Add breast cancer data exploration
This commit is contained in:
Jake Walker
parent
809b79e208
commit
df28f7edaf
1 changed files with 641 additions and 1 deletions
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@ -10,11 +10,651 @@
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"\n",
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"From: <https://www.datacamp.com/tutorial/principal-component-analysis-in-python>"
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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": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd"
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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": [
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"## Breast Cancer Data Exploration"
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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": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.datasets import load_breast_cancer\n",
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"\n",
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"breast = load_breast_cancer()\n",
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"breast_data = breast.data"
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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": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(569, 30)"
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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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"breast_data.shape"
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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": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(569,)"
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]
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},
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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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"breast_labels = breast.target\n",
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"breast_labels.shape"
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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": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"labels = np.reshape(breast_labels, (569, 1))"
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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": 13,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(569, 31)"
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},
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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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"final_breast_data = np.concatenate([breast_data, labels], axis=1)\n",
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"final_breast_data.shape"
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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": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"breast_dataset = pd.DataFrame(final_breast_data)"
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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": 16,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
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" 'mean smoothness', 'mean compactness', 'mean concavity',\n",
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" 'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
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" 'radius error', 'texture error', 'perimeter error', 'area error',\n",
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" 'smoothness error', 'compactness error', 'concavity error',\n",
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" 'concave points error', 'symmetry error',\n",
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" 'fractal dimension error', 'worst radius', 'worst texture',\n",
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" 'worst perimeter', 'worst area', 'worst smoothness',\n",
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" 'worst compactness', 'worst concavity', 'worst concave points',\n",
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" 'worst symmetry', 'worst fractal dimension'], dtype='<U23')"
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]
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},
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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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"features = breast.feature_names\n",
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"features"
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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": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"features_labels = np.append(features, \"label\")\n",
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"breast_dataset.columns = features_labels"
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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": 18,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"<div>\n",
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"<style scoped>\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>mean radius</th>\n",
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" <th>mean texture</th>\n",
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" <th>mean perimeter</th>\n",
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" <th>mean area</th>\n",
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" <th>mean smoothness</th>\n",
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" <th>mean compactness</th>\n",
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" <th>mean concavity</th>\n",
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" <th>mean concave points</th>\n",
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" <th>mean symmetry</th>\n",
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" <th>mean fractal dimension</th>\n",
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" <th>...</th>\n",
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" <th>worst texture</th>\n",
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" <th>worst perimeter</th>\n",
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" <th>worst area</th>\n",
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" <th>worst smoothness</th>\n",
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" <th>worst compactness</th>\n",
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" <th>worst concavity</th>\n",
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" <th>worst concave points</th>\n",
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" <th>worst symmetry</th>\n",
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" <th>worst fractal dimension</th>\n",
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" <th>label</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>17.99</td>\n",
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" <td>10.38</td>\n",
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" <td>122.80</td>\n",
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" <td>1001.0</td>\n",
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" <td>0.11840</td>\n",
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" <td>0.27760</td>\n",
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" <td>0.3001</td>\n",
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" <td>0.14710</td>\n",
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" <td>0.2419</td>\n",
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" <td>0.07871</td>\n",
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" <td>...</td>\n",
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" <td>17.33</td>\n",
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" <td>184.60</td>\n",
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" <td>2019.0</td>\n",
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" <td>0.1622</td>\n",
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" <td>0.6656</td>\n",
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" <td>0.7119</td>\n",
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" <td>0.2654</td>\n",
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" <td>0.4601</td>\n",
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" <td>0.11890</td>\n",
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" <td>0.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>20.57</td>\n",
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" <td>17.77</td>\n",
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" <td>132.90</td>\n",
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" <td>1326.0</td>\n",
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" <td>0.08474</td>\n",
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" <td>0.07864</td>\n",
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" <td>0.0869</td>\n",
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" <td>0.07017</td>\n",
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" <td>0.1812</td>\n",
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" <td>0.05667</td>\n",
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" <td>...</td>\n",
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" <td>23.41</td>\n",
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" <td>158.80</td>\n",
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" <td>1956.0</td>\n",
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" <td>0.1238</td>\n",
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" <td>0.1866</td>\n",
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" <td>0.2416</td>\n",
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" <td>0.1860</td>\n",
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" <td>0.2750</td>\n",
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" <td>0.08902</td>\n",
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" <td>0.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>19.69</td>\n",
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" <td>21.25</td>\n",
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" <td>130.00</td>\n",
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" <td>1203.0</td>\n",
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" <td>0.10960</td>\n",
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" <td>0.15990</td>\n",
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" <td>0.1974</td>\n",
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" <td>0.12790</td>\n",
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" <td>0.2069</td>\n",
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" <td>0.05999</td>\n",
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" <td>...</td>\n",
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" <td>25.53</td>\n",
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" <td>152.50</td>\n",
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" <td>1709.0</td>\n",
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" <td>0.1444</td>\n",
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" <td>0.4245</td>\n",
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" <td>0.4504</td>\n",
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" <td>0.2430</td>\n",
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" <td>0.3613</td>\n",
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" <td>0.08758</td>\n",
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" <td>0.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>11.42</td>\n",
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" <td>20.38</td>\n",
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" <td>77.58</td>\n",
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" <td>386.1</td>\n",
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" <td>0.14250</td>\n",
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" <td>0.28390</td>\n",
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" <td>0.2414</td>\n",
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" <td>0.10520</td>\n",
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" <td>0.2597</td>\n",
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" <td>0.09744</td>\n",
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" <td>...</td>\n",
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" <td>26.50</td>\n",
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" <td>98.87</td>\n",
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" <td>567.7</td>\n",
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" <td>0.2098</td>\n",
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" <td>0.8663</td>\n",
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" <td>0.6869</td>\n",
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" <td>0.2575</td>\n",
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" <td>0.6638</td>\n",
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" <td>0.17300</td>\n",
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" <td>0.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>20.29</td>\n",
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" <td>14.34</td>\n",
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" <td>135.10</td>\n",
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" <td>1297.0</td>\n",
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" <td>0.10030</td>\n",
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" <td>0.13280</td>\n",
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" <td>0.1980</td>\n",
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" <td>0.10430</td>\n",
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" <td>0.1809</td>\n",
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" <td>0.05883</td>\n",
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" <td>...</td>\n",
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" <td>16.67</td>\n",
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" <td>152.20</td>\n",
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" <td>1575.0</td>\n",
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" <td>0.1374</td>\n",
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" <td>0.2050</td>\n",
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" <td>0.4000</td>\n",
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" <td>0.1625</td>\n",
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" <td>0.2364</td>\n",
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" <td>0.07678</td>\n",
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" <td>0.0</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>5 rows × 31 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" mean radius mean texture mean perimeter mean area mean smoothness \\\n",
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"0 17.99 10.38 122.80 1001.0 0.11840 \n",
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"1 20.57 17.77 132.90 1326.0 0.08474 \n",
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"2 19.69 21.25 130.00 1203.0 0.10960 \n",
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"3 11.42 20.38 77.58 386.1 0.14250 \n",
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"4 20.29 14.34 135.10 1297.0 0.10030 \n",
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"\n",
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" mean compactness mean concavity mean concave points mean symmetry \\\n",
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"0 0.27760 0.3001 0.14710 0.2419 \n",
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"1 0.07864 0.0869 0.07017 0.1812 \n",
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"2 0.15990 0.1974 0.12790 0.2069 \n",
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"3 0.28390 0.2414 0.10520 0.2597 \n",
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"4 0.13280 0.1980 0.10430 0.1809 \n",
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"\n",
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" mean fractal dimension ... worst texture worst perimeter worst area \\\n",
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"0 0.07871 ... 17.33 184.60 2019.0 \n",
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"1 0.05667 ... 23.41 158.80 1956.0 \n",
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"2 0.05999 ... 25.53 152.50 1709.0 \n",
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"3 0.09744 ... 26.50 98.87 567.7 \n",
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"4 0.05883 ... 16.67 152.20 1575.0 \n",
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"\n",
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" worst smoothness worst compactness worst concavity worst concave points \\\n",
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"0 0.1622 0.6656 0.7119 0.2654 \n",
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"1 0.1238 0.1866 0.2416 0.1860 \n",
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"2 0.1444 0.4245 0.4504 0.2430 \n",
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"3 0.2098 0.8663 0.6869 0.2575 \n",
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"4 0.1374 0.2050 0.4000 0.1625 \n",
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"\n",
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" worst symmetry worst fractal dimension label \n",
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"0 0.4601 0.11890 0.0 \n",
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"1 0.2750 0.08902 0.0 \n",
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"2 0.3613 0.08758 0.0 \n",
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"3 0.6638 0.17300 0.0 \n",
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"4 0.2364 0.07678 0.0 \n",
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"\n",
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"[5 rows x 31 columns]"
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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"source": [
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"breast_dataset.head()"
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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": 19,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\JakeWalker\\AppData\\Local\\Temp\\ipykernel_17096\\3450579118.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
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"The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
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"\n",
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"For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
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"\n",
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"\n",
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" breast_dataset[\"label\"].replace(0, \"Benign\", inplace=True)\n"
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]
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}
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],
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"source": [
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"breast_dataset[\"label\"].replace(0, \"Benign\", inplace=True)\n",
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"breast_dataset[\"label\"].replace(1, \"Malignant\", inplace=True)"
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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": 20,
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>mean radius</th>\n",
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" <th>mean texture</th>\n",
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" <th>mean perimeter</th>\n",
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" <th>mean area</th>\n",
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" <th>mean smoothness</th>\n",
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" <th>mean compactness</th>\n",
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" <th>mean concavity</th>\n",
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" <th>mean concave points</th>\n",
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" <th>mean symmetry</th>\n",
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" <th>mean fractal dimension</th>\n",
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" <th>...</th>\n",
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" <th>worst texture</th>\n",
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" <th>worst perimeter</th>\n",
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" <th>worst area</th>\n",
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" <th>worst smoothness</th>\n",
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" <th>worst compactness</th>\n",
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" <th>worst concavity</th>\n",
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||||
" <th>worst concave points</th>\n",
|
||||
" <th>worst symmetry</th>\n",
|
||||
" <th>worst fractal dimension</th>\n",
|
||||
" <th>label</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>564</th>\n",
|
||||
" <td>21.56</td>\n",
|
||||
" <td>22.39</td>\n",
|
||||
" <td>142.00</td>\n",
|
||||
" <td>1479.0</td>\n",
|
||||
" <td>0.11100</td>\n",
|
||||
" <td>0.11590</td>\n",
|
||||
" <td>0.24390</td>\n",
|
||||
" <td>0.13890</td>\n",
|
||||
" <td>0.1726</td>\n",
|
||||
" <td>0.05623</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>26.40</td>\n",
|
||||
" <td>166.10</td>\n",
|
||||
" <td>2027.0</td>\n",
|
||||
" <td>0.14100</td>\n",
|
||||
" <td>0.21130</td>\n",
|
||||
" <td>0.4107</td>\n",
|
||||
" <td>0.2216</td>\n",
|
||||
" <td>0.2060</td>\n",
|
||||
" <td>0.07115</td>\n",
|
||||
" <td>Benign</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>565</th>\n",
|
||||
" <td>20.13</td>\n",
|
||||
" <td>28.25</td>\n",
|
||||
" <td>131.20</td>\n",
|
||||
" <td>1261.0</td>\n",
|
||||
" <td>0.09780</td>\n",
|
||||
" <td>0.10340</td>\n",
|
||||
" <td>0.14400</td>\n",
|
||||
" <td>0.09791</td>\n",
|
||||
" <td>0.1752</td>\n",
|
||||
" <td>0.05533</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>38.25</td>\n",
|
||||
" <td>155.00</td>\n",
|
||||
" <td>1731.0</td>\n",
|
||||
" <td>0.11660</td>\n",
|
||||
" <td>0.19220</td>\n",
|
||||
" <td>0.3215</td>\n",
|
||||
" <td>0.1628</td>\n",
|
||||
" <td>0.2572</td>\n",
|
||||
" <td>0.06637</td>\n",
|
||||
" <td>Benign</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>566</th>\n",
|
||||
" <td>16.60</td>\n",
|
||||
" <td>28.08</td>\n",
|
||||
" <td>108.30</td>\n",
|
||||
" <td>858.1</td>\n",
|
||||
" <td>0.08455</td>\n",
|
||||
" <td>0.10230</td>\n",
|
||||
" <td>0.09251</td>\n",
|
||||
" <td>0.05302</td>\n",
|
||||
" <td>0.1590</td>\n",
|
||||
" <td>0.05648</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>34.12</td>\n",
|
||||
" <td>126.70</td>\n",
|
||||
" <td>1124.0</td>\n",
|
||||
" <td>0.11390</td>\n",
|
||||
" <td>0.30940</td>\n",
|
||||
" <td>0.3403</td>\n",
|
||||
" <td>0.1418</td>\n",
|
||||
" <td>0.2218</td>\n",
|
||||
" <td>0.07820</td>\n",
|
||||
" <td>Benign</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>567</th>\n",
|
||||
" <td>20.60</td>\n",
|
||||
" <td>29.33</td>\n",
|
||||
" <td>140.10</td>\n",
|
||||
" <td>1265.0</td>\n",
|
||||
" <td>0.11780</td>\n",
|
||||
" <td>0.27700</td>\n",
|
||||
" <td>0.35140</td>\n",
|
||||
" <td>0.15200</td>\n",
|
||||
" <td>0.2397</td>\n",
|
||||
" <td>0.07016</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>39.42</td>\n",
|
||||
" <td>184.60</td>\n",
|
||||
" <td>1821.0</td>\n",
|
||||
" <td>0.16500</td>\n",
|
||||
" <td>0.86810</td>\n",
|
||||
" <td>0.9387</td>\n",
|
||||
" <td>0.2650</td>\n",
|
||||
" <td>0.4087</td>\n",
|
||||
" <td>0.12400</td>\n",
|
||||
" <td>Benign</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>568</th>\n",
|
||||
" <td>7.76</td>\n",
|
||||
" <td>24.54</td>\n",
|
||||
" <td>47.92</td>\n",
|
||||
" <td>181.0</td>\n",
|
||||
" <td>0.05263</td>\n",
|
||||
" <td>0.04362</td>\n",
|
||||
" <td>0.00000</td>\n",
|
||||
" <td>0.00000</td>\n",
|
||||
" <td>0.1587</td>\n",
|
||||
" <td>0.05884</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>30.37</td>\n",
|
||||
" <td>59.16</td>\n",
|
||||
" <td>268.6</td>\n",
|
||||
" <td>0.08996</td>\n",
|
||||
" <td>0.06444</td>\n",
|
||||
" <td>0.0000</td>\n",
|
||||
" <td>0.0000</td>\n",
|
||||
" <td>0.2871</td>\n",
|
||||
" <td>0.07039</td>\n",
|
||||
" <td>Malignant</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>5 rows × 31 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" mean radius mean texture mean perimeter mean area mean smoothness \\\n",
|
||||
"564 21.56 22.39 142.00 1479.0 0.11100 \n",
|
||||
"565 20.13 28.25 131.20 1261.0 0.09780 \n",
|
||||
"566 16.60 28.08 108.30 858.1 0.08455 \n",
|
||||
"567 20.60 29.33 140.10 1265.0 0.11780 \n",
|
||||
"568 7.76 24.54 47.92 181.0 0.05263 \n",
|
||||
"\n",
|
||||
" mean compactness mean concavity mean concave points mean symmetry \\\n",
|
||||
"564 0.11590 0.24390 0.13890 0.1726 \n",
|
||||
"565 0.10340 0.14400 0.09791 0.1752 \n",
|
||||
"566 0.10230 0.09251 0.05302 0.1590 \n",
|
||||
"567 0.27700 0.35140 0.15200 0.2397 \n",
|
||||
"568 0.04362 0.00000 0.00000 0.1587 \n",
|
||||
"\n",
|
||||
" mean fractal dimension ... worst texture worst perimeter worst area \\\n",
|
||||
"564 0.05623 ... 26.40 166.10 2027.0 \n",
|
||||
"565 0.05533 ... 38.25 155.00 1731.0 \n",
|
||||
"566 0.05648 ... 34.12 126.70 1124.0 \n",
|
||||
"567 0.07016 ... 39.42 184.60 1821.0 \n",
|
||||
"568 0.05884 ... 30.37 59.16 268.6 \n",
|
||||
"\n",
|
||||
" worst smoothness worst compactness worst concavity \\\n",
|
||||
"564 0.14100 0.21130 0.4107 \n",
|
||||
"565 0.11660 0.19220 0.3215 \n",
|
||||
"566 0.11390 0.30940 0.3403 \n",
|
||||
"567 0.16500 0.86810 0.9387 \n",
|
||||
"568 0.08996 0.06444 0.0000 \n",
|
||||
"\n",
|
||||
" worst concave points worst symmetry worst fractal dimension label \n",
|
||||
"564 0.2216 0.2060 0.07115 Benign \n",
|
||||
"565 0.1628 0.2572 0.06637 Benign \n",
|
||||
"566 0.1418 0.2218 0.07820 Benign \n",
|
||||
"567 0.2650 0.4087 0.12400 Benign \n",
|
||||
"568 0.0000 0.2871 0.07039 Malignant \n",
|
||||
"\n",
|
||||
"[5 rows x 31 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"breast_dataset.tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## CIFAR-10 Data Exploration"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "data-science-research-W2__OPFf-py3.11",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python"
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
|
|||
Loading…
Reference in a new issue