Pandas to_html formatters: Customizing Table Data Display

The formatters parameter in Pandas to_html allows you to apply formatting functions to specific columns. This ensures that your data is accurately represented.

In this tutorial, you’ll learn how to customize your output HTML tables from to_html using formatters parameter.

 

 

Types of Formatters

The most commonly used formatters are functions and lambda functions.

Functions

You can pass regular Python functions as formatters. These functions should take a single value as input and return the formatted output.

This is useful when you have a specific formatting requirement that applies to an entire column.

import pandas as pd
data = {'A': [1, 2, 3], 'B': [4, 5, 6]}
df = pd.DataFrame(data)
def format_func(value):
    return f'${value:.2f}'
html_table = df.to_html(formatters={'A': format_func})
print(html_table)

Output:

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>A</th>
      <th>B</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>$1.00</td>
      <td>4</td>
    </tr>
    <tr>
      <th>1</th>
      <td>$2.00</td>
      <td>5</td>
    </tr>
    <tr>
      <th>2</th>
      <td>$3.00</td>
      <td>6</td>
    </tr>
  </tbody>
</table>

In this example, the format_func adds a dollar sign and formats the numbers in column ‘A’ to two decimal places.

Lambda Functions

Lambda functions or anonymous functions in Python allow you to format columns in one line.

html_table = df.to_html(formatters={'B': lambda x: f'<b>{x}</b>'},escape=False)
print(html_table)

Output:

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>A</th>
      <th>B</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1</td>
      <td><b>4</b></td>
    </tr>
    <tr>
      <th>1</th>
      <td>2</td>
      <td><b>5</b></td>
    </tr>
    <tr>
      <th>2</th>
      <td>3</td>
      <td><b>6</b></td>
    </tr>
  </tbody>
</table>

Here, the lambda function wraps the values in column ‘B’ in bold tags.

The escape=False parameter ensures that HTML tags in the DataFrame are correctly interpreted as HTML.

 

Formatting Numerical Data

Formatting numerical data effectively in an HTML table ensures clarity and readability, especially when dealing with a mix of floats, integers, and other numerical types.

Formatting Floats

You can use either a custom function or a lambda function to format floats.

import pandas as pd
data = {'Floats': [3.14159, 2.71828, 1.61803], 'Integers': [1, 2, 3]}
df = pd.DataFrame(data)
float_formatter = lambda x: f'{x:.2f}'
html_table = df.to_html(formatters={'Floats': float_formatter})
print(html_table)

Output:

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Floats</th>
      <th>Integers</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>3.14</td>
      <td>1</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2.72</td>
      <td>2</td>
    </tr>
    <tr>
      <th>2</th>
      <td>1.62</td>
      <td>3</td>
    </tr>
  </tbody>
</table>

In this example, the lambda function formats the ‘Floats’ column to two decimal places.

Handling Precision

Controlling precision is vital, especially in scientific or financial data, to convey the correct level of accuracy.

df['Precise Floats'] = [0.123456, 0.654321, 0.987654]
precise_formatter = lambda x: f'{x:.4f}'
html_table = df.to_html(formatters={'Precise Floats': precise_formatter})
print(html_table)

Output:

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Floats</th>
      <th>Integers</th>
      <th>Precise Floats</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>3.14159</td>
      <td>1</td>
      <td>0.1235</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2.71828</td>
      <td>2</td>
      <td>0.6543</td>
    </tr>
    <tr>
      <th>2</th>
      <td>1.61803</td>
      <td>3</td>
      <td>0.9877</td>
    </tr>
  </tbody>
</table>

In this case, the lambda function formats the ‘Precise Floats’ column to four decimal places.

 

Round Numbers

You can use formatters in to_html to apply rounding directly during the HTML conversion process.

import pandas as pd
data = {'Values': [3.142, 1.713, 2.987]}
df = pd.DataFrame(data)
round_formatter = lambda x: f'{x:.1f}'
html_table = df.to_html(formatters={'Values': round_formatter})
print(html_table)

Output:

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Values</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>3.1</td>
    </tr>
    <tr>
      <th>1</th>
      <td>1.7</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3.0</td>
    </tr>
  </tbody>
</table>

 

Formatting Date and Time

Pandas formatters parameter allows you to customize the presentation of date and time in your HTML tables.

This can range from simply changing the date format to adjusting the representation to fit regional preferences.

import pandas as pd
data = {'Dates': pd.to_datetime(['2023-01-01', '2023-06-15', '2023-12-31'])}
df = pd.DataFrame(data)
date_formatter = lambda x: x.strftime('%d-%m-%Y')
html_table = df.to_html(formatters={'Dates': date_formatter})
print(html_table)

Output:

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Dates</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>01-01-2023</td>
    </tr>
    <tr>
      <th>1</th>
      <td>15-06-2023</td>
    </tr>
    <tr>
      <th>2</th>
      <td>31-12-2023</td>
    </tr>
  </tbody>
</table>

In this example, the lambda function converts the date format to dd-mm-yyyy.

Time Formatting

Similar to dates, you can format time, such as displaying time in a 12-hour format instead of the 24-hour format.

import pandas as pd
data = {'Dates': pd.to_datetime(['2023-01-01', '2023-06-15', '2023-12-31'])}
df = pd.DataFrame(data)
data = {'Times': pd.to_datetime(['14:30', '09:15', '23:45']).time}
df['Times'] = data['Times']
time_formatter = lambda x: x.strftime('%I:%M %p')
html_table = df.to_html(formatters={'Times': time_formatter})
print(html_table)

Output:

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Dates</th>
      <th>Times</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>01-01-2023</td>
      <td>02:30 PM</td>
    </tr>
    <tr>
      <th>1</th>
      <td>15-06-2023</td>
      <td>09:15 AM</td>
    </tr>
    <tr>
      <th>2</th>
      <td>31-12-2023</td>
      <td>11:45 PM</td>
    </tr>
  </tbody>
</table>

Here, the time is formatted to a 12-hour clock with AM/PM notation.