,value in vocabs.items():
|
||||
print('{} ({})\n {}'.format(key,value[1],value[0]))
|
||||
@@ -0,0 +1,36 @@
|
||||
vocabs = {'programming': ['the activity or job of writing computer programs','_______ming'],
|
||||
'python': ['a very large snake that kills animals for food by wrapping itself around them and crushing them', '___hon'],
|
||||
'fun': ['pleasure, enjoyment, or entertainment', '__n']}
|
||||
|
||||
|
||||
def new_word():
|
||||
try:
|
||||
word, definition, hint = input('Enter a new word (word|definition|hint): ').split('|')
|
||||
print('{} ({})\n {}'.format(word,hint,definition))
|
||||
except ValueError:
|
||||
print('Please make sure your format is correct!')
|
||||
except:
|
||||
print('Something is wrong')
|
||||
|
||||
new_word()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def new_word():
|
||||
try:
|
||||
word, definition, hint = input("Enter a new words (word|definition|hint)").split("|")
|
||||
print("{}".format(word))
|
||||
print(" - {} ({})".format(definition, hint))
|
||||
print('----------------------')
|
||||
except ValueError:
|
||||
print("Value Error")
|
||||
new_word()
|
||||
except:
|
||||
print("Something is wrong")
|
||||
new_word()
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,13 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -ex
|
||||
|
||||
python -m pip install pipenv
|
||||
|
||||
pipenv sync
|
||||
|
||||
pipenv run \
|
||||
jupyter-notebook \
|
||||
--allow-root \
|
||||
--ip=0.0.0.0
|
||||
|
||||
@@ -0,0 +1,15 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -ex
|
||||
|
||||
pipenv install jupyter
|
||||
pipenv install jupyter notebook
|
||||
|
||||
pipenv install pandas
|
||||
pipenv install quandl
|
||||
|
||||
pipenv install seaborn
|
||||
pipenv install scikit-learn
|
||||
|
||||
# jupyter-notebook
|
||||
|
||||
|
|
@@ -0,0 +1,15 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -ex
|
||||
|
||||
docker run -it \
|
||||
-v $PWD:/app \
|
||||
-w /app \
|
||||
-v /var/run/docker.sock:/var/run/docker.sock \
|
||||
-v ~/.ssh/id_rsa:/home/node/.ssh/id_rsa:ro \
|
||||
-v ~/.ssh/known_host:/home/node/.ssh/known_hosts:ro \
|
||||
-p 8889:8888 \
|
||||
--rm \
|
||||
python:latest \
|
||||
bash
|
||||
|
||||
@@ -0,0 +1,52 @@
|
||||
"state","abbreviation"
|
||||
"Alabama","AL"
|
||||
"Alaska","AK"
|
||||
"Arizona","AZ"
|
||||
"Arkansas","AR"
|
||||
"California","CA"
|
||||
"Colorado","CO"
|
||||
"Connecticut","CT"
|
||||
"Delaware","DE"
|
||||
"District of Columbia","DC"
|
||||
"Florida","FL"
|
||||
"Georgia","GA"
|
||||
"Hawaii","HI"
|
||||
"Idaho","ID"
|
||||
"Illinois","IL"
|
||||
"Indiana","IN"
|
||||
"Iowa","IA"
|
||||
"Kansas","KS"
|
||||
"Kentucky","KY"
|
||||
"Louisiana","LA"
|
||||
"Maine","ME"
|
||||
"Montana","MT"
|
||||
"Nebraska","NE"
|
||||
"Nevada","NV"
|
||||
"New Hampshire","NH"
|
||||
"New Jersey","NJ"
|
||||
"New Mexico","NM"
|
||||
"New York","NY"
|
||||
"North Carolina","NC"
|
||||
"North Dakota","ND"
|
||||
"Ohio","OH"
|
||||
"Oklahoma","OK"
|
||||
"Oregon","OR"
|
||||
"Maryland","MD"
|
||||
"Massachusetts","MA"
|
||||
"Michigan","MI"
|
||||
"Minnesota","MN"
|
||||
"Mississippi","MS"
|
||||
"Missouri","MO"
|
||||
"Pennsylvania","PA"
|
||||
"Rhode Island","RI"
|
||||
"South Carolina","SC"
|
||||
"South Dakota","SD"
|
||||
"Tennessee","TN"
|
||||
"Texas","TX"
|
||||
"Utah","UT"
|
||||
"Vermont","VT"
|
||||
"Virginia","VA"
|
||||
"Washington","WA"
|
||||
"West Virginia","WV"
|
||||
"Wisconsin","WI"
|
||||
"Wyoming","WY"
|
||||
|
@@ -0,0 +1,53 @@
|
||||
state,area (sq. mi)
|
||||
Alabama,52423
|
||||
Alaska,656425
|
||||
Arizona,114006
|
||||
Arkansas,53182
|
||||
California,163707
|
||||
Colorado,104100
|
||||
Connecticut,5544
|
||||
Delaware,1954
|
||||
Florida,65758
|
||||
Georgia,59441
|
||||
Hawaii,10932
|
||||
Idaho,83574
|
||||
Illinois,57918
|
||||
Indiana,36420
|
||||
Iowa,56276
|
||||
Kansas,82282
|
||||
Kentucky,40411
|
||||
Louisiana,51843
|
||||
Maine,35387
|
||||
Maryland,12407
|
||||
Massachusetts,10555
|
||||
Michigan,96810
|
||||
Minnesota,86943
|
||||
Mississippi,48434
|
||||
Missouri,69709
|
||||
Montana,147046
|
||||
Nebraska,77358
|
||||
Nevada,110567
|
||||
New Hampshire,9351
|
||||
New Jersey,8722
|
||||
New Mexico,121593
|
||||
New York,54475
|
||||
North Carolina,53821
|
||||
North Dakota,70704
|
||||
Ohio,44828
|
||||
Oklahoma,69903
|
||||
Oregon,98386
|
||||
Pennsylvania,46058
|
||||
Rhode Island,1545
|
||||
South Carolina,32007
|
||||
South Dakota,77121
|
||||
Tennessee,42146
|
||||
Texas,268601
|
||||
Utah,84904
|
||||
Vermont,9615
|
||||
Virginia,42769
|
||||
Washington,71303
|
||||
West Virginia,24231
|
||||
Wisconsin,65503
|
||||
Wyoming,97818
|
||||
District of Columbia,68
|
||||
Puerto Rico,3515
|
||||
|
File diff suppressed because it is too large
Load Diff
Binary file not shown.
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user