Adventures in Machine Learning

Styling Data Frames in Python: Create Engaging Presentations

Introduction to Styling with Python

Data analysis is a powerful way to extract insights from vast quantities of information. However, when it comes to presenting your work to others, you need to make it look good.

That’s where the Styler object comes in. The Styler object is a cool tool that lets you format your data and make it eye-catching.

In this article, we’ll go over the basics of the Styler object and how you can use it to make your code look slick and professional. We’ll also delve into some of the different styling options you can try out.

Overview of Styler object

The Styler object is a valuable tool that provides an opportunity to customize and format data in a variety of ways. You can use it to change the font, size, and color of cell values, add background colors or gradients, format borders, and more.

The Styler object can be called to stylize any type of data, but it is particularly useful when working with data frames. To call it up, all you need to do is invoke the .style method on a data frame.

Using accessor to modify styler object

The accessor is a useful tool that you can use to modify the styler object further. It allows you to change the way rows and columns are displayed, limit the displayed decimal points, add a color scale, and hide the index column, among other things.

The accessor can be accessed by the .apply and .applymap methods.

Different styling options to be attempted

There are various styling options that you can experiment with to improve the visual appeal of your data. Some of these options include:


You can use the .highlight method on a Styler object to highlight a specific cell or group of cells that meet a certain condition. This can be a useful tool for highlighting cells with minimum or maximum values, cells with null values, or cells that contain specific string values.

Minimum and maximum values

The .highlight_min and .highlight_max methods are two functions that can be used to highlight the minimum and maximum values, respectively. This feature can be used to show which values in a dataset are at either end of the spectrum.

It can also be used to help identify the range of values in a given data set.

Null values

The .highlight_null method is another useful feature available in the Styler object. It can be used to highlight cells in a data frame that contain null values.

This can be particularly useful if you’re looking for patterns in the data and need to identify columns or rows with missing data.

Color table

Color tables are a way of showing data values using color. They can be used to create heatmaps that visually represent the distribution of data across a range of values.

Color tables can be used to highlight areas of high or low density in the data, which may be useful when making comparisons or identifying trends.

Truncate decimals

The .set_precision method can be used to limit the number of decimal places displayed in the data. This can be useful if you have a large number of values with many decimal places that make it difficult to read the data.

Hide index

Sometimes, the index column of a data frame can be redundant or too crowded, making it difficult to read the other columns. You can hide the index column by accessing the Styler object and using the .hide_index() method.

Export to Excel

The Styler object is not limited to formatting data for display exclusively in Python. It also allows you to export data to other file formats, such as an Excel file.

Once exported, you can take advantage of any formatting features that Excel has to offer to create visually appealing and easy-to-read reports.


Styling with Python is an important tool for making data more presentable and appealing to the eye. The use of the Styler object and its accessor can make any data set look professional and polished.

By experimenting with highlighting, limiting decimal points, creating color tables, hiding the index column, and exporting to Excel, you can take your data analysis to the next level and create engaging reports that will impress your colleagues and clients.

Highlighting Null Values

Null values are a common occurrence in datasets, and sometimes it is important to highlight these values to identify any missing information in the data. In Python, the Styler object can be used to highlight null values in a data frame by leveraging the .highlight_null() method.

This method applies a specific style to any cells with null or missing values, making them stand out in the data. To highlight null values, you can start by creating a DataFrame with some null values:


import pandas as pd

import numpy as np

d = {‘A’: [1, 2, np.nan, 4], ‘B’: [5, 6, 7, np.nan], ‘C’: [9, np.nan, 11, 12]}

df = pd.DataFrame(d)


This will create a DataFrame with null or missing values across columns. The next step is to apply .highlight_null() to the data frame to highlight any cells that contain null values.



By executing this code, all null cells in the data frame are highlighted in red.

Coloring Table Borders and Text Data

Styling a table in Python goes beyond just coloring individual cells and highlighting null values; you can also modify the properties of tables with the Styler object using .set_properties. The .set_properties method is used to modify the properties of all cells within a table.

This can include properties like the tables background color, font size or style, and even the color of the borders. To modify the table properties, you can start by creating a dataframe and applying the .set_properties method.


import pandas as pd

d = {‘Name’: [‘Alice’, ‘John’, ‘Mary’], ‘Age’: [28, 23, 33], ‘Score’: [85, 92, 88]}

df = pd.DataFrame(d)

.set_properties(**{‘background-color’: ‘cyan’, ‘color’: ‘black’, ‘border’: ‘1px solid black’, ‘font-size’: ’12pt’})


In this example, we have applied a series of table properties to change its appearance. The ** operator unpacks the dictionary to create key-value pairs, allowing us to set multiple properties at once.

This modified table will now appear in a cyan background color with black text and a solid black border. The font size has also been increased to 12pt.

The Styler object is a powerful tool for modifying the appearance of data frames in Python. With different methods and options available, you can easily highlight specific values, create heatmaps, or even export the data to other file formats like Excel.

This makes it easy to share your data and insights with others without having to worry about the complexities of manual formatting. Now that you have a good understanding of how to style data frames, you can apply these techniques to your own data to create tables that are both functional and aesthetically pleasing.

Truncating Decimal Display

When working with data analysis, we often deal with numbers that have many decimal places, which can make the data difficult to read. One way to deal with this is to truncate the number of decimal places displayed.

In Python, we can use the Styler object to control the number of decimal places displayed in a data frame using the .set_precision() method. The .set_precision() method allows us to control the number of decimal places displayed in a data frame by passing a value that represents the maximum number of digits beyond the decimal point to display.

It is important to note that this method only affects the display of the data and does not change the actual values of the data. To demonstrate how to use .set_precision(), we can create a DataFrame with several decimal places.


import pandas as pd

import numpy as np

data = {‘col1’: [1.2345678, 3.1415926, 9.87654321], ‘col2’: [0.111111, 0.222222, 0.333333]}

df = pd.DataFrame(data)


The first column has seven decimal places, which can be difficult to read as is. We can use .set_precision() to truncate the number of decimal places displayed.



In this example, we set the number of decimal places to 3, which means that the data will be displayed with only three digits beyond the decimal point.

Hiding DataFrame Index

Data frames in Python come with an index column that identifies each row of data. Sometimes, the index column is not relevant to the analysis and can clutter the display.

In such cases, hiding the index can make the data frame appear neater and be easier to interpret. To hide the index using the Styler object, we can use the .hide_index() method.

To demonstrate how to use .hide_index(), we can create a DataFrame with an index column. “`

import pandas as pd

data = {‘col1’: [‘A’, ‘B’, ‘C’], ‘col2’: [1, 2, 3], ‘col3’: [4, 5, 6]}

df = pd.DataFrame(data, index=[‘row1’, ‘row2’, ‘row3’])


By default, the index column is displayed in the data frame. To hide it, we can use the .hide_index() method.



Executing this code will hide the index column.


In conclusion, the Styler object in Python provides many powerful tools for styling data frames. By controlling the number of decimal places displayed, truncation can make the data more readable, especially when dealing with large amounts of data with many digits behind the decimal point.

Likewise, hiding the index column can simplify the display of data and make it easier to read. By combining these tools with other stylistic methods such as highlighting null values and coloring table borders and text data, you can take your data frame styling to the next level.

With these techniques at your fingertips, you can present your data in a way that looks professional and is easy to understand, making it more likely that you will get your message across.

Exporting Styled Data to Excel

Once you have styled your data frame using the Styler object in Python, you may want to export it to a file format that is easily shareable or presentable to others. One such file format is Excel.

In Python, you can use the .to_excel() method to export styled data in a data frame to an Excel file.

However, exporting styled data to Excel requires some additional steps to ensure that the styling is preserved in the Excel sheet.

One way to achieve this is by specifying the openpyxl engine when calling the .to_excel() method. The openpyxl engine is a powerful library that provides flexibility in working with Excel files.

To demonstrate exporting styled data to Excel, we can create a DataFrame and style it. “`

import pandas as pd

data = {‘Name’: [‘Alice’, ‘John’, ‘Mary’], ‘Age’: [28, 23, 33], ‘Score’: [85.5678, 92.1234, 88.8765]}

df = pd.DataFrame(data)

styled_df =






In this example, we used various styling options to highlight null values, highlight maximum values, set the number of decimal places displayed to two, and hide the index column. To export the styled data frame to an Excel file, we can use the .to_excel() method, specifying the openpyxl engine.


styled_df.to_excel(‘styled_data.xlsx’, engine=’openpyxl’)


Executing this code will create an Excel file with the styled data. When you open the file in Excel, you will see that all the styling options you previously applied are still intact.


The Styler object in Python is a powerful tool that can be used to customize and format data frames, making them more presentable and easy to understand. By combining different methods of styling like highlighting null values and maximum values, changing number decimal places, and hiding the index column, you can achieve a more polished look.

To ensure that the styling is preserved when sharing data, you can export it to an Excel file using the openpyxl engine. Alternatively, the to_html() method can be used to export styled data to an HTML file so that it can be viewed as a webpage.

By understanding how to style data and export it, you can create professional and engaging data presentations with ease. In summary, styling data frames in Python using the Styler object can make them more appealing, readable and less cluttered, producing a more professional and engaging presentation of the data.

The Styler object gives users control over formatting, especially by applying highlight styles to minimum and maximum data values, null values, and showing them using color tables. Additionally, it is possible to modify table border colors and text using the dataframe attributes and accessor methods.

Finally, exporting styled data to Excel, using the openpyxl engine, ensures that the styling is preserved and that the data is in a file format easily presentable and shareable. With these tools, data analysts can create professional, easy to read and engaging tables that deliver insights that are easily understandable to any audience.

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