Introduction_to_pandas.ipynb

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    "# Introduction to pandas (not the animal)\n",
    "\n",
    "Pandas is a python package for handling tabular data. It is the python equivalent of excel, calc or simmilar.\n",
    "<br>Pandas is part of anaconda and already installed in your environment."
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    "import pandas as pd # first import the package"
   ]
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    "## Loading / generating data\n",
    "You can either import data from a file or generate it yourself directly here in the note book.\n",
    "<br>We start with the import of text based files (.csv, .tsv,...). The function `read_csv()` is all you need.\n",
    "<br>If you want to oepn a .csv (comma separated values) file the default works fine, for for other text file formats you must add the correct delimiter.\n",
    "\n",
    "As example for a .tsv (tab separated values) file you must provide the string '\\t'."
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       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>59 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    0  0.1  1  3  1.1  2  4  7  8  3.1  ...  4.3  4.4  5.1  7.6  3.4  4.5  \\\n",
       "0   0    1  2  1    2  1  3  2  2    6  ...    3    5    4    4    5    5   \n",
       "1   0    1  1  3    3  2  6  2  5    9  ...   10    5    4    2    2    3   \n",
       "2   0    0  2  0    4  2  2  1  6    7  ...    3    5    6    3    3    4   \n",
       "3   0    1  1  3    3  1  3  5  2    4  ...    9    6    3    2    2    4   \n",
       "4   0    0  1  2    2  4  2  1  6    4  ...    8    4    7    3    5    4   \n",
       "5   0    0  2  2    4  2  2  5  5    8  ...    8    8    4    2    3    5   \n",
       "6   0    0  1  2    3  1  2  3  5    3  ...    4    9    3    5    2    5   \n",
       "7   0    0  0  3    1  5  6  5  5    8  ...    4    6    4    7    6    3   \n",
       "8   0    1  1  2    1  3  5  3  5    8  ...    2    5    4    5    1    4   \n",
       "9   0    1  0  0    4  3  3  5  5    4  ...    4    3    4    5    5    3   \n",
       "10  0    1  0  0    3  4  2  7  8    5  ...    8    3    5    4    5    5   \n",
       "11  0    0  2  1    4  3  6  4  6    7  ...    5    4    7    3    5    4   \n",
       "12  0    0  0  0    1  3  1  6  6    5  ...    5    8    7    4    6    4   \n",
       "13  0    1  2  1    1  1  4  1  5    2  ...    8    2    5    1    3    4   \n",
       "14  0    1  1  0    1  2  4  3  6    4  ...   10    9    5    6    5    3   \n",
       "15  0    0  0  0    2  3  6  5  7    4  ...    9    8    7    5    3    1   \n",
       "16  0    0  0  1    2  1  4  3  6    7  ...    2    3    6    5    4    2   \n",
       "17  0    0  2  1    2  5  4  2  7    8  ...    6    9    2    1    1    2   \n",
       "18  0    1  2  0    1  4  3  2  2    7  ...    6    6    6    1    1    2   \n",
       "19  0    1  1  3    1  4  4  1  8    2  ...    3    2    4    3    1    5   \n",
       "20  0    0  2  3    2  3  2  6  3    8  ...    8    5    6    6    1    4   \n",
       "21  0    0  0  3    4  5  1  7  7    8  ...    4    4    8    2    6    5   \n",
       "22  0    1  1  1    1  3  3  2  6    3  ...    5    3    5    1    1    4   \n",
       "23  0    1  1  1    2  3  5  3  6    3  ...    5    5    6    1    1    1   \n",
       "24  0    0  2  1    3  3  2  7  4    4  ...    8    5    7    2    2    4   \n",
       "25  0    0  1  2    4  2  2  3  5    7  ...    7    4    8    2    2    1   \n",
       "26  0    0  1  1    1  5  1  5  2    2  ...    9    4    5    3    2    5   \n",
       "27  0    0  2  2    3  4  6  3  7    6  ...    7    7    8    3    5    4   \n",
       "28  0    0  0  1    4  4  6  3  8    6  ...    6    9    5    5    2    5   \n",
       "29  0    1  1  0    3  2  4  6  8    6  ...   10    4    2    6    5    5   \n",
       "30  0    0  2  3    3  4  5  3  6    7  ...    3    6    6    4    5    2   \n",
       "31  0    1  2  2    2  3  6  6  6    7  ...    5    8    5    2    5    5   \n",
       "32  0    0  2  1    3  5  6  7  5    8  ...    2    9    7    2    4    2   \n",
       "33  0    0  1  2    4  1  5  5  2    3  ...    5    6    6    2    3    5   \n",
       "34  0    0  0  3    1  3  6  4  3    4  ...    3    9    5    1    6    5   \n",
       "35  0    1  2  2    2  5  5  1  4    6  ...    6    4    5    4    6    3   \n",
       "36  0    1  1  2    3  1  5  1  2    2  ...    9    9    5    4    4    2   \n",
       "37  0    1  0  3    2  4  1  1  5    9  ...    5    5    2    1    1    1   \n",
       "38  0    1  1  3    1  1  5  5  3    7  ...    2    3    6    3    3    5   \n",
       "39  0    0  0  2    2  1  3  4  5    5  ...    2    9    6    2    2    5   \n",
       "40  0    0  1  3    3  1  2  1  8    9  ...    4    8    2    6    6    4   \n",
       "41  0    1  1  3    4  5  2  1  3    7  ...    5    8    5    5    6    1   \n",
       "42  0    0  1  3    1  4  3  6  7    8  ...   10    2    5    1    5    4   \n",
       "43  0    1  1  3    3  4  4  6  3    4  ...   10    6    8    7    2    5   \n",
       "44  0    1  2  2    4  3  1  4  8    9  ...    5    8    4    4    5    2   \n",
       "45  0    0  2  3    4  5  4  6  2    9  ...    6    7    6    5    1    3   \n",
       "46  0    1  1  3    1  4  6  2  8    2  ...    6    9    5    6    1    1   \n",
       "47  0    0  1  3    2  5  1  2  7    6  ...   10    7    6    3    1    5   \n",
       "48  0    0  1  2    3  4  5  7  5    4  ...    4    6    2    4    1    4   \n",
       "49  0    1  2  1    1  3  5  3  6    3  ...    7    9    3    3    6    3   \n",
       "50  0    1  2  2    3  5  2  4  5    6  ...    8    5    4    1    3    2   \n",
       "51  0    0  0  2    4  4  5  3  3    3  ...   10    8    7    5    2    2   \n",
       "52  0    0  2  1    1  4  4  7  2    9  ...    7    6    5    4    1    4   \n",
       "53  0    1  2  1    1  4  5  4  4    5  ...    4    5    5    2    2    5   \n",
       "54  0    0  1  3    2  3  6  4  5    7  ...    3    5    3    5    4    5   \n",
       "55  0    1  1  2    2  5  1  7  4    2  ...    7    7    5    6    3    4   \n",
       "56  0    1  1  1    4  1  6  4  6    3  ...    8    6    6    4    3    5   \n",
       "57  0    0  0  1    4  5  6  3  8    7  ...   10    8    8    6    5    5   \n",
       "58  0    0  1  0    3  2  5  4  8    2  ...    8    5    3    5    4    1   \n",
       "\n",
       "    2.1  3.5  0.2  0.3  \n",
       "0     1    1    0    1  \n",
       "1     2    2    1    1  \n",
       "2     2    3    2    1  \n",
       "3     2    0    1    1  \n",
       "4     4    3    2    1  \n",
       "5     4    1    1    1  \n",
       "6     3    2    2    1  \n",
       "7     2    1    0    0  \n",
       "8     1    2    0    0  \n",
       "9     3    2    2    1  \n",
       "10    4    0    1    1  \n",
       "11    2    3    0    1  \n",
       "12    1    3    0    0  \n",
       "13    2    0    2    0  \n",
       "14    4    2    2    0  \n",
       "15    4    0    2    1  \n",
       "16    3    0    1    0  \n",
       "17    2    0    1    0  \n",
       "18    4    3    1    1  \n",
       "19    4    2    2    0  \n",
       "20    3    0    2    0  \n",
       "21    1    0    1    0  \n",
       "22    4    1    2    0  \n",
       "23    1    0    2    1  \n",
       "24    1    1    1    0  \n",
       "25    3    0    1    1  \n",
       "26    4    3    2    1  \n",
       "27    1    3    1    0  \n",
       "28    2    1    0    1  \n",
       "29    2    3    2    1  \n",
       "30    2    3    0    0  \n",
       "31    2    0    2    1  \n",
       "32    1    2    1    1  \n",
       "33    2    1    1    1  \n",
       "34    4    2    2    0  \n",
       "35    4    3    2    1  \n",
       "36    1    0    1    0  \n",
       "37    1    3    0    1  \n",
       "38    4    3    2    1  \n",
       "39    3    0    0    1  \n",
       "40    2    2    0    0  \n",
       "41    2    1    2    0  \n",
       "42    2    1    0    1  \n",
       "43    4    3    1    1  \n",
       "44    4    1    1    0  \n",
       "45    1    0    0    0  \n",
       "46    2    1    2    1  \n",
       "47    4    3    0    0  \n",
       "48    2    2    2    1  \n",
       "49    4    1    2    0  \n",
       "50    1    3    1    0  \n",
       "51    4    1    2    1  \n",
       "52    2    2    2    1  \n",
       "53    1    0    0    1  \n",
       "54    3    3    0    1  \n",
       "55    2    2    1    1  \n",
       "56    2    1    1    1  \n",
       "57    2    0    2    0  \n",
       "58    3    1    1    0  \n",
       "\n",
       "[59 rows x 40 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('data/inflammation-01.csv')\n",
    "# for a .tsv file: pd.read_csv('path to a file', delimiter='\\t')\n",
    "\n",
    "\n",
    "display(df) # display shows our table in a nice way (works only in jupyter!!!)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50e3b421-158d-4405-b351-3661a7a265c1",
   "metadata": {},
   "source": [
    "If we add a `.head()` behind the variable containing our tabular data only the first five rows will be displayed. The equivalent to display the last five rows is `.tail()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   ],
   "source": [
    "display(df.head())\n",
    "display(df.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "594e1669-512a-4c73-987c-400502f7fe0d",
   "metadata": {},
   "source": [
    "We loaded the file but the first rwo of data was taken as column headers.\n",
    "<br>To add the correct headers we must first define a list containing matching column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3505a199-8852-4031-8861-00cb8e112620",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Day1', 'Day2', 'Day3', 'Day4', 'Day5', 'Day6', 'Day7', 'Day8', 'Day9', 'Day10', 'Day11', 'Day12', 'Day13', 'Day14', 'Day15', 'Day16', 'Day17', 'Day18', 'Day19', 'Day20', 'Day21', 'Day22', 'Day23', 'Day24', 'Day25', 'Day26', 'Day27', 'Day28', 'Day29', 'Day30', 'Day31', 'Day32', 'Day33', 'Day34', 'Day35', 'Day36', 'Day37', 'Day38', 'Day39', 'Day40']\n"
     ]
    }
   ],
   "source": [
    "columns = []\n",
    "for i in range(1, 41):\n",
    "    columns.append(f'Day{i}')\n",
    "\n",
    "print(columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd410526-9fc1-4d34-aec5-30ae5ea62c41",
   "metadata": {},
   "source": [
    "To load the file with our custom column names we must provide them through the names argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3af7c261-9280-4269-9c08-731045beb41a",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Day1  Day2  Day3  Day4  Day5  Day6  Day7  Day8  Day9  Day10  ...  Day31  \\\n",
       "0     0     0     1     3     1     2     4     7     8      3  ...      4   \n",
       "1     0     1     2     1     2     1     3     2     2      6  ...      3   \n",
       "2     0     1     1     3     3     2     6     2     5      9  ...     10   \n",
       "3     0     0     2     0     4     2     2     1     6      7  ...      3   \n",
       "4     0     1     1     3     3     1     3     5     2      4  ...      9   \n",
       "\n",
       "   Day32  Day33  Day34  Day35  Day36  Day37  Day38  Day39  Day40  \n",
       "0      4      5      7      3      4      2      3      0      0  \n",
       "1      5      4      4      5      5      1      1      0      1  \n",
       "2      5      4      2      2      3      2      2      1      1  \n",
       "3      5      6      3      3      4      2      3      2      1  \n",
       "4      6      3      2      2      4      2      0      1      1  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_with_headers = pd.read_csv('data/inflammation-01.csv', names=columns)\n",
    "display(df_with_headers.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "749b4a38-a45a-4dc6-963d-b0bd20d70aa2",
   "metadata": {},
   "source": [
    "The moment we loaded our data from the file into a variable we created an object called a pandas `DataFrame`. This is just a container for our data with many usefull functions. \n",
    "<br>Similar to a numpy array we can ask it for its shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "45eb3ed0-b48f-4405-ba7f-0bc2dee88ed3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 40)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_headers.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bae95827-ae1d-4419-9956-1a174d715130",
   "metadata": {},
   "source": [
    "Our dataframe has the correct number of rows and columns.\n",
    "<br>Let's add a meaningfull index to the table.\n",
    "<br>First we have to create the index. Therefore, we first create a list containing all the row IDs in our case the patient numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7bbb190b-8614-4847-a228-aa2fa0cc9b1e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Patient1', 'Patient2', 'Patient3', 'Patient4', 'Patient5', 'Patient6', 'Patient7', 'Patient8', 'Patient9', 'Patient10', 'Patient11', 'Patient12', 'Patient13', 'Patient14', 'Patient15', 'Patient16', 'Patient17', 'Patient18', 'Patient19', 'Patient20', 'Patient21', 'Patient22', 'Patient23', 'Patient24', 'Patient25', 'Patient26', 'Patient27', 'Patient28', 'Patient29', 'Patient30', 'Patient31', 'Patient32', 'Patient33', 'Patient34', 'Patient35', 'Patient36', 'Patient37', 'Patient38', 'Patient39', 'Patient40', 'Patient41', 'Patient42', 'Patient43', 'Patient44', 'Patient45', 'Patient46', 'Patient47', 'Patient48', 'Patient49', 'Patient50', 'Patient51', 'Patient52', 'Patient53', 'Patient54', 'Patient55', 'Patient56', 'Patient57', 'Patient58', 'Patient59', 'Patient60']\n"
     ]
    }
   ],
   "source": [
    "index = []\n",
    "for i in range(1,61):\n",
    "    index.append(f'Patient{i}')\n",
    "print(index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ef1cf66-f919-407d-a87b-aee55b734f45",
   "metadata": {},
   "source": [
    "In the next step we must change the list to an actual pandas `Index` object and then add it to our dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6c93367e-98b7-49ba-a0bd-ff389dcfb6d0",
   "metadata": {
    "tags": []
   },
   "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>Day1</th>\n",
       "      <th>Day2</th>\n",
       "      <th>Day3</th>\n",
       "      <th>Day4</th>\n",
       "      <th>Day5</th>\n",
       "      <th>Day6</th>\n",
       "      <th>Day7</th>\n",
       "      <th>Day8</th>\n",
       "      <th>Day9</th>\n",
       "      <th>Day10</th>\n",
       "      <th>...</th>\n",
       "      <th>Day31</th>\n",
       "      <th>Day32</th>\n",
       "      <th>Day33</th>\n",
       "      <th>Day34</th>\n",
       "      <th>Day35</th>\n",
       "      <th>Day36</th>\n",
       "      <th>Day37</th>\n",
       "      <th>Day38</th>\n",
       "      <th>Day39</th>\n",
       "      <th>Day40</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Patient1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patient2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patient3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patient4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patient5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Day1  Day2  Day3  Day4  Day5  Day6  Day7  Day8  Day9  Day10  ...  \\\n",
       "Patient1     0     0     1     3     1     2     4     7     8      3  ...   \n",
       "Patient2     0     1     2     1     2     1     3     2     2      6  ...   \n",
       "Patient3     0     1     1     3     3     2     6     2     5      9  ...   \n",
       "Patient4     0     0     2     0     4     2     2     1     6      7  ...   \n",
       "Patient5     0     1     1     3     3     1     3     5     2      4  ...   \n",
       "\n",
       "          Day31  Day32  Day33  Day34  Day35  Day36  Day37  Day38  Day39  Day40  \n",
       "Patient1      4      4      5      7      3      4      2      3      0      0  \n",
       "Patient2      3      5      4      4      5      5      1      1      0      1  \n",
       "Patient3     10      5      4      2      2      3      2      2      1      1  \n",
       "Patient4      3      5      6      3      3      4      2      3      2      1  \n",
       "Patient5      9      6      3      2      2      4      2      0      1      1  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "real_index = pd.Index(index) # here we change our list index to an index object\n",
    "df_with_index_and_headers = df_with_headers.set_index(real_index) # we set the index of our dataframe\n",
    "display(df_with_index_and_headers.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5a11a28-dfcf-4c77-8482-66bdd0c8d858",
   "metadata": {},
   "source": [
    "We can load data from an excel files as well with the function `read_excel()`!\n",
    "<br>`read_excel` needs an additional dependency not part of Anaconda: openpyxl\n",
    "<br>We can install python packages directly here in the notebook using the following comands:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46483044-95f5-4477-a3a3-61aec229e800",
   "metadata": {},
   "outputs": [],
   "source": [
    "conda install openpyxl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "613d161d-6329-427f-a037-eca71940d400",
   "metadata": {},
   "source": [
    "Now we can open the excel file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c22fcc6f-4305-4c83-be9e-c7f3fc4b2aa2",
   "metadata": {
    "tags": []
   },
   "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>FamilyName</th>\n",
       "      <th>GivenName</th>\n",
       "      <th>Age</th>\n",
       "      <th>Size</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Schmidt</td>\n",
       "      <td>Tim</td>\n",
       "      <td>11</td>\n",
       "      <td>1.20</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Müller</td>\n",
       "      <td>Sarah</td>\n",
       "      <td>57</td>\n",
       "      <td>1.56</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nauroth</td>\n",
       "      <td>Chris</td>\n",
       "      <td>32</td>\n",
       "      <td>1.83</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kreß</td>\n",
       "      <td>Julia</td>\n",
       "      <td>28</td>\n",
       "      <td>1.70</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  FamilyName GivenName  Age  Size  Weight\n",
       "0    Schmidt       Tim   11  1.20      36\n",
       "1     Müller     Sarah   57  1.56      52\n",
       "2    Nauroth     Chris   32  1.83      72\n",
       "3       Kreß     Julia   28  1.70      66"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_from_excel = pd.read_excel('data/excel_example_data.xlsx')\n",
    "display(df_from_excel)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2854e82-d297-43d6-b7da-64e3c60fa8e4",
   "metadata": {},
   "source": [
    "Previously, we loaded data different example files and added meaningful column names and row indices, let's try to make an entirely new table next.\n",
    "<br>Therefore, we use the pandas `DataFrame` class directly and fill it from a python dictionary (`dict`). A dictionary works similar to a real life dictionary, \n",
    "<br>you have keys you can look for and values that are affiliated with a key: 'animal': 'Tier'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "14715703-62fb-4e96-bee7-57de9ee30748",
   "metadata": {
    "tags": []
   },
   "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>col1</th>\n",
       "      <th>col2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1 col2\n",
       "0     1    a\n",
       "1     2    b\n",
       "2     3    c"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mytable = pd.DataFrame(dict(\n",
    "    col1=[1,2,3], # this is our first column with three rows\n",
    "    col2=['a','b','c'] # this is our second column\n",
    "))\n",
    "display(mytable)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bb44d22-f73c-4d29-9f9a-c19d84579bc1",
   "metadata": {},
   "source": [
    "It is important that each column has the same number of elements (rows), otherwise ou will run into an error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5eae6d87-3dcb-41ac-ad37-920de13b05ad",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "arrays must all be same length",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/69/qmk06lyd6n5_h18fgrhh1c5c0000gn/T/ipykernel_16312/3823887217.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m mytable = pd.DataFrame(dict(\n\u001b[1;32m      2\u001b[0m     \u001b[0mcol1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mcol2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'b'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m ))\n\u001b[1;32m      5\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmytable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    527\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    528\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 529\u001b[0;31m             \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minit_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    530\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    531\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36minit_dict\u001b[0;34m(data, index, columns, dtype)\u001b[0m\n\u001b[1;32m    285\u001b[0m             \u001b[0marr\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_datetime64tz_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    286\u001b[0m         ]\n\u001b[0;32m--> 287\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marrays_to_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36marrays_to_mgr\u001b[0;34m(arrays, arr_names, index, columns, dtype, verify_integrity)\u001b[0m\n\u001b[1;32m     78\u001b[0m         \u001b[0;31m# figure out the index, if necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m             \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     81\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m             \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36mextract_index\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    399\u001b[0m             \u001b[0mlengths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_lengths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    400\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 401\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"arrays must all be same length\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    402\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    403\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhave_dicts\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: arrays must all be same length"
     ]
    }
   ],
   "source": [
    "mytable = pd.DataFrame(dict(\n",
    "    col1=[1,2,3,4,5],\n",
    "    col2=['a','b','c']\n",
    "))\n",
    "display(mytable)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec31b41f-f5fa-4091-aeb4-e62afbb1f7f7",
   "metadata": {},
   "source": [
    "## Navigating a table\n",
    "We can select single or multiple columns of our dataframe by their names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "848436d7-744d-4268-bdf8-d2ff8ea1d79d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Schmidt\n",
       "1     Müller\n",
       "2    Nauroth\n",
       "3       Kreß\n",
       "Name: FamilyName, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_from_excel['FamilyName'] # select a single column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f89fe33f-3c4b-4183-8f07-870b0a508532",
   "metadata": {
    "tags": []
   },
   "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>FamilyName</th>\n",
       "      <th>GivenName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Schmidt</td>\n",
       "      <td>Tim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Müller</td>\n",
       "      <td>Sarah</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nauroth</td>\n",
       "      <td>Chris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kreß</td>\n",
       "      <td>Julia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  FamilyName GivenName\n",
       "0    Schmidt       Tim\n",
       "1     Müller     Sarah\n",
       "2    Nauroth     Chris\n",
       "3       Kreß     Julia"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_from_excel[['FamilyName','GivenName']] # select multiple columns (care to not forget the double brackets)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5a20be1-d9fd-4459-9ae9-54aafa4b5d06",
   "metadata": {},
   "source": [
    "When we select multiple columns the order of the column names defines the order the columns are displayed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b7c5e48d-7da3-4a9c-a4d1-dd04d5daa985",
   "metadata": {
    "tags": []
   },
   "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>GivenName</th>\n",
       "      <th>FamilyName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tim</td>\n",
       "      <td>Schmidt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>Müller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chris</td>\n",
       "      <td>Nauroth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Julia</td>\n",
       "      <td>Kreß</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  GivenName FamilyName\n",
       "0       Tim    Schmidt\n",
       "1     Sarah     Müller\n",
       "2     Chris    Nauroth\n",
       "3     Julia       Kreß"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_from_excel[['GivenName', 'FamilyName']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "189db077-a88d-4e23-9080-3e810fd4b70c",
   "metadata": {},
   "source": [
    "We can as well select a single or multiple rows of our dataframe. Therefore, we have to use `.loc`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d4bf533e-581c-4e92-8cbe-c64d025b9592",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FamilyName    Nauroth\n",
       "GivenName       Chris\n",
       "Age                32\n",
       "Size             1.83\n",
       "Weight             72\n",
       "Name: 2, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_from_excel.loc[2] # single row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ffb92b68-dc93-48af-a098-33a8c4b05f5c",
   "metadata": {
    "tags": []
   },
   "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>FamilyName</th>\n",
       "      <th>GivenName</th>\n",
       "      <th>Age</th>\n",
       "      <th>Size</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Müller</td>\n",
       "      <td>Sarah</td>\n",
       "      <td>57</td>\n",
       "      <td>1.56</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nauroth</td>\n",
       "      <td>Chris</td>\n",
       "      <td>32</td>\n",
       "      <td>1.83</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  FamilyName GivenName  Age  Size  Weight\n",
       "1     Müller     Sarah   57  1.56      52\n",
       "2    Nauroth     Chris   32  1.83      72"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_from_excel.loc[1:2] # multiple rows, mind that the upper und lower bounds are bouth included"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99da7f8a-fbf4-4fc7-b10d-95080c7896bc",
   "metadata": {},
   "source": [
    "It is important to use the actual indices of our table! If we modify the index to custom names we have to use the custom names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1a57623f-9c79-4537-a652-bfb8ed87efba",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Day1      0\n",
       "Day2      0\n",
       "Day3      1\n",
       "Day4      3\n",
       "Day5      1\n",
       "Day6      2\n",
       "Day7      4\n",
       "Day8      7\n",
       "Day9      8\n",
       "Day10     3\n",
       "Day11     3\n",
       "Day12     3\n",
       "Day13    10\n",
       "Day14     5\n",
       "Day15     7\n",
       "Day16     4\n",
       "Day17     7\n",
       "Day18     7\n",
       "Day19    12\n",
       "Day20    18\n",
       "Day21     6\n",
       "Day22    13\n",
       "Day23    11\n",
       "Day24    11\n",
       "Day25     7\n",
       "Day26     7\n",
       "Day27     4\n",
       "Day28     6\n",
       "Day29     8\n",
       "Day30     8\n",
       "Day31     4\n",
       "Day32     4\n",
       "Day33     5\n",
       "Day34     7\n",
       "Day35     3\n",
       "Day36     4\n",
       "Day37     2\n",
       "Day38     3\n",
       "Day39     0\n",
       "Day40     0\n",
       "Name: Patient1, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_index_and_headers.loc['Patient1']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8addc28a-9ff3-46be-b6d5-5f6cfed19fa9",
   "metadata": {},
   "source": [
    "We can use the row and columns selection together to select a specific subset of our table or a specific cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5ce027e9-7389-462b-89f5-9db0863c2e9c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Nauroth'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_from_excel.loc[2, 'FamilyName'] # specific cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6234cca7-12ef-47ed-abb8-5300e902d4eb",
   "metadata": {
    "tags": []
   },
   "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>FamilyName</th>\n",
       "      <th>GivenName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Müller</td>\n",
       "      <td>Sarah</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nauroth</td>\n",
       "      <td>Chris</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  FamilyName GivenName\n",
       "1     Müller     Sarah\n",
       "2    Nauroth     Chris"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_from_excel.loc[1:2, ['FamilyName', 'GivenName']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9a9e20c-dcec-4581-973c-978180fcbe60",
   "metadata": {},
   "source": [
    "## Modifying a table\n",
    "We can modify dataframes in many ways using pandas. I will just give two simple examples, for more take a look at the pandas documantation (https://pandas.pydata.org/docs/index.html) please.\n",
    "<br>The first example will be to add a new column. To add a new column we can simply tell the dataframe the name of the column and its content."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "25b1ac96-04ca-43cf-88f5-38cea0137967",
   "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>FamilyName</th>\n",
       "      <th>GivenName</th>\n",
       "      <th>Age</th>\n",
       "      <th>Size</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Schmidt</td>\n",
       "      <td>Tim</td>\n",
       "      <td>11</td>\n",
       "      <td>1.20</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Müller</td>\n",
       "      <td>Sarah</td>\n",
       "      <td>57</td>\n",
       "      <td>1.56</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nauroth</td>\n",
       "      <td>Chris</td>\n",
       "      <td>32</td>\n",
       "      <td>1.83</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kreß</td>\n",
       "      <td>Julia</td>\n",
       "      <td>28</td>\n",
       "      <td>1.70</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  FamilyName GivenName  Age  Size  Weight\n",
       "0    Schmidt       Tim   11  1.20      36\n",
       "1     Müller     Sarah   57  1.56      52\n",
       "2    Nauroth     Chris   32  1.83      72\n",
       "3       Kreß     Julia   28  1.70      66"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_from_excel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9ce1118e-d122-4448-bb36-e5970704861d",
   "metadata": {
    "tags": []
   },
   "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>FamilyName</th>\n",
       "      <th>GivenName</th>\n",
       "      <th>Age</th>\n",
       "      <th>Size</th>\n",
       "      <th>Weight</th>\n",
       "      <th>FaveColor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Schmidt</td>\n",
       "      <td>Tim</td>\n",
       "      <td>11</td>\n",
       "      <td>1.20</td>\n",
       "      <td>36</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Müller</td>\n",
       "      <td>Sarah</td>\n",
       "      <td>57</td>\n",
       "      <td>1.56</td>\n",
       "      <td>52</td>\n",
       "      <td>yellow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nauroth</td>\n",
       "      <td>Chris</td>\n",
       "      <td>32</td>\n",
       "      <td>1.83</td>\n",
       "      <td>72</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kreß</td>\n",
       "      <td>Julia</td>\n",
       "      <td>28</td>\n",
       "      <td>1.70</td>\n",
       "      <td>66</td>\n",
       "      <td>orange</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  FamilyName GivenName  Age  Size  Weight FaveColor\n",
       "0    Schmidt       Tim   11  1.20      36     green\n",
       "1     Müller     Sarah   57  1.56      52    yellow\n",
       "2    Nauroth     Chris   32  1.83      72      blue\n",
       "3       Kreß     Julia   28  1.70      66    orange"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_from_excel['FaveColor'] = ['green', 'yellow', 'blue', 'orange']\n",
    "display(df_from_excel)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9083959d-4ac6-4815-aa86-04030c8381d8",
   "metadata": {},
   "source": [
    "The second example is to combine two dataframes. We can use the `concat()` function to combine two dataframes in various ways.\n",
    "<br>Here we will just append. First we create a new dataframe with additional persons (just for this example), afterwards we combine the two dataframes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a0fef48a-bc7b-485a-a95b-41ed784efc64",
   "metadata": {
    "tags": []
   },
   "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>FamilyName</th>\n",
       "      <th>GivenName</th>\n",
       "      <th>Age</th>\n",
       "      <th>Size</th>\n",
       "      <th>Weight</th>\n",
       "      <th>FaveColor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Schmidt</td>\n",
       "      <td>Tim</td>\n",
       "      <td>11</td>\n",
       "      <td>1.20</td>\n",
       "      <td>36</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Müller</td>\n",
       "      <td>Sarah</td>\n",
       "      <td>57</td>\n",
       "      <td>1.56</td>\n",
       "      <td>52</td>\n",
       "      <td>yellow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nauroth</td>\n",
       "      <td>Chris</td>\n",
       "      <td>32</td>\n",
       "      <td>1.83</td>\n",
       "      <td>72</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kreß</td>\n",
       "      <td>Julia</td>\n",
       "      <td>28</td>\n",
       "      <td>1.70</td>\n",
       "      <td>66</td>\n",
       "      <td>orange</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Turner</td>\n",
       "      <td>Hanna</td>\n",
       "      <td>5</td>\n",
       "      <td>1.14</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bently</td>\n",
       "      <td>Joe</td>\n",
       "      <td>83</td>\n",
       "      <td>1.67</td>\n",
       "      <td>82</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  FamilyName GivenName  Age  Size  Weight FaveColor\n",
       "0    Schmidt       Tim   11  1.20      36     green\n",
       "1     Müller     Sarah   57  1.56      52    yellow\n",
       "2    Nauroth     Chris   32  1.83      72      blue\n",
       "3       Kreß     Julia   28  1.70      66    orange\n",
       "0     Turner     Hanna    5  1.14      21       NaN\n",
       "1     Bently       Joe   83  1.67      82       NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df = pd.DataFrame(dict(\n",
    "    FamilyName=['Turner','Bently'],\n",
    "    GivenName=['Hanna','Joe'],\n",
    "    Age=[5, 83],\n",
    "    Size=[1.14, 1.67],\n",
    "    Weight=[21, 82],\n",
    ")) # we do not add their fave color for a specific reason, just take a look at the table below\n",
    "\n",
    "combined_df = pd.concat([df_from_excel, new_df]) # the brackets are important\n",
    "display(combined_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffa145d9-f274-4a99-b0d7-2c20aedba5a5",
   "metadata": {},
   "source": [
    "We did not add the fave color to our new dataframe, because we did not need to. `concat()` can combine dataframes even if columns are missing in one or both of the dataframes.\n",
    "<br>Notice that the index is made from both dataframes. If the values of the indices do not have any special meaning you can reset them with the argument `ignore_index = True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "852cc70b-fdff-4ecb-ae31-128c1479089f",
   "metadata": {
    "tags": []
   },
   "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>FamilyName</th>\n",
       "      <th>GivenName</th>\n",
       "      <th>Age</th>\n",
       "      <th>Size</th>\n",
       "      <th>Weight</th>\n",
       "      <th>FaveColor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Schmidt</td>\n",
       "      <td>Tim</td>\n",
       "      <td>11</td>\n",
       "      <td>1.20</td>\n",
       "      <td>36</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Müller</td>\n",
       "      <td>Sarah</td>\n",
       "      <td>57</td>\n",
       "      <td>1.56</td>\n",
       "      <td>52</td>\n",
       "      <td>yellow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nauroth</td>\n",
       "      <td>Chris</td>\n",
       "      <td>32</td>\n",
       "      <td>1.83</td>\n",
       "      <td>72</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kreß</td>\n",
       "      <td>Julia</td>\n",
       "      <td>28</td>\n",
       "      <td>1.70</td>\n",
       "      <td>66</td>\n",
       "      <td>orange</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Turner</td>\n",
       "      <td>Hanna</td>\n",
       "      <td>5</td>\n",
       "      <td>1.14</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bently</td>\n",
       "      <td>Joe</td>\n",
       "      <td>83</td>\n",
       "      <td>1.67</td>\n",
       "      <td>82</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  FamilyName GivenName  Age  Size  Weight FaveColor\n",
       "0    Schmidt       Tim   11  1.20      36     green\n",
       "1     Müller     Sarah   57  1.56      52    yellow\n",
       "2    Nauroth     Chris   32  1.83      72      blue\n",
       "3       Kreß     Julia   28  1.70      66    orange\n",
       "4     Turner     Hanna    5  1.14      21       NaN\n",
       "5     Bently       Joe   83  1.67      82       NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined_df = pd.concat([df_from_excel, new_df], ignore_index=True)\n",
    "display(combined_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c028eaff-35e4-4a5f-bb33-068613545ec4",
   "metadata": {},
   "source": [
    "## Ploting from a table\n",
    "In th past we used matplotlib.pyplot to plot data from a numpy array. We can plot from a pandas `DataFrame` directly, without importing the package.\n",
    "<br>If we want to modify the plots or save then we do need matplotlib.pyplot again.\n",
    "<br>Let's plot the sympthoms of patient one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fe891886-4109-4352-aa1c-faa43d1a05c9",
   "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>Day1</th>\n",
       "      <th>Day2</th>\n",
       "      <th>Day3</th>\n",
       "      <th>Day4</th>\n",
       "      <th>Day5</th>\n",
       "      <th>Day6</th>\n",
       "      <th>Day7</th>\n",
       "      <th>Day8</th>\n",
       "      <th>Day9</th>\n",
       "      <th>Day10</th>\n",
       "      <th>...</th>\n",
       "      <th>Day31</th>\n",
       "      <th>Day32</th>\n",
       "      <th>Day33</th>\n",
       "      <th>Day34</th>\n",
       "      <th>Day35</th>\n",
       "      <th>Day36</th>\n",
       "      <th>Day37</th>\n",
       "      <th>Day38</th>\n",
       "      <th>Day39</th>\n",
       "      <th>Day40</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Patient1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Day1  Day2  Day3  Day4  Day5  Day6  Day7  Day8  Day9  Day10  ...  \\\n",
       "Patient1     0     0     1     3     1     2     4     7     8      3  ...   \n",
       "\n",
       "          Day31  Day32  Day33  Day34  Day35  Day36  Day37  Day38  Day39  Day40  \n",
       "Patient1      4      4      5      7      3      4      2      3      0      0  \n",
       "\n",
       "[1 rows x 40 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hT61qUXBUldfYzJ2wP0iTuhGr4hWPCL2gK+fHYxU3qRE9EBPgQqLs+Yg+R+qmCHbFnpypm8oQ+nSpG6fNgunZUMGlrcLSRYRe0BWl4mZz8+KIvq3eBncghGmNufOx6VkQAfX29CWOCrVVZrhsZt1TNyYDwZa075CM3VJeB0tPGp8bhXqbGczzn0qKzc9OXMb//eXZktxLUIcIvaArfSNeGFIqbhTaC0ypjPnm0GC3wmTM/WOrt4ulYn+QqSPXXjERfbqqm9I5WDIzPv/0KXzrpXNFv5egHhF6QVf6R71oq7ehyrw48m0t0J5gdDp7s1TqvYoh9JmoqVIi+vJsxiacKzOkboDSdMeeuuzFuTE/Jv1BRCVVVDGI0Au60jfiS7sRC8znzrV2rY75cjdLKbQ32HDRPYNwJKrpXqlMz4QWDQVPZj6iL8+mpycQhIFiaatUkq2Ki0137zAAIMrz+xpC+RGhF3QjGI7i/Lg/7UYsUHjuPJ+Ivq3ehnCUMTylj12xJ5A9ordbY59gyjV8xB0Ioq7aDEMaD34lnTNZ5IiemdF9fBim+Bom/XNFvZ+gHhF6QTdeH/cjHOUF1gepaHWxjEYZ43lE9G31dgD6Vd7kSt04yjxO0B0Ipc3PA/MlocVO3Zwc9uL1cT/u3LEaADDhk9r9SkGEXtCNvvhUqXQVNwptDXZNXvHuQBDhKKuP6Bv09aXPJfTVZiMMVM6qm2DmGn+rCSYDFb2Wvvv4JRgNhHde0wYAmPCL0FcKIvSCbvTHK242NNkzXtNWX42Lnvxz52MJ+4PsXbEKq2urYDbqY1ccjTKmZ7MLPRHBbjXBO1seoZ/0Z47oiajo3bHMjO7eYdywsQGb4qk7EfrKQYRe0I2+ER/WN9jTVtwotNXbENGQOx+dzj4rNhWjgdDq0sfczDsXBnPmZimFcg4fiUX0mfsLXDZzUTdjX700jfMTAezvaEn8wpnwSY6+Usgp9ET0HSIaJaITScfqiehZIuqP/+nK8Nq9RHSaiM4Q0Uf1XLhQefSNerE5w0asgpI7zzelkrA/cKgTeiBWYjlQgFumQi5DMwW71QR/sHx19OlKKxVixmbFi7C7jw/DaCDcuX01zEYDnDYzJiWirxjURPTfA7A35dhHATzPzJsBPB9/vgAiMgL4OoC7AGwDcD8RbStotULFMheOYGAikHUjFtBuT5CwP1AZ0QPxpikdcvS5vOgV7FZTWapuZkMRzIaiabtiFZw2c+LvoTdK2ubGTY2JNdTbLbIZW0GkH4CZBDO/SETrUw7fDeCW+ON/BvALAB9JueYaAGeY+RwAENG/xV/3mvblCsUgFInCSJS2NE8t58b8iEQ5bUdsMqtrq2AxGvIW+jHvHBxWE2yWnD+yCdobbJieDaNvxJuoikmlttqc8ZxCLudKhZoypW7mDc2yR/RHhzxFuf+Ji9MYnAzgvW/alDjWYLdgQsorKwb1/2sWsoqZhwGAmYeJqDnNNWsBDCU9vwDgWo33E4oEM+POL7+IAx0t+MAdV2p+H6XiJldEbzQQ1rmq8x5AMuqdVV1aqXBFYyxNdMeXXsx4TV21Gb/9+G1Z9xUSQp9FSIFYLb2SYiolSu69PluO3m6BJxACM2e0cdDKk8cvwWQg3LF9VeJYg92Ks2M+Xe8jaEer0Ksh3U9Txp5oInoAwAMA0NbWVqw1CSkMTc7g3JgfL5weK0jo+0d8MBooa8WNghZ7glEVIwRTuXlLE/7hvl2YDaVPp/SP+PDtl1/HiYtT2LO+PuP7qI3oY6mb0kf0niwWxQoumxnBSBSBYCTRxasHzIyDx2Npm+T71zss+N15Sd1UClq/4yNE1BKP5lsAjKa55gKA1qTn6wBcyvSGzPwwgIcBYM+ePWKSUSJ6Bt0AgJPD05gJRhZMhcqH/lEv2htssJpyv769wYYj8fuqZdw7h61ravN6jdlowN271mZ+T98cvv3y6+gZdKsS+kyDwRUcZdqMTfjcpJkXq6BUwrgDQV2F/vjFKQxNzuAvbt284Hij3YLJQBCRKMNYQEpQ0Aet5ZVPAPjj+OM/BvAfaa75HYDNRHQFEVkA3Bd/nVBBKEIfjjJ6L3g0v0//iA9bsjRKJdNWH8ud51PXPepVb3+glkaHFW31NvQMeLJe55kJwmI0oMqc/b+LPZ6jL/U4wWzOlQrOIvnddPcOw2wk3Llt9YLj9XYLmEtjpCbkRk155SMAfg3gSiK6QETvAfA5ALcTUT+A2+PPQURriOggADBzGMB7ATwN4CSAR5n51eL8NQStHB5wY2tLLFI+nGeUrTAbiuD8RGaPm1Ra8xz1FwiG4ZsL5526UUNXmxOHB91ZxXl6JoTaLBbFCg6rCaEIYy6sj5GaWjxqNmOLYFXMzHiydxg3bWpctH/REC+DlaapykBN1c39GU7dlubaSwD2JT0/COCg5tUJRSUQDOPUZS/+7I0bMReK5IxsM3FuzI8oI6NrZSrtSfYEneucOa8fUzkrVgtXt7vwk6OXcME9k/gFlErM/iB3uiPZ7ybb5q7euAMh2CzGrGmzhIOljkJ/7MIULnpm8Je3b1l0rsGuNE0FgVWLTgslRjpjVzDHhqYQiTK62p3Y3ebCkRyRbSb6R9VV3Ci0uvKL6NXOitXC7rZYr19Plk8zuXxuFOzW8njSx5qlsu8fzHvS65e66e69BLORcPu2xUo+H9FLiWUlIEK/glHEbXerC13tTkz4g5q8YfpGvDAZKFHOmAu71YRGh0W1PYHaWbFauGp1DWwWI44MejJeMzUTylrRouBIWBWXdkPW7c9saKbgrNY3oleapG7e3JT2l6Ay7lG6YysDEfoVTM+AGxua7HDZLbi6PRbZHh7IP0/fN+LD+kY7LCb1P05t9TbVNghjRRR6k9GAznV1Wf/eeUf0Ja68yWZRrGAyGlBTZdItoj8y5MGlqVns72xJe95lM4MIGJfu2IpAhH6Fwsw4MuRBVzx1sbm5Bg6rKWsKIxP9I17VG7EK+cx0HfXOwmSgnGKmlavbXYny0nTkGjqioOTofSV2sMxmUZyMnn433b3DsBgNeHOatA0Q+8XirDbL8JEKQYR+hXJ+IoBJfzAh9EYDYVerM+8N2dlQBAOTgawe9Oloa7BjeGoGQRUVKmPeOTQ6rAVZNGSjq82Vsbw0EmV4Z8OozUfoS526URHRA3EHSx0i+miU8dTxYdy8pSnt6EKFBodV/G4qBBH6FUpPPFWhpGyAWKnhqcvTeQnVmVEfmNVvxCq01dsQZeCiZybntVq6YvNhfkPWs+icd1ZdVyyQvBlbOqGPxL3ysxmaKcRsEAoXXiVtcyBD2kah3m6R8soKQYR+hXJ40I0aqwmbk0zIutpdiDLQm4f51e/OTwIAtufZtdqWRy19PrNitVBvt2BDoz1tnn6+K1a90Jcyop+aCYEZWS2KFfRK3fz67DgA4Nat6Syu5ml0WMSTvkIQoV+h9Ay4savNuSAdsrs1d6lhKgePD+Oq1TVYr7LiRqE9D7visTxmxWolU3mpWp8boDypGzVdsQpOmxkeHTpjBycDaK6xZk3bALFfoFJ1UxmI0K9AvLMh9I14EykLhTqbGZuaHWlTGOm4PDWL3513Y39H9o/w6WhyWGE1GTA4kd3FMhJlTPiKG9EDyFhemvCiVxExGw2EarOxpKkbNV2xCi6bBd65sKp9kWwMTAQSn8iy0WC3wjMTyntspKA/IvQrkGNDU4jywvy8QlebEz0qG6cOHh8GAOzLkatNh8FAqlwsJ/xziHJxmqWSUTalU9M3+UT0QOmHjyjeNWo3Y4GYd08hDE0GEgNkstHgiPndFHsouZAbEfoViJKa2dXqXHTu6nYXPIEQzo3n9ovvPj6MrS212NiUX2mlQnu9DYOT2TdjlVmxaoeCa2XLqvTlpfkKvcNa2og+v9RN4d2xc+EIhqdnVUf0gDRNVQIi9CuQnkE3Njc70oqXEtn25GicuuSZweEBd87Ki2y01tswOOHP+ulhzFc8+4NkMpWXaonoS5u6iW8WZ7EoVkhYFRcgvBfcM2CGKqGvT/jdyIZsuRGhX2FEo4wjg560aRsA2NjkQG2VKWeePpG20ZCfV2hvsMEfjGSN+Mami9cVm4pSXpos1NMzIVhNBtUmZQ6rCd4SCv1kIAiTgVCjwmM+YVVcQESvpNraVaRuGh1xoZeIvuyI0K8wzo37MDUTSkTuqRgMhF1trpwRfffxYWxrqVXtb5MONSWWpYroAWB3vLz0WFJ5qVr7AwVHySP6WFesmvGASq19IbX0yrD1TE6fyUhEXzmI0K8wlNREV7sz4zVXt7nQN+rF9Gz6yO+CO4Ajg56MPidqUSP0o9OzqK0ylcT2tytNeala+wOFUqdu3H51hmtAslVxYRF9tdmIJkfuX7xOmwVEkqOvBEToVxg9g27UVZuxoTHzBmpXuxOcEtkm89TxywCgqawymcQAkizmZmO+OTTXFncjViFdeWm+EX3Jq24CQVXNUgBQbTbCYjIUFtFPxkor1XyCMBoI9TYLxkXoy44I/QqjZ9CN3SmNUqnsanWCKLOTZffxYexYW5t3k1QqVWYjVtVaMZA1op9TFT3qRWp5af6pm1LX0auP6Iko7ndTWOpGTdpGod5uwaT43ZQd/aYECxXP1EwIfSM+HOhck/W6mioztjTXpN2QHZoM4OiQBx/Ze5Uua2qrt6G7dxiH4lYKqVz0zOCuHYV9csiHrjYXHj10AefG/djY5MDUTAhXtaj38XFYzZgJRRCORGEyFj+OcgeCactkMxGzQdCWumFmDE4GcOOmRtWvaXBYZPhIBSBCv4I4Gk/FZNqITaar3YUney8hGuUF0f9TJ2LVNoWmbRQefONGPHHsUsbzu1qd+P+ubdflXmpQqpF6BtzY2OTAdN6pm9hegj8YQV11cYWemWMRvYrSSgWXTbux2bgviJlQRFXFjUKD3YqTl6c13U/QDxH6FUTPgBsGAna21uW8tqvNiUdeGcTZMd+CWbDdvcPoXFenqjNSDbdtXYXbtlbOUNHk8tJ7dq+Fdy6cd9UNEHOwzOd1WggEIwhGonn59LvsZpy+7NV0v8HJWBOdmhp6hQaHRayKKwDJ0a8gegbd2LKqBjU5zKiAWEQPLMzTD00GcOzClG7RfCWSXF46HR8gku9mLFAaq+L5rlj163PaLJo7Y5XqqHxz9FMzIYTE76asaBZ6IrqSiI4mfU0T0ftTrrmFiKaSrvlkwSsWNBGNMo4OehICnosNjXY4beYFpYbdOjRJLQWU8tKL7pg9Q14RfVVM6EvRNJXois0noreZ4ZkJaRoCPzARABGwzlWt+jXKkPBCunGFwtGcumHm0wB2AQARGQFcBPB4mktfYuYDWu8j6EP/qA/euTCuVpGfB2IVGl1trgUbst29w9jZ6swroluKKOWlL/aPAVDnDKngKEtEn4/QW+LDSvJPLQ1OBrC6tiqvnoYG+3x3bKnKZIXF6JW6uQ3AWWYe0On9BJ1RInO1ET0Qy9OfGfXBEwhiYMKP4xencGCZR/PAfHnpz0+NAsgzdWMppdArzpX5pW4Abd2xQ5Pq7ImTSQi95OnLil5Cfx+ARzKcu56IjhHRU0S0PdMbENEDRHSIiA6NjY3ptCxB4fCAG/V2C9bnsYmqVOccGfIk0jZ3dawuyvoqCaW89Ej8l6OWzdhSNE0p6ZB8UzeAtu5YtT70yTQk/G6kxLKcFCz0RGQB8FYAj6U53QOgnZl3AvgqgJ9keh9mfpiZ9zDznqampkKXJaTQM+jG7lanqo5GhZ2tThgIODLgRnfvMHa1OrHOtbzTNgrKWEUAqgaDKyTKK0uYuskntaT8Usi3aWomGMGod05DRB/L0UtEX170iOjvAtDDzCOpJ5h5mpl98ccHAZiJSH23haALnkAQ58b8eaVtgFgFyVWra/HT3mG8emm6IEvipUZXmzPxWMtmbCnGCXoCIdRYTTDn0ZiVGD6Sp9BfcMcqbvItq62rNsNoIPG7KTN6CP39yJC2IaLVFA8hieia+P0mdLinkAdH4huqahqlUulqd+L1+BCSu1ZAfl5B+aVYbTbCalK/+Wg1GWE2UkmE3h0I5tUsBSR70ueXuhmI+xHlG9EbDASXTbpjy01BQk9ENgC3A/hx0rEHiejB+NN7AZwgomMAvgLgPtZS1yUUxOEBN4wGUtUolYryy6GrzYm1TvVldUsdpbxUS9OTHg6WoUgUlzzZp2+5AyHU55GfB2JpKAPlH9ErNfT5Cj0Q25CV1E15KagzlpkDABpSjn0z6fHXAHytkHsIhTEbiuDgiWFsX1MLmyX/b/cb1tfDQMDdu9YWYXWVCxHhuisaMOKdzfu1doup4Ij+n391Hp976hQe//Mb0bEu/S9oTyCYV2klEHOUrKs2570ZOzgZgMNqSnjM50ODwyKpmzIjFgjLnC8914dzY37887uv0fT61nobnvnLNxY0YGSp8vdv70RQQ0enw2qCb7Ywof/12QmEo4wPPXYMT/zFjWnTR+5AEBs0fF9cNgsmNUT0rSrtiVOpt1vw6iXxuyknYoGwjOkZdONbL57DfW9oxRu3aK9k2tTsgDGLrfFypc5m1jTZylFlgj+oXeiZGT2DbmxssuP0iBdfeb4/7XWePIaOJOO0mTWlbtrqtaXuGh1WmTJVZkTolymzoQg+9NgxrK6twif2by33clYUhQ4feX3cD3cghP/2exvwjqvX4Ru/OLtoCEwoEoV3Lpx36gaIWxXnsRkbjcbsidsbtH2qq7dbMD0bRjAsfjflQoR+mfLFZ07j3Jgfn793pyoTM0E/Ch0+othOdLW78NcHtmFVbRU++NgxzIbmf3koPjeuPKtuAMXYTH1EP+qdQzAc1Wx9oeT1Cxl4IhSGCP0y5ND5SXz75dfxzmvbcNNmaVsoNXZLYVU3PYNu1FSZsKnJgbpqMz739k6cGfXhy8/Np3A8gfy7YhViU6bUR/SFVNwAQGO8O3Zc0jdlQ4R+mTETjODDP+zFWmc1PrZPUjblwFFV2GZsz4Abu1rnxz2+cUsT7r+mFQ+/eDbhWaTF50bBZbdgJhRZ8AkhGwMTsT6Kds0RfWyfQypvyocI/TLjC0+fxuvjfnz+3s6E74pQWhzW2GaslpYR72wIp0e8iUlXCh/ftxUtddX4UDyFo4imlhy9M9Edqy6qH5oMwEDAGo19FAm/G6mlLxsi9MuI356bwHd/9TredX07btgoKZtyYbeaEGVgRmXEnMyxoSkwL+5irqky4+/f3olzY3588ZnTSakbDRF9nn43g5MBrHFWw2LSJhfJVsVCeRChXyYEgmF8+Ie9aHXZdBvcLWjDbtXud3N4wA0iYFeS147CTZsb8c5r2/Dtl1/HcydjFsqFRPRqhX5Agz1xMrVVZpgMJCWWZUSEfpnw+Z+dxuBkAF+4tzMhNEJ5cMQdLLXk6XsG3djSXIPaDJVSH9u3FWud1Xju5AgsRgNsFvU+PAquhCe9+tRNIUJvMBBcdumOLSci9MuAV16fxPd+dR5/cuN6XLuhIfcLhKLisMZE2p9nLX00yjgy6EZXuzPLe5vw+Xs7AcRKK7V2qgLqInr/XBjjvmDBw+Ab7BaM65Cj986G8KVn++Cd1Tb3dqUiQr8MeLL3EhxWE/7qTknZVAKKJ32+qZtz4z5Mz4axO4fL6A0bG/H+N2/Gm7eu0rS+ersFDqsJvUNTOa8ttLRSIeZ3U3jq5tNPvoZ/eL4fT7+6yBVdyIII/TJgYCKA9Y02VGv4GC/oj9a5sYcH4uMeVdhJv//NW/CZezryXxwAs9GA27etwtOvXUYoh5ePbkJvtxa8GfvCqVE8eugCgPl/K0EdIvTLgKHJANrrV57pWKWi7JHk63fTM+CB02bWZFSWL/s7WuAJhPCrs9nHQwzGfegL/fmqt1swWUDqZioQwkd/3Istqxy4bkN9YsyjoA4R+iVOJMq44J7R3J4u6E9NXOi9eW7GKuMeDSUwkPu9LY2osZrQ3Xsp63WDkwHUVplQp6GMM5lGhwXeuTDmwto8gD715GsY9wXxxXfswnUbGnB6xCt5+jwQoV/iXJ6eRTASLfijtaAfdg2pm6lACP2jPk1TwLRgNRlx+/ZVePrVkaxmY4OTgYI3YoHCumOfe20EP+q5gIdu2YiOdXXoanOBGTiaYvQmZEaEfomT+Gitw39GQR9sFiOI8hP6I0Px/Hyec30L4UBnC6ZmQvjPs+MZrxnUKS2otTvWEwjiY48fx1Wra/DeWzcDiPUYEMVSXYI6ROiXOEM6bZYJ+kFE8SlT6tMUPYMeGAjY2eos3sJSuGlTE2qqTOjuHU57PpYWDOiSFtTaHfu3T7wKtz+I//2OnYnO3NoqM7Y01yR8f4TciNAvcQYm/TAZCC11VeVeipCE3WqEb059DvnIoBtXrq4tqT+RxWTAndtX4+lXL6dN31yenkUowroEEQ2OWOomn+7Yp1+9jJ8cvYT33roJO9YuHKfY1e7EkUE3olEZQa0GEfolzuDkDNa6qmEyyreyknBYTaobpiJRxpFBD7rS2B4Um/0dLfDOhvHymbFF5xKulbrk6GMRvdoc/aQ/iE88fhzbWmrx0Js2LTq/u82F6dkwzo75Cl7bSqAgdSCi80R0nIiOEtGhNOeJiL5CRGeIqJeIugq5n7CYwQLb04Xi4LCqHxDeP+qFby5cso3YZG7c1IjaKhOeTJO+0TMtWFtlgtlIqlM3f/PEq5iaCeGLf7AT5jRBjOLuKekbdegRBr6JmXcx85405+4CsDn+9QCAb+hwPyGJwQm/CH0FYreqHz6ibCqmWhOXAiV98+yrI4tKHwcnA7qlBYkI9XaLqtTNwePD+OmxS/jvt27G1pbatNdsaLTDaTPLhqxKiv15/24A/8IxfgPASUQtRb7nimF6NgR3ICRCX4HY84joDw+4UW+3lK1yan9nC7xzYbzUt7D6ZmAioGtasMFuzZm6mQ1F8D9+cgIda+vw4C0bM15HRNjd6sRhiehVUeh3kAE8Q0SHieiBNOfXAhhKen4hfmwRRPQAER0iokNjY4vzhcJilNJKEfrKI5/UzZFBN7ranJoMyvTgxk2NqKs2o/v4wvRNoa6VqTQ4chub/bJvDBP+ID5855VpUzbJXN3uwplRH6byGIu4UilU6G9k5i7EUjQPEdHNKefT/eSm3SZn5oeZeQ8z72lqaipwWSuDRA5VaugrDofK1M2kP4hz4/6cRmbFxGw0YO/21Xj2tZEF4wUL9aFPpV6FVXF37zBcNjNu2JjbhVXZ01B6EITMFCT0zHwp/ucogMcBXJNyyQUArUnP1wHI3nMtqEYxnBL7g8rDrrLqRvFsKUd+Ppn9nS3wzYXxYl/s0/TUTAgendOCDXZr1hz9bCiC506OYO+OFlXpop2tThgoNmNXyI5moSciOxHVKI8B3AHgRMplTwB4V7z65joAU8ycvjtDyJuByQBcNnPGIRVC+XBYjQhGojm9XXoG3TAaCJ3r6rJeV2yu39gAl82Mg/H0TTEa8RocFviDmYeS/+L0KALBCPZ3qNvGs1tNuGp1LXoGPbqtcblSSES/CsDLRHQMwCsAupn5Z0T0IBE9GL/mIIBzAM4A+BaAPy9otcIChiYDaGsQ18pKZN7vJofQD3iwtaUGNkt5p4KZjQbs3TGfvhksQlowV3fsk73DqLdbcN2GetXv2dXuxNEhDyLSOJUVzULPzOeYeWf8azszfyZ+/JvM/M34Y2bmh5h5IzN3MPOiWntBO1JDX7mo8aQPR6I4dsGDq8uYn09mf8ca+IMR/LJvrChpwUTTVJoN2ZlgBM+fHMXeHavzqvLpanPBNxdG/6hXt3UuR6SdcokSjkRx0T2DdhH6isShYkD4qcteBIKRkhqZZeO6DfWot1vQ3TuMwSKkBRUbhPE0k6ZeOD2KmVAEB1SmbRSUDVkZRJIdEfolyvDULMJRfXxIBP1RY1WsbMSWoyM2HSZjrHnquZMj6Lvs1T0t2JAlou8+PoxGhwXXXKE+bQPE7Bka7BZpnMqBCP0SZWBCKm4qGbuKiL5n0INGhxXrXNWlWlZODnS2IBCM4NCAW/cgImFVnBLRB4Jh/FxD2gaIN061uWTiVA5E6JcoSg5VfOgrEzWpm8MDblzdXr5GqXRce0V9IvJuq9f3F5DDaoLFaFi0GfvCqTHMhCLY37FG0/t2tTtxbtyvaajJSqG8W/3LiIueGUSjXLIIe3AyAIvRgFW1Yk9ciTiqYv+1fnV2AukKQpTKlnde21bilWXHFK+++cFvB3WfQ0xEaHBYFg0f6T5+CY0Oa95pGwVlM/vIoBu3bV1V0BrPjflQbTGipa5yPmXpgQi9Tnzg34/CHQjimb98Y0nuNzjpxzpXNYwlmC8q5I/LZkaV2YB//e0g/vW3gxmvu2FjYwlXpY63da3FI68MYtua9IZihZDaHeufC+Pnp0bxB3taNf8sd65zwmQg9BQo9CPTs/j9r/8nrlpdi0cfvF7z+1QiIvQ6wMx4bXga3tkw+ka82LKqpuj31GuWp1AcbBYT/vMjt8KdxYfFZjFijbPyIser2+tx9G/uKEojXoNjYXfsz0+NYjYUVd0klY5qixFbW2oL2pBlZnz8x8cxPRvGK+cncXlqFquX0TAfydHrwMj0HLyzsVxsOl/vYjA4ITX0lU6Dw4pNzY6MX5Uo8grF6rZusFsW5Oi7e4fRXGPFnvXa0jYKV7e7cHTIg3Ak86DzbPyo5yKePzWKP7wulko7eHx5NfCL0OtA30isWSM2f/MSmIvbpecJBDE9GxahF5YcDfb5HL1vLowXTo9iX0dLwSnI3W1OzIQiOHU5/8ap4akZ/N1PX8U16+vxqbfuwNaW2kVOnksdEXodUIT+PTddgbNjfpweKW6X3qAMBBeWKPUOC2ZCEQSCYTx/cgRz4Sj2dxY+oqIraUM2H5gZH/3RcYQjjM/f2wmDgbC/YzUOD7hxyTNT8LoqBRF6Hegf8aHebsE7r22HgWIfR4uJUkMvOXphqdFoV4aEB9HdO4xVtVZdLCDWuarRXGPNu0P20UND+GXfGD5611VY3xirMtoX3y946sTlgtdVKYjQ60DfqBebmx1oqrHiug0N6O4dLmr6RiJ6Yami+N0MTQbwi74x3LWjBQYdKseICF1trrycLC96ZvDpJ0/iug31+KPr2hPHNzQ5sK2lFt29y8dRXYS+QJgZZ0Z8iUqb/Z0tODfux8nh4qVvhiYDaHRYy+54KAj5onTHPnpoCMFwFAd0SNsodLU7MTgZwJg391xaZsZHftiLKDO+cO/ORb9s9ne2oGfQg4vLJH0jQl8gw1Oz8M6FsWWVAwCwd/tqGKi4u/YDEwHduxYFoRQ0xFM33ceHsbq2SlefH+W9elTk6f/1lUG8fGYcH9+3NW2To1Lu+dQy2ZQVoS8QZSN2czyib3BYccPGRnQfL176ZnAygHbxoReWIEpEH4ow9nXok7ZR2LG2DmYj5RT6ockAPtt9EjduasjYmby+0Y4da2tLVi5dbEToC6R/xAcAC5qk9ne24PVxP14bntb9fsFwFMNTM2JmJixJbBYjrKaY7OhRbZNMldmI7WvqcCRL41Q0yvjIj3oBAH//9s6sPkP7O9bg6JAnMW1rKSNJ3gLpG/Gi0WFJbDIBwJ3bV+Ovf3IC3b3D2L5G3xFxFz0ziLJsxApLEyJKmKbtbnXq/v5dbS58/zcDeNd3Xkl73j8XxuEBN/7X2zqwzpX9/9D+jhb8/c9O4akTw3jg5o057z3hm8OXnuvDn968seICMYnoC6Rv1IfNzQstD+rtFtywsaEo6RtxrRSWOn94fTs+cMeVuqZtFO7ZvRad6+owPRNK+xWJMt5z0xW47w2tOd+rrcGGznV1qsqlmRl//ZMT+P5vBvGBR48iWmGjDSWiL4BYxY0X9169btG5/R0t+OiPj+PVS9PYsVa/qH5wwg9AInph6fLnt2wq2nt3rKvDD//sBt3eb19HCz731CkMTQayRuk/7R3GUycu4/oNDfj1uQl891fn8Z6brtBtHYUiEX0BXPTMwB+MJDZik7lz+2oYDaT7Zs7gZABWkwHNNVZd31cQhMUo1TfZquhGvbP45H+cwK5WJ/7/91yDW69qxud/dgrnxnylWmZONAs9EbUS0QtEdJKIXiWi96W55hYimiKio/GvTxa23Moi3UasgstuwY2bGtF9XF/vG2UgeCUNqxCE5UprvQ0719Vl9L5hZnzi8RMIBCP43+/YCZPRgP/1tg5YTQZ8+Ie9iFRICqeQiD4M4IPMvBXAdQAeIqJtaa57iZl3xb8+VcD9Kg6ltFKpoU/lQEcLhiZncPzilG73HBDXSkEoKfs7W9B7YQqDE4urb/7j6CU8+9oIPnzHldjUHNOBVbVV+Lu7t+PwgBvfefn1Ui83LZqFnpmHmbkn/tgL4CSAtXotbCnQN+JDU40VTpsl7fk7tq+CyUC6ed8wM4bEh14QSorifZMa1Y9Mz+JvnngVV7e78O6UfPzv71qL27etwheeOY0zo+VP4eiSoyei9QB2A/htmtPXE9ExInqKiLZneY8HiOgQER0aGxvTY1lFp3/UmzGaBwCnzYKbNjfiSZ28byb9QfiDEYnoBaGErHPZsKvVie7j8943yqCS2VAEX7i3c5HNMhHhM/fsgM1ixAcfO6bZJ18vChZ6InIA+BGA9zNzaodQD4B2Zt4J4KsAfpLpfZj5YWbew8x7mpqaCl1W0YlGGf0ji0srU9nf0YKLnhkcu1B4+mZAzMwEoSwc6GzBiYvTOD8eq3pTBpX81d6rsKEpfbDXXFOFT929A8eGPPjWS+VN4RQk9ERkRkzkf8DMP049z8zTzOyLPz4IwExElTckUwMXPTOYCUVyjg28Y9tqmI2ki/fNkNTQC0JZuCspfZM8qORPblif9XVv6WzB3u2r8aVn+9Bf5DkV2Sik6oYA/BOAk8z8fzJcszp+HYjomvj9JrTes5LoH82+EatQZzPjpk2NulgXKz70uTr6BEHQl7XOauxuc+LJ3uFFg0qyQUT4n/fsgKPKVNYUTiER/Y0A/gjArUnlk/uI6EEiejB+zb0AThDRMQBfAXAfF3vOXgHk803oi5dWpquhT2V/5xpc9Mzgpf5xjHnn0n5NZRkirTA4GcCqWiuqzEbV6xQEQR/2d7Tg5PA0ftk3ho/svTIxqCQXjQ4rPn33DvRemML/ffFckVeZHs2dscz8MoCsv86Y+WsAvqb1HqXkhVOj+PMf9OCZv7xZlU9F34gXq2qtqKvOPUT59m2rYDEaMvpvAICBgM/c04H7r0nvpgfEXSvrxbVSEMrBvo4WfObgSVyzvh7vun59Xq/d39mCgyda8OXn+nDrVc3Y2lJbnEVmQCwQ4jx6aAgzoQieOHYJD70pd4t2f9KwkVzUVZvxvXe/AWfH/Bmv+enRS/jUT1/DjRsbM5ZPDk4EcOOmZbHFIQhLjjXOanz/Pddia0utJp+eT9+9A789N4EPPXYMP3noRpiNpTMmEKFHzNHu56dGAcTmveYS+miUcWbUlzX6TuWGjY24YWNmkb7tqmbc+aUX8eEfHsMj/+26RT9Is6EILk/PykasIJSRQgKtersF//P3O/Dg9w/jH184i/e9ebOOK8uOeN0AeP7UKObCUdy5fRVeG57O6VFxwa1U3GTfiM2HNc5q/I8D2/Db1yfxL78+n/aegJRWCsJSZu+O1bh71xp89ef9ePWSfh3zuRChB9DdewnNNVZ88i2xfq5cpZCpU6X04h171uGWK5vwuZ+dStTrKgxOxp5Xms+1IAj58bdv2Q6nzYIPPdaLYLg0VTgrXuh9c2G8cHoM+zpasNZZjT3trpyOk32jitDrF9EDsVKsz72tE2ajAR967NgCQyTFZ0NSN4KwtHHZLfjsPTtwcngaX3vhTEnuueKF/vmTIwiGo4mxZvs6WnDqshdns6Rv+kd8aKmrQm1V7oqbfFldV4W/fct2HBpw47v/Od9NNzAZgM1iTEznEQRh6XLH9tV42+61+PoLZ3BCR9PDTKx4oX+ydxiraq24Oj5BXjEwOpglqu8b8Sac6orB27rW4s1bm/GFp08n9guGxJ5YEJYVf/OW7WiwW/DBR49hLhwp6r1WtNB7Z0P4Zd/Ygmn0q+uq8Ib1roz+05F4xY3a0kotEBE+e08HqszGRApH8aEXBGF5UGcz43Nv78DpES++8nx/Ue+1ooX++ZOjCIajOJAyjX5/PH1zZnSxN8XQZABz4aiuFTfpaK6twt+9dTt6Bj349kvnROgFYRly61Wr8I6r1+GbvzyHY0Oeot1nRQv9k73DaKmrwu5W14Ljd3W0gAjo7r286DXFqrhJx9271uCObavwhadPYzYUFR96QViG/PWBbWhyWPGhx45hNlScFM6KFfrp2RBeTEnbKKyqrcIb1tcv8J9W6I8PEdhcxBy9QszTugM1VbG+NonoBWH5UVcdS+H0j/rw5eeKk8JZsUL/3GsjCEbmq21SOdDZgr4RXyKCV+gb8WJNXRVqilBxk46mGis+e08H6qrN2FZifwxBEErDLVc24743tOKJoxcRCIZ1f/8VK/TdvcMx69FWZ9rze3esjqdvFm7K9o34SpK2SeaujhYc/eTtaK6tKul9BUEoHZ/YvxVPve9m2Cz6O9OsSKGfmgnhxf4x7OtYnbFcsbmmCtdeUb+gSzYSZZwd8xV9IzYdUlYpCMubmioz6mzFyRSsSKF/9rURhCKM/Z1rsl63v3MN+kfn0zcDE34Ew9GSR/SCIAiFsCKFvrv3EtY6q7FzXV3W6/ZuXw0DIWGJoAwbKWYNvSAIgt6sOKGfCoTwUv84DnS25EyHNNVYcd2GBnT3XgIzJ2Y+lqLiRhAEQS9WnNA//dplhKOcsDrIxb6OFpwd8+P0iBd9oz6sdVbDbhUbf0EQlg4rTugPHh/GOlc1OnOkbRT27oilb7p7h9E/4i3LRqwgCEIhrCih9wSCeLl/HPtVpG0UGh1WXL+xAT89dgnnxvySnxcEYclRkNAT0V4iOk1EZ4joo2nOExF9JX6+l4i6CrlfoTzz6gjCUcaBjuzVNqns71iD8xMBBCNScSMIwtJDs9ATkRHA1wHcBWAbgPuJaFvKZXcB2Bz/egDAN7TeTw+ePD6MtnobdqzNr8P0zu2rYIzbJEjqRhCEpUYhu4rXADjDzOcAgIj+DcDdAF5LuuZuAP/CzAzgN0TkJKIWZs4+wkkjb/nqy1lNgc6O+fCnb9yYd/NRg8OKGzY24KX+8aL60AuCIBSDQoR+LYChpOcXAFyr4pq1ABYJPRE9gFjUj7a2Nk0L2thkRzCSeQbj9jW1+MPr2jW99wdu34Lf29xYlPZkQRCEYlKIaqULi1nDNbGDzA8DeBgA9uzZk/aaXHz5vt1aXqaK3W0u7G5z5b5QEAShwihkM/YCgNak5+sApPr6qrlGEARBKCKFCP3vAGwmoiuIyALgPgBPpFzzBIB3xatvrgMwVaz8vCAIgpAezakbZg4T0XsBPA3ACOA7zPwqET0YP/9NAAcB7ANwBkAAwJ8UvmRBEAQhHwraWWTmg4iJefKxbyY9ZgAPFXIPQRAEoTBWVGesIAjCSkSEXhAEYZkjQi8IgrDMEaEXBEFY5lBsv7SyIKIxAAMaX94IYFzH5eiJrE0bsjZtyNq0sVTX1s7MTelOVKTQFwIRHWLmPeVeRzpkbdqQtWlD1qaN5bg2Sd0IgiAsc0ToBUEQljnLUegfLvcCsiBr04asTRuyNm0su7Utuxy9IAiCsJDlGNELgiAISYjQC4IgLHOWjdDnGlReLoiolYheIKKTRPQqEb2vjGupIqJXiOhYfC1/l+aad8YHufcS0a+IaGcJ12ckoiNE9GSac1cR0a+JaI6IPlSqNWUiPhbzh0R0Kv69vb5M67iSiI4mfU0T0ftTrrmFiKaSrvlkCdeX7Xt6d/zn7CgRHSKim0q1LhVrK9u/WTFYFnPxkgaV347YsJPfEdETzPxa9leWhDCADzJzDxHVADhMRM+WaW1zAG5lZh8RmQG8TERPMfNvkq55HcAbmdlNRHchtvmTOiKyWLwPwEkA6aa3TwL47wB+v0RrycU/APgZM98bn8dgK8cimPk0gF1A4v/BRQCPp7n0JWY+UMKlKWT7nj4P4AlmZiLqBPAogKsqZG1A+f7NdGe5RPSJQeXMHASgDCovO8w8zMw98cdexH6w1pZpLczMvvhTc/yLU675FTO7409/g9hUsKJDROsA7Afw7XTnmXmUmX8HIFSK9WSDiGoB3AzgnwCAmYPM7CnromLcBuAsM2vtKtcVFd9TH89Xg9iRYcxoOda23FguQp9pCHlFQUTrAewG8NsyrsFIREcBjAJ4lpmzreU9AJ4qycKALwP4KwCZp7tXDhsAjAH4bvyj/7eJyF7uRSE25e2RDOeuj6fsniKi7SVaz5eR43tKRPcQ0SkA3QDeXaJ1Aep+3srxb1YUlovQqx5CXi6IyAHgRwDez8zT5VoHM0eYeRdikfo1RLQj3XVE9CbEhP4jxV4TER0AMMrMh4t9L50wAegC8A1m3g3AD6Cs+0Lx9NFbATyW5nQPYj4oOwF8FcBPSrAeVd9TZn6cma9CLCX36WKvK4+1lfzfrJgsF6Gv6CHk8Xz4jwD8gJl/XO71AEA81fALAHtTz8Xzpd8GcDczT5RgOTcCeCsRnUcs7XYrEX2/BPfVygUAF5I+Df0QMeEvJ3cB6GHmkdQTzDytpOziU+HMRNRY5PXk9T1l5hcBbCzBulStrUz/ZkVjuQi9mkHlZYGICLFc7klm/j9lXksTETnjj6sBvBnAqZRr2gD8GMAfMXNfKdbFzB9j5nXMvB6x793PmfkPS3FvLTDzZQBDRHRl/NBtAMq98X8/MqRtiGh1/OcQRHQNYv/vi/oLXM33lIg2Ja2rC4Cl2OvKY20l/zcrJsui6ibToPIyL0vhRgB/BOB4PDcOAB+PRwmlpgXAP8erMwwAHmXmJ2nhQPdPAmgA8I/xn/NwuZz8ktdFRKsBHEKsQiIaLyHcVsY02F8A+EE8sDiHMg6+JyIbYhVnf5p0LPl7ei+APyOiMIAZAPclbYKWeq3J63o7gHcRUSi+rv9SrnWlWVvF/JvpgVggCIIgLHOWS+pGEARByIAIvSAIwjJHhF4QBGGZI0IvCIKwzBGhFwRBWOaI0AuCICxzROgFQRCWOf8P36/gsZBwNS0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_with_index_and_headers.loc['Patient1':'Patient1'])\n",
    "df.loc[2].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30dc1ba5-97be-43be-a3d9-5ca1e784b558",
   "metadata": {},
   "source": [
    "As you can see it worked, but the axes are not labeled and the x-axis has some strange values. The pandas plotting methods should be used mainly for data exploration. If you want to generate proper figures you need to modify then using matplotlib.pyplot."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfabca0a-98a7-46f0-8457-82f37d914fd1",
   "metadata": {},
   "source": [
    "## Saving a table\n",
    "The last step when handling dataframes might be to save them back to a file you can store somewhere or give your collegues.\n",
    "<br>To save da dataframe just call the `.to_csv()` method to save as a text file or the `.to_excel()` method to save as an excel file.\n",
    "<br>If your dataframe does not have an index of any meaning we do not save it by adding the argument: `index=False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1d26d4da-ec12-4875-abe4-31578a61df18",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_from_excel.to_csv('data/my_fancy_table.csv', index=False) # save to a .csv file, ignore the index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "47c2cdc1-7117-4cf4-8172-8ee3a77f019b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_from_excel.to_excel('data/my_fancy_table.xlsx', index=False) # save to an excel file, ignore the index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b83c3f76",
   "metadata": {},
   "source": [
    "# Converting a Pandas table to a Numpy array\n",
    "\n",
    "\n",
    "In order to apply data manipulation and evaluation that we learned using Numpy, we would like to convert the Pandas table to a Numpy array.\n",
    "\n",
    "Recall that our data looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "21de630e-23e0-4da4-b639-156f44571fb7",
   "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>Day1</th>\n",
       "      <th>Day2</th>\n",
       "      <th>Day3</th>\n",
       "      <th>Day4</th>\n",
       "      <th>Day5</th>\n",
       "      <th>Day6</th>\n",
       "      <th>Day7</th>\n",
       "      <th>Day8</th>\n",
       "      <th>Day9</th>\n",
       "      <th>Day10</th>\n",
       "      <th>...</th>\n",
       "      <th>Day31</th>\n",
       "      <th>Day32</th>\n",
       "      <th>Day33</th>\n",
       "      <th>Day34</th>\n",
       "      <th>Day35</th>\n",
       "      <th>Day36</th>\n",
       "      <th>Day37</th>\n",
       "      <th>Day38</th>\n",
       "      <th>Day39</th>\n",
       "      <th>Day40</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Patient1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patient2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patient3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patient4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patient5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Day1  Day2  Day3  Day4  Day5  Day6  Day7  Day8  Day9  Day10  ...  \\\n",
       "Patient1     0     0     1     3     1     2     4     7     8      3  ...   \n",
       "Patient2     0     1     2     1     2     1     3     2     2      6  ...   \n",
       "Patient3     0     1     1     3     3     2     6     2     5      9  ...   \n",
       "Patient4     0     0     2     0     4     2     2     1     6      7  ...   \n",
       "Patient5     0     1     1     3     3     1     3     5     2      4  ...   \n",
       "\n",
       "          Day31  Day32  Day33  Day34  Day35  Day36  Day37  Day38  Day39  Day40  \n",
       "Patient1      4      4      5      7      3      4      2      3      0      0  \n",
       "Patient2      3      5      4      4      5      5      1      1      0      1  \n",
       "Patient3     10      5      4      2      2      3      2      2      1      1  \n",
       "Patient4      3      5      6      3      3      4      2      3      2      1  \n",
       "Patient5      9      6      3      2      2      4      2      0      1      1  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_with_index_and_headers.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0ef87e0",
   "metadata": {},
   "source": [
    "Use the function `to_numpy()` on the data set to convert it to a numpy array.\n",
    "For this array, row and column names are being ignored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1c87d93e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 1 ... 3 0 0]\n",
      " [0 1 2 ... 1 0 1]\n",
      " [0 1 1 ... 2 1 1]\n",
      " ...\n",
      " [0 1 1 ... 1 1 1]\n",
      " [0 0 0 ... 0 2 0]\n",
      " [0 0 1 ... 1 1 0]]\n",
      "(60, 40)\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "new_array = df_with_index_and_headers.to_numpy()\n",
    "print(new_array)\n",
    "print(new_array.shape)\n",
    "print(type(new_array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74d60f82",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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