![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "yXA3GwWhY9KL"
+ },
+ "source": [
+ "# Part 1 - Scalars and Vectors\n",
+ "\n",
+ "For the questions below it is not sufficient to simply provide answer to the questions, but you must solve the problems and show your work using python (the NumPy library will help a lot!) Translate the vectors and matrices into their appropriate python representations and use numpy or functions that you write yourself to demonstrate the result or property. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "oNOTv43_Zi9L"
+ },
+ "source": [
+ "## 1.1 Create a two-dimensional vector and plot it on a graph"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "5oCHTpESqmk4"
+ },
+ "source": [
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XNqjzQzrkVG7",
+ "outputId": "412d60bd-ee5d-449e-d2a2-033b2310c333",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 281
+ }
+ },
+ "source": [
+ "# 2D vector\n",
+ "vec_2d = np.array([2, 5])\n",
+ "\n",
+ "# Graph the vector\n",
+ "plt.arrow(0,0,vec_2d[0],vec_2d[1], color='green')\n",
+ "plt.xlim(0, 3)\n",
+ "plt.ylim(0, 5)\n",
+ "plt.title(\"2-Dimensional Vector\")\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GSNiYn8lr6nN"
+ },
+ "source": [
+ "# Statistics"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3d4izUhQvh2_"
+ },
+ "source": [
+ "## 1.1 Sales for the past week was the following amounts: [3505, 2400, 3027, 2798, 3700, 3250, 2689]. Without using library functions, what is the mean, variance, and standard deviation of of sales from last week? (for extra bonus points, write your own function that can calculate these two values for any sized list)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "YgCoLzDSwCvf"
+ },
+ "source": [
+ "# Function for finding sales stats\n",
+ "def sales_stats(sales):\n",
+ " sales_mean = 0.0\n",
+ " sales_variance = 0.0\n",
+ " sales_std = 0.0\n",
+ " \n",
+ " # Calculate mean\n",
+ " sum = 0\n",
+ " for x in range(len(sales)):\n",
+ " sum += sales[x]\n",
+ " sales_mean = sum / len(sales)\n",
+ "\n",
+ "# Calculate variance\n",
+ " sum = 0\n",
+ " for x in range(len(sales)):\n",
+ " sum += (sales[x] - sales_mean) ** 2\n",
+ " sales_variance = sum / (len(sales) -1)\n",
+ "\n",
+ " # Calculate standard deviation\n",
+ " sales_std = sales_variance ** (1/2)\n",
+ "\n",
+ " return sales_mean, sales_variance, sales_std\n"
+ ],
+ "execution_count": 26,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "w1iZfYvBtEA1",
+ "outputId": "2e9d19e5-7e09-4755-ae8a-ebe232dff5ba",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 105
+ }
+ },
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "weekly_sales = np.array([3505, 2400, 3027, 2798, 3700, 3250, 2689])\n",
+ "sales_mean, sales_variance, sales_std = sales_stats(weekly_sales)\n",
+ "\n",
+ "print(f\"\"\"\n",
+ "Sales mean = ${round(sales_mean, 2)}\n",
+ "Variance = ${round(sales_variance, 2)}\n",
+ "Standard deviation = ${round(sales_std, 2)}\n",
+ "\"\"\")"
+ ],
+ "execution_count": 25,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Sales mean = $3052.71\n",
+ "Variance = $214387.9\n",
+ "Standard deviation = $463.02\n",
+ "\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "oh63KaOctEp_"
+ },
+ "source": [
+ "## 1.2 Find the covariance between last week's sales numbers and the number of customers that entered the store last week: [127, 80, 105, 92, 120, 115, 93] (you may use librray functions for calculating the covariance since we didn't specifically talk about its formula)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "G7ZB0krot564",
+ "outputId": "a4ac2b4d-c595-400b-f456-a49b32f2dc32",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 105
+ }
+ },
+ "source": [
+ "weekly_customers = np.array([127, 80, 105, 92, 120, 115, 93])\n",
+ "\n",
+ "# Transform sales and customer data into dataframe\n",
+ "data = {\n",
+ " 'weekly_sales': weekly_sales,\n",
+ " 'weekly_customers': weekly_customers\n",
+ "}\n",
+ "sales_df = pd.DataFrame(data)\n",
+ "\n",
+ "# Calculate covariance between sales and customers\n",
+ "cov = sales_df.cov()\n",
+ "print(f\"\"\"Covariance between weekly sales and customers:\n",
+ "{cov}\n",
+ "\"\"\")"
+ ],
+ "execution_count": 3,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Covariance between weekly sales and customers:\n",
+ " weekly_sales weekly_customers\n",
+ "weekly_sales 214387.904762 7604.357143\n",
+ "weekly_customers 7604.357143 290.952381\n",
+ "\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "J9SbUY9mt66I"
+ },
+ "source": [
+ "## 1.3 Find the standard deviation of customers who entered the store last week. Then, use the standard deviations of both sales and customers to standardize the covariance to find the correlation coefficient that summarizes the relationship between sales and customers. (You may use library functions to check your work.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vFJms2YRrKhY",
+ "outputId": "6cbb6dcb-01df-4c5d-d836-e4c676a6b03c",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 105
+ }
+ },
+ "source": [
+ "customer_mean, customer_variance, customer_std = sales_stats(weekly_customers)\n",
+ "print(f\"\"\"\n",
+ "Standard deviation:\n",
+ " Sales = ${round(sales_std, 2)}\n",
+ " Customers = {round(customer_std, 3)}\n",
+ "\"\"\")"
+ ],
+ "execution_count": 4,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Standard deviation:\n",
+ " Sales = $463.02\n",
+ " Customers = 17.057\n",
+ "\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ENyVw3U_f-bp",
+ "outputId": "adc28dc3-22fa-4af1-8aa1-e22ea50904b8",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 157
+ }
+ },
+ "source": [
+ "# Calculate the correlation coefficient\n",
+ "combined_std = sales_std * customer_std\n",
+ "combined_cov = cov['weekly_sales']['weekly_customers']\n",
+ "corr = combined_cov / combined_std\n",
+ "print(f\"\"\"The correlation between sales and customers last week:\n",
+ " r = {round(corr, 3)}\n",
+ "\n",
+ "Pandas correlation matrix:\n",
+ "{sales_df.corr()}\n",
+ "\"\"\")"
+ ],
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "The correlation between sales and customers last week:\n",
+ " r = 0.963\n",
+ "\n",
+ "Pandas correlation matrix:\n",
+ " weekly_sales weekly_customers\n",
+ "weekly_sales 1.000000 0.962834\n",
+ "weekly_customers 0.962834 1.000000\n",
+ "\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IbZVf7nmujPJ"
+ },
+ "source": [
+ "## 1.4 Use pandas to import a cleaned version of the titanic dataset from the following link: [Titanic Dataset](https://raw.githubusercontent.com/Geoyi/Cleaning-Titanic-Data/master/titanic_clean.csv)\n",
+ "\n",
+ "## Calculate the variance-covariance matrix and correlation matrix for the titanic dataset's numeric columns. (you can encode some of the categorical variables and include them as a stretch goal if you finish early)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0TWgUIiaCFzq",
+ "outputId": "d4787592-85be-4079-d13d-ff01d04315b8",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 559
+ }
+ },
+ "source": [
+ "# Load Titanic dataset\n",
+ "data_url = 'https://raw.githubusercontent.com/Geoyi/Cleaning-Titanic-Data/master/titanic_clean.csv'\n",
+ "titanic_data = pd.read_csv(data_url)\n",
+ "titanic_data.drop(columns='Unnamed: 0', inplace=True)\n",
+ "print(titanic_data.shape)\n",
+ "titanic_data.head()"
+ ],
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "(1310, 15)\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "| \n", + " | pclass | \n", + "survived | \n", + "name | \n", + "sex | \n", + "age | \n", + "sibsp | \n", + "parch | \n", + "ticket | \n", + "fare | \n", + "cabin | \n", + "embarked | \n", + "boat | \n", + "body | \n", + "home.dest | \n", + "has_cabin_number | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1.0 | \n", + "1.0 | \n", + "Allen, Miss. Elisabeth Walton | \n", + "female | \n", + "29.0000 | \n", + "0.0 | \n", + "0.0 | \n", + "24160 | \n", + "211.3375 | \n", + "B5 | \n", + "S | \n", + "2 | \n", + "NaN | \n", + "St Louis, MO | \n", + "1 | \n", + "
| 1 | \n", + "1.0 | \n", + "1.0 | \n", + "Allison, Master. Hudson Trevor | \n", + "male | \n", + "0.9167 | \n", + "1.0 | \n", + "2.0 | \n", + "113781 | \n", + "151.5500 | \n", + "C22 C26 | \n", + "S | \n", + "11 | \n", + "NaN | \n", + "Montreal, PQ / Chesterville, ON | \n", + "1 | \n", + "
| 2 | \n", + "1.0 | \n", + "0.0 | \n", + "Allison, Miss. Helen Loraine | \n", + "female | \n", + "2.0000 | \n", + "1.0 | \n", + "2.0 | \n", + "113781 | \n", + "151.5500 | \n", + "C22 C26 | \n", + "S | \n", + "NaN | \n", + "NaN | \n", + "Montreal, PQ / Chesterville, ON | \n", + "1 | \n", + "
| 3 | \n", + "1.0 | \n", + "0.0 | \n", + "Allison, Mr. Hudson Joshua Creighton | \n", + "male | \n", + "30.0000 | \n", + "1.0 | \n", + "2.0 | \n", + "113781 | \n", + "151.5500 | \n", + "C22 C26 | \n", + "S | \n", + "NaN | \n", + "135.0 | \n", + "Montreal, PQ / Chesterville, ON | \n", + "1 | \n", + "
| 4 | \n", + "1.0 | \n", + "0.0 | \n", + "Allison, Mrs. Hudson J C (Bessie Waldo Daniels) | \n", + "female | \n", + "25.0000 | \n", + "1.0 | \n", + "2.0 | \n", + "113781 | \n", + "151.5500 | \n", + "C22 C26 | \n", + "S | \n", + "NaN | \n", + "NaN | \n", + "Montreal, PQ / Chesterville, ON | \n", + "1 | \n", + "
| \n", + " | pclass | \n", + "survived | \n", + "age | \n", + "sibsp | \n", + "parch | \n", + "fare | \n", + "body | \n", + "has_cabin_number | \n", + "
|---|---|---|---|---|---|---|---|---|
| pclass | \n", + "0.701969 | \n", + "-0.127248 | \n", + "-3.954605 | \n", + "0.053090 | \n", + "0.013287 | \n", + "-24.227788 | \n", + "-2.876653 | \n", + "-0.249992 | \n", + "
| survived | \n", + "-0.127248 | \n", + "0.236250 | \n", + "-0.314343 | \n", + "-0.014088 | \n", + "0.034776 | \n", + "6.146023 | \n", + "0.000000 | \n", + "0.061406 | \n", + "
| age | \n", + "-3.954605 | \n", + "-0.314343 | \n", + "165.850021 | \n", + "-2.559806 | \n", + "-1.459378 | \n", + "114.416613 | \n", + "81.622922 | \n", + "1.463138 | \n", + "
| sibsp | \n", + "0.053090 | \n", + "-0.014088 | \n", + "-2.559806 | \n", + "1.085052 | \n", + "0.336833 | \n", + "8.641768 | \n", + "-8.708471 | \n", + "-0.003946 | \n", + "
| parch | \n", + "0.013287 | \n", + "0.034776 | \n", + "-1.459378 | \n", + "0.336833 | \n", + "0.749195 | \n", + "9.928031 | \n", + "4.237190 | \n", + "0.013316 | \n", + "
| fare | \n", + "-24.227788 | \n", + "6.146023 | \n", + "114.416613 | \n", + "8.641768 | \n", + "9.928031 | \n", + "2678.959738 | \n", + "-179.164684 | \n", + "10.976961 | \n", + "
| body | \n", + "-2.876653 | \n", + "0.000000 | \n", + "81.622922 | \n", + "-8.708471 | \n", + "4.237190 | \n", + "-179.164684 | \n", + "9544.688567 | \n", + "3.625689 | \n", + "
| has_cabin_number | \n", + "-0.249992 | \n", + "0.061406 | \n", + "1.463138 | \n", + "-0.003946 | \n", + "0.013316 | \n", + "10.976961 | \n", + "3.625689 | \n", + "0.174613 | \n", + "
| \n", + " | pclass | \n", + "survived | \n", + "age | \n", + "sibsp | \n", + "parch | \n", + "fare | \n", + "body | \n", + "has_cabin_number | \n", + "
|---|---|---|---|---|---|---|---|---|
| pclass | \n", + "1.000000 | \n", + "-0.312469 | \n", + "-0.366370 | \n", + "0.060832 | \n", + "0.018322 | \n", + "-0.558629 | \n", + "-0.034642 | \n", + "-0.713857 | \n", + "
| survived | \n", + "-0.312469 | \n", + "1.000000 | \n", + "-0.050199 | \n", + "-0.027825 | \n", + "0.082660 | \n", + "0.244265 | \n", + "NaN | \n", + "0.302250 | \n", + "
| age | \n", + "-0.366370 | \n", + "-0.050199 | \n", + "1.000000 | \n", + "-0.190747 | \n", + "-0.130872 | \n", + "0.171892 | \n", + "0.059059 | \n", + "0.271887 | \n", + "
| sibsp | \n", + "0.060832 | \n", + "-0.027825 | \n", + "-0.190747 | \n", + "1.000000 | \n", + "0.373587 | \n", + "0.160238 | \n", + "-0.099961 | \n", + "-0.009064 | \n", + "
| parch | \n", + "0.018322 | \n", + "0.082660 | \n", + "-0.130872 | \n", + "0.373587 | \n", + "1.000000 | \n", + "0.221539 | \n", + "0.051099 | \n", + "0.036806 | \n", + "
| fare | \n", + "-0.558629 | \n", + "0.244265 | \n", + "0.171892 | \n", + "0.160238 | \n", + "0.221539 | \n", + "1.000000 | \n", + "-0.043110 | \n", + "0.507253 | \n", + "
| body | \n", + "-0.034642 | \n", + "NaN | \n", + "0.059059 | \n", + "-0.099961 | \n", + "0.051099 | \n", + "-0.043110 | \n", + "1.000000 | \n", + "0.083796 | \n", + "
| has_cabin_number | \n", + "-0.713857 | \n", + "0.302250 | \n", + "0.271887 | \n", + "-0.009064 | \n", + "0.036806 | \n", + "0.507253 | \n", + "0.083796 | \n", + "1.000000 | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7wMWCkE1RZpM"
+ },
+ "source": [
+ "# Vertical Line Test"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "W0-g7aprRv2j"
+ },
+ "source": [
+ "## 1.1 Create two graphs, one that passes the vertical line test and one that does not."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "-tPn7C1yChre"
+ },
+ "source": [
+ "%matplotlib inline\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from sklearn.decomposition import PCA"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "fIJhCtF6RW_U",
+ "outputId": "b5f5f300-63f3-497c-8d82-a5fec1cf1065",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 823
+ }
+ },
+ "source": [
+ "# Function that passes the vertical line test\n",
+ "def f(x):\n",
+ " y = 3 *x -4\n",
+ " return y\n",
+ "\n",
+ "# Graph the function\n",
+ "x_vals = [-2, -1, 0, 1, 2]\n",
+ "y_vals = [f(x) for x in x_vals]\n",
+ "vertical_x = np.ones(13)\n",
+ "vertical_y = np.arange(-10, 3)\n",
+ "\n",
+ "plt.style.use('seaborn-bright')\n",
+ "fig, ax = plt.subplots(figsize=(12, 16))\n",
+ "ax.set_title(\"Passes the Vertical Line Test\")\n",
+ "ax.set_xlim(-3, 3)\n",
+ "ax.set_ylim(-10, 2)\n",
+ "ax.plot(x_vals, y_vals, '-', color='g', linewidth=1.5)\n",
+ "ax.plot(vertical_x, vertical_y, '.', color='k')\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "| \n", + " | CONTROL | \n", + "AGE1 | \n", + "METRO3 | \n", + "REGION | \n", + "LMED | \n", + "FMR | \n", + "L30 | \n", + "L50 | \n", + "L80 | \n", + "IPOV | \n", + "BEDRMS | \n", + "BUILT | \n", + "STATUS | \n", + "TYPE | \n", + "VALUE | \n", + "VACANCY | \n", + "TENURE | \n", + "NUNITS | \n", + "ROOMS | \n", + "WEIGHT | \n", + "PER | \n", + "ZINC2 | \n", + "ZADEQ | \n", + "ZSMHC | \n", + "STRUCTURETYPE | \n", + "OWNRENT | \n", + "UTILITY | \n", + "OTHERCOST | \n", + "COST06 | \n", + "COST12 | \n", + "COST08 | \n", + "COSTMED | \n", + "TOTSAL | \n", + "ASSISTED | \n", + "GLMED | \n", + "GL30 | \n", + "GL50 | \n", + "GL80 | \n", + "APLMED | \n", + "ABL30 | \n", + "... | \n", + "COST08RELPOVCAT | \n", + "COST08RELFMRPCT | \n", + "COST08RELFMRCAT | \n", + "COST12RELAMIPCT | \n", + "COST12RELAMICAT | \n", + "COST12RELPOVPCT | \n", + "COST12RELPOVCAT | \n", + "COST12RELFMRPCT | \n", + "COST12RELFMRCAT | \n", + "COSTMedRELAMIPCT | \n", + "COSTMedRELAMICAT | \n", + "COSTMedRELPOVPCT | \n", + "COSTMedRELPOVCAT | \n", + "COSTMedRELFMRPCT | \n", + "COSTMedRELFMRCAT | \n", + "FMTZADEQ | \n", + "FMTMETRO3 | \n", + "FMTBUILT | \n", + "FMTSTRUCTURETYPE | \n", + "FMTBEDRMS | \n", + "FMTOWNRENT | \n", + "FMTCOST06RELPOVCAT | \n", + "FMTCOST08RELPOVCAT | \n", + "FMTCOST12RELPOVCAT | \n", + "FMTCOSTMEDRELPOVCAT | \n", + "FMTINCRELPOVCAT | \n", + "FMTCOST06RELFMRCAT | \n", + "FMTCOST08RELFMRCAT | \n", + "FMTCOST12RELFMRCAT | \n", + "FMTCOSTMEDRELFMRCAT | \n", + "FMTINCRELFMRCAT | \n", + "FMTCOST06RELAMICAT | \n", + "FMTCOST08RELAMICAT | \n", + "FMTCOST12RELAMICAT | \n", + "FMTCOSTMEDRELAMICAT | \n", + "FMTINCRELAMICAT | \n", + "FMTASSISTED | \n", + "FMTBURDEN | \n", + "FMTREGION | \n", + "FMTSTATUS | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "'100003130103' | \n", + "82 | \n", + "'3' | \n", + "'1' | \n", + "73738 | \n", + "956 | \n", + "15738 | \n", + "26213 | \n", + "40322 | \n", + "11067 | \n", + "2 | \n", + "2006 | \n", + "'1' | \n", + "1 | \n", + "40000 | \n", + "-6 | \n", + "'1' | \n", + "1 | \n", + "6 | \n", + "3117.394239 | \n", + "1 | \n", + "18021 | \n", + "'1' | \n", + "533 | \n", + "1 | \n", + "'1' | \n", + "169.000000 | \n", + "213.750000 | \n", + "648.588189 | \n", + "803.050535 | \n", + "696.905247 | \n", + "615.156712 | \n", + "0 | \n", + "-9 | \n", + "73738 | \n", + "15738 | \n", + "26213 | \n", + "40322 | \n", + "51616.6 | \n", + "20234.571429 | \n", + "... | \n", + "4 | \n", + "72.898038 | \n", + "2 | \n", + "48.402635 | \n", + "2 | \n", + "290.250487 | \n", + "4 | \n", + "84.001102 | \n", + "2 | \n", + "37.077624 | \n", + "2 | \n", + "222.339102 | \n", + "4 | \n", + "64.346936 | \n", + "2 | \n", + "'1 Adequate' | \n", + "'-5' | \n", + "'2000-2009' | \n", + "'1 Single Family' | \n", + "'2 2BR' | \n", + "'1 Owner' | \n", + "'4 200%+ Poverty' | \n", + "'4 200%+ Poverty' | \n", + "'4 200%+ Poverty' | \n", + "'4 200%+ Poverty' | \n", + "'3 150-200% Poverty' | \n", + "'2 50.1 - 100% FMR' | \n", + "'2 50.1 - 100% FMR' | \n", + "'2 50.1 - 100% FMR' | \n", + "'2 50.1 - 100% FMR' | \n", + "'1 LTE 50% FMR' | \n", + "'2 30 - 50% AMI' | \n", + "'2 30 - 50% AMI' | \n", + "'2 30 - 50% AMI' | \n", + "'2 30 - 50% AMI' | \n", + "'2 30 - 50% AMI' | \n", + "'.' | \n", + "'2 30% to 50%' | \n", + "'-5' | \n", + "'-5' | \n", + "
| 1 | \n", + "'100006110249' | \n", + "50 | \n", + "'5' | \n", + "'3' | \n", + "55846 | \n", + "1100 | \n", + "17165 | \n", + "28604 | \n", + "45744 | \n", + "24218 | \n", + "4 | \n", + "1980 | \n", + "'1' | \n", + "1 | \n", + "130000 | \n", + "-6 | \n", + "'1' | \n", + "1 | \n", + "6 | \n", + "2150.725544 | \n", + "4 | \n", + "122961 | \n", + "'1' | \n", + "487 | \n", + "1 | \n", + "'1' | \n", + "245.333333 | \n", + "58.333333 | \n", + "1167.640781 | \n", + "1669.643405 | \n", + "1324.671218 | \n", + "1058.988479 | \n", + "123000 | \n", + "-9 | \n", + "55846 | \n", + "17165 | \n", + "28604 | \n", + "45744 | \n", + "55846.0 | \n", + "19911.400000 | \n", + "... | \n", + "4 | \n", + "120.424656 | \n", + "3 | \n", + "103.094063 | \n", + "6 | \n", + "275.768999 | \n", + "4 | \n", + "151.785764 | \n", + "3 | \n", + "65.388468 | \n", + "4 | \n", + "174.909320 | \n", + "3 | \n", + "96.271680 | \n", + "2 | \n", + "'1 Adequate' | \n", + "'-5' | \n", + "'1980-1989' | \n", + "'1 Single Family' | \n", + "'4 4BR+' | \n", + "'1 Owner' | \n", + "'3 150-200% Poverty' | \n", + "'4 200%+ Poverty' | \n", + "'4 200%+ Poverty' | \n", + "'3 150-200% Poverty' | \n", + "'4 200%+ Poverty' | \n", + "'3 GT FMR' | \n", + "'3 GT FMR' | \n", + "'3 GT FMR' | \n", + "'2 50.1 - 100% FMR' | \n", + "'3 GT FMR' | \n", + "'4 60 - 80% AMI' | \n", + "'4 60 - 80% AMI' | \n", + "'6 100 - 120% AMI' | \n", + "'4 60 - 80% AMI' | \n", + "'7 120% AMI +' | \n", + "'.' | \n", + "'1 Less than 30%' | \n", + "'-5' | \n", + "'-5' | \n", + "
| 2 | \n", + "'100006370140' | \n", + "53 | \n", + "'5' | \n", + "'3' | \n", + "55846 | \n", + "1100 | \n", + "13750 | \n", + "22897 | \n", + "36614 | \n", + "15470 | \n", + "4 | \n", + "1985 | \n", + "'1' | \n", + "1 | \n", + "150000 | \n", + "-6 | \n", + "'1' | \n", + "1 | \n", + "7 | \n", + "2213.789404 | \n", + "2 | \n", + "27974 | \n", + "'1' | \n", + "1405 | \n", + "1 | \n", + "'1' | \n", + "159.000000 | \n", + "37.500000 | \n", + "1193.393209 | \n", + "1772.627006 | \n", + "1374.582175 | \n", + "1068.025168 | \n", + "28000 | \n", + "-9 | \n", + "55846 | \n", + "13750 | \n", + "22897 | \n", + "36614 | \n", + "44676.8 | \n", + "19937.500000 | \n", + "... | \n", + "4 | \n", + "124.962016 | \n", + "3 | \n", + "109.452905 | \n", + "6 | \n", + "458.339239 | \n", + "4 | \n", + "161.147910 | \n", + "3 | \n", + "65.946449 | \n", + "4 | \n", + "276.153890 | \n", + "4 | \n", + "97.093197 | \n", + "2 | \n", + "'1 Adequate' | \n", + "'-5' | \n", + "'1980-1989' | \n", + "'1 Single Family' | \n", + "'4 4BR+' | \n", + "'1 Owner' | \n", + "'4 200%+ Poverty' | \n", + "'4 200%+ Poverty' | \n", + "'4 200%+ Poverty' | \n", + "'4 200%+ Poverty' | \n", + "'3 150-200% Poverty' | \n", + "'3 GT FMR' | \n", + "'3 GT FMR' | \n", + "'3 GT FMR' | \n", + "'2 50.1 - 100% FMR' | \n", + "'2 50.1 - 100% FMR' | \n", + "'4 60 - 80% AMI' | \n", + "'5 80 - 100% AMI' | \n", + "'6 100 - 120% AMI' | \n", + "'4 60 - 80% AMI' | \n", + "'4 60 - 80% AMI' | \n", + "'.' | \n", + "'3 50% or More' | \n", + "'-5' | \n", + "'-5' | \n", + "
| 3 | \n", + "'100006520140' | \n", + "67 | \n", + "'5' | \n", + "'3' | \n", + "55846 | \n", + "949 | \n", + "13750 | \n", + "22897 | \n", + "36614 | \n", + "13964 | \n", + "3 | \n", + "1985 | \n", + "'1' | \n", + "1 | \n", + "200000 | \n", + "-6 | \n", + "'1' | \n", + "1 | \n", + "6 | \n", + "2364.585097 | \n", + "2 | \n", + "32220 | \n", + "'1' | \n", + "279 | \n", + "1 | \n", + "'1' | \n", + "179.000000 | \n", + "70.666667 | \n", + "1578.857612 | \n", + "2351.169341 | \n", + "1820.442900 | \n", + "1411.700224 | \n", + "0 | \n", + "-9 | \n", + "55846 | \n", + "13750 | \n", + "22897 | \n", + "36614 | \n", + "44676.8 | \n", + "17875.000000 | \n", + "... | \n", + "4 | \n", + "191.827492 | \n", + "3 | \n", + "161.926709 | \n", + "7 | \n", + "673.494512 | \n", + "4 | \n", + "247.752301 | \n", + "3 | \n", + "97.224801 | \n", + "5 | \n", + "404.382763 | \n", + "4 | \n", + "148.756610 | \n", + "3 | \n", + "'1 Adequate' | \n", + "'-5' | \n", + "'1980-1989' | \n", + "'1 Single Family' | \n", + "'3 3BR' | \n", + "'1 Owner' | \n", + "'4 200%+ Poverty' | \n", + "'4 200%+ Poverty' | \n", + "'4 200%+ Poverty' | \n", + "'4 200%+ Poverty' | \n", + "'4 200%+ Poverty' | \n", + "'3 GT FMR' | \n", + "'3 GT FMR' | \n", + "'3 GT FMR' | \n", + "'3 GT FMR' | \n", + "'2 50.1 - 100% FMR' | \n", + "'6 100 - 120% AMI' | \n", + "'7 120% AMI +' | \n", + "'7 120% AMI +' | \n", + "'5 80 - 100% AMI' | \n", + "'4 60 - 80% AMI' | \n", + "'.' | \n", + "'1 Less than 30%' | \n", + "'-5' | \n", + "'-5' | \n", + "
| 4 | \n", + "'100007130148' | \n", + "26 | \n", + "'1' | \n", + "'3' | \n", + "60991 | \n", + "737 | \n", + "14801 | \n", + "24628 | \n", + "39421 | \n", + "15492 | \n", + "2 | \n", + "1980 | \n", + "'1' | \n", + "1 | \n", + "-6 | \n", + "-6 | \n", + "'2' | \n", + "100 | \n", + "4 | \n", + "2314.524902 | \n", + "2 | \n", + "96874 | \n", + "'1' | \n", + "759 | \n", + "5 | \n", + "'2' | \n", + "146.000000 | \n", + "12.500000 | \n", + "759.000000 | \n", + "759.000000 | \n", + "759.000000 | \n", + "759.000000 | \n", + "96900 | \n", + "0 | \n", + "60991 | \n", + "14801 | \n", + "24628 | \n", + "39421 | \n", + "48792.8 | \n", + "16651.125000 | \n", + "... | \n", + "3 | \n", + "102.985075 | \n", + "3 | \n", + "55.308707 | \n", + "3 | \n", + "195.972115 | \n", + "3 | \n", + "102.985075 | \n", + "3 | \n", + "55.308707 | \n", + "3 | \n", + "195.972115 | \n", + "3 | \n", + "102.985075 | \n", + "3 | \n", + "'1 Adequate' | \n", + "'Central City' | \n", + "'1980-1989' | \n", + "'5 50+ units' | \n", + "'2 2BR' | \n", + "'2 Renter' | \n", + "'3 150-200% Poverty' | \n", + "'3 150-200% Poverty' | \n", + "'3 150-200% Poverty' | \n", + "'3 150-200% Poverty' | \n", + "'4 200%+ Poverty' | \n", + "'3 GT FMR' | \n", + "'3 GT FMR' | \n", + "'3 GT FMR' | \n", + "'3 GT FMR' | \n", + "'3 GT FMR' | \n", + "'3 50 - 60% AMI' | \n", + "'3 50 - 60% AMI' | \n", + "'3 50 - 60% AMI' | \n", + "'3 50 - 60% AMI' | \n", + "'7 120% AMI +' | \n", + "'0 Not Assisted' | \n", + "'1 Less than 30%' | \n", + "'-5' | \n", + "'-5' | \n", + "
5 rows × 99 columns
\n", + "| \n", + " | CONTROL | \n", + "AGE1 | \n", + "METRO3 | \n", + "REGION | \n", + "LMED | \n", + "FMR | \n", + "L30 | \n", + "L50 | \n", + "L80 | \n", + "IPOV | \n", + "BEDRMS | \n", + "BUILT | \n", + "STATUS | \n", + "TYPE | \n", + "VALUE | \n", + "VACANCY | \n", + "TENURE | \n", + "NUNITS | \n", + "ROOMS | \n", + "WEIGHT | \n", + "PER | \n", + "ZINC2 | \n", + "ZADEQ | \n", + "ZSMHC | \n", + "STRUCTURETYPE | \n", + "OWNRENT | \n", + "UTILITY | \n", + "OTHERCOST | \n", + "COST06 | \n", + "COST12 | \n", + "COST08 | \n", + "COSTMED | \n", + "TOTSAL | \n", + "ASSISTED | \n", + "GLMED | \n", + "GL30 | \n", + "GL50 | \n", + "GL80 | \n", + "APLMED | \n", + "ABL30 | \n", + "... | \n", + "COST08RELPOVCAT | \n", + "COST08RELFMRPCT | \n", + "COST08RELFMRCAT | \n", + "COST12RELAMIPCT | \n", + "COST12RELAMICAT | \n", + "COST12RELPOVPCT | \n", + "COST12RELPOVCAT | \n", + "COST12RELFMRPCT | \n", + "COST12RELFMRCAT | \n", + "COSTMedRELAMIPCT | \n", + "COSTMedRELAMICAT | \n", + "COSTMedRELPOVPCT | \n", + "COSTMedRELPOVCAT | \n", + "COSTMedRELFMRPCT | \n", + "COSTMedRELFMRCAT | \n", + "FMTZADEQ | \n", + "FMTMETRO3 | \n", + "FMTBUILT | \n", + "FMTSTRUCTURETYPE | \n", + "FMTBEDRMS | \n", + "FMTOWNRENT | \n", + "FMTCOST06RELPOVCAT | \n", + "FMTCOST08RELPOVCAT | \n", + "FMTCOST12RELPOVCAT | \n", + "FMTCOSTMEDRELPOVCAT | \n", + "FMTINCRELPOVCAT | \n", + "FMTCOST06RELFMRCAT | \n", + "FMTCOST08RELFMRCAT | \n", + "FMTCOST12RELFMRCAT | \n", + "FMTCOSTMEDRELFMRCAT | \n", + "FMTINCRELFMRCAT | \n", + "FMTCOST06RELAMICAT | \n", + "FMTCOST08RELAMICAT | \n", + "FMTCOST12RELAMICAT | \n", + "FMTCOSTMEDRELAMICAT | \n", + "FMTINCRELAMICAT | \n", + "FMTASSISTED | \n", + "FMTBURDEN | \n", + "FMTREGION | \n", + "FMTSTATUS | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "0 | \n", + "82 | \n", + "2 | \n", + "0 | \n", + "73738 | \n", + "956 | \n", + "15738 | \n", + "26213 | \n", + "40322 | \n", + "11067 | \n", + "2 | \n", + "2006 | \n", + "0 | \n", + "1 | \n", + "40000 | \n", + "-6 | \n", + "1 | \n", + "1 | \n", + "6 | \n", + "3117.394239 | \n", + "1 | \n", + "18021 | \n", + "1 | \n", + "533 | \n", + "1 | \n", + "0 | \n", + "169.000000 | \n", + "213.750000 | \n", + "648.588189 | \n", + "803.050535 | \n", + "696.905247 | \n", + "615.156712 | \n", + "0 | \n", + "-9 | \n", + "73738 | \n", + "15738 | \n", + "26213 | \n", + "40322 | \n", + "51616.6 | \n", + "20234.571429 | \n", + "... | \n", + "4 | \n", + "72.898038 | \n", + "2 | \n", + "48.402635 | \n", + "2 | \n", + "290.250487 | \n", + "4 | \n", + "84.001102 | \n", + "2 | \n", + "37.077624 | \n", + "2 | \n", + "222.339102 | \n", + "4 | \n", + "64.346936 | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "5 | \n", + "1 | \n", + "2 | \n", + "0 | \n", + "4 | \n", + "4 | \n", + "4 | \n", + "4 | \n", + "3 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "2 | \n", + "0 | \n", + "2 | \n", + "0 | \n", + "0 | \n", + "
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| 2 | \n", + "2 | \n", + "53 | \n", + "4 | \n", + "2 | \n", + "55846 | \n", + "1100 | \n", + "13750 | \n", + "22897 | \n", + "36614 | \n", + "15470 | \n", + "4 | \n", + "1985 | \n", + "0 | \n", + "1 | \n", + "150000 | \n", + "-6 | \n", + "1 | \n", + "1 | \n", + "7 | \n", + "2213.789404 | \n", + "2 | \n", + "27974 | \n", + "1 | \n", + "1405 | \n", + "1 | \n", + "0 | \n", + "159.000000 | \n", + "37.500000 | \n", + "1193.393209 | \n", + "1772.627006 | \n", + "1374.582175 | \n", + "1068.025168 | \n", + "28000 | \n", + "-9 | \n", + "55846 | \n", + "13750 | \n", + "22897 | \n", + "36614 | \n", + "44676.8 | \n", + "19937.500000 | \n", + "... | \n", + "4 | \n", + "124.962016 | \n", + "3 | \n", + "109.452905 | \n", + "6 | \n", + "458.339239 | \n", + "4 | \n", + "161.147910 | \n", + "3 | \n", + "65.946449 | \n", + "4 | \n", + "276.153890 | \n", + "4 | \n", + "97.093197 | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "1 | \n", + "4 | \n", + "0 | \n", + "4 | \n", + "4 | \n", + "4 | \n", + "4 | \n", + "3 | \n", + "2 | \n", + "2 | \n", + "2 | \n", + "1 | \n", + "2 | \n", + "3 | \n", + "4 | \n", + "5 | \n", + "3 | \n", + "4 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "0 | \n", + "
| 3 | \n", + "3 | \n", + "67 | \n", + "4 | \n", + "2 | \n", + "55846 | \n", + "949 | \n", + "13750 | \n", + "22897 | \n", + "36614 | \n", + "13964 | \n", + "3 | \n", + "1985 | \n", + "0 | \n", + "1 | \n", + "200000 | \n", + "-6 | \n", + "1 | \n", + "1 | \n", + "6 | \n", + "2364.585097 | \n", + "2 | \n", + "32220 | \n", + "1 | \n", + "279 | \n", + "1 | \n", + "0 | \n", + "179.000000 | \n", + "70.666667 | \n", + "1578.857612 | \n", + "2351.169341 | \n", + "1820.442900 | \n", + "1411.700224 | \n", + "0 | \n", + "-9 | \n", + "55846 | \n", + "13750 | \n", + "22897 | \n", + "36614 | \n", + "44676.8 | \n", + "17875.000000 | \n", + "... | \n", + "4 | \n", + "191.827492 | \n", + "3 | \n", + "161.926709 | \n", + "7 | \n", + "673.494512 | \n", + "4 | \n", + "247.752301 | \n", + "3 | \n", + "97.224801 | \n", + "5 | \n", + "404.382763 | \n", + "4 | \n", + "148.756610 | \n", + "3 | \n", + "1 | \n", + "0 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "0 | \n", + "4 | \n", + "4 | \n", + "4 | \n", + "4 | \n", + "4 | \n", + "2 | \n", + "2 | \n", + "2 | \n", + "2 | \n", + "2 | \n", + "5 | \n", + "6 | \n", + "6 | \n", + "4 | \n", + "4 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "
| 4 | \n", + "4 | \n", + "26 | \n", + "0 | \n", + "2 | \n", + "60991 | \n", + "737 | \n", + "14801 | \n", + "24628 | \n", + "39421 | \n", + "15492 | \n", + "2 | \n", + "1980 | \n", + "0 | \n", + "1 | \n", + "-6 | \n", + "-6 | \n", + "2 | \n", + "100 | \n", + "4 | \n", + "2314.524902 | \n", + "2 | \n", + "96874 | \n", + "1 | \n", + "759 | \n", + "5 | \n", + "1 | \n", + "146.000000 | \n", + "12.500000 | \n", + "759.000000 | \n", + "759.000000 | \n", + "759.000000 | \n", + "759.000000 | \n", + "96900 | \n", + "0 | \n", + "60991 | \n", + "14801 | \n", + "24628 | \n", + "39421 | \n", + "48792.8 | \n", + "16651.125000 | \n", + "... | \n", + "3 | \n", + "102.985075 | \n", + "3 | \n", + "55.308707 | \n", + "3 | \n", + "195.972115 | \n", + "3 | \n", + "102.985075 | \n", + "3 | \n", + "55.308707 | \n", + "3 | \n", + "195.972115 | \n", + "3 | \n", + "102.985075 | \n", + "3 | \n", + "1 | \n", + "1 | \n", + "3 | \n", + "5 | \n", + "2 | \n", + "1 | \n", + "3 | \n", + "3 | \n", + "3 | \n", + "3 | \n", + "4 | \n", + "2 | \n", + "2 | \n", + "2 | \n", + "2 | \n", + "3 | \n", + "2 | \n", + "2 | \n", + "2 | \n", + "2 | \n", + "7 | \n", + "1 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "
5 rows × 99 columns
\n", + "| \n", + " | 0 | \n", + "1 | \n", + "2 | \n", + "3 | \n", + "4 | \n", + "5 | \n", + "6 | \n", + "7 | \n", + "8 | \n", + "9 | \n", + "10 | \n", + "11 | \n", + "12 | \n", + "13 | \n", + "14 | \n", + "15 | \n", + "16 | \n", + "17 | \n", + "18 | \n", + "19 | \n", + "20 | \n", + "21 | \n", + "22 | \n", + "23 | \n", + "24 | \n", + "25 | \n", + "26 | \n", + "27 | \n", + "28 | \n", + "29 | \n", + "30 | \n", + "31 | \n", + "32 | \n", + "33 | \n", + "34 | \n", + "35 | \n", + "36 | \n", + "37 | \n", + "38 | \n", + "39 | \n", + "... | \n", + "59 | \n", + "60 | \n", + "61 | \n", + "62 | \n", + "63 | \n", + "64 | \n", + "65 | \n", + "66 | \n", + "67 | \n", + "68 | \n", + "69 | \n", + "70 | \n", + "71 | \n", + "72 | \n", + "73 | \n", + "74 | \n", + "75 | \n", + "76 | \n", + "77 | \n", + "78 | \n", + "79 | \n", + "80 | \n", + "81 | \n", + "82 | \n", + "83 | \n", + "84 | \n", + "85 | \n", + "86 | \n", + "87 | \n", + "88 | \n", + "89 | \n", + "90 | \n", + "91 | \n", + "92 | \n", + "93 | \n", + "94 | \n", + "95 | \n", + "96 | \n", + "97 | \n", + "98 | \n", + "
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| 2 | \n", + "1.215270 | \n", + "0.370168 | \n", + "-3.248606 | \n", + "-2.123899 | \n", + "1.229951 | \n", + "-2.559945 | \n", + "0.984291 | \n", + "0.088488 | \n", + "-0.209300 | \n", + "-0.715159 | \n", + "-0.243996 | \n", + "1.168680 | \n", + "-0.136100 | \n", + "0.491252 | \n", + "0.358669 | \n", + "-0.462415 | \n", + "-1.494570 | \n", + "0.114951 | \n", + "-0.160159 | \n", + "-0.889462 | \n", + "0.678651 | \n", + "-0.002222 | \n", + "0.162761 | \n", + "-0.738769 | \n", + "-0.844579 | \n", + "0.332651 | \n", + "-0.014602 | \n", + "0.525549 | \n", + "-0.134705 | \n", + "0.832489 | \n", + "-0.366875 | \n", + "0.429854 | \n", + "-0.639612 | \n", + "-0.112777 | \n", + "-0.114059 | \n", + "0.258893 | \n", + "0.974611 | \n", + "0.167855 | \n", + "0.177374 | \n", + "-0.033658 | \n", + "... | \n", + "-0.037108 | \n", + "0.019828 | \n", + "-0.032203 | \n", + "0.003294 | \n", + "0.006182 | \n", + "0.000272 | \n", + "-0.008307 | \n", + "0.000210 | \n", + "0.000053 | \n", + "-3.011104e-11 | \n", + "-3.448156e-11 | \n", + "-2.696771e-12 | \n", + "-1.342672e-11 | \n", + "3.055270e-11 | \n", + "-1.853116e-11 | \n", + "6.526787e-12 | \n", + "1.171080e-11 | \n", + "2.918130e-11 | \n", + "1.978506e-14 | \n", + "-6.917647e-15 | \n", + "-8.690510e-15 | \n", + "3.585682e-15 | \n", + "-4.658132e-15 | \n", + "-8.145799e-15 | \n", + "-4.673181e-16 | \n", + "-5.639147e-15 | \n", + "-2.337729e-15 | \n", + "-5.838861e-15 | \n", + "2.205696e-15 | \n", + "-2.530473e-15 | \n", + "-4.543976e-16 | \n", + "-6.528784e-15 | \n", + "-2.303640e-15 | \n", + "-2.500232e-15 | \n", + "2.893038e-15 | \n", + "2.086186e-15 | \n", + "1.598883e-15 | \n", + "3.318875e-15 | \n", + "8.989284e-16 | \n", + "-3.788372e-17 | \n", + "
| 3 | \n", + "2.821837 | \n", + "1.723711 | \n", + "-4.375157 | \n", + "-1.479343 | \n", + "0.085327 | \n", + "-1.755050 | \n", + "0.710440 | \n", + "-0.064843 | \n", + "0.487051 | \n", + "-0.175074 | \n", + "-0.947811 | \n", + "0.273949 | \n", + "-0.083829 | \n", + "-0.606772 | \n", + "-0.058791 | \n", + "0.293943 | \n", + "-1.319074 | \n", + "1.225816 | \n", + "-0.688633 | \n", + "-0.437567 | \n", + "-0.155818 | \n", + "-0.934189 | \n", + "0.070781 | \n", + "0.082882 | \n", + "0.259157 | \n", + "-0.305200 | \n", + "-0.157419 | \n", + "0.136505 | \n", + "-0.678651 | \n", + "0.363593 | \n", + "-0.497262 | \n", + "0.453585 | \n", + "0.099106 | \n", + "0.302715 | \n", + "-0.319196 | \n", + "0.502472 | \n", + "-0.056209 | \n", + "0.043986 | \n", + "0.284494 | \n", + "0.152548 | \n", + "... | \n", + "0.051131 | \n", + "-0.016479 | \n", + "0.032716 | \n", + "-0.001735 | \n", + "-0.006142 | \n", + "-0.000083 | \n", + "0.003233 | \n", + "0.000369 | \n", + "0.000047 | \n", + "1.570527e-11 | \n", + "-2.956002e-11 | \n", + "-4.246039e-11 | \n", + "-3.625665e-11 | \n", + "-3.904595e-11 | \n", + "-8.910011e-12 | \n", + "2.530238e-12 | \n", + "7.069996e-12 | \n", + "3.740412e-13 | \n", + "8.286938e-15 | \n", + "6.592696e-16 | \n", + "-1.280837e-15 | \n", + "9.213680e-16 | \n", + "-3.869037e-17 | \n", + "1.861092e-16 | \n", + "-3.535306e-16 | \n", + "2.137629e-15 | \n", + "-3.969571e-16 | \n", + "-1.665115e-15 | \n", + "2.604798e-15 | \n", + "-3.388406e-15 | \n", + "3.325411e-16 | \n", + "-1.605029e-15 | \n", + "-2.185016e-15 | \n", + "-2.579443e-15 | \n", + "3.379958e-15 | \n", + "1.414309e-15 | \n", + "-3.038426e-15 | \n", + "-1.618005e-15 | \n", + "-2.137277e-16 | \n", + "-7.535854e-16 | \n", + "
| 4 | \n", + "-1.570106 | \n", + "-0.878362 | \n", + "-3.307062 | \n", + "0.785881 | \n", + "-2.310400 | \n", + "3.881736 | \n", + "-0.645952 | \n", + "0.464725 | \n", + "-0.092147 | \n", + "0.439502 | \n", + "0.680427 | \n", + "1.297333 | \n", + "-2.176527 | \n", + "0.873973 | \n", + "-1.258729 | \n", + "0.078789 | \n", + "-1.274484 | \n", + "-1.278863 | \n", + "-0.049121 | \n", + "-1.107579 | \n", + "-1.315707 | \n", + "0.183007 | \n", + "-0.463981 | \n", + "-0.126541 | \n", + "-0.632876 | \n", + "-0.490356 | \n", + "0.227040 | \n", + "0.448714 | \n", + "0.362058 | \n", + "0.192815 | \n", + "-0.061116 | \n", + "0.423652 | \n", + "0.060251 | \n", + "0.066255 | \n", + "0.087248 | \n", + "0.049826 | \n", + "-0.007892 | \n", + "-0.222354 | \n", + "-0.017775 | \n", + "0.482700 | \n", + "... | \n", + "0.029508 | \n", + "0.017652 | \n", + "-0.007901 | \n", + "0.005932 | \n", + "-0.008180 | \n", + "0.001159 | \n", + "0.004948 | \n", + "0.003858 | \n", + "-0.000114 | \n", + "-1.210293e-12 | \n", + "-7.386035e-13 | \n", + "1.444818e-12 | \n", + "-7.904880e-13 | \n", + "-9.223507e-14 | \n", + "-6.033953e-13 | \n", + "5.752033e-14 | \n", + "-3.907023e-13 | \n", + "8.020451e-13 | \n", + "-3.283658e-13 | \n", + "5.266361e-14 | \n", + "3.634224e-15 | \n", + "1.832897e-14 | \n", + "4.621295e-14 | \n", + "-3.589585e-14 | \n", + "-1.776807e-14 | \n", + "-2.737603e-14 | \n", + "-8.598189e-15 | \n", + "-3.794475e-14 | \n", + "-1.916136e-14 | \n", + "-8.228730e-15 | \n", + "-6.056303e-15 | \n", + "-1.204971e-14 | \n", + "-1.099331e-14 | \n", + "7.764073e-15 | \n", + "-3.602489e-15 | \n", + "9.824194e-15 | \n", + "3.711774e-15 | \n", + "5.388211e-15 | \n", + "-7.548762e-16 | \n", + "-5.272142e-17 | \n", + "
5 rows × 99 columns
\n", + ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "y-3rVFtGLMJM"
+ },
+ "source": [
+ "# K-Means Clustering"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_VS3FFSFLR3a"
+ },
+ "source": [
+ "# 1) Use the \"Breast Cancer Wisconsin (Diagnostic) Data Set\" from Kaggle to try and cluster types of cancer cells. \n",
+ "\n",
+ "Here's the original dataset for your reference:\n",
+ "\n",
+ "| \n", + " | id | \n", + "diagnosis | \n", + "radius_mean | \n", + "texture_mean | \n", + "perimeter_mean | \n", + "area_mean | \n", + "smoothness_mean | \n", + "compactness_mean | \n", + "concavity_mean | \n", + "concave points_mean | \n", + "symmetry_mean | \n", + "fractal_dimension_mean | \n", + "radius_se | \n", + "texture_se | \n", + "perimeter_se | \n", + "area_se | \n", + "smoothness_se | \n", + "compactness_se | \n", + "concavity_se | \n", + "concave points_se | \n", + "symmetry_se | \n", + "fractal_dimension_se | \n", + "radius_worst | \n", + "texture_worst | \n", + "perimeter_worst | \n", + "area_worst | \n", + "smoothness_worst | \n", + "compactness_worst | \n", + "concavity_worst | \n", + "concave points_worst | \n", + "symmetry_worst | \n", + "fractal_dimension_worst | \n", + "Unnamed: 32 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "842302 | \n", + "M | \n", + "17.99 | \n", + "10.38 | \n", + "122.80 | \n", + "1001.0 | \n", + "0.11840 | \n", + "0.27760 | \n", + "0.3001 | \n", + "0.14710 | \n", + "0.2419 | \n", + "0.07871 | \n", + "1.0950 | \n", + "0.9053 | \n", + "8.589 | \n", + "153.40 | \n", + "0.006399 | \n", + "0.04904 | \n", + "0.05373 | \n", + "0.01587 | \n", + "0.03003 | \n", + "0.006193 | \n", + "25.38 | \n", + "17.33 | \n", + "184.60 | \n", + "2019.0 | \n", + "0.1622 | \n", + "0.6656 | \n", + "0.7119 | \n", + "0.2654 | \n", + "0.4601 | \n", + "0.11890 | \n", + "NaN | \n", + "
| 1 | \n", + "842517 | \n", + "M | \n", + "20.57 | \n", + "17.77 | \n", + "132.90 | \n", + "1326.0 | \n", + "0.08474 | \n", + "0.07864 | \n", + "0.0869 | \n", + "0.07017 | \n", + "0.1812 | \n", + "0.05667 | \n", + "0.5435 | \n", + "0.7339 | \n", + "3.398 | \n", + "74.08 | \n", + "0.005225 | \n", + "0.01308 | \n", + "0.01860 | \n", + "0.01340 | \n", + "0.01389 | \n", + "0.003532 | \n", + "24.99 | \n", + "23.41 | \n", + "158.80 | \n", + "1956.0 | \n", + "0.1238 | \n", + "0.1866 | \n", + "0.2416 | \n", + "0.1860 | \n", + "0.2750 | \n", + "0.08902 | \n", + "NaN | \n", + "
| 2 | \n", + "84300903 | \n", + "M | \n", + "19.69 | \n", + "21.25 | \n", + "130.00 | \n", + "1203.0 | \n", + "0.10960 | \n", + "0.15990 | \n", + "0.1974 | \n", + "0.12790 | \n", + "0.2069 | \n", + "0.05999 | \n", + "0.7456 | \n", + "0.7869 | \n", + "4.585 | \n", + "94.03 | \n", + "0.006150 | \n", + "0.04006 | \n", + "0.03832 | \n", + "0.02058 | \n", + "0.02250 | \n", + "0.004571 | \n", + "23.57 | \n", + "25.53 | \n", + "152.50 | \n", + "1709.0 | \n", + "0.1444 | \n", + "0.4245 | \n", + "0.4504 | \n", + "0.2430 | \n", + "0.3613 | \n", + "0.08758 | \n", + "NaN | \n", + "
| 3 | \n", + "84348301 | \n", + "M | \n", + "11.42 | \n", + "20.38 | \n", + "77.58 | \n", + "386.1 | \n", + "0.14250 | \n", + "0.28390 | \n", + "0.2414 | \n", + "0.10520 | \n", + "0.2597 | \n", + "0.09744 | \n", + "0.4956 | \n", + "1.1560 | \n", + "3.445 | \n", + "27.23 | \n", + "0.009110 | \n", + "0.07458 | \n", + "0.05661 | \n", + "0.01867 | \n", + "0.05963 | \n", + "0.009208 | \n", + "14.91 | \n", + "26.50 | \n", + "98.87 | \n", + "567.7 | \n", + "0.2098 | \n", + "0.8663 | \n", + "0.6869 | \n", + "0.2575 | \n", + "0.6638 | \n", + "0.17300 | \n", + "NaN | \n", + "
| 4 | \n", + "84358402 | \n", + "M | \n", + "20.29 | \n", + "14.34 | \n", + "135.10 | \n", + "1297.0 | \n", + "0.10030 | \n", + "0.13280 | \n", + "0.1980 | \n", + "0.10430 | \n", + "0.1809 | \n", + "0.05883 | \n", + "0.7572 | \n", + "0.7813 | \n", + "5.438 | \n", + "94.44 | \n", + "0.011490 | \n", + "0.02461 | \n", + "0.05688 | \n", + "0.01885 | \n", + "0.01756 | \n", + "0.005115 | \n", + "22.54 | \n", + "16.67 | \n", + "152.20 | \n", + "1575.0 | \n", + "0.1374 | \n", + "0.2050 | \n", + "0.4000 | \n", + "0.1625 | \n", + "0.2364 | \n", + "0.07678 | \n", + "NaN | \n", + "
| \n", + " | id | \n", + "radius_mean | \n", + "texture_mean | \n", + "perimeter_mean | \n", + "area_mean | \n", + "smoothness_mean | \n", + "compactness_mean | \n", + "concavity_mean | \n", + "concave points_mean | \n", + "symmetry_mean | \n", + "fractal_dimension_mean | \n", + "radius_se | \n", + "texture_se | \n", + "perimeter_se | \n", + "area_se | \n", + "smoothness_se | \n", + "compactness_se | \n", + "concavity_se | \n", + "concave points_se | \n", + "symmetry_se | \n", + "fractal_dimension_se | \n", + "radius_worst | \n", + "texture_worst | \n", + "perimeter_worst | \n", + "area_worst | \n", + "smoothness_worst | \n", + "compactness_worst | \n", + "concavity_worst | \n", + "concave points_worst | \n", + "symmetry_worst | \n", + "fractal_dimension_worst | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "842302 | \n", + "17.99 | \n", + "10.38 | \n", + "122.80 | \n", + "1001.0 | \n", + "0.11840 | \n", + "0.27760 | \n", + "0.3001 | \n", + "0.14710 | \n", + "0.2419 | \n", + "0.07871 | \n", + "1.0950 | \n", + "0.9053 | \n", + "8.589 | \n", + "153.40 | \n", + "0.006399 | \n", + "0.04904 | \n", + "0.05373 | \n", + "0.01587 | \n", + "0.03003 | \n", + "0.006193 | \n", + "25.38 | \n", + "17.33 | \n", + "184.60 | \n", + "2019.0 | \n", + "0.1622 | \n", + "0.6656 | \n", + "0.7119 | \n", + "0.2654 | \n", + "0.4601 | \n", + "0.11890 | \n", + "
| 1 | \n", + "842517 | \n", + "20.57 | \n", + "17.77 | \n", + "132.90 | \n", + "1326.0 | \n", + "0.08474 | \n", + "0.07864 | \n", + "0.0869 | \n", + "0.07017 | \n", + "0.1812 | \n", + "0.05667 | \n", + "0.5435 | \n", + "0.7339 | \n", + "3.398 | \n", + "74.08 | \n", + "0.005225 | \n", + "0.01308 | \n", + "0.01860 | \n", + "0.01340 | \n", + "0.01389 | \n", + "0.003532 | \n", + "24.99 | \n", + "23.41 | \n", + "158.80 | \n", + "1956.0 | \n", + "0.1238 | \n", + "0.1866 | \n", + "0.2416 | \n", + "0.1860 | \n", + "0.2750 | \n", + "0.08902 | \n", + "
| 2 | \n", + "84300903 | \n", + "19.69 | \n", + "21.25 | \n", + "130.00 | \n", + "1203.0 | \n", + "0.10960 | \n", + "0.15990 | \n", + "0.1974 | \n", + "0.12790 | \n", + "0.2069 | \n", + "0.05999 | \n", + "0.7456 | \n", + "0.7869 | \n", + "4.585 | \n", + "94.03 | \n", + "0.006150 | \n", + "0.04006 | \n", + "0.03832 | \n", + "0.02058 | \n", + "0.02250 | \n", + "0.004571 | \n", + "23.57 | \n", + "25.53 | \n", + "152.50 | \n", + "1709.0 | \n", + "0.1444 | \n", + "0.4245 | \n", + "0.4504 | \n", + "0.2430 | \n", + "0.3613 | \n", + "0.08758 | \n", + "
| 3 | \n", + "84348301 | \n", + "11.42 | \n", + "20.38 | \n", + "77.58 | \n", + "386.1 | \n", + "0.14250 | \n", + "0.28390 | \n", + "0.2414 | \n", + "0.10520 | \n", + "0.2597 | \n", + "0.09744 | \n", + "0.4956 | \n", + "1.1560 | \n", + "3.445 | \n", + "27.23 | \n", + "0.009110 | \n", + "0.07458 | \n", + "0.05661 | \n", + "0.01867 | \n", + "0.05963 | \n", + "0.009208 | \n", + "14.91 | \n", + "26.50 | \n", + "98.87 | \n", + "567.7 | \n", + "0.2098 | \n", + "0.8663 | \n", + "0.6869 | \n", + "0.2575 | \n", + "0.6638 | \n", + "0.17300 | \n", + "
| 4 | \n", + "84358402 | \n", + "20.29 | \n", + "14.34 | \n", + "135.10 | \n", + "1297.0 | \n", + "0.10030 | \n", + "0.13280 | \n", + "0.1980 | \n", + "0.10430 | \n", + "0.1809 | \n", + "0.05883 | \n", + "0.7572 | \n", + "0.7813 | \n", + "5.438 | \n", + "94.44 | \n", + "0.011490 | \n", + "0.02461 | \n", + "0.05688 | \n", + "0.01885 | \n", + "0.01756 | \n", + "0.005115 | \n", + "22.54 | \n", + "16.67 | \n", + "152.20 | \n", + "1575.0 | \n", + "0.1374 | \n", + "0.2050 | \n", + "0.4000 | \n", + "0.1625 | \n", + "0.2364 | \n", + "0.07678 | \n", + "
| \n", + " | radius_mean | \n", + "texture_mean | \n", + "perimeter_mean | \n", + "area_mean | \n", + "smoothness_mean | \n", + "compactness_mean | \n", + "concavity_mean | \n", + "concave points_mean | \n", + "symmetry_mean | \n", + "fractal_dimension_mean | \n", + "radius_se | \n", + "texture_se | \n", + "perimeter_se | \n", + "area_se | \n", + "smoothness_se | \n", + "compactness_se | \n", + "concavity_se | \n", + "concave points_se | \n", + "symmetry_se | \n", + "fractal_dimension_se | \n", + "radius_worst | \n", + "texture_worst | \n", + "perimeter_worst | \n", + "area_worst | \n", + "smoothness_worst | \n", + "compactness_worst | \n", + "concavity_worst | \n", + "concave points_worst | \n", + "symmetry_worst | \n", + "fractal_dimension_worst | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "
| mean | \n", + "14.127292 | \n", + "19.289649 | \n", + "91.969033 | \n", + "654.889104 | \n", + "0.096360 | \n", + "0.104341 | \n", + "0.088799 | \n", + "0.048919 | \n", + "0.181162 | \n", + "0.062798 | \n", + "0.405172 | \n", + "1.216853 | \n", + "2.866059 | \n", + "40.337079 | \n", + "0.007041 | \n", + "0.025478 | \n", + "0.031894 | \n", + "0.011796 | \n", + "0.020542 | \n", + "0.003795 | \n", + "16.269190 | \n", + "25.677223 | \n", + "107.261213 | \n", + "880.583128 | \n", + "0.132369 | \n", + "0.254265 | \n", + "0.272188 | \n", + "0.114606 | \n", + "0.290076 | \n", + "0.083946 | \n", + "
| std | \n", + "3.524049 | \n", + "4.301036 | \n", + "24.298981 | \n", + "351.914129 | \n", + "0.014064 | \n", + "0.052813 | \n", + "0.079720 | \n", + "0.038803 | \n", + "0.027414 | \n", + "0.007060 | \n", + "0.277313 | \n", + "0.551648 | \n", + "2.021855 | \n", + "45.491006 | \n", + "0.003003 | \n", + "0.017908 | \n", + "0.030186 | \n", + "0.006170 | \n", + "0.008266 | \n", + "0.002646 | \n", + "4.833242 | \n", + "6.146258 | \n", + "33.602542 | \n", + "569.356993 | \n", + "0.022832 | \n", + "0.157336 | \n", + "0.208624 | \n", + "0.065732 | \n", + "0.061867 | \n", + "0.018061 | \n", + "
| min | \n", + "6.981000 | \n", + "9.710000 | \n", + "43.790000 | \n", + "143.500000 | \n", + "0.052630 | \n", + "0.019380 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.106000 | \n", + "0.049960 | \n", + "0.111500 | \n", + "0.360200 | \n", + "0.757000 | \n", + "6.802000 | \n", + "0.001713 | \n", + "0.002252 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.007882 | \n", + "0.000895 | \n", + "7.930000 | \n", + "12.020000 | \n", + "50.410000 | \n", + "185.200000 | \n", + "0.071170 | \n", + "0.027290 | \n", + "0.000000 | \n", + "0.000000 | \n", + "0.156500 | \n", + "0.055040 | \n", + "
| 25% | \n", + "11.700000 | \n", + "16.170000 | \n", + "75.170000 | \n", + "420.300000 | \n", + "0.086370 | \n", + "0.064920 | \n", + "0.029560 | \n", + "0.020310 | \n", + "0.161900 | \n", + "0.057700 | \n", + "0.232400 | \n", + "0.833900 | \n", + "1.606000 | \n", + "17.850000 | \n", + "0.005169 | \n", + "0.013080 | \n", + "0.015090 | \n", + "0.007638 | \n", + "0.015160 | \n", + "0.002248 | \n", + "13.010000 | \n", + "21.080000 | \n", + "84.110000 | \n", + "515.300000 | \n", + "0.116600 | \n", + "0.147200 | \n", + "0.114500 | \n", + "0.064930 | \n", + "0.250400 | \n", + "0.071460 | \n", + "
| 50% | \n", + "13.370000 | \n", + "18.840000 | \n", + "86.240000 | \n", + "551.100000 | \n", + "0.095870 | \n", + "0.092630 | \n", + "0.061540 | \n", + "0.033500 | \n", + "0.179200 | \n", + "0.061540 | \n", + "0.324200 | \n", + "1.108000 | \n", + "2.287000 | \n", + "24.530000 | \n", + "0.006380 | \n", + "0.020450 | \n", + "0.025890 | \n", + "0.010930 | \n", + "0.018730 | \n", + "0.003187 | \n", + "14.970000 | \n", + "25.410000 | \n", + "97.660000 | \n", + "686.500000 | \n", + "0.131300 | \n", + "0.211900 | \n", + "0.226700 | \n", + "0.099930 | \n", + "0.282200 | \n", + "0.080040 | \n", + "
| 75% | \n", + "15.780000 | \n", + "21.800000 | \n", + "104.100000 | \n", + "782.700000 | \n", + "0.105300 | \n", + "0.130400 | \n", + "0.130700 | \n", + "0.074000 | \n", + "0.195700 | \n", + "0.066120 | \n", + "0.478900 | \n", + "1.474000 | \n", + "3.357000 | \n", + "45.190000 | \n", + "0.008146 | \n", + "0.032450 | \n", + "0.042050 | \n", + "0.014710 | \n", + "0.023480 | \n", + "0.004558 | \n", + "18.790000 | \n", + "29.720000 | \n", + "125.400000 | \n", + "1084.000000 | \n", + "0.146000 | \n", + "0.339100 | \n", + "0.382900 | \n", + "0.161400 | \n", + "0.317900 | \n", + "0.092080 | \n", + "
| max | \n", + "28.110000 | \n", + "39.280000 | \n", + "188.500000 | \n", + "2501.000000 | \n", + "0.163400 | \n", + "0.345400 | \n", + "0.426800 | \n", + "0.201200 | \n", + "0.304000 | \n", + "0.097440 | \n", + "2.873000 | \n", + "4.885000 | \n", + "21.980000 | \n", + "542.200000 | \n", + "0.031130 | \n", + "0.135400 | \n", + "0.396000 | \n", + "0.052790 | \n", + "0.078950 | \n", + "0.029840 | \n", + "36.040000 | \n", + "49.540000 | \n", + "251.200000 | \n", + "4254.000000 | \n", + "0.222600 | \n", + "1.058000 | \n", + "1.252000 | \n", + "0.291000 | \n", + "0.663800 | \n", + "0.207500 | \n", + "
| \n", + " | radius_mean | \n", + "texture_mean | \n", + "perimeter_mean | \n", + "area_mean | \n", + "smoothness_mean | \n", + "compactness_mean | \n", + "radius_worst | \n", + "texture_worst | \n", + "perimeter_worst | \n", + "area_worst | \n", + "smoothness_worst | \n", + "compactness_worst | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "569.000000 | \n", + "
| mean | \n", + "14.127292 | \n", + "19.289649 | \n", + "91.969033 | \n", + "654.889104 | \n", + "0.096360 | \n", + "0.104341 | \n", + "16.269190 | \n", + "25.677223 | \n", + "107.261213 | \n", + "880.583128 | \n", + "0.132369 | \n", + "0.254265 | \n", + "
| std | \n", + "3.524049 | \n", + "4.301036 | \n", + "24.298981 | \n", + "351.914129 | \n", + "0.014064 | \n", + "0.052813 | \n", + "4.833242 | \n", + "6.146258 | \n", + "33.602542 | \n", + "569.356993 | \n", + "0.022832 | \n", + "0.157336 | \n", + "
| min | \n", + "6.981000 | \n", + "9.710000 | \n", + "43.790000 | \n", + "143.500000 | \n", + "0.052630 | \n", + "0.019380 | \n", + "7.930000 | \n", + "12.020000 | \n", + "50.410000 | \n", + "185.200000 | \n", + "0.071170 | \n", + "0.027290 | \n", + "
| 25% | \n", + "11.700000 | \n", + "16.170000 | \n", + "75.170000 | \n", + "420.300000 | \n", + "0.086370 | \n", + "0.064920 | \n", + "13.010000 | \n", + "21.080000 | \n", + "84.110000 | \n", + "515.300000 | \n", + "0.116600 | \n", + "0.147200 | \n", + "
| 50% | \n", + "13.370000 | \n", + "18.840000 | \n", + "86.240000 | \n", + "551.100000 | \n", + "0.095870 | \n", + "0.092630 | \n", + "14.970000 | \n", + "25.410000 | \n", + "97.660000 | \n", + "686.500000 | \n", + "0.131300 | \n", + "0.211900 | \n", + "
| 75% | \n", + "15.780000 | \n", + "21.800000 | \n", + "104.100000 | \n", + "782.700000 | \n", + "0.105300 | \n", + "0.130400 | \n", + "18.790000 | \n", + "29.720000 | \n", + "125.400000 | \n", + "1084.000000 | \n", + "0.146000 | \n", + "0.339100 | \n", + "
| max | \n", + "28.110000 | \n", + "39.280000 | \n", + "188.500000 | \n", + "2501.000000 | \n", + "0.163400 | \n", + "0.345400 | \n", + "36.040000 | \n", + "49.540000 | \n", + "251.200000 | \n", + "4254.000000 | \n", + "0.222600 | \n", + "1.058000 | \n", + "
| \n", + " | radius_mean | \n", + "texture_mean | \n", + "perimeter_mean | \n", + "area_mean | \n", + "smoothness_mean | \n", + "compactness_mean | \n", + "radius_worst | \n", + "texture_worst | \n", + "perimeter_worst | \n", + "area_worst | \n", + "smoothness_worst | \n", + "compactness_worst | \n", + "clusters | \n", + "diagnosis | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "17.99 | \n", + "10.38 | \n", + "122.80 | \n", + "1001.0 | \n", + "0.11840 | \n", + "0.27760 | \n", + "25.38 | \n", + "17.33 | \n", + "184.60 | \n", + "2019.0 | \n", + "0.1622 | \n", + "0.6656 | \n", + "0 | \n", + "M | \n", + "
| 1 | \n", + "20.57 | \n", + "17.77 | \n", + "132.90 | \n", + "1326.0 | \n", + "0.08474 | \n", + "0.07864 | \n", + "24.99 | \n", + "23.41 | \n", + "158.80 | \n", + "1956.0 | \n", + "0.1238 | \n", + "0.1866 | \n", + "0 | \n", + "M | \n", + "
| 2 | \n", + "19.69 | \n", + "21.25 | \n", + "130.00 | \n", + "1203.0 | \n", + "0.10960 | \n", + "0.15990 | \n", + "23.57 | \n", + "25.53 | \n", + "152.50 | \n", + "1709.0 | \n", + "0.1444 | \n", + "0.4245 | \n", + "0 | \n", + "M | \n", + "
| 3 | \n", + "11.42 | \n", + "20.38 | \n", + "77.58 | \n", + "386.1 | \n", + "0.14250 | \n", + "0.28390 | \n", + "14.91 | \n", + "26.50 | \n", + "98.87 | \n", + "567.7 | \n", + "0.2098 | \n", + "0.8663 | \n", + "1 | \n", + "M | \n", + "
| 4 | \n", + "20.29 | \n", + "14.34 | \n", + "135.10 | \n", + "1297.0 | \n", + "0.10030 | \n", + "0.13280 | \n", + "22.54 | \n", + "16.67 | \n", + "152.20 | \n", + "1575.0 | \n", + "0.1374 | \n", + "0.2050 | \n", + "0 | \n", + "M | \n", + "
| diagnosis | \n", + "B | \n", + "M | \n", + "All | \n", + "
|---|---|---|---|
| clusters | \n", + "\n", + " | \n", + " | \n", + " |
| 0 | \n", + "0.175747 | \n", + "22.671353 | \n", + "22.8471 | \n", + "
| 1 | \n", + "62.565905 | \n", + "14.586995 | \n", + "77.1529 | \n", + "
| All | \n", + "62.741652 | \n", + "37.258348 | \n", + "100.0000 | \n", + "
| \n", + " | 0 | \n", + "1 | \n", + "2 | \n", + "3 | \n", + "
|---|---|---|---|---|
| 0 | \n", + "5.069569 | \n", + "2.399980 | \n", + "-3.432181 | \n", + "1.555644 | \n", + "
| 1 | \n", + "3.747829 | \n", + "-2.669786 | \n", + "-1.423654 | \n", + "-0.560089 | \n", + "
| 2 | \n", + "4.423904 | \n", + "0.135107 | \n", + "-0.756282 | \n", + "-0.089172 | \n", + "
| 3 | \n", + "1.343376 | \n", + "6.903362 | \n", + "0.178411 | \n", + "1.265439 | \n", + "
| 4 | \n", + "3.438885 | \n", + "-1.149978 | \n", + "-2.827432 | \n", + "-0.460856 | \n", + "
| \n", + " | PC1 | \n", + "PC2 | \n", + "PC3 | \n", + "PC4 | \n", + "
|---|---|---|---|---|
| 0 | \n", + "5.069569 | \n", + "2.399980 | \n", + "-3.432181 | \n", + "1.555644 | \n", + "
| 1 | \n", + "3.747829 | \n", + "-2.669786 | \n", + "-1.423654 | \n", + "-0.560089 | \n", + "
| 2 | \n", + "4.423904 | \n", + "0.135107 | \n", + "-0.756282 | \n", + "-0.089172 | \n", + "
| 3 | \n", + "1.343376 | \n", + "6.903362 | \n", + "0.178411 | \n", + "1.265439 | \n", + "
| 4 | \n", + "3.438885 | \n", + "-1.149978 | \n", + "-2.827432 | \n", + "-0.460856 | \n", + "
| \n", + " | PC1 | \n", + "PC2 | \n", + "PC3 | \n", + "PC4 | \n", + "
|---|---|---|---|---|
| count | \n", + "5.690000e+02 | \n", + "5.690000e+02 | \n", + "5.690000e+02 | \n", + "5.690000e+02 | \n", + "
| mean | \n", + "-7.024258e-17 | \n", + "-1.139491e-16 | \n", + "-4.760886e-17 | \n", + "3.258475e-17 | \n", + "
| std | \n", + "2.778672e+00 | \n", + "1.519665e+00 | \n", + "1.284437e+00 | \n", + "7.701293e-01 | \n", + "
| min | \n", + "-4.260721e+00 | \n", + "-3.246944e+00 | \n", + "-3.432181e+00 | \n", + "-1.693007e+00 | \n", + "
| 25% | \n", + "-2.054315e+00 | \n", + "-1.073269e+00 | \n", + "-8.626120e-01 | \n", + "-5.033166e-01 | \n", + "
| 50% | \n", + "-1.008188e+00 | \n", + "-2.037317e-01 | \n", + "-1.121198e-01 | \n", + "-7.409345e-02 | \n", + "
| 75% | \n", + "1.625827e+00 | \n", + "9.362490e-01 | \n", + "7.179154e-01 | \n", + "4.322451e-01 | \n", + "
| max | \n", + "1.116828e+01 | \n", + "6.903362e+00 | \n", + "4.707096e+00 | \n", + "3.152067e+00 | \n", + "
| diagnosis | \n", + "B | \n", + "M | \n", + "All | \n", + "
|---|---|---|---|
| clusters | \n", + "\n", + " | \n", + " | \n", + " |
| 0 | \n", + "0.000000 | \n", + "27.416520 | \n", + "27.41652 | \n", + "
| 1 | \n", + "62.741652 | \n", + "9.841828 | \n", + "72.58348 | \n", + "
| All | \n", + "62.741652 | \n", + "37.258348 | \n", + "100.00000 | \n", + "