5 Best Ways to Center Align Column Headers of a Pandas Dataframe

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💡 Problem Formulation: When displaying a pandas DataFrame, the column headers default to left alignment, which might not be visually appealing or meet the requirements for certain reports or presentations. Suppose you have a DataFrame with financial data and you want the headers centered over each column for better readability. You’re looking to transform the default header alignment from:

  name  |  revenue  |  expenses  
---------|-----------|----------
 Company |    10000  |    8000

to

  name   |  revenue  | expenses
---------|-----------|----------
 Company |    10000  |    8000

The purpose of this article is to show you different methods to achieve centered column headers easily.

Method 1: Using the Styler Object

Using the Styler object provided by pandas is an efficient way to centralize the header by applying CSS styling to the DataFrame. The set_table_styles function can be used to add CSS directives to alter the display of the headers.

Here’s an example:

import pandas as pd

df = pd.DataFrame({'name':['Company'], 'revenue':[10000], 'expenses':[8000]})
styles = [dict(selector="th", props=[("text-align", "center")])]
df.style.set_table_styles(styles)

This code example will modify the DataFrame’s header alignment to be center-aligned instead of the default left-aligned.

In this method, we’re taking advantage of pandas’ built-in styling functionality. set_table_styles accepts a list of style specifications, which include a CSS selector and properties to apply. The "th" selector targets the table headers, and the text-align property is set to center.

Method 2: Using HTML and the to_html Method

You can convert your DataFrame to HTML and include <style> tags to centralize text in the headers. The to_html method seamlessly converts a DataFrame into an HTML table that can be further styled with CSS.

Here’s an example:

df = pd.DataFrame({'name':['Company'], 'revenue':[10000], 'expenses':[8000]})
html = df.to_html(index=False)
html = f"<style>th {text-align: center;}</style>{html}"
print(html)

The output will be an HTML representation of the DataFrame with the column headers centered.

This approach manually constructs an HTML table, incorporating the to_html() method to get the basic table structure. Then, it uses string formatting to prepend a <style> element that contains CSS styles which apply to all <th> elements, ensuring headers are centered.

Method 3: Modifying pandas DataFrame Output Options

Pandas allows you to modify display options that can globally affect how DataFrames are rendered. The set_option function can be used to adjust the maximum display width for the column headers and align text to the center.

Here’s an example:

pd.set_option('display.colheader_justify', 'center')
df = pd.DataFrame({'name':['Company'], 'revenue':[10000], 'expenses':[8000]})
print(df)

With this code, pandas will adjust all future DataFrame displays so that the headers are centered.

The strength of this approach is its simplicity and its application to all DataFrame representations throughout the session. The display.colheader_justify option is changed to 'center', realigning the headers center globally. However, it’s important to remember that this setting persists until reset, affecting all DataFrames in the current Python session.

Method 4: Extending the Pandas Styler with Custom Functions

The pandas Styler object can be extended with custom functions, which allows developers to have finer control of DataFrame styling. You can create a function to apply center alignment to headers and then apply it using the apply method on the Styler.

Here’s an example:

def center_headers(styler):
    styles = [dict(selector="th", props=[("text-align", "center")])]
    return styler.set_table_styles(styles)

df = pd.DataFrame({'name':['Company'], 'revenue':[10000], 'expenses':[8000]})
df.style.pipe(center_headers)

The provided code will result in a DataFrame with headers that are center-aligned.

By defining a center_headers function and passing it to the Styler’s pipe method, we apply custom styling logic modularly. This method offers increased flexibility and reusability, as the same function can easily be applied to style multiple DataFrames.

Bonus One-Liner Method 5: Using Styler Set Properties

For a quick fix without going into too much syntax, pandas styler’s set_properties can be used to centrally align headers with a simple one-liner.

Here’s an example:

df = pd.DataFrame({'name':['Company'], 'revenue':[10000], 'expenses':[8000]})
df.style.set_properties(**{'text-align': 'center'}, subset=['name', 'revenue', 'expenses'])

This will output the DataFrame styled with centered headers.

This method applies the text-align property directly to the DataFrame’s columns. While it’s practical for quick edits, it doesn’t provide a global styling solution and needs to be applied each time the DataFrame is displayed.

Summary/Discussion

  • Method 1: Using the Styler Object. Involves styling the DataFrame directly utilizing pandas’ built-in Styler capabilities. Strengths include simplicity and integration within pandas. Weaknesses may include reduced control over more complex styling needs.
  • Method 2: Using HTML and the to_html Method. Offers a lot of flexibility by providing the DataFrame as HTML and styling it manually. Strengths lie in the compatibility with web presentations. A weakness might be the extra steps for styling if you are not well-versed in HTML/CSS.
  • Method 3: Modifying pandas DataFrame Output Options. Allows global changes to DataFrame display settings. Strengths include its effect on all DataFrames without the need to apply styles individually. However, this can be a weakness since it may lead to unwanted changes in all DataFrame outputs.
  • Method 4: Extending the Pandas Styler with Custom Functions. Enables the creation of reusable custom styles that can be easily applied to multiple DataFrames. Strengths are its modularity and reusability. Weakness comes from the necessity to understand custom function creation.
  • Bonus One-Liner Method 5: Using Styler Set Properties. Quick and easy for on-the-fly styling but not suitable for persistent or complex styles. The main strength is its conciseness, while the weakness is the lack of global application.