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@@ -0,0 +1,1881 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Exercise 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 603,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import csv as C\n",
+ "import numpy as N\n",
+ "import pandas as P"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1.1 Data structures"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 604,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0 5\n",
+ "1 8\n",
+ "2 7\n",
+ "3 6\n",
+ "4 8\n",
+ "5 4\n",
+ "Name: A, dtype: int64\n",
+ "0 1.3\n",
+ "1 2.1\n",
+ "2 1.8\n",
+ "3 1.2\n",
+ "4 1.4\n",
+ "5 2.3\n",
+ "Name: B, dtype: float64\n",
+ "0 y\n",
+ "1 y\n",
+ "2 n\n",
+ "3 y\n",
+ "4 n\n",
+ "5 n\n",
+ "Name: C, dtype: object\n"
+ ]
+ }
+ ],
+ "source": [
+ "A = P.Series([5,8,7,6,8,4],\n",
+ " name = \"A\")\n",
+ "B = P.Series([1.3, 2.1, 1.8, 1.2, 1.4, 2.3],\n",
+ " name = \"B\")\n",
+ "C = P.Series([\"y\",\"y\",\"n\",\"y\",\"n\",\"n\"],\n",
+ " name = \"C\")\n",
+ "\n",
+ "print(A)\n",
+ "print(B)\n",
+ "print(C)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 605,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>A</th>\n",
+ " <th>B</th>\n",
+ " <th>C</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>5</td>\n",
+ " <td>1.3</td>\n",
+ " <td>y</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>8</td>\n",
+ " <td>2.1</td>\n",
+ " <td>y</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>7</td>\n",
+ " <td>1.8</td>\n",
+ " <td>n</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>6</td>\n",
+ " <td>1.2</td>\n",
+ " <td>y</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>8</td>\n",
+ " <td>1.4</td>\n",
+ " <td>n</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>4</td>\n",
+ " <td>2.3</td>\n",
+ " <td>n</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " A B C\n",
+ "0 5 1.3 y\n",
+ "1 8 2.1 y\n",
+ "2 7 1.8 n\n",
+ "3 6 1.2 y\n",
+ "4 8 1.4 n\n",
+ "5 4 2.3 n"
+ ]
+ },
+ "execution_count": 605,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1 = P.concat([A,B,C], axis=1)\n",
+ "df1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 606,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1.8"
+ ]
+ },
+ "execution_count": 606,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1.iloc[2,1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 607,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "A 6\n",
+ "B 1.2\n",
+ "C y\n",
+ "Name: 3, dtype: object"
+ ]
+ },
+ "execution_count": 607,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1.iloc[3]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 608,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>B</th>\n",
+ " <th>C</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2.1</td>\n",
+ " <td>y</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>1.8</td>\n",
+ " <td>n</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>1.2</td>\n",
+ " <td>y</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>1.4</td>\n",
+ " <td>n</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " B C\n",
+ "1 2.1 y\n",
+ "2 1.8 n\n",
+ "3 1.2 y\n",
+ "4 1.4 n"
+ ]
+ },
+ "execution_count": 608,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "subset = df1.iloc[1:5,[1,2]]\n",
+ "subset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 609,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>0</th>\n",
+ " <th>1</th>\n",
+ " <th>2</th>\n",
+ " <th>3</th>\n",
+ " <th>4</th>\n",
+ " <th>5</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>A</th>\n",
+ " <td>5</td>\n",
+ " <td>8</td>\n",
+ " <td>7</td>\n",
+ " <td>6</td>\n",
+ " <td>8</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>B</th>\n",
+ " <td>1.3</td>\n",
+ " <td>2.1</td>\n",
+ " <td>1.8</td>\n",
+ " <td>1.2</td>\n",
+ " <td>1.4</td>\n",
+ " <td>2.3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>C</th>\n",
+ " <td>y</td>\n",
+ " <td>y</td>\n",
+ " <td>n</td>\n",
+ " <td>y</td>\n",
+ " <td>n</td>\n",
+ " <td>n</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " 0 1 2 3 4 5\n",
+ "A 5 8 7 6 8 4\n",
+ "B 1.3 2.1 1.8 1.2 1.4 2.3\n",
+ "C y y n y n n"
+ ]
+ },
+ "execution_count": 609,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1.transpose()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1.2 Thyroid Disease"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 610,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df2 = P.read_csv('allbp.data', header=None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 611,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
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+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
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+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>0</th>\n",
+ " <th>1</th>\n",
+ " <th>2</th>\n",
+ " <th>3</th>\n",
+ " <th>4</th>\n",
+ " <th>5</th>\n",
+ " <th>6</th>\n",
+ " <th>7</th>\n",
+ " <th>8</th>\n",
+ " <th>9</th>\n",
+ " <th>...</th>\n",
+ " <th>20</th>\n",
+ " <th>21</th>\n",
+ " <th>22</th>\n",
+ " <th>23</th>\n",
+ " <th>24</th>\n",
+ " <th>25</th>\n",
+ " <th>26</th>\n",
+ " <th>27</th>\n",
+ " <th>28</th>\n",
+ " <th>29</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>41</td>\n",
+ " <td>F</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>...</td>\n",
+ " <td>t</td>\n",
+ " <td>125</td>\n",
+ " <td>t</td>\n",
+ " <td>1.14</td>\n",
+ " <td>t</td>\n",
+ " <td>109</td>\n",
+ " <td>f</td>\n",
+ " <td>?</td>\n",
+ " <td>SVHC</td>\n",
+ " <td>negative.|3733</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>23</td>\n",
+ " <td>F</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>...</td>\n",
+ " <td>t</td>\n",
+ " <td>102</td>\n",
+ " <td>f</td>\n",
+ " <td>?</td>\n",
+ " <td>f</td>\n",
+ " <td>?</td>\n",
+ " <td>f</td>\n",
+ " <td>?</td>\n",
+ " <td>other</td>\n",
+ " <td>negative.|1442</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>46</td>\n",
+ " <td>M</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>...</td>\n",
+ " <td>t</td>\n",
+ " <td>109</td>\n",
+ " <td>t</td>\n",
+ " <td>0.91</td>\n",
+ " <td>t</td>\n",
+ " <td>120</td>\n",
+ " <td>f</td>\n",
+ " <td>?</td>\n",
+ " <td>other</td>\n",
+ " <td>negative.|2965</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>70</td>\n",
+ " <td>F</td>\n",
+ " <td>t</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>...</td>\n",
+ " <td>t</td>\n",
+ " <td>175</td>\n",
+ " <td>f</td>\n",
+ " <td>?</td>\n",
+ " <td>f</td>\n",
+ " <td>?</td>\n",
+ " <td>f</td>\n",
+ " <td>?</td>\n",
+ " <td>other</td>\n",
+ " <td>negative.|806</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>70</td>\n",
+ " <td>F</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>...</td>\n",
+ " <td>t</td>\n",
+ " <td>61</td>\n",
+ " <td>t</td>\n",
+ " <td>0.87</td>\n",
+ " <td>t</td>\n",
+ " <td>70</td>\n",
+ " <td>f</td>\n",
+ " <td>?</td>\n",
+ " <td>SVI</td>\n",
+ " <td>negative.|2807</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 30 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " 0 1 2 3 4 5 6 7 8 9 ... 20 21 22 23 24 25 26 27 28 \\\n",
+ "0 41 F f f f f f f f f ... t 125 t 1.14 t 109 f ? SVHC \n",
+ "1 23 F f f f f f f f f ... t 102 f ? f ? f ? other \n",
+ "2 46 M f f f f f f f f ... t 109 t 0.91 t 120 f ? other \n",
+ "3 70 F t f f f f f f f ... t 175 f ? f ? f ? other \n",
+ "4 70 F f f f f f f f f ... t 61 t 0.87 t 70 f ? SVI \n",
+ "\n",
+ " 29 \n",
+ "0 negative.|3733 \n",
+ "1 negative.|1442 \n",
+ "2 negative.|2965 \n",
+ "3 negative.|806 \n",
+ "4 negative.|2807 \n",
+ "\n",
+ "[5 rows x 30 columns]"
+ ]
+ },
+ "execution_count": 611,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df2.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 612,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<class 'pandas.core.frame.DataFrame'>\n",
+ "RangeIndex: 2800 entries, 0 to 2799\n",
+ "Data columns (total 30 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 0 2800 non-null object\n",
+ " 1 1 2800 non-null object\n",
+ " 2 2 2800 non-null object\n",
+ " 3 3 2800 non-null object\n",
+ " 4 4 2800 non-null object\n",
+ " 5 5 2800 non-null object\n",
+ " 6 6 2800 non-null object\n",
+ " 7 7 2800 non-null object\n",
+ " 8 8 2800 non-null object\n",
+ " 9 9 2800 non-null object\n",
+ " 10 10 2800 non-null object\n",
+ " 11 11 2800 non-null object\n",
+ " 12 12 2800 non-null object\n",
+ " 13 13 2800 non-null object\n",
+ " 14 14 2800 non-null object\n",
+ " 15 15 2800 non-null object\n",
+ " 16 16 2800 non-null object\n",
+ " 17 17 2800 non-null object\n",
+ " 18 18 2800 non-null object\n",
+ " 19 19 2800 non-null object\n",
+ " 20 20 2800 non-null object\n",
+ " 21 21 2800 non-null object\n",
+ " 22 22 2800 non-null object\n",
+ " 23 23 2800 non-null object\n",
+ " 24 24 2800 non-null object\n",
+ " 25 25 2800 non-null object\n",
+ " 26 26 2800 non-null object\n",
+ " 27 27 2800 non-null object\n",
+ " 28 28 2800 non-null object\n",
+ " 29 29 2800 non-null object\n",
+ "dtypes: object(30)\n",
+ "memory usage: 656.4+ KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "df2.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 613,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>2800</td>\n",
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+ " <td>2800</td>\n",
+ " <td>2800</td>\n",
+ " <td>2800</td>\n",
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+ " <td>2800</td>\n",
+ " <td>2800</td>\n",
+ " <td>2800</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>unique</th>\n",
+ " <td>94</td>\n",
+ " <td>3</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
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+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>...</td>\n",
+ " <td>2</td>\n",
+ " <td>218</td>\n",
+ " <td>2</td>\n",
+ " <td>139</td>\n",
+ " <td>2</td>\n",
+ " <td>210</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>5</td>\n",
+ " <td>2800</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>top</th>\n",
+ " <td>59</td>\n",
+ " <td>F</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
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+ " <td>f</td>\n",
+ " <td>?</td>\n",
+ " <td>other</td>\n",
+ " <td>negative.|3733</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>freq</th>\n",
+ " <td>75</td>\n",
+ " <td>1830</td>\n",
+ " <td>2470</td>\n",
+ " <td>2760</td>\n",
+ " <td>2766</td>\n",
+ " <td>2690</td>\n",
+ " <td>2759</td>\n",
+ " <td>2761</td>\n",
+ " <td>2752</td>\n",
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+ " <td>2616</td>\n",
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+ " <td>2800</td>\n",
+ " <td>2800</td>\n",
+ " <td>1632</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
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+ "</table>\n",
+ "<p>4 rows × 30 columns</p>\n",
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+ },
+ "execution_count": 613,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df2.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 614,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df2.columns =[\n",
+ " \"age\",\n",
+ " \"sex\",\n",
+ " \"on thyroxine\",\n",
+ " \"query on thyroxine\",\n",
+ " \"on antithyroid medication\",\n",
+ " \"sick\",\n",
+ " \"pregnant\",\n",
+ " \"thyroid surgery\",\n",
+ " \"I131 treatment\",\n",
+ " \"query hypothyroid\",\n",
+ " \"query hyperthyroid\",\n",
+ " \"lithium\",\n",
+ " \"goitre\",\n",
+ " \"tumor\",\n",
+ " \"hypopituitary\",\n",
+ " \"psych\",\n",
+ " \"TSH measured\",\n",
+ " \"TSH\",\n",
+ " \"T3 measured\",\n",
+ " \"T3\",\n",
+ " \"TT4 measured\",\n",
+ " \"TT4\",\n",
+ " \"T4U measured\",\n",
+ " \"T4U\",\n",
+ " \"FTI measured\",\n",
+ " \"FTI\",\n",
+ " \"TBG measured\",\n",
+ " \"TBG\",\n",
+ " \"referral source\",\n",
+ " \"classes\"\n",
+ "]"
+ ]
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+ "execution_count": 615,
+ "metadata": {},
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+ "text/plain": [
+ " age sex on thyroxine query on thyroxine on antithyroid medication sick \\\n",
+ "0 41 F f f f f \n",
+ "1 23 F f f f f \n",
+ "2 46 M f f f f \n",
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+ "4 70 F f f f f \n",
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+ "1 f f f f ... t \n",
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+ "4 f f f f ... t \n",
+ "\n",
+ " TT4 T4U measured T4U FTI measured FTI TBG measured TBG referral source \\\n",
+ "0 125 t 1.14 t 109 f ? SVHC \n",
+ "1 102 f ? f ? f ? other \n",
+ "2 109 t 0.91 t 120 f ? other \n",
+ "3 175 f ? f ? f ? other \n",
+ "4 61 t 0.87 t 70 f ? SVI \n",
+ "\n",
+ " classes \n",
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+ "4 negative.|2807 \n",
+ "\n",
+ "[5 rows x 30 columns]"
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+ },
+ "execution_count": 615,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df2.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 616,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(2800, 30)"
+ ]
+ },
+ "execution_count": 616,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df2.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- How many observations and how many variables are there in the data? <br>\n",
+ "2800 observations, 30 variables\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 617,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/fp/cf7b8z110lj8yjpy9rj8f5fr0000gn/T/ipykernel_5773/4070518099.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
+ " df2.replace(['?', 'nan', 'missing'], N.nan, inplace=True)\n"
+ ]
+ }
+ ],
+ "source": [
+ "df2.replace(['?', 'nan', 'missing'], N.nan, inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 618,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "age 1\n",
+ "sex 110\n",
+ "on thyroxine 0\n",
+ "query on thyroxine 0\n",
+ "on antithyroid medication 0\n",
+ "sick 0\n",
+ "pregnant 0\n",
+ "thyroid surgery 0\n",
+ "I131 treatment 0\n",
+ "query hypothyroid 0\n",
+ "query hyperthyroid 0\n",
+ "lithium 0\n",
+ "goitre 0\n",
+ "tumor 0\n",
+ "hypopituitary 0\n",
+ "psych 0\n",
+ "TSH measured 0\n",
+ "TSH 284\n",
+ "T3 measured 0\n",
+ "T3 585\n",
+ "TT4 measured 0\n",
+ "TT4 184\n",
+ "T4U measured 0\n",
+ "T4U 297\n",
+ "FTI measured 0\n",
+ "FTI 295\n",
+ "TBG measured 0\n",
+ "TBG 2800\n",
+ "referral source 0\n",
+ "classes 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 618,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df2.isna().sum()"
+ ]
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+ {
+ "cell_type": "code",
+ "execution_count": 619,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "age object\n",
+ "sex object\n",
+ "on thyroxine object\n",
+ "query on thyroxine object\n",
+ "on antithyroid medication object\n",
+ "sick object\n",
+ "pregnant object\n",
+ "thyroid surgery object\n",
+ "I131 treatment object\n",
+ "query hypothyroid object\n",
+ "query hyperthyroid object\n",
+ "lithium object\n",
+ "goitre object\n",
+ "tumor object\n",
+ "hypopituitary object\n",
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+ "TSH measured object\n",
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+ "TBG float64\n",
+ "referral source object\n",
+ "classes object\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 619,
+ "metadata": {},
+ "output_type": "execute_result"
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+ ],
+ "source": [
+ "df2.dtypes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 620,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "columns_to_change = [\"age\",\"TSH\", \"T3\", \"TT4\", \"T4U\", \"FTI\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 621,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for column in columns_to_change:\n",
+ " df2[column] = df2[column].astype(float)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 622,
+ "metadata": {},
+ "outputs": [
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+ "data": {
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+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>...</td>\n",
+ " <td>t</td>\n",
+ " <td>155.0</td>\n",
+ " <td>t</td>\n",
+ " <td>1.05</td>\n",
+ " <td>t</td>\n",
+ " <td>148.0</td>\n",
+ " <td>f</td>\n",
+ " <td>NaN</td>\n",
+ " <td>SVI</td>\n",
+ " <td>negative.|3689</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2796</th>\n",
+ " <td>73.0</td>\n",
+ " <td>M</td>\n",
+ " <td>f</td>\n",
+ " <td>t</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>...</td>\n",
+ " <td>t</td>\n",
+ " <td>63.0</td>\n",
+ " <td>t</td>\n",
+ " <td>0.88</td>\n",
+ " <td>t</td>\n",
+ " <td>72.0</td>\n",
+ " <td>f</td>\n",
+ " <td>NaN</td>\n",
+ " <td>other</td>\n",
+ " <td>negative.|3652</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2797</th>\n",
+ " <td>75.0</td>\n",
+ " <td>M</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>...</td>\n",
+ " <td>t</td>\n",
+ " <td>147.0</td>\n",
+ " <td>t</td>\n",
+ " <td>0.80</td>\n",
+ " <td>t</td>\n",
+ " <td>183.0</td>\n",
+ " <td>f</td>\n",
+ " <td>NaN</td>\n",
+ " <td>other</td>\n",
+ " <td>negative.|1287</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2798</th>\n",
+ " <td>60.0</td>\n",
+ " <td>F</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>...</td>\n",
+ " <td>t</td>\n",
+ " <td>100.0</td>\n",
+ " <td>t</td>\n",
+ " <td>0.83</td>\n",
+ " <td>t</td>\n",
+ " <td>121.0</td>\n",
+ " <td>f</td>\n",
+ " <td>NaN</td>\n",
+ " <td>other</td>\n",
+ " <td>negative.|3496</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2799</th>\n",
+ " <td>81.0</td>\n",
+ " <td>F</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>f</td>\n",
+ " <td>...</td>\n",
+ " <td>t</td>\n",
+ " <td>114.0</td>\n",
+ " <td>t</td>\n",
+ " <td>0.99</td>\n",
+ " <td>t</td>\n",
+ " <td>115.0</td>\n",
+ " <td>f</td>\n",
+ " <td>NaN</td>\n",
+ " <td>SVI</td>\n",
+ " <td>negative.|724</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>2800 rows × 30 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " age sex on thyroxine query on thyroxine on antithyroid medication sick \\\n",
+ "0 41.0 F f f f f \n",
+ "1 23.0 F f f f f \n",
+ "2 46.0 M f f f f \n",
+ "3 70.0 F t f f f \n",
+ "4 70.0 F f f f f \n",
+ "... ... .. ... ... ... ... \n",
+ "2795 70.0 M f f f f \n",
+ "2796 73.0 M f t f f \n",
+ "2797 75.0 M f f f f \n",
+ "2798 60.0 F f f f f \n",
+ "2799 81.0 F f f f f \n",
+ "\n",
+ " pregnant thyroid surgery I131 treatment query hypothyroid ... \\\n",
+ "0 f f f f ... \n",
+ "1 f f f f ... \n",
+ "2 f f f f ... \n",
+ "3 f f f f ... \n",
+ "4 f f f f ... \n",
+ "... ... ... ... ... ... \n",
+ "2795 f f f f ... \n",
+ "2796 f f f f ... \n",
+ "2797 f f f f ... \n",
+ "2798 f f f f ... \n",
+ "2799 f f f f ... \n",
+ "\n",
+ " TT4 measured TT4 T4U measured T4U FTI measured FTI TBG measured \\\n",
+ "0 t 125.0 t 1.14 t 109.0 f \n",
+ "1 t 102.0 f NaN f NaN f \n",
+ "2 t 109.0 t 0.91 t 120.0 f \n",
+ "3 t 175.0 f NaN f NaN f \n",
+ "4 t 61.0 t 0.87 t 70.0 f \n",
+ "... ... ... ... ... ... ... ... \n",
+ "2795 t 155.0 t 1.05 t 148.0 f \n",
+ "2796 t 63.0 t 0.88 t 72.0 f \n",
+ "2797 t 147.0 t 0.80 t 183.0 f \n",
+ "2798 t 100.0 t 0.83 t 121.0 f \n",
+ "2799 t 114.0 t 0.99 t 115.0 f \n",
+ "\n",
+ " TBG referral source classes \n",
+ "0 NaN SVHC negative.|3733 \n",
+ "1 NaN other negative.|1442 \n",
+ "2 NaN other negative.|2965 \n",
+ "3 NaN other negative.|806 \n",
+ "4 NaN SVI negative.|2807 \n",
+ "... ... ... ... \n",
+ "2795 NaN SVI negative.|3689 \n",
+ "2796 NaN other negative.|3652 \n",
+ "2797 NaN other negative.|1287 \n",
+ "2798 NaN other negative.|3496 \n",
+ "2799 NaN SVI negative.|724 \n",
+ "\n",
+ "[2800 rows x 30 columns]"
+ ]
+ },
+ "execution_count": 622,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 623,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "age float64\n",
+ "sex object\n",
+ "on thyroxine object\n",
+ "query on thyroxine object\n",
+ "on antithyroid medication object\n",
+ "sick object\n",
+ "pregnant object\n",
+ "thyroid surgery object\n",
+ "I131 treatment object\n",
+ "query hypothyroid object\n",
+ "query hyperthyroid object\n",
+ "lithium object\n",
+ "goitre object\n",
+ "tumor object\n",
+ "hypopituitary object\n",
+ "psych object\n",
+ "TSH measured object\n",
+ "TSH float64\n",
+ "T3 measured object\n",
+ "T3 float64\n",
+ "TT4 measured object\n",
+ "TT4 float64\n",
+ "T4U measured object\n",
+ "T4U float64\n",
+ "FTI measured object\n",
+ "FTI float64\n",
+ "TBG measured object\n",
+ "TBG float64\n",
+ "referral source object\n",
+ "classes object\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 623,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df2.dtypes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1.3 Thyroid disease (continued)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 624,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "yes_no_columns = [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 22, 24, 26]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 625,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Column: on thyroxine %Yes: 11.79%\n",
+ "\n",
+ "Column: query on thyroxine %Yes: 1.43%\n",
+ "\n",
+ "Column: on antithyroid medication %Yes: 1.21%\n",
+ "\n",
+ "Column: sick %Yes: 3.93%\n",
+ "\n",
+ "Column: pregnant %Yes: 1.46%\n",
+ "\n",
+ "Column: thyroid surgery %Yes: 1.39%\n",
+ "\n",
+ "Column: I131 treatment %Yes: 1.71%\n",
+ "\n",
+ "Column: query hypothyroid %Yes: 5.82%\n",
+ "\n",
+ "Column: query hyperthyroid %Yes: 6.18%\n",
+ "\n",
+ "Column: lithium %Yes: 0.50%\n",
+ "\n",
+ "Column: goitre %Yes: 0.89%\n",
+ "\n",
+ "Column: tumor %Yes: 2.54%\n",
+ "\n",
+ "Column: hypopituitary %Yes: 0.04%\n",
+ "\n",
+ "Column: psych %Yes: 4.82%\n",
+ "\n",
+ "Column: TSH measured %Yes: 89.86%\n",
+ "\n",
+ "Column: T3 measured %Yes: 79.11%\n",
+ "\n",
+ "Column: TT4 measured %Yes: 93.43%\n",
+ "\n",
+ "Column: T4U measured %Yes: 89.39%\n",
+ "\n",
+ "Column: FTI measured %Yes: 89.46%\n",
+ "\n",
+ "Column: TBG measured %Yes: 0.00%\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "for column in yes_no_columns:\n",
+ " yes_count = ((df2.iloc[:, column] == \"t\").sum())/2800\n",
+ " print(f\"Column: {df2.columns[column]} %Yes: {yes_count:.2%}\\n\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 626,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "calculate_columns = [\"TSH\", \"T3\", \"TT4\", \"T4U\", \"FTI\", \"TBG\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 627,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "481.7251481915739"
+ ]
+ },
+ "execution_count": 627,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "((df2[\"TSH\"] ** 2).sum())/df2[\"TSH\"].notna().sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 628,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0 1.212020e+06\n",
+ "1 1.212020e+06\n",
+ "2 1.212020e+06\n",
+ "3 1.212020e+06\n",
+ "4 1.212020e+06\n",
+ " ... \n",
+ "2795 1.212020e+06\n",
+ "2796 inf\n",
+ "2797 inf\n",
+ "2798 1.212020e+06\n",
+ "2799 1.212020e+06\n",
+ "Name: TSH, Length: 2800, dtype: float64\n",
+ "0 10588.025\n",
+ "1 10588.025\n",
+ "2 inf\n",
+ "3 10588.025\n",
+ "4 10588.025\n",
+ " ... \n",
+ "2795 inf\n",
+ "2796 10588.025\n",
+ "2797 inf\n",
+ "2798 inf\n",
+ "2799 10588.025\n",
+ "Name: T3, Length: 2800, dtype: float64\n",
+ "0 34397613.32\n",
+ "1 34397613.32\n",
+ "2 34397613.32\n",
+ "3 34397613.32\n",
+ "4 34397613.32\n",
+ " ... \n",
+ "2795 34397613.32\n",
+ "2796 34397613.32\n",
+ "2797 34397613.32\n",
+ "2798 34397613.32\n",
+ "2799 34397613.32\n",
+ "Name: TT4, Length: 2800, dtype: float64\n",
+ "0 2587.103636\n",
+ "1 inf\n",
+ "2 2587.103636\n",
+ "3 inf\n",
+ "4 2587.103636\n",
+ " ... \n",
+ "2795 2587.103636\n",
+ "2796 2587.103636\n",
+ "2797 2587.103636\n",
+ "2798 2587.103636\n",
+ "2799 2587.103636\n",
+ "Name: T4U, Length: 2800, dtype: float64\n",
+ "0 33454030.13\n",
+ "1 inf\n",
+ "2 33454030.13\n",
+ "3 inf\n",
+ "4 33454030.13\n",
+ " ... \n",
+ "2795 33454030.13\n",
+ "2796 33454030.13\n",
+ "2797 33454030.13\n",
+ "2798 33454030.13\n",
+ "2799 33454030.13\n",
+ "Name: FTI, Length: 2800, dtype: float64\n",
+ "0 NaN\n",
+ "1 NaN\n",
+ "2 NaN\n",
+ "3 NaN\n",
+ "4 NaN\n",
+ " ..\n",
+ "2795 NaN\n",
+ "2796 NaN\n",
+ "2797 NaN\n",
+ "2798 NaN\n",
+ "2799 NaN\n",
+ "Name: TBG, Length: 2800, dtype: float64\n"
+ ]
+ }
+ ],
+ "source": [
+ "for column in calculate_columns:\n",
+ " calculation = ((df2[column] ** 2).sum())/(df2[column].notna())\n",
+ " print(calculation)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}