How to create a pandas table

import pandas as pd
# Create a sample DataFrame
data = {
    'Name': ['Anna', 'Ben', 'Charlie', 'Diana'],
    'Age': [23, 30, 35, 29],
    'Occupation': ['Engineer', 'Teacher', 'Doctor', 'Designer']
}
df = pd.DataFrame(data)
# Display the DataFrame as a table
print(df)Here, the pandas DataFrame is displayed as a simple table in the console. The output looks like this:

Name  Age    Occupation

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If you’re using **[Jupyter Notebook](t3://page?uid=2063)**, you don’t even need to use the `print()function. **Simply calling** the DataFrame will result in it being displayed in a tabular format.
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### `to_string()— DataFrames in full
The built-in pandas function `DataFrame.to_string()converts a **DataFrame into a [Python string](t3://page?uid=3826)**, ensuring that the DataFrame is shown in its entirety, no matter how large it is. Let’s take a look at how this option works using the example from above:
```python
# Show the entire DataFrame as a table
print(df.to_string())Here’s what the output looks like:

Name    Age    	Occupation

### `style` — Styling capabilities
With the `style` property, pandas also offers a built-in way to highlight and format DataFrames. In the following example, we’re going to highlight the maximum values of the dataset from above:
```python
# Display the DataFrame with the maximum values highlighted
df.style.highlight_max(axis=0)In a Jupyter Notebook, the DataFrame will be shown with the maximum values highlighted.
### `to_html()— Best for web frameworks
With `DataFrame.to_html(), you can represent your DataFrame as an HTML table with just one function call. This makes it easy to directly embed DataFrames in web applications. All you need to do is call the function on the DataFrame:
```python
# Display the DataFrame as an HTML table
html_table = df.to_html()
print(html_table)You’ll then receive HTML code that you can embed in websites:
```html
<table border="1" class="dataframe">
    <thead>
        <tr style="text-align: right;">
            <th></th>
            <th>Name</th>
            <th>Age</th>
            <th>Occupation</th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <th>0</th>
            <td>Anna</td>
            <td>23</td>
            <td>Engineer</td>
        </tr>
        <tr>
            <th>1</th>
            <td>Ben</td>
            <td>30</td>
            <td>Teacher</td>
        </tr>
        <tr>
            <th>2</th>
            <td>Charlie</td>
            <td>35</td>
            <td>Doctor</td>
        </tr>
        <tr>
            <th>3</th>
            <td>Diana</td>
            <td>29</td>
            <td>Designer</td>
        </tr>
    </tbody>
</table>

from tabulate import tabulate
# Display the DataFrame as a table in GitHub Markdown format
print(tabulate(df, headers='keys', tablefmt='github))Here’s the output: